biomass resource analyses & future bioenergy scenarios

Biomass Resource Analyses & Future

Bioenergy Scenarios

A Thesis submitted to The University of Manchester for the degree of:

Doctor of Philosophy in Environmental Engineering

In the Faculty of Engineering & Physical Sciences

2014

Andrew James Welfle

Tyndall Centre for Climate Change Research, School of Mechanical Aerospace

& Civil Engineering

Andrew Welfle - ID: 81163530

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Table of Contents List

Abstract 10

Declaration 11

Copyright Statement 12

Acknowledgements 13

PhD Programme Outputs 14

1) Published Work 14

2) Other Work 14

3) Notable Activities 15

4) Presentations 15

Chapter 1 - Introduction .................................................................................................................................... 17

1.1 Opening Statement 18

1.2 Research Context 20

1.2.1 The Wider Research Context - Climate Change 20

1.2.2 Energy Profiles 24

1.2.3 The Global Energy Consumption Profile 25

1.2.4 UK Energy Consumption Profile 26

1.2.5 Renewable Energy & the UK’s Renewables Profile 28

1.2.6 Energy Scenarios – Modelling Future Trends 30

1.2.7 UK Climate Change, Energy & Bioenergy Policy 31

1.2.8 The Emergence & Development of the Climate Change Policy Agenda 31

1.2.9 UK Climate Change & Renewable Energy Policy Timeline 32

1.2.10 Key UK & EU Policy Mechanisms & Instruments 34

1.3 Thesis Rationale 40

1.4 Research Problem Statement 41

1.5 Research Aims & Objectives 42

1.5.1 Research Aim 1 42



1.6 Thesis Storyline 43

1.6.1 Chapter 1 – Introduction 44

1.6.2 Chapter 2 – Biomass as a Renewable Energy Resource 44

1.6.3 Chapter 3 – Biomass Resource Modelling 44

1.6.4 Chapter 4 – Developing the Biomass Resource Model 44

1.6.5 Chapter 5 – Drivers Influencing Biomass Resource Availability & Bioenergy 45

1.6.6 Chapter 6 – UK Biomass Resource Scenarios 45

1.6.7 Chapter 7 – The Future UK Bioenergy Sector 46

1.6.8 Chapter 8 – Global Biomass Trade: Supply, Demand, Limitations & Sustainability 47

1.6.9 Chapter 9 – Case Study: Brazil’s Biomass Resource Analysis 47

1.6.10 Chapter 10 – An Alternative UK Bioenergy Strategy 48

1.6.11 Chapter 11 – Thesis Conclusions 48

1.6.12 Thesis References 48

1.6.13 Thesis Appendices 48

Chapter 2 - Biomass as a Renewable Energy Resource .................................................................................. 49

2.1 Biomass as a Renewable Energy Resource 50

2.1.1 A Renewed Interest in Biomass as a Fuel 50

2.1.2 The Storage of Energy within Biomass 50

2.1.3 Biomass Composition, Characteristics & Fuel Properties 51

2.1.4 The Variable Characteristics of Biomass Materials 55

2.1.5 The Negative Impacts of Biomass for Energy Generation 56


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2.2 Biomass Feedstocks, Resources & Energy Pathways 58

2.2.1 Categories of Biomass 58

2.2.2 Lignocellulosic Biomass Resources 62

2.3 Energy Generation from Biomass & Conversion Pathways 64

2.3.1 Biomass Conversion Pathways 64

2.3.2 Direct Combustion Processes 65

2.3.3 Thermochemical Conversion Processes 68

2.3.4 Biochemical Conversion Processes 71

2.3.5 Comparing Biochemical versus Thermochemical Processes 73

2.3.6 Conversion of Lignocellulosic Materials 74

2.3.7 The UK’s Bioenergy & Bio-refining Systems & Infrastructure 74

Chapter 3 - Biomass Resource Modelling ......................................................................................................... 76

3.1 Biomass Resource Modelling 77

3.1.1 An Extensive Global Resource 77

3.1.2 An Introduction to Resource Modelling 77

3.1.3 Biomass Resource Modelling 81

3.1.4 Estimating the Global Biomass Resource 84

3.1.5 Modelling the UK’s Indigenous Biomass Resource 86

Chapter 4 - Developing the Biomass Resource Model ..................................................................................... 89

4.1 Introducing the Biomass Resource Modelling 90

4.1.1 Aims & Objectives of the Biomass Resource Model 90

4.1.2 Influential Studies & Research 93

4.1.3 New Modelling Knowledge & Niche 94

4.1.4 The Biomass Resource Model Structure 96

4.1.5 Constructing & Navigating the Biomass Resource Model 100

4.1.6 Applying the Biomass Resource Model 103

4.2 UK BRM - Developing the Stage One Methodology 106

4.2.1 UK Population Dynamics 106

4.2.2 UK Built-Up Land Area 107

4.2.3 UK Forests, Woodlands & Plantations 108

4.2.4 UK Food & Agriculture Systems 109

4.2.5 Agriculture & Biomass Productivity Yields 112

4.2.6 Land Area to Meet Food Commodity Demands 115

4.2.7 UK Land Availability 116

4.3 UK BRM - Stage One Modelling Mechanics 117

4.3.1 Stage One Analysis Calculation Equations Key 117

4.3.2 Modelling Food & Agriculture Systems to 2050 118

4.3.3 Modelling Land-Use Dynamics to 2050 119

4.4 UK BRM - Developing the Stage Two Methodology 121

4.4.1 UK Forest System Productivity & Characteristics 121

4.4.2 Forestry Residues 126

4.4.3 Forestry-Industry Dynamics 127

4.4.4 Industrial Residues 130

4.4.5 Agricultural Residues 130

4.4.6 Straw Agricultural Residues 131

4.4.7 Slurry Agricultural Residues 132

4.4.8 Arboriculture (Arb) Residues 132

4.4.9 Wastes 133

4.4.10 Sewage Waste 136

4.4.11 Grown Biomass & Energy Crops 136

4.5 UK BRM - Stage Two Modelling Mechanics 139

4.5.1 Stage Two Analysis Calculation Equations Key 139

4.5.2 Modelling Forest-Industry Dynamics 140


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4.5.3 Modelling Biomass Resource Availability Dynamics 141

4.6 UK BRM – Developing the Stage Three Methodology 144

4.6.1 Resource Availability for the Bioenergy Sector 144

4.6.2 Energy Content of Resources 145

4.6.3 Developing a Methodology for Analysing Bioenergy Potentials 145

4.6.4 Pre-Treatment Pathways 146

4.6.5 Energy Conversion Pathways 146

4.6.6 Preferred Bioenergy Conversion Pathway 147

4.6.7 Total Bioenergy Potential 148

4.6.8 Energy Targets & Demands 149

4.7 UK BRM - Stage Three Modelling Mechanics 150

4.7.1 Stage Three Analysis Calculation Equations Key 150

4.7.2 Modelling Bioenergy Potential of Available Biomass Resources 150

Chapter 5 - Drivers Influencing Biomass Resource Availability & Bioenergy ........................................... 153

5.1 Drivers Influencing Biomass Resource Availability 154

5.1.1 Biomass Resource Drivers 155

5.1.2 The BRM’s Analysis Drivers 156

5.1.3 Discussion of Drivers Analysed within the BRM 157

5.1.4 Discussion of Drivers Not Directly Analysed within the BRM 164

5.2 UK BRM Drivers Sensitivity Analysis 170

5.2.1 Developing a Sensitivity Analysis Methodology 170

5.2.2 UK Baseline Scenario – Forecast Biomass Resource Availability 171

5.2.3 Biomass Resources Demonstrating Potential for the UK Bioenergy Sector 176

5.2.4 BRM Drivers Sensitivity Analysis 177

5.2.5 Key Chapter & Sensitivity Analysis Outputs & Conclusions 191

Chapter 6 - UK Biomass Resource Scenarios ................................................................................................. 194

6.1 Developing UK Biomass Resource Scenarios 195

6.1.1 An Introduction to Scenario Based Analysis 195

6.1.2 Developing Biomass Resource Scenarios 197

6.1.3 Developing the UK Food Focus (Foo-F) Biomass Resource Scenario 199

6.1.4 Developing the UK Economic Focus (Eco-F) Biomass Resource Scenario 200

6.1.5 Developing the UK Conservation Focus (Con-F) Biomass Resource Scenario 202

6.1.6 Developing the UK Energy Focus (Ene-F) Biomass Resource Scenario 204

6.2 UK Biomass Availability & Bioenergy 207

6.2.1 Biomass Resource Scenarios – Land Utilisation Forecasts 207

6.2.2 Biomass Resource Scenarios – Resource Availability Forecasts 210

6.2.3 Biomass Resource Scenarios – Bioenergy Potential Forecasts 214

6.2.4 Key Scenario Analyses Conclusions 217

Chapter 7 - The Future UK Energy & Bioenergy System ............................................................................ 219

7.1 Current & Future UK Bioenergy Sector 220

7.1.1 The Current & Future UK Bioenergy Sector 221

7.1.2 The UK Bio-Heat Sector 222

7.1.3 The UK Bio-Power Sector 224

7.1.4 The UK Bio-Fuel Sector 228

7.1.5 UK Biomass Resource Import Forecasts 230

7.1.6 Trends and Conclusions of Future UK Bioenergy 232

7.2 Biomass Resource Balance Analysis 234

7.2.1 Resource Balance Analysis Methodology 234

7.2.2 Resource Balance Analysis Results 237

7.2.3 Resource Balance Analysis Discussions 238

7.2.4 Chapter Conclusions & Consequences for the Future UK Bioenergy Sector 240

Chapter 8 - Global Biomass Trade - Supply, Demand, Limitations & Sustainability ................................ 242

8.1 Global Biomass Trade – Supply, Demand, Limitations & Sustainability 243


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8.1.1 Increasing Global Demand for Biomass 243

8.1.2 Global Biomass Trade Markets 244

8.1.3 Biomass Resources Key Global Trade Flows 247

8.1.4 Global Biomass Trade Limitations & Uncertainty 249

8.1.5 Sustainability of Global Biomass Resource Production 252

8.1.6 Chapter Conclusions & Consequences for the UK Bioenergy Sector 258

Chapter 9 - Case Study: Brazilian Biomass Resource Analyses ................................................................... 260

9.1 Case Study – Brazil’s Biomass Resource Analyses 261

9.2 The Brazil Biomass Resource Model – Stage One Analyses 263

9.2.1 Brazil Population Dynamics 263

9.2.2 Brazil Built-Up Land Area 263

9.2.3 Brazil Forests, Woodlands & Plantations 264

9.2.4 Brazil Food & Agriculture Systems 264

9.2.5 Agriculture & Biomass Productivity Yields 265

9.2.6 Land Area to Meet Food Commodity Demands 265

9.2.7 Brazil Land Availability 266

9.3 The Brazil Biomass Resource Model – Stage Two Analyses 267

9.3.1 Forestry System Productivity & Characteristics 267

9.3.2 Forestry Residues 270

9.3.3 Industrial Residues 270

9.3.4 Straw Agricultural Residues 271

9.3.5 Slurry Agricultural Residues 271

9.3.6 Arboriculture Residues 272

9.3.7 Wastes 273

9.3.8 Sewage Waste 274

9.3.9 Grown Biomass & Energy Crops 275

9.4 The Brazil Biomass Resource Model – Stage Three Analyses 278

9.5 Brazil Biomass Resource Availability 279

9.5.1 Brazil Biomass Supply Chain Dynamics to 2030 279

9.5.2 Brazil Baseline Scenario – Land-Use Analysis 280

9.5.3 Brazil Baseline Scenario – Biomass Resource Availability Analysis 282

9.6 Brazil Bioenergy Scenarios 286

9.6.1 Brazil Current Energy System 286

9.6.2 Brazil’s Future Energy Strategy & Targets 291

9.6.3 Global Comparisons – Leading Energy Targets & Strategies 294

9.6.4 Developing Brazil Bioenergy Scenarios 296

9.6.5 Brazil Bioenergy Scenarios – Bioenergy Potentials 302

9.6.6 Brazil Bioenergy Scenarios – Resource Balance Analysis 304

9.6.7 Chapter Conclusions & Consequences for the Future UK Bioenergy Sector 307

Chapter 10 - An Alternative UK Bioenergy Strategy .................................................................................... 310

10.1 An Alternative UK Bioenergy Strategy 311

10.1.1 Thesis Analysis Key Conclusions 312

10.2 Alternative Policy & Strategy Options 315

10.2.1 UK Grown Biomass & Energy Crops 315

10.2.2 Plant Based Agricultural Residues 318

10.2.3 Animal Based Agricultural Residues 322

10.2.4 Household & Organic Wastes 328

10.2.5 Promoting the UK Bio-Heat Sector 331

Chapter 11 - Thesis Conclusions ..................................................................................................................... 336

11.1 Thesis Conclusions 337

11.1.1 Summary of Thesis Conclusions 337

11.2 Deductions & Implications 341

11.2.1 Conclusion – Develop a Bioenergy Sector Compatible with UK Resources 341


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11.2.2 Conclusion - Developing Mobilisation & Utilisation Strategies 342

11.2.3 Conclusion – Placing Greater Focus on Indigenous Resources 343

11.3 Original Contributions to Knowledge 345

11.3.1 Biomass Resource Model 345

11.3.2 Food & Industry Biomass Demands 347

11.3.3 UK Indigenous Biomass Resource Analyses 347

11.3.4 Brazil Biomass Resource Forecasts & Bioenergy Scenarios 348

11.4 Limitations & Further Work 350

11.4.1 Changing Food Diets 350

11.4.2 Climate Change Impacts 350

11.4.3 Spatial Scale & Distribution of Modelling 351

11.4.4 Energy Conversion Modelling 351

11.4.5 Chemical Industry 352

11.4.6 Supply Chain Drivers 352

11.5 Concluding Statement 354

Thesis References .............................................................................................................................................. 356

Thesis Appendices............................................................................................................................................. 380

85,995 Words


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Tables List Table 1.1: Climate Change Characteristics of the Core Greenhouse Gases ......................................................... 21

Table 2.1: Comparison of Typical Fuel Characteristic Values for Biomass & Fossil Fuels ................................. 53

Table 2.2: Typical Pyrolysis of Wood Product Yields ......................................................................................... 69

Table 2.3: Comparison of Biochemical & Thermochemical Biofuel & Energy Yields ....................................... 73

Table 2.4: Summary of the UK and EU’s Operating & Planned Bioenergy Systems .......................................... 75

Table 3.1: Global Biomass Resource Estimates ................................................................................................... 77

Table 3.2: Summary & Characteristics of Existing Biomass Resource Models ................................................... 84

Table 4.1: Summary of the Analysed Biomass Categories & Specific Resources ............................................... 99

Table 4.2: Summary of Excel Tabs within the BRM .......................................................................................... 101

Table 4.3: Overview of the BRM’s Main Control Panel Variables .................................................................... 103

Table 4.4: UK Population Forecasts ................................................................................................................... 107

Table 4.5: FAO Food Commodity Datasets Utilised Within the BRM .............................................................. 110

Table 4.6: References Providing Data on Current UK Agriculture Productivity Yields .................................... 114

Table 4.7: References Providing Data on Future Agriculture Productivity Yields ............................................. 115

Table 4.8: Equation Calculations Key for the BRM’s Stage One Analyses ....................................................... 117

Table 4.9: Forest Productivity Scenarios for Forestry Commission Estate Forests ............................................ 122

Table 4.10: Forest Productivity Scenario for Forest Systems within the Private Sector Estate .......................... 124

Table 4.11: Waste Streams & Availability for the Bioenergy Sector ................................................................. 134

Table 4.12: DEFRA Scenarios for UK Waste Generation to 2050 ..................................................................... 135

Table 4.13: DEFRA Scenarios for UK Waste Management to 2050.................................................................. 136

Table 4.14: BRM Default Biomass Resource & Energy Crop Planting Strategies ............................................ 138

Table 4.15: Equation Calculations Key for the BRM’s Stage Two Analyses .................................................... 139

Table 4.16: Equation Calculations Key for the BRM’s Stage Three Analyses .................................................. 150

Table 5.1: Drivers Influencing Biomass Resources & the BRM’s Analysis Capability ..................................... 156

Table 5.2: Summary of Key Drivers Influencing UK Biomass Resource Availability ...................................... 157

Table 5.3: Reports Studies & Research Influencing the UK Baseline Scenario ................................................. 171

Table 5.4: UK BRM Drivers Influencing the Availability of Grown Resources ................................................ 179

Table 5.5: UK BRM Drivers Influencing the Availability of Residue Resources .............................................. 184

Table 5.6: UK BRM Drivers Influencing the Availability of Waste Resources ................................................. 189

Table 5.7: Research Summary Ranking Indigenous Resource Influences & Contributors ................................ 192

Table 6.1: UK BRM Biomass Resource Scenarios ............................................................................................ 198

Table 6.2: Summary of Biomass Resource Scenario Characteristics & Forecast Assumptions ......................... 206

Table 7.1: Bioenergy Contribution of Resources within the UK Bioenergy Sector (2012)................................ 221

Table 7.2: UK Bioenergy Sector Installed Capacity & Generation (2012) ........................................................ 221

Table 7.3: UK Renewable Energy Roadmap (2011) Near-Term Bioenergy Estimates by 2020 ........................ 222

Table 7.4: Feedstock Co-fired with Fossil Fuels in the UK ................................................................................ 227

Table 7.5: UK Bioenergy Sector Utilisation of Feedstocks to Produce Biofuels ............................................... 230

Table 7.6: Future Bioenergy Sector Trends and Key Resource Demands .......................................................... 233

Table 7.7: Scenarios Mid and Long-Term Forecasts of UK Biofuels Demand .................................................. 236

Table 7.8: UK BRM Resources Compatible with Future UK Bioenergy Sector Demands ................................ 236

Table 9.1: Brazil Population Forecasts ............................................................................................................... 263

Table 9.2: Brazil Urban Development Land Area Forecasts (Hectares) ............................................................. 264

Table 9.3: Brazil BRM Forestry Growth Scenarios ............................................................................................ 269

Table 9.4: Brazil BRM Forestry Productivity Scenarios .................................................................................... 270

Table 9.5: Waste Streams & Availability for the Bioenergy Sector ................................................................... 273

Table 9.6: Reports Studies and Research Influencing the Brazil Baseline Scenarios ......................................... 280

Table 9.7: Overview of Brazil Bioenergy Scenario Themes and Targets ........................................................... 302

Table 9.8: Brazil BRM Resources Identified as Potentially Suitable for Exportation ........................................ 305

Table 10.1: UK Plant Based Farming Renewable Energy Characteristics ......................................................... 318

Table 10.2: UK Animal Based Farming Renewable Energy Characteristics ..................................................... 323


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Figures List Figure 1.1: Combustion Greenhouse Gas Calculations ........................................................................................ 20

Figure 1.2: Schematic Demonstrating the Greenhouse Effect .............................................................................. 21

Figure 1.3: The Utilisation of Fossil Fuels since the Industrial Revolution ......................................................... 22

Figure 1.4: Graphs Demonstrating the Atmospheric CO2 since the Industrial Revolution ................................... 23

Figure 1.5: Energy Source Contributions to Global Primary Energy Consumption (2011) ................................. 25

Figure 1.6: Global Discrepancy between Population and Energy Consumption .................................................. 26

Figure 1.7: UK Total Annual Primary Energy Consumption 1970-2010 ............................................................. 27

Figure 1.8: Contribution of Energy Source to UK’s Annual Energy Consumption 1970-2010 ........................... 27

Figure 1.9: Contribution of the UK Renewable Energy Technologies in 2010 .................................................... 30

Figure 1.10: Development Timeline of the UK’s Key Climate Change & Energy Policies ................................. 34

Figure 1.11: Energy Policy Framework ................................................................................................................ 35

Figure 2.1: The Photosynthesis Reaction ............................................................................................................. 51

Figure 2.2: Moisture Content Calculations ........................................................................................................... 52

Figure 2.3: The Principal Components of Biomass Material ................................................................................ 55

Figure 2.4: Biomass Combustion Reactions ......................................................................................................... 56

Figure 2.5: Typical Biomass Conversion Pathway & Pollutants .......................................................................... 56

Figure 2.6: Biomass Resource Categories & the Key Life-Cycle Processes ........................................................ 59

Figure 2.7: Overview of Key Biomass Conversion Pathways & Products ........................................................... 64

Figure 2.8: Summary of the Core Biomass Conversion Pathways & Resulting Products .................................... 65

Figure 2.9: Biomass Gasification Conversion Processes ...................................................................................... 70

Figure 3.1: Non-Renewable Resource Economic Dynamics ................................................................................ 79

Figure 3.2: Ratio of Global Fossil Fuel Consumption to Years of Remaining Reserve ....................................... 80

Figure 3.3: Biomass Modelling Resolution Potentials.......................................................................................... 82

Figure 3.4: Typical Structure & Analysis Flow for a Resource Focused Biomass Model ................................... 83

Figure 3.5: Range of Global Biomass Resource Estimates .................................................................................. 85

Figure 3.6: Range of Biomass Resource and Related Land Category Estimates .................................................. 86

Figure 4.1: The Biomass Resource Model Methodology Architecture ................................................................ 97

Figure 4.2: UK Population Forecasts .................................................................................................................. 107

Figure 4.3: UK Change in Built-Up Area Forecasts ........................................................................................... 108

Figure 4.4: UK Forested Area Forecasts ............................................................................................................ 109

Figure 4.5: Evaluating Productivity Yields within the BRM .............................................................................. 113

Figure 4.6: Forecast Land Area Required to Meet Total Food Commodity Demands ....................................... 115

Figure 4.7: Calculation Equations Applied within the BRM Agricultural System & Food Demand Analyses.. 119

Figure 4.8: Calculation Equations Applied within the BRM’s Land-Use Analyses ........................................... 120

Figure 4.9: Forestry Commission Estate Standing Volume Forecasts ................................................................ 123

Figure 4.10: Forestry Commission Productivity Forecasts ................................................................................. 123

Figure 4.11: Private Sector Estate Standing Volume Forecasts .......................................................................... 125

Figure 4.12: Private Sector Estate Productivity Forecasts .................................................................................. 125

Figure 4.13: Forestry System & Industry Dynamics Analysed within the BRM................................................ 129

Figure 4.14: Analysis of Straw Agricultural Residues within the BRM ............................................................. 131

Figure 4.15: Analysis of Slurry Agricultural Residues within the BRM ............................................................ 132

Figure 4.16: Modelling Future Planting Strategies within the BRM .................................................................. 137

Figure 4.17: Modelling Resource Flows within the BRM’s Forestry-Industry Analysis Module ...................... 141

Figure 4.18: Calculation Equations Applied within the BRM’s Resource Availability Analyses ...................... 143

Figure 4.19: Modelling the Biomass Resource Bioenergy Conversion Pathway ............................................... 145

Figure 4.20: Description of BRM’s Resource-Bioenergy Filter Analysis Methodology .................................... 146

Figure 4.21: Preferred Bioenergy Conversion Pathways within the BRM ......................................................... 148

Figure 4.20: Analysing the Bioenergy Potentials Generated from Resources within the BRM ......................... 149

Figure 4.22: Biomass Flow & Modelling Mechanics of the BRM’s Pre-Treatment Processing Analyses ........ 151

Figure 4.23: Calculation Equations Applied within the BRM’s Bioenergy Potential Analyses ......................... 152

Figure 5.1: Availability of UK Biomass Resource Categories within the Baseline Scenario ............................. 173

Figure 5.2: UK Baseline Scenario Biomass Resource Availability .................................................................... 176

Figure 5.3: UK Baseline Scenario – Proportional Contribution of Key Biomass Resources ............................. 177

Figure 5.4: Biomass Grown Resources Sensitivity Analysis Radar Chart ......................................................... 178

Figure 5.5: Biomass Residue Resources Sensitivity Analysis Radar Chart ........................................................ 183

Figure 5.6: Biomass Waste Resources Sensitivity Analysis Radar Chart ........................................................... 188

Figure 6.1: Visual Conceptualisation of Scenario Pathways Based Analysis ..................................................... 196

Figure 6.2: UK BRM Scenarios Analysis - UK Land Utilisation Profiles ......................................................... 208

file:///E:/Tyndall%20Centre%20Work/3)%20Third%20Year%20-%20THESIS/Final%20Thesis%20Documents/Andrew%20Welfle%20-%20PhD%20Thesis%20-%2081163530%20-%20Biomass%20Resource%20Analyses%20&%20Future%20Bioenergy%20Scenarios%20COMBINED%20FINAL%202.docx%23_Toc403316519

file:///E:/Tyndall%20Centre%20Work/3)%20Third%20Year%20-%20THESIS/Final%20Thesis%20Documents/Andrew%20Welfle%20-%20PhD%20Thesis%20-%2081163530%20-%20Biomass%20Resource%20Analyses%20&%20Future%20Bioenergy%20Scenarios%20COMBINED%20FINAL%202.docx%23_Toc403316531


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Figure 6.3: UK BRM Scenarios Analyses – Biomass Resource Availabilities .................................................. 212

Figure 6.4: UK BRM Scenario Analyses – Forecast Bioenergy Potentials & Energy Target Comparisons ...... 216

Figure 7.1: Capacity of Operational and Planned UK Bio-power Projects ........................................................ 225

Figure 7.2: Potential Delivered Energy from Use of Biomass in Transport ....................................................... 229

Figure 7.3: Forecast Range of Domestic & Imported Biomass Resource Utilisation ......................................... 231

Figure 7.4: Analysis of UK Wood Fibre Resource Availability, Future Demand & Deficits to 2025 ............... 237

Figure 7.5: Analysis of UK Indigenous Biofuel Energy, Future Demand & Deficits in 2030 ........................... 238

Figure 8.1: Predominant Global Trade Flows of Biomass Resources for Energy End Uses .............................. 245

Figure 9.1: Brazil Forestry Area & Designations to 2050 within the Scenario 3 Pathway ................................. 269

Figure 9.2: Brazil Plantation Strategy to 2050 within the Brazil Baseline Scenarios ......................................... 277

Figure 9.3: Brazil Land-Use within the Baseline Scenario to 2030 .................................................................... 281

Figure 9.4: Brazil Biomass Resource Availability within the Baseline Scenario to 2030 .................................. 283

Figure 9.5: Brazil Baseline Scenario – Validation of Wood Resource Availability Forecasture ....................... 284

Figure 9.6: Brazil Baseline Scenario – Validation of Energy Crop Availability Forecast.................................. 285

Figure 9.7: Energy Technologies Contributing to Brazil’s Total Primary Energy Supply (2011) ..................... 287

Figure 9.8: Fuels & Resources Contributing to Generate Brazil’s Power (2011) ............................................... 288

Figure 9.9: Fuels & Resources Utilised within Brazil’s Transport Sector (2011) .............................................. 289

Figure 9.10: Brazil Bioenergy Scenarios – Contribution from Energy Technologies ........................................ 298

Figure 9.11: Brazilian Primary Energy & Bioenergy Demand Forecasts ........................................................... 303

Figure 9.12: Brazilian Bioenergy Sector Resource Demands & Surplus Resources Available for Export ........ 306

Figure 10.1: Density of Plant Based Agriculture Compared to Transport Network Development .................... 320

Figure 10.2: Density of Animal Based Agriculture Compared to Transport Network Development ................. 325


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Abstract

The University of Manchester

Andrew James Welfle

Biomass Resource Analyses & Future Bioenergy Scenarios

March 2014

___________________________________________________________________________

The United Kingdom has committed itself to ambitious and legally-binding Greenhouse Gas

emission reduction, and renewable energy contribution targets. Energy production from

biomass is expected to play a significant role in achieving these targets. The PhD Research

Project as presented in this Thesis provides an analysis of the UK’s indigenous biomass

resources, and the potential they offer in servicing domestic bioenergy requirements.

The biomass resource supply chain dynamics within the UK, govern the availability of these

indigenous resources. By modelling these supply chain dynamics, an assessment has been

undertaken; the principle aim of which was to evaluate the potential contribution that

indigenous biomass resources can make towards the UK’s future energy mix.

This Research finds that the United Kingdom has considerable indigenous biomass resources

that could potentially be made available, if the UK were able to develop its supply chains to

appropriately mobilise these resources. However, the specific demands and the direction of

development of the UK’s future bioenergy sector, as driven by the UK Government’s current

strategies and policies; demonstrate degrees of incompatibility with the forecast potential of

biomass resource availability.

The consequence of this disparity is likely to result in rising biomass resource imports to

balance the UK’s future energy demands. Further analysis highlights the potential impacts,

inherent uncertainties, and risks to the United Kingdom’s bioenergy sector; associated with

trade within future global biomass resource markets.

The concluding themes are based on analyses and discussions that indicate that the UK

should implement strategies to develop its indigenous resources, and develop its supply

chains to optimise these resources; rather than become heavily reliant on imports from the

global markets.


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Declaration


Andrew James Welfle


March 2014

___________________________________________________________________________

No portion of the work referred to in this Thesis has been submitted in support of an

application for another degree or qualification, at The University of Manchester or any other

University or other Institute of learning.


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Copyright Statement


Andrew James Welfle


March 2014

___________________________________________________________________________

The author of this Thesis (“the Thesis”), including any appendices and / or schedules to this

Thesis; owns certain copyright or related rights within it (“the Copyright”) and he has given

The University of Manchester certain rights to use such Copyright, including for

administrative purposes.

Copies of this Thesis, either in full or in extracts, and whether in hard or electronic copy, may

be made only in accordance with the ‘Copyright, Designs, and Patents Act 1988’ (as

amended) and regulations issued under it or, where appropriate, in accordance with licensing

agreements which the University has from time to time. This page must form part of any such

copies made.

The ownership of certain Copyright, patents, designs, trademarks, and other intellectual

property (“the Intellectual Property”) and any reproductions of copyright works in the Thesis,

for example graphs and tables (“Reproductions”), which may be described in this Thesis;

may not be owned by the author and may be owned by third parties. Such Intellectual

Property and Reproductions cannot and must not be made available for use without the prior

written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions.

Further information on the conditions under which disclosure, publication and

commercialisation of this Thesis, the Copyright and any Intellectual Property and / or

Reproductions described in it may take place, is available in the University IP Policy (see

http://www.campus.manchester.ac.uk/medialibrary/policies/intellectual-property.pdf), in any

relevant Thesis restriction declarations deposited in the University Library, The University

Library’s regulations (see http://www.manchester.ac.uk/library/aboutus/regulations) and in

The University’s policy on presentation of Theses.

http://www.campus.manchester.ac.uk/medialibrary/policies/intellectual-property.pdf

http://www.manchester.ac.uk/library/aboutus/regulations


13

Acknowledgements


Andrew James Welfle


March 2014

___________________________________________________________________________

This Thesis has been prepared and written following a three-year PhD Programme carried out

at the Tyndall Centre for Climate Change Research, within The University of Manchester

School of Mechanical Aerospace and Civil Engineering.

Special thanks for help and support during the PhD programme are given to:

Dr. Paul Gilbert

Dr. Patricia Thornley

Professor Kevin Anderson

And all the Researchers & Staff of the Tyndall Centre for Climate Research, University of

Manchester.


14

PhD Programme Outputs

The key research outputs and outreach activities undertaken during the PhD Programme are

summarised in the Tables below. Information about further activities is also shown within

Appendix 17.0.

1) Published Work

1)

Title: Meeting Bioenergy Targets with Reduced Imports

Reference:

Welfle. A, Gilbert. P, Thornley. P, 2013, ‘Meeting Bioenergy Targets with

Reduced Imports’, 21st European Biomass Conference & Exhibition,

Copenhagen, Denmark, Proceedings, pp. 21-29

Details Submitted at the Conference in addition to carrying out an oral presentation

Status: Published within the European Biomass Conference 2013 Proceedings

2)

Title: Securing a Bioenergy Future without Imports

Reference: Welfle. A, Gilbert. P, Thornley. P, 2014, ‘Securing a Bioenergy Future without

Imports’, Energy Policy, Vol. 68, pp.1-14

Details A copy of this paper can be found in Appendix 15.0 of this Thesis.

Status: Accepted and Published within Energy Policy Journal

3)

Title: Increasing Biomass Resource Availability through Supply Chain Analysis

Reference: Welfle. A, Gilbert. P, Thornley. P, 2014, ‘Refocusing Bioenergy Strategies’,

Biomass & Bioenergy, In Press

Details A copy of this paper can be found in Appendix 15.0 of this Thesis.

Status: Accepted and Published within Biomass & Bioenergy Journal

2) Other Work

1)

Title: Global Biomass Trade Caution (preliminary title)

Reference: Welfle. A, 2014, ‘Global Biomass Trade Caution’, Journal to be Confirmed

Details A third research paper from this PhD’s output has been written, but to date it is

undecided to which Journal it will be submitted.

Status: Draft Paper Written and Awaiting Submittal to Selected Journal

2)

Title: DECC Bioenergy Calculator – Model Validation Report

Details

Carried out a validation of the UK Department for Energy & Climate Change’s

Bioenergy Calculator Tool in a consultancy role. This involved carrying out a

quality assurance exercise checking Calculator Tool, before it was subject to a

stakeholder consultation process and eventual publication. .

Status: Submitted to the DECC Science & Innovation Team


15

3) Notable Activities

1)

Event DECC Workshop: Carbon Assessment of Biomass Feedstock

Time 08th

March 2013, London

Details Attended event and contributed to the stakeholder consultation process following on

from the validation Report produced for DECC.

2) Event Tyndall Centre for Climate Change Research, Annual Assembly 2012

Time 11th-13th September 2012, Cardiff University

Details Attended event, representing the Manchester University Tyndall Centre.

3) Event Tyndall Centre for Climate Change Research, Annual Assembly 2013

Time 11th-13th September 2013, University of East Anglia

Details Attended event, representing the Manchester University Tyndall Centre.

4) Event Global Young Scientists Summit, 2014

Time 19th-24th January 2014, Nanyang Technological University, Singapore

Details Attended Summit representing the University of Manchester.

5)

Event BBC Radio Manchester Live Interview

Time 19th February 2014

Details

Following on from a Press Release from the University of Manchester, ‘UK Failing to

Harness Bioenergy Potential’. Carried out a live radio interview with BBC Radio

Manchester discussing our research and the key outputs and implications from the

research Paper, ‘Meeting Bioenergy Targets with Reduced Imports’

4) Presentations

1)

Title Availability & Sustainability of Biomass for Heating in the UK

Event IMechE Warming to Biomass Conference 2011

Date 21st September 2011

Location London

Role Produced Presentation with Dr. Patricia Thornley

Details Oral Presentation

2)

Title Modelling the UK’s Indigenous Biomass Resource Potential

Event Tyndall Centre PhD Conference 2012, ‘Knowledge Gaps’

Date 11th

-13th

April 2012

Location University of East Anglia

Role Primary Presenter

Details A copy of this Poster Presentation can be found in Appendix 16.0 of this Thesis

http://www.synertree.co.uk/1/post/2013/03/synertree-to-attenddecc-workshop-carbon-assessment-of-biomass-feedstock.html


16

3)

Title Presenting the Biomass Resource Model

Event Presentation to DECC’s Bioenergy & Energy Policy Teams

Date 1st October 2012

Location Whitehall, London



4)

Title UK Biomass Resource Scenarios

Event Tyndall Centre PhD Conference 2013, ‘Climate Transitions’

Date 03rd

-05th

April 2013

Location University of Cardiff



5)

Title Availability & Characteristics of the Future UK Biomass Resource

Event Sustainability Live Conference 2013

Date 16th

-18th

April 2013

Location NEC, Birmingham

Role Produced Presentation with Dr. Patricia Thornley


6)

Title Meeting Bioenergy Targets with Reduced Imports

Event European Biomass Conference 2013, Copenhagen

Date 3rd

-6th

June 2013

Location Bella Conference Centre, Copenhagen



7)

Title Developing Biomass Strategies to Maximise Indigenous Resource Potential

Event International Bioenergy Conference 2014

Date 11th

-13th

March 2014

Location Manchester Central Convention Complex



http://www.sustainabilitylive.com/page.cfm/action=seminar/libID=1/libEntryID=264/listID=1


17

Chapter 1 - Introduction


18

1.1 Opening Statement

“Observations throughout the world make it clear that climate change is occurring, and

rigorous scientific research demonstrates that the greenhouse gases emitted by human

activities are the primary driver” American Association for the Advancement of Science,

(2009) [1]. Consensus around the world relating to actions for the mitigation of

anthropogenic climate change predominantly focus on policies for reducing greenhouse gas

emissions from human activities, increasing the proportion of energy generated from

renewable and low carbon energy sources, and reducing energy consumption all together [2].

European Governments have greenhouse gas emission and renewable energy targets that are

bound by the baseline requirements of the Kyoto Protocol [3], and the European

Commission’s Renewable Energy Requirements [4], [5]. In addition, the UK is legally bound

by the 2008 Climate Change Act [6], to achieve a mandatory 80% cut in the UK’s carbon

emissions by 2050, and a benchmark 35% reduction by 2020; below 1990 levels [7]. The aim

is to encourage a transition towards a low-carbon UK economy through unilateral binding

emissions reduction targets [8], [9].

A key route to achieving these targets is to replace fossil fuel based energy with renewable

and low carbon energy technologies. There is a progressive consensus that having a broad

energy mix is likely to be the best method to achieving energy and climate change targets

[10].

Biomass as a renewable energy source can contribute towards reducing greenhouse gas

emissions, the decarbonisation of energy systems, the diversification of fuel supplies; and can

contribute towards the development of long-term replacements for fossil fuels [11].

Bioenergy is also a key component of European energy strategies [12]. The European

Commission estimates [13] that two-thirds of the EU’s 2020 target for 20% contribution by

renewable energy resource, may be from biomass.

In the context of these tough targets, the UK’s Renewable Energy Strategy [7] also confirms

the high likelihood that bioenergy systems will contribute an increasingly important role in

the UK achieving its Global, European, and National climate change, emission reduction and

renewable energy targets. In addition, bioenergy will most likely contribute significantly to

the UK’s future energy portfolio.


19

However, as bioenergy pathways are being assumed in many national energy strategies

globally [14], critical assessment of biomass resource availabilities is essential as increased

mobilisation of biomass resource will be required to meet the growing demand. Most energy

strategies of EU Member States assume the utilisation of non-EU sourced biomass to meet

their forecast demands [15]; so there is also likely to be increased demand (competition) [16]

for globally traded biomass resources in the future. As a consequence, there are many

limitations and notable sustainability implications relating to the global trade and reliance on

imported biomass resource [17].

However, the factors that determine the availability of biomass resources are not always

clear. Currently in the UK the biomass resources and feed-stocks utilised for the generation

of heat, power, and transport fuels, are a combination of indigenous resource and materials

imported from both the EU and wider global markets [8], [9].

The focus of the PhD Research Project on which this Thesis is based, was to gain a greater

understanding of the potential and limitations of biomass resource availability in the UK, and

how competing demands for the same biomass resources are likely to influence the UK’s

bioenergy sector. The UK’s ability to meet its own biomass requirements with indigenous

resources, determines the extent to which the UK may have to compete for biomass resource

commodities on the global markets. This Thesis presents research that focuses on the

following: analysis of the UK’s biomass resource supply chain dynamics, evaluating the

types and extent of indigenous biomass resources that the UK can practically mobilise,

analysing the UK’s potential future biomass resource demands, forecasting the extent that the

UK may have to import biomass resources to balance these demands, analysing the global

biomass trade markets, and undertaking a case-study analysis of a large biomass exporting

country in order to evaluate the nature of the future global markets from which the UK may

have to trade, and finally, to develop a series of strategies that the UK could potentially

implement in order to maximise its utilisation of indigenous resource.


20

1.2 Research Context

This section of the Introduction Chapter provides further background context to the wider

research project. This includes a literature review driven summary of the concepts of climate

change, an introduction to the UK’s energy systems; including how we currently generate our

energy. This section also includes a discussion of renewable energy technologies, their role

within the UK, and their contribution to mitigating anthropogenic climate change.

1.2.1 The Wider Research Context - Climate Change

A. Anthropogenic Climate Change

Anthropogenic climate change is summarised by MacKay (2008) [18] in three simple steps:

Fossil fuels are burnt through human activities, causing atmospheric CO2

concentrations to increase.

CO2 is a greenhouse gas.

Increasing the atmospheric CO2 concentrations, results in increasing global

temperatures and many other impacts.

B. What are Greenhouse Gases

C + O2 CO2 + Energy

Carbon + Oxygen Carbon Dioxide + Energy 1) Figure 1.1: Combustion Greenhouse Gas Calculations

Figure 1.1: Combustion Greenhouse Gas Calculations

As the equation in Figure 1.1 highlights, when any carbon based fuel is combusted, be it

wood, coal, petroleum or natural gas, greenhouse gases (GHG’s) and particularly CO2 will be

produced. The natural process of photosynthesis locks atmospheric CO2 into plant matter,

which when combusted however many years later or in whatever form, releases the CO2 back

into the atmosphere. The problem we face (the global population), is that CO2 levels are

increasing at a rate of about 0.4% per annum, and with relentless human activities continually

consuming fossil fuels this is likely to increase further [19]. The GHG’s that currently

provide the greatest concerns in terms of climate impact are; Carbon dioxide (CO2), Methane

(CH4), Nitrous Oxide (N2O), Chlorofluorocarbons (CFC’s) and Tropospheric Ozone (O3).

CO2 now accounts for ~57% of the ‘greenhouse effect’, CH4 ~17% and CFC’s ~5% [19].

Table 1.1 provides a summary of the characteristics of key GHG’s and their respective

impacts toward climate change.


21

Table 1.1: Climate Change Characteristics of the Core Greenhouse Gases Table 1) Table 1.1: C limate Change C haracteristics of the Core Greenhouse Gases

Greenhouse Gas Preindustrial

Concentration

Concentration in

1994

Annual Rate of

Increase

Lifetime

(Years)

Importance to

Greenhouse Effect

Carbon Dioxide (CO2) ~ 280 ppmv 358 ppmv 0.40 % 50-200 57%

Methane (CH4) ~ 700 ppbv 1,720 ppbv 0.60 % 12 17%

Nitrous Oxide (N2O) ~ 275 ppbv 312 ppbv 0.25 % 120 5%

CFC-11 0 268 pptv 0.00 % 50

5% CFC-12 0 503 pptv 0.00 % 102

HCFC-22 0 110 pptv 5.00 % 12

CF4 0 72 pptv 2.00 % 50,000

Data taken from [20]

C. The Greenhouse Effect

The Earth’s climate is powered by the sun radiating energy at very short wavelengths –

predominantly in the visible to near-visible end of the spectrum. Of this solar energy reaching

Earth, roughly one third is reflected away by the atmosphere, the remaining two thirds is

absorbed by the Earth and to a lesser extent the atmosphere. To balance this incoming

absorbed energy the Earth radiates on average the same levels of energy back to space, albeit

it at much longer wavelengths - primarily at the infrared end of the spectrum. This thermal

radiation is emitted by the land and the oceans and either passes through the atmosphere to

space, is absorbed by the atmosphere or is radiated back to Earth. This process is basically the

greenhouse effect [20]. Figure 1.2 visually demonstrates these processes.

2) Figure 1.2: Sche matic Demonstrating t he Greenhouse Effect

Figure 1.2: Schematic Demonstrating the Greenhouse Effect Adapted from [20]

The atmosphere’s two most abundant gases, Nitrogen (78%) and Oxygen (21%) exert almost

no greenhouse effect. It is the more complex molecules also known as the greenhouse gases

that exert this effect – water vapour and Carbon Dioxide contributing the most. It is the

Earth’s natural greenhouse effect that enables life as we know it. However a series of human

activities, in particularly the combustion of fossil fuels and deforestation, have greatly

increased this natural greenhouse effect. By increasing the abundance of greenhouse gases –

more ‘escaping’ energy is radiated back to Earth, increasing this warming effect [21].


22

D. Increasing Greenhouse Gas Emissions

In 1769 James Watt patented his efficient steam engine design, which in turn kick started a

series of events that led to what we know as the Industrial Revolution. The UK, the birthplace

of the Revolution was heavily endowed with coal supplies which, coupled with growing

industries, resulted in exponential and unprecedented growth and prosperity. Our love affair

with fossil fuels for powering our endeavors continues to this day. Figure 1.3 demonstrates

the exponential growth in consumption of fossil fuels since the Industrial Revolution. Figure

1.4 demonstrates the atmospheric CO2 concentrations for the past 1100 years based on air

trapped in ice cores (up to 1977) and directly measured in Hawaii (from 1958) – the ‘famous

hockey stick graph’ [22]. The overwhelming majority of scientific consensus and the entire

climate scientist community, place the combustion of fossil fuels as the principal reason why

atmospheric CO2 concentrations have increased since the Industrial Revolution, and are still

rising [18].

3) Figure 1.3: The Uti lisation of F ossi l Fuels since the Industrial Revo lut ion

Figure 1.3: The Utilisation of Fossil Fuels since the Industrial Revolution Taken from [22]


23

4) Figure 1.4: Graphs De monstrating t he Atmospheric CO2 since the Industrial Revolution

Figure 1.4: Graphs Demonstrating the Atmospheric CO2 since the Industrial Revolution

Taken from [22]

E. Quantifying Climate Change

Climate Sensitivity is the measure of how climate systems change with sustained radiative

forcing. The extent of this sensitivity is measured as a function of the extent of global surface

warming, as a result of a doubling of atmospheric GHG concentrations from pre-industrial

levels (CO2 280ppmv). To add context, current CO2 concentrations are around 385ppmv,

although when other GHG are also considered; by giving them a ‘carbon equivalent value’,

current GHG concentrations are estimated as being closer to 430ppmv [20].

In 2007 the Intergovernmental Panel on Climate Change (IPCC) estimated [2] that the likely

range of climate sensitivities, with a doubling of atmospheric GHG’s; would result in a 2˚C to

4.5˚C increase, with the best estimate being 3˚C. Therefore, the IPCC’s stance is, that it is

‘likely’ that should GHG concentrations stabilise at 560ppmv (double pre-industrial levels),

there would be a 3˚C temperate rise above pre-industrial levels.

Further analysis also demonstrates that should CO2 levels stabilise at 450ppmv, there would

be a 26%-78% chance that the global temperature would experience a greater than 2˚C

increase. This forecast increases to a 63-99% chance, should CO2 levels stabilise at 550ppmv

[23].


24

F. Uncertainty & Response to Climate Change

There remains a huge degree of uncertainty in the level at which atmospheric CO2

concentrations may peak, and even more uncertainty regarding the impacts that different

levels of global temperature rise may produce. The predicted list of potential drastic impacts

is long, without mentioning the potential melting of the Greenland Icecap and resulting sea

level rises. However, it is conceivable that ecosystems around the globe would be

significantly altered to the extent that the supply of certain goods and services we now take

for granted, may end [18].

In the absence of any explicit global consensus regarding the values or metrics that reflect

‘dangerous’ from ‘acceptable’ climate change, European leaders have worked together to

ensure that the EU takes the international lead in ensuring that global average temperature

increases, do not exceed pre-industrial levels by more than 2˚C [24]. Despite all the

uncertainties that still surround the issues of climate change, the IPCC highlight [2] the

undisputed issues crucial to the climate change and energy debate:

Global consumption of energy derived from fossil-fuel sources continues to increase.

This continuing and rising consumption is resulting in increasing CO2, which is the

‘‘the most important anthropogenic greenhouse gas’’.

Global unprecedented effort will be required over the next decade if CO2 emissions

are to peak and begin to decline, through the reduction of energy consumption and

decarbonisation of supplies.

Climate change and energy issues have shot up the political agenda over the past 20 years.

The UK’s emissions did reduce during the 1990’s, but this is predominantly a consequence of

declining manufacturing industries, and a large scale switch from coal to gas energy

generation. Subsequent recent policies have largely failed to prevent further growth in

emissions [25].

1.2.2 Energy Profiles

This next section focuses on evaluating the types and contribution of different energy sources

to the overall global demand, and what types of energy source are used in the UK. Renewable

energy technologies are reviewed in the context of the wider context. This section is designed

to demonstrate the current dominance of fossil fuels across much of the world, but also how

energy modelling and scenario development, can steer future energy decisions towards

technologies such as those of the bioenergy sector.


25

1.2.3 The Global Energy Consumption Profile

Global annual consumption of all forms of primary energy, increased more than ten-fold

during the 20th century [26]. The dominance of fossil fuels across the globe is clearly

highlighted by Figure 1.5, where a breakdown and contribution of different energy sources to

global primary consumption in 2011 is demonstrated.

5) Figure 1.5: Energy Source Contributions to Global Primary Energy Consumption (2011)

Figure 1.5: Energy Source Contributions to Global Primary Energy Consumption (2011)

Data Taken from [27]

No essential relationship exists between countries’ GDP per capita and the standard of living,

but both of these values are often highly correlated with energy use. A citizen of a developing

country may consume equivalent to less than one barrel of oil annually, compared to a citizen

from an industrialised country where their annual energy consumption may be 20-60 barrels

of oil-equivalent per capita [19].

This major global discrepancy in the spread of energy use may have massive implications for

future energy consumption, carbon emissions and climate change - as the developing world

becomes more industrialised and their energy demands and consumptions increase. This

global mismatch of populations and energy consumptions per capital is demonstrated clearly

within Figure 1.6, showing that North America consumes nearly 5x the world average, people

of Europe consuming half this value, and the rest of the world only about a fifth.

Global Primary Energy

Consumption by Source (2011)


26

6) Figure 1.6: Global D iscrepancy between Population and Energy Consumpt ion

Figure 1.6: Global Discrepancy between Population and Energy Consumption

Taken from [18]

A. Fossil Fuel Durability

With such reliance on fossil fuels for our energy, major concerns exist relating to the

durability of these resources - let alone the side-effect climate implications of continuing to

use fossil fuels [26].

If current consumption rates continue, proven world coal reserves should last for about 200

years, oil reserves for approximately 40 years, and natural gas reserves for around 60 years. It

is also predicted that the production of liquid fuels, including non-conventional as well as

conventional sources; is likely to peak between 2005 and 2015. The peak production of

natural gas is predicted for around 2030, and from then onward despite large quantities of oil

and gas reserves remaining, the overall resource will decline [28]. Therefore it is clearly time

to start looking seriously, at alternative energy options to take over from fossil fuels.

1.2.4 UK Energy Consumption Profile

In 2010 the UK’s overall primary energy consumption was 218.5 Mtoe, a 3% rise on 2009;

although primary energy consumption levels in 2009 experienced a 20 year low – attributed

to the economic downturn [29]. The Graphs in both Figures 1.7 and 1.8, demonstrate trends

in both the UK’s total annual primary energy consumption over the past 40 years, and also a

breakdown of the energy sources providing our energy.


27

7) Figure 1.7: U K Tota l Annual Primary Energy Consumption 1970-2010

Figure 1.7: UK Total Annual Primary Energy Consumption 1970-2010

Taken from [18]

8) Figure 1.8: Contribution of Energy Source to UK’s A nnua l Energy Consumption 1970-2010

Figure 1.8: Contribution of Energy Source to UK’s Annual Energy Consumption 1970-2010

Taken from [18]

UK Primary Energy Consumption

UK Primary Energy Consumption by Source


28

Figure 1.7 highlights that the UK’s primary energy consumption in 2010 was at its lowest

since 1987 and between 1990 and 2010, primary energy consumption on a temperature

corrected basis decreased by 4% [29]. Figure 1.8 demonstrates the changing contribution of

different energy sources to the UK’s energy mix. The rise of energy from gas over the past 40

years is clearly demonstrated, along with the decline of energy from coal – in part

demonstrated by the ‘solid fuels’ category. Another rising contributor clearly evident from

the 2010 data category is energy from biomass resources.

1.2.5 Renewable Energy & the UK’s Renewables Profile

Renewable energy has been defined as:

‘Energy obtained from the continuous or repetitive currents of energy recurring in the

natural environment'.

Twidell & Weir (2006) [30]

A. An Introduction to Renewable Energy Sources

Boyle (2004) [26] categorises sources of renewable energy into three distinct groups: those

that directly use the sun; those that indirectly use the sun; and renewable sources non-reliant

on the sun. These categories can be further explained as follows [26]:

i. Direct Solar Energy Renewable Sources

Solar energy can be directly converted using various renewable technologies.

Solar-Thermal: Solar energy can be absorbed by solar-thermal hot water collectors

to provide hot water or space heating.

Solar Concentrated Power: The concentration of direct solar radiation by mirrors

can also produce the high temperatures to power solar concentrator power stations.

Photovoltaic Panels: Direct solar radiation can also be converted into electricity

using photovoltaic panels that are strategically orientated on roofs and facades.

Passive Design: The utilisation of passive design techniques in buildings can also

enhance the use of direct solar energy for lighting and space heating requirements.

ii. In-Direct Solar Energy Renewable Sources

It is the evaporation of water as a result of direct solar radiation that powers our rivers once

precipitated back to Earth.

Hydro-Power: Powerful water flows around the globe allowing the generation of

hydroelectric power.


29

Wave & Wind Power: The differential contact of solar radiation across the globe as

a result of varying latitude, essentially powers our ocean currents and winds as

massive heat flow takes place towards the poles. This allows the generation of both

wave and wind power.

Bioenergy: Solar energy has its most obvious role in the growing of plant life.

Biomass materials are essentially formed from air and water through the influence of

sunlight. Biomass has been an important and essential source of energy for millennia.

The chemical energy stored within biomass during photosynthesis is released through

either combustion of the biomass, or after its conversion to another source of fuel.

During photosynthesis, radiant solar energy drives the chemical reactions in which the

carbon atoms from CO2 and the Hydrogen atoms from water combine to produce

carbohydrate compounds of different lengths, and Oxygen. It is this process that locks

up CO2, making biomass fuels carbon neutral; the CO2 released during combustion

balancing that taken up during photosynthesis.

iii. Non-Solar Energy Renewable Sources

The remaining major sources of renewable energy that are not reliant on solar radiation are

tidal energy and geothermal energy.

Tidal Energy: Harnessing the power of the oceans tides by directing the tidal flow of

water to either power turbines or other mechanical generators.

Geothermal Energy: Utilisation of heat energy from within the Earth, that results

from the gravitational contraction of the planet and decay of radioactive elements.

B. The UK’s Renewable Energy Mix

In 2010, electricity generated from renewable sources in the UK reached 25.7 TWh.

Collectively renewable sources generated 6.8% of the UK’s total electricity generation. Heat

from renewable sources reached 1,212 Ktoe, and renewable biofuel for transportation reached

1,214 Ktoe [29].

Figure 1.9 demonstrates a breakdown of the UK’s total renewable energy generation in 2010

as produced from different energy technologies. This highlights the large contribution of

energy from biomass energy sources with further notable contributions from wind power.


30

9) Figure 1.9: Contribution of the U K Renewable Energy Techno logies in 2010

Figure 1.9: Contribution of the UK Renewable Energy Technologies in 2010

Taken from [31]

1.2.6 Energy Scenarios – Modelling Future Trends

Energy scenario analysis first emerged during the 1970s-80s, as a response to limitations in

trend forecasting and the requirement to forward plan energy decisions. Our ability to predict

medium-long term is potentially dangerous due to the high complexity of human systems. As

a result, the concepts of energy scenario analysis were developed with the aim of exploring

alternative futures, and to enable decision making to be based on broad assessments [25].

Energy modelling and its output results are becoming increasingly important, as the future

world may very well be dependent on decisions made, based on today’s modelling scenarios.

Therefore, the management of energy resources and modelling of scenarios in an optimal

fashion has become mandatory amongst energy planners and policy makers. Energy scenario

modelling involves the effective planning and utilisation of all the energy resources available,

and the reliable supply and efficient management of resources – essentially a fully integrated

approach that considers options on technical, organisational, and behavioral levels [32].

Energy forecasting models are typically developed specifically for a Nation or utility, and are

normally dependent on a series of pre-determined prevailing economic and market

conditions. Some prominent energy scenario models developed for UK systems are as

follows:

A. DECC 2050 Pathways Analysis [33], [34]

DECC’s 2050 Pathways Analysis Tool is designed to help policymakers, the energy industry,

and to help the public understand these choices. For each sector of the economy four scenario

Contribution of UK Renewable Technologies (2010)


31

trajectories are developed, ranging from little/no effort to reduce emission or save energy,

through to ambitious targets that push towards the extremes of what can be achieved.

DECC’s 2050 Pathways Calculator is publically available, allowing users to develop their

own scenarios of changes; for achieving an 80% reduction in greenhouse gas emissions by

2050, while ensuring that energy supply balances demand.

B. MARKAL Energy Model [35]

The MARKAL Model is a numerical model that is designed to carry out economic analysis

of different energy systems at the National level, to represent its evolution over a period up to

50 years. The MARKAL model measures a series of parameters including energy costs, plant

costs, plant performances, and building performance. The model is designed to evaluate the

optimal technology scenario to suite the required energy demands.

C. Tyndall Decarbonisation Scenarios [36], [37]

The Tyndall decarbonisation scenarios project outlines alternative pathways for achieving the

60% reduction in CO2 emissions from 1990 levels by 2050, as adopted as a goal by the UK

Government.

1.2.7 UK Climate Change, Energy & Bioenergy Policy

In the previous section it was discussed how energy scenario models are being developed to

help planners direct their future energy decisions, to aid the reduction of carbon emissions

and to focus on renewable technologies. This next section provides an introduction to the

policies, instruments, and mechanisms that are currently being used to steer such decisions.

This section is of particular importance to the overall research focus, as it provides

background information regarding the forces that are helping the development of the

bioenergy sector in the UK. The specific policies and policy mechanisms discussed within

this section, demonstrating particular relevance to this research.

1.2.8 The Emergence & Development of the Climate Change Policy

Agenda

The fundamental aims of renewable energy and climate change policies are to increase the

uptake of renewable technologies, reduce GHG emissions, mitigate environmental impacts,

and enhance energy security. The main challenge for policy makers in this area is to create a

framework that will accelerate progress towards carbon targets, and prevent undesired

negative impacts along the way [26].


32

The optimal energy technology mix will likely require research, and development of policy

that invests in a portfolio of technologies. Although it is not clear which technologies this

portfolio should contain, as the process of research and development is inherently uncertain

[38].

Globally, a wide range of policy responses and instruments are being implemented in

response to global climate change. Near term policy responses include both restrictions on

emissions through limits or taxes, and the investment in environmentally friendly

technologies [39].

The UK has always positioned itself towards the forefront of policy making in this area. A

Report by the Royal Commission [40] developed a series of themes and concepts aimed at

addressing the issue of climate change, and relationships with the supply of energy. The

principal recommendation of the Report’s analysis was that the CO2 emissions from the UK’s

human activities should be reduced by 60% below 1998 levels by 2050. At the time, this

target stood out as being too radical to be politically acceptable; as it went well beyond the

binding commitments of the Kyoto Protocol [3]. However, as the agenda hardened, the UK

has legislated well beyond this proposed target through the development of the Climate

Change Act [6], that requires an 80% reduction in 1990 level CO2 emissions by 2050.

1.2.9 UK Climate Change & Renewable Energy Policy Timeline

The UK has experienced gradual progression in its development of climate change and

energy policy. Figure 1.10 provides a timeline of this step-by-step policy development

process, also providing a summary description of each key stage. The colour classifications

identify the different types of policies relevant to each theme.


33

Climate Change Focus Energy Focus Bioenergy Focus

Policy Timeline Summary Description

1989

The Non Fossil Fuel

Obligation (NFFO)

Scottish Renewables

Obligation (SRO)

Established under the 1989 Electricity Act, these were originally intended to support nuclear electricity generation, but in 1990 this was extended to include renewables. Both

the NFFO and SRO were funded by a Fossil Fuel Levy paid buy suppliers of electricity

from fossil fuels.

1996 White Paper on Energy EU White Paper with the primary policy objectives of; energy supply security, improving

competiveness of European business and taking environmental aspects into account.

1997 White Paper on

Renewable Energy

This White Paper provided a review of renewable energy across Europe, considered potential technical contributions for each sector and also defined a strategy and actions for

progressing the shift to renewable energy sources.

Specific to bioenergy, Member States were required to adopt national goals and strategies, to be compared alongside European wide progress in promoting progressively increased

market share of liquid biofuels.

2000 Climate Change

Programme

Policies and action priorities from both the UK and internationally. This was updated in

2006 with the aim of reducing CO2 emissions by 15-18% on 1990 levels by 2010, and overall GHG emissions by 23-25%.

2001 Renewable Energy

Sources Directive

This represented the first piece of European legislation aimed at promoting the production of energy from renewable sources. The Directive’s aim being to increase the contribution

of green electricity from 14 to 22% and to double the share of renewable energy from 6%

to 12% of gross energy consumption by 2010.

2001 The Climate Change

Levy (CCL)

Replacing the Fossil Fuels Levy, the CCL taxes the non-domestic energy use by industry and the public sector. Designed to incentivise energy efficiency and emission reductions.

2002

The Renewables

Obligation (RO)

Replacing the NFFO and SRO as the primary renewable energy policy instrument. The RO requires electricity end-suppliers to source a specific proportion of their annual electricity

supply from specific renewable technologies – receiving tradable Renewable Obligation

Certificated (ROCs) in the process.

The Energy Efficiency

Commitment (EEC)

The EEC requires energy suppliers to achieve 62TWh energy savings up to 2005 through assisting the implementation of home energy efficiency improvements – equivalent to a

~1% reduction in domestic emissions.

2003 Directive on Promotion

of Biofuels

This Directive required Member States to set national targets for the minimum level of bio or other renewable transport fuels on their markets. The reference levels recommended in

the Directive were 2% by 2005 and 5.75% by 2010.

2004

Communication on Share

of Renewable Energy in

the EU

This Communication evaluated the progress of Member States towards the 2001 RES Directive targets.

2005

Communication from the

Commission on “The

Support of Electricity

from Renewable Energy

Sources”

This Communication evaluated the widely varying potentials and developments in

different Member States, in the harmonisation of a renewable energy strategy.

In relation to the biomass energy sector, this Communication concluded that the

effectiveness of policy mechanisms being deployed in the Member States for solid biomass electricity was significantly lower than for wind.

2005 Biomass Action Plan

(BAP)

The BAP was produced following widespread stakeholder consultation across the EU, with a series of proposals aimed at promoting the development of the bioenergy sector.

2005

European Union

Emissions Trading

System (EU ETS)

The EU’s trading scheme aimed at ensuring Kyoto obligation compliance. Member states submitting National Allocation Plans (NAPs) to the EC, for the allocation of each

country’s emission totals for each sector; 2008-2012.

2006 Energy Green Paper

This Green Paper aimed to develop a common, coherent European energy policy, with

targets to take a global lead in the energy debate. Focusing on the following key areas: green jobs in the EU, promoting an internal energy market, tackling energy security, and

tackling climate change.

2006 EU Strategy for Biofuels

This Strategy looked specifically at the role of biofuels in reducing the EU’s dependency

on imported oil. Discussions focusing on the requirements for Member States to give favourable treatment to 2nd generation biofuels and the promotion of clean vehicles.

The Strategy also looked at the sustainability of different feedstock cultivations and

explored further optimisation of greenhouse gas benefits.

2007 The Code for Sustainable

Homes

Establishment of minimum performance standards for the design and construction of

homes, covering energy, water, materials and waste.

2008

Climate Change Act This Act set a legally binding target of an 80% reduction in emissions from 1990 to 2050.

With a medium-term target of a 34% reduction by 2020.

Carbon Emission

Reduction Target

(CERT)

Replacing the Energy Efficiency Commitment, the CERT places greater focus on the substantial domestic energy saving measures such as insulation.

Renewable Transport

Fuel Obligation (RTFO)

Administered by the Renewable Fuels Agency, the RTFO requires suppliers of road transport fuels to include a specified percentage of renewable fuels.

Energy Performance

Certificates (EPC) Requires that whenever a building is built, sold or rented, an Energy Performance


34

Certificate will be included to reflect the building performance.

2009

Community Energy

Saving Programme

(CESP)

Complementing CERT, the CESP scheme aims to reduce carbon emissions and fuel

poverty in the most deprived areas of England, Scotland and Wales.

2010

Carbon Reduction

Commitment Energy

Efficiency Scheme (CRC

EES)

Established under the Climate Change Act 2008, the CRC EES scheme targets emissions

by companies and public bodies not already subject to the EU system. The scheme comprising both reporting requirements and a carbon levy.

Feed-In Tariffs (FITs)

The FITs offer payment for small-scale low-carbon electricity generated by households, businesses and communities, with additional payments provided for electricity fed into the

grid.

Carbon Capture and

Storage (CCS)

Demonstration Project

A Government commitment of £1 billion capital funding for the first full-scale CCS demonstration project in the UK.

2011 Carbon Plan A Government-wide carbon reduction plan, including both domestic and international

emissions. The vision, plan and timetable for meeting the UK’s 2020 targets.

2011 Electricity Market

Reform

A series of proposals developed by the Government, aimed at reforming the UK’s power markets; by providing further support and incentives for low carbon technologies, whilst

ensuring sufficient backup capacity for periods of intermittent renewable generation.

2012

Green Investment Bank

(GIB). The GIB is developed to unlock finance for the transition to low-carbon growth.

UK Bioenergy Strategy Strategy document laying out the current status and planned direction for the UK

bioenergy sector.

Renewable Heat

Incentive (RHI)

The RHI provides long-term financial support across a wide range of renewable heat installations installed after 15 July 2009.

The Energy Bill This Bill includes provisions for a ‘Green Deal’ on energy efficiency, greater security of

energy supplies and more low-carbon electricity.

2014 Smart Meter Roll-Out

In response to a consultation period, this obligation requires energy suppliers to roll out

smart meters to all homes in the United Kingdom, over the period 2014 to 2019. The roll-out programme includes the replacement of around 53 million gas and electricity meters.

10) Figure 1.10: Development Timeline of the U K’s Key Climate Change & Energy Policies

Figure 1.10: Development Timeline of the UK’s Key Climate Change & Energy Policies

Adapted from [41], [42]

1.2.10 Key UK & EU Policy Mechanisms & Instruments

When developing energy policies, Governments are faced with many complex issues that

need to be balanced. In energy policy terms, this typically concerns the supply and demand of

energy, and the social and environmental considerations that result. Figure 1.11 provides an

illustration of a typical energy policy framework, in which policy mechanisms and

instruments are designed to promote renewable technologies. As an EU Member State, the

UK is free to set up its own energy, renewable technology, and climate change policies.

However, these policies are heavily influenced by European level legislation, policies and

activities. For example, the policies of EU Member States have to be consistent with EU

legislation restrictions and procurement laws. National level policies will likely be influenced

by the targets and requirements of European Directives, and National level policies will likely

duplicate but support requirements at EU level; and National level activities may be driven

through EU research or programme funding [42].


35

11) Figure 1.11: Energy Policy Framewor k

Figure 1.11: Energy Policy Framework Adapted from [43]

A. Policy Mechanisms

The following specific policy mechanisms have been set up within the UK and EU, with the

aim of directly influencing the development, within the bioenergy and other renewable

energy technology sectors.

i. European Union Emission Trading Scheme (EU-ETS)

The EU-ETS was agreed by the European Council and the European Parliament in 2003 and

became active on the 1st January 2005. This mechanisms works on the ‘cap and trade’

principle, where there is a limit on the total amount of certain GHG’s that can be emitted by

industry, power plants, and other installations within the system. Within the EU-ETS,

companies receive emission allowances, which they can sell or buy within a market as

required. At the close of each year, companies must surrender enough allowances to cover

their emissions, or otherwise incur heavy fines. Companies able to reduce their emissions are

eligible to either store their allowance for future needs, or sell them to other companies. The

overall quantity of allowances is reduced over time, with the overall aim of forcing the

market so that sectors cut their emissions [44].


36

ii. European Carbon Capture & Storage (CCS)

In 2009 the European Commission initiated the ‘CCS Directive’ [45] and set out supporting

mechanisms for the first CCS demonstration plants, under the ‘European Economic Program

for Recovery’ (EEPR) [46]. Across the EU, there is a common understanding that CCS

technologies will play a key role in reducing global CO2 emissions in the future.

CCS is proposed to provide an important contribution, in assisting the UK in meeting its CO2

emission reduction targets. The EU CCS initiative in the UK is becoming a reality in the form

of the White Rose Project under development across Yorkshire [47].

iii. Emission Performance Standards (EPS)

EPS’s are a method of setting benchmarks for the maximum levels of GHG emissions that

can be emitted for a certain amount of energy generated. In the UK, EPS’s have had previous

success in reducing the NOx, SOx, and particulate emissions from different installations [48].

The UK Government regards the EPS as a ‘regulatory backstop’; an important part of a

combined package of mechanisms, to prevent the unabated construction and operation of new

coal-fired power stations [49]

B. Policy Instruments

The next section provides a summary and discussion of some of the different policy

instruments that have been developed, and which are currently being adopted globally; across

the EU and within the UK. These include both statutory requirements, taxation based policy

instruments, and policy instruments driven by incentive. Each is designed to increase the

uptake of renewable technologies, reduce emissions, and mitigate climate change.

C. Policy Instruments - Statutory Requirement & Taxation Based

The following policy instruments have been designed and are currently being implemented to

promote the uptake of renewable technologies and climate change preventative actions; as a

statutory requirement or through promotion as a result of taxation.

i. Climate Change Levy (CCL)

The UK CCL is a taxation based policy instrument, focusing on the energy delivered to non-

domestic users throughout the UK. It is directly linked to the energy delivered and is

designed to incentivise energy efficiency and reduce carbon emissions. Electricity generated

from renewable technologies and approved co-generation schemes are exempt from the Levy.

Electricity generated from nuclear facilities is also subject to the Levy, despite the minimal

direct emissions resulting from this generation method [50].


37

For the UK bioenergy sector the CCL has been judged to be ineffective [51]. This is likely

due to the relatively small costs of the Levy itself – fundamentally making it more

economical to remain with fossil fuel based sources, than to switch to renewable technology

alternatives that attract higher capital costs. Similar policy instruments appear to have a much

greater influence in other EU Member States, notably in Germany and Sweden [51].

ii. The UK Renewable Obligation (RO) Scheme

The RO is the main policy instrument for promoting renewable electricity projects throughout

the UK. The focus of the RO is to place an obligation on all licensed suppliers of electricity

in the UK, to source an increasing proportion of their electricity from renewable sources.

Alternatively, the licensed electricity suppliers may hold ‘Renewable Obligation Certificates’

(ROCs) equal to the required percentage [52].

ROCs are essentially Green certificates that are issued to the generators of ‘eligible

renewable electricity’ within the UK, and supplied to customers within the UK by licensed

suppliers. In summary one ‘ROC’ is issued for each megawatt hour (MWh) of ‘eligible

renewable electricity’ output generated. The overall design of the RO in conjunction with

ROCs provides an incentive for the continual development of renewable electricity

generation capacity [52].

In relation to the bioenergy sector, the introduction of the RO has failed to demonstrate any

significant increases in capacity compared to other renewable technologies. This failure is

attributed to the single price offered through the RO for all renewable electricity; it being

uneconomical for bio-electricity to compete with cheaper options for the UK, such as wind

power; although RO updates will likely attempt to address this [53].

iii. UK Renewable Transport Fuel Obligation (RTFO)

The RTFO is a requirement for transport fuel suppliers to ensure that ≥5% of fuel for road

vehicles, is sourced from sustainable energy sources. The RTFO also requires additional

reporting of the carbon related impacts throughout feedstock production stages [54]. All

suppliers operating within the UK with a biofuel threshold usage of over 450,000 l/yr. are

required to report their performance [55].

In practice, the RTFO is mostly achieved by blending bio-ethanol, bio-methanol, biodiesel,

and biogas (derived from a wide range of biomass resource feedstock) with fossil fuels. The

UK Department for Transport (DfT) estimates [54] that the RTFO will help promote a

http://en.wikipedia.org/wiki/Ethanol_fuel

http://en.wikipedia.org/wiki/Methanol_fuel

http://en.wikipedia.org/wiki/Biodiesel

http://en.wikipedia.org/wiki/Biogas

http://en.wikipedia.org/wiki/Fossil_fuel


38

movement towards a target of 1/3 of all UK transport sector fuel, being produced

domestically. The RTFO also keeps the UK ahead of related EU level legislation.

iv. Electricity Market Reform

The Government is consulting on a series of options for reforming the UK electricity

markets. These being designed to balance the best deals for customers, whilst providing

existing and potential new entrants to the energy sector, with the certainty they need to

generate investment. The focus is to ensure that low carbon technologies become increasingly

attractive choices for investors, and ensuring that backup capacity is adequately rewarded.

Key proposals are [56]:

Feed-in Tariffs: Long-term contracts that are designed to provide greater revenue

certainty for low-carbon generation. A ‘contract for difference’ (CFD) model for low-

carbon generation is proposed; that should control consumer costs, provide stable

investor returns, and maintain the market incentives to generate when electricity

demand is high.

Capacity Payments: Targeted payment schemes to encourage and ensure security of

supply and the construction of flexible reserve plants or demand reduction measures.

These capacity payments ensure that there is a generation capacity buffer during

periods of intermittent and inflexible low-carbon generation.

Emissions Performance Standards: Upper limits to the extent that carbon intensive

power stations can emit emissions. Enforcing existing requirements for the building of

no new coal plants without effective carbon capture and storage capabilities.

D. Policy Instruments – Incentive & Investment Based

An alternative policy instrument approach for promoting and developing the renewable

energy and specifically biofuel sectors, are based on incentives such as energy subsidies.

Energy subsidies keep processes for consumers below market levels, or for producers above

market levels. The primary motive is to make renewable technologies highly economical and

viable for investment by the private sector [51]. A discussion of some incentive and

investment based schemes are discussed in the following section.

i. UK Renewable Heat Incentive (RHI)

The RHI is the world’s first and longest running financial support programme for renewable

heat generation. The RHI pays incentives to participants of the scheme, who generate and use

renewable energy to heat their buildings. The RHI is applicable to solar-thermal, ground-

source, and water-source heat pumps and biomass boiler systems [57].


39

Although the RHI has delivered modest success since its launch, it took 6 years to finalise,

following its initial introduction by the UK Department for Energy & Climate Change

(DECC). This period was very damaging for the development of the bio-heat sector that has

seen a large policy focus shift towards bio-power energy pathways [58].

ii. Green Certificates

Green Certificates are typically a tradable commodity that may be gained as a function of the

amounts of renewable energy generated. It is typical for one certificate to represent the

generation of 1 MWh of generation.

Green certificates in the UK have not been universally successful for the bioenergy sector in

comparison to the success found with other renewable technologies [51].

iii. Feed-In-Tariff (FiT)

The UK FiT policy instruments have been developed for application to predominantly small-

scale electricity generation systems, where a fixed sum is paid for the generation

technologies. In the UK, the FiT was designed with a few standout features applicable to the

bioenergy sector – including Combined Heat and Power (CHP). The majority of EU Member

States selected FiT’s and price premiums as the policy instruments of choice to support the

introduction of renewable technologies [48].

The UK Government estimated [51] that FiT’s supporting small-scale renewable energy

generation, would cost £8.6 billion up to 2030; although only produce a carbon savings worth

£0.42 billion. The cost effectiveness and efficiency of such a policy instrument must

therefore remain questionable.


40

1.3 Thesis Rationale

The rationale for undertaking this Research Project stems from a series of uncertainties and

questions. The designed research framework aims and objectives; are developed to address

each of these themes:

Legislated Targets – the UK has ambitious GHG gas reduction and renewable

energy contribution targets [59].

Bioenergy - the UK has plans for energy from biomass resources to make large

contributions to these targets.

Uncertainty – a comprehensive review is required of the UK’s biomass resource

supply chains, to further understand how much the UK’s indigenous resource will be

able to contribute towards the future UK bioenergy sector’s resource demands.

Bioenergy Sector – further understanding is required about how the UK’s bioenergy

and the biomass resource supply chain dynamics may develop in the future.

Imports – if the UK is going to have to import more biomass in the future to balance

its demands, the implications of this are still relatively unknown.

Trade – with biomass being identified as a key part of energy strategies in countries

all around the world, there are still many more questions than answers about how the

global biomass markets will develop, and whether supplies will be able to balance

rapidly growing demands.


41

1.4 Research Problem Statement

The following statement reflects the structure of the fundament research problem that the

PhD Research Project has been developed to address. The statement represents the

overarching questions, and the gaps in current knowledge that the PhD Research has been

undertaken to fill.

The UK has ambitious GHG reduction and renewable energy technology contribution targets,

that it is legislated meet through a series of progressive benchmark targets to the year 2050.

Energy from biomass is proposed to make large contributions towards the UK’s ability to

meet these targets. Questions remain as to the types and extent that UK indigenous biomass

resources may be available to contribute to balancing future bioenergy demands. Also, what

types and extent of biomass resources will the UK potentially need to import from the global

biomass trade markets, if indigenous resources are found to be insufficient to balance

demand? With the UK and many other countries around the world developing energy

strategies that rely on bioenergy pathways, further major questions remain as to how the

future global biomass trade markets will develop; and fundamentally whether supply will be

able to meet growing global demand? There are also many further unanswered questions

relating to how the UK Government could further develop its bioenergy strategies and

policies; to help mobilise and utilise a greater proportion of indigenous biomass resource, in

order to reduce the extent of biomass that may need to be imported in the future?


42

1.5 Research Aims & Objectives

This section discusses the key aims and objectives of the PhD Research. These aims and

objectives steer the research and analysis directions; undertaken during the project as

documented in this Thesis.

1.5.1 Research Aim 1

Develop a modelling tool that would enable the analysis of biomass supply chain dynamics

for a chosen country or geographic area.

A. Objectives

The objectives of Research Aim 1 are as follows:

Through the undertaking of wide literature reviews, develop a modelling tool that can

be applied to reflect the biomass resource supply chain dynamics in both the UK and

Brazil; in order to evaluate both the UK’s and Brazil’s biomass resource potentials.

Develop the modelling tool so that agricultural land requirements and the biomass

dependent industry’s resource demands, are forecast and fully accounted; so that any

biomass resource availability forecasts are in addition to food and industry demands

being met.

Through further wide literature reviews, develop the modelling tool so that bioenergy

potential forecasts, can be developed using the resources identified as being available;

taking into consideration the broad range of biomass resources, bioenergy conversion

process, and potential forms of bioenergy that could be generated.


Determine the UK’s indigenous biomass resource potential, and the availability of indigenous

resource that may be utilised by the UK bioenergy sector, without impacting food systems or

existing biomass resource markets.

A. Objectives


Through the development of a series of biomass resource scenarios that reflect

different potential pathways that the UK could take, with varying focus on developing

the UK bioenergy sector; analyse the potential availability of UK indigenous biomass

resource that could be used by the UK bioenergy sector.


43

Compare the biomass resource availability forecasts from each of the biomass

scenarios, against forecasts of UK bioenergy sector’s resource demands; in order to

determine whether the extent and types of available UK biomass resource, may be

able to balance the UK’s resource demands.

Evaluate the extent and types of biomass resources that the future UK bioenergy

sector may need to import from the global biomass trade markets, if indigenous

resources are insufficient to balance UK demands.


Evaluate the nature and characteristics of the global biomass trade markets, and analyse how

these may change in the future.

A. Objectives


Determine the major supply and demand regions and countries that are currently

driving global biomass resource trade, and evaluate whether these may change in the

future.

Identify the current trade flows of key biomass resources, and also evaluate how these

may evolve in the future.

Through focusing on the biomass supply chain dynamics of a key biomass exporting country

(Brazil), evaluate whether future biomass exports traded on the global biomass markets, may

be sufficient to balance demands.

1.6 Thesis Storyline

This final section of the Introduction Chapter presents the framework of the ‘Thesis

Storyline’. This represents the structure developed to present the literature reviews,

methodologies, analyses, results, data, discussions, and conclusions of the wider PhD

Research Project, as reflected within this Thesis. The structure has been designed to flow,

presenting a ‘story’ of the research. The Thesis Chapters have been developed to provide a

background to the research, followed by a presentation of the methodologies; moving on to

present the analyses, results, discussions, and finally the conclusions. As the Chapters

progress, further methodologies are presented followed by their respective results,

discussions, and conclusions. This specific approach was chosen to reflect the actual

progression and chronological order in which the research was undertaken. The conclusions


44

derived from each theme of the research; in turn, influence the successive steps taken within

the PhD Research Project.

The Thesis Storyline progresses through the following structure of Chapters:

1.6.1 Chapter 1 – Introduction

This Introduction Chapter is listed as Chapter 1 within the chronological ordering of the

Thesis Chapters.

1.6.2 Chapter 2 – Biomass as a Renewable Energy Resource

Chapter 2 presents a literature review driven analysis of the different types and forms of

biomass, and the range of bioenergy pathways that make biomass resources attractive energy

options. The Chapter aims to set the scene in discussing the broad range of biomass

resources, and their varying characteristics. The Chapter also discusses the equally wide

range of bioenergy conversion pathway options, and potential end products that make

biomass a versatile renewable energy resource.

Chapter 2 links to other Chapters, in that it provides the knowledge based framework that is

required when assessing different types of biomass, and their applicability to different

bioenergy conversion pathways to produce various forms of energy or related products.

1.6.3 Chapter 3 – Biomass Resource Modelling

Chapter 3 presents a literature review driven discussion of the concepts of biomass resource

modelling. This includes an analysis of the different modelling methodologies, developed to

analyse biomass resources at various geographic and spatial scales. Also, methodologies

developed to analyse different themes of biomass research that have varying focus, such as

modelling biomass economic feasibilities or resource potentials.

Chapter 3 is essential to the Thesis Storyline as it introduces the concepts of biomass resource

modelling that are integrated into the development of the Biomass Resource Model – the

primary tool developed to carry out all analyses throughout the research.

1.6.4 Chapter 4 – Developing the Biomass Resource Model

Chapter 4 represents the primary methodology Chapter within the Thesis. This includes a full

discussion of how the Biomass Resource Model (BRM) was developed, and a presentation of

the specific calculation equations that make up the modelling mechanics, of each analysis

theme within the BRM. The BRM is designed as a flexible tool that can be adapted to model

the full biomass resource supply chain dynamics, and bioenergy systems of a given country


45

or geographic region. Chapter 4 focuses on the development of the UK BRM; designed to

analyse the UK’s biomass resource supply chains, and forecast how the UK’s indigenous

biomass resource may develop in the future.

The methodologies developed within Chapter 4 are important and closely linked to each of

the other Chapters where analysis is undertaken, using the BRM.

1.6.5 Chapter 5 – Drivers Influencing Biomass Availability & Bioenergy

Chapter 5 represents the first Chapter where the research analysis is undertaken. The Chapter

starts with a literature review driven assessment, to identify and further understand the broad

range of biomass resource supply chain drivers that are closely linked, and provide influence

in determining the potential availability of different biomass resources. A wide literature

review is also undertaken to gain an understanding of the UK’s current supply chain

characteristics, and also to determine how these may change in the future.

The UK BRM is calibrated to reflect the ‘literature review informed’ future UK biomass

supply chain characteristics. Thus a ‘UK Baseline Scenario’ is developed within the UK

BRM, and an analysis is undertaken to evaluate the UK’s potential biomass resource

availability for this scenario. A sensitivity analysis is then carried out, to determine the extent

to which each specific driver within the UK BRM influences the different biomass resource

availability forecasts. Chapters 5’s results, discussions, and conclusions, focus on

highlighting the specific supply chain drivers that are identified as providing the greatest and

least influence in determining the potential availability of biomass resources.

The analysis undertaken in Chapter 5 is linked closely to that undertaken in Chapter 6, where

scenarios are developed; and Chapter 10 where the BRM is adapted to reflect Brazil’s

biomass supply chains.

The UK BRM baseline scenario analyses and the supply chain driver sensitivity analyses

undertaken in Chapter 5, reflect work submitted and reviewed by Elsevier’s Biomass and

Bioenergy Journal under the title of: ‘Increasing Biomass Resource Availability through

Supply Chain Analysis’. Further details of this submission are discussed in the PhD Outputs

Section, and the Paper is presented in Appendix 15.

1.6.6 Chapter 6 – UK Biomass Resource Scenarios

Chapter 6 progresses the Thesis Storyline on from the analysis undertaken in Chapter 5. In

Chapter 6, further literature review driven analysis is undertaken to understand how the UK’s

biomass supply chain characteristics may change if the UK were to follow a series of


46

potential future pathways, where varying degrees of focus are placed on developing the UK’s

bioenergy sector and mobilisation of UK biomass resource. Respective scenarios are

developed within the UK BRM representing these potential future pathways and biomass

resource availability, and bioenergy potential forecasts are developed and extensively

analysed.

Analyses within Chapter 6 identify the specific types of biomass resource that demonstrate

the highest potential value for the UK bioenergy sector. In addition an analysis is undertaken

to forecast the UK’s potential maximum resource mobilisation levels, and how these

indigenous biomass resources may contribute to the UK’s energy targets.

The analysis undertaken in Chapter 6 builds upon that undertaken in Chapter 5, and also

forms the basis for the discussions and analysis undertaken in Chapter 7.

The UK BRM scenarios analysis undertaken in Chapter 6 reflects work published within

‘Elsevier’s Energy Policy Journal’, under the title: ‘Securing a Bioenergy Future without

Imports’. Further details of this submission are discussed in the PhD Outputs Section, and the

Paper is presented in Appendix 15.

1.6.7 Chapter 7 – The Future UK Bioenergy Sector

Chapter 7 starts with a literature review driven analysis of the UK’s current bioenergy

strategies and policies. This is undertaken to gain a greater understanding of where the UK’s

bioenergy sector currently stands, and the likely directions it may take in the near, medium,

and long-terms. Following this review, an analysis is undertaken to evaluate the types and

extent of biomass resources that the UK bioenergy sector will likely require, if the current

strategies and plans were to be realised.

Drawing on the results of the scenario analyses in Chapter 6, a resource balance analysis is

undertaken that compares the specific potential future demands of the UK bioenergy sector,

against the potential indigenous biomass resource availabilities forecast for each scenario.

The main conclusions from Chapter 7 are the evaluations of whether or not the UK is found

to have sufficient types of, and the extent of, indigenous biomass resources; in order to meet

its future demands. The assumptions being that the UK will have to import resources to

balance its demands, if indigenous resources are found to be insufficient.

The analysis undertaken in Chapter 7, builds directly on from that undertaken in Chapter 6, in

that the scenario forecasts from Chapter 6, are used within Chapter 7’s biomass resource


47

balance analysis assessments. The nature of the results presented in Chapter 7, also heavily

influenced the successive directions of the PhD Research Project; therefore the successive

Chapters of the Thesis are heavily linked to Chapter 7.

The UK BRM biomass resource balance analysis undertaken in Chapter 7 reflects the work

published within Elsevier’s Energy Policy Journal under the title, ‘Securing a Bioenergy

Future without Imports’. Further details of this submission are discussed in the PhD Outputs

Section, and the Paper is listed in Appendix 15.

1.6.8 Chapter 8 – Global Biomass Trade: Supply, Demand, Limitations &

Sustainability

Chapter 8 presents a further literature review driven analysis; this time focusing on the global

biomass trade markets. The concept of this Chapter is to further analyse and understand the

global markets, in which countries have to trade if they require biomass resource imports to

balance their demands. The discussion areas focus on identifying: the key supply and demand

regions for biomass trade; the major trade flows of different types of biomass; how the

market might develop; the potential limitations; and the sustainability impacts associated with

the global trade of biomass.

Chapter 8 is closely linked to Chapter 7 in that it discusses the global markets in which the

UK will have to trade, if the UK faces a deficit of indigenous resources to meet its demands.

As before, Chapter 9 is heavily influenced by Chapter 8, in which Brazil is highlighted as a

case study of a country with a large biomass resource, and the export potential to drive the

global biomass trade markets.

The analysis of global trade markets discussed in Chapter 8 reflects the work of a third

Journal Paper with the preliminary title, ‘Global Biomass Trade Caution’. Further details of

this proposed submission are discussed in the PhD Outputs Section.

1.6.9 Chapter 9 – Case Study: Brazil’s Biomass Resource Analysis

Chapter 9 focuses on Brazil, as a case study of a country that exports, and is forecast to

increasingly export, biomass resources for global trade. The Chapter presents methodologies,

describing how the Biomass Resource Model was adapted to reflect the biomass resource

supply chains of Brazil (Brazil BRM). Drawing on the methodologies of Chapter 5, a

‘literature review informed’ Brazil BRM Baseline Scenario is developed, to forecast the

extent and types of biomass resources that Brazil may have in the future, and how much they

may be able to export.


48

A unique area of analyses is undertaken in Chapter 9, in developing a series of ‘Brazil

Bioenergy Scenarios’. Again drawing on literature and influenced by a series of Case Study

Countries; scenarios are developed that evaluate how Brazil’s energy strategies and policies

may develop in the future, if the Brazilian Government were to set targets with varying

ambitions, to increase the renewable energy technology contribution towards the Brazilian

energy mix. The key conclusions from Chapter 9, discuss the potential impacts to the global

biomass trade markets and future net importing countries; if countries such as Brazil were to

start utilising greater proportions of their biomass resources to deliver domestic energy

demands, rather than importing them.

The Brazilian bioenergy scenario analyses of Chapter 9, reflects the core body of work of a

third Journal Paper; with the preliminary title, ‘Global Biomass Trade Caution’. Further

details of this proposed submission are discussed in the PhD Outputs Section.

1.6.10 Chapter 10 – An Alternative UK Bioenergy Strategy

Chapter 10 brings the focus of the Thesis back to the UK. In reflecting on the conclusions

drawn from each of the previous Thesis Chapters, Chapter 10 draws on literature and key

case studies, to present a series of themes, strategies, and policy innovations; the proposal of

which present potential alternative UK bioenergy strategy options. These potential strategies

are designed with the aim of increasing the UK’s focus, on utilising key biomass resources

that demonstrate the greatest potential. Thus, the Chapter presents alternative UK bioenergy

strategy options.

1.6.11 Chapter 11 – Thesis Conclusions

Chapter 11 presents the PhD Conclusions including: an analysis of the research results and

conclusions, with respect to the initial PhD Research aims and objectives; an assessment of

the implications of the research conclusions; identification of the limitations of the work; and

suggested areas of additional work that would further develop the research.

1.6.12 Thesis References

This section within the Thesis presents the complete list of references. These are ranked

chronologically in relation to their reference points throughout the Thesis texts.

1.6.13 Thesis Appendices

The final section of the Thesis presents the various Appendices referenced throughout the

Thesis.


49

Chapter 2 - Biomass as a Renewable Energy Resource


50

2.1 Biomass as a Renewable Energy

Resource

The following Chapter introduces and discusses the various characteristics of biomass

materials that make them important fuels. In the context of the overall Thesis structure, this

Chapter is important in developing the background information; discussing why different

biomass materials have varying properties, and why biomass is flexible in allowing a range of

energy generation or conversion pathways.

2.1.1 A Renewed Interest in Biomass as a Fuel

There is a renewed interest in the widespread utilisation of biomass resources for the

generation of energy. Interest is focused on: the conversion of energy crops to produce

transport fuels, the direct combustion of resource to generate heat, or the co-firing of biomass

with other fossil fuels to produce power. Compared to other renewable energy supplies,

biomass offers the distinct advantage of being a readily storable energy resource, with a wide

geographic spread [60].

This trend has been reaffirmed by the European Parliament, in noting: “Biomass has many

advantages over conventional energy sources, as well as over some other renewable

energies; in particular: relatively low costs, less dependence on short-term weather changes,

promotion of regional economic structures, and provision of alternative sources of income

for farmers” [61].

Energy produced from biomass and its conversion products, represents an ever-increasing

proportion of today’s energy mix. Biomass materials can be converted into liquids, solids or

gaseous fuels; through a series of conversion processes, in order to enhance its energy

content. The ability to convert biomass materials through physical, chemical or biological

processes, allows the transformation of carbonaceous solid materials that may be awkward to

handle, bulky and of low energy concentration; into fuels with enriched physicochemical

characteristics, with the added options for economic storage and transferability [62].

2.1.2 The Storage of Energy within Biomass

The energy within biomass originates from the Sun, as a result of the processes of

photosynthesis. Essentially, CO2 from the air is converted into other Carbon based molecules

such as sugars (Figure 2.1). As the Sun’s energy is intercepted by the photosynthetic active


51

elements within plants, it is ‘fixed’ through this process. These sugars or ‘Carbohydrates’ are

stored within plants, or in animals when eaten; to be expelled as their wastes [63].

Water + Carbon Dioxide + Sunlight → Glucose + Oxygen

6 H2O + 6 CO2 + Radiant Energy → C6H12O + 6 O2 12) Figure 2.1: The Photosynt hesis Reaction

Figure 2.1: The Photosynthesis Reaction

Biomass resources are harvested or collected as part of a constantly replenishing system; as

crops grow, wastes continue to be generated, and residues collect. This cycle creates a closed

Carbon loop, effectively making biomass materials Carbon neutral fuel sources [64].

2.1.3 Biomass Composition, Characteristics & Fuel Properties

Biomass materials have diverse characteristics that influence their performance as a fuel, and

also the types of bioenergy pathways that can be applied. The list below, and in following

section, provides a description of the key fuel property characteristics of biomass materials:

Calorific Value Moisture Content Bulk Density

Original Source Chemical Content Energy Density

Ash Content Mechanical Durability Particle Size/Dimensions

A. Calorific Value

The calorific value (CV) of any fuel is highly important, as it documents its heating potential

– the measure of the fuel’s energy content. The CV unit for biomass fuels expresses the value

of heat released per unit of fuel during complete combustion. MJ/kg is the unit typically used.

The gross calorific value (GCV) of a fuel is the total heat generated during its complete

combustion, when the water vapour released is condensed, and the heat from vaporisation is

recovered. This is generally not applicable to biomass fuels, as flue gases are not cooled

below ~130˚C, and therefore water cannot condense [65].

The net calorific value (NVC) of a fuel is the value of heat generated by the full combustion

of a unit of the fuel, whilst water vapour expelled remains as a vapour, and the heat of

vaporisation is not recovered. In the UK when considering biofuels, the NVC is used more

widely than the GCV [65].

The fundamental characteristic determining a biomass fuel’s calorific value is its moisture

content (MC). The MC of biomass materials vary greatly, and as a result the CV of different

biomass materials also differ [65].


52

B. Moisture Content (MC)

The MC of biomass materials is expressed as a percentage of either a ‘wet’ or ‘dry’ basis.

Although both these units are used throughout the biomass industry, the wet basis calculation

method is used by suppliers, as it provides a clearer indication of a material’s water content.

The calculation equations within Figure 2.2 demonstrate how MC is calculated; the ‘oven dry

mass’ being that with all the moisture removed.

Typically the higher the MC the lower the CV, as every unit of fuel contains less oven dry

mass – the portion of the fuel that will convert to release heat energy; the impact of MC being

most noticeable in systems where the released water vapour cannot be condensed – the

energy within the water vapour therefore not being recovered [66].

Wet Basis Calculation: MC = (

Fresh Mass - Oven Dry Mass

) x 100% -----------------------------------------

Fresh Mass

Dry Basis Calculation: MC = (

Fresh Mass - Oven Dry Mass

) x 100% -----------------------------------------

Oven Dry Mass

Figure 2.2: Moisture Content Calculations [66] 13) Figure 2.2: Moisture Content Calculations

C. Bulk Density

The bulk density of materials is the value of mass of the material, divided by the volume in

which it exists; the higher the bulk density of a material, the more mass and value of fuel

within its given volume. A material’s MC and the way in which it is processed, are key

factors in determining its bulk density. For example, the bulk density of wood pellets vs.

wood chips, are typically ~660kg/m³ compared to ~250kg/m³ respectively [65].

D. Energy Density

The energy density of a material is another highly important characteristic of a fuel. This

being the value of energy contained with a given unit of the material (for example, MJ/m³).

The energy density is calculated through multiplying the CV (MJ/kg) with the bulk density

(kg/m³). The energy density value allows the estimation of specific fuels’ ‘volumetric fuel

consumption rates’, which is important when calculating the volume of resources required to

meet a given demand [66]. Table 2.1 below documents and provides easy comparison of the

typical CV, bulk density, and energy density values for a number of biomass and fossil fuel

resources.


53

Table 2.1: Comparison of Typical Fuel Characteristic Values for Biomass & Fossil Fuels Table 2) Table 2.1: Comparison of Typical F uel C haracteristic Values for Biomass & Foss il F uels

Fuel Material Net CV

(MJ/kg)

CV

(kWh/kg)

Bulk Density

(kg/m³) Energy Density

(MJ/m³)

Energy Density

(kWh/m³)

Bio

ma

ss

Woodchips (30% MC) 12.5 3.5 200 - 250 2,500 - 3,125 694 – 868

Log Wood (20% MC) 14.6 4.1 350 - 500 5,110 - 7,300 1,419 - 2,028

Wood (Solid Oven Dried) 18.6 5.2 400 - 600 7,440 - 11,160 2,067 - 3,100

Wood Pellets 17.0 4.7 600 - 700 10,200 - 11,900 2,833 - 3,306

Miscanthus (Bale – 25% MC) 12.1 3.4 140 - 180 1,694 - 2,178 471 – 606

Fo

ssil

Fu

els

House Coal 29.0 8.1 850 24.7 6,847

Anthracite 32.1 8.9 1,100 35.3 9,808

Oil 41.5 11.5 865 35.9 9,972

Natural Gas - - - 36.0 10.1

LPG 46.9 13.0 500 23.5 6.5


E. Particle Size/Dimensions

Biomass like coal is heterogeneous, but it is also a low density material that can be difficult to

reduce to uniform particle sizes through standard techniques. Woody biomass, agricultural

residues, and energy crops, are all examples of biomass resources that have relatively large

particle size distribution, even after typical milling or other size reduction processes [68].

Despite the difficulty in ensuring uniformity of size of biomass particles, biomass combustion

and processing plants, are typically designed to operate efficiently with materials of specific

sizes and dimensions. Heating and processing systems, also involve mechanisms for the

handling and transferring of material from storage to processing areas. Therefore, the particle

size and dimensions of the materials, is a major characteristic determining the smooth

operation of these processes [68].

F. Mechanical Durability

The mechanical durability of any fuel, is a key characteristic in that it determines the types of

bioenergy plant that are able to process/combust the material. Durability being the primary

form of measurement, to classify pellet quality in the biomass feed pelleting industry [69].

An example of this can be demonstrated by biomass pellet fuels, that can sometimes

disintegrate and breakup as a result of the handling process – decreasing the combustion

efficiency of the fuel. Good quality pellets typically have a mechanical durability factor of at

least 97.5%; therefore any pellets of this standard will experience ~2.5% less breakup prior to

combustion [69].


54

G. Original Source

The original source characteristics of a fuel are important, as they determine the classification

of the fuel and specifically whether the fuel is classified as a waste – and therefore relate to

permit issues. Other original source characteristics may also determine whether the material

has any physical or chemical contaminants, potentially impacting the combustion process and

resulting products [65].

H. Ash Content

The ash content characteristic of biomass materials relate to the extent of ash produced as a

result of its combustion. The ash content is closely linked to both the chemical properties of

the specific material, and also the type and performance of the bioenergy plant utilised. Ash

produced from the combustion of wood materials, is highly dependent on the bark layers

within the tree. It is the concentration of specific minerals within the bark that determines the

characteristics of the ash produced; the dominant constituents being Calcium, Silicon,

Aluminium, Potassium and Magnesium [70].

I. Chemical Composition

The chemical composition of biomass materials is an important determinant of the properties

and content of the combustion products produced. Probably the most important influence of a

material’s chemical composition, relates to energy generation - as the material’s calorific

value is basically a function of the percentage content of carbon and hydrogen [71].

Great care is required when determining the types of biomass materials to be utilised. For

example, materials such as straw typically demonstrate high alkaline metal salts content that

may damage the plant; creating high levels of slagging, ash content, and gaseous/particulate

emissions. Also, if high levels of sand are present in fuels, glass formation may occur during

the combustion process, leading to potential damage to the energy plant [65].

The prominent chemical constituents of plants are summarised in Figure 2.3, the major

constituents of lignocellulosic materials being: cellulose (~43%), lignin (~36%), and

hemicellulose (~22%). In depth chemical analysis of dry wood, demonstrates a typical

breakdown of elemental constituents of: Carbon (~52%), Hydrogen (~6.3%), Oxygen

(~40.5%), and Nitrogen (~0.4%) [71].


55

14) Figure 2.3: The Principal Components of Biomass Material

Figure 2.3: The Principal Components of Biomass Material Adapted from [72]

2.1.4 The Variable Characteristics of Biomass Materials

It is often difficult to establish a representative biomass composition breakdown, for all the

different biomass feedstocks; due to the large range of variations. In some cases this makes

reliance on biomass as the sole fuel source, problematic. There are various solutions to this

issue; including pre-treatments that may remove undesired contaminants such Chlorine or

Potassium [73]. One of the best ways to anticipate the performance of different biomass

types; is through the accumulation of knowledge relevant to each type of biomass.

A. The Phyllis Database

Until relatively recently, information relating to the composition and characteristics of

different biomass types was scattered amongst various texts, and was expressed in varying

units. The ‘Phyllis Database’, designed by the Energy Centre for the Netherlands, is a

comprehensive source of information detailing the average compositions of different biomass

types and wastes. As of 1998, the Phyllis Database has been made available for third parties,

and the data records continue to develop [74].

B. Biomass Combustion Models

Another way of categorising the characteristics of different biomass materials is through a

biomass combustion model; the main biomass combustion reactions summarised by Figure

2.4. Biomass combustion models are a method of analysing combustion data, at either

macroscopic or microscopic scales. The macroscopic properties are derived following a

‘macroscopic analysis’, that typically looks at values for: ultimate analysis, heating value,

moisture content, particle size, bulk density, and ash fusion temperature. Values derived


56

following a Microscopic Analysis, may include: thermal, chemical kinetic, and specific

mineral data [73].

Non-

Reacting Solid

→

Heat

+ Drying

→ Pyrolysis →

Pre-

Combustion Reactions

→

Primary Gas

Phase Combustion

→ Secondary

Combustion →

Effluent

Stack Gas

Figure 2.4: Biomass Combustion Reactions [73] 15) Figure 2.4: Biomass Combustion Reactions

2.1.5 The Negative Impacts of Biomass for Energy Generation

Whilst biomass resources represent many advantages as fuels, there are also problems

relating to the utilisation of biomass for energy pathways. Often highlighted impacts include

environmental pollution resulting directly from the energy conversion processes and also

problems relating to land-use change such as deforestation or impacts on food productivity.

A. Pollution Impacts

In all cases of combustion, there is potential for the release of airborne pollutants. Figure 2.5

provides a step-by-step demonstration of a typical conversion pathway of biomass materials,

and highlights the stages of pollutant generation. The predominant pollutants relating to the

conversion of biomass materials being as follows:

Methane

Nitrogen Oxides

Furans

Sulphur Oxides

Hydrogen Chloride

Dioxins

Organic & Inorganic Air Particulates

Polyaromatic Hydrocarbons (PAHs)

Volatile Organic Compounds (VOCs)

Wet Biomass → Heating /

Drying →

Dry

Biomass

Biomass →

Volatiles

(tar &

gases)

+ Char

Volatiles + Air → CO + CO2 (+PAH + Unburned Hydrocarbons

+ Soot + Inorganic Aerosols)

Char + Air → CO + CO2

Volatiles (N, S, K) → N, S, K based Pollutants

Char (N, S, K) → N, S, K based Pollutants 16) Figure 2.5: Typical Biomass Conversion Pathway & Po llutants

Figure 2.5: Typical Biomass Conversion Pathway & Pollutants Adapted from [60]

Biomass materials can broadly be classified into groups based on an evaluation of their

source. Although the chemical and physical properties of different biomass types in each

group don’t always correlate, there is growing suggestion that these groupings can indicate

the types of pollutants that may occur. For example, the extent of smoke is likely a function


57

of the lignin content, and NOx pollutants typically depend on the combustion environment.

All biomass materials contain: Nitrogen, Potassium, Phosphorus, Calcium, Magnesium,

Sodium, and Silicon; with other species also containing: Manganese, Iron, Molybdenum,

Copper, and Zinc; in varying compositions based on the growing conditions and time of

harvesting. It is the composition of these elements in different biomass samples that

ultimately determine the pollutants produced [60].

B. Land-Use Impacts

Another major issue related to biomass, and particularly the growth of energy crops; relates

to land-use change. The widespread planting of energy crops on deforested land, or land that

may otherwise be used for the production of foods; is a major issue.

The impact of land-use changes with a shift towards the production of biofuels; has been

demonstrated to have large ‘unseen’ carbon impacts, depending on types of vegetation

present on the land before clearing [75]. Whilst the widespread growth of biofuels in poorer

areas of the world, on land that would otherwise be used for food production, has sometimes

been demonstrated to result in food shortages, and large food price spikes for the local

populations [76].

The potential impacts of land-use change and on food systems, relating to the production of

biomass; are key themes addressed and discussed further within Chapters 4 and 8 of this

Thesis.


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2.2 Biomass Feedstocks, Resources &

Energy Pathways

Building on the previous section’s review of the properties and characteristics of biomass

materials; this next section introduces the different biomass resources and feedstocks that are

available for the bioenergy sector. This categorises the different types of biomass in relation

to their sources and properties, and places them within the context of the research project,

linking different biomass resources to particular energy generation and conversion pathways.

2.2.1 Categories of Biomass

Biomass materials by definition; are any organic matter that has been derived from plants.

This includes plant and animal materials such as: wood from forests, crops, seaweed, material

unused from agricultural and forestry processes, as well as organic industrial, human and

animal wastes [63].

In energy terms, globally the majority of bioenergy generated comes from wood and wood

wastes (~64%), municipal solid wastes (MSW) (~24%), agricultural wastes and residues

(~5%), and landfill gases resulting from the decomposition of organic wastes (~5%) [71].

However, the range of biomass materials used for the production of biofuels, bio-chemicals,

and direct energy generation, is extensive. The list below provides a brief snapshot of just

some of the prominent biomass feedstocks and resources that are used by the bio-energy

industry.

Straw Waste Vegetable Oil Waste Wood

Poultry Litter Switchgrass Reed Canary Grass

Wood Pellets Short Rotation Coppice Short Rotation Forestry

Wheat Palm Oil MSW

Oil Seed Rape Sweet Sorghum Animal Slurry

Jatropha Olive Residues Palm Kernels

Sugar Cane Forestry Residues Mechanically & Biologically

Treated Waste (MBT) Arb Arisings Sawmill Co-Products

Figure 2.6 demonstrates the principal categories of biomass resources and feedstocks that are

typically used within bioenergy pathways, within the UK. The diagram also provides an

introduction to the key supply chain processes, conversion options and end use outputs,

depending on the biomass types involved and the desired end uses.


59

17) Figure 2.6: Biomass Resource Categories & the Key Life-Cycle Processes

Figure 2.6: Biomass Resource Categories & the Key Life-Cycle Processes

Adapted from [77]

A. Categorising Biomass

Biomass resources can be categorised into three groups [64]:

Natural Feedstocks – utilised within energy pathways in the form that they are

directly provided to us by nature.

First Generation Feedstocks – otherwise edible resources that can be used for the

production of bio-fuels, such as: sunflower seeds, soya oil, wheat, and sugar cane.

Second Generation Feedstocks – non-edible biomass resources that can be utilised

directly as fuels or converted into other materials. Examples of these include:

lignocellulosic feedstocks, forest residues, and agricultural residues such as straws.

The specific biomass resources and feedstocks predominantly utilised for energy pathways,

can be grouped further as discussed below.

B. Virgin Wood

Virgin woods are classified as wood-based materials that have had no chemical treatments or

the application of finishes. Their physical forms include: barks, arboricultural arisings, logs,

sawdust, wood chips, pellets, and briquettes. Virgin wood may have a range of moisture

content (from oven dry to >60% in freshly harvested/green wood samples). Virgin wood may

also include chemical contaminants taken up from the soil, water or air – although these will

be generally quite low. Virgin wood materials are suitable for a wide range of energy


60

applications, as they can be directly combusted to generate heat and power at various scales,

or can be converted through technologies to produce a range of liquid of gaseous biofuels

[78].

i. Virgin Wood from Forestry:

Forestry represents the primary source of timber and virgin woods. Harvested materials have

a range of physical shapes, sizes, characteristics, and moisture content – meaning pre-

treatments process will be necessary, to render the materials suitable for bioenergy systems

[78].

ii. Virgin Wood from Arboricultural (Arb) Arisings:

This virgin wood material will include residues from the managements of municipal/private

parks and gardens, tree surgery processes, and the maintenance of infrastructure such as

railways and road margins. Like forestry sourced material, virgin wood from arboricultural

arisings, will likely need pre-treatment and processing, as due to the nature of the materials

there may be a high proportion of brash material, bark and leaves. Chipping onsite being the

typical processing method adopted [78].

iii. Virgin Wood from Industry:

Wood processing industries such as mills and timber merchants are another major source of

wood; although the forms and characteristics of the materials are likely to vary [78]:

Sawmill resources are likely to include a high proportion of bark content.

Other resources may have been kiln dried (resulting in extremely low moisture

content level), making it highly suitable for pelletising or blending with other

materials.

Sawdust collected from different stages of processing will likely have a range of

moisture content and will be highly suitable for pelletising.

C. Energy Crops

Energy crops are those that are grown specifically as fuels, and provide high output per

hectare of growth. Extensive research has been undertaken to determine which strains of crop

perform best under UK conditions. The key biomass categories being: Short Rotation Energy

Crops, Grasses & Non-Woody Energy Crops, and Agricultural Energy Crops [78].

The ultimate aim is to maximise the growth output of energy crops from each harvest. This

being measured either in the crop yield per unit area, or the quantities of biofuels generated as

a result [78].


61

In order to achieve high levels of biomass production, processes have to be weighed against

the potentially damaging environmental impacts of some intensive crop management

techniques. For example excessive use of fertiliser may have several impacts; including

emissions of NOx or release of Nitrogen based compounds into groundwater. The ultimate

goal being to produce high yields of energy crops, with high outputs and minimal or zero

inputs [78].

D. Agricultural Residues

Agricultural residues consist of a range of products that stem from agricultural processes,

some of which are potentially, highly valuable bioenergy fuels. These range from dry

materials such as straws and poultry litter, to wet materials such as slurry and silage; dry

materials being highly suitable for direct combustion conversion pathways, whereas the wet

materials are energetically inefficient for combustion, may be awkward/expensive to

transport, and are therefore typically utilised in close proximity to the production site in

biochemical based systems.

Another important issue with agricultural residues is that many of the materials have

alternative markets or uses, competing for their bioenergy value. For example, these residues

may be used for soil nutrient recycling processes; displacing the use of conventional synthetic

fertilisers or products [78].

E. Food Waste

At each stage of the food supply chain, residues and waste are likely to be generated. It has

been calculated that up to one third of all food grown for human consumption in the UK, is

actually thrown away [79]!

Food wastes can be divided into dry and wet waste categories, although the majority have

relatively high moisture content. This represents a massive potential biomass resource for the

energy sector – particular through the biochemical conversion pathways [78].

F. Industrial Waste & Co-Products

Many industrialised processes produce biomass wastes and residues that offer potential for

the bioenergy sector.

The predominant wood based materials from industry include: untreated woods, treated

woods, wood composites, and laminates. These materials can contain contaminants such as

heavy metals, arsenic and halogens, that are likely to cause hazardous or toxic by-products,

bottom ash, fly ash, and flue gas emissions.


62

The basic technologies that enable conversion and energy generation from virgin woods are

the same for treated woods. However regulatory and environmental restraints will often

require these materials to be pre-treated, removing contaminants before processing by the

energy sector [78].

Industrial processes also generate wastes; residues and co-products that are not wood based

but are still biomass derived. These include: paper pulp and wastes, textiles, and sewage

sludge [78].

2.2.2 Lignocellulosic Biomass Resources

Lignocellulosic biomass resources are typically: low-cost crops, forestry residues, wood

process wastes, ‘woody’ vegetation, and the partial organic matter fraction of municipal solid

wastes. Wherever these materials are available, there is potential to produce biofuels and

generate energy, with minimal if any impact on food or fibre crop production [80].

A. Composition of Lignocellulosic Materials

Lignocellulosic biomass materials are made up of three major components: lignin, cellulose

and hemicellulose [64]:

Cellulose – made up of linear polysaccharides in the cell walls of wood fibres.

Biomass typically comprises 40-50% cellulose.

Hemicellulose - surrounds the cellulose fibres and provides the linkage between

cellulose and lignin within plant structure (15-30%).

Lignin – A highly complex material that is concentrated between the outer layers of

plant fibres. This provides structural rigidity, holding polysaccharides fibres together

(15-30%).

B. Pre-Treatment of Lignocellulosic Materials

The main focus when pre-treating lignocellulosic material is to break down the complex

molecular structures of the material. This allows the material to easily undergo bioenergy

conversion processes, in order to produce usable hydrocarbons as fuels, chemicals, and other

products [64].

The main obstacle to utilising simple pre-treatment processes on lignocellulosic materials is

that the major components of the material; lignin and cellulose, are tough to break down; due

to the rigidity and protective nature provided by lignin and hemicellulose, surrounding the

cellulose micro fibrils. Therefore alternative pre-treatment processes are required to

effectively breakdown the cellulose [64].


63

Pre-treatment methods are continually subject to intense global research. The predominant

pre-treatment methods can be categorised as follows; although some of these methods are yet

to be developed to the extent of feasibility in large scale systems [64]:

Physical Pre-treatments – include: milling, irradiation (gamma ray, electron beam,

and microwave), hydrothermal, high-pressure steaming, expansion, intrusion, and

pyrolysis.

Chemical & Physiochemical Pre-treatments – include: explosion, alkali treatment,

acid treatment (sulphuric, hydrochloric and phosphoric), gas treatment (Chlorine

Dioxide, Nitrogen Dioxide, and Sulphur Dioxide), addition of oxidizing agents, or

solvent extraction of lignin.

Biological Pre-treatments – Through fungi or Actinomycetes.

C. Lignocellulosic Material as an Energy Resource

Lignocellulosic materials are prominent renewable energy resources, and in the correct form

are highly combustible. This results from the high volatility of the material and the char

materials that result; although compared to the energy potential of solid fossil fuels such as

coals, lignocellulosic materials contain less carbon and more oxygen – resulting in overall

lower heating values [73].

The key energy generation pathways for lignocellulosic materials are through direct

combustion, either in dedicated biomass energy systems or co-fired with fossil fuels.

However, another major growth area is the generation of bio-ethanol from lignocellulosic

biomass. This being produced through the fermentation of by-product sugar streams. In

summary, lignocellulosic materials are becoming an increasingly important feedstock for the

bioenergy sector [81].


64

2.3 Energy Generation from Biomass &

Conversion Pathways

The processes in which biomass resources and feedstocks are utilised for energy generation

and how they are converted into alternative products, is crucial to the bioenergy sector. This

section introduces the main energy pathways, and builds on the previous section by analysing

how specific biomass types are best processed for optimum energy utilisation.

2.3.1 Biomass Conversion Pathways

It is the high versatility of biomass that allows so many products to be produced and derived;

depending on the different conversion technologies and processes applied. Many of these

products are flexible and can be adapted to compete with fossil fuel based products, using the

existing energy and vehicle fuel infrastructures. Figure 2.7 provides a high level summary list

of some of these different conversion pathways and potential products.

18) Figure 2.7: Overview of Key Biomass Conversion Pathw ays & Products

Figure 2.7: Overview of Key Biomass Conversion Pathways & Products

Adapted from [81]

Biomass materials in their primary forms are not always the ideal fuels ready for utilisation.

To maximise the levels of energy potentially generated, biomass material can be ‘enhanced’

using various conversion pathways. These primary conversion pathways can be separated

into four basic process categories: Direct Combustion, Thermochemical, Biochemical, and

Agrochemical. Figure 2.8 provides an overview of the different products, and forms of

energy typically generated through each conversion pathway.


65

19) Figure 2.8: Summary of the Core Biomass Conversion Pat hways & Resulting Products

Figure 2.8: Summary of the Core Biomass Conversion Pathways & Resulting Products

Adapted from [82]

2.3.2 Direct Combustion Processes

Despite the growth in prominence of a series of conversion processes, the direct combustion

of biomass still accounts for ~97% of global bio-energy generation [82]. The direct

combustion conversion pathway is the complete combustion of biomass material, to convert

biomass energy to heat and/or electricity.

Heat generation through direct combustion, is the most common use of biomass material;

ranging from small scale domestic bioenergy systems up, to large scale 100MW plants. This

biomass conversion pathway also includes the combustion of biomass materials within

existing and new generation coal fired power plants [82].

The electrical efficiencies of biomass combustion power plants range from 20-40%, with the

typical trend of efficiency increasing with plant size. At the other end of the scale, smaller

systems are typically inefficient in comparison, with heat transfer losses up to 30-90% [71].

Before biomass materials are combusted, it is typical for pre-treatment processes to take place

to ready the material – particularly related to the removal of water. Due to the high water

content typically associated with virgin biomass materials; without pre-treatment, the

calculated heat content of the material would have to be corrected, to correspond with the

extent of water content. Research demonstrating that high water content within biomass

material, can reduce the net heat generated by as much as 20% [71].


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A. Co-firing Biomass Resources with Coal

Co-firing simply refers to the combustion of biomass material alongside coal for the

generation of power. As biomass material is classified as being carbon neutral; the dual

combustion of biomass with coal brings down the total carbon emissions per unit of energy

produced, compared to the scenario where coal is combusted on its own.

Coal and biomass materials are quite different in composition, and as a result, the content of

the emissions produced during co-firing are different to the conventional combustion of either

fuel. Research has shown that the co-firing of biomass material with coal is capable of

reducing both NOx and SOx emissions from those of conventional coal plants. Co-firing may

also result in reduced fuel costs, reductions in wastes, and also lower soil and water pollution;

dependent on the properties of the biomass material utilised. Co-firing can therefore have a

positive effect on both the environment and the economics of power generation [82]. The

advantages and disadvantages of co-firing wood based biomass with coal, at an existing coal-

fired power plant are summarised below [83]:

i. Advantages:

Utilisation of existing coal-fired power plant infrastructure.

Promoting the development of wood based biomass fuel industry.

Reduction in CO2 emissions compared to conventional coal generation.

Prolonging the resource life of fossil fuels.

Reducing the emissions content for Sulphur, Nitrogen and ash – in turn reducing the

environmental impacts and the investment/maintenance costs.

ii. Disadvantages:

Decline in thermal efficiency performance compared to generating with coal alone.

Raising the procurement and utilisation costs of resources – as a result of transporting

woody biomass.

Difficulties in effective use of mixed ash.

Eroding the concept of bioenergy being a clean renewable energy option.

Potentially delaying the utilisation of biomass material alone for energy generation, and

the development of new technologies and processes.


67

Co-firing is a popular and often convenient option for reducing Carbon emissions, whilst

utilising existing energy infrastructure. As a result of the UK’s reliance and legacy of coal

fired power stations, co-firing biomass is becoming an increasingly utilised generation

technology in the UK [84]. As a result, there is a growing requirement for suitable biomass

for these power plants; with increasing volumes of resource being imported [85] – a key

theme that will be analysed and discussed later in this Thesis.

B. Different Fuels for Different Combustion Plant Systems

A further consideration when utilising biomass for energy pathways, is the range of

bioenergy plant specifications that can be applied; each typically requiring feedstocks with

varying properties. An example of this is demonstrated below, where the combustion systems

highlighted, require biomass materials with different specific properties for optimal

operation.

i. Suspended Systems

This system typically requires biomass feedstock particles that are reduced to a range of

10µm–1000 µm in order to ensure complete burnout within a few seconds of resident time in

the combustion chamber [60].

ii. Fluidised Bed Systems

For this type of system the biomass feedstocks are typically pelletised or chipped to 2-5mm

to satisfy the fluidisation requirements. Although this system type can also take larger

particles, that would in turn minimise the amount of energy that may be used during

processing phases [60].

iii. Packed-Bed Systems

In this type of system the biomass feedstocks can be fired either in the form they are received,

or with minimum level of pre-processing required. Biomass particle sizes typically ranging

from 5-100mm, or even higher with logs up to 50cm [60].

iv. Direct Firing, Power Stations & Co-Firing

Power stations would typically require biomass fuels that have been pulverised to a similar

size to that of coal, in order to achieve the high level combustion intensities sought. The

pulverisation of biomass or co-firing with coal; requiring particles ground to less than 1mm in

size [60].

However, this can present a problem for fibrous biomass fuel types, as it is difficult and

expensive in energy/cost terms; fibrous biomass requiring typically 5x more energy than coal


68

when pulverising. Potential options available include the process of torrefaction to modify the

nature of the material, to allow easier milling; or the utilisation of alternative feedstocks that

are less fibrous such as palm kernel expeller or olive waste [60].

2.3.3 Thermochemical Conversion Processes

The core Thermochemical conversion processes applied to biomass materials, are pyrolysis

and gasification.

A. Pyrolysis

Pyrolysis is the thermal decomposition of biomass in the absence of Oxygen. It is the first

step in the combustion and gasification of biomass materials, although in these processes the

pyrolysis stage is followed by the total or partial oxidation of the primary products. In

pyrolysis, the biomass material decomposes to generate: aerosols, vapours, and some

charcoal – each having value in energy terms. In summary, pyrolysis is the thermal

degradation of biomass material into more useful fuels [86].

The pyrolysis of biomass material is an attractive conversion option as it enables solid

biomass and waste materials, to be readily converted into liquid products. Liquid fuels having

advantages in terms of transportation, storage, combustion, retrofitting, and flexibility in

production and marketing [72].

i. Pyrolysis – Conversion Modes

Depending on the conversion reaction conditions and the specification of the biomass

material reacted, pyrolysis conversion will produce a wide range of products with differing

attributes; the predominant products being: bio-oil/crude (liquid), charcoal, non-condensable

gases, acetic acid, acetone, and methanol [86].

Conventional slow pyrolysis of biomass materials has been undertaken for 1000’s of years in

the production of charcoal. In this mode of pyrolysis, the feedstock is held at constant

temperature or slowly heated. This process enables vapours to be continually removed – the

resulting products having high charcoal content. Alternatively, during flash pyrolysis, small

dried biomass particles are thermo-chemically converted into a range of products that are up

to 75% liquid fuels, with smaller quantities of charcoal and non-condensable gases [72].

Table 2.2 summarises typical product yields with the pyrolysis of wood under different

reaction conditions.


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Table 2.2: Typical Pyrolysis of Wood Product Yields Table 3) Table 2.2: Typical Pyro lysis of Wood Product Yields

Conversion Mode Conditions Liquid Solid Gas

Fast Pyrolysis ~500ºC Short Hot Vapour Residence ~1s 75% 12% char 13%

Intermediate Pyrolysis ~500ºC Hot Vapour Residence ~10-30s 50% 25% char 25%

Slow Pyrolysis – Torrefaction ~290ºC Solids Residence Time ~30mins - 77% char 23%

Slow Pyrolysis – Carbonisation ~400ºC Long Vapour Residency ~hours-days 30% 35% char 35%

Gasification ~800ºC 5% 10% char 85%

Adapted from [86]

Therefore, depending on the types of end products desired, the parameters of the pyrolysis

process can be tailored. The pyrolysis process can be varied greatly in order to generate

different end products, with a typical fuel-to-feed efficiency of 95.5%. This high ’fuel to

feed’ ratio makes pyrolysis the most efficient process for biomass conversion, and also a

process with great potential of competing with non-renewable fossil fuels. At present, the

preferred pyrolysis methodology is fast or flash pyrolysis; at high temperatures with very

short residence times, resulting in proportionately high liquid fuel end products [72].

ii. Pyrolysis – Production of Bio-Char

The slow heating of wood in air tight conditions is known as carbonisation, and results in the

production of char or charcoal end products. Char is an extremely important principal fuel for

many households, both across the developed and developing world [72].

Wood carbonisation typically occurs at ~400⁰C, and produces char with approximate content

of: 80% Carbon, 1-3% ash, and 12-15% volatile compounds. Although the typical end

products and compositions are highly dependent on the feedstock input and the conversion

parameters applied [72].

iii. Pyrolysis – Production of Bio-Oil

Extractives from biomass materials following pyrolysis include oils and other valuable

chemicals, which can be converted into biodiesel through a trans-esterification process with

methanol. These bio-oils are highly valuable conversion products, as they have high energy

densities compared to those of most other biomass materials, and are readily available when

fast pyrolysis processes are applied to biomass material in systems such as a fluidised bed

setup [18].

Bio-oils are an important source of fuel for remote areas, where it is economical to convert

biomass into bio-oil. Further refinement of bio-oils can then result in a long list of potential

products - most noticeably those used as transport fuels, either as a blend, or through sole

combustion.


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B. Gasification

Biomass gasification is a form of extreme pyrolysis conversion that is carried out at high

temperatures, in order to maximise the production of gases. These expelled gases are known

as ‘syn-gases’ and are composed of: Carbon Monoxide, Hydrogen, Methane, Carbon

Dioxide, and Nitrogen. The biogas produced has high versatility compared to the original

solid biomass, and can be combusted to produce heat and/or steam for electricity generation,

or used in further bio-refining processes. Figure 2.9 documents the main biomass gasification

conversion processes, potential products at each step, and the potential forms of bioenergy

that could be generated.

20) Figure 2.9: Biomass Gasification Conversion Processes

Figure 2.9: Biomass Gasification Conversion Processes

Adapted from [72]

Biomass gasification systems are part of the latest generation of biomass energy conversion

processes that are being applied to enhance the efficiency, and reduce the investment costs of

bio-electricity generation. Gasification plants are produced in a range of sizes and types that

can operate on a variety of fuels, ranging from woods and charcoal to coconut shell and even

rice husks.

Biomass Gasification

Pathways, Products &

Potential Forms of Bioenergy


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Efficiencies up to 50% have been found to be achievable, using combined-cycle gas turbine

systems where waste gases are recovered to operate steam turbines; demonstrating that such

biomass plants can be as economical as conventional coal-fired plants. The power output

being determined by the economic and ready supply of the biomass fuels [87].

C. Gasification - Fischer Tropsch Synthesis

The Fischer-Tropsch Synthesis (FTS) process was developed in 1923 by German scientists

Franz Fischer and Hans Tropsch. The aim of FTS was to produce long chained hydrocarbons

from a CO and H2 gas mix. The FTS process is now well established for producing a variety

of fuels. FTS operates at low temperatures (LTFT), to produce a ‘syn-crude’ that has a large

fraction of heavy, waxy hydrocarbons. When FTS is operated at higher temperatures (HTFT),

light syn-crude and olefins are produced. The HTFT primary products can be refined into

environmentally friendly gasoline and diesel, solvents and olefins. The LTFT heavy

hydrocarbons products can be refined to waxes, or if hydrocracked and/or isomerised, to

produce diesel, base-stock for lube oils and naphtha [71]. The production of fuels through

FTS has several notable advantages and disadvantages [88]:

i. Advantages

The FTS process can produce hydrocarbons of different lengths from any carbon based

feedstock, which can then be refined to produce transportable liquid fuels.

As FTS products are functionally similar to conventional refinery products, they can be

processed by existing transportation, storage and refueling infrastructure as used for

petroleum based products. The products are also compatible with current vehicles, and

blendable with current petroleum based fuels.

Products from the FTS process are of high quality, being free of Nitrogen, aromatics,

and other contaminants that are typically found in petroleum based products.

ii. Dis-advantages

The capital costs of FTS conversion plants are higher and the energy efficiency of

producing FTS liquid fuels is comparatively lower than that for alternative fuels, such

as Hydrogen, Dimethyl Ether, Methanol, and conventional biofuels.

2.3.4 Biochemical Conversion Processes

As biomass is a natural material, nature has developed numerous highly efficient biochemical

processes for breaking down the constituent molecules within biomass. Many of these

processes can be harnessed to convert biomass into a wide range of products. Biomass


72

biochemical conversion processes, differ to thermochemical processes in that they proceed at

lower temperatures and lower reaction rates. Both produce a wide range of products. Biomass

materials and feedstocks with higher moisture content are generally best suited for

biochemical conversion. The biochemical conversion processes of greatest interest to the

bioenergy sector being: fermentation and anaerobic digestion.

A. Biomass Fermentation

Ethanol can be produced directly from biomass through the fermentation conversion

pathway. The best know source of ethanol is sugar cane, but there is an extensive list of

alternate materials that can be used including: wheat, sugar beet, and even wood. The choice

of the biomass feedstock utilised is highly important to the fermentation process, as it

typically makes up 55-80% of the fuels end selling price - starch based biomass materials

being typically the cheapest, but requiring additional processing [89].

The production of (bio)-ethanol from biomass is one way to reduce both the consumption of

crude oil and environmental pollution. Conversion technologies for producing ethanol from

cellulosic biomass resources such as: forest materials, agricultural residues, and urban wastes,

are major areas for research focus, and are continually edging towards commercial viability.

Although in order to produce ethanol from cellulosic biomass, pre-treatment processes are

required to reduce the sample size, break down the hemicelluloses to sugars, and open up the

structural components of the cellulose [89].

Bio-ethanol is an important bio-fuel that can be widely utilised, in some cases directly

substituting for conventional transport fuel. The leading case study being Brazil, where

widespread use of feedstocks such as sugarcane are utilised for the production of bio-ethanol

transport fuels, on an industrial scale [89].

B. Anaerobic Digestion

Within the anaerobic digestion conversion pathway, biomass material is decomposed through

bacterial action in the absence of Oxygen. Anaerobic digestion typically takes place within

digesters that range from household systems (1m³), to large commercial installations (up to

~2000m³) [71]. The predominant useful products are [66]:

Biogases – principally methane that can be combusted directly within internal

combustion engines to generate electricity.

Solid Residues – known as fibre or digestate, these are similar to and can be used as

compost material.


73

Liquid Liquor – this liquid portion can be used as a fertiliser that may or may not

contain useful levels of nitrate and phosphate, depending on the input feedstocks.

Depending on the feedstock, heavy metal contamination can also occur.

The anaerobic digestion process itself can take place over different temperature ranges and

time periods. ‘Mesophilic Digestion’ takes place between the 20-40˚C temperature range with

the reaction lasting over a month or two. While ‘Thermophilic Digestion’ takes place

between the 50-65˚C temperature range over a shorter period of time [66].

2.3.5 Comparing Biochemical versus Thermochemical Processes

No clear financial or technological advantages currently exist, between biomass biochemical

and thermo-chemical conversion pathways. Although scientific consensus accepts that both

pathways still have significant technical and environmental barriers to overcome to improve

the processes, if they are to truly compete with the fossil fuel industry [80].

Table 2.3 provides a summary of some of the bio-fuel and energy generation yields for

sample biochemical and thermochemical processes.

Table 2.3: Comparison of Biochemical & Thermochemical Biofuel & Energy Yields Table 4) Table 2.3: Comparison of Bioc hemical & Thermoche mical Biof uel & Energy Yields

Processes Biofuel Yield

(l/dry t) Energy Content

(MJ/l) (LHV) Energy Yields

(GJ/t)

Biochemical

Conversion Enzymatic Hydrolysis Ethanol 110 - 300 21.1 2.3 - 6.3

Thermochemical

Conversion

Syngas-to-Fischer Tropsch Diesel 75 - 200 34.4 2.6 - 6.9

Syngas-to-Ethanol 120 - 160 21.1 2.5 - 3.4


A key difference between biochemical and thermochemical processes is that the lignin

component of biomass materials becomes a residue during biochemical conversion processes,

whilst it becomes a further fuel for heat and power generation, during thermochemical

processes. Another fundamental difference between the processes is the range of end use

products; biochemical processes predominantly produce bio-ethanol, whilst the

thermochemical process routes, can produce synthesis gases that may be utilised to produce a

wider range of longer chained hydrocarbons [80].

The main improvements that are being pursued across all the conversion process pathways

are: the refinement of the input feedstock characteristics, improvement of pre-treatment

processes, enhancing system efficiencies, lowering overall production costs, and improving

the general process integration [80].


74

2.3.6 Conversion of Lignocellulosic Materials

Producing biofuels and generating energy from lignocellulosic feedstock material, can be

achieved using both biochemical and thermochemical conversion processes. Biomass

materials, particularly lignocellulosic biomass feedstocks, are prominent alternative energy

sources, as in the correct form they are highly combustible – a result of the high volatility of

the fuel and resulting char material. However, compared to comparable solid fossil fuels such

as coal, biomass contains less carbon, more oxygen and has an overall lower heating value

[73]. Current scientific focus is looking heavily into the following pathways for optimum use

of lignocellulosic materials:

Biochemical – Enzymes and micro-organisms are utilised to convert the cellulose and

hemicellulose fractions of the feedstock material into sugars. These are then converted

into bio-ethanol through fermentation.

Thermochemical – Pyrolysis and gasification techniques are utilised to produce

synthesis gases – CO and H2. From these synthesis gases, multiple long carbon-chain

hydrocarbon biofuel products are generated, such as synthetic diesel or ethanol.

[73]

The generation of bio-ethanol from lignocellulosic biomass, is a major industry growth area.

Lignocellulosic materials are particularly sought after as an appropriate input-feedstock in

this process, as they are not directly linked to food production. There is wide expectation that

the on-going ‘biotechnology revolution’, driven by commercial aspirations will result in large

cost reductions in lignocellulosic bio-ethanol production, over the coming years [81].

2.3.7 The UK’s Bioenergy & Bio-refining Systems & Infrastructure

Bioenergy and refinery facilities are where biomass conversion processes take place on large

scales. Bio-refining processes, typically integrate a series of biomass conversion processes

and technologies, to produce a range of desired products: biofuels, bio-power, bio-heat, and

‘value-added’ chemicals etc. Bio-refineries being the bioenergy equivalent of the modern

crude oil refinery, producing multiple grades of petroleum based fuels and products.

Table 2.4 below, provides a summary of the types of bioenergy and refinery system

infrastructures that are currently operating across the EU, and within the UK. Information and

data relating the UK’s planned and current operating bioenergy infrastructures, can be

sourced from, the Office of Gas & Electricity Markets (Ofgem) or The National Non-Food

Crops Centre (NNFCC) [90]–[92].


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Table 2.4: Summary of the UK and EU’s Operating & Planned Bioenergy Systems Table 5) Table 2.4: Summary of the UK and EU’s Operating & P lanned Bioe nergy Systems

Bioenergy Systems Typical Capacity

Range

Net Efficiency

(LHV basis) Status and Deployment in the UK & EU

Biogas

Production

Anaerobic

digestion

Up to several MWe

10–15%

(electrical)

Well-established technology.

Widely applied for homogeneous wet organic

waste streams and wastewater.

Landfill gas Up to several 100

kWe

Equivalent to

Engine

Efficiency

Very attractive GHG mitigation option.

Widely applied in EU and generally part of waste

treatment policies in most countries.

Combustion

Heat Domestic 1–5

MWth

From Very Low

(Classic Fireplaces)

up to 70–90% for Modern Furnaces.

Classic firewood use still widely utilised across

Europe, but decreasing. Replacement by modern heating systems.

CHP

0.1–1 MWe 60–90% (overall) Widely deployed in Scandinavia countries,

Austria, Germany and to a lesser extent France.

Increasing in scale and electrical efficiency with time.

1–10 MWe 80–100% (overall)

Stand Alone 20–100 MWe 20–40% (electrical)

Well-established technology, especially deployed

in Scandinavia. Various advanced concepts include using Fluid

Bed technology to give high efficiency, low costs

with high flexibility. Mass burning or waste incineration has much

higher capital costs and lower efficiencies,

although widely applied in Netherlands & Germany.

Co-

Combustion

5–20MWe at

Existing Coal-

Fired Stations. Higher for New

Multi-Fuel Plants.

30–40% (electrical)

Widely deployed in many EU countries.

Interest for larger biomass co-firing systems is growing.

Gasification

Heat Usually Smaller

Capacity Range

80–90%

(overall)

Commercially available and deployed. However the total contribution to energy

production in the EU is very limited.

CHP gas

engine 0.1–1 MWe 15–30%

Deployment limited due to relatively high costs,

critical operational demands and fuel quality.

Biomass Integrated

Gasification

Combined Cycle

30–100 MWe

40–50% (or higher

electrical

efficiency)

Demonstration phase at 5–10 MWe range

obtained.

Rapid development in the 1990’s has stalled in recent years.

First generation concepts proving capitally

expensive.

Pyrolysis Bio-oil

Generally Smaller

Capacities are

Proposed of Several 100 kWth

60–70% Heat

Content of Bio-oil/

Feedstock.

Not commercially available; mostly considered a

pre-treatment option for longer distance transport.

Adapted from [82]


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Chapter 3 - Biomass Resource Modelling


77

3.1 Biomass Resource Modelling

This Chapter introduces the concept of quantifying biomass resource levels at various scales.

Focus is placed on evaluating different models that forecast the extent of biomass resource

availability, at both the global and UK scale. The methods applied by different models in

undertaking their analyses, being fundamental in influencing the design and development of

the BRM.

This Chapter is important to the overall Thesis Storyline, as it introduces the concepts of

biomass modelling that are strongly linked in to the methodologies developed in Chapter 4.

3.1.1 An Extensive Global Resource

Obtaining an all-inclusive reliable estimate of the total global biomass resource is currently a

near impossible task. Unlike fossil fuel resources, no global companies are currently

undertaking global assessments that gather up-to-date information on biomass production,

consumption etc. Table 3.1 provides some estimate data for world biomass totals; these

values are still subject to much uncertainty and are clearly large numbers! However, there is

little doubt that biomass resources are a major energy provider for much of the world.

Estimates place energy from biomass, as accounting for up to 1/3 of total primary energy

consumption in developing countries, and up to 90% in some of the poorest world regions

[26].

Table 3.1: Global Biomass Resource Estimates Table 6) Table 3.1: Global Biomass Resource Estimates

Global Biomass Resource Estimates

Total Mass of Living Matter 2,000 billion tonnes (inc. moisture)

Total Mass in Land Plants 1,800 billion tonnes

Total Mass in Forests 1,600 billion tonnes

Energy Stored in Terrestrial Biomass 25,000 EJ

Net Annual Production of Terrestrial Biomass 400,000 Mt yr-1

Biomass Energy Consumption 56 EJ yr-1 (1.6 TW)


3.1.2 An Introduction to Resource Modelling

Human history has always been closely linked to the control, extraction and use of resources.

Although over recent decades, the demand for resources has increased to the extent that it is

now widely considered to be a limiting factor and serious threat, to the functionality of

economies and society [93]. As a result, resource modelling is becoming an increasingly

utilised and important tool, with projections potentially impacting economies, growth trends,

and also having fundamental environmental impacts.


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Resource modelling is essentially the science of estimating supply versus demand, and

attempting to quantify resource reserves. Projections of future resource supplies and demands

therefore rely heavily on estimations of resource availability. A fundamental question for the

developers of resource models, is whether the past can provide a guide to the future [94]? The

reality is, that resource modelling scenarios in the future may be vastly different from

historical experiences, therefore rendering simple predictions based on extrapolation of past

trends and behaviours, highly suspect. Resource projections are further complicated by

technological uncertainties, political impediments, and potential unseen changes affecting

supplies [95].

Resource modelling and data analysis, of some form or another, has arguably been

undertaken for as long as goods and resources have been in-demand and traded. Although on-

going analysis over recent times has revealed two distinct trends [93]:

A decoupling is occurring between global resource extraction and use, from economic

growth. Perhaps highlighting that on a global scale, economic output may be becoming

less resource dependent; and ‘scale effect’ influences may be operating.

At the same time global resource extraction in absolute terms, is increasing in all

regions – a trend clearly incompatible with the concepts of sustainable development.

A. Why Model Resources

All governmental and management actions, decisions, regulations, and controls; rely on

quantitative contexts and numerical information to provide drivers, precautionary indicators,

reference points and cost-benefit analyses [96]. Projections developed through modelling,

provide the structure for many resource governance, and management decisions. However,

the vast array of resource types and characteristics means that no set modelling

methodologies can be universally applied; resource modelling projections varying in both

accuracy and relevance based on the resource, modelling methodology applied, and scales

[96].

B. Modelling Non-Renewable Resources

As by definition non-renewable resources are finite, the availability and abundance of these

resources will drive any supply and demand projections. History has proved, that the

pathways of non-renewable resource extraction can be predicted through the application of

‘Hotelling’s Rule’. This states that: extraction trends through time will follow the most


79

socially and economically profitable pathways, determined by net revenues and rates of

interest increase; thus ensuring the maximisation of resource stock value through time [97].

An example of the dynamics of resource prices, and quantities through time, is demonstrated

by Figure 3.1 that presents a screenshot of a Dynamic Economic Resource Model. In the top

left graph, the price of a resource is shown to be linked to its quantity - the price increasing as

quantity decreases. The bottom left graph shows the quantity of a resource is also linked to

time - as time progresses the quantity of the resource will decrease. Bringing these dynamics

together, the top right graph highlights that as time progresses, the price of the resource will

also increase.

21) Figure 3.1: Non-Renewable Resource Economic Dynamics

Figure 3.1: Non-Renewable Resource Economic Dynamics

Screenshot Taken from [98]

i. Non-Renewable Resource Case Study

The most frequently modelled non-renewable resources are fossil fuels. The vast majority of

fossil fuel reserve, and supply/demand trends, are not forecast based on scientific knowledge

of global availability, but on economic parameters - consumption and price [99]. Fuel prices

also significantly dictate consumption levels; providing an additional feedback loop, and

highlighting the complexity of fossil fuel modelling [100].

Fossil Fuel reserves have recently been forecast in the region of: 1,300 billion barrels of oil,

6,100 trillion ft³ of gas [27], and 850 billion tonnes of coal [101]. However, over recent


80

decades, overall fossil fuel reserves have not experienced any significant decrease in trends;

with predictions that fossil fuels would run out, failing to materialise [102]. This is probably

due to improved data, and technological advancements, that have opened up reserves

previously deemed economically inaccessible. Fuel reserve forecasts, fluctuate in relation to

global economic conditions - ‘proven reserve levels’ falling when prices are too low for fossil

fuels to be recovered economically, and increasing when economic prices deem that fuels are

economically recoverable [100].

22) Figure 3.2: Ratio of Global Fossil Fuel C onsumption to Years of Remaining Reserve

Figure 3.2: Ratio of Global Fossil Fuel Consumption to Years of Remaining Reserve

Graph Taken from [99]

This paradigm can be seen within Figure 3.2, where the ratio of global fossil fuel

consumption, compared to the number of years of remaining fuel reserves, are plotted

through time (1980-2006). The ratios for oil and gas have been relatively constant for

decades – suggesting that reserves have been increasing at the same rate as consumption.

Whilst at the same time, the coal consumption to reserve ratio, has fallen intermittently.

Shafiee & Topal (2008) [99] explain these trends, linking them to improving data, and also

political management, steering trends in accordance with Hotelling’s Rule (1931) [97].

i. Coal Consumption to Reserve Ratio

The decreases in the ratio of coal consumption to reserves, are linked to improvements in

data; previous coal reserve projections being found to be inaccurate. The levelling-off of the

Global Fossil Fuel Consumption to

Years Remaining Reserve Ratio


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ratio at certain periods through time, being linked to technological advancements, making

more reserves economically accessible [99].

ii. Oil & Gas Consumption to Reserve Ratio

This relatively constant ratio is also linked to technological advancements opening up new

reserves, but also to politico-economic incentives; in only disclosing information relating to

reserve abundance and sizes, to maintain maximised fuel prices [99].

C. Modelling Renewable Resources

Approaches and strategies for modelling renewable resources tend to differ from those for

non-renewables, as an additional dynamic exists – the resources are not finite. However, the

focus of these models still revolves around projecting trends of supply and demand, and

quantifying the extent of the resource reserve. Modelling biomass resources is an excellent

case study for the modelling of renewable resources.

3.1.3 Biomass Resource Modelling

“The global biomass potential and its use to provide energy, cannot be measured; it

can only be modelled”

UKERC (2011) [103]

Biomass resource modelling, typically aims to produce estimates of the availability of the

biomass materials, from a range of sources, at a chosen geographic scale. Biomass resource

models are highly variable in complexity, but all aim to utilise data and information, to

estimate an aspect of the bioenergy sectors future development. Forecasts of potential

biomass resource availability can typically be categorised, into one of four resolution groups,

as discussed below and highlighted within Figure 3.3.

A. Theoretical or Ultimate Modelling Forecasts

Biomass resource forecasts, of the potential quantity of resource that can be grown

/harvested/collected annually; are limited only by fundamental physical and/or biological

barriers. These may be the biological plant growing conditions that could be linked to climate

change, or the physical land surface area available for growing biomass. This scenario

category, is not typical as a useful means of estimating biomass production levels, but could

be used as a comparative analysis tool [104].


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B. Technical or Geographic Modelling Forecasts

Biomass resource forecasts, of the potential quantity of resource that could be collected;

taking into consideration all technical constraints such as: ecological impacts, land area

constraints, agro-technological constraints, and even topographic problems. The technical

potential estimates may therefore increase, as technological advancements occur [104].

C. Economical Modelling Forecasts

Biomass resource forecasts, of the potential quantity of resource, based on economic

considerations – are fundamentally driven by supply-demand curves. This is a highly variable

quantification method, as economic conditions readily change over time, and more

specifically, markets for different biomass feedstocks are likely to evolve [104].

D. Implementation or Realistic Modelling Forecasts

Biomass resource forecasts, of the potential quantity of resource available, without inducing

detrimental environmental, social or economic impacts. This is typically measured as a

function of the levels of resource that are considered to be recoverable, or accessible for use.

Although deciding the most appropriate levels with respect to each consideration, may be a

matter for expert opinion [104].

23) Figure 3.3: Biomass Modelling Reso lution P otentials

Figure 3.3: Biomass Modelling Resolution Potentials

Adapted from [105]

A. Structure of Biomass Resource Models

Biomass resource models have no set design, and have been developed using a broad range of

methodology frameworks. The structure and approach of the models, plays an important role

in determining the types of outputs and results that a model may generate, and explains why


83

biomass resource analyses, often differ from one study compared to another [106]. The two

main categories of biomass resource models developed; are either ‘Resource Focused’, or

‘Demand Driven’.

i. Resource Focused Biomass Models

Resource focused models, typically aim to build an inventory of the different available

biomass resources, based on a function of the availability of supply. This is typically, the land

available determining the extent of energy crops and forestry, and the state of industries and

on-going activities, that may be providing wastes and residues. The results of resource

focused models, are highly dependent on the methodologies utilised for quantifying changes

in production systems, and also the boundary conditions set from the offset – the number and

types of resources modelled [103]. Figure 3.4 provides an example structure of a resource

focused biomass model.

24) Figure 3.4: Typical Structure & Ana lysis Flow for a Resource Focuse d Biomass Model

Figure 3.4: Typical Structure & Analysis Flow for a Resource Focused Biomass Model

Adapted from [107]

ii. Demand Driven Biomass Models

In contrast, demand driven models, focus specifically on the competitiveness of bioenergy in

comparison to conventional energy sources. These models, estimating the quantities of

biomass required to meet various energy, biomass, and renewable energy targets. Many

demand driven models are produced as part of wider energy-economic modelling, analyses

[106]. Limitations of demand driven models, are related to their assumptions which are often

aggregated and sometimes undefined. So, although these studies provide insight into the

likelihood of increases in biomass utilisation, they provide little useful analysis into the size

of the technical biomass potential [103].


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3.1.4 Estimating the Global Biomass Resource

Estimating the global biomass resource is fraught with difficulty, and is by no means a

straightforward process. Therefore, the estimates that we do have; are derived from the realm

of biomass resource modelling work, of which there is plenty. A list of some of the existing

studies that have utilised or developed biomass resource models, are shown and described

within Table 3.2.

Table 3.2: Summary & Characteristics of Existing Biomass Resource Models Table 7) Table 3.2: Summary & Characteristics of Existing Biomass Resource Models

Timeframe Biomass Model References Resolution Approach

2020 Bauen et al (2004) [108] Theoretical + Realistic R-F

FAO (2010) [109] Demand D-D

2025 Sims (2006) [110] Theoretical + Technical R-F

2030 IEA (2008) [111] Demand D-D

Moreira (2006) [112] Technical + Economical R-F

2050

Beringer et al (2011) [113] Realistic R-F

Cannell (2003) [114] Theoretical + Realistic R-F

de Vries et al (2007) [115] Theoretical + Technical + Economical R-F

Erb et al (2009) [116] Theoretical + Technical + Economical R-F-L

Field et al (2008) [117] Theoretical R-F-L

Fischer & Schrattenholzer (2001) [118] Technical R-F

Haberl et al (2010) [119] Geographic A-S

Hall et al (1993) [120] Technical R-F

Hoogwijk et al (2003) [106] Geographic A-S

Hoogwijk (2004) [106] Geographic + Technical + Economical R-F

Johansson et al (1993) [121] Technical R-F

Lysen et al (2008) [122] Geographic R-F

Smeets et al (2007) [123] Technical R-F

Thran et al (2010) [124] Technical R-F

WEA (2000) [125] Technical R-F

Wolf et al (2003) [126] Technical R-F

Schubert et al (2009) [127] Technical R-F

2050-2100 Hoogwijk et al (2005) [104] Geographic + Technical D-D

Yamamoto et al (2000) [128] Ultimate + Realistic R-F-L

2100



Rokityanskiy et al (2007) [131] Economical R-F-L

Key: Approach

R-F : Resource Focus Biomass Models

R-F-L : Resource Focus Biomass Models Driven by Land-Use Assessments

D-D : Demand Driven Biomass Model

The UK Energy Research Centre (UKERC), and Imperial College London, completed a

comparison study that looked at and analysed over 120 different global biomass resource

estimations, based on numerous biomass models [103]. The range of timescales analysed

varied from short (to 2020), medium (to 2050), and long term (to 2100). The majority, focus


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on the medium term, reflecting the importance of 2050 as an important deadline for many

mandated energy targets around the world [132]–[134].

Figure 3.5, plots the range of global biomass resource estimates, from the different biomass

models analysed within the UKERC and Imperial College London study. Each vertical line

within the graph, demonstrates the estimated extent of global biomass resources, from a

different model/study. The height and range of each of these lines, documents the estimated

biomass resource range from each model. Figure 3.5 clearly highlights that the estimated

global biomass resource, differs significantly; according to literature and based on different

biomass resource models. This large estimate range stems from the varying scenarios,

modelled variables, and the many other considerations that may differ from one particular

study’s methodology and model, to another. As a result, no two sets of biomass resource

estimations can be directly comparable [103]. A further study undertaken at Lund University

[135], found comparable results at the National scale for Sweden; highlighting the difficulty

of accurately evaluating and validating bioenergy potential estimates, as a result of differing

methodologies and extent that different variables were modelled.

25) Figure 3.5: Range of Global Biomass Resource Estimates

Figure 3.5: Range of Global Biomass Resource Estimates


Breaking down this analysis further, Figure 3.6 provides a summary of the how the different

analyses covered within the UKERC and Imperial College London study; estimate the

Forecast Range of Biomass Resource

Potential for Global Studies


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resource potentials from different categories of biomass. A wide range of estimates can be

seen, resulting from variations in how different studies analyse and define resources – again

highlighting the caution to be applied when interpreting this Graph. However, what Figure

3.6 does highlight, is that the greatest potential global biomass resource contributor, is likely

to be from energy crop resources.

26) Figure 3.6: Range of Biomass Resource and Related Land Category Estimates

Figure 3.6: Range of Biomass Resource and Related Land Category Estimates


3.1.5 Modelling the UK’s Indigenous Biomass Resource

The UK’s indigenous biomass resource is an area of research that has been looked at by a

number of bodies, and through a series of studies. These focus on, quantifying the extent of

the UK’s biomass resource from different perspectives, including models that take both

resource-focused and demand-driven, approaches. The following discussion introduces some

of these studies, and goes on to highlight the focus of each; also introducing potential

knowledge gaps in current research that this PhD research aims to fill.

A. DECC - 2050 Pathways Analysis

The DECC 2050 Pathways Analysis tool, was designed to provide full energy-industry

scenarios, for meeting the UK’s 2050 carbon targets. The 2050 Pathways Calculator,

allowing users to develop their own combinations of levels of change, to achieve an 80%

reduction in carbon emissions by 2050; while ensuring that energy supply meets demand

Forecast Range of Global Biomass Potential

for Resource Categories


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[34]. The Report itself, describes six different Pathways Scenarios, selected to demonstrate

varied routes to 2050, ranging from: a scenario requiring significant effort across all energy

sectors, to scenarios with minimal or high contribution from particular technologies,

including bioenergy.

The DECC 2050 Pathways Analysis, is an energy Demand Driven study – looking at the

issues from a top down perspective; estimating the demand of biomass resource, based on the

extent that the bioenergy sector, is selected to contribute to the UK’s overall energy mix.

Therefore, although this study provides a valuable tool, for analysing the potential of the

bioenergy sector in the UK, in comparison to other energy technologies for meeting the

carbon targets; it does not provide analysis on the resource potentials of different indigenous

biomass resources or feedstocks.

B. AEA Consulting - UK and Global Bioenergy Resource Analysis

This analysis, produced by AEA Consulting for DECC, looks at the UK’s indigenous

biomass resource available for the bioenergy sector, over the 2010-2030 timescale. The aims

and objectives of this analysis are to assess the potential of UK’s biomass resources and

feedstocks that are available to the market; taking into consideration, barriers of deployment

for the different resources, under different assumptions. AEA’s analysis methodology is

resource-focused, estimating biomass resource availability under different development

scenarios: Business as Usual Scenario, High Investment Scenario, and a Low Development

Scenario [136], [137].

AEA’s analysis follows a rigid format that systematically looks at each biomass resource,

with respect to these three development scenarios. The analysis methodology is heavily

weighted in analysing the economic potential forecasts, and to a certain extent, technological

potential forecasts, for each biomass resource in the UK. The analysis’ resource-focused

approach includes an assessment of many of the variables associated with quantifying each

resource.

C. E4Tech - Biomass Supply Curves for the UK Analysis

Also developed for DECC, E4Tech’s report focuses on analysing resource availability supply

curves, for different biomass resource categories; for those produced indigenously in the UK,

but also for potentially available material, imported from EU and other global markets. The

analysis looks at a snapshot of timeframes: 2008, 2010, 2015, and 2030; applying the

following scenarios: Business as Usual Scenario, Introduction of the EU Renewable Energy


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Directive Scenario, High Sustainability Focus Scenario, and a High

Industrial/Economic/Technological Growth Scenario [138].

Similar to the AEA analysis, the E4Tech study is heavily focused on economic and

technological potential forecasts, of biomass resources produced in the UK. The study also

focuses on the EU and global markets competing with the UK, for potential imports of

biomass resource. E4tech adopt a resource-focused modelling approach, although much of

the analysis focuses on estimating the potential costs to the UK, in meeting its renewable

energy targets, while having to compete globally for imported material. The indigenous

resource quantification aspects of the analysis, notably explores how the UK could best

utilise its indigenously available resources; in order to minimise the levels of biomass imports

potentially required.


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Chapter 4 - Developing the Biomass Resource Model


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4.1 Introducing the Biomass Resource

Model

The following Chapter of the Thesis introduces the Biomass Resource Model (BRM); the key

analysis tool developed during the PhD programme. This builds upon the theoretical

framework developed in Chapters 2 and 3, and provides a full walk-through and discussion of

the BRM’s developed methodologies. The specific calculation equations applied within the

BRM, are also listed; these reflecting the modelling mechanics developed for each area of

analysis undertaken.

The BRM has been developed to provide a tool capable of providing answers to the research

questions at the heart of this PhD; and to occupy a niche allowing research to be undertaken,

in areas previously not covered by pre-existing biomass resource models.

This Chapter provides an overview of all the literature, research, studies, reports, and data

that have been utilised in developing the BRM. Existing biomass resource models that

influenced the BRM’s design are highlighted, and reasoning is provided where new

methodologies are developed.

Initially, the Chapter discusses the aims and objectives of the BRM, presenting the BRM’s

structure; including the tools and software applied in its development. Then it moves on to

discuss the progressive development of each analysis stage within the BRM, and the specific

modelling mechanics applied to undertake each stage of the analysis.

4.1.1 Aims & Objectives of the Biomass Resource Model

During development of the BRM, it was important to have clear and succinct aims and

objectives of what the Model would allow and provide, in terms of its analysis capability, and

desired outputs. Therefore, the parameters of the PhD research questions and the aims as

discussed in the Introduction (Chapter 1); were the key factors governing its design. Thus the

primary aim was to develop the BRM into a functional tool, capable of performing

appropriate analyses and addressing each of the PhD research questions in turn.

A. Aims of the Biomass Resource Model

The aim was to create a flexible tool that could reflect upon, analyse and provide forecasts of:

indigenous biomass resource supply chains, availability of potential biomass resources for the

bioenergy sector, and ultimately the bioenergy potential from indigenous resources within the

desired country or geographic region.


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To reflect the many dynamics, drivers, and variables that may influence the availability of

different forms of biomass resources, and the bioenergy generated; the BRM sets out the

following specific aims:

Land Based Biomass – aims to provide estimates of the resource extent and potential

availability for the bioenergy sector, of all land-based biomass resources to 2050;

within the desired country or region.

Land-Use Influences – aims to model the land-use dynamics and influences that

determine the extent and availability of all land-based biomass resources to 2050;

within the desired country or region.

Food Systems – aims to model the land-based food systems to 2050, within the desired

country or region - specifically focusing on the nexus between food and biomass for

energy. The BRM, aiming to evaluate biomass resource availability scenarios that do

not adversely impact food system productivity.

Competing Markets – aims to evaluate, quantify, and model the resource demands of

industries and other markets that may compete with the bioenergy sector for biomass

resource, to 2050; within the desired country or region. The BRM, aiming to evaluate

biomass resource availability scenarios that do not adversely impact the ability of

competing markets to meet their own biomass resource demand.

Bioenergy Pathways – aims to allow the analysis and evaluation of scenarios of

biomass resource utilisation, within different bioenergy pathways. The BRM, analysing

the bioenergy potential from indigenous resources, and comparing these to the relevant

energy targets.

Developing Scenarios – aims to provide a modelling interface, and ability to control

influencing drivers within the BRM, so that resource availability and bioenergy

potential scenarios can be developed; reflecting different potential future pathways

within the desired country or region, to 2050.


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B. Objectives of the Biomass Resource Model

A set of key objectives were developed that each reflect a different aspect of the modelling

methodology, and collectively allow the analysis required to answer the key PhD research

questions. These objectives are as follows:

Influences, Variables & Drivers – the objective is to model and understand all the

different influences, variables and drivers within biomass supply chains, that work in

determining the extent and availability of different biomass resources.

o Modelling the interactions and influences that land-use may have on biomass

resource availability to 2050. Whether the land is: forested, built-up, pastoral or

arable agriculture, un-utilised, or dedicated for the production of biomass potentially

available for the energy sector.

o Modelling the interactions and influences that population and social behaviours may

have on biomass resource availability to 2050. Including the extent and types of

foods required to meet demand, waste generation, and management behaviours.

o Modelling the interactions and influences that food production systems may have on

biomass resource availability to 2050. Including the extent and land requirements to

continue to meet food demands, and the resource opportunities that agriculture and

food product systems may provide for the bioenergy sector.

Quantifying Indigenous Resources – the objective is to model and quantify the extent

of all land based biomass resources, and evaluate their potential availability for the

bioenergy sector.

o Modelling and evaluating biomass resource availability and opportunities to 2050.

Including: resources grown specifically for the bioenergy sector, wastes and residues

collected from on-going activities, such as: wood, food or agricultural based

industries, and emerging resources opportunities such as arboriculture arisings,

forestry residues, and organic wastes.

o Modelling and evaluating the resource demands of industries and markets that may

compete with the bioenergy sector for resources, to 2050.

o Modelling and evaluating the bioenergy sectors resource demands to 2050. To

develop resource balance analyses, and to determine the extent that indigenous

resource may be able to meet this demand.


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Bioenergy Potential & Targets – the objective is to model and evaluate the bioenergy

generation potential of indigenous biomass resources, through the range of conversion

pathways; comparing these to renewable energy and bioenergy targets.

o Model and evaluate the bioenergy potential of indigenous resource to 2050. Taking

into consideration and allowing for the potential utilisation of the range of bioenergy

conversion and pre-treatment pathways, applicable to different biomass resources.

o Model and evaluate the primary energy, renewable energy, and bioenergy targets of

the desired country or region, to 2050. Evaluating the extent that bioenergy from

indigenous biomass resource, may contribute to these.

o Model and determine the extent that indigenous resources are in deficit or surplus in

the desired country or region, to 2050. Evaluating the biomass resource trade

balances that reflect the desired country or region’s required strategy; to meet their

biomass resource demands and energy targets.

4.1.2 Influential Studies & Research

A key stage in developing the BRM to meet the required aims and objectives was the

evaluation of existing studies, models, and methodologies; and to draw influence from these

when developing the BRM’s analysis methodology. As discussed in Chapter 3, a large

number of relevant studies have been undertaken, applying different methodologies and

modelling approaches; applicable to different geographic scales. As part of the process for

developing the BRM design concept; many of these studies were evaluated, with the merits

and limitations of the different methodologies considered.

Following this review process, it was decided to develop a modelling methodology with a

bottom-up, resource-focused approach. This would enable the full biomass resource potential

to be analysed, based on predicted levels of production and supply change interactions; rather

than changes in demand related to top-down forces. Development of the specific BRM

structure and approaches are discussed in greater detail, later in this Chapter. Two existing

studies were identified, that utilised relevant methodologies that could be adapted for the

BRM to meet the prescribed aims and objectives. These studies described below, provided

the greatest influence when developing the BRM’s methodologies and structure.


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A. Fischer et al (2007) - Assessment of Biomass Potentials for Biofuel Feedstock

Production in Europe

The Fischer et al (2007) study [139], was carried out under the Renewable Fuels for Europe

Project (REFUEL), in collaboration with the International Institute for Applied Systems

Analysis (IIASA). This study implements a resource-focused modelling approach;

specifically focusing on land-use influences across Europe, on biomass resource, and

feedstock productivity.

The notable and relevant aspects of this study include the methodologies applied and

scenarios developed to reflect agricultural systems, and the potential interactions with the

bioenergy sector. Future available land for potential bio-fuel production, being forecast,

whilst satisfying projected food and feed demands; to maintain European self-reliance levels.

B. Smeets et al (2004) - A Quickscan of Global Bio-Energy Potentials to 2050

The Smeets et al (2004) study [140], utilises a biomass resource model that applies a bottom-

up, resource-focused methodology, to analyse the global bioenergy potentials to 2050. The

analysis methodology is based on the evaluation of data, studies, and a range of relevant

factors that may influence bioenergy potential, such as: population growth, per capita food

consumption, and the efficiency of food production systems.

The types of biomass resources and feedstocks that the study models, includes: dedicated

bioenergy crops, forestry growth, waste streams, and agricultural and forestry residues.

Further relevant aspects of the methodology include the evaluation of bioenergy potential;

influenced by the demands for food, industrial round-wood, traditional wood fuels, and the

requirements to maintain forestry systems to protect biodiversity. In addition, the study is

notable in placing special attention on the technical potential of reducing the land area

required for food production, to meet demand; through increased productivity and efficiency

of food systems. Only agricultural land surplus to the requirement of meeting food demand is

considered, for the potential growth of biomass for the bioenergy sector.

This methodology was also developed following an extensive review of existing studies, and

therefore represents a key influential reference study for the BRM.

4.1.3 New Modelling Knowledge & Niche

As there are numerous existing biomass resource models, each with wide ranging

characteristics, it is important that the BRM does not simply replicate these. The BRM has

been designed to draw influence from existing leading research and methodologies, and also


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provide an original tool to progress and bridge gaps in knowledge. An overview of the

BRM’s characteristics and features, differentiate it from pre-existing models, and further

knowledge in this research area can be summarised as follows:

Geographic Flexibility – the BRM is designed to be flexible to geographic scale. The

structure of the BRM has been developed to be rigid, but the data applicable to each

analysis area, can be amended and updated to reflect different countries or geographic

regions. The vast majority of existing biomass resource models are developed to focus

on specific geographic areas, countries or regions.

Modelling Approach – the BRM adopts a resource-focused modelling approach,

analysing the theoretical biomass resource potential of the desired country or region.

Through calibration of the BRM to reflect the supply chain dynamics of future potential

scenarios, theoretical potential forecasts can be refined into realistic biomass resource

potential forecasts. In the UK where the majority of this PhD research is focused, a

large portion of models are demand driven [34], or resource focused [136]–[138] with

economic or technical biomass resource potential forecasts being prioritised. For the

UK, the BRM therefore provides a further analysis tool that is flexible in allowing the

evaluation of biomass resource potentials, beyond economic and technical constraints;

as discussed in greater detail with Chapter 3.

Whole System Dynamics – The BRM has been designed to reflect the whole system

and supply chain dynamics that may influence the availability of all land based

indigenous biomass resources, for the desired country or region. The majority of related

studies and models also analyse how different drivers and variables may influence

resource availability. However, they usually focus on one or a limited range of

resources and also focus on specific drivers that influence availability. The whole

system analysis approach adopted by the BRM therefore goes further than most studies;

allowing the scenarios and resource forecasts that are developed, to be more holistic.

Biomass Resource Scenarios – the BRM has been developed to be highly flexible, in

that each of the variables and drivers that reflect the dynamics of biomass supply

chains, can be controlled. Calibrating the dynamics of the Model in this way enables

future scenarios to be developed, and biomass resource potential estimates to be

forecast under these. Many existing related studies produce or make reference to


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resource scenarios. The flexibility of the BRM in both geographic extent, and its ability

to calibrate all influences, makes it a valuable additional analysis tool.

Biomass, Food & Competing Markets – Building on the methodologies developed by

Smeets et al (2004) [140], and Fischer et al (2007) [139]; the BRM has been developed

to produce biomass resource forecasts, in parallel with the ongoing need to meet food

system requirements, industrial land, and other resource demands. The model allows

modulation of these other essential requirements, while determining the extent of

resources available for the bioenergy sector. The BRM joins a short list of existing

models and studies that allow this format of analysis. The BRM provides advancement

over other models in being highly flexible and with the ability to analyse scenarios:

evaluating the availability of biomass resources where there is a desire to increase food

production, or conversely, where there is increased economic focus and therefore

greater dedication of resources to industry, rather than the bioenergy sector.

4.1.4 The Biomass Resource Model Structure

The following section introduces the BRM’s structure and design, providing an overview

description of the aims of each analysis stage within the Model.

The Model’s analysis has been designed to progress in three key stages; albeit with many

interlinking variables between each stage. The BRM’s architecture is reflected by Figure 4.1,

where the overarching design and progression of the analysis is shown. The three analysis

stages are represented by different colours within Figure 4.1; which also highlights each of

the variables and key steps that take place within each stage.

The BRM’s analysis timeframe spans the period from 2010 to 2050, with individual analysis

time-interval forecasts provided for the years: 2015, 2020, 2030, and 2050. The year 2010 is

chosen as the baseline for the BRM’s analysis, as this was latest year demonstrating complete

datasets at the time of initial development of the BRM. The year 2050 is chosen as the

analysis end point, as this represents the timeframe for key UK target strategies; these being:

energy, carbon mitigation, and other relevant strategy targets. The 2015 analysis timeframe is

chosen to provide a near-term forecast. The 2020 and 2030 analysis time frames are chosen

as they provide a mid-term bridge to the 2050 endpoint; and also represent the dates of

further key strategy targets.


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Figure 4.1: The Biomass Resource Model Methodology Architecture

27) Figure 4.1: The Biomass Resource Model Methodology Architecture


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A. Analysis Stage One – Assessment of Land-Use Influences on Biomass Resource

Availability

Analysis Stage One, allows an assessment of the land-use influences that may impact

biomass resource availability. This analysis calculates the area of UK land utilised to meet

various demands, including: food production, further urban development, and forestry; to

2050. The remaining suitable UK land area is then highlighted as being potentially available

for the production of biomass resources, for the bioenergy sector. The key of aims of

Analysis Stage One are:

The Aims of Analysis Stage One:

Determine the domestic food consumption and total demands, based on future population

scenarios leading to 2050. Estimate the land requirement to produce this quantity of food.

Determine the extent of urban and built-up land area, and forecast how this may change, to

2050.

Determine the extent of woodland and forested land area, and forecast how this may change,

to 2050.

Estimate the area of land that may be available for alternative uses; including the potential

production of biomass resource for the bioenergy sector to 2050.

The analysis progresses by defining the types and areas of land in the UK, and forecasting

how these may change in the future. Ultimately, this assessment defines the land area which

may potentially be available, for the production of biomass resource for the bioenergy sector.

B. Analysis Stage Two – Biomass Resource Availability

Analysis Stage Two of the BRM is designed to provide an analysis of the potential

availability of different biomass resources to 2050; for the bioenergy sector. The key focus of

Stage Two is the quantification of all biomass resources, whether they are wastes and

residues from on-going activities, or specifically grown for purpose. Also, following on

directly from the analysis of Stage One, biomass resource planting scenarios and strategies

are also explored; utilising the land identified as being potentially available and suitable for

resource growth. The key aims of Analysis Stage Two are:

The Aims of Analysis Stage Two:

Using the available land identified within Stage One of the Model, analyse the potential

biomass resource that can be grown for the bioenergy sector to 2050.

Quantify the indigenous land based biomass resource that may be potentially available, to

the bioenergy sector to 2050; including: grown resources, wastes and residues.

Quantify the biomass resources that may be potentially required, by competing markets other

than the bioenergy sector to 2050.


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A summary of the specific biomass resources and feedstocks analysed within the Model, and

how they are categorised, is demonstrated within Table 4.1. These resources and feedstocks

demonstrating the default design of the BRM; but this list can be extended to include more or

less resources depending on the country or geographic region modelled.

Table 4.1: Summary of the Analysed Biomass Categories & Specific Resources Table 8) Table 4.1: Summary of the Analyse d Biomass C ategories & Specific Resources

Resource Categories Resources & Feedstocks

Energy &

Biomass

Crops

Grown on

Available

Land

Biomass Crops (non-food crops)

Grasses Switch Grass / Reed Canary Grass / Miscanthus

Short Rotation Coppice Poplar / Willow

Short Rotation Forestry Eucalyptus / Beech / Sycamore

Other Forestry Pine / Birch / Spruce / Fir / Ash

Energy Crops

(food crops)

Cereal Crops Barley / Oats / Wheat

Oil Crops Oilseed Rape / Palm / Soya / Sunflower

Sugar Crops Sugarcane / Sorghum / Sugar Beet

Forestry Resource Direct Forestry

Production Softwood - Wood Fuel / Hardwood - Wood Fuel

Biomass

Residues

Resources

Agricultural

Residues

Straw Wheat / Barley / Oats / Other Cereals / Oilseed Rape / Others

Slurry & Manure Cow Manure / Pig Manure / Poultry Litter / Sheep Manure

Forestry Residues Wood Residues Forest Residues

Arboriculture

Arisings

Collected from Urban

Areas Total Arboriculture Arising

Industry Residues Residues from Wood

Industries Wood Chips / Bark / Sawdust & Other

Biomass

Waste

Resources

Household,

Industry & Other

Wastes

Wastes

Chemicals / Used Oils / Health Care & Biological / Metallic / Glass /

Paper & Card / Rubber / Plastic / Wood / Textile / Containing PCB’s /

Minerals / Sludge / Animal & Vegetable / Animal Wastes / Food Preparation & Products / Animal Faeces, Urine & Manure /

Household & Similar / Mixed & Undifferentiated Materials / Sorting

Residues / Others

Sewage Water Treatment Works Sewage Sludge

The analysis in Stage Two quantifies and forecasts the extent and availability of the biomass

resource listed in Table 4.1; taking into consideration the demands and requirements of

industries, potentially competing with the bioenergy sector for resources. This includes the

analysis of factors such as: the extent that resources are collected / harvested / generated; and

drivers, such as: economic and industry productivity trends, and the ways in which resources

are managed through their life-cycle, and supply chains.

C. Analysis Stage Three – Bioenergy Potentials & Demand

Analysis Stage Three of the Model is designed to evaluate the bioenergy potential of the

specific indigenous resource quantities calculated within Stage Two; comparing this against

projected primary energy demand, and mandated or forecast energy, renewable energy, and

bioenergy targets to 2050. The Stage Three analysis allows an assessment of the bioenergy

potential of indigenous resources, and the determination of whether the country or geographic


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region may have a surplus or deficit or resource, to meet prescribed targets. The key of aims

of Analysis Stage Three are:

The Aims of Analysis Stage Three:

Evaluate the specific types and quantities of indigenous biomass resource identified in Stage

Two and compare these against forecasts of the resource demands of the bioenergy sector to

2050.

Based on the specific types and quantities of indigenous biomass resource identified in Stage

Two of the Model, analyse the bioenergy potentials to 2050.

Compare the bioenergy potentials against total energy, renewable energy and bioenergy

targets to 2050.

Determine the types and quantities of biomass resource that may potentially be in surplus /

deficit to satisfy forecast demands.

The analysis of Stage Three, calculates the bioenergy potential of the specific resource

quantities, taking into consideration the wide range of pre-treatment and bioenergy generation

pathway options, applicable to the different types of biomass. Bioenergy values are

calculated, taking account of each resource’s potential energy value, the energy and mass

losses applicable to different pre-treatment processes, and the energy conversion efficiencies

for the applied bioenergy generation pathway. Each specific resource within the BRM are

assigned ‘preferred’ conversion pathways; as reflected within the breadth of literature

discussed in Section 4.6.6, and detailed specifically within Appendix 1.0.

The resultant potential heat, power, and transport-fuel, bioenergy thus generated; are

compared against the specific or forecast targets, for the modelled country or geographic

region. This allows the Stage Three analysis to determine the specific types and extent of

bioenergy that may be potentially generated, from indigenous resources; and determines the

types and extent of resources that may be in deficit or surplus, to balance demand.

4.1.5 Constructing & Navigating the Biomass Resource Model

This section provides details of the specific software utilised in constructing the BRM, and

provides an overview of how to navigate and control the Model. This includes descriptions of

the BRM’s user-interface, and the controllability that allows the development of biomass

resource scenarios.

A. Application of Software & Tools

The BRM has been developed, using Microsoft Excel (2010) [141] software. The analysis

figures and tables presented within this Thesis, have been developed, utilising Microsoft

Word (2010) [142], and Microsoft PowerPoint (2010) [143] software. The references applied


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in developing the BRM, and in producing this Thesis, were collated and presented utilising

Mendeley (2010) [144] software.

B. Modelling User Interface

The structure and interfaces of the BRM’s Excel Spreadsheet are summarised by Table 4.2.

This Table summarises the different functions and names of the BRM Excel Spreadsheet’s

Tabs, and provides respective descriptions for each. The different colour designations within

Table 4.2 for each Tab; provide an overview of the function of each. Screenshots from the

BRM’s Excel Spreadsheet, for each of these Tabs, are also shown and described within

Appendix 14.0.

Table 4.2: Summary of Excel Tabs within the BRM Table 9) Table 4.2: Summary of Excel Tabs within the BRM

Function Excel Tab Labels

within the BRM Description of Role within the BRM

Presentation

of Analysis

Output

Analysis Charts Presents graphs and charts that analyse the data outputs from the biomass resources

scenarios, as calibrated within the BRM.

Output Summary Page

Presentation of the key data outputs from the BRM, for the given biomass resource

scenario. This includes both the combined and resource-specific, biomass resource

availability forecasts. Also, the combined and resource-specific, bioenergy potential forecasts.

This tab also presents the total forecast values of: bioheat, biopower, and transport

biofuel, energy forecasts; for the given biomass resource scenarios.

Import Deficit

Analysis

Presentation of the analysis, comparing the forecasts of biomass resource availability and bioenergy potential; with forecasts of resource, energy and bioenergy demands; for the

modelled scenario and geography.

BRM

Controls Main BRM Control

Panel

This tab is the BRM’s main control panel. Users can develop biomass resource scenarios though the calibration of a series of drop down menus, each of which control specific

analysis areas within the BRM.

Agricultural

& Food

Systems

Yields

Presentation of the agricultural and crop productivity yield data, applied within the BRM.

Values are presented for all the food commodities, and for each of the grown biomass resource and energy crops; analysed within the BRM. This tab also presents analyses that

demonstrate productivity yield changes, in relation to the BRM’s analysis timeframe.

Food Demand by

Commodity (BASE,

2015, 2020, 2030, 2050)

These tabs undertake the analysis of how the demand for each specific food commodity, may change over the analysis timeframe. This includes analysis of: changing domestic

supply quantities, production, import quantities, stock variations, export quantities, seed,

waste, processing, and other uses for each commodity.

Food Waste Scenarios

This tab is the analysis module that allows the integration of changing food commodity

waste dynamics, into the biomass resource scenarios. Within this tab, increases or

reductions in food wastes; influence the quantities of each food commodity that will need to be produced, to balance demands.

Summary of Food

Demand

This tab presents a summary of the food commodity demands, over the analysis

timeframe.

Feed Conversion Rates

Analysis of the extent and types of feeds required to produce animal products, to balance demands. This includes an evaluation of the quantity of grown food and non-food

commodities, and pasture land required to feed livestock.

Processed Feed Data Analysis of the specific types of food and non-food commodities, required to produce the

feed for the growth of different livestock, and within different farming systems.

Land-Use

Analysis

Crop Commodity

Land

Calculations of the land area required to produce the quantities of each food crop

commodity, over the analysis timeframe, to balance demands.

Animal Feed Land Calculations of the land area required to produce the quantities of each animal feed

commodity, over the analysis timeframe, to balance demands.

Summary of Land for

Food

A summary of the total land area required to produce both crop and animal based food

commodities, over the analysis timeframe, to balance demands.


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Bioenergy

Forecast

Analysis

Bio-energy Conversion

Matrix

This is the control panel that allows users to designate bioenergy conversion pathways

for each available biomass resource, in order to forecast bioenergy potential calculations.

Biomass Pre-

Treatment Pathways

This is the control panel that allows users to designate biomass pre-treatment processes and pathways, for each available biomass resource; prior to them being subject to

respective bioenergy conversion pathways.

Energy Targets A summary of the energy and bioenergy targets over the analysis timeframe, to be integrated within the bioenergy analysis calculations.

Energy Demand A summary of the energy and bioenergy demands over the analysis timeframe, to be

integrated within the bioenergy analysis calculations.

Bioenergy Conversion

Efficiencies

A database of bioenergy conversion efficiencies, for the wide range of processes applicable to the bioenergy forecast calculations.

Feedstock Calorific

Values

A database of the caloric values of all the biomass resources, for analysis within the

bioenergy forecast calculations.

Bioenergy Summary

Tables

A summary of the total bioenergy generation forecasts for the given biomass resource scenario.

Key Data Population Data Data and forecast analysis of changing population scenarios.

Land-Use Data Data and forecast analysis of changing land-use scenarios.

Analysing

Specific Biomass

Resource

Availabilities

MSW & Waste Wood Analysis and forecast scenarios of the extent and types of waste generated over the

analysis timeframe.

Waste Management &

Generation

Analysis and forecast scenarios of waste generation and waste management strategies,

over the analysis timeframe.

Industry Forestry

Dynamics

Analysis and calculations of the dynamics between forestry systems, wood based

industries, and the bioenergy sector (Figure 4.13).

Energy Crop Scenarios

Control panel for calculations and analysis: for developing energy crop planting

scenarios, and forecasting the potential availability of these resources over the analysis

timeframe.

Straw Calculations Calculations and analysis, forecasting the availability of straw resources.

Animal Manures &

Slurries Calculations and analysis, forecasting the availability of manure and slurry resources.

Sewage Sludge Calculations and analysis, forecasting the availability of sewage sludge resources.

Key Excel Tab Primary Functions:

BRM Control Panel Analysis Database Results & Outputs

C. Modelling Controls

As highlighted within Table 4.2, the BRM’s main controls are undertaken on a dedicated

Tab; ‘Main BRM Control Panel’ within the Excel Spreadsheet. Table 4.3 provides a further

breakdown and descriptions of the specific control variables of the main control Tab. Further

controls for development of scenarios relating to the bioenergy conversion pathways, and for

the development of biomass and energy crop planting strategies on available land; are further

described later in this Chapter and depicted by screenshots of the BRM Excel Spreadsheet in

the Appendix 14.0.


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Table 4.3: Overview of the BRM’s Main Control Panel Variables Table 10) Table 4.3: Overview of the BRM’s Main Control P anel V ariables

Themes BRM Main Control

Panel Variables Descriptions

Development Built Up Land Area

% Change in built up land area between each analysis timeframe. Or the option to link

the expansion of built up land area to the changing population rate.

Population Change Low, Medium and High population change forecasts over the analysis time frame.

Agricultural

Productivity

& Food

Systems

Agricultural Productivity

Yields

Options for Lower Range, Upper Range, Medium and Mean yield levels for all

agricultural production processes.

Projected Agricultural

Productivity Yields

Options for Lower Range, Upper Range, Medium and Mean forecast increases in

productivity yields for all agricultural processes.

Food Commodity Waste

Scenarios

Option to activate or disable scenarios with varying food waste levels.

% Change in the rate of food waste generation between each analysis timeframe.

Food Commodity Export

Scenarios

Option to activate or disable scenarios with varying food commodity exports.

% Change in the rate of food commodity exports between each analysis timeframe.

Food Commodity Import

Scenarios

Option to activate or disable scenarios with varying food commodity imports.

% Change in the rate of food commodity imports between each analysis timeframe.

Forestry &

Wood Based

Industry

Dynamics

Expansion & Productivity

of Forestry

Options of a series of scenarios for forestry expansion and productivity, as developed

by specialised research, such as that carried out by the Forestry Commission.

Wood Based Industry Demand

% Change in the rate of wood based industry resource demands.

Forestry Product Export

Scenarios % Change in the rate of forestry product exports between each analysis timeframe.

Forestry Product Import Scenarios

% Change in the rate of forestry product imports between each analysis timeframe.

Waste

Resource

Dynamics

Waste Generation Trends Options of a series of scenarios for waste generation as developed by specialised

research such as that by DEFRA.

Waste Management Trends

Options of a series of scenarios for waste management as developed by specialised research such as that by DEFRA.

Further

Biomass

Resource

Dynamics

Forestry Residues % Change in the extent that this resource is collected and made available to the

bioenergy sector.

Arboricultural Arisings % Change in the extent that this resource is collected and made available to the bioenergy sector.

Straw Agricultural

Residues

% Change in the extent that this resource is harvested and made available to the

bioenergy sector.

Slurry & Manure Agricultural Residues

% Change in the extent that this resource is collected and made available to the bioenergy sector.

Biomass &

Energy Crop

Planting

Strategies

Biomass & Energy Crop

Planting Scenarios

Option to activate a default or to develop a customised biomass and energy crop

planting scenario - making use of the land forecast as being available for growth.

Proportion of Available Land Utilised for Growth

% of the area of land forecast as being suitable and available for biomass growth that is to be utilised within planting strategies between each analysis timeframe.

4.1.6 Applying the Biomass Resource Model

The BRM is applied within this research to reflect and analyse the biomass resource supply

chain dynamics of two geographies; that of the United Kingdom and that of Brazil. This

section describes the key analysis processes undertaken for each country, through application

of the BRM.

A. The UK Biomass Resource Model

In the first instance, BRM is developed to reflect the biomass resource supply chain dynamics

within the UK. The UK is chosen as the first analysis case study, as this research has been


104

funded by the UK Engineering & Physical Sciences Research Council (EPSRC).

Furthermore, the UK represents an ideal example of a developed nation with strong

bioenergy aspirations, but with uncertain indigenous biomass resource availability. The UK

BRM analyses as discussed in this Thesis focus on the following:

Drivers – the variables that influence the availability of biomass resources in the UK

are evaluated through application of the BRM to the UK’s biomass resource supply

chains (Chapter 5). Each of these supply chain drivers are listed and their influence on

biomass availability, discussed. This analysis is undertaken to gain a greater

understanding of which drivers are the most, and the least influential, in determining

resource availability. In addition the BRM is used to evaluate the ‘availability

robustness’ of specific biomass resources; the level of influence specific resource

availabilities have on supply chain dynamics. The desired outcome of this research

being: to identify those drivers most influential in effecting the availability of the most

needed biomass resources. And then to review current actions, strategies and policies,

in the light of this new information; with a view to optimizing future availability of

indigenous biomass resource and subsequent bioenergy potential (Chapter 10).

Biomass Resource Scenarios – a series of scenarios are developed and constructed

within the UK BRM, to represent the different pathways the UK could take, to 2050

(Chapter 6). These scenarios forecast the extent of the UK’s indigenous biomass

resource availability, and the bioenergy potential. This analysis is undertaken in order

to gain a greater understanding of the different levels of bioenergy that could be

generated from indigenous resource to 2050; with varying future commitments for

mobilising resources and developing the UK bioenergy sector.

Bioenergy Sector Potential & Targets - further analysis is undertaken to determine

the forms and extent of future possible bioenergy generation, in relation to the UK’s

energy targets (Chapter 7). Also, carrying out analyses to evaluate the types and extent

of resources that will be potentially required by the future UK bioenergy sector. A

resource-trade-balance analysis is then undertaken, to evaluate the specific types of

biomass resource that the UK may need to import, to balance the future bioenergy

sector’s demands; and comparing these to the indigenous domestic resources

potentially available.


105

B. The Brazil Biomass Resource Model

The BRM is then adapted to reflect the biomass resource supply chain dynamics within

Brazil. Brazil is chosen as the second analysis case study, as Brazil represents a developing

nation with extensive biomass resources, and is a predominant contributor to the global

biomass trade market. The Brazil BRM analyses within this Thesis progresses as follows:

Bioenergy Sector – following an extensive literature review process, a baseline

scenario is developed to reflect Brazil’s biomass resource supply chains. The Brazil

BRM is utilised to forecast Brazil’s biomass resource availability and bioenergy

potential to 2050 (Chapter 9). This analysis is undertaken to understand the specific

types and extent of biomass resources within Brazil, and the potential forms and extent

of bioenergy generation. This analysis allows a biomass resource balance assessment to

be undertaken, to evaluate the types and extent of resources that may potentially be

available to the international biomass trade markets.

Bioenergy Scenarios – a literature review process is undertaken to analyse Brazil’s

energy and bioenergy targets to 2050 (Chapter 9). A further literature review is carried

out to assess and compare the energy and bioenergy targets and strategies of a series of

countries, who are currently leading the way in terms of energy efficiency, renewable

energy contribution, and bioenergy. Scenarios are then developed within the Brazil

BRM to understand how Brazil’s biomass resource balance may change, if Brazil were

to adopt targets and strategies comparable to those of leading countries. This analysis is

undertaken to evaluate how the global biomass trade markets may be impacted if

countries such as Brazil, were to adopt strategies requiring them to utilise higher

proportion of their biomass resources for domestic use, with less available for export

(Chapter 9).


106

4.2 UK BRM - Developing the Stage One

Methodology

The following section of the Thesis discusses in detail, how the Stage One analyses of the

BRM were developed. This section focuses on presenting the key studies and methodologies

that influenced the design of the Stage One analyses; with all the data sources utilised, being

referenced. The specific discussions in this Chapter focus on the development of the UK

BRM; with adaptations integrated to develop the Brazil BRM, discussed later in Chapter 9.

Section 4.3 of this Chapter, supported by screenshots of the BRM Excel Spreadsheet in

Appendix 14.0; goes onto present the precise calculations and mechanics, and to show how

the Stage One analyses of the BRM are modelled.

A step-by-step walk through of the Stage One BRM methodologies is provided; focusing on

the following key analysis areas:

Population Dynamics Food & Agriculture Systems

Built-Up Land Area Agriculture Land

Land Availability Growing Biomass

Food Commodity Demands Agriculture & Biomass Yields

Woodlands, Forests & Plantations

4.2.1 UK Population Dynamics

The confirmed official UK population data for 2010 has been taken to provide baseline

figures for the UK BRM’s analysis. High, Medium, and Low population change forecasts

have been sourced from the United Nation’s ‘Population Division’ [145]. Highlighted within

Table 4.4 and Figure 4.2, these provide the population change scenarios for the analysis

timeframes, within the Model.

Following the methodologies developed by Smeets et al (2004) [140], and Fischer et al

(2007) [139]; population change scenarios provide much of the basis for calculating future

food production, and commodity requirements to meet domestic food demand. The data of

the chosen population forecast scenarios is thus used consistently throughout the BRM; with

the Medium Forecast applied as the default, wherever population dynamics are required.


107

Table 4.4: UK Population Forecasts Table 11) Table 4.4: UK Population Forecasts

Country Forecast Baseline 2015 2020 2030 2050

UK

High 62,036,000 64,444,000 67,138,000 72,749,000 82,045,000

Medium 62,036,000 63,935,000 65,802,000 69,314,000 72,817,000

Low 62,036,000 63,426,000 64,466,000 65,889,000 64,268,000


28) Figure 4.2: UK Population Forecasts

Figure 4.2: UK Population Forecasts


4.2.2 UK Built-Up Land Area

The UK BRM utilises Food & Agriculture Organisation (FAO) data (2011) [146], to quantify

the area of UK ‘built-up’ land area, for the analysis base year (2010). This area of land

includes residential, commercial, industrial, and infrastructure built-up land area

classifications. Within the BRM, this land is identified as being unsuitable for any form of

agricultural or biomass resource growth.

Future forecasts for the expansion of built-up land area in the UK, have been derived from

the EU ‘Modelling Opportunities & Limits for Restructuring Europe’ Project (MOSUS).

High, Medium, and Low development forecasts are utilised, as depicted within Figure 4.3;

with the Medium Forecast applied as the default within the BRM. Expansion of built-up land

area is assumed to take up some of land that would otherwise be potentially available for

biomass resource growth.

United Nation’s UK Population Change Forecast to 2050


108

29) Figure 4.3: UK C hange in B uilt-Up Area Forecasts

Figure 4.3: UK Change in Built-Up Area Forecasts

Data Taken from [146], [147]

4.2.3 UK Forests, Woodlands & Plantations

The characteristics of forests, woodlands, and plantations are a major analysis focus-area

within the BRM. The extent, types, productivity, and characteristics of UK forestry are well

documented by the UK Forestry Commission. Within Stage One of the BRM, the current

forested land area and the forecast trends for how this may change, form the key areas of

analysis. Figure 4.4, documents scenarios for the future UK forested area, developed by the

Forestry Commission [148]. The Medium forecast represents the default within the UK

BRM. The productivity of forest systems and their interaction with industry and the

bioenergy sector, are discussed later in this Chapter when introducing the BRM’s Stage Two

analysis.

Forecasts of Changes to UK Built-Up Land Area to 2050


109

30) Figure 4.4: UK Forested Area Forecasts

Figure 4.4: UK Forested Area Forecasts

Data developed from, [148]

4.2.4 UK Food & Agriculture Systems

Food and agriculture systems are a further important analysis theme within Stage One of the

BRM. The BRM analysis progresses through evaluating the demands for different food

commodities, and forecasting how these may change to 2050. Food commodity requirements

to meet animal feed demands are also analysed. The area of land required to produce the

quantities of food commodities to balance demands are calculated as a function of the

applicable agricultural productivity, and yield data, for each food commodity.

A. Food Commodity Demands

The Food and Agriculture Organisation’s ‘FAO Stat’ website and database [146], provides

food commodity data for all countries and provides the basis for food-related data, applied

within the BRM. Developing the BRM to be compatible with FAO data also provides

flexibility, in that the required food data can be easily updated; allowing the BRM to reflect

the dynamics of different countries or geographic regions.

The BRM is developed to allow analysis of all food commodity types and categories analysed

by the FAO; these being listed within Appendix 2.0. Table 4.5 provides a summary and

description of the FAO Stat food commodity datasets, utilised within the BRM.

The modelling calculation equations and mechanics, and how these are applied within the

BRM, are discussed later in Section 4.3 of this Chapter.

Forecasts of UK Forested Land Area to 2050


110

Table 4.5: FAO Food Commodity Datasets Utilised Within the BRM Table 12) Table 4.5: FAO Food Co mmodity D atasets Utilised Within the BRM

Food Commodity Dataset Descriptions

Demand of Commodity The total demand of each food commodity required per annum. (tonnes)

Food per capita Consumption The per capita consumption of each food commodity per annum. (tonnes/person/annum)

Processed Food The demand of each food commodity required for all processed food per annum. (tonnes)

Other Food The demand of each food commodity required for all other uses per annum. (tonnes)

Feed for Pastoral Total demand of commodities for feed as a requirement of animal production systems. (tonnes)

Waste Estimation The typical waste ratios for each food commodity. (tonnes)

Seed The seed requirements for each food commodity. (tonnes)

Export Levels The total export of each crop and animal product from the UK. (tonnes)

Import Levels The total import values of each crop and animal product to the UK. (tonnes)

Descriptions Adapted from [146]

B. Forecasting Changing Food Demands

Based on the large number of reports and studies with varying conclusions; forecasting how

the demand for different food commodities will change to 2050, is subject to much

speculation. However, the undeniable primary driver that will accurately predict future food

commodity demands, are the rates of population growth [149]. Therefore, once again

following the methodologies developed and applied by Smeets et al (2004) [140], and Fischer

et al (2007) [139]; the key drivers responsible for changing food demands within BRM, are

the population scenarios. This concept is also discussed further within Chapter 5.

C. Animal Based Food Feed Demands

A further agricultural system dynamic analysed within the BRM, are the land and resource

demands required to produce animal based food commodities. Animal based agriculture can

be pastoral, landless, or mixed, in terms of direct land-use; with additional land being

required to produce the crop commodities, required to feed the livestock. This section

discusses the animal based agriculture systems applied within the UK, and describes how the

land and resource requirements are analysed within the UK BRM. Much of the analysis

within the UK BRM applies the data and assumptions, developed and applied by Bouwman

et al (2004) [150].

i. UK Agriculture Production Systems

The first step in analysing the animal based agriculture feed demands within Stage One of the

UK BRM, is the evaluation of the types of agriculture systems typically utilised within the

UK. The two main categories of agricultural practice being: Pastoral Agriculture, where

animals are grazed on areas of land, and gain a large proportion of their feed directly from

this land; and Mixed & Landless Agriculture, where animals spend their time either housed,

or on pasture land dependent on seasonal variations. With regard to the latter, food crop


111

commodities and other resources are thus required to feed the livestock during the periods

when they are housed. A list of the key animal based product categories as analysed within

the BRM are as follows:

Beef Meat Based Products Poultry Based Products

Mutton & Goat Based Products Other Products with Pastoral Land Requirements

Pork Based Products Other Products with Mixed & Landless Requirements

Milk & Dairy Based Products

Appendix 3.0 presents data sourced from Bouwman et al (2004) [150], reflecting the

proportions of time taken, by each animal based product category, while utilising

predominantly pastoral, and mixed and landless systems, in the UK.

ii. Feeding Practices

The next step the BRM undertakes is the evaluation of the specific feed requirements of

livestock produced within both pastoral, and mixed & landless systems, in the UK. This

analysis step is undertaken to determine if there are any, additional land requirements needed

to grow the food commodities, to balance the feed demands, for all animal based agricultural

practices. Also, to identify the areas of pasture land required, to balance the demands of

pastoral agricultural practices.

The BRM again utilises the data and methodology applied by Bouwman et al (2004) [150],

which lists the typical feed requirements, for producing different livestock. This essentially

provides agricultural practice assumptions for different regions, including: the extent of feed

that each different category of livestock will consume, the specific food crops and animal

based products that will have to be produced for feed requirements, the extent that residues

and fodder will be consumed, the quantities of grass that will be required either directly from

the land, or grown and harvested, and/or the extent that scavenging for food, (with no

accounted land or resource requirements) may be adopted as the agricultural system method.

The feed data utilised within the UK BRM to reflect UK livestock feed agricultural practices,

is listed within Appendix 3.0.

iii. Animal Feed Raw Material Composition

The BRM then undertakes analyses to evaluate the typical compositions of feed for different

livestock categories. This is carried out to determine the land area that may be required to

produce the quantities of crop and animal food commodities, to balance the feed demands.


112

The UK BRM utilises typical feed composition data provided by the UK Department for

Environment, Food and Rural Affairs (DEFRA) [151]. This is listed within Appendix 3.0.

iv. Feed Conversion Efficiencies

The BRM then applies feed conversion efficiency data, to determine the typical quantities of

feed (kg) that are required to produce an equivalent mass (kg) of animal food product. This is

an important analysis step, as it allows the total demand of different crop commodities to be

calculated, to produce the required quantities of animal product. The BRM adopts a similar

methodology for determining feed conversion efficiencies, as that developed for analysing

crop productivity yields. A literature review was undertaken to determine the range of feed

conversion efficiency values, calculated for different livestock categories in the UK. The key

reports, studies and research, providing UK feed conversion efficiency data, are listed below.

The specific data utilised within the UK BRM are listed in Appendix 3.0; with the Mean

values applied as the default control options, within the BRM.

Bouwman et al (2004) [150] DairyCo (2010) [152]

FAO (1996, 2010, 2011) [109], [153], [154] Jolly & Wallace (2007) [155]

v. Land Requirement to Produce Animal Based Food Commodities

The final step in this phase of the Stage One BRM analyses, are calculations of the total land

area that would be required to produce the animal based food commodities, to balance

demands; including areas of pasture land and additional lands, required to produce the

commodities for making animal feed.

The BRM approaches this analysis through linking these animal feed land requirements, with

that required to produce the crop commodities, for meeting overall demands. Thus the

production of additional crop commodities for animal feed is modelled, in addition to that of

crops for food consumption. The land area required for pastoral agricultural practices is

analysed as a function of the typical grass productivity yields in the UK, and the amount of

food (grass) necessary to produce the required quantities of animal product. Data reflecting

these dynamics are listed in Appendix 3.0.

4.2.5 Agriculture & Biomass Productivity Yields

Agriculture and biomass resource productivity yields are key data utilised within the BRM, to

forecast the areas of land that may be required to produce the desired levels of commodities.

The productivity yields are also used within the BRM, as a function for modelling potential


113

stresses to agricultural systems; such as that caused by climate change. This is discussed in

further detail within Chapters 5 and 8.

A. Modelling Productivity Yields

Agriculture yield forecasts are undertaken in practically all countries, with estimations of

crop yields being based on the evaluation of sample harvests at a various geographic scales

[156]. This process can result in a broad range of yield forecasts, and this is documented in a

wide range of literature and reports. To allow for this wide ranging yield data, the analysis

within the BRM takes account of such variances. Productivity yield data is collated for each

food commodity and biomass resource, and simple statistical analysis is undertaken within

the BRM to evaluate the range in yield values. Figure 4.5 reflects the interface developed

within the BRM, with the Mean production yield values being utilised as the default for all

food commodities and biomass resources. A screenshot of the BRM Spreadsheet

demonstrating this analysis is also shown in Appendix 14.0.

Food

Commodity /

Biomass

Resource

Production Yields Range of

Values Low 1st ¼ Mean 3rd ¼ High Custom

List of the food

commodities

and biomass

resources

The lowest

yield value

from the range

of sourced

values.

The first

quartile yield

value from the

range of

sourced values

when ranked.

The mean yield

value from the

range of

sourced values.

The third

quartile yield

value from the

range of

sourced values

when ranked.

The highest

yield value

from the range

of sourced

values.

Flexibility to

allow a custom

yield value to

be utilised

within the BRM

independent of

those sourced.

Collation of all

the productivity

yield data

sourced from a

wide range of

reports, studies

and literature.

Figure 4.5: Evaluating Productivity Yields within the BRM 31) Figure 4.5: Eva luating Productivity Yields wit hin the BRM

B. Current UK Productivity Yields

A literature review was undertaken to develop a database of agricultural yield data for all the

food and biomass commodities analysed within the UK BRM. Table 4.6 provides a summary

of the key studies, reports, and research; from which this data was sourced. The production

yields for each commodity and resource are also included in Appendix 4.0 of this Thesis.


114

Table 4.6: References Providing Data on Current UK Agriculture Productivity Yields Table 13) Table 4.6: References Providing Data on Current UK Agricult ure Productivity Yields

Productivity Yield Forecasts References

Yield data for food commodities that are

utilised within animal feed mixes. Bouwman et al (2004) [150] EBLEX (2008) [157]

Yield data for a wide range of food

commodities.

Bruinsma (2003, 2009) [158], [159]

DEFRA (2012) [151]

EUROSTAT (2011, 2012) [160], [161] Ewert et al (2005) [162]

FAO (2009, 2011) [146], [163]

Hafner (2003) [164]

Fischer et al (2007) [139] Nixon et al (1997) [165]

Meehl (2007) [166] Smeets et al (2004) [140]

Smil (2005) [167]

Yield data for a wide range of biomass

resources categories that are utilised by the bioenergy sector.

AEA (2010), (2011) [136], [137]

European Commission (2012) [168] EUROSTAT (2011, 2012) [160], [161]

Fischer et al (2007) [139]

Lauer (2009) [107]

Smeets et al (2004) [140] Thornley et al (2008) [169]

Yield data specifically for grass biomass

resource categories utilised by the bioenergy sector.

Riche (2006) [170]

Yield data specifically for forestry related

biomass resource categories utilised by the

bioenergy sector. Boadmeadow (2007) [171]

Forestry Commission & Biomass

Energy Centre (2012) [172]

Yield data specifically for energy crop biomass

resource categories utilised by the bioenergy

sector

Cook (2008) [173]

Hafner (2003) [164] Spink et al (2009) [174]

C. Future UK Productivity Yields

There is also great conjecture and opinions as to where future agriculture productivity yields

will stand. Research typically provides global, regional, or country specific yield change

forecasts. Similar to the methodology applied to analyse current yield values; a wide

literature review was undertaken to evaluate the breadth of literature forecasts.

At the global level, forecasts range from 1.1% annual productivity yield increases [175], to

optimistic 2.1% increases, factoring-in technological innovation [176]. Although without

technological intervention, productivity yields have been forecast to level out and the rate of

increase, to slow [163]. The global average productivity yield has also been forecast to

increase by ~100% by 2050 [177].

Within Europe, literature largely acknowledges that productivity yields of core crops such as

cereal and oil crops, are increasing; with annual increment rates of 0.8-3.0% forecast [178]–

[180]. Overall average increases in European crop productivity have been forecast to be up to

~150% by 2050 [181].

At the UK national level, productivity yields are also expected to increase [182]. The UK

invests heavily in agricultural research [183], and is a country likely to benefit from short-

term climate change, in terms of its indigenous productivity yields [184], [185]. By 2050, UK

productivity yields are forecast to increase by 40-140% within cereal crops, 20-50% in grass

crops, and 20-70% for other crops, using a 1990 baseline [186].


115

The UK BRM applies these considerations, working on the assumption that the maximum

increment in crop productivity yields to 2050, will by 150%; with further commodity specific

forecasts appropriately accounted for. A summary of the key documents utilised within the

literature review process, are shown in Table 4.7.

Table 4.7: References Providing Data on Future Agriculture Productivity Yields Table 14) Table 4.7: References Providing Data on Fut ure Agricult ure Productiv ity Yields

Productivity Yield Forecasts References

Future Productivity Forecasts for a wide

range of food commodities.

Alexandratos et al (2006) [187]

Bruinsma (2003, 2009) [158], [159]

Deepal et al (2013) [178]

Erb et al (2009) [116] Ewert et al (2005) [162]

FAO (2009) [163]

IPCC (2007) [184] van der Mensbrugghe et al (2011) [176]

Moeller & Grethe (2010) [179]

Nelson et al (2010) [188]

Nikos & Bruinsma (2012) [175] Randers (2012) [177]

Tilman et al (2011) [189]

UK Government (2012) [186] Jaggard et al (2010) [181]

DEFRA (2010) [182]

Future Productivity Forecasts for a wide

range for biomass resource categories

that are utilised by the bioenergy sector.

AEA (2010, 2011) [136], [137]

E4Tech (2009) [138] Haberl et al (2011) [180]

IPCC (2007) [184] Moeller & Grethe (2010) [179]

Randers (2012) [177]

4.2.6 Land Area to Meet Food Commodity Demands

Once the demand for each food commodity is determined, the BRM forecasts the land area

that may be required to produce the quantities of food commodities, to balance demands. This

land area being a function of: the arable and pastoral food commodity demands, the animal

feed commodity demands, arable and pastoral agricultural productivity yields, and population

change scenarios. The overview of this dynamic is highlighted within Figure 4.6, and the

calculation mechanics are specifically documented in Section 4.3 of this Chapter.

32) Figure 4.6: Forecast Land Area Required to Meet Total F ood C ommodity Demands

Figure 4.6: Forecast Land Area Required to Meet Total Food Commodity Demands


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A. Prioritising Land for Food Production

A key focus area of the BRM’s analysis methodology is the prioritisation of land area to meet

food demands, before it is identified as being potentially available for the production of

biomass resources. This reflects similar methodologies developed by Smeets et al (2004)

[140] and Fischer et al (2007) [139].

4.2.7 UK Land Availability

The final analysis step within Stage One analyses of the BRM is to forecast the total area of

land that may be available and suitable, for the production of biomass resources. This land

area is calculated in reflection of the methodology development, by Fischer et al (2007)

[139]. The calculation mechanics of this analysis is specifically documented in Section 4.3 of

this Chapter.


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4.3 UK BRM - Stage One Modelling

Mechanics

The following section presents details of the precise calculations and modelling mechanics,

developed and applied within the Stage One analysis of the BRM. These calculations are

supported by screenshots and descriptions of the BRM Excel Spreadsheet; presented within

Appendix 14.0. The BRM’s calculation equations for the following key analysis themes are

presented in this section:

Food & Agriculture Systems Land-Use

4.3.1 Stage One Analysis Calculation Equations Key

Table 4.8 presents the key for the calculation equations listed in this Section. These are

presented in alphabetical order.

Table 4.8: Equation Calculations Key for the BRM’s Stage One Analyses Table 15) Table 4.8: Equation C alculations Key for the BRM’s Stage One A nalyses

Symbol Label Description

AFP Animal Food Product Total quantity (Tonnes) of animal products produced.

APFeD Animal Product Feed Demand Total quantity (Tonnes) of animal product feed required for livestock production.

APFeP Animal Product Feed Proportion Proportion (%) of animal product resources making up livestock feed.

BULA Built-Up Land Area Total area (Ha) of built-up land.

CF Consumed Food Total quantity (Tonnes/person/year) of a food commodity directly consumed

CPY Crop Productivity Yield Productivity yields (Tonnes/Ha) for crop commodities

DCD Domestic Commodity Demand Total quantity (Tonnes) of domestically produced food commodities (excl. imports).

FCD Food Commodity Demand Total quantity (Tonnes) of food commodity required to balance demands.

FCE Feed Conversion Efficiency Quantity (Tonnes/Tonnes) of animal feed required to produce animal product.

FCFeP Food Crop Feed Proportion Proportion (%) of food crop commodities making up livestock feed.

FCFeD Food Crop Feed Demand Total quantities (Tonnes) of crops required for animal feed.

FeCLA Feed Crop Land Area Total area (Ha) of land required to produce crop commodities for feed.

Fex Food Exports Total quantity (Tonnes/person/year) of a food commodity to be exported.

FF Feed Food Total quantity (Tonnes/person/year) of a food commodity for livestock feed.

FIm Food Imports Total quantity (Tonnes/person/year) of a food commodity imported.

FLA Forested Land Area Total area (Ha) of forests, woodlands, and plantations.

FoCLA Food Crop Land Area Total area (Ha) of land required to produce crop commodities for food.

GFeD Grass Feed Demand Total quantity (Tonnes) of grass feed required for livestock production.

GFP Grass Feed Proportion Proportion (%) of grass resource making up livestock feed.

MLP Mixed & Landless Practices Proportion (%) of mixed and landless practices utilised in livestock production.

OF Other Food Total quantity (Tonnes/person/year) of a food commodity for other activities.

PF Processed Food Total quantity (Tonnes/person/year) of a food commodity for processing activities.

PLA Pasture Land Area Total area (Ha) of pasture land required to produce livestock.

Pop. Population Population quantity.

PP Pastoral Practices Proportion (%) of pastoral practices utilised in livestock production.

SALA Suitable & Available Land Area Land area (Ha) suitable and available for potential biomass resource growth.

SF Seed Food Total quantity (Tonnes/person/year) of a food commodity required for seeding.

TASL Total Area of Suitable Land Total area (Ha) of suitable land modelled for country/region.

TALA Total Agricultural Land Area Total area (Ha) of agricultural land including both arable and pastoral practices.

TPGD Total Pastoral Grass Demands Total quantity (Tonnes) of pastoral grass resource required for livestock.

RFFeD Residue & Fodder Feed Demand Total quantity (Tonnes) of residue and fodder feed required for livestock production.

RFFeP Residue & Fodder Feed Proportion Proportion (%) of residue and fodder resources making up livestock feed.

WF Waste Food Total quantity (Tonnes/person/year) of a food commodity that will be wasted.


118

4.3.2 Modelling Food & Agriculture Systems to 2050

The following section presents the calculation equations applied within the BRM for the

modelling of agricultural systems, and the food demand analyses undertaken. These

equations are described below and listed in Figure 4.7.

Calculating Food Commodity Demands (Eq.1a)

The calculation equation applied for analysing specific food commodity demands

within the BRM.

Calculating Domestic Food Commodity Demands (Eq.1b)

The calculation equation applied for analysing the domestic production of each food

commodity within the BRM. This calculation excludes the influence of food imports.

Calculating Total Pastoral Grass Required for Livestock (Eq.1c)

The calculation equation applied within the BRM, for analysing the total grass

resource demands for livestock production, utilising pastoral agricultural systems.

Calculating Total Grass Resource Required for Livestock Feed (Eq.1d)

The calculation equation applied within the BRM, for analysing the total quantities of

grass feed demands required by livestock, produced within mixed and landless

agricultural systems.

Calculating Total Food Crop Commodities Required for Livestock Feed (Eq.1e)


food crop feed commodity demands, for livestock produced within mixed and

landless agricultural systems.

Calculating Total Residue & Fodder Resource Required for Livestock Feed (Eq.1f)


residue and fodder feed resource demands, for livestock produced within mixed and


Calculating Total Animal Product Resource Required for Livestock Feed (Eq.1g)


animal product feed resource demands, for livestock produced within mixed and



119

Calculating Total Food Commodities Required for Livestock Feed (Eq.1h)

Documents the calculation equation applied within the BRM, for analysing the total

quantity of agricultural products and resources for feed, for livestock produced within

mixed and landless agricultural systems.

Eq.1a) Calculating Quantities of Food Commodities Required to Balance Demands

FCD = Pop. ( CF + PF + OF + WF + FF + SF + FIm + FEx )

Eq.1b) Calculating Domestic Food Commodity Production Required to Balance Demands

DCD = Pop. ( CF + PF + OF + WF + FF + SF + FEx )

Eq.1c) Calculating Total Pastoral Grass Required for Livestock

TPGD = ( ( ( AFP ) ( PP ) ) ( FCE ) )

Eq.1d) Calculating Total Grass Feed Required for Livestock

GFeD = ( ( ( ( AFP ) ( MLP ) ) ( GFP ) ) ( FCE ) )

Eq.1e) Calculating Total Food Crop Feed Required for Livestock

FCFeD = ( ( ( ( AFP ) ( MLP ) ) ( FCFeP ) ) ( FCE ) )

Eq.1f) Calculating Total Residue & Fodder Feed Required for Livestock

RFFeD = ( ( ( ( AFP ) ( MLP ) ) ( RFFeP ) ) ( FCE ) )

Eq.1g) Calculating Total Animal Product Feed Required for Livestock

APFeD = ( ( ( ( AFP ) ( MLP ) ) ( APFeD ) ) ( FCE ) )

Eq.1h) Calculating Total Agricultural Products & Resource for Feed Required for Livestock

FF = GFeD + FCFeD + RFFeD + APFeD

33) Figure 4.7: Calculation Equations Applied w ithin the BRM’s Agricultural Syste m & Food De mand A nalyses

Figure 4.7: Calculation Equations Applied within the BRM’s Agricultural System & Food

Demand Analyses

4.3.3 Modelling Land-Use Dynamics to 2050

The following section presents the calculation equations applied within the BRM, for the land

-use analyses undertaken. These equations are described below and listed in Figure 4.8.

Calculating Land Area Required to Produce Crop Commodities for Food (Eq.2a)

The calculation equation applied for analysing the total land area required to produce

crop commodities to balance food demands.


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Calculating Land Area Required to Produce Crop Commodities for Feed (Eq.2b)

The calculation equation applied for analysing the total land area required to produce

crop commodities, to balance animal feed demands.

Calculating Pasture Land Area Required for Livestock (Eq.2c)

The calculation equation applied for analysing the total area of pasture land required

to balance the grass feed demands of livestock.

Calculating Total Agriculture Land Area (Eq.2d)

The calculation equation applied for analysing the total land area required for all

agricultural processes, to balance domestic food commodity supply demands.

Calculating the Area of Land Suitable & Available for Biomass Growth (Eq.2e)

The calculation equation applied for analysing the area of available and suitable land,

for the potential growth of biomass resources and energy crops within the BRM.

Eq.2a) Calculating Land Area Required to Produce Food Crop Commodities

FoCLA = DCD / CPY

Eq.2b) Calculating Land Area Required to Produce Feed Crop Commodities

FeCLA = FCFeD / CPY

Eq.2c) Calculating Pasture Land Area Required for Livestock

PLA = TPGD / CPY

Eq.2d) Calculating Total Agricultural Land Area Required to Balance Domestic Production Demands

TALA = FoCLA + FeCLA + PLA

Eq.2e) Calculating Land Area Suitable & Available for Biomass Resource & Energy Crop Growth

SALA = TASL - ( FLA + BULA + TALA )

34) Figure 4.8: Calculation Equations Applied w ithin the BRM’s Land Use Analyses

Figure 4.8: Calculation Equations Applied within the BRM’s Land-Use Analyses


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4.4 UK BRM - Developing the Stage Two

Methodology

The following section of the Thesis discusses in detail, the development of Stage Two

analyses of the BRM. The BRM’s Stage Two analysis, aims to quantify and forecast the

availability of different resources and feedstocks for the bioenergy sector. The specific

resources analysed are listed in Table 4.1. At each stage, the key studies and methodologies

that influenced the design of the BRM are listed, and all the data sources utilised are

referenced.

Section 4.5 of this Chapter, supported by screenshots of the BRM Excel Spreadsheet in

Appendix 14.0, goes on to provide further discussions of the precise calculations mechanics,

of how the Stage Two analyses of the BRM are modelled.

A step-by-step walk through of the Stage Two BRM methodology is provided, focusing on


Forest Productivity Forestry Residues Industry – Forestry Dynamics

Industrial Residues Agricultural Residues Arboriculture Residues

Waste Generation Waste Management Biomass Planting Strategies

4.4.1 UK Forest System Productivity & Characteristics

The UK Forestry Commission produces a wide range of reports and statistics that describe

the current and forecast forestry systems. These document the extent, types, and

characteristics of forests, woodlands, and plantations in the UK. Within the Stage Two

analysis of the BRM, forestry systems are important as they provide resources to both UK

industries, and the bioenergy sector. The productivity of forestry systems are therefore a key

focus for the analysis.

A. UK Forest Productivity Forecasts

The UK BRM utilises the Forestry Commission’s productivity scenarios, to forecast potential

pathways that UK forestry could take [190]–[197]. The Forestry Commission develop

separate forecasts, for forestry within the Forestry Commission Estate, and forecasts for the

Private Sector Estate. These forecasts are derived by assessing: the forested area, the forestry

characteristics within the area, the rapidity of tree growth (yield class), and options for

when/if the trees are harvested.


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i. Forestry Forecasts for the Forestry Commission Estate

Forestry Commission Estate information, pertaining to forestry area and characteristics, are

extracted from the Forestry Commission’s well-established ‘Sub-Compartment Database’.

This database provides a periodically updated record, of all land managed by the Forestry

Commission. Each stand of trees is represented spatially, with further information detailing

individual characteristics, such as: species, planting, year, spacing, and yield class.

Information from the Sub-Compartment Database is utilised to estimate the standing-volume

for each spatial area. Forestry Commission ‘growth and yield’ models, are then applied to

‘grow’ the stands. The further application of harvesting events, relating to the felling or

thinning of the forests over the forecast period; allow the development of Standing Volume,

and Increment and Production Volumes projections, for each forecast. The timing and scale

of felling and thinning events, ultimately determining the nature of each forecast [192], [195].

The Forestry Commission have two main scenarios that forecast the productivity of forestry

systems on their Estate. These scenarios are described in Table 4.9. The Forestry Standing

Volumes, and Forestry Productivities data, from these scenarios, are reflected within Figures

4.9 and 4.10 respectively. The UK BRM utilises the ‘Market Behaviour’ forecast scenario as

the default, to reflect the productivity of forestry systems within the Forestry Commission

Estate.

Table 4.9: Forest Productivity Scenarios for Forestry Commission Estate Forests Table 16) Table 4.9: Forest Productiv ity Scenarios for Forestry Commiss ion Estate Forests

Forestry Commission

Scenarios Description

Biological Potential (BP)

This scenario reflects the forecast productivity of forestry systems where they

are left to achieve their biological potential, only constrained by physical

factors. Limited thinning and felling activities are assumed to reflect moderate

wind-risk measures.

Market Behaviour (MB)

This scenario reflects the Forestry Commission Estate management plans.

Management of forestry systems including felling and thinning strategies take

place to maximise the health and productivity of the forestry systems.



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35) Figure 4.9: Forestry Commission Estate Standing V olume Forecasts

Figure 4.9: Forestry Commission Estate Standing Volume Forecasts

Scenario Data from, [190]–[197]

36) Figure 4.10: Forestry Commission Productiv ity Forecasts

Figure 4.10: Forestry Commission Productivity Forecasts


ii. Forestry Forecasts for Private Sector Estate

For the Private Sector Estate, information of forested area and characteristics are derived

from results from the ‘National Forest Inventory’. This inventory is composed of two

Forestry Commission Estate Standing Volume Forecasts

Forestry Commission Estate Forest Productivity Forecasts

2010-15 2016-20 2021-25 2026-30 2036-40 2041-45 2046-50

2010-15 2016-20 2021-25 2026-30 2036-40 2041-45 2046-50


124

elements: woodland map, and a field survey. The woodland map covers all forests in the UK

that are over 0.5 hectares, and have a canopy cover of at least 20%. This map is periodically

validated using satellite imagery, and refined through field-survey work [198].

Forestry Commission growth and yield models are then applied to forecast: future growth,

standing volume, and potential harvested productivity. The Forestry Commission has

developed a range of forecast scenarios that explore the productivity outcomes based on:

different yields, biological potentials, approaches to felling, and thinning and management for

wind-risk [192], [195]. These scenarios are described in Table 4.10, and the Forestry

Standing Volume, and Forestry Productivity forecasts data, is documented within Figures

4.11 and 4.12 respectively. The UK BRM utilises the ‘Biological Potential 2’ forecast

scenario as the default; to reflect the productivity of forestry systems within the Private

Sector Estate.

Table 4.10: Forest Productivity Scenarios for Forest Systems within the Private Sector Estate Table 17) Table 4.10: Forest Productivity Sce nario for Forest Syste ms w ithin the Private Sector Estate

Forestry Commission

Scenarios Description

No Harvesting (NH) This scenario, also known as ‘zero intervention’; assumes that there is no felling

or thinning throughout the forestry system.

Biological Potential 1

(BP-1)

This scenario reflects the approach of allowing forestry systems to develop

without thinning, to reach their biological potential. Clear-felling takes place at

the year of mean annual increment (MAI), to maximise productivity.


(BP-2)

This scenario reflects the approach of allowing forestry systems to develop to

reach their biological potential. Thinning takes places based on observed

activity, but no special wind-risk measures are applied. Clear-felling is

undertaken at the first year of MAI to maximise productivity.


(BP-3)

This scenario reflects the approach of allowing forestry systems to develop to

reach their biological potential. This scenario maximises productivity, by

thinning and felling, and at the ages of MAI; irrespective of wind-risk.

Modified Biological

Potential 1 (MBP-1)

This scenario reflects the approach where thinning and felling are actively

undertaken, assuming moderate wind-risk measures. This scenario takes

account of wind-risk, but assumes a relatively risk-tolerant approach, when

applying wind-risk constraints to harvesting practice. Felling takes place at the

year of MAI.

Modified Biological

Potential 2 (MBP-2)

This scenario reflects the approach where thinning and felling are actively

undertaken, assuming strong wind-risk measures. This scenario takes account of

wind-risk, but assumes a relatively risk-tolerant approach, when applying wind-

risk constraints to harvesting practice. Felling takes place at the year of MAI.

Industry View (IV)

This scenario reflects harvesting and productivity, based on an industry-view of

future harvesting practice; in relation to the age of felling and types of thinning.

This scenario being developed in consultation with private sector growers and

processors.



125

37) Figure 4.11: Private Sector Estate Standing Volume Forecasts

Figure 4.11: Private Sector Estate Standing Volume Forecasts


38) Figure 4.12: Private Sector Estate Productivity Forecasts

Figure 4.12: Private Sector Estate Productivity Forecasts


iii. Restocking Forecast

The Forestry Commission forecasts, for both the Forestry Commission and Private Sector

Estates, apply the assumption that when stands are felled within the forecast period, they are

Private Sector Estate Standing Volume Forecasts

Private Sector Estate Forest Productivity Forecasts

2010-15 2016-20 2021-25 2026-30 2036-40 2041-45 2046-50

2010-15 2016-20 2021-25 2026-30 2036-40 2041-45 2046-50


126

restocked with tree species with the same yield-class (like-for-like). The impact of restocking,

on production and standing volumes within the forecast analysis is minimal for the forecast

period; as restocked areas will mostly mature and produce resources after the forecast period.

The Forestry Commission’s ‘like-for-like’ assumption, is only one possible scenario for

restocking; the National Forest Inventory, aiming in future, to provide a range of restocking

scenarios to explore the impacts of this assumption [195].

4.4.2 Forestry Residues

Forestry residues are typically low value, low density resources, such as: stem-wood, stem

tips and branches collected from the forest floor, or as a result of timber harvesting or

thinning processes. The resource is often regarded as an afterthought and not specifically

targeted, but its inclusion as a potential resource opportunity for the bioenergy sector, can

provide valuable increases to harvesting efficiencies [78].

A. Current Resource

Forest residues in the UK are virtually untapped as a resource, and may have significance for

the bioenergy sector. The UK BRM utilises data calculated for the UK’s ‘managed forested

area’; produced by McKay (2003) [199], to reflect the current resource. No studies have been

carried out that focus on analysing the potential resource from the ‘unmanaged forested area’;

as by its very nature, forest management is required to harvest this resource. The BRM’s

analysis of forestry residues therefore reflects that from managed forests.

B. Availability for the Bioenergy Sector

The main driver determining the availability of forestry residues for the bioenergy sector is

the extent to which the resource may be harvestable. The UK BRM’s analysis reflects that

developed by E4tech (2009) [138]; where a limit of 50% of the available resource is deemed

harvestable by 2015, and up to 100% thereafter. A further key variable that may limit the

extent that forestry residues are available to the bioenergy sector, relates to whether

competing markets will emerge for this resource; McKay et al (2003) [199], predicting that

there may be up to an 83% reduction in resource availability for the bioenergy sector, if

competing markets emerge.

C. Future Resource

The future potential of forestry residues for the bioenergy sector in the UK, are analysed

within the UK BRM using the methodology developed by E4tech (2009) [138]. This

forecasts the potential levels of resource, as a function of changing managed forested area.


127

The forestry residue availability analysis within the BRM is thus linked to the Forestry

Commission’s forecasts [148], [200]. The BRM is also developed to allow the analyses of

potential resource availability for the bioenergy sector, with and without competition for the

resource from other industries.

Further details of the precise calculation mechanics within the BRM, relating to the analysis

of forestry residue resources, are documented in Section 4.5 of this Chapter.

4.4.3 Forestry-Industry Dynamics

One of the most important themes modelled within the BRM is that relating to the dynamics

between forestry systems, and the industries that rely on forest productivity. These forestry-

industry dynamics, and the flow of resource between them, offer a wide range of

opportunities for the bioenergy sector; in terms of resources sourced directly from the

forestry systems themselves, and residues resulting from industrial processes.

The following section describes the approach and methodologies, for how these dynamics are

modelled within the BRM. Further detailed descriptions and the precise calculation

mechanics of the BRM, are documented in Section 4.5 of this Chapter.

A. Modelling Structure

The analysis module within the BRM that models all the forestry-industry dynamics is

represented as a sub-model, within the wider BRM structure. The aim when developing this

forestry-industry analysis module of the BRM was to create a tool that allows a ‘closed-loop’

evaluation, of forestry systems and their numerous links to industries. This starts with the

evaluation of the extent and productivity of forestry systems (Section 4.4.1), followed by the

tracking and analysis of all resources produced by these forests. The BRM tracks the current

flow, and forecasts potential future flows of forest products and resources, distributed

between each of the demand industries. It also evaluates their respective industry

productivities, and at the same time accounts for all wastes and residues that are generated;

these being potentially available and suitable, for utilisation as feedstocks within bioenergy

pathways. Imports and exports, of both raw forestry resources and wood based products are

also accounted for.

Figure 4.13 provides a high-level overview of the modelling structure of the forestry-industry

analysis module, developed within the BRM. The individual industries, product categories,

and resource distribution flows, have been designed following close evaluation of the FAO’s

‘Global Wood Fibre Reports’ [201], [202]. These are used as the base for the modelling


128

structure. This provides the BRM with flexibility, allowing easy amendment of data so that

analyses can be undertaken for alternative countries or regions. Further data relevant to the

UK forestry-industry dynamics, were also utilised from the European Commission’s EuroStat

Database [160].

B. Model Flexibility & Scenarios

The next step in analysis progression is exploring and evaluating, how the flows of different

resources and products may change; if variables within the system change. As such, the

forestry-industry analysis module in the BRM is developed to allow a series of key drivers

that control the flow of resources, to be varied in reflection of potential future scenarios. The

key applicable variables that can be controlled within the BRM are:

Forestry Extent – Future scenarios for the extent of forested area.

Forest Productivity – The productivity of forest systems, and quantity of resource

available to industries.

Industrial Productivity - The extent and trends reflecting the productivity of industries

that utilise forestry resources. This variable, determining the overall resource demands

of industry.

Imports – The extent and trends of raw forest resource, and wood product imports.

This variable influences the demands placed on indigenous forests, to produce the

resources required to balance the demands of industry. Also influencing the extent that

industries have to produce products, to meet overall demand.

Exports – The extent and trends of raw forest resource, and wood product exports. This

variable influences the demands placed on indigenous forests, to produce the resources

required to balance the demands of industry. Also influencing the extent that industries

have to produce products, to meet overall demand.

Industrial Efficiency – This variable influences the overall efficiency of wood based

industries. This in part, determining the extent that industry produces wastes and

residues that are surplus to their process requirements.

Energy Focus – This variable reflects the extent that generated wastes and residues are

made available for bioenergy pathways, rather than alternative distribution strategies.

Further discussions of specific Scenarios developed within the BRM, are discussed within

Chapter 6 of this Thesis.


129

Figure 4.13: Forestry System & Industry Dynamics Analysed within the BRM

39) Figure 4.13: Forestry System & Industry Dynamics Analysed within the BRM


130

4.4.4 Industrial Residues

Many industrial processes and manufacturing operations produce wastes and residues that

represent potential resources opportunities, for the bioenergy sector [78].

A. Current Resource

The current levels of biomass resources produced from UK industries, is generally well

documented. The Forestry Commission provide widespread data [200] that documents

resource flows to and from industries, particularly for the wood mill sector. The UK BRM is

developed to utilise this data, in addition to datasets provided by the FAO [201]. The

methodology for evaluating the extent that industry-residue resources, are potentially

available for the bioenergy sector, are adapted from those applied by E4tech (2009) [138],

and AEA Consulting (2010, 2011) [136], [137].


The key variables determining the availability of industrial residues for the bioenergy sector

within the BRM are: the extent to which residues are generated, and the end-use distribution

of these resources. The UK BRM links residue generation, to industry productivity and

economic trends [203]–[206]. The default distribution scenario for residues, including those

available for the bioenergy sector, is assumed to progress at a constant rate within the BRM.

However, the BRM is flexible; allowing alternative scenarios to be developed by varying the

extent of the available resource, potentially available for bioenergy pathways (discussed

further in Chapter 6).

C. Future Resource

The future availability of industrial residues for the bioenergy sector within the BRM, are

dependent on the precise scenarios developed within the BRM, and linked to industry

productivity and residue distribution trends.

4.4.5 Agricultural Residues

Agricultural residues are a series of materials resulting from agricultural processes, some of

which are potentially highly valuable for the bioenergy sector. These range from

predominantly dry materials such as, straws and poultry litter, to wet materials such as, slurry

and silage [78]. The BRM’s analyses, categorises agricultural residues into two groups:

Straws (dry), and Slurries (wet).


131

This section discusses the approach for how agricultural residues are modelled within the

BRM. The precise calculation mechanics of this analysis, within the BRM, are documented in

Section 4.5 of this Chapter.

4.4.6 Straw Agricultural Residues

A. Current Resource

Straw is a major agricultural residue in the UK, and a widespread resource with great

potential for the bioenergy sector. The main crop contributors to straw production in the UK

are the cereal and oil based crops [207]. The UK BRM utilises data relating to the production

of straws, and other related agricultural residues, from the Central Science Laboratory (CSL)

(2008) [207]. The CSL also provides data on the current end-uses, and competing markets,

for straws in the UK; predominantly for animal feed and bedding, and also the mushroom

industry.

Figure 4.14 provides a summary of the step-by-step analysis processes, applied within the

BRM to analyse current and forecast quantities, of straw potentially available to the

bioenergy sector.

Crops &

Practices

Production

(Tonnes)

Residue

Production

Potential

(Tonnes)

Residue

Recoverability

(%)

Harvested

Residue

Potential

(Tonnes)

Competing Uses

and Markets

(Tonnes)

Availability for

Bioenergy

Sector

(Tonnes)

The crops and

agricultural

practices

providing dry agricultural

residues

Production

extent of crops

with associated residues.

The extent of

residue

production per tonnes of crop.

The maximum

proportion of crop

residue that may be harvested with

current

technology & practices.

The extent

of residues

harvested.

The extent that the

residues are

utilised by competing

markets other than


The potential

extent that the

residues may be

available to the bioenergy

sector.

Figure 4.14: Analysis of Straw Agricultural Residues within the BRM 40) Figure 4.14: Ana lysis of Straw Agric ultura l Residues w ithin the BRM


The predominant factor, determining the availability of straw resources, is the extent that it

can be harvested. The UK BRM’s analysis, reflects the work carried out by AEA (2010,

2011) [136], [137], and E4tech (2009) [138]; who estimate that current UK harvesting

capabilities to be ~20% of the overall resource.

C. Future Resource

It is estimated that up to 100% of all straws may be harvestable by 2020, through the

development of technological and practical advances [138]. A series of harvest forecasts, and

an evaluation of competing markets [136]–[138], [207], are reflected within the BRM. The

average of these values derived from literature, is set as the default within the BRM.


132

4.4.7 Slurry Agricultural Residues

A. Current Resource

The agriculture residue resource produced by livestock, is typically calculated as a function

of the number and types of animals, excreta rates, housing practices, and the manure

management methods applied. Figure 4.15 provides a summary of the step-by-step analysis

processes applied within the BRM, to analyse current and forecast quantities of slurry

resource, potentially available to the bioenergy sector.

Animals &

Practices

Production

(Tonnes)

Number

of

Animals

Manure Factor

(Tonnes/Year) Indoor Occupancy

(%)

Competing Uses

and Markets

(Tonnes)

Availability for

Bioenergy Sector

(Tonnes)

The animals

and agricultural

practices

providing wet agricultural

residues

Production

extent of animal

based food

production.

The

number

of

animals.

The typical

extent of

manure,

produced per

animal, per

year.

The proportion of

time the animals are housed, assuming

that manure is

typically only collected during this

period.

The extent that the

residues are utilised by

competing

markets, other than the bioenergy

sector.

The potential

extent that the

residues may be

available to the bioenergy sector.

Figure 4.15: Analysis of Slurry Agricultural Residues within the BRM 41) Figure 4.15: Ana lysis of Slurry Agricultural Res idues wit hin the BRM

The BRM’s analysis utilises the animal based food commodity data, discussed in Section

4.2.4, to determine the quantities and types of livestock farmed. A literature review was

undertaken [208]–[212], to evaluate the ‘manure factor per animal, per year’; to determine

the total levels of resource that may be produced. The UK BRM applies data from Bouwman

(2004) [150], and DEFRA (2011) [213], to evaluate typical farming practices, and to

determine the extent that different livestock are housed – allowing the estimation of the

maximum levels of resource that may be collectable.


The extent that manures and slurry resources, may be available to the bioenergy sector, are

determined following a further literature review [77], [136]–[138]. The key factors

determining availability being: the extent that the resource can be collected (the majority

being predominantly used onsite), and that required by competing markets [138].

C. Future Resource

The future potential of manure and slurry resources, are analysed within the BRM, utilising

the methodology developed by E4tech (2009) [138]. This focuses on forecasts, for the extent

that different livestock are farmed (Appendix 3.0), and changing farming practices such as

the livestock housing practices, and manure extraction rates over time.

4.4.8 Arboriculture (Arb) Residues

Arboricultural arising, predominantly consist of: low density branches and brash materials

that result from tree surgery work, maintenance of municipal and domestic gardens, and from


133

the verges of roads and railways. Collectively, this resource category can yield large

quantities of arisings and residues that may be a suitable for the bioenergy sector, if

appropriately handled and processed [78].

This section, discusses the methods adopted in the BRM, for analysing the potential

availability of arboricultural residue resources. Further details of the precise modelling

calculations and mechanics of the BRM, are documented in Section 4.5 of this Chapter.

A. Current Resource

The BRM’s analysis methodology, accounting for the potential availability of arboricultural

residues; reflects that developed by MacKay (2003) [199], and Holmes & Nevin (2001)

[214].


The BRM utilises a methodology for analysing the resource potential for the bioenergy

sector, reflecting that developed by E4tech (2009) [138]; where all wood is assumed to have

an average moisture content of 60%, and a mixed range of wood types, species, and

classifications are assumed.

C. Future Resource

Forecasts for the future arboriculture resource potential are predicted by the Wood Panel

Industries Federation (2010) [204], and are thought unlikely to vary much, until 2025 and

beyond; although there are likely to be trends relating to tree planting strategies in urban

areas. Therefore, the BRM’s analysis links future arboriculture resource availability with the

scenarios of changing built-up land area (discussed further in Chapter 6).

4.4.9 Wastes

Waste resources are a significant resource, and are becoming increasingly utilised within

energy pathways [78]. Therefore, modelling of waste resources within the BRM represents an

important area of analysis. Waste resources constitute a wide range of material types, with

many characteristics, from: woods, packaging, foods, and demolition and construction

outputs. Extensive existing work has been carried out, to develop and understand waste

resource categories, and to quantify them [206], [215]–[219]. Modelling the potential

availability of any given waste resource, for the bioenergy sector, requires an assessment of

how much waste is generated, and an understanding of the waste management strategies in

place that determines how it is distributed.


134

This section describes the modelling approaches applied within the BRM, to analyse waste

generation, and management dynamics. The precise calculations and modelling mechanics

developed in the BRM, applicable to waste resources; are documented in Section 4.5 of this

Chapter.

A. The BRM’s Waste Resource Categories

The BRM categorises different waste resources, in reflection of the groupings utilised by

DEFRA [219], [220]. As documented by Table 4.11, waste resources in the BRM are

identified as being either: hazardous, or non-hazardous; and their suitability for potential

bioenergy pathways is identified.

Table 4.11: Waste Streams & Availability for the Bioenergy Sector Table 18) Table 4.11: Waste Streams & Ava ilability for the Bioenergy Sector

Waste Categories Hazard Classification Availability to Bioenergy Sector

Chemical Wastes Non-Hazardous Chemical Wastes excl. Used Oils Hazardous X

Used Oils Hazardous X

Healthcare & Biological Wastes Non-Hazardous

Healthcare & Biological Wastes Hazardous

Metallic Wastes Non-Hazardous X

Metallic Wastes Hazardous X

Glass Wastes Non-Hazardous X

Glass Wastes Hazardous X

Paper & Cardboard Wastes Non-Hazardous Rubber Wastes Non-Hazardous X

Plastic Wastes Non-Hazardous X

Wood Wastes Non-Hazardous Wood Wastes Hazardous

Textile Wastes Non-Hazardous

Waste Containing PCB Hazardous X

Animal & Vegetal Wastes Non-Hazardous

Animal Waste of Food Preparation & Products Non-Hazardous

Animal Faeces, Urine & Manure Non-Hazardous

Household & Similar Wastes Non-Hazardous

Mixed & Undifferentiated Materials Non-Hazardous X

Mixed & Undifferentiated Materials Hazardous X

Sorting Residues Non-Hazardous X

Sorting Residues Hazardous X

Common Sludges Non-Hazardous Mineral Wastes Non-Hazardous X

Mineral Wastes Hazardous X

Other Wastes Non-Hazardous X

Other Wastes Hazardous X



135

B. Waste Generation Forecast Scenarios

Many previous studies [221]–[223], have utilised methodologies that develop scenarios to

forecast waste generation trends. The UK BRM integrates DEFRA’s waste generation

forecast scenarios [206], for the UK. These reflect different potential pathways, where the

quantities of waste generated differ, depending on the approaches to waste generation within

the scenarios. The scenarios are described in Table 4.12, and the relevant specific data

utilised within the BRM is listed in Appendix 5.0.

Table 4.12: DEFRA Scenarios for UK Waste Generation to 2050 Table 19) Table 4.12: DEFRA Scenarios for UK Waste Generation to 2050

DEFRA Scenarios Description

Current Rate Scenario

Within this baseline scenario, waste generation trends continue over the analysis

period. This scenario representing, a ‘business as usual’ future pathway with no

specific focus on addressing the levels of waste generated.

Green Rate Scenario

Within the green rate scenario, there is increased focus on sustainability,

resulting in a reduction in the generation of wastes. This is driven be societal

decisions and behaviour change.

High-Tech Rate Scenario

Within this scenario, large-scale technological solutions are developed, with

respect to the generation of waste. Resulting in a moderate reduction in levels of

waste generated, with technological solutions being applied to deal to with all

waste.

Unlimited Rate Scenario

The unlimited rate scenario, is developed to reflect a future pathway, where

there is no focus to curb or manage waste generation trends. This scenario sees

a steady increase in waste generation; driven by wastefulness societal

characteristics, and lack of action.

Adapted from [206]

C. Waste Management Scenarios

Waste management is: the collection, processing, transportation, disposal, or monitoring of

wastes. The development of effective waste management strategies, is a further key theme

relevant to the bioenergy sector; with policy-makers forming guidelines, targets, and

strategies, for how waste is managed [224], [225]. A large number of existing studies [226]–

[229], have developed waste management scenarios for the UK that to some degree evaluate

the potential availability of waste, for energy pathways.

The UK BRM utilises waste management scenarios developed by DEFRA (2006) [217].

These forecast the extent that different waste categories are managed, either: Recycled or

Reused; Composted, Energy recovery, Land spread, or Disposal at Sea. The waste diverted to

energy recovery pathways, being of particular interest to the bioenergy sector. These

scenarios are utilised within the UK BRM and are described in Table 4.13. The relevant data

is listed in Appendix 5.0.

http://en.wikipedia.org/wiki/Waste_collection


136

Table 4.13: DEFRA Scenarios for UK Waste Management to 2050 Table 20) Table 4.13: DEFRA Scenarios for UK Waste Manage ment to 2050


Current Rate Scenario Within this baseline scenario, current estimates of waste material management

practices and distribution continue.

Resource Recovery

Scenario

Within this scenario, waste management and distribution, moves towards

increasing the upper recycling-rate limits; with focus on material recycling and

composting.

Energy Recovery

Scenario

Within this energy recovery scenario, waste management and distribution,

moves towards increased energy recovery, with focus on heat energy pathways

and anaerobic digestion, where applicable.

Combined Recovery

Scenario

The combined recovery scenario provides a balanced approach; with both

increased recycling, and energy recovery rates, above current levels.

Adapted from [217]

4.4.10 Sewage Waste

Sewage sludge is the useful by-product of waste-water treatment, where solids are separated

from liquids. This resource can be spread on farmland, incinerated, and can be utilised within

bioenergy pathways [78].

This section, describes the modelling methodologies for analysing potential sewage waste

availability for the bioenergy sector, within the BRM. Further details of the precise

calculations and modelling mechanisms applied, are documented in Section 4.5 of this

Chapter.

A. Current Resource

Sewage waste is a relatively constant and reliable biomass resource; with its availability and

utilisation within energy pathways, well documented by DECC (2011) [67].


Datasets: [67], [78], [136]–[138], [230], documenting the proportion of sewage waste

available for the bioenergy sector, are utilised within the UK BRM.

C. Future Resource

The BRM applies methodologies, developed by AEA Consulting (2010, 20110) [136], [137],

and E4tech (2009) [138]; to forecast the resource availability and bioenergy potential of

sewage wastes. This works on the assumption that resource availability forecasts will be

linked to the population change scenarios, applied within the BRM (discussed further in

Chapter 6).

4.4.11 Grown Biomass & Energy Crops

The final major group of resources analysed within Stage Two of the BRM, are the biomass

resources and energy crops, grown specifically for the bioenergy sector. The BRM’s analysis


137

focuses on the area of land identified as being available, and suitable for crop growth; from

the Stage One outputs. The BRM allows the evaluation of different planting strategies for the

utilisation of the available land, to produce biomass resources and energy crops dedicated for


As shown by Figure 4.16, the developed methodology for analysing planting strategies within

the BRM, progresses through a process of dedicating either the area, or proportion of

available land, for the growth of different biomass resources and energy crop species (the

crop species analysed in the BRM are listed in Table 4.1). The crop species’ respective

productivity yields (discussed in Section 4.2.5 of this Chapter), are then applied to evaluate

potential growth and resource quantities, that may be produced from the land available. These

resource quantities are assumed to be directly available to the bioenergy sector, and are

accounted for within the Stage Three analysis of the BRM.

The precise calculations and modelling mechanics, of analysing planting scenarios in the

BRM, are documented in Section 4.5 of this Chapter.

Biomass

Species

Future Planting Strategies

2015 2020 2030 2050

Planting

(%) Land Area

(Ha) Planting

(%)

Land

Area (Ha)

Planting

(%)

Land

Area (Ha)

Planting

(%)

Land

Area (Ha)

Planted

Biomass or

Energy Crops

Proportion of

available land

planted with

crops for the

bioenergy

sector.

Equivalent area

of available

land planted

with crops for

the bioenergy

sector.

- - - - - -

Figure 4.16: Modelling Future Planting Strategies within the BRM 42) Figure 4.16: Modell ing Fut ure Planting Strategies within the BRM

A. Dedicating Available Land for Resource Growth

The area of land dedicated for resource growth, is a key driver that will influence the

availability of grown resources for the bioenergy sector. The BRM is flexible, in that 0-100%

of the available land as determined within the Stage One analysis, can be dedicated for

resource growth; at any stage through the analysis timeframe, to 2050. A large number of

existing studies and reports [8], [9], [77], [136], [137], [139], [231]–[239], discuss the

potential and strategies for resource growth in the UK, for the bioenergy sector. The specific

extent, to which the available land is utilised to grow these resources, is reflected within the

scenarios and pathways modelled within the BRM. Such scenarios are discussed and

developed in-depth, within Chapters 5 and 6 of this Thesis.


138

B. Planting Strategies

Once the area of land dedicated for resource growth has been determined within the BRM,

the next stage is the development of planting strategies – essentially determining the specific

species being planted on the available land. The nature of the planting strategies to be

modelled, are driven by the specific focus and targets of the scenarios, modelled within the

BRM.

The BRM also has the option of applying three default planting strategy scenarios, that have

been developed in reflection of work carried out by AEA Consulting (2010, 2011) [136],

[137], and ADAS (2008) [237]. These default planting scenarios are described in Table 4.14.

Table 4.14: BRM Default Biomass Resource & Energy Crop Planting Strategies Table 21) Table 4.14: BRM Default Biomass Resource & Energy Crop Planting Strategies


Biomass Crop Max’

Scenario

This scenario assumes that the available land is dedicated for the production of

predominantly non-food biomass crop species (listed in Table 4.1). A further

assumption is made that these will be utilised predominantly within heat and

power bioenergy generation pathways.

Energy Crop Max

Scenario

This scenario assumes that the available land is dedicated for the production of

predominantly energy crop species (listed in Table 4.1). A further assumption is

made that these will be utilised predominantly within transport fuel bioenergy

generation pathways.

Balanced Crop Growth

Scenario

This scenario assumes that a balanced planting strategy will be implemented

with a mix of both biomass resource and energy crops. There is a further

assumption that these resources will be used in a mix of bioenergy generation

pathways.


139

4.5 UK BRM - Stage Two Modelling

Mechanics

The following section presents details of the precise calculations and modelling mechanics,

developed and applied within the Stage Two analysis of the BRM. These are supported by

screenshots and descriptions of the BRM Excel Spreadsheet, presented within Appendix 14.0.

The BRM’s modelling calculations for the following key analysis themes are presented in

this section:

Forestry-Industry Dynamics Forecasting Biomass Resource Availabilities

4.5.1 Stage Two Analysis Calculation Equations Key



Table 4.15: Equation Calculations Key for the BRM’s Stage Two Analyses Table 22) Table 4. 15: Equation Calculations Key for the BRM’s Stage Two A nalyses


AR Arboricultural Residues Total quantity (Tonnes eqv.) of arb residues available for the bioenergy sector.

ARBY Base Year Arboricultural Residues Total quantity (Tonnes eqv.) of arb residues available in the analysis base year.

ARC Arboricultural Residue Competition Proportion (%) of arb residues used for competing use and industries.

ARLF Arboricultural Residue Landfill Proportion (%) of arb residues sent to landfill.

BSP Bioenergy Sector Proportion Proportion (%) of waste and residue resource utilised within bioenergy pathways.

BULA Built-Up Land Area Total area (Ha) of built-up land.

ΔBULA Built-Up Land Area Change Built-up land area change between two analyses time periods.

CPY Crop Productivity Yield Productivity yields (Tonnes/Ha) for crop commodities

DECP Domestic Energy Crop Production Total quantity (Tonnes) of domestically produced straw-yielding energy crops

DFCP Domestic Food Crop Production Total quantity (Tonnes) of domestically produced straw- yielding food commodities.

EfW(s) Energy from Waste Scenario Proportion (%) of waste sent to energy recovery, within developed BRM scenario.

FA Forest Area Total area (Ha) of forested land.

ΔFA. Forested Area Change Change in forested area (Ha) between two analyses time periods.

FRBY Forest Residue Base Year Total quantity (Tonnes eqv.) of forest residues within the analysis base year.

FRC Forest Residue Competition Proportion (%) of forest residues used for competing uses or industries.

FRHP Forest Residue Harvest Proportion Proportion (%) of overall residue resource that is harvested from managed forests.

FRR Forest Residue Resource Total quantity (Tonnes eqv.) of forest residues available for the bioenergy sector.

GR Grown Resources Total quantity (Tonnes eqv.) of grown resources produced on available land.

ΔID. Change in Industry Demand Change in industry’s forest resource demand (Tonnes eq.) between time periods

IP Industry Product Industry product produced (Tonnes per Tonnes eqv.) from raw forest resources.

IRR Industry Residue Resource Total quantity (Tonnes eqv.) of industry residues available for the bioenergy sector.

IO Indoor Occupancy Proportion (%) of time livestock typically housed.

LQ Livestock Quantity Total quantity (number) of animals.

MR Manure Resource Total quantity (Tonnes eqv.) of animal manure available for the bioenergy sector.

MRA Manure Resource Availability Proportion (%) of manure resources collectable and available for further use.

MRC Manure Resource Competition Proportion (%) of manure resources used for competing uses or industries.

MF Manure Factor Manure produced (Tonnes/animal/year) typically per animal per year.

OWPI Other Wood Product Industry Industry utilising forestry resource.

PET Plantation Establishment Time Time (years) the chosen plantation species takes to achieve peak establishment.

PHC Plantation Harvest Cycle Number (number) of harvest cycles undertaken, over the timeframe.

PLD Proportion Land Dedicated Proportion (%) of available and suitable land dedicated for resource growth.

PMF Proportion Managed Forests Proportion (%) of forested area that is actively managed.

Pop. Population Population quantity.


140

ΔPop. Population Change Population quantity change between two analyses time periods.

PPI Papermill & Pulp Industry Industry utilising forestry resource.

PSW Proportion Suitable Waste Proportion (%) of generated waste suitable for bioenergy pathways.

PT Plantation Time Time (years) dedicated for resource growth on land.

SALA Suitable & Available Land Area Land area (Ha) suitable and available for potential biomass resource growth.

SR Straw Resource Total quantity (Tonnes eqv.) of straw resource available for the bioenergy sector.

SMI Saw Mill Industry Industry utilising forestry resource.

SRC Straw Resource Competition Proportion (%) of straw resources used for competing uses or industries.

SRP Straw Recoverability Portion Proportion (%) of total straw resource that can be recovered for further uses.

SRR Straw Resource Ratio Ratio of (straw: total plant) straw produced per quantity of commodity produced.

SW Sewage Waste Total quantity (Tonnes eqv.) of sewage waste available for the bioenergy sector.

SWBY Sewage Waste in Base Year Total quantity (Tonnes eqv.) of sewage waste available in the analysis base year.

WR Waste Resource Total quantity (Tonnes eqv.) of waste resources available to bioenergy sector.

WFR Wood Fuel Resource Total quantity (Tonnes eqv.) of wood fuel resource available for bioenergy sector.

WFRBY Wood Fuel Resource Base Year Total quantity (Tonnes eqv.) of wood fuel resource within the analysis base year.

WG(s) Waste Generated Scenario Total waste (Tonnes eqv.) generated within developed BRM scenario.

WPI Wood Panel Industry Industry utilising forestry resource.

ΔWPEx. Change in Wood Product Exports Change in forest resource exports (Tonnes eq.) between two analyses time periods

ΔWPIm. Change in Wood Product Imports Change in forest resource imports (Tonnes eq.) between two analyses time periods

WRBP Waste & Residue Bark Proportion Proportion (%) of bark waste and residue generated per Tonnes of product.

WRCP Waste & Residue Chip Proportion Proportion (%) of chip waste and residue generated per Tonnes of product.

WRSI Wood Resource Sent to Industry Quantity of (Tonnes eqv) of forest resource sent to industry.

WRSP Waste & Residue Dust Proportion Proportion (%) of sawdust waste and residue generated per Tonnes of product.

4.5.2 Modelling Forest-Industry Dynamics

The following section discusses the modelling mechanics applied within the BRM for

developing the Forestry-Industry Dynamics analysis module.

This section of the BRM models the specific flows of forestry resources from forests to

industry, taking account and quantifying the multiple resource uses along the way. Building

on the high level description of the modelling processes discussed in Section 4.4.3, Figure

4.17 documents the specific modelling assumptions and flows of the resources evaluated. The

key role and influence of the specific parameters of developed scenarios within the BRM are

also shown.


141

Forested Land Area (Ha)

(Varied through BRM Scenarios)

↓ ↓ ↓ ↓

Forestry Standing Volume (m³ obs)

↓ ↓ ↓ ↓

Managed Forest (%)


↓ ↓

Forest Productivity (Green Tonnes)


← Imports (Green Tonnes)


→ Exports (Green Tonnes)


↓

↓

Wood Fuel Resource

(%)

Proportion of

Resource Sent to

Industry (%)

↓

Industry Demand (odt)


↓ ↓

Production of

Industrial Residues

Available to the

Bioenergy Sector

(Varied through BRM

Scenarios)

Wood Based Products

(Tonnes)

← Imports (Tonnes)


→ Export (Tonnes)


43) Figure 4.17: Modell ing Resource Flows within t he BRM’s Forestry-Industry Ana lysis Module

Figure 4.17: Modelling Resource Flows within the BRM’s Forestry-Industry Analysis

Module

4.5.3 Modelling Biomass Resource Availability Dynamics

The following section presents the calculation equations applied within the BRM, for

analysing the availability of key biomass resources. These equations are described below and

listed in Figure 4.18.

Calculating Industry Residue Resource for the Bioenergy Sector (Eq.3a)

The calculation equation applied within the BRM, for analysing the potential

availability of industry residues for the bioenergy sector, over the analysis timeframe.

Calculating Resource Direct from Forestry Systems for the Bioenergy Sector (Eq.3b)


availability of resources direct from forests, such as wood fuels for the bioenergy

sector, over the analysis timeframe.


142

Calculating Forest Residue Resource for the Bioenergy Sector (Eq.3c)


availability of forest residues for the bioenergy sector, over the analysis timeframe.

Calculating Straw Resource for the Bioenergy Sector (Eq.3d)


availability of straw resources for the bioenergy sector, over the analysis timeframe.

Calculating Manure & Slurry Resource for the Bioenergy Sector (Eq.3e)


availability of animal manure and slurry resources for the bioenergy sector, over the

analysis timeframe.

Calculating Arboricultural Residues for the Bioenergy Sector (Eq.3f)


availability of arboricultural arising residues for the bioenergy sector, over the

analysis timeframe.

Calculating Waste Resource Available for the Bioenergy Sector (Eq.3g)


availability of different waste resources for the bioenergy sector, over the analysis

timeframe.

Calculating Sewage Resource Available for the Bioenergy Sector (Eq.3h)


availability of sewage resources for the bioenergy sector, over the analysis timeframe.

Calculating Grown Resource Potential Planted on Available & Suitable Land (Eq.3i)

The calculation equations applied within the BRM, for analysing the potential

availability of grown resources produced on suitable and available land. These

equations, reflecting the mechanisms of the BRM’s analyses, for evaluating biomass

resource and energy crop planting strategies.


143

Eq.3a) Calculating Industrial Resources Availability

IRR = {

SMI { ( ( ( ( WRSI ) ( IP ) ) ( WRBP ) ) ( BSP ) )

( ( ( ( WRSI ) ( IP ) ) ( WRCP ) ) ( BSP ) )

( ( ( ( WRSI ) ( IP ) ) ( WRSP ) ) ( BSP ) )

PPI { ( ( ( ( WRSI ) ( IP ) ) ( WRBP ) ) ( BSP ) )

( ( ( ( WRSI ) ( IP ) ) ( WRCP ) ) ( BSP ) )

( ( ( ( WRSI ) ( IP ) ) ( WRSP ) ) ( BSP ) )

WPI { ( ( ( ( WRSI ) ( IP ) ) ( WRBP ) ) ( BSP ) )

( ( ( ( WRSI ) ( IP ) ) ( WRCP ) ) ( BSP ) )

( ( ( ( WRSI ) ( IP ) ) ( WRSP ) ) ( BSP ) )

OWPI { ( ( ( ( WRSI ) ( IP ) ) ( WRBP ) ) ( BSP ) )

( ( ( ( WRSI ) ( IP ) ) ( WRCP ) ) ( BSP ) )

( ( ( ( WRSI ) ( IP ) ) ( WRSP ) ) ( BSP ) )

Eq.3b) Calculating Wood Fuel Resource Availability

WFR = ( ( ( ( ( ( WFRBY / FA ) (FA + ΔFA. ) ) ( 1 – PMF ) ) + ΔID. ) + ΔWPIm. ) ΔWPEx. )

Eq.3c) Calculating Forest Residue Resource Availability

FRR = ( ( ( ( ( FRBY / FA ) ( FA+ΔFA ) ) ( 1-PMF ) ) ( 1-FRHP ) ) ( 1-FRC ) )

Eq.3d) Calculating Straw Resource Availability

SR = {

( ( DFCP ) ( SRR ) ( SRP ) ) ( 1 – SRC )

( ( DECP ) ( SRR ) ( SRP ) ) ( 1 – SRC )

Eq.3e) Calculating Manure Resource Availability

MR = ( ( LQ ) ( MF ) ) ( ( IO ) ( 1 – MRC ) ( MRA ) )

Eq.3f) Calculating Arboricultural Arising Resource Availability

AR = ( ( ( ( ARBY / BULA ) ( BULA + ΔBULA ) ) ( 1 – ARC ) ) ( 1 – ARLF ) )

Eq.3g) Calculating Waste Resource Availability

WR = ( ( ( WG(s) ) ( PSW ) ) ( EfW(s) ) )

Eq.3h) Calculating Sewage Waste Resource Availability

SW = SWBY ( Pop. + ΔPop. )

Eq.3i) Calculating Plantation Grown Resource Availability

GR = ( ( ( ( ( SALA ) ( PLD ) ) ( CPY ) ) ( ( ( ( PT ) / ( PET ) ) / ( PHC ) ) ) )

44) Figure 4.18: Calculation Equations A pplied w ithin t he BRM’s Resource Availabil ity Ana lyses

Figure 4.18: Calculation Equations Applied within the BRM’s Resource Availability

Analyses


144

4.6 UK BRM – Developing the Stage Three

Methodology

The following section of the Thesis, discusses in detail the development of the Stage Three

analysis of the BRM. The BRM’s Stage Three analysis aims to quantify and forecast the

bioenergy potential of the resources available to the bioenergy sector, as identified within

BRM’s Stage Two analysis.

At each stage, the key studies and methodologies that influenced the design of the BRM, and

all the data sources utilised, are referenced. Section 4.7 of this Chapter, supported by

screenshots of the BRM Excel Spreadsheet in Appendix 14.0, goes onto provide further

discussions of the precise calculation equations applied; that define the mechanics for how

the Stage Three analysis of the BRM was modelled.

A step-by-step walk through of the Stage Three BRM methodology is provided, focusing on


Total Resource Availability Approach for Analysing Bioenergy Potential

Energy Content of Resources Biomass Pre-Treatment Pathways

Bioenergy Conversion Pathways Preferred Bioenergy Conversion Pathways

Total Bioenergy Potential Energy Targets & Demands

4.6.1 Resource Availability for the Bioenergy Sector

The first step, within the Stage Three analysis of the BRM is the assessment of the total

resource availability to the bioenergy sector that will be evaluated to determine the bioenergy

potential. Within the BRM, the resource outputs from each analysis step of the Stage Two

analysis are collated, and if required, converted into uniform resource quantity units, in

preparation for bioenergy conversion analysis.

The uniform unit applied within this stage of the Model, is ‘tonnes equivalent’ of each

specific resource category. For solid biomass resource categories, the combined quantity is

calculated, taking account of the varying moisture content typical of different resources [74],

[78]. Conversion factors, utilised within the BRM for calculating conversions between

different forms and states of biomass, are listed within Appendix 1.0. Potential resource

losses, and energy debts associated with any pre-treatment processes, are accounted for. For

wet biomass resource categories, the combined resource quantities are collated and presented

in both wet volume, and the oven dry tonnes equivalent. Conversion of the biomass resource


145

quantities takes place in this way, to allow ease of comparison of the BRM output results

between different scenarios, and also to generate total biomass quantity values.

4.6.2 Energy Content of Resources

The potential energy content of the wide range of resources, analysed within the BRM, are

included in an important dataset that is utilised to calculate the bioenergy potential of the

specific resources. The calorific values of each of the resources are collated in the database,

within the BRM. The Energy Centre for the Netherlands’ ‘Phyllis Database’ [74], is the

prime source of all calorific value data utilised within the BRM. In addition, further calorific

value data provided by alternative studies, reports, and literature [66], [74], [240]–[244],

applicable to the key resource categories, are also taken into consideration.

A summary of the calorific value data utilised within the BRM, and the applicable references

for each, are listed within Appendix 1.0.

4.6.3 Developing a Methodology for Analysing Bioenergy Potentials

The heat, power, and transport fuel bioenergy potentials are calculated within the Stage Three

analysis of the BRM, as a function of: the quantity of specific resources, the calorific values

of the resource, and the efficiency of the chosen bioenergy conversion pathways.

A ‘resource-bioenergy filtering methodology’ is developed that allows the allocation of

different proportions of the available biomass resources, to be subject to different custom-

developed bioenergy pathways. The resources are ‘filtered’ through a series of calculation

phases that account for pre-treatment processes, followed by bioenergy conversion pathways.

A schematic of this process is demonstrated by Figure 4.19, and described further in Figure

4.20. The precise calculations and modelling mechanics applied within BRM to undertake

this analysis, are documented in Section 4.7 of this Chapter.

45) Figure 4.19: Modell ing the Biomass Resource Bioenergy Conversion Pathway

Figure 4.19: Modelling the Biomass Resource Bioenergy Conversion Pathways


146

Resource

Category

Total Resource

(Tonnes eqv.) Calorific

Value

Pre-Treatment

Processes Bioenergy Pathway Bioenergy Potential

Process % Pathway % Heat Power Transport

Biomass

Resource

The total extent of the

resource identified as

being potentially

available for the bioenergy sector.

The energy

content

value of

the resource.

The extent that the

resource is subject to a chosen pre-

treatment process.

The extent that the

post pre-treatment

resource is subject to a

chosen bioenergy conversion pathway.

The potential heat, power and

transport fuel bioenergy

generated from the resource.

Figure 4.20: Description of BRM’s Resource-Bioenergy Filter Analysis Methodology 46) Figure 4.20: Descript ion of BRM’s Resource-Bioenergy Filter Analysis Methodology

4.6.4 Pre-Treatment Pathways

The pre-treatment of resources prior to being converted to bioenergy, is a key process within

the Stage Three analysis of the BRM. The extent and type of pre-treatments, applied to each

resource within the BRM, are subject to the focus and design of the scenario modelled within

the BRM. The following pre-treatment pathway options have been developed within the

BRM, in reflection of the work carried out by the National Non-Food Crop Centre (2010)

[245]:

No Pretreatment

Torrefaction and Pelletising

Drying

Drying & Pelletising

Chipping

Pelletising

Torrefaction

Pyrolysis

Further key calculations relating to pre-treatment analyses within the BRM, are those that

account for resource mass reductions, and energy debts; associated with converting the

resource [245].

4.6.5 Energy Conversion Pathways

The bioenergy process conversion efficiencies, for converting the biomass resources into

either: heat, power, or transport fuel bioenergy, are a further set of key data applied within the

BRM. The Stage Three analysis within the BRM, allows the allocation of custom proportions

of available resources to be converted to energy, through a wide range of bioenergy

conversion pathways:

Heat from Biomass Combustion

Co-Firing with Fossil Fuels

Conversion to Ethanol

Conversion to Biodiesel

Biomass to Liquid

Dedicated Biopower

Dedicated Biopower with CCS

Dedicated Transport Fuel

Energy from Pyrolysis – Transport

Energy from Pyrolysis – Heat

Energy from Pyrolysis - Electricity

Combined Heat & Power

Anaerobic Digestion (Biogas) – Transport

Anaerobic Digestion (Biogas) – Heat

Anaerobic Digestion (Biogas) - Electricity

Bio-Syngas Generation

The conversion efficiencies for each of these processes, and forecasts for how efficiencies

may improve over the analysis timeframe, were evaluated through undertaking a wide


147

literature review. A wide range of studies, reports and literature [30], [33], [34], [82], [169],

[235], [245]–[250], were taken into a consideration, and a bioenergy conversion efficiency

database was developed within the BRM. The specific conversion efficiencies for each

bioenergy pathway utilised within the UK BRM, are listed within Appendices 1.0.

4.6.6 Preferred Bioenergy Conversion Pathway

The BRM is designed to allow the development of scenarios, where specific resources can be

converted to different forms of bioenergy, through a wide range of processes and conversion

pathways. A ‘preferred’ bioenergy conversion pathway is developed as the default scenario,

within the BRM. This reflects the default extent, and pathways, for how the different biomass

resources analysed within the BRM, are processed and converted to different forms of

bioenergy.

The preferred conversion pathways within the BRM have been developed in reflection of a

wide range of studies, reports, and literature [8], [64], [71], [72], [77], [82], [168], [246],

[247], [251]. A summary of the BRM’s default conversion pathways for each of the analysed

resources, are shown within Figure 4.21. The specific characteristics, and extent that the

different conversion pathways are applied to each resource, are listed within Appendix 1.0.

The specific calculations and modelling mechanics of this analysis stage are documented in

Section 4.7 of this Chapter.


148

Biomass Resources Analysed within

the BRM Resource Forms

Pre-

Treatment &

Processing

Post Pre-

Treatment

Resources

Bio-energy

Conversion

Processes

Forms of

Bioenergy

Grown

Biomass &

Energy

Crops

Biomass Crops

Grasses Solid Hydrocarbon Baling Solid Fuel Combustion Heat Bioenergy

Power Bioenergy

Short

Rotation

Coppice

Solid Hydrocarbon Chipping Solid Fuel Combustion Heat Bioenergy

Pelletising Solid Fuel Combustion Power Bioenergy

Short

Rotation Forestry

Solid Hydrocarbon Chipping Solid Fuel Combustion Heat Bioenergy


Other

Forestry Solid Hydrocarbon

Chipping Solid Fuel Combustion Heat Bioenergy


Energy

Crops

Cereal Crops Solid Hydrocarbon Hydrolysis Bio-ethanol Biofuels

Generation Transport Bioenergy

Oil Crops Solid Hydrocarbon Transesterification Bio-Diesel Biofuels

Generation

Transport

Bioenergy

Sugar Crops Solid Hydrocarbon Fermentation Bio-ethanol Biofuels

Generation

Transport

Bioenergy

Direct Forestry Production Solid Hydrocarbon Chipping Solid Fuel Combustion Heat Bioenergy


Biomass

Residues

Resources

Agricultural

Residues

Straw Solid Hydrocarbon Baling Solid Fuel Combustion Heat Bioenergy

Combustion Power Bioenergy

Slurry & Manure

Liquid Hydrocarbon Anaerobic Digestion

Bio-Methane Combustion Power Bioenergy

Forestry Residues Solid Hydrocarbon Chipping Solid Fuel Combustion Heat Bioenergy

Arboriculture Arisings Solid Hydrocarbon Chipping Solid Fuel Combustion Heat Bioenergy

Industry Residues Solid Hydrocarbon Chipping Solid Fuel Combustion Heat Bioenergy


Biomass

Waste

Resources

Household, Industry & Other

Wastes

Solid Hydrocarbon Sorting Solid Fuel Combustion Power Bioenergy

Liquid Hydrocarbon Anaerobic

Digestion Bio-Methane Combustion Power Bioenergy

Sewage Liquid Hydrocarbon Anaerobic

Digestion Bio-Methane Combustion Power Bioenergy

Key

Forms of Resource Forms of Bioenergy

Solid Resources Transport Bioenergy

Liquid Resources Power Bioenergy

Gaseous Resources Heat Bioenergy

47) Figure 4.21: Preferred Bioenergy Conversion P athways within t he BRM

Figure 4.21: Preferred Bioenergy Conversion Pathways within the BRM

4.6.7 Total Bioenergy Potential

Once the bioenergy potential from all available biomass resources within the BRM has been

analysed, these values are collated to evaluate the combined totals and forms of bioenergy

generated for the given scenario modelled. As presented within Figure 4.22, these bioenergy

values form key analysis outputs that can be compared against energy targets and also enable

comparisons to be derived between different scenarios developed in the BRM.


149

Year Bioenergy Potentials

Heat Power Transport Fuels Totals

Base Year Potential heat

bioenergy generated

from biomass

resources.

Potential electrical bioenergy generated

from biomass

resources.

Potential transport fuels energy

generated from

biomass resources.

Total bioenergy

generated from biomass resources.

2015

2020

2030

2050

Figure 4.20: Analysing the Bioenergy Potentials of Resources Analysed within the BRM 48) Figure 4.20: Ana lysing t he Bioenergy Potentia ls Generated from Resources within t he BRM

4.6.8 Energy Targets & Demands

The final analysis step, within Stage Three of the BRM, is the comparison of the bioenergy

potentials of the various resources, to the energy targets and demands; within the country or

geographic region in which the BRM is focused.

As such, for the UK BRM, the UK’s energy targets and demands were analysed. There are

numerous studies and reports that attempt to forecast the UK’s energy demand into the future

[34], [111], [132], [252]–[254]. The BRM is flexible, in that any of these forecast energy

targets and demands, can be analysed through amending respective databases within the

BRM. The default forecasts of future UK energy demands within the UK BRM, are sourced

from DECC (2010) [34]. Whilst the UK’s renewable energy and bioenergy targets, accounted

for within the UK BRM, reflect those confirmed by the UK Government energy strategy

reports [8], [58], [254].

The results and further discussion of these targets, in the context of output results from the

UK BRM, are discussed widely in Chapter 6 of this Thesis.


150

4.7 UK BRM - Stage Three Modelling

Mechanics

The following section, presents details of the precise calculations and modelling mechanics,

developed and applied within the BRM’s Stage Three analysis. These are supported by

screenshots, and descriptions of the BRM Excel Spreadsheet, presented within Appendix

14.0. The BRM’s modelling calculations for the following key analysis theme is presented in

this section:

Calculating the Bioenergy Potential of Biomass Resources

4.7.1 Stage Three Analysis Calculation Equations Key



Table 4.16: Equation Calculations Key for the BRM’s Stage Three Analyses Table 23) Table 4.16: Equation Calculations Key for the BRM’s Stage Three Analyses


AR Arboricultural Residues Total quantity (Tonnes eqv.) of arb residues available for the bioenergy sector.

BCE Bioenergy Conversion Efficiency Energy efficiency (%) of the selected bioenergy conversion process.

BRCV Biomass Resource Calorific Value Calorific energy (MJ/Tonne) content of biomass resource.

FRR Forest Residue Resource Total quantity (Tonnes eqv.) of forest residues available for the bioenergy sector.

GR Grown Resources Total quantity (Tonnes eqv.) of grown resources produced on available land.

GRBPn Bioenergy from Grown Resource Bioenergy potential (MJ) from a given grown resource.

IRR Industry Residue Resource Total quantity (Tonnes eqv.) of industry residues available for the bioenergy sector.

PoPTR Post Pre-Treatment Resource Total quantity (Tonnes eqv.) of resource post pre-treatment processing.

PrPTR Pre Pre-Treatment Resource Total quantity (Tonnes eqv.) of resource sent for pre-treatment processing.

PTED Pre-Treatment Energy Debt Energy debt (MJ/Tonne) associated within the applied pre-treatment processing

RBP Resource Bioenergy Potential Bioenergy potential (MJ) of the biomass resources subject to bioenergy pathways.

RRBPn Bioenergy from Residue Resource Bioenergy potential (MJ) from a given residue resource.

LRM Loss in Resource Mass Loss in resource mass (Tonnes eqv.) resulting from pre-treatment processing.

MR Manure Resource Total quantity (Tonnes eqv.) of animal manure available for the bioenergy sector.

WRBPn Bioenergy from Waste Resource Bioenergy potential (MJ) from a given waste resource.

SR Straw Resource Total quantity (Tonnes eqv.) of straw resource available for the bioenergy sector.

SW Sewage Waste Total quantity (Tonnes eqv.) of sewage waste available for the bioenergy sector.

TGRBP Total Grown Resource Bioenergy Total bioenergy potential (MJ) of all grown resources within a given BRM scenario.

TRBP Total Resource Bioenergy Potential Total bioenergy potential (MJ) of all available resources within a given scenario.

TRRBP Total Residue Resource Bioenergy Total bioenergy potential (MJ) of all residue resources within a given BRM scenario.

TWRBP Total Waste Resource Bioenergy Total bioenergy potential (MJ) of all waste resources within a given BRM scenario.

WR Waste Resource Total quantity (Tonnes eqv.) of waste resources available to the bioenergy sector.

WFR Wood Fuel Resource Total quantity (Tonnes eqv.) of wood fuel resource available for bioenergy sector.

4.7.2 Modelling Bioenergy Potential of Available Biomass Resources

The following section, presents the calculation equations applied within the BRM, for

analysing the potential bioenergy that may be generated from the biomass resource available

to the bioenergy sector; in a given developed scenario. These equations are described below

and listed in Figure 4.23.


151

Analysing Pre-Treatment Processes within the BRM (Eq.4a)

The influence and impacts of pre-treatment processes, on biomass resources within

the BRM, are modelled through applying a ‘resource filter’ methodology. Figure 4.22

demonstrates how custom quantities of available resource within the BRM, can be

subject to different pre-treatment processes. An ‘energy debt’ and any associated

resource mass reductions are accounted for; reflective of the specific pre-treatment

processes applied within the BRM. Equation 4a within Figure 4.23, documents the

specific calculation applied within the BRM, for analysing how resource masses may

change, reflective of the specific pre-treatment processes applied.

Calculating Resource Bioenergy Potentials (Eq.4b)

Documents the calculation equation applied within the BRM, for analysing the

bioenergy potential of resources available for the bioenergy sector; over the analysis

timeframe.

Calculating Total Bioenergy Potential of Resources within BRM Scenarios (Eq.4c)

Documents the calculation equation applied within the BRM, for analysing the total

bioenergy potential of all resources considered potentially available for the bioenergy

sector, within a given scenario; over the analysis timeframe.

Available Biomass Resource Identified for Pre-Treatment Processing (Tonnes eqv.)

(Following BRM Stage Two Analysis)

↓ ↓ ↓ ↓

Selected Pre-Treatment Processing Pathway


↓ ↓

Pre-Treatment Processing of Available Biomass Resources

← Energy Required for Process (MJ/Tonne)

(Associated with Selected Process)

→ Loss in Resource Mass (Tonnes eqv.)

(Associated with Selected Process)

↓ ↓

Post Pre-Treatment Biomass Resource (Tonnes eqv.) Available

for Bioenergy Conversion Processing

49) Figure 4.22: Biomass Resource Flow & Modelling Mechanics of t he BRM’s Pre-Treatment Processing A nalyses

Figure 4.22: Biomass Resource Flow & Modelling Mechanics of the BRM’s Pre-Treatment

Processing Analyses


152

Eq.4a) Calculating Resource Mass Changes through Pre-Treatment Processing

PoPTR = PrPTR - LRM

Eq.4b) Calculating Bioenergy Potential of Biomass Resources

RBP = ( ( ( ( PoPTR ) ( AFCV ) ) ( BCE ) ) – PTED )

Eq.4c) Calculating Bioenergy Potential of Biomass Resources

TRBP = {

TGRBP = { ( ( ( ( GR – LRM ) ( AFCV ) ) ( BCE ) ) - PTED )

( ( ( ( WFR – LRM ) ( AFCV ) ) ( BCE ) ) - PTED )

TRRBP = {

( ( ( ( AR – LRM ) ( AFCV ) ) ( BCE ) ) - PTED )

( ( ( ( FRR – LRM ) ( AFCV ) ) ( BCE ) ) - PTED )

( ( ( ( IRR – LRM ) ( AFCV ) ) ( BCE ) ) - PTED )

( ( ( ( MR – LRM ) ( AFCV ) ) ( BCE ) ) - PTED )

( ( ( ( SR – LRM ) ( AFCV ) ) ( BCE ) ) - PTED )

TWRBP = { ( ( ( ( SW – LRM ) ( AFCV ) ) ( BCE ) ) - PTED )

( ( ( ( WR – LRM ) ( AFCV ) ) ( BCE ) ) - PTED )

50) Figure 4.23: Calculation Equations A pplied w ithin t he BRM’s Bioenergy Potential A nalyses

Figure 4.23: Calculation Equations Applied within the BRM’s Bioenergy Potential Analyses


153

Chapter 5 - Drivers Influencing Biomass Resource Availability & Bioenergy


154

5.1 Drivers Influencing Biomass Resource

Availability

This Chapter has been developed to provide a discussion of the different variables and drivers

within supply chains; that influence the availability of biomass resources, and resulting

bioenergy potential.

The Chapter begins with a literature review driven evaluation, of the types of drivers and

variables, within biomass supply chains. The Chapter then builds on the methodology of

Chapter 4; highlighting and evaluating the various drivers that are inherently developed

within the BRM. It is the calibration of these drivers within the BRM that enables the

modelling of different scenarios. The drivers within the BRM are listed, and a literature

review is undertaken, to analyse why these BRM drivers may influence supply chains

dynamics, and vary the availability of biomass for the bioenergy sector.

The first analysis within the Thesis, using the BRM is then introduced. A baseline UK

biomass resource scenario is modelled within the BRM. This baseline scenario reflects the

UK’s biomass supply chain dynamics to 2050; with the driver characteristics within the BRM

calibrated to reflect an averaged assessment of how these may change to 2050, as informed

by a broad literature review. A methodology is then developed and applied, carrying out a

sensitivity analysis, to evaluate the specific influences that each driver has on different types

of biomass resources. The aim being, to identify: the key biomass resource types within the

baseline scenario that demonstrate greatest potential for the bioenergy sector, the specific

drivers that have greatest influence in determining the availability of these resources, and

those drivers that have minimal or limited influence on determining the availability of any

biomass resources.

The Chapter concludes in highlighting the specific biomass resources that demonstrate the

greatest potential for the bioenergy sector, and the specific drivers that should potentially be

targeted, to maximise the availability of the specific resource. This Chapter’s relevance, in

the overall Thesis progression, is the contribution of key data and conclusions to discussions

in Chapter 10; where ideas will be proposed for the development of refocused and alternative

bioenergy strategies for the UK.


155

5.1.1 Biomass Resource Drivers

The analyses within the BRM, as with all biomass resource models, are based on developing

an understanding of, and modelling the different supply chain drivers that may influence

biomass resources. The realistic availability of any given resource for the energy sector

requires evaluation of the quantities available, and the environmental and economic viability

of making it available to the energy market [78]. These drivers can be categorised reflecting

the work carried out by AEA Consulting (2010, 2011) [136], [137]:

Policy Drivers – energy and environmental themed policies are particularly important

in determining a secure long-term energy strategy. Waste, agricultural, and forestry

policies have great influence in determining the potential availability of specific

resources.

Market Drivers – biomass is a relatively immature market in the UK. The level of

understanding that potential resource suppliers / buyers have of the UK market,

determines the levels of uncertainty, and likelihood of commitments to long term

contracts.

Technical Drivers – are the influences and barriers that may drive the actual processes

of energy generation. These may include issues such as the availability of fuel

standards, or the ability to integrate biomass resources with the existing fossil fuel

dominated network.

Infrastructure Drivers – are influences relating to the performance of all facilities

required for the bioenergy sector to operate, including: the harvesting, collection,

storage, and transport of feedstocks.

All biomass resource models and assessments revolve around analysing the influence of

different drivers. As such, the range of drivers listed within the literature that are identified as

being influential of biomass resource, is extremely broad. The studies, reports, and research

listed below, provided influence when developing the BRM. Collectively, they are

representative of a broad range of biomass research, and each analyse different drivers

relating to biomass resources. The specific drivers identified and analysed within each of

these sources, are listed and categorised within Table 5.1. Table 5.1 also highlights the

capability of the BRM, identifying which of the listed drivers can be analysed within the

BRM.

Lysen (2008) [122] Smeets & Faaij (2007) [251]


156

Long et al (2013) [255]

Hoogwijk (2004) [256]

Adams et al (2011) [257]

Thran et al (2010) [124]

Bottcher et al (2012) [258]

Haberl et al (2010) [119]

Ladanai & Vinterback (2009) [259]

Fischer & Schrattenholzer (2001) [118]

Slade et al (2011) [103]

BR&Di (2011) [260]

Table 5.1: Drivers Influencing Biomass Resources & the BRM’s Analysis Capability Table 24) Table 5.1: Drivers Influencing Biomass Resources & the BRM’s Analys is Capabil ity

Categories Specific Drivers References BRM

Capability

Economic &

Development

Drivers

Population Change [103], [118], [119], [122], [124], [255], [256],

[258]

Resource Import / Export Drivers [119], [124] Economic & Technical Development Drivers [251], [255] ~

Industry Productivity Drivers [119], [251], [255] Gross Domestic Product [122], [256], [258] ~

Rural Economy Development [257], [260] X

Infrastructure

Targets

Energy System Structure [119], [257], [259], [260] Energy Generation Plant [257], [260]

Supply Chain Development [257], [260]

Physical &

Climate

Drivers

Land-Use Change Drivers [118], [119], [122], [255], [256] Water Availability [103], [122] X

Climate Change Drivers [103], [122], [124], [255] ~ Flood Protection Land Requirements [124] X

Nature Conservation Land Requirements [124] [103] ~ Soil Degradation Drivers [124] [103] X

Food Drivers

Per-capita Food Demand & Consumption [103], [124], [258] Calorie Consumption [258] X

Diet Change [258] X Agriculture Productivity Yields [103], [118], [124], [258]

Resource

Mobilisation

Technical

Drivers

Technological Advances [118], [119], [122], [251], [256], [259] Forest System Productivity [118], [119], [122], [251], [256], [259]

Industry & Process Residue Generation [118], [119], [122], [251], [256], [259] Forestry Residues Collection [118], [119], [122], [251], [256], [259]

Resource

Demand

Drivers

Resource Use by Industry [118], [122], [124], [251], [256], [258], [259] Demand for Round Wood [118], [122], [256], [258], [259] Demand for Wood Fuel [118], [122], [251], [256], [259]

Demand for Other Resources [118], [122], [256], [258], [259]

Policy Drivers

Greenhouse Gas Emission Targets [119], [122], [257], [259] ~ Energy Efficiency & Consumption Targets [119], [122], [256], [257], [259]

Renewable & Bioenergy Targets [119], [122], [256], [257], [259] Fuel Security Drivers [119], [257]

Support Policies & Mechanisms [119], [122], [256], [257], [259], [260] X

Key

The BRM’s design and outputs allows the analysis of these drivers, in terms of their influence on biomass

resource availability, and bioenergy potential. ~ The BRM’s design and outputs allows the analysis of partial aspects of these drivers, or can provide an

indirect evaluation of the drivers’ influence on biomass resource availability, and bioenergy potential.

X The BRM’s current design and outputs do not allow the analysis of these drivers.

5.1.2 The BRM’s Analysis Drivers

This section introduces the supply chain drivers that make up the BRM, and form the baseline

for the analysis within this Chapter. The drivers are grouped into categories based on their

characteristics, as summarised and described in Table 5.2. As discussed in Chapter 4, the

drivers considered to be most influential have been identified by literature review, and these

are analysed within the BRM. This list is extensive but not exhaustive, and there are some


157

which are considered to be less influential or less relevant, and these have not been included

for analysis within the BRM.

Table 5.2: Summary of Key Drivers Influencing UK Biomass Resource Availability Table 25) Table 5.2: Summary of Key Drivers Influencing UK Biomass Resource Availability

Categories Specific Drivers Descriptions

Development

Drivers

Population Change Influence resulting from the different population scenarios within

the BRM.

Changes in Built-Up Land Area Influencing resulting from the different built-up land area scenarios

within the BRM.

Food

Production

System

Drivers

Crop & Agriculture Productivity Influence from the current and forecast crop and agriculture

productivity yields.

Food Waste Generation Influence from varying food waste generation scenarios within the

BRM.

Food Commodity Imports Influence from varying food importation scenarios.

Food Commodity Exports Influence from varying food exportation scenarios.

Utilisation of Agricultural Wastes & Residues Influence from varying levels of agricultural waste and residue

utilisation scenarios.

Forestry &

Wood-based

Industry

Drivers

Forestry Expansion & Productivity Influence from varying forestry area and productivity scenarios.

Wood-based Industry Productivity Influence from varying industry productivity and economic trend

scenarios.

Imports of Forestry Product Influence from varying forestry raw material and wood product

importation trend scenarios.

Exports of Forestry Product Influence from varying forestry raw material and wood product

exportation trend scenarios.

Biomass

Residue &

Waste

Utilisation

Drivers

Utilisation of Forestry Residues Influence from varying levels of forestry residue utilisation

scenarios.

Utilisation of Industrial Residues Influence from varying levels of industrial waste and residue

utilisation scenarios.

Utilisation of Arboriculture Arisings Influence from varying levels of arboriculture residue utilisation

scenarios.

Waste Generation Trends Influence from varying waste generation scenarios.

Waste Management Strategies. Influence from the application of different waste management

strategies.

Biomass &

Energy Crop

Strategy

Drivers

Land Dedicated for Energy Crop Growth Influence from varying the extent that available land is utilised for

the production of biomass and crops for the bioenergy sector.

Biomass & Energy Crop Planting Strategies Influence from the types and extent that different biomass and

energy crops are planted on the available land.

5.1.3 Discussion of Drivers Analysed within the BRM

This section provides a discussion of the drivers analysed within the BRM. Including a

literature review based evaluation, of how they may influence biomass resource availability

and bioenergy potential. As described later within Section 5.2, the current characteristics of

these drivers within the UK’s biomass supply chains, and how they may change to 2050; will

be analysed within an ‘supply chain sensitivity analysis’. This section also highlights the

current status and forecast trends of each of the drivers within the UK, as reflected within the

BRM.


158

A. Development Drivers

The following drivers have been grouped within the development category. These reflect the

BRM’s capability of analysing biomass resource availability, and bioenergy potential; with

varying development characteristics.

i. Population Change

Population growth is the fundamental influence for all long term outlooks relating to food and

agriculture [180]. The widely expected, large increases in global food demand to 2050 are

based on forecasts of increasing population [158]. Food and agricultural systems are closely

linked to many biomass resource supply chains; therefore population is a driver with key

influence on biomass resource availability.

Within the UK BRM, the available population forecast scenarios reflect those developed by

the United Nations Population Division [145].

ii. Built-Up Land Area

Urbanisation is a further driver that influences food and agriculture systems [261]. Changes

in the extent of built-up land area may directly influence the area of available land that could

otherwise be dedicated for biomass production. Any development on agricultural lands will

also result in a requirement for food to be produced elsewhere, to meet demand.

The UK BRM utilises forecasts of current and future built-up land areas for the UK, as

developed within the EU MOSUS Project [147].

B. Food Production System Drivers

The next series of drivers have been grouped within the food production system category.

These reflect the BRM’s capability of analysing biomass resource availability, and bioenergy

potential, with varying food production system dynamics.

i. Crop and Agriculture Productivity

The productivity of land and agricultural yields, are important drivers that directly influence

the production of biomass. Where crop yields can be increased, agricultural land may be

freed for growth of biomass and energy crops [140], [262]. Also, where biomass and energy

crop yields can be enhanced, more resource can be produced for the bioenergy sector from

the land available.

Improvements and variances in food and crop systems productivity result from the collective

influence of a range of manageable external inputs. The UK has great strength in crop

science, resulting in a greater understanding of crop responses to global climate change [233].


159

Mueller et al (2012) [263], suggest that the ‘yield gap’ - the difference between attainable and

actual yields, will continue to be reduced. Other forecasts suggest that average yield increases

of 70% by 2050, are possible for most crops; through improved nutrient management,

irrigation, and productivity techniques [263], [264].

Harberl et al (2011) [180], found that Western European yields could experience further

mean increases of >16% from CO2 fertilization by 2050; resulting from climate change forces

(>2% without CO2 fertilization).

However, whilst the main northern hemisphere producers of food and biomass crops, may

experience favourable conditions from climate change in the next 40 years, regions where

rising food demand is most pronounced, will likely see production hindered. This may lead to

a greater number of countries relying on fewer, high latitude producers; greatly increasing

vulnerability to extreme weather events, in these regions [185].

Current and forecast food and crop agricultural yields are analysed within the BRM, and

reflect those documented in a wide range of studies and literature, including those that predict

climate change impacts [107], [116], [136]–[140], [146], [150], [151], [157]–[182], [184]–

[189] (Section 4.2.5).

ii. Food Waste Generation

Food waste influences the availability of biomass resource in multiple ways. Food waste

itself, is a plausible resource for bioenergy generation pathways. At the same time, food

waste is a factor that reduces the supply chain efficiency – the greater the waste from the

system, the more land is required, to produce food commodity quantities, to meet demand.

Research estimates that 25-50% of food produced, is wasted along the supply chain [265]–

[267]. 50% of the UK’s food waste comes from households, where at some point at least 60%

of this waste could have been consumed [218]. The European Commission is targeting a 50%

reduction in food wastes by 2020 [268], and the UK Government Office for Science, suggests

that by halving food waste by 2050, this saving may be equivalent to 25% of current

productivity [269], [270].

UK food waste trends and reduction targets are analysed within the BRM, utilising a series of

forecasts [146], [269]–[272].

iii. Food Commodity Imports and Exports

Food commodity import and export trends are drivers that can influence biomass availability,

as they contribute towards determining the area of land that is required to produce the food


160

quantities, to meet demand. Any land dedicated for food production is therefore unavailable

for biomass or energy crop growth.

The majority of the UK’s imports come from the EU, with the Common Agricultural Policy

and EU Directives, strongly influencing the shape of the UK food system [273]. The UK

currently produces about half of the food it consumes, and is ~60% ‘self-sufficient’ [274].

The UK Government’s stance is, “it sees no economic or environmental rationale for

Government to set targets, to raise UK output of particular food products in step with changes

in global food demand” [273].

The BRM’s analysis takes into consideration these stances and forecasts towards future food

import/export trends; the BRM utilising data from a series of studies [146], [203], [271], to

reflect the UK’s approach.

iv. Utilisation of Agricultural Wastes and Residues

Agricultural wastes and residues reflect a resource category with sizeable potential for the

bioenergy sector [275]. The key influence determining the availability of these resources is

the extent to which they are harvested / collected, and the competition for the resource.

The BRM’s analysis, reflects a wide range of research and studies that forecast the extent and

timeframes to which these resources could be utilised, for energy generation [77], [138],

[139], [206], [207], [211]–[213], [276]. These range from 20%-100% utilisation of the total

resource, with typically half of this being available for the bioenergy sector. The UK

Department for Food & Rural Affairs, provide sustainability guidance on the extent that

agricultural residues should be returned to the soil; to protect and enhance soil and

biodiversity (10% Lower Limit, 50% Higher Limit) [9].

The range of these forecasts and the sustainability limits, form the basis of analysis of this

driver within the BRM.

C. Forestry & Wood-Based Industry Drivers

The following drivers have been grouped within the forestry and wood-based industry

category. These reflect the BRM’s capability of analysing biomass resource availability and

bioenergy potential, with varying forestry productivity levels and resource demand of

industry.

i. Forestry Expansion and Productivity

The extent and productivity of forestry systems, directly influences the availability of

resources for the bioenergy sector. Forests provide energy generation opportunities, either


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through resources specifically harvested for the bioenergy sector, or via the collection of

residues. Forests also provide indirect opportunities for the bioenergy sector, through

supplying resource to wood-based industries, that in turn produce wastes and residues that

can be utilised by the bioenergy sector.

These drivers are analysed within the BRM, through utilisation of the UK Forestry

Commission’s Forested Area and Productivity forecasts [190]–[197].

ii. Wood-based Industry Productivity

The on-going activities of wood-based industries produce wastes and residues that provide an

opportunity for the bioenergy sector. At the same time, these wood-based industries require

raw forestry products, for which it competes directly with the bioenergy sector for the lower

grades of resource. Therefore, wood-based industries represent a driver for biomass resource

that may both increase, and decrease the availability of resource for the bioenergy sector.

The BRM utilises existing data [203]–[205], and forecasts [204], that predict the trends and

directions that UK wood industries may take.

iii. Imports and Exports of Forestry Product.

Forestry raw material, and wood-product import and export trends, can influence the

availability of biomass resource, through determining the extent that the indigenous forestry

systems are utilised. Where imports are increased and exports are reduced, there will be less

strain on indigenous forestry systems, to produce the wood resource required to meet

demand. This may in turn provide increased opportunities for the bioenergy sector. Likewise,

reduced imports and increased exports would have the counter influence, putting greater

strain on indigenous forests.

The BRM again utilises existing data [203]–[205], and forecasts [204], that predict the trends

and directions that UK forestry products imports/exports may follow.

D. Biomass Residue & Waste Utilisation Drivers

The following drivers have been grouped within the biomass residue and waste utilisation

category. These reflect the BRM’s capability of analysing biomass resource availability, and

bioenergy potential, with the utilisation of different waste and residue resources.

i. Utilisation of Forestry Residues

Forestry residues represent an opportunity for the bioenergy sector that is currently un-

utilised in the UK [138]. The availability extent of this resource is dependent on: the


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proportion extracted from forestry systems, the emergence of competing markets, and the

proportions left in-situ to maintain the health of the habitat.

Forest Certification Standards, set by: the Forestry Stewardship Commission (FSC Criterion

5.3 & 6.3), Ministerial Conference on the Protection of Forests in Europe (MCPFE Criterion

2 & 3), and the Programme for the Endorsement of Forest Certification (PEFC Criterion 4);

all provide details for the minimisation of on-site harvesting and residue processing,

maintenance of ecosystem health, and function and protection of biodiversity [277].

The BRM’s analysis of this driver reflects these sustainability guidance levels, and the full

range of residue extraction levels; forecast by research and studies [138], [199], [259], [277],

[278].

ii. Utilisation of Industrial Residues

The key drivers influencing the availability of industrial residues are the extent to which they

can be collected, or processed. The total resource generated being driven by the productivity

efficiency and applied processes, of UK wood-based industries. Biomass residues from on-

going industrial processes, representing a potential opportunity for the bioenergy sector

[262]. The BRM analyses this driver through the utilisation of data that reflects current and

forecast productivity and efficiency trends, for the UK’s wood-based industries [203]–[205];

including forecasts of the extent that industry residues may potentially be available to the

bioenergy sector [138], [279], [280].

iii. Utilisation of Arboricultural Arisings

The Forestry Commission (2013) [281], confirm that UK Local Authorities and tree surgeons

produce thousands of tonnes of arboriculture arisings. The majority of this is currently land-

filled, stored for landscaping applications, or burnt onsite. With correct processing, handling,

grading and storing; these residues provide an opportunity for the bioenergy sector. The key

drivers determining resource availability are the extent to which the resource is harvested /

collected, and the competition for the resource.

The BRM utilises forecasts from a series of research and studies [138], [199], [204], that

estimate that up to 100% of arboriculture arising could be utilised by the bioenergy sector.

E. Waste Generation Trends & Waste Management Strategies

The potential availability of waste resources for the bioenergy sector is influenced by two key

drivers: the amount of waste being generated, and the strategies implemented for how this

waste is managed.


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The BRM analyses these drivers through the utilisation of a series of datasets [206], [215]–

[218], [282], that reflect the UK’s current waste system. Scenarios developed by DEFRA

[206], [217], that forecast waste generation and management trends, are utilised to analyse

how these different pathways may influence the availability of biomass resource for the

bioenergy sector.

F. Biomass & Energy Crop Strategy Drivers

The following drivers have been grouped within the biomass & energy crop strategy

category. These reflect the BRM’s capability of analysing biomass resource availability and

bioenergy potential, with varying land area, and planting strategies. The drivers within this

category and a discussion of their influence are as follows:

i. Land Dedicated for Grown Biomass Resources & Energy Crops

The area of land dedicated for biomass and energy crop growth is a fundamental driver in

determining the potential availability of grown resource. Grown biomass resources have an

important role to play in helping to achieve the UK’s renewable energy targets [8], [9]. The

UK Department for Energy & Climate Change estimate [8], [9], that for the UK to meet these

targets, approximately 0.35 MHa of land needs to be dedicated for energy crops – a large

increase from the current <0.025 MHa utilised. Although 0.35 MHa seems large it currently

reflects <2% of UK agricultural land; an area that could be easily realised through farmers

utilising un-used / marginal lands.

A large number of reports and studies estimate that varying amounts of the UK’s >17 MHa of

agriculture land, could be dedicated for biomass resource growth [231], [232]. Potential land

dedication estimates range from 0.35-1.0 MHa [77], [136], [137], [233]–[237]; whilst the

theoretical maximum available land for short rotation coppices and Miscanthus, without

impacting food systems, have been estimated to be between 0.93-3.63 MHa [8], [9].

The European Environment Agency (EEA) also reported that between 0.8-3.4 MHa of land

could be released in the UK by 2030, by reform of the Common Agricultural Policy [236].

Fischer et al (2007) [139], estimating that half of this released land would be former

grassland.

The BRM takes into consideration these estimates when analysing this key driver, and in

determining the proportion of free land to be dedicated for biomass resource growth.


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ii. Biomass & Energy Crop Planting Strategies

Strategies that determine the types and extent that different biomass and energy crops are

planted on the available land, is a further driver within the BRM; although analysis of this

driver has been excluded from the sensitivity analysis undertaken in Section 5.2. This is

because the range of variables and combination of planting strategies that could be analysed

is vast. Therefore, this driver within the BRM’s analysis is set by default to reflect the

‘balanced crop growth scenario’, as described in Table 4.14, and influenced by literature

[136], [137], [237]. Biomass and energy crop planting strategies are also discussed further

within Chapter 6 and 10.

5.1.4 Discussion of Drivers Not Directly Analysed within the BRM

The next section provides a discussion of the drivers identified within Table 5.2 that cannot

be directly analysed within the BRM. This section is important as it identifies some of the

limitations of the BRM’s analysis, and highlights further influences to biomass resources that

are not included within the BRM analysis based conclusions of this Thesis.

A. Drivers Partially or Indirectly Analysed within the BRM

The following drivers listed within Table 5.2 are identified as being partially or indirectly

analysed within the BRM:

i. Gross Domestic Product & Economic Development

The influences of gross domestic product (GDP) on biomass resources are not directly

analysed within the BRM. However, other indicators and factors closely linked to GDP are

analysed: industry productivity, forest raw materials and wood product import / export rates,

food commodity import / export rates, built-up land area, and population.

A vast range of literature has analysed the relationships between energy and economic trends.

Payne (2010) [283], provides an overview examining over 100 of these, and concludes that

there is no clear consensus about the dynamics of this relationship; although further studies

focusing on the relationship between biomass and GDP, find a positive unidirectional link

[284], and further relationships between industrial productivity and employment with

bioenergy [285].

The generation of biomass and wastes are also themselves, important drivers in the energy-

GDP relationship [286]. Analysis carried out by Ohler & Fetters (2014) [286], also confirmed

the complexity of this relationship, but concluded that a 1% increase in bioenergy generation

could be linked to a 0.129% increase in real GDP. At the same time, their short-term


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causality analysis concluded that there was negative short-term impact on GDP from

bioenergy growth, but also a positive impact on bioenergy from GDP growth.

ii. Nature Conservation Land Requirements & Environmental Protection Policies

The influences of nature conservation land requirements on biomass resources are also not

directly analysed within the BRM. However, other indicators and factors closely linked to

conservation are analysed: forest productivity, forestry residue utilisation, built-up land area,

and the waste drivers. A conservation focused scenario is also developed within the BRM, as

discussed widely within Chapter 6.

The bioenergy sector’s increasing demand for resources will add additional pressure on land

systems. According to Melillo et al (2009) [287], a future without forestry conservation may

lead to large scale increases in cropland expansion into formally unused / forested land.

Restricting land availability for biomass plantations, by conserving natural forests will

require additional efforts in the agricultural sector, principally to increase their productivity

yields, but will also likely lead to a decrease in the availability of biomass resources [288].

In further considering the conservation of forested land, an increasing number of papers

[289]–[297], have discussed the relationship between biodiversity and growth of resources

for the bioenergy sector. The overwhelming consensus from these papers highlights a

negative impact on biodiversity [298].

The general conclusions are that the following widespread biomass production practices

would have to be limited, if full policies to preserve biodiversity are implemented; these

likely resulting in reduced biomass resource productivity:

Reduction in intensification practices such as converting long-term or permanent

crops into annual harvest, and management practices [293], [294].

Reduced of limited utilisation of pesticides

Reduced use of land management practices that may lead to pronounced losses of

habitat biodiversity such as, landscaping or maintenance of hedgerows [299], [300].

Reduced planting of species that impact soil organic stocks; as demonstrated to

potentially take place with Miscanthus plantations [301].


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iii. Climate Change Drivers

The influences of climate change on biomass resources and bioenergy also cannot directly be

analysed within the BRM. However agricultural productivity yields are analysed, these being

utilised within the BRM to reflect potential climate change influence on food systems.

The relationships between climate change drivers and bioenergy are complex and extensive.

Bioenergy being a key energy pathway increasingly utilised to mitigate fossil fuel energy-

driven climate change, but climate changes also having large feedback effects, in terms of the

levels of bioenergy potential [302].

Climate change will likely have large impacts on both food and biomass productivity systems

(discussed further in Chapters 6 and 8). A notable component of the IPCC’s scenarios [303]

forecasting potential climate change impacts are their projected impacts on biomass and

biofuel production. Their conclusions being, that climate change may have major

implications for forests and related industries. Further studies have focused specifically on

these relationships, such as Kirlenk & Sedjo (2007) [304], who reviewed historic climate

change events and their impact on forestry systems.

A number of studies have concluded that climate change drivers may lead to increased global

forest productivity and a migration towards the poles, as a result of increased CO2

concentrations, increased temperatures, and extended growing seasons; this potentially

leading to increased resource availability and hence lower prices [305]–[309]. However,

these studies fail to address the forecast increases in productivity limitation factors that will

likely result from climate change, such as an increase in pests and weeds [163], [304], [309].

On the energy demand side, consumption of biomass resources for energy pathways have

been forecast to increase by as much as 5-7 fold by 2050, due to climate change drivers, such

as rising fossil fuel energy prices, and technological advances in the bioenergy sector [310].

Many studies estimating the impacts of climate change, fail to take account of these

potentially large increases in demand [304]. Raunikar et al (2010) [311], highlight that any

climate change impacts that result in a narrowing of biomass resource fuel wood prices,

towards that of roundwood used by industries, would lead to a major unbalancing of market

competition for resource between industries.

There is a strong acknowledged link between land-use change, and climate change. The

increased production of biomass resource is a major cause of land-use change in many

regions of the world. Berndes et al (2010) [302], highlighted that land-use change emissions,


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predominantly resulting from the conversion of forests to agricultural land, have contributed

roughly 1/3 of GHG emissions since 1850.

Land-use change impacts, can be categorised as being either direct or indirect. An example of

direct land-use change being the clearing of forests to produce biomass resource for

agriculture. Whilst indirect land-use change could be the displacement of food producers,

who go onto re-establish their operations and generate land-use change elsewhere. Land-use

change emissions arising when the biomass of the cleared forest or natural ecosystems are

burned, or when land management practices result in a release of soil carbon stocks [302].

iv. Greenhouse Gas Emission Targets

The influences of GHG emission targets on biomass resources and bioenergy cannot directly

be analysed within the BRM. However, the influences of linked energy, energy efficiency,

renewable energy, and bioenergy targets, can be analysed within the BRM to reflect GHG

targets. This is undertaken later in the Thesis within Chapter 9.

GHG emission targets are a prominent driving force, promoting the development of the

bioenergy sector [3]–[8], [10], [12], [13], [312]. The more stringent and ambitious the targets,

the greater the focus placed on alternative and renewable energy technologies, including

bioenergy.

B. Drivers Not Analysed with the BRM

The following drivers listed within Table 5.2, cannot currently be directly analysed within the

BRM.

i. Rural Economy Development

Most biomass resources are located within rural areas, and therefore the development and

characteristics of these areas can have large influences on bioenergy [313]. Hillring (2002)

[314], found a close relationship between the development of rural industries and bioenergy

supply chains; identifying a series of key characteristics that may result in large increases in

bioenergy potential. These being:

Direct employment from the rural areas in production and processing related

industries, results in a greater ‘buy-in’ to the bioenergy sector [314].

The availability of local people, who are skilled, educated and suitable to work within

related industries, can have large influences on bioenergy potentials [315].


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The development of local infrastructure is a major factor influencing the potential

utilisation of the local resources [314].

Integration of supply chains and production processes linked to end-use bioenergy

systems, and maintenance within a given area, can result in large increases in

efficiencies and bioenergy potentials [316].

ii. Water Availability

There is an inextricable link between bioenergy and water. Water quantity and quality being

identified as emerging issues of concern for the bioenergy sector; the availability of water

being a major factor in determining the extent that bioenergy may contribute to the overall

energy balance. Water is already a scarce resource in many regions of the world, so the

expansion and intensification of bioenergy production, is likely to contribute to existing

pressures [317].

Water utilisation typically occurs throughout biomass resource production cycles, including

the processed and energy conversion steps [317]. Therefore, the practices and strategies

relating to how biomass resources are produced are major determinants of both the

potentially positive and negative impacts, on water systems [318].

iii. Flood Protection Land Requirements

Flood plains and land dedicated for the management of water systems can have a close

relationship with biomass resources. Growth of biomass on this land can improve the flood

protective performance of the land, as well as produce opportunities for the bioenergy sector

[319]. The production of biomass on this land may also provide water quality and flood

control characteristics, during flood events [320], [321].

iv. Soil Degradation Drivers

As with all agricultural production, the growing of biomass resources can degrade soil

fertility, if not managed sustainably. This is particularly reflective of monoculture that can

leach and acidify soil [322]. All forms of land degradation, ultimately results in reductions in

soil fertility and productivity. This can cause loss of protective soil cover, resulting in

increased vulnerability and further degradation [323].

All forms of biomass cultivation should attempt to increase soil health, and also decrease the

risk of soil degradation. Monitoring of the soil organic matter content, can ensure that soil

health is maintained or enhanced under local conditions [322].


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A further relationship between soil degradation and bioenergy is that particular biomass

species can be produced on degraded or marginal lands, unsuitable for food commodity

crops. Degraded lands, therefore represent an opportunity as well as a potential problem for

biomass resource production [324].

v. Diet Change & Calorie Consumption

Haberl et al (2011) [180] and Alexandratos et al (2006) [187], have undertaken analyses that

in part, looks at the relationships between changing diet and calorie food trends, on biomass

resource production and bioenergy potential. These studies developed and analysed scenarios

where populations vary their calorie intake, and also focus on different food commodities

(change their diets). The conclusions highlighted a strong link between ‘richer’ diets, and

increased meat compositions; with reduced levels of biomass availability resulting from the

increased land requirement, to produce the food commodities for these dietary choices.

Alternatively, the ‘frugal’ diet and lower calorie scenario within the study, was found to be

highly linked to bioenergy potentials. In developed counties such as those in Western Europe,

population diets and calorific intakes are considered to have peaked, in terms of impacts on

bioenergy potentials [116].

vi. Support Policies & Mechanisms

There is a close link between success and development of the bioenergy sector, and support

policies and mechanisms; although there does not appear to be any ‘correct’ combinations of

policies or strategies, to fit all circumstances [325]. Thornley & Cooper (2008) [51], carried

out an analysis of the types and effectiveness of different policy instruments in promoting

bioenergy. An overview of these key policy instrument groups applied by Governments, were

summarised within the Introduction (Chapter 1).


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5.2 UK BRM Drivers Sensitivity Analysis

The following section includes the sensitivity analysis, undertaken to evaluate the extent that

different drivers within the BRM influence biomass resource. The section starts with an

introduction to the methodology developed to undertake the sensitivity analysis; going on to

present the results of the analysis for the UK BRM, and finishing with discussions and

conclusions based on the analysis results. The outputs from this Chapter contribute towards

the analysis undertaken within Chapter 7, where the UK’s bioenergy future is discussed, and

Chapter 10, where ideas are expressed for the development of an alternative Bioenergy

Strategy for the UK.

5.2.1 Developing a Sensitivity Analysis Methodology

This section describes the methodology developed for analysing the extent that different

drivers influence resource availability, within the BRM. A sensitivity analysis approach is

applied, as this form of analysis is highly applicable to modelling assessments where a

measurement of uncertainty can be apportioned to different input variables [326]. A series of

existing studies have undertaken similar sensitivity analyses, focusing on biomass supply

chain interactions and dynamics [327], [328], resource harvesting dynamics [329], and

biomass technical and economic analysis assessment [330].

A. Developing a Baseline UK Biomass Resource Scenario

The first step taken to carry out the sensitivity analysis was the development of a baseline UK

biomass resource scenario, within the BRM. This is carried out in order to generate some

biomass resource availability and bioenergy potential forecasts for the UK. The measurement

of variations from these baseline values can then be calculated, and the attributed influence

from different drivers accounted for. Developing a UK Baseline Scenario also enables an

initial assessment of the specific types of biomass resources that may present the greatest

opportunities for the bioenergy sector.

A literature review was carried out to analyse the current characteristics of drivers within the

UK, and to develop an idea of how these may change, to 2050 (Section 5.1). A database was

then produced, collating the range of values that literature and studies forecast for each of

these. This ‘values-database’ was then analysed to develop a series of average or mean values

for each of the drivers, to 2050. These values therefore representing a ‘literature informed’

mean ‘Baseline Scenario’ of how the UK’s biomass supply chains may function. Calibrating

the BRM to reflect this baseline enables an evaluation of the ‘average’ availability, and the


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bioenergy potential of each resource, to 2050. A summary of the reports, studies, and

research contributing to the development of the baseline scenario is shown in Table 5.3, and

the key information included within the ‘values-database’ is listed within Appendix 6.0.

Table 5.3: Reports, Studies & Research Influencing the UK Baseline Scenario Table 26) Table 5.3: Reports St udies & Research Influe ncing the UK Baseline Scenario

Categories Specific Drivers References Contributing to UK Baseline

Scenario

Development Drivers 1) Population Change [145]

2) Changes in Built-Up Land Area [147]

Food Production

System Drivers

3) Crop & Agriculture Productivity [107], [116], [136]–[140], [146], [150], [151],

[157]–[182], [184]–[189], [233], [262]–[264]

4) Food Waste Generation [146], [218], [265]–[272]

5) Food Commodity Imports [146], [203], [271], [273], [274]

6) Food Commodity Exports [146], [203], [271], [273], [274]

7) Utilisation of Agricultural Wastes & Residues [9], [77], [138], [139], [206], [207], [211]–[213],

[275], [276]

Forestry & Wood-

based Industry Drivers

8) Forestry Expansion & Productivity [190]–[197]

9) Wood-based Industry Productivity [203]–[205]

10) Imports of Forestry Product [203]–[205]

11) Exports of Forestry Product [203]–[205]

Biomass Residue &

Waste Utilisation

Drivers

12) Utilisation of Forestry Residues [138], [199], [259], [277], [278]

13) Utilisation of Industrial Residues [138], [203]–[205], [279], [280]

14) Utilisation of Arboriculture Arisings [138], [199], [204], [281]

15) Waste Generation Trends [206], [215]–[218], [282]

16) Waste Management Strategies. [206], [215]–[218], [282]

Biomass & Energy

Crop Strategy Drivers 17) Land Dedicated for Energy Crop Growth [8], [9], [77], [136], [137], [139], [231]–[237]

B. Analysing the Influence of the BRM’s Drivers

The BRM’s result-outputs for the UK Baseline Scenario, represents the mean biomass

resource availability, and bioenergy potentials to 2050, as informed by literature.

The sensitivity analysis proceeds by calibrating all the drivers within the BRM to reflect the

Baseline Scenario. The drivers within the BRM are then individually varied to reflect the

range of values represented in the ‘values-database’, and the subsequent changes in biomass

resource availability and bioenergy potential, are noted. Progressively running this analysis

for each driver whilst keeping all other drivers set at the baseline; allows the extent that each

driver influences the availability of each biomass resource, to be determined.

The key outputs from this analysis are also developed to inform the discussions in Chapter

10, where the potential opportunities for the UK bioenergy sector are highlighted.

5.2.2 UK Baseline Scenario – Forecast Biomass Resource Availability

This section presents the BRM’s analysis results for the UK Baseline Scenario. Figure 5.1

demonstrates the combined availability of each category of biomass resources to 2050, within


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the UK Baseline Scenario. A further dynamic highlighted within Figure 5.1, is the extent that

availability of each of the resource categories may increase, over the analysis timeframe. This

important dynamic is highlighted, as it provides an initial insight into the levels of work or

actions that may be required if the higher levels of resource availability are to be achieved.

Figure 5.2 then demonstrates a greater depth analysis; documenting the potential availability

of each specific biomass resource forecast within the UK Baseline Scenario, to 2050. The

stacked bar charts highlight the availability of each specific resource type; whilst the stacked

line graphs connecting each of the bars, provide differentiation between resource categories:

grown resources, residue resources, and waste resources.

Each of these Figures are presented and discussed within the following sections:

A. UK Baseline Scenario - Biomass Resource Category Analysis

i. Results

Figure 5.1 shows the UK Baseline Scenario forecasts of biomass availability within each of

the three resource categories, to 2050. Figure 5.1 clearly documents trends of increasing

resource availability, as forecast by literature and reflected within this scenario.

‘Grown Resources’ are shown to have a relatively low availability in 2015

(>2,044,000 Tonnes), but this potentially increases by >1050% by 2050 (to

>23,533,000 Tonnes).

‘Residue Resources’ in 2015 are shown to have a potential availability of >10,929,000

Tonnes, potentially increasing by >115% by 2050 (to >23,447,000 Tonnes).

‘Waste Resources’ in 2015 are shown to have a potential availability >15,220,000

Tonnes, potentially increasing by >491% by 2050 (to >90,000,000 Tonnes).


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51) Figure 5.1: Availability of U K Biomass Resource Categories within t he Baseline Scenario

Figure 5.1: Availability of UK Biomass Resource Categories within the Baseline Scenario

ii. Discussion

The results presented within Figure 5.1 represent three levels of analysis for the UK Baseline

Scenario: the extent that the different categories of biomass may be available over the

analysis timeframe, the levels of resource category availability in the near-term (by 2015),

and the potential increment in availability between 2015 and 2050.

Analysing the potential availability of different categories of resource over the analysis

timeframe is important, as it identifies how much resource could potentially be mobilised for

the bioenergy sector under Baseline Scenario conditions. The near-term forecasts are

important as they provide an insight into how much resource may be available, without

extensive further actions and management of supply chains. The analysis of the forecast

increment range from 2015-2050 provides an indication of the potential effort and actions

that may be required to achieve resource availability levels, that reflect the higher levels

Using this premise to analyse the three categories, Figure 5.1 shows that ‘Grown Biomass

Resources’ are forecast to have relatively low near-term availability, but large potential

availability by 2050. The large forecast increase in potential availability over the analysis

timeframe, suggests that the availability of resources from within this category may be highly

variable, depending on the characteristics of influential drivers. The changing characteristics

of one or multiple drivers, are potentially forcing this trend. Also, the large resource

Forecast Availability of Biomass Resource

Categories within the UK Baseline Scenario


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availability increment from the near term to 2050, highlighting that substantial effort may be

required to manage these drivers, if the upper limits of resource availability are to be

achieved, from the low base forecast for 2015.

‘Residue Biomass Resources’ in Figure 5.1 are shown to have ‘medium’ near-term

availability compared to the other two resource categories. This increases at a steady rate to

2050, although the availability of biomass residue resources, documents the lowest overall

increment of the three resource categories. The relatively continuous and uniform incremental

rate of resource availability over the analysis period, suggests that residue resources may be

relatively robust to influencing drivers. The small forecast increment in residue resource

availability from the near-term to 2050, suggests that lower levels of effort may be required

to achieve the higher levels of the resource availability forecast. However, the small increase

in resources availability at a narrowing increment rate over the analysis timeframe, could also

suggest a resource that is getting close to its maximum availability potential [331].

Figure 5.1 shows that ‘Waste Biomass Resources’ within the UK Baseline Scenario, have

near-term availability that exceeds the other two categories, and the potential forecast by

2050 represents the greatest opportunity for the UK bioenergy sector. A large overall increase

in waste resource availability is forecast; this taking place at a rapid rate. This suggests that

waste resources may also be highly susceptible to changes in the characteristics of supply

chain drivers. The near-term high availability of waste resources may represent a good

opportunity for the UK bioenergy sector; although significant effort and actions may be

required to manage the influential supply chain drivers, if the upper limits of resource

availability as forecast, are to be realised.

B. UK Baseline Scenario - Biomass Resource Availability

i. Results

Figure 5.2 and the data within Appendix 6.0 document the forecast availability of each

specific biomass resource within the UK Baseline Scenario, to 2050. This analysis also

clearly reflects the overall increase in resource availability forecast, over the analysis

timeframe. Deeper evaluation of Figure 5.2 highlights that several specific biomass resources

dominate the analysis, contributing a majority proportion to the overall biomass resource

available.

Within the Grown Resources category, ‘biomass and energy crop resources’ grown

specifically for the bioenergy sector dominate, especially towards the end of the analysis


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timeframe. These make up: 43% (873,000 Tonnes) of the available resource within the

category in the near future (2015,), 60% (2,908,000 Tonnes) by 2020, 71% (7,316,000

Tonnes) by 2030, and 97% (24,416,000 Tonnes) by 2050.

Within the Residue Resources category, ‘agricultural residues’ dominate throughout the

analysis timeframe. The contribution proportion of both ‘plant and animal based agriculture

residues’ to this category remains relatively constant with: 94% (10,238,000 Tonnes) of the

available resource within the category in the near future (2015,), 93% (11,081,000 Tonnes)

by 2020, 94% (16,110,000 Tonnes) by 2030, and 96% (22,424,000 Tonnes) by 2050.

The dominant resources within the Waste Resource category are ‘household wastes’. The

contribution proportion of ‘household wastes’ to the overall wastes resource category also

remains relatively constant with: 45% (6,780,000 Tonnes) of the available resource within the

category in the near future (2015,), 48% (16,149,000 Tonnes) by 2020, 48% (34,848,000

Tonnes) by 2030, and 45% (40,716,000 Tonnes) by 2050.

‘Food and organic wastes’ are also shown to be biomass resource that may demonstrate large

potential for the bioenergy sector in terms of resource availability. The ‘other wastes’ types

listed within the analysis, represented a collation of all other waste streams (Table 4.1) that

were not within the categories of: household, food, or organic. Collectively ‘other wastes’ are

also forecast to represent a potentially significant resource available for the bioenergy sector.


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52) Figure 5.2: UK Baseline Scenario Biomass Resource Availability

Figure 5.2: UK Baseline Scenario Biomass Resource Availability

ii. Discussion

The analysis documented within Figure 5.2 and corresponding data within Appendix 6.0,

highlights a series of specific biomass resources that may demonstrate particular potential for

the bioenergy sector, in terms of the potential extent of their availability.

5.2.3 Biomass Resources Demonstrating Potential for the UK Bioenergy

Sector

As described within the previous sections, ‘household wastes’, ‘agricultural residues’ and

‘biomass and energy crops’ are shown within the UK Baseline Scenario to demonstrate

particular potential for the UK bioenergy sector, in terms of their potential availability. These

representing the dominant resources contributing within each of the biomass categories

analysed within the BRM.

Figure 5.3 highlights the proportional contribution of these resources to the total available

biomass resource, and the total bioenergy potential, analysed within the UK Baseline

Scenario. The bioenergy potential documented within this analysis reflects the conversion of

the resources quantified within Figure 5.3, through application of the ‘preferred bioenergy

conversion pathways’ (Appendix 1.0) for each specific resource.

Within the UK Baseline Scenarios, the combined contribution from these three resources are

forecast to make up >55% of total available biomass, and >60% bioenergy potential by 2050.

Forecast Availability of Specific Biomass

Resources within the UK Baseline Scenario


177

These resources therefore represent the greatest potential for the UK bioenergy sector. If the

UK is to increase its utilisation of indigenous resources, the increased mobilisation and

availability of these resources will be of key importance. Thus, identifying the drivers and

supply chain characteristics that influence their availability to the bioenergy sector, is also of

key importance.

53) Figure 5.3: UK Baseline Scenario – Proportiona l Contribution of Key Biomass Resources

Figure 5.3: UK Baseline Scenario – Proportional Contribution of Key Biomass Resources

5.2.4 BRM Drivers Sensitivity Analysis

The following section provides the results and discussions of the sensitivity analyses

undertaken. The results are demonstrated within a series of radar charts, labelled as Figures

5.4, 5.5, and 5.6; supported by the corresponding data listed within Appendix 7.0. The radar

charts document the resource availability of each of the selected biomass resources, over the

analysis timeframe. The 17 spokes of the radar charts each represent the influence of one of

the BRM supply chain drivers; the numbers of each driver corresponding to those listed

within Table 5.3. The specific availabilities of the resources mapped on each spoke within the

radar chart, represent the maximum resource availability forecast when the corresponding

driver has been varied within the BRM to reflect the range of characteristic values identified

by literature; and all other drivers are controlled to reflect UK Baseline Scenario conditions

(Section 5.2.1 and Appendix 6.0).

Forecast Contribution of the Key Resources to Overall

Biomass Availability within the UK Baseline Scenario


178

Where the resource availability mapped on a given spoke demonstrates notable variation

from that on other spokes for the same time period, the supply chain driver within the UK

BRM is assumed to command influence in determining the availability of the resource. The

extent that the availability mapped on a given spoke, varies from the notable baseline is

assumed to reflect the acuteness of influence, commanded by the corresponding supply chain

driver [332].

A. UK BRM Driver Sensitivity Analysis – Grown Biomass Resources

i. Results

54) Figure 5.4: Biomass Grown Resources Sensitivity A na lysis Radar Chart

Figure 5.4: Biomass Grown Resources Sensitivity Analysis Radar Chart

The results of the sensitivity analysis undertaken for ‘grown biomass resources’ are

documented within the radar chart of Figure 5.4, and supporting data as listed within

Appendix 7.0. This highlights that the availability of these resources are predominantly

influenced by the supply chain characteristics of the following drivers:

UK BRM Driver 1 – Population Change

UK BRM Driver 3 – Crop & Agriculture Productivity

UK BRM Driver 8 – Forestry Expansion & Productivity

Influence of Supply Chain Drivers in

Determining the Availability of UK

Grown Biomass Resources


179

UK BRM Driver 17 – Land Dedicated for Energy Crop Growth

Four of the seventeen analysed supply chain drivers within the UK BRM, are highlighted as

commanding degrees of influence on the availability of ‘grown biomass resources’.

Population change dynamics (Driver 1), and variations in the expanse and productivity of

forest systems (Driver 8), are shown to have only marginal influence on the availability of

these resources. Changes in crop and agricultural productivity (Driver 3) are shown to control

a greater influence, especially towards the end of the analysis timeframe where the potential

availability of ‘grown biomass resources’, can be seen to be noticeably higher than that of

Baseline Scenario conditions. However, the extent that land is made available and utilised for

resource growth (Driver17) can be clearly identified as the key characteristic within the BRM

that influences the availability of these resources. The sensitivity analysis highlights that if

the upper limits of land are made available and utilised for resource growth, as documented

by literature, the availability of ‘biomass and energy crops’ for the bioenergy sector may be

>87% greater in 2050, compared to scenarios where lower limits of land are utilised.

Following the sensitivity analysis undertaken on the combined grown biomass resource

category, further sensitivity analyses were undertaken for each individual biomass resource

within the category. The supply chain influences on each of these are listed in Table 5.4,

reflecting the data of Appendix 7.0.

Table 5.4: UK BRM Drivers Influencing the Availability of Grown Resources Table 27) Table 5.4: UK BRM Drivers Influencing t he Availability of Grown Resources

Grown Resources UK BRM Drivers Influencing Resource Availability

Biomass & Energy

Crops

Population Change (Driver 1)

Changes in Built-Up Land Area (Driver 2)

Crop & Agriculture Productivity (Driver 3)

Food Waste Generation (Driver 4)

Land Dedicated for Energy Crop Growth (Driver 17)

Dedicated Forestry

Resources Forestry Expansion & Productivity (Driver 8) Wood-based Industry Productivity (Driver 9)

ii. Discussion

The sensitivity analysis for the grown biomass resources category, found that the availability

of these resources were predominantly influenced by four drivers within the BRM. Marginal

influences were shown to come from population change trends, and the changing expanse and

productivity of forestry systems. Stronger influences were found to be linked to the

productivity of crops and agricultural systems, and the extent that available land is utilised for

resource production. These linkages are discussed as follows:

Population Change – Population is a key driver, providing influence throughout the

BRM. For the Baseline Scenario the sensitivity analysis has highlighted a potential

increase in the availability of grown resources, when the different population forecasts


180

are applied within the BRM. Population is closely linked to the food commodity

demand and land requirements analysis, within the BRM; with lower population

forecasts resulting in reduced demands for land to produce food, and an increased

availability of land that may potentially be available for alternative uses. The main

output from this analysis, highlights and stresses the inverse relationship between

population growth and availability of land.

Forestry Systems – The expanse and productivity of forestry systems within the

BRM can be linked directly to the potential availability of grown biomass resources.

In addition to providing resource to a wide range of wood-based and competing

industries, forests also potentially provide resources directly to the bioenergy sector,

as accounted for within the BRM.

Productivity Yields – The relationship between crop yields and the potential

availability of grown resources for the bioenergy sector is also easily explained. The

sensitivity analysis confirms that in circumstances where increased yield and

productivity levels are achieved, a greater extent of resource will be produced and

available to the bioenergy sector, from the land utilised. Also where the productivity

of agricultural land increases, less area will be required to produce the food levels to

meet demand. Thus more land may be utilised for alternative activity such as resource

production for the bioenergy sector. Of the drivers analysed within the BRM, the

productivity of crop and agricultural systems is shown to be the second most

influential supply chain characteristic on determining the availability of grown

resources.

Utilising Available Land – The extent that available land is utilised for resource

production is shown to be a highly influential driver. The greater the area of available

land utilised and dedicated for the growth of resources for the bioenergy sector, the

greater the availability of the resource. If this characteristic is managed appropriately

resulting in increases in available land utilisation, the sensitivity analysis highlights

that large increases in resource availability may be achieved. The influence of this

driver on determining the availability of grown resources surpasses all others within

the BRM.

Other UK BRM drivers that were identified to provide influence on the availability of

specific grown biomass resources were: changes in area of built-up land, food waste


181

generation trends, and the productivity of wood-based industries. The influences of these

drivers on resource availability were found to be relatively minor; as they did not heavily

register within the combined grown resource category analysis. They are discussed as

follows:

Built-Up Land Area Trends – There is an inverse relationship between changes in

the built-up land area and the availability of biomass and energy crops. Any land

being developed as reflected within the BRM is identified as no longer being available

for resource production, resulting in an overall reduction in potential resource

availability. The relatively minor infringement of new developments on UK land that

could otherwise be utilised for resource growth, explains the minor influence of this

driver within the analysis.

Food Waste Trends – A further relationship exists between food waste trends and the

potential availability of biomass and energy crop resources. The extent and

characteristics of food waste within any food system, plays a large role in determining

the efficiency of the system. The greater the extent of food wastes, the more land and

food commodity production will be required to meet demands. Therefore, as the

sensitivity analysis has flagged the characteristic of food waste trends as having a

degree of influence in determining the extent of land that may be available for

alternative uses; this will ultimately have some influence on the potential availability

of biomass and energy crops.

Industry Productivity Trends – The sensitivity analysis highlighted a relationship

between the productivity of wood-based industries, and the availability of resources

for the bioenergy sector sourced directly from forestry systems. This link is also

straightforward to explain, as wood-based industries compete directly with the

bioenergy sector for certain grades of resource from forests. Changes in the

productivity and hence demand of these industries, will result in more / less resource

being available to the bioenergy sector. The design of the Baseline Scenario within

the UK BRM also ensures that industry requirements for resources are delivered

ahead of those being identified as required for the bioenergy sector; thus this scenario

dynamic promotes this inverse relationship.

A further insight that can be derived from sensitivity analysis radar charts are the rates of

resource availability increment, over the analysis timeframe. The spacing between the lines


182

represents the time interval over the analysis timeframe. Figure 5.4 demonstrates that the

near-term availability of grown biomass category resources, according to the UK Baseline

Scenario; is small. This reaffirms that analysis shown within Figure 5.1, showing that there is

huge scope for increasing biomass and energy crop resource levels, but upper limits of

growth would likely require sector transformations with substantial commitment / investment

changes.

iii. Resources that Demonstrate the Greatest Potential for the UK Bioenergy Sector

As described earlier in this Section, ‘biomass and energy crops’ were identified as resources

that may demonstrate particular potential for the UK bioenergy sector in the future. The

sensitivity analysis identifies that the productivity of crops and agriculture systems (Driver

3), and the area of available land dedicated for resource growth (Driver 17), are the key

influences.

The influence of realising higher levels of crop and agricultural productivity is shown to

potentially increase the availability of this resource by >30% by 2050; whilst realising higher

levels of available land utilisation as forecast by literature, is found to potentially improve

resource availability by as much as >87% by 2050. Highlighting that if the UK wants to

increase its biomass and energy crop resource; focusing on anything other than increasing

land availability, is unlikely to deliver the same scale of results.

These analysis outputs and conclusions are taken forward to Chapter 10, where potential

strategies are discussed for increasing the availability and utilisation of biomass and energy

crops.


183

B. UK BRM Driver Sensitivity Analysis – Residue Resources

i. Results

55) Figure 5.5: Biomass Residue Resources Se nsitiv ity Analys is Radar Chart

Figure 5.5: Biomass Residue Resources Sensitivity Analysis Radar Chart

The results of the sensitivity analysis undertaken for ‘residue biomass resources’ are

documented within the radar chart of Figure 5.5, supported by data listed within Appendix

7.0. This highlights that the availability of these resources are predominantly influenced by

the supply chain characteristics of the following drivers:


UK BRM Driver 7 – Utilisation of Agricultural Wastes & Residues

UK BRM Driver 8 – Forestry Expansion & Productivity

UK BRM Driver 9 – Wood-based Industry Productivity

Four of the seventeen analysed supply chain drivers within the UK BRM are also highlighted

as commanding degrees of influence on the availability of ‘residue biomass resources’. The

analysis demonstrates that none of the drivers reflected within the BRM command significant

influence in determining the availability of these resources. Varying population change

dynamics (Driver 1), and the extent and productivity of forestry systems (Driver 8), are



Residue Biomass Resources


184

shown to provide the greatest influence. The Baseline Scenario forecasts suggest that

realisation of higher population change forecasts, may result in their being >12.6% increased

availability of residue resources, compared to pathways where lower levels of population

change occur. Also, greater expansion and productivity of UK forestry systems by 2050 are

forecast to potentially result in >11.6% increased availability of residue resources in 2050,

compared to pathways where less focus is places on forest system growth and utilisation.

Variations in the extent that agricultural wastes and residues are utilised (Driver 7), and the

productivity trends of wood-based industries (Driver 9), are also shown to command

marginal influence in determining the availability of residues resources for the bioenergy

sector.

Following the sensitivity analysis undertaken on the combined residue biomass resource

category, further sensitivity analyses were undertaken for each individual resource within the

category. The supply chain influences for each of these are listed in Table 5.5, reflecting the

data of Appendix 7.0.

Table 5.5: UK BRM Drivers Influencing the Availability of Residue Resources Table 28) Table 5.5: UK BRM Drivers Influencing t he Availability of Res idue Resources

Residue Resources UK BRM Drivers Influencing Resource Availability

Plant Agricultural

Residues


Utilisation of Agricultural Residues (Driver 7) Land Dedicated for Resource Growth (Driver 17)

Animal Agricultural

Residues


Food Commodity Imports (Driver 5) Food Commodity Exports (Driver 6)

Arboricultural

Residues Population Change (Driver 1) Changes in Built-Up Land Area (Driver 2)

Forestry Residues Forestry Expansion & Productivity (Driver 8) Utilisation of Forestry Residues (Driver 12)

Industry Residues Forestry Expansion & Productivity (Driver 8)

Wood-based Industry Productivity (Driver 9) Utilisation of Industrial Residues (Driver 13)

ii. Discussion

The sensitivity analysis focusing on the combined forecast data for all the residue biomass

category resources, found that the availability of these resources were also predominantly

influenced by four drivers within the BRM. Marginal influences were shown to come from

the extent that agricultural wastes and residues were utilised, and the productivity of wood

based industries. Stronger influences were found to be linked to population change dynamics

and the expanse and productivity of forestry systems. These linkages are discussed as

follows:

Agricultural Residue Utilisation – The relationship between this driver and the

availability of residue resources for the bioenergy sector is straightforward. The

characteristics of this driver reflect the extent that agricultural residues can be


185

harvested, and the extent that these resources are potentially distributed between the

bioenergy sector and other competing industries or uses. Thus, as highlighted by the

sensitivity analysis, this driver has direct influence on resources potentially available

for energy pathways.

Industry Productivity Trends – A further straightforward relationship exists

between the productivity of wood-based industries and the availability of residue

resources. As accounted for within the BRM, wood-based industries may provide a

source of resource for the bioenergy sector, in the form of wastes and residues from

their on-going processes. Therefore, as the sensitivity analysis highlights, a link is

shown between the productivity of industries and the amount of resource they

potentially produce that could potentially be utilised by the bioenergy sector.

Population Change – The sensitivity analysis identified a strong relationship

between population change and the availability of residue resources. This relationship

can be explained by the linkages within the BRM between population and food

commodity demand and production. Changes in population result in responding

changes in the level of food commodities produced, as adjustments are made to meet

demand. As both plant and animal based agricultural residues contribute a large

proportion of the overall resources within this category, changes in the production of

crops or animals will likewise result in notable changes in the availability of

agricultural residues.

Forestry Systems – A strong relationship is also shown to exist between forestry

systems and the availability of residue resources. This link can be explained by the

key role that forest systems have in influencing the levels of wood-based resources

that move through supply chains, as reflected within the BRM. The productivity of

forestry systems directly influencing the amount of forestry residues that are

potentially available, the levels of resource that are distributed to industry, and also

directly to the bioenergy sector – opportunities for residue generation and availability,

potentially occurring at every step along these supply chains.

Other UK BRM drivers that were identified to provide influence on the availability of

specific residue resources were: utilisation of available land, food commodity imports, food

commodity exports, changes in area of built-up land, utilisation forestry residues, and

utilisation of industrial residues. The influences of these drivers on resource availability were


186

found to be relatively minor, as they did not heavily register within the combined residue

resource category analysis. They are discussed as follows:

Utilisation of Available Land – Available land is utilised within the UK Baseline

Scenario for production of resources directly for the bioenergy sector. A proportion of

this dedicated land is utilised for the production of biomass and energy crop species

that generate plant based agricultural residues, during the production cycle. Therefore,

as highlighted within the sensitivity analysis, a relationship exists between this driver

and the availability of residue resources.

Food commodity Imports & Exports – The relationship between food commodity

imports and exports on agricultural residues, stems from the linkages within the BRM

between the levels of food commodities produced to meet demand. The UK Baseline

Scenario within the BRM is developed to ensure that current food demand is always

sufficed, with variations in import and export levels influencing the extent that food

commodities are produced in the UK. This having a direct link with the levels of

agricultural residues that may potentially be available.

Changes in Area of Built-Up Land – Arboriculture arisings are linked directly to

changes in built-up land area within the BRM. Therefore, this relationship highlighted

by the sensitivity analysis, is straightforward. The overall availability of arboriculture

arising in comparison to the other residue resources, explains why variation in this

resource’s availability and the influence of this driver are not notable within the

combined residue resource category analysis.

Utilisation Forestry Residues – This driver reflect the extent that forestry residues

are collected and the proportion that may potentially be available for the bioenergy

sector. Therefore, as the sensitivity analysis highlighted, the characteristic of this

driver has clear influences on the availability of the resource.

Utilisation of Industrial Residues – Similar to the previous driver, this characteristic

reflects the extent that industrial residues are made potentially available to the

bioenergy sector. Thus, this driver is confirmed to have a clear degree of influence on

determining the availability of industrial residue resources.

Figure 5.5 also provides further insights through analysing the changing rates of resource

availability, over the analysis timeframe. The spacing between the lines, representing each

time interval over the analysis timeframe; shows a gradual and steady increase in residue


187

resource availability. This reaffirms the analysis shown within Figure 5.1, where residue

resources are highlighted as providing a relatively large near-term resource opportunity for

the UK bioenergy sector. The availability of residues increases at a steady rate to 2050,

suggesting the relative robustness of residue resources, in resisting influence from changing

supply chain characteristics.


As described earlier within this Section, ‘agricultural residues’ combining both plant and

animal based resources, are identified as the resources that may demonstrate particular

potential for the UK bioenergy sector, in the future. The sensitivity analysis identifies that

population change (Driver 1), and the extent that agricultural residues are utilised (Driver 7),

are the key drivers that provide influence in determining the availability of these resources.

Agricultural residues are shown to have relatively high near–term and continuing availability,

that is shown to be relatively constant and robust to major fluctuations caused by the

influencing drivers. This resource availability is forecast to exceed 10,238,000 Tonnes in

2015, and steadily increase by >109% by 2050. Based on this analysis, agricultural residues

are justifiably highlighted as potentially providing reliable and robust opportunities for the

bioenergy sector.

These analysis outputs and conclusions are again taken forward to Chapter 10, where

potential strategies are discussed for increasing the availability and utilisation of agricultural

residues.


188

C. UK BRM Driver Sensitivity Analysis – Biomass Waste Resources

i. Results

56) Figure 5.6: Biomass Waste Resources Se nsitiv ity Ana lys is Radar Chart

Figure 5.6: Biomass Waste Resources Sensitivity Analysis Radar Chart

The results of the sensitivity analysis undertaken for ‘waste biomass resources’ are

documented within the radar chart of Figure 5.6, supported by the corresponding data listed

within Appendix 7.0. This highlights that the availability of these resources are

predominantly influenced by the supply chain characteristics of the following drivers:


UK BRM Driver 15 – Waste Generation Trends

UK BRM Driver 16 – Waste Management Strategies

Three of the seventeen analysed supply chain drivers within the UK BRM are shown to carry

varying degrees of influence on the availability of ‘waste biomass resources’. Population

change dynamics (Driver 1), again demonstrates influence in determining resource

availability. The focus of implemented waste generation strategies (Driver 15) can also be

seen to control influence. However, the focus of the implemented waste management

strategies (Driver 16) is clearly the key driver within the UK BRM that determines potential



Waste Biomass Resources


189

resource availability for the bioenergy sector. The Baseline Scenario analysis forecasts,

demonstrate that the implementation of a waste management strategy that complements the

bioenergy sector, may result in acute increases in the availability of waste resources by as

much as: >318% by 2020, >476% by 2030, and as much as >500% by 2050; compared to

scenarios where waste management strategies distribute lower levels of waste to the

bioenergy sector.

Following the sensitivity analysis undertaken on the combined waste biomass resource

category, further sensitivity analyses were undertaken for each individual waste resource. The

supply chain influences on each of these are listed in Table 5.6, reflecting the data of

Appendix 7.0.

Table 5.6: UK BRM Drivers Influencing the Availability of Waste Resources Table 29) Table 5.6: UK BRM Drivers Influencing t he Availability of Waste Resources

Waste Resources UK BRM Drivers Influencing Resource Availability

Household Wastes Population Change (Driver 1)

Waste Generation Trends (Driver 15) Waste Management Strategies (Driver 16)

Food & Organic

Wastes


Food Waste Generation (Driver 4)

Waste Generation Trends (Driver 15)

Waste Management Strategies (Driver 16)

Other Wastes Population Change (Driver 1)


Sewage Wastes Population Change (Driver 1)


ii. Discussion

The sensitivity analysis focusing on the combined forecast data for all the waste biomass

category resources, found that the availability of these resources were also predominantly

influenced by three drivers within the BRM. Influences were shown to come from population

change dynamics, and the focus of applied waste generation strategies. Acute influence was

found to be linked to the focus of applied waste management strategies. The sensitivity

analysis also highlighted that of all the drivers analysed within the BRM, these three

characteristics are applicable to all waste resources analysed; with no further drivers

generating major influences to any of the specific waste resources. The linkages of these key

drivers are discussed as follows:

Population Change – The relationship between population and the generation of

waste resources potentially available for the bioenergy sector is also straightforward.

The large majority of waste streams analysed within the BRM are linked to population

activities, with a minority of waste streams linked more directly to alternative

influences, such industry productivity. Thus, changing population dynamics, as


190

highlighted within the sensitivity analysis, have a telling influence on determining the

extent that a large proportion of wastes are generated.

Waste Generation Strategies – The implementation of waste strategies aimed at

influencing the extent that wastes are generated also has a clear and obvious link with

the potential availability of wastes for the bioenergy sector. The UK BRM focuses on

potential future waste generation strategies that range from, increasing the levels of

waste generated, to the vast minimisation of wastes by 2050; compared to current

levels (Section 4.3.9). The sensitivity analysis confirming the link between the focus

of the waste generation strategy and the amount of resource that may be available to


Waste Management Strategies – The nature and focus of the implemented waste

management strategy, was found to have strong links in determining the extent that

waste resources may be available to the bioenergy sector. The strength of this

relationship can be explained through further highlighting the nature of this driver.

Regardless of the types and extent that different waste streams are generated, the

strategy for how the wastes are managed is the determinant factor relevant to the

bioenergy sector. How wastes are managed and distributed, including to the bioenergy

sector; being the key influence that will determine potential availability.

Figure 5.6 documents a very different picture to that for the other biomass resource

categories. The near-term availability of resources forecast within the UK Baseline Scenario

is shown to be relatively large, reaffirming the trends shown in Figure 5.1. The increment in

waste resource availability without the influence of the waste management strategy driver;

can be seen to be gradual and relatively robust over the analysis timeframe. The upper limits

of waste availability in 2050 are forecast to be far less significant without the inclusion of a

complementary waste management strategy.

The influence of the waste management strategy is clearly acute, and it can be concluded that

the management of this driver to complement the bioenergy sector is mandatory, if the upper

limits of waste resources potential for the bioenergy sector are to be realised.


As described earlier in this Section, ‘household wastes’ are identified as the resource that

may demonstrate particular potential for the UK bioenergy sector in the future. The

sensitivity analysis identified that population change (Driver 1), waste generation strategies


191

(Driver 15), and waste management strategies (Driver 16), are the key drivers that provide

influence in determining the availability of this resource.

The analysis finds that implementation of a waste management strategy that focuses on

energy from waste generation pathways, could provide >40,716,000 Tonnes of household

wastes for the bioenergy sector by 2050. Household wastes therefore represent a very

substantial opportunity for the bioenergy sector, albeit reliant completely on the development

of a complementary waste management strategy. Aside from this standout opportunity, the

analysis also highlights that if a waste management strategy is not complementary to the

bioenergy sector, the baseline levels of resource availability are much less attractive.

These analysis outputs and conclusions are again taken forward to Chapter 10, where

potential avenues for developing complementary waste management strategies are discussed,

for increasing the availability and utilisation of household waste resources by the bioenergy

sector.

5.2.5 Key Chapter & Sensitivity Analysis Outputs & Conclusions

The following section provides an overview of the key analysis outputs and conclusions from

Chapter 5. These are taken forward to Chapter 10, where discussions focus on potential

avenues for the development of alternative bioenergy strategies, with increased bioenergy

generation utilising indigenous biomass resources.

A. The Influence of Supply Chain Drivers on Biomass Resource Availability

This Chapter has analysed a wide range of drivers, finding large variances in their influence

in determining biomass availability for the bioenergy sector. The research also highlights that

particular resources demonstrate significantly greater availability potential than others.

Therefore, if the contribution of indigenous resources is to be maximised, the research

suggests that investments in bioenergy strategies should perhaps be increasingly focused and

targeted.

Table 5.7 provides a key summary of this Chapter’s analysis; ranking the specific resources

and drivers, based on their analysed contribution towards increasing the potential of each

indigenous resource in the UK. Table 5.7 highlights the specific resources that are determined

to demonstrate the greatest and least potential for the UK bioenergy sector, in terms of their

availability; also the specific drivers that have been determined to command the greatest and

least influence in determining resource availability.


192

Table 5.7: Research Summary Ranking Indigenous Resource Influences & Contributors Table 30) Table 5.7: Research Summary Ranking Indige nous Resource Influe nces & Contributors

Ranking

Ranked UK BRM Drivers Demonstrated to

Command Levels of Influence on Determining

Biomass Resource Availability

Ranked Potential of Resources for the UK

Bioenergy Sector in Terms of Availability &

Bioenergy Potential

High Ranking

Drivers & Resources with

Greatest Influence &

Contribution Potential

Waste Management Strategies

Land Dedicated for Energy Crop Growth

Agricultural Residues

Household Wastes

Biomass & Energy Crops

Other Wastes

Medium Ranking


Mid Influence &


Crop & Agriculture Productivity

Population Change

Changes in Built-Up Land Area

Food Waste Generation

Utilisation of Agricultural Wastes & Residues

Forestry Expansion & Productivity

Waste Generation Trends

Dedicated Forestry Resources

Forestry Residues

Food & Organic Wastes

Low Ranking


Lowest Influence &


Food Commodity Imports

Food Commodity Exports

Wood-based Industry Productivity

Imports of Forestry Product

Exports of Forestry Product

Utilisation of Forestry Residues

Utilisation of Industrial Residues

Utilisation of Arboriculture Arisings

Sewage Wastes

Industry Residues

Arboricultural Residues

The analyses results from Chapter 5 highlight the potential importance of applying a targeted

approach for increasing the availability and utilisation of UK indigenous resources. This is in

contrast to the relatively broad policy focus approach, currently being implemented within

the UK. The present UK Bioenergy Strategy aiming to maximise the opportunities for

improving biomass supplies from all feedstocks, through policies aimed at managing a broad

range of drivers [8]; as discussed in greater detail within Chapters 7 and 10.

B. Grown Resources Demonstrating Greatest Potential for the UK Bioenergy Sector

UK grown biomass resources and energy crops are being identified in the grown resource

category, to demonstrate the greatest potential for the UK bioenergy sector. The standout

driver influencing the availability of these resources was identified as the utilisation of

available land, dedicated for their growth. However, the analysis also highlighted that this

resource currently has a relatively low starting base, and therefore concerted efforts will be

required in managing the drivers that influence availability; if anywhere near the upper levels

of resource forecasts are to be realised. These may include the implementation of policies that

encourage or incentivise the utilisation of available land, for the growth of resource dedicated

for the bioenergy sector.

C. Residue Resources Demonstrating Greatest Potential for the UK Bioenergy Sector

UK agricultural residues (straws & slurries) are being identified as the resources from the

residue resource category that demonstrate the greatest potential for the UK bioenergy sector;

whilst also continuing to be utilised by competing industries and to maintain soil systems.

The availability of residues was found to be comparatively robust to influencing drivers; the


193

extent that resources are harvested / collected, being the most influential factor. Availability

of residues is forecast to steadily increase without any major influences from supply chain

drivers - confirming that residues may represent a continuous and reliable, near and long-term

indigenous resource option for the bioenergy sector.

D. Waste Resources Demonstrating Greatest Potential for the UK Bioenergy Sector

Household wastes are being identified as the resources from the waste resource category that

demonstrate the greatest potential for the UK bioenergy sector. Waste resources are found to

be highly influenced by one key driver; the waste management system adopted. The

availability of waste resources was found to be much diminished when the adopted waste

management strategy was uncomplimentary to the bioenergy sector; such as strategies

focussing on resource recovery, or continuation of landfill trends. Therefore, if wastes are

going to be increasingly utilised by the bioenergy sector, the analysis confirms the

importance of developing policies for the implementation of more effective waste

management strategies.


194

Chapter 6 - UK Biomass Resource Scenarios


195

6.1 Developing UK Biomass Resource

Scenarios

This Chapter explores the different scenario pathways that the UK could take in the future

with regard to the development of its bioenergy sector, and resource supply chains; analysing

within each scenario, the fluctuation of the availability of the different biomass resources for

the bioenergy sector. The Chapter also undertakes an assessment of which pathways the UK

could pursue, in order to best utilise the available indigenous resources. Chapter 6 aims to

build on the analysis undertaken within Chapter 5, where the influences of each individual

driver within the BRM, were assessed in isolation. The development of scenarios, allows the

simultaneous calibration of these same supply chain drivers, to reflect whole system

dynamics within the UK. This facilitates a deeper evaluation of how the different supply

chain drivers analysed within the BRM can form relationships, linkages, and feedbacks; that

may collectively influence the potential availability of UK indigenous resource for the UK

bioenergy sector.

The Chapter starts within an introduction and discussion of the nature of the different

scenarios, and why this form of analysis is highly relevant to the wider research project. The

Chapter goes on to introduce the concepts of the specific biomass resource scenarios

developed; providing in-depth discussions and literature review driven reasoning for the

supply chain characteristics reflected within the BRM, for each scenario. The Chapter then

presents the results of the analysis; principally documenting the levels of resource availability

and bioenergy potentials analysed. The key relationships and feedbacks that emerge are

discussed, and the favourability of different supply chain characteristics and bioenergy

conversion pathways, are highlighted.

The main analysis outputs and conclusions from Chapter 6 are then taken forward and

contribute towards the discussions within Chapter 10; where alternative bioenergy strategy

options for the UK are proposed.

6.1.1 An Introduction to Scenario Based Analysis

This section provides an introduction to scenario based analysis, and explains why this

analysis approach is highly relevant to this research project.

Scenario analysis is the evaluation of potential future events through the consideration of

alternative plausible, although not equally likely states of the world (scenarios) [333].


196

The IPCC provide a further definition of scenarios when representative of natural science

research: “A scenario is a coherent, internally consistent and plausible description of a

possible future state of the world. It is not a forecast; rather, each scenario is one alternative

image of how the future can unfold” [334].

Therefore, based on this definition, scenarios are not predictions of forecasts, but are a

dynamic view of future potential pathways based on the chosen trajectory variables. This

definition and thought process is reflected by a schematic in Figure 6.1.

57) Figure 6.1: Visual Conceptualisation of Scenario Pathw ays Based A na lysis

Figure 6.1: Visual Conceptualisation of Scenario Pathways Based Analysis

Adapted from [335]

The broad range of outputs that can result from scenario based analysis provides the

advantage of illustrating potential directions, and illuminating events that may otherwise be

missed. This being especially important when placed in the context of both short and long

term decision making processes; such as are highly relevant to the research. As Means et al

(2005) [336] state: “The great value of scenario planning lies in its articulation of a common

future view, to enable more coordinated decision-making and action”.

Time

Co

nce

pt

Alternative Futures

Scenario

Forecast

Range


197

A. Drawing Influence from Existing Biomass Resource Scenario Research

There will undoubtedly be countless expected, and also many currently unforeseen variables

that will influence UK biomass resource supply chains through to 2050. The only certainty

about carrying out a long-term analysis, such as the 2050 analysis timeframe adopted within

this research; is that there are likely to be many uncertainties. Therefore, a scenario approach

has been identified as the best option for exploration of the potential outcomes of pathways

subject to alternative assumptions.

Comparative biomass resource scenario approaches, have been utilised by a series of existing

studies. These develop scenarios that forecast global biomass resource potential [59], [124],

or specifically focus on the potential of specific resources within a set geography [337],

[338].

The flexibility of the BRM enables any developed scenarios to command a degree of realism,

in terms of reflecting supply chain interactions. Reflecting on previous relevant studies, the

parameters within each scenario developed within this research, are built up through the

calibration of the BRM drivers. The characteristics, extent, and the direction in which these

drivers are varied within each scenario, will reflect a wide range of previous research and

studies that provide forecasts. These are introduced and discussed later in this Chapter.

6.1.2 Developing Biomass Resource Scenarios

Four core biomass resource scenarios have been developed, that are designed to reflect

different potential pathways that the UK could take, to 2050. Each of these potential

pathways will be analysed to determine: the potential extents and types of biomass resource

that may be available to the UK bioenergy sector by 2050, and the forms and levels of

bioenergy that could potentially be generated. A further three sub-scenarios are also

developed to evaluate the bioenergy potential from UK indigenous resource, when alternative

bioenergy conversion pathways are prioritised. These scenarios are introduced and described

in Table 6.1.


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Table 6.1: UK BRM Biomass Resource Scenarios Table 31) Table 6.1: UK BRM Biomass Resource Scenarios

Scenarios Concept Future Pathway Key Focus Areas

Co

re

Scen

ari

os

Food Focus Scenario

(Foo-F)

The food focus scenario is developed to reflect a potential

pathway the UK could take, placing greater focus on food

issues. This includes placing greater policy influence on enhancing food security, and increasing self-sufficiency.

The bioenergy conversion analysis element of this scenario,

applies the ‘preferred’ conversion pathway approach, as described earlier within the Thesis.

This scenario evaluates the impacts that such a scenario could

have on the UK bioenergy sector, and utilisation of UK indigenous resources

Increasing crop yield productivity.

Decreasing food waste.

Reduced food imports, replaced by

domestic growth.

Emphasis on agriculture over

forestry expansion.

Dedication of available land for agriculture ahead of bioenergy

crop growth.

Economic Focus

Scenario

(Eco-F)

The economic focus scenario is developed to reflect a potential pathway the UK could take, placing greater focus on

economic issues. This includes placing greater policy influence on growing industry productivity, and increasing

development within the UK.

The bioenergy conversion analysis element of this scenario applies the ‘preferred’ conversion pathway approach, as

described earlier within the Thesis.

This scenario evaluates the impacts that such a scenario could have on the UK bioenergy sector and utilisation of UK

indigenous resources.

Reduced restrictions on built-up

land area expansion.

Decreased focus on forestry

expansion and productivity.

Utilisation of forestry residues.

Increased exportation rates of food

commodities & forestry products.

Waste generation rates driven by

economic growth and

technological advancement.

Conservation Focus

Scenario

(Con-F)

The conservation focus scenario is developed to reflect a

potential pathway the UK could take, placing greater emphasis

on conservation issues. This includes placing greater policy influence on conservation, and resource protection.

The bioenergy conversion analysis element of this scenario



have on the UK bioenergy sector and utilisation of UK


Restricted expansion of built-up land area.

Increased focus on forestry

expansion & preservation.

Lower limit utilisation of forestry

& agricultural residues for energy.

Decreased levels of waste generation.

Waste management strategies

focusing on resource recovery.

Reduced dedication of available

land for bioenergy crop growth.

Energy Focus

Scenario

(Ene-F)

The energy focus scenario is developed to reflect a potential pathway the UK could take, placing greater focus on

developing the UK’s bioenergy sector. This includes placing

greater policy influence on mobilising resource, and expanding the bioenergy sector.

The bioenergy conversion analysis element of this scenario



have on the UK bioenergy sector and utilisation of UK indigenous resources.

Increased dedication of available

land for bioenergy crop growth.

Increasing focus on forestry

expansion & productivity.

Increased utilisation of forestry residues, agricultural residues and

arboriculture arising by the

bioenergy sector.

Waste management focusing on

energy recovery.

Su

b S

cen

ario

s

Energy (Heat) Focus

Scenario The energy focus sub scenarios evaluate different bioenergy conversion options for the resources

accounted for within the core ‘Ene-F’ scenario. Each of the sub scenarios analyse the bioenergy potential when the biomass resource is converted to power, heat, and transport fuels; according to their

most efficient or ‘preferred pathways’.

However, in the Energy (Heat) Focus Scenario, heat energy generation is prioritised where possible; in the Energy (Power) Focus Scenario, electrical energy generation is prioritised where possible; and in the

Energy (Transport Fuel) Focus Scenario, all suitable resources are utilised to produce biofuels.

Energy (Power)

Focus Scenario

Energy (Transport

Fuel) Focus Scenario

These scenarios have been developed to represent different policy, target, and priority themes

that may emerge in the UK’s future. As described by Anderson et al (2008a-b) [36], [37], the


199

scenarios have been designed to be prospective, quantitative, and normative; in that they

reflect probable future pathways based on the extension of key variable quantities. In

developing each scenario, it is also important to highlight and stress the key design

parameters that ensure that the footprint from each of the developed scenarios, have minimal

impacts in the following key areas:

In each scenario throughout the analysis timeframe, the UK continues to produce food

commodities that at a minimum represent a continuation of current self-sufficiency. In

other words, the production of biomass resources within each scenario, does not

negatively impact the UK’s ability to feed itself.

In each scenario throughout the analysis timeframe, UK industries and all other

activities that currently or may in the future compete for resources with the bioenergy

sector; will be allocated resource to meet their demands ahead of being identified as

being potentially available for the bioenergy sector. In other words, the production of

biomass resources within each scenario, does not negatively impact the UK’s wood-

based industries from continuing to work.

6.1.3 Developing the UK Food Focus (Foo-F) Biomass Resource Scenario

The Foo-F Scenario has been developed to analyse forecasts of biomass resource availability

for the bioenergy sector; within a future pathway where prime focus has been placed on

improving food security and self-sufficiency.

A. Scenario Context

Since World War Two, European agricultural policy has focused on enhancing food self-

sufficiency for the European population, and as demonstrated by recent overproduction of

food, has been highly successful [339]. However, an enormous future challenge looms -

having to feed up to 9-10 billion people by 2050, globally [271]. Agricultural systems are

highly sensitive to climate fluctuations, and a 2°C rise in mean global temperature reflecting

the Intergovernmental Panel on Climate Change’s lowest emission scenarios, is predicted to

result in widespread destabilisation of farming systems across the world [340]. In addition to

the uncertainty regarding food systems, the large-scale production of biofuels is becoming a

significant competitor for agricultural land – and whilst energy security concerns may justify

the production of biofuels, the proposed scale of production raises questions about the trade-

offs between biofuels and food crops [341]. The key issue relating to future food systems

remains, whether these can keep pace with steep growing demands and dietary transitions, in


200

an environment of climate change and numerous other drivers [341]. This strain will put

pressure on Europe’s future supply chains. Enhancing food security and self-sufficiency may

re-emerge as prominent areas of concern for future governments.

B. A Future Pathway with Food Focus

Although complete food self-sufficiency is not a current target for the UK, it is important that

food systems adapt, so that the UK is able to cope with future stresses in the food system

[269], [270]. The UK currently produces about half of the food it consumes, and is ~60%

‘self-sufficient’ [151]. Recognised strategies to address future food issues include: closing the

yield gap – the difference between attainable yields and realised yield by increasing

agricultural productivity through technologies, research, and investment; reducing wastes

from food systems, changing diets, and expanding aquaculture opportunities [271].

UK agricultural productivity has been increasing at a steady trajectory through time, and

increased research, development, and investment in the sector is likely to see this trend

continue [79], [188], [342]. Estimates also suggest that 30-50% of food grown worldwide

may be lost or wasted, before and after it reaches the consumer. Therefore, future emphasis

should be placed on addressing wastes – the UK Government Office for Sciences suggesting

that food waste could be realistically halved by 2050; equivalent to as much as 25% of

current productivity [269], [270].

All actions should be realised through coordinated and multifaceted strategies, where

sustainability is key. This ‘sustainable intensification’ involves an enhancement of current

business-as-usual trends; where agricultural systems remain largely unchanged and demands

follow current projections, but agricultural productivity becomes increasingly efficient [343].

A summary of the key focus areas and an overview of the studies, reports, and literature

utilised to develop the future Foo-F pathway, are shown in Table 6.2. An overview of the

specific UK BRM supply chain driver characteristics and values for the scenario, are listed

within Appendix 8.0.

6.1.4 Developing the UK Economic Focus (Eco-F) Biomass Resource

Scenario

The Eco-F Scenario has been developed to analyse forecasts of biomass resource availability

for the energy sector; in a future where emphasis is placed on economic growth over all other

considerations.


201

A. Scenario Context

Following the 2008 - 09 financial crisis, an agenda aimed at encouraging economic growth

and a return to financial stability, is currently the major driving force behind UK policy. In

the UK, timber and wood-based industries are well established and contribute about 1.5% to

UK exports; equivalent to 2.5% of the global share [203]. The flow of resources between the

environment and industry constitutes the physical foundations of economies – this ‘economic

metabolism’ being a key indicator of economic health [161], [344]. It is the growth and

dynamics associated with the wood-based industries that will be a key influence in

determining the availability of biomass resources available for the bioenergy sector, within

this scenario.

In terms of development of the bioenergy sector, many countries are showing considerable

interest in bioenergy from an economic viewpoint because of the value-added (income) and

employment opportunities that bioenergy can bring, especially in the rural areas where the

resources are produced / collected [345]. However, studies such as those of Marques &

Fuinhas (2012) [346], have concluded that the high costs associated with supporting

renewable energy options are actually an economic burden, as polices such as paying

elevated tariffs for electricity generated by renewable energy, results in an economically

counter-productive effect, and deceleration in economic activity. As things stand, European

countries have energy systems and infrastructures that are deeply grounded in fossil fuel

provision [346]. Therefore, if future policy, finance, and focus are not directed towards

renewable energy pathways, it is unlikely that there will be a widespread move away from

conventional fossil fuel generation. A future pathway focused on economic growth may not

specifically focus on the development of the bioenergy sector through the mobilisation of

biomass resource or the building of bioenergy infrastructure; but through increased on-going

activities within wood-based industries, there may still be opportunities for the bioenergy

sector.

B. A Pathway with Economic Focus

A future pathway with economic focus will reflect policies designed to encourage the growth

of industry, which in turn may compete for biomass resource but also provide new

opportunities for the bioenergy sector. The UK Wood Panel Industry Federation (WPIF)

identifies the growth of the bioenergy sector as a major concern in competing for resource,

“As subsidised energy generators can afford to out-pay the wood panel industry for primary

raw material” [205]. Therefore, a future pathway with economic focus would ensure that the


202

wood industry’s resource demands are prioritised above those of the bioenergy sector.

Industry’s future resource demands have been forecast by the WPIF [204], and forestry

expansion and productivity scenarios are forecast by the Forestry Commission [190]–[197].

To reflect market behaviour these forecasts are utilised within the Eco-F Scenario.

Meta-analysis of a series of studies [347], concludes that there is statistical significance

between economic growth and aggregate export levels, especially relating to both

manufactured and energy-based export categories. Therefore, it should be expected that a

future pathway with economic focus may reflect increases in export levels, particularly wood

products with relevance to the bioenergy sector.

Greyson (2007) [348], states that; “realising zero waste and sustainability with continued

economic growth may not be achievable within the scope of current practices”. Therefore,

the future patterns of waste generation and management within this scenario may reflect a

continuation of current or very similar trends. To model this, the waste strategies within this

scenario utilise DEFRA’s technologically driven forecasts [206], [215]–[218], [282]. These

predict that large-scale solutions and technology will be key factors in dealing with waste

generation issues.


utilised to develop the future Eco-F pathway, are shown in Table 6.2. An overview of the UK

BRM supply chain driver characteristics for this scenario, are listed within Appendix 8.0.

6.1.5 Developing the UK Conservation Focus (Con-F) Biomass Resource

Scenario

The Con-F Scenario has been developed to analyse forecasts of biomass resource availability

for the bioenergy sector, within a future pathway where emphasis is placed on a paradigm of

enhanced conservation and preservation of biodiversity and resources.

A. Scenario Context

A century ago, forestry cover in the UK was at an all-time low; although following a series of

phases of forestry focus this increased by two and a half times to the ~13% cover present

today. However, planting rates in recent years have once again stagnated, leading to

recognition that it is time to regain focus and ‘up the game’, particularly when measured

against the context of having to mitigate and adapt to climate change [349].

The UK’s approach to conservation relies on a series of partnerships between statutory,

voluntary, academic, and business sectors; at both the National and local scale. The prime


203

focus being to maintain and create habitats and ecosystems, halt the decline of biodiversity,

and enhance the robustness of sites to environmental change [350]. This is backed up by a

wide spectrum of legislative requirements that aim at safeguarding forestry, biodiversity, and

conservation [351].

The UK has well established wood based industries that rely on forestry productivity, but at

the same time there is increasing inclusion of forestry resources within renewable energy

strategies [259], and the awareness of forestry resources and ancient woodlands in terms of

ecological value, is increasing [352]. Collectively these three competing demands and

priorities will shape the pathway for utilisation of forestry in the future.

A further increasingly prominent conservation issue is resource availability and scarcity - a

theme motivating new waste management strategies at both the European and UK level

[353]. There is great scope for improvement in the UK, where recycling levels stand at about

39% of municipal waste compared to >60% in leading places such as Austria and Germany

[354]. To help develop innovative and exemplary practices that drive behaviour towards

enhanced sustainability, the UK Government has on-going ‘Zero Waste Places’ initiatives

[355] - “A simple way of encapsulating the aim to go as far as possible in reducing the

environmental impact of waste. It is a visionary goal which seeks to prevent waste occurring,

conserves resources and recovers all value from materials” [356].

B. A Pathway with Conservation Focus

A trade-off exists between biodiversity, conservation, and optimal biomass resource

production for the bioenergy sector. Erb, et al [357], found that estimates of global biomass

crop potential are lowered by 9-32% when land areas of wilderness, of biodiversity

importance, and with protection status; are excluded from assessments. The German

Advisory Council on Global Change also found that a minimum of 10-20% of global land

should be protected if the biosphere’s functions, such as climate regulation and biodiversity

are to be preserved [358]. 14% of land is currently protected globally [267]; meaning a

further 6%, equivalent to 540,000 km² is required [359]. In summary, a future pathway with

conservation focus will undoubtedly result in lower levels of biomass resource being

available for the bioenergy sector.

In the UK, the Forestry Commission have a wide range of forestry expansion and

productivity scenarios that reflect varying levels of forest growth and utilisation [190]–[197].


204

A future pathway with conservation focus will reflect the upper projections for forestry

expansion, with the management and felling of these forests reflective of this pathway.

The approach of the UK forestry industry has progressively shifted primarily from timber

production, to increasingly multi-purpose values that include conservation [360]. Forestry

industries having an important role to play in conservation, as the industry’s long-term

sustainability depends on the resource [361]. Therefore, industry within a future pathway of

conservation focus would continue to utilise forests, albeit strongly abiding to the

requirement of the ‘UK Forestry Standard’ [362].

Research [363], [364], also highlights that the extraction of residues from both forestry and

agricultural systems may pose a risk for the maintenance of soil fertility; with the essential

requirement to ensure that removal of residues don’t exceed levels required to maintain food

and habitats for organisms, and to ensure protection against soil compaction, and for the

maintenance of soil fertility [277], [278]. Therefore, a future pathway with a conservation

focus, will likely avoid the upper limit utilisation of both forestry and agricultural residues.

Waste generation and management strategies will reflect future pathways of reduced waste

generation, and increased levels of resource recovery from waste streams. Scenarios

reflecting these pathways have been forecast by DEFRA [206], [215]–[218], [282], and will

form the basis of future waste analysis within the Con-F Scenario.


utilised to develop the future Con-F pathway, are shown in Table 6.2. An overview of the UK

BRM supply chain driver characteristic data, are listed within Appendix 8.0.

6.1.6 Developing the UK Energy Focus (Ene-F) Biomass Resource

Scenario

The Ene-F Scenario has been developed to analyse forecasts of biomass resource availability

for the energy sector, in a future pathway where prime focus has been placed on expanding

the UK bioenergy sector.

A. Scenario Context

As already discussed within the Thesis Introduction (Chapter 1), the UK has legally binding

CO2 emission and renewable energy targets; including a series of bioenergy targets relating to

heat, power, transport fuel, and overall energy generation [7], [58], [254]. The Energy Focus

Scenario sets out a future pathway where the maximum achievable levels of bioenergy are

generated, from indigenous biomass resources. The strategy is for the UK to maximise its


205

bioenergy generation potential through the utilisation of indigenous resources, and reduce

potential reliance on imported resources.

B. A Pathway with Energy Focus

The concept behind this scenario is to explore the upper limits of indigenous biomass

resources that could realistically be mobilised for the bioenergy sector, to 2050. This involves

mobilising and pushing the limits on resource availability, across the range of biomass

categories.

Within this future pathway the upper limits of available and suitable land (after food demands

are met), is dedicated for the potential growth of biomass resources and energy crops [136],

[137], [237]. The energy focus scenario will reflect the Forestry Commission’s Forest

Expansion and Productivity Forecasts [190]–[197], that provide the greatest resource

potential for the bioenergy sector. A further future opportunity explored, is highlighted by

The Independent Panel on Forestry [365], “Only 52% of UK forests and woodland are

currently actively managed, so major resource use opportunities may exist if progress is

made in this area”.

There are also notable biomass resource opportunities potentially available for the bioenergy

sector, in the form of wastes and residues from on-going activities in the UK [275]. As such,

a future pathway with energy focus will work towards achieving increased harvest and

collection (biological & realistic) limits for biomass residues, from forestry, agricultural, and

industrial processes. The waste generation and management strategies adopted, also reflect

DEFRA’s forecast pathways where energy recovery is the focus [206], [215]–[218], [282].


utilised to develop the future Ene-F pathway, are shown in Table 6.2. An overview of the UK

BRM supply chain driver characteristic data, are listed within Appendix 8.0.


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Table 6.2: Summary of Biomass Resource Scenario Characteristics & Forecast Assumptions Table 32) Table 6.2: Summary of Biomass Resource Scenario C haracteristics & Forecast Assumptions

Drivers within the Biomass Resource Model

Forecasts Informing

the BRM Scenario

Characteristics

Focus of BRM Supply Chain Drivers

within each Scenario

Foo-F Eco-F Con-F Ene-F

UK

Development

Population Change [145] ●● ●● ●● ●●

Changes in Built-Up Land Area [147] ●● ●●● ● ●●

Food

Production

Systems


[107], [116], [136]–[140],

[146], [150], [151],

[157]–[182], [184]–[189],

[233], [262]–[264]

●●● ●● ●● ●●

Food Waste Generation [146], [218], [265]–[272] ● ●● ● ●●

Food Commodity Import & Exports [146], [203], [271], [273],

[274] ● ●● ●● ●●

Utilisation of Agricultural Wastes & Residues [9], [77], [138], [139],

[206], [207], [211]–[213],

[275], [276] ●● ●● ● ●●●

Forestry &

Wood Based

Industries

Forestry Expansion & Productivity [190]–[197] ●● ●●● ●●● ●●●

Wood Based Industry Productivity [203]–[205]

●● ●●● ●● ●●

Imports & Export of Forestry Product ●● ●●● ●● ●●

Biomass

Wastes &

Residues

Utilisation of Forestry Residues [138], [199], [259], [277],

[278] [138], [199], [259],

[277], [278] ●● ●● ● ●●●

Utilisation of Industry Residues [138], [203]–[205], [279],

[280] [138], [203]–[205],

[279], [280] ●● ●● ●● ●●●

Utilisation of Arboriculture Arising [138], [199], [204], [281] ●● ●● ●● ●●●

Waste Generation Forecasts [206], [215]–[218], [282]

●● ●● ● ●●

Waste Management Strategies ●● ●● ● ●●●

Biomass &

Energy Crop

Strategy

Land Dedicated for Crop Growth [8], [9], [77], [136], [137],

[139], [231]–[237]

● ●● ● ●●●

Biomass & Energy Crop Planting Strategies ●● ●● ●● ●●

●●● Future supply chain characteristics within the scenario reflect upper limits of forecasts within the literature*.

●● Future supply chain characteristics within the scenario reflect mid-range values forecasts within the literature*.

● Future supply chain characteristics within the scenario reflect lower limits of forecasts within the literature*.


207

6.2 UK Biomass Availability & Bioenergy

The following section presents and discusses the results of the scenario analyses, undertaken

within the UK BRM. This includes an analysis of UK land utilisation, biomass resource

availability, and bioenergy potential; from each of the scenarios over the analysis timeframe.

The key scenario analysis outputs and conclusions are then summarised and taken forward to

Chapter 10, where they are discussed in relation to the wider research findings.

6.2.1 Biomass Resource Scenarios – Land Utilisation Forecasts

The first analysis presented, documents how UK land is utilised within each of the developed

scenarios. This is reflected by Figure 6.2, where the stacked bar charts reflect the breakdown

of UK land between the different key land-use categories. The corresponding data from this

analysis is also listed within Appendix 8.0. Areas of land with characteristics unsuitable for

crop growth such as: rivers, mountains, coasts and lakes; are excluded from the analysis and

are not reflected within Figure 6.2.

The ‘Other Land’ category within Figure 6.2 represents the area of land that could potentially

be utilised for resource growth, but is currently un-utilised in that respect. The ‘Land

Dedicated for Biomass & Energy Crops’ category, reflects the area of land within each

scenario that has been specifically dedicated for growth of biomass, for the bioenergy sector.

The ‘Built-Up Land Area’ category reflects land that is developed; such as that utilised for

buildings, roads and infrastructure. ‘Agriculture Land’ represents the land area within each

scenario dedicated to both pastoral and arable food productivity. The ‘Forestry and

Woodland’ category reflects the area of both managed and unmanaged forests / woodlands,

within each scenario.

A. Scenario Analysis Results

Figure 6.2 allows the evaluation of how, shifts in land-use change occur within each of the

scenarios, over the analysis timeframe. Many of these trends are straightforward to explain;

however, a number of further emerging trends are worthy of further analysis.

As may be expected, the proportion of UK land dedicated for forests is shown to be greatest

within the Con-F scenario; although overall there is only marginal variation in the area of

forested land area between each of the scenarios. A similar level of variation is demonstrated

for the Con-F, Eco-F, and Ene-F scenarios, in terms of the land area dedicated to agriculture.

An interesting trend is shown within the Foo-F scenario, where the land dedicated for


208

agriculture is less than that of the other scenarios; despite the scenario’s priority focus being

the production of food. The developed area of UK land within each scenario is also shown to

be relatively even across the scenarios; albeit with notably lower levels of land dedication

within the Con-F scenario.

The area of land utilised for the production of biomass and energy crops shows variation

across the scenarios. As may be expected, the Ene-F scenario has the largest dedicated areas

of land for biomass resource production over the analysis timeframe. However, this is closely

followed by the land utilisation profile of the Foo-F scenario. The area of land dedicated for

resource growth within the Con-F, and Eco-F scenarios, are relatively well aligned.

The ‘Other or Un-utilised land’ as characterised by the analysis, also demonstrates varying

dynamics across the scenarios. Again, and as may be expected, the Con-F scenario has the

greatest extent of land within this category. The Foo-F scenario is second followed by the

Eco-F scenario; with the Ene-F scenario demonstrating the lowest level of un-utilised lands.

Figure 6.2: UK BRM Scenarios Analysis - UK Land Utilisation Profiles

58) Figure 6.2: UK BRM Scenarios Analys is - UK Land Utilisation Profiles

UK Land-Uses within the Biomass Resource Scenarios


209

B. Scenario Analysis Discussions

There are a series of obvious and interesting trends demonstrated within the land utilisation

analysis, for each scenario. The main focus in carrying out this evaluation is to provide a

greater depth of understanding of the dynamics within each scenario.

An example of a relatively obvious analysis outcome can be seen in the evaluation of the

extent that UK land is dedicated to forests, within each scenario. As maybe expected, the

Con-F scenario dedicates the largest area of land of all the scenarios to forests; reflecting the

scenario’s focus on forestry growth and lower limits of exploitation. The relatively small

range of variation between all the scenarios perhaps reflects a parameter limitation of the

BRM. The developed scenarios all utilise UK Forestry Commission forest system scenario

forecasts; thus the limits of forestry expansion within the UK BRM are reflective of the

Forestry Commission’s outlook.

The agricultural land utilisation profiles within each scenario, document some noteworthy

trends. Little variation exists between the agricultural land within the Ene-F, Con-F, and Eco-

F scenarios, but in the Foo-F scenarios where a greater extent of agricultural land may be

expected, the opposite trend is actually realised. Despite the focus of this scenario being to

enhance food productivity, this analysis highlights that the combined characteristics of this

scenario, result in less agricultural land being required to produce more food. This suggests

that a future pathway that focuses on enhancing the productivity of the land and agricultural

systems, in addition to increasing the efficiency of food systems through reducing wastes;

may have important feedback benefits for the land system.

A further straight forward observation is seen within the built-up land area analysis, where

the Con-F scenario is shown to have lower trends of developed land area compared to the

other scenarios. This can be directly linked back to the development of the scenarios, where

the Con-F scenario future pathway, assumes that lower levels of future development will take

place on land that could otherwise be useful to agricultural systems.

The area of available land utilised for the production of resources for the bioenergy sector, is

shown to be greatest within the Ene-F scenario; this dynamic reflecting the scenario’s aim to

maximise resource availability, including the higher limit utilisation of available land. The

interesting outcome from this section of the analysis comes from the Foo-F scenario, where a

greater proportion of land can be seen to be utilised for biomass resource and energy crop

growth, exceeding that within the Eco-F and Con-F scenarios. Despite this scenario’s focus


210

being the production of food, the Foo-F scenario is shown to be able to achieve this target

whilst also dedicating larger areas of land for biomass and energy crop growth for the

bioenergy sector. The root of this dynamic, once again can be linked back to the increased

productivity of the food systems, and the feedback benefit of more land being available for

alternative uses.

This dynamic is further highlighted within the ‘Other land’ category of the analysis, where

the Foo-F scenario again demonstrates comparatively large areas of land that are un-utilised

in terms of the analysis characteristics. Only the Con-F scenario is documented to have

greater land in this category, reflecting this scenario’s focus on reduced land for biomass

resource production, and natural resource utilisation.

6.2.2 Biomass Resource Scenarios – Resource Availability Forecasts

Figure 6.3 and the data within Appendix 8.0 document the forecast availability of each

specific biomass resource within each of the developed scenarios, to 2050. The stacked bar

charts within Figure 6.3, demonstrate the availability of different biomass resources over the

analysis timeframe. The stacked lines joining these bars provide segregation, highlighting the

different resources within each of the biomass categories (Table 4.1). The grouping of the

resources into their respective categories allows higher level analysis, and identification of

the changing trends to 2050.


A high level evaluation of Figure 6.3 demonstrates that biomass resource availability across

the scenarios reflect a wide range of dynamics and trends. This section sets out to highlight

these, so they can be discussed in depth.

The standout observation from Figure 6.3 is the large potential availability of resources

forecast within the Ene-F scenario, far exceeding that of the other scenarios. The Foo-F

scenario is forecast to have the second greatest abundance of resources, with the Eco-F and

Con-F scenarios trailing closely behind.

UK biomass resources within the Ene-F scenario is forecast to have upper near-term

(2015) availability of >46,000 Tonnes, increasing by 89% to >87,000 Tonnes in 2020,

to >168,000 Tonnes (266% overall increment) by 2030, and to >237,000 Tonnes

(416% overall increment by 2050).

UK biomass resources within the Foo-F scenario is forecast to have upper near-term



211

to >63,000 Tonnes (121% overall increment) by 2030, and to >112,000 Tonnes

(297% overall increment by 2050).

UK biomass resources within the Eco-F scenario is forecast to have upper near-term


to >54,000 Tonnes (98% overall increment) by 2030, and to >90,000 Tonnes (235%

overall increment by 2050).

UK biomass resources within the Con-F scenario is forecast to have upper near-term


to >40,000 Tonnes (87% overall increment) by 2030, and to >80,000 Tonnes (272%

overall increment by 2050).

The availability of agricultural residues, grown biomass and energy crops, and household

wastes, are again shown to be dominant across the scenarios; reflecting the results from

Chapter 5. A reverse to this trend is seen within the Con-F scenario, where the availability of

household and all other waste resources, can be seen to gradually decline over the analysis

timeframe. Resources directly available to the bioenergy sector from forestry systems are also

seen to be a notable contributor, within the Ene-F scenario.

Further evaluation of the grown biomass category resources highlights that there is only

marginal variation in the overall availability of these resources, across the scenarios. A

notable dynamic can be seen between the Foo-F and Ene-F scenarios where there is an

overall greater availability of grown resources within the Ene-F scenario; but the Foo-F

scenario can be seen to have a greater proportion of biomass and energy crop resources

available. Little variation in grown resource availability is shown between the Eco-F and

Con-F scenarios.

A further dynamic shown through closer evaluation of the residue biomass category

resources, is the relatively continuous and similar availability of these resources across the

scenarios. Marginally greater levels of agricultural residues are shown to be available within

the Foo-F and Ene-F scenarios; whilst marginally greater levels of industrial residues are

shown to be potentially available within both the Eco-F and Ene-F scenarios. The Con-F

scenario is shown to have marginally lower levels of residue resource availability, compared

to each of the other scenarios.

The availability of waste biomass category resources across the scenarios, demonstrates great

variances. The availability of wastes within the Ene-F scenario far exceeds that of any other


212

scenario. At the same time, the availability of waste resources within the Con-F scenario

progressively reduces over the analysis timeframe. Little variance is demonstrated between

the availability of waste resource forecast, within both the Foo-F and Eco-F scenarios.

59) Figure 6.3: UK BRM Scenarios Analys is – Biomass Resource Availabil ities

Figure 6.3: UK BRM Scenarios Analyses – Biomass Resource Availabilities


The range and extent of resources forecast to be potentially available within the Ene-F

scenario may be regarded within this analysis, as the upper limits of resource mobilisation

within the UK. This scenario provides an opportunity to highlight the maximum potential the

UK indigenous resources could provide for the UK bioenergy sector. However, the

indigenous resource potentials forecast within the other scenarios may represent future

pathways the UK could take, and may represent a more credible reality. Therefore, it is

important to not only understand what the upper limit potentials may be, but also to gain a

greater understanding of some of the factors differentiating the other three scenarios. Thus,

the discussions within this section aim to evaluate these key factors and influences.

Across the scenario analyses, a series of key resource categories were again identified as

demonstrating greatest potential availability; these reflecting those previously identified

within the UK Baseline Scenario (Chapter 5), being: household wastes, agricultural residues

and grown biomass, and energy crops. There were a few exceptions such as within the Con-F

scenario, where the scenario’s overarching focus to increasingly reduce, reuse, and recycle

Forecast Availability of Specific Resources for

the Biomass Resource Scenarios


213

wastes over the analysis timeframe, resulted in much reduced waste availability forecasts.

Another exception was found within the Ene-F scenario, where the forecast availability of

resources direct from forestry systems are shown to be significant; reflecting this scenarios

overarching focus to increase the productivity of forestry systems.

The analysis has highlighted that across all of the scenarios, the variation in the availability of

resources within the grown biomass category is only marginal. Notable levels of resources

available directly from forestry systems, are forecast within the Ene-F scenario which skews

the availability of grown biomass resource within the Ene-F scenario, upwards. If this

dynamic is overlooked, the overall scenario analyses highlight that the availability of grown

biomass resources and energy crops, reflect only marginal variances. This suggests that there

may be common influences across the scenarios, determining the availability of forestry

resources. As highlighted within the sensitivity analysis (Chapter 5), the utilisation of

available land may be the prime determining factor influencing the availability of these

resources. Differentiation in the extent of land utilisation is defined between the scenarios,

however this marginal variance may be a function of the UK land area itself; with further

variations in proportional utilisation of a limited land area, also equating to limited variance

in resource availability.

The further dynamic identified through closer evaluation of the grown biomass resources

category, is the greater availability of biomass resource and energy crops within the Foo-F

scenario, compared to that within the Ene-F scenario. This is unexpected, as one of the

overarching themes of the Ene-F scenario is the greater utilisation of available land. This

dynamic can again be explained by highlighting areas of positive feedback, resulting from

placing greater focus on food and agricultural systems. Enhancement of, and working

towards greater productivity of both land and agricultural systems, such as reflected within

the Foo-F scenario, also has benefits for resource production for the bioenergy sector. Despite

the Foo-F scenario dedicating a smaller proportion of land for resource growth (Figure 6.2)

compared to the Ene-F scenario; the Foo-F scenario is able to produce more resources from

the land utilised.

The standout result, when focusing on the residue resources analyses, is the continuous

availability of this resource. Both over the analysis timeframe, and when comparing

scenarios, the availability of residue resource is relatively constant; reflecting the key

conclusions from the previous sensitivity analysis sections.


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However, upon closer inspection of the residue resource analysis, trends and relationships do

emerge. Marginally greater levels of agricultural residues are forecast to be available within

both the Foo-F and Ene-F scenarios. This link can be explained within the Foo-F scenario due

to the dynamic that more food commodities are being produced – resulting in greater

opportunities for the availability of residues. Whilst within the Ene-F scenario, this dynamic

may be reflective of the upper limits of agricultural residue collection; harvesting and

utilisation.

A further less pronounced trend demonstrated within both the Eco-F and Ene-F scenarios, are

greater levels of industrial residues forecast as being available. These can be attributed to the

greater forest industry productivity embedded within the Eco-F scenario, and again the

greater utilisation of industrial residues within the Ene-F scenario. The reduced utilisation of

both forestry and agricultural residues within the Con-F scenario is a further dynamic that

sets this scenario apart from the others. It also further explains why overall resource

availability is forecast to be lowest within the Con-F scenario.

Within the scenario analyses, the potential availability of waste resources for the bioenergy

sector is a story of two strategies. The Ene-F scenario adopts a waste generation strategy that

reflects business-as-usual trends over the analysis timeframe, but adopts a waste management

strategy that strongly focuses on energy recovery. As such, the large potential of waste

resources for the bioenergy sector is clearly demonstrated within the Ene-F scenario. In

contrast, the Con-F scenario adopts a waste generation strategy that moves towards waste

reduction over the analysis timeframe, and a waste management strategy that focuses on the

reuse and recycling of waste resources. The available waste resources within the Con-F

scenario fall over the analysis period, reflecting a much less promising opportunity for the

bioenergy sector. This analysis reaffirms the conclusions developed within the previous

sensitivity analysis sections, once again stressing the importance of a waste strategy focus if

energy from wastes is targeted.

6.2.3 Biomass Resource Scenarios – Bioenergy Potential Forecasts

The analysis focusing on the bioenergy potential of the different scenarios is documented

within Figure 6.4, and supported by the data listed within Appendix 8.0. Figure 6.4 highlights

the bioenergy generation potential for each of the core scenarios, and also for the Ene-F sub-

scenarios; where the prioritisation of the different conversion pathways is explored. The

bioenergy potential forecasts are plotted for comparison against DECC’s (2010) [34] forecast

range for future UK primary energy demand. UK renewable energy and bioenergy


215

contribution targets [8], [357], [358], are also highlighted within Figure 6.4; represented by

the triangle and dash markers respectively.


Figure 6.4 highlights the broad range of bioenergy potential forecasts of the different

scenarios; these energy values representing the potential levels of bioenergy generated when

all of the resources quantified within Figure 6.4, are converted to bioenergy.

Comparisons of the bioenergy potential analysed for each of the core scenarios, demonstrates

that the Ene-F scenario is forecast to provide the greatest contribution of bioenergy; followed

by the Foo-F scenario, which is in turn followed by the Con-F and Eco-F scenarios.

Indigenous biomass resources within the Ene-F scenario are shown to be able to potentially

contribute as much as 20.8-32.6% towards the UK’s primary energy demand by 2050.

Resources within the Foo-F scenario potentially contributing 16.0-20.0% by 2050; and

between 12.3-19.3% and 11.9-18.6% potential bioenergy contributions from indigenous

resource; respectively for the Con-F and Eco-F scenarios.

Comparisons of the Ene-F sub-scenarios highlight that future pathways with focus on heat

conversion pathways, would generate the greatest bioenergy potential from the resources

available. The transport fuel focused sub-scenario, showing the least energy potential from

the resources available, and the power focused sub-scenario placed between these. The Ene-F

heat sub-scenario, demonstrates that UK indigenous resources could generate between 28.0-

43.9% of the UK’s primary energy demand by 2050. The Ene-F power sub-scenario showing

that between 20.8-32.6% of UK primary energy demand could be delivered; with the Ene-F

transport fuel scenario highlighting that 13.7-21.4% of demand could be met by 2050, from

UK indigenous biomass resources.

In summary, Figure 6.4 highlights that bioenergy generated from indigenous resources within

each of the developed scenarios, could potentially provide a highly significant contribution

towards the UK’s future renewable energy, and bioenergy targets. Bioenergy from these

scenarios are forecast to potentially contribute between 16.3-49.5% towards the UK’s 2020

renewable energy target, and 17.3-53.5% towards the 2030 target. Indigenous resources

within all of the scenarios, aside from the Eco-F and Con-F scenarios; are forecast to be able

to potentially contribute levels of bioenergy, above the UK’s 2050 bioenergy target.


216

60) Figure 6.4: UK BRM Scenario Ana lyses – F orecast Bioenergy Potentials & Energy Target Comparisons

Figure 6.4: UK BRM Scenario Analyses – Forecast Bioenergy Potentials & Energy Target

Comparisons


The scenario forecasts of bioenergy potential analyses, demonstrate that if the UK were to

follow these various pathways to mobilise biomass resources in reflection of any of the

developed scenarios; UK indigenous resource could potentially make a significant

contribution to meeting primary energy demand.

As perhaps expected, the Ene-F scenario demonstrates the highest bioenergy potential

forecast; this reflecting the upper limit of biomass resource mobilisation. The Ene-F core and

its related sub-scenarios, providing a potential ‘high level watermark’ of the contributions

that UK indigenous biomass resource could make to the UK energy mix. The main Ene-F

core scenario reflects the conversion of available biomass resource through a broad and

evenly balanced range of conversion pathways, to generate different forms of bioenergy. The

Ene-F sub-scenario forecasts provide an opportunity to analyse the different levels of

bioenergy potentials that may be generated, when different forms of bioenergy are prioritised.

Figure 6.4 clearly highlights that utilising UK indigenous biomass resource within the bio-

heat conversion pathway would generate the most energy, from the resources available.

This is an important analysis output that needs to be analysed further, and is therefore

identified as a key dynamic to be taken forward to the discussions within Chapter 10.

Forecast Bioenergy Potential of Resources

for the Biomass Resource Scenarios


217

A further important output from this analysis is the identification that a future pathway that

focuses on the food sector (Foo-F scenario), may also present great opportunities for the

bioenergy sector. Figure 6.4 also demonstrates that the bioenergy potential forecast generated

by the Foo-F scenario noticeably exceeds those of either the Eco-F, or Con-F scenarios. This

reinforces the perceived relationship that exists between food systems and opportunities for

the bioenergy sector. This analysis appears to demonstrate that there may be a stronger link

between the bioenergy sector and the development of food systems, to a far greater extent

than observed with scenarios promoting economic development, or a move towards increased

conservation practices.

The bioenergy potential forecasts from both the Eco-F and Con-F scenarios were found to be

highly aligned. Although the forecast bioenergy potentials from these scenarios don’t appear

as attractive as to those of the Ene-F and Foo-F scenarios; they highlight that there may still

be significant opportunities derived from the mobilisation of large levels of biomass resource

for the bioenergy sector; where wider focus of policies and strategies are directed towards

economic or conservation priorities.

6.2.4 Key Scenario Analyses Conclusions

The following section presents the key conclusions from the biomass resource scenario

analyses undertaken in Chapter 6. These are taken forward to Chapter 10, where discussions

focus on the potential avenues for the development of alternative bioenergy strategies that

could potentially optimise the generation of bioenergy from UK indigenous biomass

resource.

A. High Availability of Biomass Resource without Impacting Food and Industrial Systems

Each of the scenarios developed within this Chapter were designed to evaluate the potential

availability of indigenous resources within the UK, for the bioenergy sector; without

adversely impacting on food and industrial productivities. The scenario analyses found that

high levels of indigenous resources could be mobilised without impacting food systems;

including the growth of biomass resources and energy crops without an adverse effect on

food production; and even enhancing food production in the case of the Foo-F scenario. A

wide range of other biomass resources were also identified as providing potential

opportunities for the bioenergy sector without limiting the productivity of UK wood based

industries; and even enhancing the productivity of related industries within the Eco-F

scenario.


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B. Robust & Continuous Resource Availability from On-going UK Activities

Residue resources including: agricultural, forestry, industrial, and arboriculture arisings, were

again found to represent a continuous and robust resource that maintained a high availability,

regardless of the scenario, or time-interval within the analysis timeframe. Agricultural

residues, particularly both straws and slurries were identified as major potential opportunities

for the UK bioenergy sector due to their high abundance, availability robustness, and current

under-utilisation [67].

C. Large Potential from Waste Resources

The potential availability of future waste resources for utilisation within the UK bioenergy

sector was found to hinge on the nature of the focus of implemented waste strategies. Within

the Ene-F scenario where the adopted waste management strategy emphasised energy

recovery, the potential waste resource availability for the bioenergy sector, was shown to be

highly substantial by 2050. Likewise within the Con-F scenario, where a strategy of reduced

waste generation and resource recovery was adopted; the potential for the bioenergy sector

was much less attractive. The abundance of household, food, and organic waste streams, were

again documented as showing particular potential for the UK bioenergy sector.

D. Food Focus Positive Feedback Benefit

Throughout the scenario analyses, it was increasingly highlighted that a future pathway that

emphasised an increase in food productivity, and reduction of wastes from food systems, as

modelled within the Foo-F scenario; resulted in large potential feedback benefits for the

bioenergy sector. Increasing the productivity of the land not only resulted in increased food

security and self-sufficiency, but ultimately resulted in less land being required to produce

more food; potentially freeing up additional land for biomass resource and energy crop

growth.

E. Heat Conversion Pathways Providing Most Energy Efficient Use of Resources

The energy sub-scenario analyses focusing on different bioenergy conversion pathways,

highlighted that the prioritisation of heat bioenergy conversion pathways with suitable

resources; resulted in the greatest levels of bioenergy generation. This suggests that possibly

the best pathway for the UK to optimise the energy potential of its indigenous biomass

resources, may be to direct selected resources for the bio-refinery industries, with all

remaining suitable resources being dedicated for heat generation pathways. Generation of

renewable electricity, potentially being better achieved through alternative technology

pathways.


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Chapter 7 - The Future UK Energy & Bioenergy System


220

7.1 Current & Future UK Bioenergy Sector

Chapter 7 directs focus of the Thesis onto the UK bioenergy sector. The Chapter includes a

description of the present-day status of the UK bioenergy sector, and a discussion of the

possible future directions that the sector could take to 2050. The nature and direction of the

UK bioenergy sector being a key theme, and an important moving component within the

wider context of this research project.

In the previous Chapter, biomass resource scenarios were developed to evaluate different

pathways that the UK could take; analysing the potential availability of different UK biomass

resources within each scenario, and evaluating the bioenergy that could potentially be

generated from these resources. Chapter 7 continues the progress of the research, taking the

next step in asking, what types and how much biomass, will the UK bioenergy sector actually

require in the future? Chapter 7 links the conclusions from these discussions with those of the

scenario analyses from Chapter 6; highlighting approximately what the UK may be able to

produce / mobilise indigenously, and what may need to be imported from abroad in order to

balance demands.

Chapter 7 starts by highlighting the various UK Government reports and studies that can be

utilised to gain a greater understanding of the nature of the current UK bioenergy sector.

These also allow a greater understanding of how current UK strategies and policies are

steering the bioenergy sector, and provide an indication of the possible future state of the

sector, if these strategies and policies are implemented to fruition.

The Chapter then provides a discussion regarding the various industries that comprise the UK

bioenergy sector, and then moves on to provides an overview of the current status and

possible future development of the UK’s bio-heat, bio-power, and biofuel sectors. The

Chapter also focuses on the specific resources and feedstocks that each of these sectors may

require. Conclusions are then developed, forecasting the predominant biomass resource and

feedstock requirements of the UK bioenergy sector; for the near, mid and long terms, to 2050.

Chapter 7 then moves on to a section of further analysis, where a methodology is developed

to undertake ‘biomass resource balance analyses’. This compares the biomass resource

availability forecasts from each of the scenarios developed within Chapter 6, against the

perceived future resource requirements of the UK bioenergy sector. The conclusions from

these analyses highlighting the likely types, and the extent to which different resources may


221

have to be imported to the UK; for each potential scenario pathway applicable to the future

UK bioenergy sector.

7.1.1 The Current & Future UK Bioenergy Sector

The nature of the UK’s current bioenergy sector is reflected within the data of Tables 7.1 and

7.2. These document DECC’s Digest of United Kingdom Energy Statistic (DUKES) values,

for the UK bioenergy sector in 2012 [67]; including details of the bioenergy generated from

different resources (Table7.1), and the installed capacity and generating potential of

bioenergy sector plant (Table 7.2).

Table 7.1: Bioenergy Contribution of Resources within the UK Bioenergy Sector (2012) Table 33) Table 7.1: Bioenergy Contribut ion of Resources within t he UK Bioe nergy Sector (2012)

DECC Digest of UK Energy Statistics – Utilisation of Biomass Resources Bioenergy Potential (TWh)

Wood Wastes 3.52

Wood Resource 5.30

Animal Based Agricultural Residues & Wastes 5.15

Plant Based Biomass Resources 20.02

Sewage Waste Resources 3.58

Waste Resource 19.63

Liquid Biofuels 11.14


Table 7.2: UK Bioenergy Sector Installed Capacity & Generation (2012) Table 34) Table 7.2: UK Bioenergy Sector Instal led Capacity & Generation (2012)

DECC Digest of UK Energy Statistics – Bioenergy Capacity & Generation Capacity (MW) Generation(TWh)

Bioenergy from Sewage Sludge 199 0.72

Bioenergy from Bio-degradable Wastes 593 2.28

Bioenergy from Anaerobic Digestion 110 0.52

Bioenergy from Animal Based Biomass 111 0.64

Bioenergy from Plant Based Biomass 1203 4.10

Bioenergy Co-fired with Fossil Fuels 204 1.78


The UK has a series of strategies and policy documents that when reviewed, provide a

general framework for evaluating the directions that the UK bioenergy sector may take over

the near, mid and long-terms. The UK Renewable Energy Roadmap [366], [367], UK

Bioenergy Strategy [8], and associated analysis [9]; highlight the potential overarching

directions and nature of the future UK bioenergy sector. Further specific documentation such

as the UK Heating Strategy [58], Renewable Heat Incentive [368], Renewable Transport

Fuels Obligation [54], [254], [369], [370], and Biomass for Electricity [371], reports; provide

insights into the extent that bioenergy may contribute to the overall UK energy mix.

The underlying principles and targets that the UK has for its future bioenergy sector, can be

summarised within the following themes set out within the UK Bioenergy Strategy [8]:


222

Bioenergy is identified as an important part of the UK energy mix that is required if

the UK is to meet its carbon reduction, and renewable technology contribution targets.

Key focus is placed on bioenergy heat and power generation pathways, applying CHP

systems that efficiently utilise recoverable wastes.

Bioenergy heat generation pathways are targeted for high temperature industrial

processes. Also, to provide a transitional energy role for the heating of buildings, and

the use of recoverable waste heat from bio-power and industrial processes.

Target to increasingly utilise biofuels from sustainably sourced feedstocks to produce

biofuels for the transport sector.

Bioenergy technologies are to be utilised to decarbonise current coal plants, through

co-firing and full plant conversions.

Imported resources to the UK are expected to form the majority of that utilised.

Whilst indigenous resources are expected to form a cost effective, sustainable secure

resource base for the UK bioenergy sector.

Table 7.3: UK Renewable Energy Roadmap (2011) Near-Term Bioenergy Estimates by 2020 Table 35) Table 7.3: UK Renewable Energy Roadmap (2011) Near-Term Bioe nergy Estimates by 2020

UK Renewable Energy Roadmap - Bioenergy Pathways UK Bioenergy in 2020 (TWh)

(Central Range Estimate)

UK Biomass Resource Demand for

Energy in 2020 (Mt/yr) (Central Range Estimate)

Bio-power Generation Pathways 32-50 16-22

Bio-Heat Generation Pathways 36-50 5-10

Biofuel Pathways for Transport Up to 48 7-15


Table 7.3 documents the central range near-term estimates by DECC [367], for how much

energy the UK bioenergy sector could generate by 2020, and how much resource the sector

may require. The following sections provide more detailed discussions, describing the nature

of the current, forecast future, and specific resource requirements of the UK bioenergy sector.

7.1.2 The UK Bio-Heat Sector

This section describes the nature of the current UK bio-heat sector, also describing how the

sector may evolve if the current planned directions for UK bio-heat generation are realised. A

further discussion is provided describing the predominant resources and feedstocks utilised

by the current UK bio-heat sector; also highlighting how these may change in the future.

A. The Current UK Bio-Heat Sector

The contribution of bio-heat systems to the UK’s heating demand is currently small,

reflecting around 1% of total heating demand [8]. However DECC’s Heating Strategy [58]


223

confirms that the UK market for lower carbon heating is rapidly expanding, with bio-heat

pathways heavily contributing to this growth. The Strategy confirms the potential application

of bio-heat pathways for space-heating, industrial applications, within wider heat networks,

and to a limited extent; to displace natural gas in the grid with bio-methane.

Although the UK Bioenergy Strategy [8] confirms that within the wider context of

decarbonising the heat sector, bio-heat may only be able to contribute a marginal role; bio-

heat applications focusing on areas of the heat network that may otherwise be difficult to

decarbonise, such as within high temperature industrial processes.

B. The Future UK Bio-Heat Sector

However, from the UK’s current low base of bio-heat contribution, the introduction of the

Renewable Heat Incentive [368], has committed the Government to an ambition for 12% of

the Country’s heat to be sources from renewable pathways; with much of this being from

biomass [372]. This would equate to bio-heat contributing around 6% of the UK’s heat

energy demands by 2020 [8].

The UK Bioenergy Strategy [8], also lays out a number of key potential applications for

bioenergy heating that are deemed to be cost-effective, and low risk for the near and medium

term. They can be summarised as follows:

Large on-going role in replacing conventional fossil fuel based high temperature

industrial processes.

A near and medium term transitional role in decarbonising both domestic and non-

domestic heating demands. Particularly where alternative low carbon technologies are

not suitable.

A potential medium term role for biogas for space heating applications, and within the

longer term for high-temperature industrial process heating.

Increasing role in heating buildings through networks utilising waste heat from

biomass based power plants, and bioenergy driven industrial processes.

The long term future for UK bio-heat applications is expected to become increasingly

important with the further development and deployment of carbon capture and storage

technologies; especially within district heating and industrial applications [8]. An

increasingly greater role from bioenergy heating pathways is also expected, if higher levels of

biomass resources are available in the future [366].


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C. UK Bio-Heat Sector Feedstock and Resource Demands

Currently in the UK, both wood pellets and chips are the predominant feedstocks utilised

within bio-heat systems; the Forestry Commission [373], confirming that only one sixth of

pellets currently utilised within the UK are produced from UK forestry systems; the

remaining being imported. Pellets in the UK are used extensively in both the domestic, and

small and medium commercial sectors [374].

The Forestry Commission [373], also confirm that almost all wood chips currently utilised

within the UK bio-heat systems, are sourced from the UK; the chips predominantly composed

of wood from UK industry residues, forestry residues, arboriculture arisings, and residues

from other processes.

DUKES [67], also confirms that bio-heat generation is being undertaken directly or following

applicable pre-treatments, using the following resources: sewage sludge, wood fuel, animal

based wastes and residues, plant based residues, and biodegradable wastes.

Future potential growths of the UK’s bio-heat sector will likely lead to an increased demand

for imported wood pellets and chips, if current trends continue; and also increased demands

for feedstocks to meet the requirements of bioenergy conversion pathways to produce

biogases [136]–[138], [374].

7.1.3 The UK Bio-Power Sector

This section describes the nature of the current UK bio-power sector, also describing how the

sector may evolve if the current planned directions for UK bio-power are realised. Further

focus is also placed on discussing the role that biomass fulfils within UK’s co-firing

bioenergy pathways. The predominant resources and feedstocks utilised to generate direct

bio-power, and / or within co-firing pathways, are highlighted. Future potential resource

utilisation trends are also discussed.

A. The Current UK Dedicated Bio-Power Sector

In 2011, bio-power generation contributed less than 3% to the total electricity generated in

the UK [8]; although the relative cost-effectiveness of bio-power pathways, compared to

alternative renewable technologies, makes it an attractive option for increased contribution in

the UK, towards delivering renewable energy targets [9].

Currently a broad range of dedicated bio-power technologies are utilised in the UK, including

notable electricity generation contributions from: anaerobic digestion, direct combustion, and


225

combined heat and power pathways; bio-power pathways accounting for 37% of renewable

electricity generation [366].

Increased utilisation of biomass for power generation in the UK was the result of the

introduction of the Renewables Obligation (RO) policy in 2002. As highlighted, some

dedicated biomass plants have been commissioned, but the largest contribution of bio-power

has emerged through the conversion of existing UK coal plants, to co-firing with biomass

resources [372].

B. The Future UK Dedicated Bio-Power Sector

The UK Bioenergy Strategy [8], indicates that going forward, bio-power generation has an

important role to play; providing cost-effective transitional options towards meeting

renewable energy targets. Thus, as confirmed by analyses from the UK Renewable Energy

Road Map [366], highlighted by the Graph within Figure 7.1, there are a number of bio-

power projects currently operational and within the planning pipeline. The Graph within

Figure 7.1 documents the status of projects, as of the end of June 2013.

61) Figure 7.1: Capacity of Operational and Planned U K Bio-power Projects

Figure 7.1: Capacity of Operational and Planned UK Bio-power Projects

The UK’s current focus on bio-power generation is reflected within DECC’s forecast range

[8]; that bio-power generation pathways may deliver between 20-40 TWh of energy by 2020,

accounting for between 5-11% of total UK power generation.


226

However, beyond 2020, the analyses of the Bioenergy Strategy [8], [9], forecasts that the

cost- effectiveness of deploying bio-power generation pathways is likely to significantly fall.

This forecast trend resulting from the currently perceived need during this timeframe, to

tackle the ‘harder-to-decarbonise’ sectors, such as aviation and shipping. The nature of the

UK bio-power sector after 2020, being forecast to be limited to generation pathways that will

focus on contribution towards peak electricity, utilising wastes, CHP systems, and the

application of bio-methane in high-efficiency generation systems.

The Bioenergy Strategy [8], confirms that in the longer term up to 2050, the prospect of

utilising biomass for power generation should be treated with caution; unless effective linked

carbon capture and storage technologies emerge.

C. Current UK Bio-Power Sector Co-firing with Fossil Fuels

In 2010, coal generation in the UK provided 108 GWh of power, representing 28% of the

total power generated [67]. The utilisation of biomass co-fired with coal within existing

converted coal fired plants, is both a low-cost renewable energy, and a technology that is

reducing the carbon footprint of the UK energy system [8]. The combustion systems of

modern power plants allow the substitution of coal with biomass, up to approximately 5-10%

biomass contribution; without the need for significant mechanical changes [67].

DECC’s analysis carried out within the UK Bioenergy Strategy [8], [9], confirms that as

biomass displaces coal within the feedstock mix, the cost of each tonne of CO2 mitigated

within a co-firing power station, is significantly less compared to a dedicated bio-power

plant. Also, within the current sustainability criteria, the analysis also documents that the

conversion of existing coal plant, may lead to a carbon saving of at least 624 kg of CO2/MWh

of power generated. The capacity of bio-power co-firing plants in the UK in 2012 was 204

MW (Table 7.2) [67].

Initial policies that cleared the path for the emergence of bioenergy co-firing pathways in the

UK were aimed at being transitional technologies. The target was to encourage the

establishment of large-scale UK energy crop production, and the commissioning of a series of

dedicated bio-power plants. However, it has become increasingly clear that these intentions

are not feasible in practice - resulting from the lack of confidence in the long term value of

the technology pathway, given the uncertain lifespan of coal plants, and the perception of

there being insufficient UK biomass to satisfy a rapidly growing demand for suitable biomass

resource [372].


227

D. Future UK Bio-Power Sector Co-firing with Fossil Fuels

Whilst the deployment of dedicated bio-power plants may be less cost-effective in their

abatement of carbon emissions compared to co-fired converted plant; dedicated bio-power

plants will largely achieve higher bioenergy conversion efficiencies, and will have much

longer life spans than converted plants [8].

It is expected that in the 2020’s, over 20 GW of the UK’s existing coal and nuclear power

plants will close. This being the result of many of these power plants reaching the end of their

natural life-spans [372], but also significantly for the fossil fuel based plants, the added

economic pressures from the EU Emissions Trading System (EU ETS) [375], and the

Industrial Emissions Directive (IED) [376].

As such, bio-power co-firing is very much regarded as a short to mid-term option within the

UK that allows the rapid decarbonisation of the UK power sector, the extension of the

lifespan of generating assets, and the time to allow transition to alternative more sustainable

low carbon generation technologies [366].

E. UK Bio-Power Sector Feedstock and Resource Demands

A large number of reports and studies describe the biomass resources currently utilised within

the UK bio-power sector [67], [371], [377]–[379]. DUKES statistics [67], confirm that the

predominant feedstocks are sewage sludge, biodegradable wastes, and both plant and animal

based biomass resources. DECC collates databases describing the utilised feedstocks; for the

development of analyses in relation to the Renewable Obligation (RO) reporting. For the

period 2011-2012 [371], wood based feedstocks contributed 1.7 million odt of resource to

UK bioenergy pathways (52% UK sourced, 48% imported), and non-wood based feedstock

contributed 4.0 million odt of resource (70% UK sources, 30% imported).

Table 7.4: Feedstock Co-fired with Fossil Fuels in the UK Table 36) Table 7.4: Feedstoc k Co-fired w ith Fossil Fuels in the UK

Feedstocks Utilised within UK Bio-Power Co-firing Pathways Capacity (TWh)

Biomass & Energy Crops (SRC, Willow, Miscanthus) UK

Shea Residues (Meal & Pellets) Imported

Sunflower Pellets Imported

Sewage Sludge & Waste Derived Fuels UK

Cereal Crop Agricultural Residues & Pellets UK

Tallow UK

Olive Wastes (Residue & Expeller) Imported

Wood (Sawdust, Chips, Pellets) UK & Imported

Palm Residues (Palm Kernel Expeller, Shell, Pellets, Oil) Imported

Taken from [77], [380], [381]


228

Table 7.4 demonstrates the typical contribution of different feedstocks to co-firing pathways

in the UK. Also, highlighting the typical location from where these resources are sourced. As

described within the previous sections, power generation from co-firing pathways is likely to

experience large increases over the near and medium terms in the UK. Therefore, it is the

demand for the resources listed within Table 7.4 that will most likely reflect the sharpest

increases; with a large proportion of future resources having to be imported to balance

demand.

7.1.4 The UK Bio-Fuel Sector

This section describes the nature of the current UK bio-fuel sector, also describing how the

sector may evolve, if the current planned directions for UK bio-fuel contribution to the

transport sector are realised. A further discussion is provided, describing the predominant

resources and feedstocks utilised to currently produce biofuels in the UK; also highlighting

how these may change in the future.

A. The Current UK Bio-Fuels Sector

For as long as the UK utilises fossil fuels to power its transport sector, sustainable first

generation biofuels such as biodiesel, bio-ethanol, and bio-methane; may provide a cost-

effective contribution option for reducing the carbon emission of UK transport [8].

The Renewable Transport Fuel Obligation (RFTO) is the UK Government’s primary policy

for reducing the carbon emissions from the transport sector [369]. Biofuels are at the heart of

this policy, where in 2010 >1.5 million litres of biofuels were blended with conventional

fuels and utilised within the UK transport system; equivalent to 12.8 TWh of energy and

3.3% of total road fuel demand [8]. In 2010, biodiesel contributed 71% towards this total with

bio-ethanol providing the remaining. However, the composition of biofuels utilised within the

UK has since been shifting and currently reflect a near balance between biodiesel and bio-

ethanol [366].

B. The Future UK Bio-Fuels Sector

In the medium term, the UK Government believes that biofuels will provide an increasing

contribution towards balancing fuel demands. Building on the base developed by the RFTO,

the growth of advanced biofuels from lignocellulosic feedstocks such as woods and wastes;

are forecast to play an increasing role in reducing transport emissions through the 2020’s [8],

[366].


229

However, analysis carried out by DECC linked to the UK Bioenergy Strategy [8], [9],

concluded that the potential long-term contribution of biofuels within the transport sector, is

uncertain; the future role of biofuels likely linked to the availability of feedstocks, the

adherence to widening sustainability requirements, the costs of biofuels, and emerging

competition from other technologies such as ‘Ultra Low Emission Vehicles’.

Figure 7.2 presents a graph produced by DECC [8], which demonstrates the current long term

uncertainty of the contribution of biofuels in the UK. The Graph highlights the range of

biofuel contribution forecasts developed through DECC’s scenarios analysis. The presented

results demonstrate that the future availability of different feedstocks, and the success of

future bio-carbon-capture-storage (Bio-CCS) technologies; may be key drivers influencing

the future contribution of different biofuels for transportation categories.

62) Figure 7.2: Potential Delivered Energy from U se of Biomass in Transport

Figure 7.2: Potential Delivered Energy from Use of Biomass in Transport

Taken from [8]

DECC’s key conclusion from this particular analysis was: “the importance to keep options

open, neither picking winners nor abandoning the range of low carbon technologies” [8].

This is a stance from the Government that echoes through large areas of UK bioenergy policy

[382].

C. UK Bio-Fuel Sector Feedstock and Resource Demands

As discussed in the previous section, bio-diesel and bio-ethanol are the predominant biofuels

developed and utilised within the UK bioenergy sector. The specific biomass resources and

feedstocks utilised to produce these fuels within UK bio-refineries, are discussed within a

wide range of reports and studies [67], [370], [379], [383]. However, the most descriptive


230

statistics can be sourced from the UK Department for Transport; where large databases for

the RTFO are collated to account and verify the sustainability, and carbon intensity of

biofuels produced.

Table 7.5: UK Bioenergy Sector Utilisation of Feedstocks to Produce Biofuels Table 37) Table 7.5: UK Bioenergy Sector Utilisation of Feedstoc ks to Produce Biofuels

Biofuel Type Feedstock Resource Origin

UK Imported

Biodiesel

Oilseed Rape 20.0% 80.0%

Palm Oil 0.0% 100.0%

Soy 0.0% 100.0%

Tallow (category 1) 44.9% 55.1%

Tallow (category 3 or unknown) 100.0% 0.0%

Used Cooking oil 40.6% 59.4%

Bio-ethanol

Corn from the EC 0.0% 100.0%

Corn from outside the EC 0.0% 100.0%

Sugar Beet 57.9% 42.1%

Sugar Cane 0.0% 100.0%

Wheat 0.0% 100.0%

Biogas Municipal Organic Wastes 100.0% 0.0%

Bio-methanol Crude Glycerine 0.0% 100.0%

MTBE Crude Glycerine 0.0% 100.0%

Pure Vegetable oil Used cooking oil 100.0% 0.0%

Proportion of UK Feedstocks 17%

Proportion of Imported Feedstocks 83%


Table 7.5 highlights such data, representing the feedstocks utilised to produce biofuels over

RTFO Period 5 (15th April 2012 – 14th April 2013) [370]. This table highlights the typical

feedstocks utilised to produce each type of biofuel, and the proportion of these resources that

have been sourced either from the UK, or have been imported. The statistics for this RTFO

Period demonstrate the current heavy reliance on imported resources; a trend that is highly

likely to intensify as the demands for biofuels increase.

7.1.5 UK Biomass Resource Import Forecasts

The accompanying analyses of the 2009 UK Renewable Energy Strategy [7], optimistically

indicated that there could be sufficient biomass resource potential within the UK, to meet the

2020 bio-heat and bio-power demands; also noting albeit without quantification, that

imported biomass resources are likely to play a role within the future UK bioenergy sector.

Successive reports have since increasingly acknowledged the importance of biomass imports,

for meeting the demands of the UK bioenergy sector - given the limited availability of

suitable indigenous resources [384].


231

The UK’s increasing demands for biofuels over the long-term, and for feedstocks to co-fire

with coal over the short to mid-term; are identified as the key drivers for increased imports.

The UK having insufficient availability of suitable indigenous resources to balance the

increasing demands of these bioenergy pathways [8], [372], [385].

The graph demonstrated within Figure 7.3 is taken from the UK Bioenergy Strategy [8],

where DECC provides forecast ranges of the potential future domestic, and imported resource

utilisation by the UK bioenergy sector. The graph highlights the assumptions of the analysis.

These being: the potential range of domestic resource utilisation may plateau beyond 2030,

imported resources will be increasingly utilised in reflection of the current bioenergy

strategy’s pathway - peaking in the mid-term to 2030 before dropping, assumedly as

alternative low carbon technologies increase their contribution to the UK energy system.

63) Figure 7.3: Forecast Range of Domestic & Imported Biomass Resource Utilisation

Figure 7.3: Forecast Range of Domestic & Imported Biomass Resource Utilisation


However, developing appropriate scenarios that attempt to analyse future biomass import

scenarios is highly complex; due to the multi-level influences and uncertainties related to:

food, energy, competing resource market trends, global emissions policies, and ultimately

costs [103]. Important resource assessments are illustrative rather that robust forecasts of

future trends [384]. An assumption chosen by a number of studies [384], [386], is that 2% of

the total resource available on the global markets may be available for the UK. This 2% value

is chosen as it reflects the UK’s share of global primary energy consumption.


232

With this in mind, the only certainties are that if the pathways set out by the current UK

Bioenergy Strategy, mature [8]; the UK will require increasing levels of biomass resource

imports to balance its demands and in the future the UK will be competing within a

potentially volatile global market for resources.

Further potential impacts associated with imported resources include the potentially negative

embodied sustainability concerns ranging from: ecosystem and biodiversity damage, food

security issues for the indigenous rural poor, and concerns resulting from both direct and

indirect land-use change [372]. The future costs associated with ‘secure’ sustainability

certification have been estimated to range from 10-50% added premium [387], [388], which

only serves to add further potential uncertainties for the future bioenergy sector. These issues

are discussed further in Chapter 8 of this Thesis.

7.1.6 Trends and Conclusions of Future UK Bioenergy

Based on discussions within this Chapter, the key trends and conclusions describing the

nature of the potential future UK bioenergy sector are summarised within Table 7.6. The

trends are highlighted, describing how the different forms of the bioenergy sector may change

in the near, medium, and long terms. The predominant types of resources required to meet the

demands of the future bioenergy sector based on current views, are also shown. These

conclusions form the basis for comparisons within the resource balance analysis undertaken

in the following sections.


233

Table 7.6: Future Bioenergy Sector Trends and Key Resource Demands Table 38) Table 7.6: Fut ure Bioenergy Sector Trends and Key Resource Demands

UK Bioenergy Sector 2010 2020 2030 2040 2050

Near-Term Mid-Term Long-Term

Bio-Heat

Sector

Trends

Gradual increase in resource demand for bio-heat

generation pathways. Reflecting both increased transitional and specialist roles targeted for bio-

heat.

Gradual decline in resource demand for bio-heat

generation pathways. Reflecting the targeted

focus on emerging alternative low carbon heat technologies in the long term. Bio-heat continuing

within specialist roles such as industry

applications.

Predominant

Resource

Demands

Wood based resources

(pellets & chip)

Wood based resources (pellets & chips)

Feedstocks for advanced bioenergy technologies

Bio-Power

Sector

Trends

Sharp increase in resource demand for the bio-power sector, driven by the increased and further

conversion of conventional power plants to allow

co-firing with biomass.

Gradual decline in resource demand for bio-power

sector, as co-firing plants are expected to

gradually close. Continuation of resource demand for bio-power applications, contributing to

balance peak-energy demands.

Predominant

Resource

Demands

Solid biomass resources (wood, animal,

plant, wastes)

Solid biomass resources (wood, animal,

plant, wastes)

Biofuel

Sector

Trends

Sharp increase in resource demand for biofuel production; as biofuels increasingly contribute

towards the decarbonisation of the fuel-based

transport system.

High uncertainty is currently forecast for the long-term biofuel sector, due to the potential

emergence of alternative low-carbon transport

technologies during this period.

Predominant

Resource

Demands Energy crops

Energy crops

Lignocellulosic resources


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7.2 Biomass Resource Balance Analysis

The second part of Chapter 7 introduces the resource balance analysis undertaken within the

research project. This reflects a comparison of the types and extent of resources, forecast to

be required by the future UK bioenergy sector; with the availability of UK indigenous

resources previously forecast within the biomass resource scenarios analysis of Chapter 6.

The main conclusions from this analysis are the evaluation of the extent, and types of

resources that the UK may have to import to balance future demands.

This section progresses with an introduction to the methodologies developed, followed by the

results and discussions, before drawing conclusions for the consequences for the future UK

bioenergy sector.

7.2.1 Resource Balance Analysis Methodology

The methodology for undertaking the resource balance analysis within the research represents

a straightforward comparison of forecasts of future demands, against the potential availability

of resources forecast within scenarios analysis (Chapter 6).

As summarised by Table 7.6, the future demands of the UK bioenergy sector are likely to

vary greatly in both the type, and extent of resources required to 2050; the longer-term

demand forecasts over this timeframe reflecting many uncertainties. Therefore, the resource

balance analysis focuses on the predominant resource requirements of the UK bioenergy

sector, to the medium-term of the analysis timeframe (2020-2030).

The summaries within Table 7.6 conclude that the predominant resource demands of the UK

bio-power and bio-heat sectors, to this medium-term time period; are likely to be typically

wood-based feedstocks for the respective power and heat generation technologies. Whilst the

resource demands for the UK biofuel sector to this medium term, are forecast to be

predominantly energy crops, and later, lignocellulosic resources. Future forecast demands for

each of these resource groups are discussed below.

A. Forecasting the Feedstock Demands of the Future UK Bio-Power and Bio-Heat Sectors

The UK’s future feedstock and resource demands for its bio-power and bio-heat sectors, have

been analysed and forecast by many reports and studies [8], [136]–[138], [372]. The

predominant resource types required to service these sectors are wood-based biomass

resources. These resources are also extensively utilised by a wide range of UK industries that

are competing, or will compete with the UK bioenergy sector.


235

Therefore, the resource balance analysis opts to utilise the future UK wood-fibre demand

forecasts, as developed by the ‘Confederation of Forest Industries’ (ConFor), ‘UK Forest

Products Association’ (UKFPA), and the ‘Wood Panel Industries Federation’ (WPIF); in

close collaboration with the UK Forestry Commission [204], [205]. These resource demand

forecasts span to 2025, taking consideration of: the wood-fibre requirements for all UK

wood-based industries, the growing demand for biomass co-firing plants, and the resource

requirements of dedicated bioenergy technologies. The forecasts of this assessment are also

validated through their relative alignment with studies making similar assessments; produced

for DECC [136]–[138].

B. Forecasting the Feedstock Demands of the Future UK Biofuel Sector

There are likewise, many studies and developed scenarios that analyse how the UK’s demand

for biofuels and the feedstocks required to produce biofuels; may change in the future. The

UK Energy Research Centre Report, ‘The UK Energy System in 2050’ [389], evaluates and

compares scenarios developed in a range of key studies; that in part analyse the future

potential directions that the UK transport sector could take, and relevantly, the extent that

biofuels may be utilised. The forecasts of the studies evaluated within this Report [389], form

the baseline for determining the UK’s future biofuel demands within the biomass resource

balance analyse. The scenarios and studies are:

i. UK Energy Research Centre (UKERC) Scenarios

Reports [252], [389]–[391], produced by UKERC; have developed a series of ‘Energy 2050

Scenarios’ over two phases. These reflect a broad range of forecasts for biofuels utilisation by

the future UK transport sector. These scenarios evaluate different pathways with: varying

ranges of carbon reduction targets to 2050 (40-90% from baseline levels), the implementation

of different technologies, the near-term response and implementation of carbon reduction

actions, and different rates of carbon reduction actions over the timeframe. The range of

biofuel utilisation forecasts from the UKERC scenarios, are reflected within the data of Table

7.7.

ii. The UK Committee on Climate Change (CCC) Scenarios

The CCC’s ‘Fourth Carbon Budget Report’ [392], develops a series of scenarios that

represent future biofuel demand forecasts that take a cautionary approach to their wider

utilisation. Within this Report [392], the CCC forecast lower-end biofuel utilisation levels

compared to forecasts produced by other studies; this reflecting the on-going sustainability

concerns relating to land-use stresses, and other wider impacts associated with biofuel


236

feedstock production. Also, forecasting increased efficiency of successive vehicle vintages,

and wide-spread electrification or increased biofuel utilisation. The high degree of

uncertainty with longer-term biofuel use is also reflected, as summarised within Table 7.7.

iii. AEA Technology Scenarios Developed for DECC

The AEA ‘Pathways to 2050 Report’ [393], [394], produced for DECC, also develops

scenarios that forecast the UK’s future utilisation of biofuels. Within the AEA scenarios, the

greater utilisation and substitution of conventional fuels with biofuels is forecast to take

place; predominantly from 2035 onwards. The diversity of the technology mix contributing to

balancing UK transport demands through to 2050, reflecting a large step away from the

present contributions. This high-level uptake of biofuels taking place later in the timeframe to

2050, is reflected within the data presented in Table 7.7.

Table 7.7: Scenarios Mid and Long-Term Forecasts of UK Biofuels Demand Table 39) Table 7.7: Scenario s Mid and Long-Term Forecasts of UK Biofuels Demand

Scenarios 2030 Forecast Range of UK

Bioenergy Demand (TWh)

2050 Forecast Range of UK

Bioenergy Demand (TWh)

UK Energy Research Centre (UKERC) Scenarios 21.4 - 94.7 162.2 - 288.8

The UK Committee on Climate Change (CCC) Scenarios 10.0 - 13.6 2.2 - 3.6

AEA Technology Scenarios Developed for DECC 10.3 - 13.6 68.3 - 69.4

Forecasts Data Taken from [252], [389]–[394]

C. Analysing the Suitability of Available Resources Forecast within the Biomass Resource

Scenarios

Having developed a framework for analysing the potential demands of the UK bioenergy

sector, the next step is to identify the extents that the resources forecast, as being available

within the biomass resource scenario; may be suitable to balance these future demands. This

analysis ultimately allowing an assessment of the types and extent to which, different

resources may need to be imported to the UK.

Table 7.8 provides a summary of the specific resources from the UK BRM’s analysis that are

identified as being suitable to meet the predominant resource / feedstock demands of the

future UK bioenergy sector. Table 4.1 earlier in the Thesis, provides further details of the

specific biomass resources within each of the categories listed.

Table 7.8: UK BRM Resources Compatible with Future UK Bioenergy Sector Demands Table 40) Table 7.8: UK BRM Resources Compatible with F uture UK Bioenergy Sector Demands

UK Bio-Power Sector Demands UK Bio-Heat Sector Demands UK Biofuel Sector Demands

Grasses Short Rotation Coppices

Short Rotation Forestry

Other Forestry Direct Forestry Production

Forestry Residues

Arboriculture Residues Industrial Residues

Grasses Short Rotation Coppices

Short Rotation Forestry

Other Forestry Direct Forestry Production

Forestry Residues

Arboriculture Residues Industrial Residues

Grasses

Cereal Crops

Oil Crops Sugar Crops



237

7.2.2 Resource Balance Analysis Results

This section presents the results of the resource balance analysis in the form of Figures 7.4

and 7.5, supported by corresponding data within Appendix 9.0. Figure 7.4 demonstrates a

comparison of the UK’s forecast demands for wood-fibre resources to 2025, against the

availability of suitable UK indigenous resources to meet this demand; as forecast by each of

the UK BRM analysis scenarios. Figure 7.5 provides a snapshot of the UK’s biofuel energy

demands for the transport sector in 2030, according to the discussed scenario forecasts. These

are also compared to the forecast energy potentials of the biofuels potentially produced from

UK indigenous feedstocks; for each of the UK BRM analysis scenarios.

A. Resource Balance Analysis – UK Wood-Fibre Demand

Figure 7.4 demonstrates the analysis comparing UK wood-fibre demand, indigenous

availability, and the deficits. The black analysis line reflects the forecast wood-fibre resource

demand of UK industries and the UK bioenergy sector, to 2025. The coloured lines of Figure

7.5, document the forecast availability of suitable UK indigenous resources for each of the

UK BRM analysis scenarios. The area shaded below the demand line to each of the scenario

lines; representing the forecast UK deficit of suitable resources. This deficit having to be

balanced by resources imported to the UK.

64) Figure 7.4: Analysis of UK Indigenous Wood Fibre Resource Availability, F uture De mand & Deficits to 2025

Figure 7.4: Analysis of UK Indigenous Wood-Fibre Resource Availability, Future Demand

& Deficits to 2025

Forecast UK Bioenergy Sector & Industry Resource Demands, and

Indigenous Availability within the Biomass Resource Scenarios


238

B. Resource Balance Analysis – UK Biofuels Demand

Figure 7.5 demonstrates the analysis comparing forecasts of UK biofuel energy demand in

2030, to the potential biofuel energy that could be produced from UK indigenous feedstocks

forecast by the UK BRM analysis scenarios. Each of the grey bars within Figure 7.5 reflects

the range of biofuels energy demand forecasts; as developed by UKERC, CCC and AEA. The

coloured lines within Figure 7.5 represent the biofuels energy from UK indigenous

feedstocks, as forecasts within each UK BRM analysis scenario. The space above or below

these lines to the demand bars, represents the potential deficit or surplus of UK indigenous

resources required to balance demand, if any of the corresponding scenarios are to be

realised.

65) Figure 7.5: Analysis of UK Indigenous Biofuel Energy, F uture De mand & Deficits in 2030

Figure 7.5: Analysis of UK Indigenous Biofuel Energy, Future Demand & Deficits in 2030 7.2.3 Resource Balance Analysis Discussions

The high level conclusions highlighted by both Figures 7.4 and 7.5, and the corresponding

data in Appendix 9.0, is that if the UK bioenergy sector continues in the direction it is headed

in and current plans mature; the UK will likely have a significant biomass resource deficit to

balance demands.

The resource balance analysis focusing on future UK wood-fibre demand, demonstrated a

sharp near-term increase in resource demand, reflecting the UK’s predicted continuation of

focus on biomass co-firing pathways, and the large resource demands associated with these.

UK Biofuel Energy Demand Forecasts and Forecast

Availability from Indigenous Resources within the

Biomass Resource Scenarios (2030)


239

The demand for wood-fibre resources is shown to plateau through the 2020’s in Figure 7.4,

reflecting the expected ‘end to fossil fuel plant conversions’ leading to the gradual closures of

co-firing plants.

The impact of the UK’s pathway towards increased co-firing generation, on the UK biomass

supply chains, can be seen through focusing on the Ene-F scenario. This scenario represents

the research’s upper-limits view of UK indigenous biomass resource availability; in terms of

growth and mobilisation of resource for the bioenergy sector. Figure 7.4 demonstrates that for

much of the analysis timeframe to 2025, suitable UK resources within the Ene-F scenario,

even at their maximum levels, appear to meet less than half of the total wood-fibre resource-

demand. If considering that the Ene-F scenario may be the least likely of the scenarios to be

realised; the resource deficits will probably be more accurately reflected by that represented

with the other three scenarios. The resource deficit is thus likely to be considerable.

Similar conclusions can be drawn through careful evaluation of the resource balance analysis

which focuses on future UK biofuels demand. This analysis provided a snapshot of how the

biofuels resource balance may look in 2030, according to a series of different scenarios.

Analysis of the UKERC scenarios, which looked at various scenarios for future utilisation of

biofuels in the UK, resoundingly highlights the potential deficit for suitable UK indigenous

resource availability. The forecast range in biofuel demands largely exceeds the potential of

indigenous resources; according to the UK BRM analysis scenarios. The rapid utilisation of

biofuels to 2050, forecast by the UKERC scenarios (Table 7.7), highlights that the resource-

deficit trend would most likely intensify only if the corresponding scenarios were to be

realised.

The CCC scenarios, which took a cautious lower-limit stance towards future biofuel

utilisation in the UK, also forecast a high likelihood that in the medium-term to 2050, the UK

would most likely have a resource deficit, if it is to produce sufficient biofuels to balance

future demands. The CCC scenario forecasts to 2050 (Table 7.7), documented a gradual

decline in biofuels utilisation, suggesting that if any of the CCC scenarios were to be realised,

the UK may have a greater availability of resources in the long-term that could be distributed

for alternative uses.

The AEA scenarios for DECC, reflected medium and long-term increases in biofuel demand

to 2050, with rapid utilisation taking place beyond 2030. These scenarios present a slow and

gradual incremental utilisation of biofuels, to 2030. Despite this slow-start trend, Figure 7.5


240

still predicts that the UK may likely have insufficient suitable resources to balance demands;

leading to resource deficits and the need for feedstock imports.

The biomass resource balance analysis focusing on biofuels, once again highlights the

uncertainty of the extent to which biofuels will contribute to the future UK transport energy

sector. However, the analysis shows with some certainty, that it is highly likely whichever

scenario is realised; the UK may face potentially large deficits of suitable feedstocks to

produce the biofuels, needed to balance future demand.

The biomass resource balance analysis presents a juxtaposition of the key conclusions arrived

at in Chapter 6. Those conclusions postulated that the biomass resource availability and

bioenergy potential projected from each scenario, especially the Ene-F scenario; were capable

of contributing significantly to the UK’s renewable energy targets and primary energy

demand.

7.2.4 Chapter Conclusions & Consequences for the Future UK Bioenergy

Sector

Chapter 7 provides a greater understanding of the several future pathways the UK bioenergy

sector may follow; as directed by current biomass resource strategies and policies. It also

provides an analysis of the types of resources required, and the extent to which the UK may

need to import biomass resource and feedstocks, in order to balance future demand.

The following key analysis outputs and conclusions are deemed to be important and highly

relevant to the wider research project. These concepts and themes will be taken forward to

Chapter 10, where they will contribute to the discussion of possible future alternative UK

bioenergy strategies.

A. Predominant Resource Types Required by the Future UK Bioenergy Sector

The UK’s current and planned bioenergy sector directions focus predominantly on power

bioenergy pathways, and like many countries the UK will require mainly woody biomass

(primarily wood pellets and to a lesser extent wood chips) for this energy generation

pathway. Biofuels (biodiesel and bio-ethanol) and suitable feedstocks to produce them are

also widely sought, for current and future transport sector developments [395].

B. Uncertainties with the UK Bioenergy Sector Future Directions

Chapter 7 has highlighted many uncertainties relating to the future of the UK bioenergy

sector. The wide range of characteristic variances found within the demand scenarios

analysed within this Chapter; highlights the limited consensus shared amongst ‘UK bioenergy


241

experts’. The UK Government Strategy and Policy documents reviewed in the early stages of

this Chapter, also acknowledge this lack of clarity. These uncertainties will have to be

resolved if the UK bioenergy sector is to continue to develop and to meet bioenergy demands

and satisfy key carbon emission targets. This is something that will be addressed further

within Chapter 10.

C. Large Indigenous Resource Deficits to Balance Future Demand

Overall the key output from this Chapter is that the UK has high potential availability of

specific indigenous biomass resources, capable of making a significant contribution towards

meeting energy targets (Chapter 6). However, large potential resource deficits are forecast for

the specific types of biomass that will be needed for the proposed future UK bioenergy

industry.

D. UK Biomass Resource Trade Requirements

The forecasts highlighted in this Chapter lead to the conclusion that the UK will very likely

have to trade extensively within global markets for the biomass resources it needs to balance

future energy demand. For the currently held bioenergy strategies to mature, the UK could

become increasingly dependent on imported resources. The international markets for biomass

resources are still relatively immature and therefore add yet another future uncertainty [136],

[137]. Issues of trade uncertainties will be covered in Chapters 8 and 9 of this Thesis.


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Chapter 8 - Global Biomass Trade - Supply, Demand, Limitations & Sustainability


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8.1 Global Biomass Trade – Supply,

Demand, Limitations & Sustainability

Chapter 8 introduces and provides a description of the global biomass trade market. The

Chapter is literature review driven, discussing the key biomass resource supply and demand

regions around the world, and also provides an overview of the major flows of biomass

resource currently taking place. The Chapter also discusses how these flows may change as

the global trade markets mature. The Chapter goes on to provide discussions of the key

limitations and barriers that may limit increased global biomass trade, and also the

sustainability issues and implications, associated with the expansion of biomass resource

productivity to serve global markets.

Chapter 8 is well placed within the Thesis, providing an insight into the current and future

biomass trade markets, in which the UK will have to operate, in order to balance its

increasing resource demands. The Chapter also discusses those countries that may offer

potential trading opportunities for biomass resource, and highlights some possible limitations,

risks, and sustainability implications for these trade flows. Chapter 8 provides specific

sections that focus on the UK’s requirements, and its position within the global market. A

section also highlights the position of Brazil in the global market. Brazil provides the case-

study region under focus within Chapter 9; representing an example of a key country,

possibly capable of providing large amounts of resource for future global markets, including

the UK.

8.1.1 Increasing Global Demand for Biomass

Energy from biomass pathways currently contributes approximately 10% of total global

energy supply. Two thirds of this bioenergy is utilised within developing countries, the

remaining being generated within the industrialised world [10]. Bioenergy is an attractive

energy option for all stages of industrial development, due to its flexibility, potential for

integration with world-wide development strategies [259], and the general acceptance that

greenhouse gas emissions from biomass resources are carbon-neutral [13].

The IEA / IRENA Global Renewable Energy Policies and Measures Database [396],

confirms that more than 60 countries currently have national targets or policies supporting

renewable energy. In countries such as the EU Member States, biomass is expected to play a

major role in contributing over 50% towards their renewable energy targets [397], [398].


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Despite widespread growing global demand, biomass is unevenly distributed; with some

regions with the greatest demands, having comparatively low resource availability potentials

[106], [131], [251], [377]. Trade has an important role to play, with biomass being described

as, “The most important renewable energy carrier worldwide” [124]. Short-term trends

appear to show that countries and regions with strong economies and development will

increase their requirement for imported biomass resource; whilst less developed countries

will continue their development largely reliant on fossil fuels [399].

As a result of recent energy policies, Europe has become the prime market for the trade of

biomass for energy [400]. More than 30% of biomass resource currently consumed in the EU

is imported [401], and demand is forecast to rise by almost 50% between 2010 and 2020

[402]. Demands for biofuels are expected to rise sharply, driven by Europe’s ambitious

biofuel mandates; whilst the demand for wood pellets are forecast to increase three-fold by

2020, as governments offer renewable energy subsides [258].

In summary, developed countries and regions such as the EU, are set on a trajectory of

increased utilisation of bioenergy pathways, to meet energy demands. As their renewable

energy policies are implemented and targets met, ever greater quantities of biomass resource

will probably have to be imported to generate this energy.

8.1.2 Global Biomass Trade Markets

The key forces driving the global trade in biomass resources for the bioenergy sector are: the

price of fossil fuels (especially oil), the implementation of policy mandates aimed at reducing

GHG emissions, and increasing policy and financial mechanisms supporting bioenergy

pathways [17].

Figure 8.1, highlights the major trade flows of biomass for bioenergy, around the world.

Brazil is the major exporter of bio-ethanol; predominately to the EU, the United States and

Japan [403]. The United States, Argentina, Indonesia, and Malaysia, are the largest exporters

of biodiesel; mainly to the EU [403]. The major exporters of wood pellets are Canada, the

United States, Russia and the Balkan States; with Europe once again being the largest

importing region [404]–[406].


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66) Figure 8.1: Predominant Global Trade F lows of Biomass Resources for Energy End Uses

Figure 8.1: Predominant Global Trade Flows of Biomass Resources for Energy End Uses

A. Focus on the UK & Biomass Trade

The UK Department for Transport reported that around 25% of feedstocks purchased for

current UK bioenergy plants, were indigenous resources; the remaining being imported from

the EU, North America, Russia, South Africa, New Zealand (predominantly wood pellets);

Brazil (biofuels), and Malaysia and Indonesia (palm oil) [254].

Important drivers influencing the demand levels of imported resources are: insufficient

indigenous resources to balance demand (Chapter 7), and expensive domestic resources that

are already widely utilised by a series of existing demand markets [407].

UK resource exports for energy end-uses are highly limited. They are restricted

predominantly to residue resource categories and are usually kept within the frameworks of

large multinational companies; this trade therefore largely representing a redistribution of

resources rather than stand-alone trades. This explains the data secrecy and why it is hard to

obtain an accurate picture of these on-going intra-organisation trades [407].

B. Focus on Brazil & Biomass Trade

The 1973 oil crisis and the recession that followed to 1975 saw oil prices soar, hindering

economies all around the World. This triggered Brazil to initiate its Ethanol Program which

has evolved to the present day, where almost all road transport is powered by fuel containing

between 25-100% ethanol [408].

The Brazilian Government have also developed a series of renewable energy policies [132]–

[134] that target increased energy contributions from a broad range of low carbon

technologies; with Brazilian States such as Sao Paulo going even further with such targets

Key Global Biomass

Resource Trade Flows


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[409]. These renewable energy policies require extensive use of indigenous biomass

resources, making Brazil a country whose infrastructure and focus are heavily directed

towards bioenergy [410].

i. Bio-ethanol Production & Trade Status

Globally, Brazil is the second largest producer of bio-ethanol, behind the United States.

However, Brazil is also the largest exporter, driven by a Brazilian bio-ethanol industry that

has lower production costs than the United States. It also has the potential to significantly

enlarge its already vast production of feedstock crops such as sugarcane - the predominant

feedstock for the bio-ethanol industry [407]. Thus Brazil is highly important for the global

biofuel market.

Traditionally in the most favourable production years, Brazilian exports of bio-ethanol have

never represented more than 15% of total production, which highlights the importance of the

domestic market. Investments have, and are continuing to be focused on developing the

export market; although perspectives continue to predict only gradual growth over the near-

term [407].

To satisfy the large domestic market and growth in export demands, sugarcane production

rates have reflected average annual increases of 9.7% in recent years; this trend resulting

from continual improvements in productivity, high domestic and international demands for

sugar, and the continuing focus on the export market that drives a continual flow of

investment [395].

Brazil is well placed to continue this production trend as it has vast areas of arable savannah

that could potentially be utilised to produce crops, without risk of deforestation [410].

Brazil’s Ministry of Agriculture, Livestock & Food Supply [411]; estimates that the potential

scope for crop expansion exceeds 119 MHa; through the cultivation of savannah (69 MHa),

and the conversion of pasturelands (50 MHa).

ii. Biodiesel Production & Trade Status

Brazil is a large consumer of biodiesel which is overwhelmingly imported; there being little

or no data describing Brazilian biodiesel exports, so this is taken to be negligible [407], [412].

iii. Wood Biomass Production & Trade Status

Brazil has a large, albeit predominately domestic market for wood biomass resource. This is

utilised largely for charcoal and wood briquette production [413]. There is only meagre and


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highly dispersed information documenting wood biomass resource exports; aside from small

quantities of pellets [407].

However, the Brazilian market is considered by some, especially within the EU; to be a

highly promising future trading-partner for wood biomass. Brazil’s proximity, established

trade routes for biofuel-based resources, and the large relatively undeveloped wood-based

market, driving this enthusiasm; although questions still remain as to the extent that wood-

based biomass exports from Brazil to the EU, could meet the EU’s stringent sustainability

requirements [16].

There are a growing number of Brazilian companies developing information about the

European markets, with a view to establishing wood pellet trade links. This is widely

regarded as being a good opportunity for Brazil, but remains in an early phase and far from

widespread deployment [407].

The Brazilian Association of Industry Biomass & Renewable Energy ABIB [412], confirmed

that Brazil currently has 10 plants producing wood pellets, predominantly using pine and

eucalyptus residues as feedstocks. These have a reported capacity of about 320,000 tonnes

per year [412]. More pellet plants are currently under development which will increase pellet

production to over 3 million tonnes by the end of 2014; with a series of further plants

following by 2018 / 19. The realisation of these plans will place Brazil as a major future

producer and exporter of pellets; the majority expected to go to Europe [414].

8.1.3 Biomass Resources Key Global Trade Flows

This next section takes a broader approach, describing the major flows of different biomass

resources around the World; focusing on the trade of biofuel and wood-based biomass

resources.

A. Bio-ethanol – Key Global Trade Flows

As already highlighted, there are two leading producers of ethanol that dominate the global

market; the United States and Brazil. These account for over 85% of the world market [407].

Brazil is the largest exporting country with the United States and the EU being the greatest

importers, followed by Canada and Japan [17].

The UK and Sweden are amongst the largest importers of ethanol in the EU, of which 32% in

2009 was estimated to be used to power the transport sector [17]; Brazil being the

predominant supplier of bio-ethanol to the EU [16]


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In 2009 it was estimated that between 40 and 51 PJ of ethanol fuels were traded globally

[403]. The uncertainty range of this data, relates to the imprecise accounting methods for

ethanol end-uses. For example: uses for transport fuels, industrial processes, or by beverage

industries; also the improper utilisation of trade-codes and categories for both ethanol

biofuels and the producing feedstocks. All this adds to the uncertainty [407].

B. Biodiesel – Key Global Trade Flows

The global production of biodiesel has been increasing strongly since 2005 [17]. This trend is

reflected in the rise of global production from 20 PJ in 2000 to around 565 PJ in 2009 [403].

Driven by the European biofuels mandates [258], the EU has the World’s most developed

biodiesel industry, producing two thirds of global productivity [17]. Germany, France, Spain,

and Italy, being the leading producers [415]; with Rapeseed oil produced within the EU being

the major feedstock providing two thirds of total production. Imported feedstocks such as

Soybean oil, Palm oil, and to a lesser extent further Rapeseed oil; representing the final third

[416].

Other major biodiesel producers include the United States, Argentina, and Brazil. With more

than 95% of global biodiesel exports being directed toward the EU [417]; Germany and

France consuming almost half of this amount [407].

C. Wood Pellets - Key Global Trade Flows

The global biomass pellet market has also been growing exponentially for the past 10 years,

with levels currently comparable to those of both ethanol and biodiesel, in terms of traded

volumes [17].

Driven by European Commission mandates and incentives to increase renewable energy

generation and reduce carbon emissions, the EU is also the World’s largest wood pellet

market [416]; wood pellets replacing or being co-fired with coal to either generate electricity

[258], or to produce heat with reduced greenhouse gas emissions [416]. Thus the EU has the

largest demand for pellets with Sweden leading the way; followed closely by Denmark, the

Netherlands, Belgium, Germany, and Italy [407]. To meet this demand the EU is also the

largest importer of pellets with about 3.4 million tonnes imported in 2010; of which about

half can be assumed to have been intra-traded inside the EU [418].

North America is the largest exporter of pellets, the majority of these again going to the EU.

Canada is the dominant exporter who also supply pellets to the United States. In

Scandinavian countries despite there being significant domestic pellet production, large


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increases in demand are outpacing production, thus increasing volumes of pellets are

imported from the Baltic Regions and Russia, to balance their demand [416]. Further minor

biomass pellet trade-flows to the EU are also taking place and growing, from Australia,

Argentina, and South Africa [398].

The European Biomass Association (AEBIOM) [398], forecasts that the EU’s consumption

of wood pellets will continue its steep rise; estimating increases from the 2.5 Mtoe in 2008 to

as much as 20 - 32 Mtoe in 2020. North American exports being targeted by the EU to

provide the major share of these near and mid-term demands [17].

D. Wood Chip - Key Global Trade Flows

For many years over the past decade, Japan has been the largest importer of wood chips,

attracting over 50% of the global trade in this resource. Although China is expected to

become the predominant importer within the next few years [419].

The greatest producers of wood chips in recent time have been: Canada (37%), Australia

(8%), Sweden (7%), Russia (6%), and China / Finland (each with 5% of global production)

[420]; all these countries incidentally and relevantly, having large pulp and paper production

industries.

Once again, largely due to recent energy policies, price competitiveness, and a strong forestry

sector; the EU has become a growing major market for the wood-chip trade [421]. The EU is

a net importer of wood chips but also demonstrates the dynamics of EU Member State intra-

trading. Within the EU, Sweden, Finland, Austria, and Italy, are major EU Member State

importers; with Germany, Latvia, and Estonia being the major exporters [422]. From outside

the EU, wood chips are increasingly being sourced from Russia, Uruguay, Brazil, and Canada

[422].

Potential trends in the future trade of wood chips may be indicated by the on-going shift in

the production of pulp and paper industries; moving from the Northern to Southern

hemispheres [400].

8.1.4 Global Biomass Trade Limitations & Uncertainty

This next section of Chapter 8 provides a discussion of the limitations and uncertainties that

relate to the global trade of biomass. These having great influence in acting as potential

barriers to the future growth of the global biomass market; and are highly relevant to this

research as they may control the levels of resource that demand-led countries such as the UK,

may be able to import.


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These barriers can principally be defined as: any feature, mechanism, or issue that either

directly or indirectly hinders the development of the international trade of biomass

commodities for energy end-uses [17]. The key barriers to the growth of the global biomass

markets are highlighted by a number of studies [17], [423]–[425]. Each of these barriers is

discussed and their relevance to the UK, highlighted in the following sections.

A. Technical Barriers

Technical barriers to the continued growth of the global biomass trade markets essentially set

the benchmark for the extent that available resources around the World are suitable, and can

therefore be traded to demand regions; technical constraints representing the quality control,

and fuel characteristics requirements of the current and future bioenergy sectors [425].

Technical standards are the key mechanisms applied to ensure that these minimum resource

and fuel requirements are met. Adherence to these standards reduces the risks to potential

importers, whilst also ensuring the safety to potential customers; as in the case for biofuels. A

key example being: the maximum proportions of either bio-ethanol or biodiesel that can be

blended with fossil fuels, to ensure that the imported biofuels are regulatory compliant and

meet technical performance requirements [17].

Technical barriers are an important mechanism required by potential importing countries

such as the UK, to reduce risk. The barriers themselves reduce the overall extent that

resources may be available for trade, but in the long-term act as a driver for increasing the

overall quality and performance of the resources traded globally.

B. Economic & Trade Barriers

The economic and financial mechanisms applicable to the global trade markets are perhaps

the most essential drivers, and sometimes barriers to the growth of biomass trade. These

potential barriers are applicable at each stage within supply chains, but most importantly for

the growth in trade of biomass; economic influences that both enable and constrain resource

imports, exports and utilisation; are most relevant [425].

Export subsidies or tariffs are a key example of a potential economic trade barrier that may

impact the trade of biomass commodities; influencing the extent that countries develop export

markets. The influence of export economic mechanisms on the global biomass markets can

be seen within a case study of Argentina. Here, a differential in export tax was set for

finished biodiesel products (20%), and the feedstocks for biodiesel production (32%). This

differential in tax provided an incentivisation for the domestic production and export of


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finished biodiesel; rather than the exportation of raw feedstocks for biodiesel generation

elsewhere [426]. This distortion, although generating growth in the Argentinean bioenergy

sector, continues to create a disadvantage for other biodiesel producing regions that may rely

on the importation of raw feedstocks [427].

Import tariffs represent a further driver that may either incentivise or render the importation

of biomass resources uneconomical. The bio-ethanol import tariffs of countries range from

6%, up to 186% in the case of India. Biodiesel import tariffs are typically lower than that for

bio-ethanol [428], whilst countries typically have no tariffs for the importation of solid

biomass such as pellets [17].

In the UK, the import duty rates for biomass pellets is 0%, with standard rate VAT. In the UK

budget of 1st April 2012, the duty rate for liquid biofuels was raised to equal that of

conventional fuels. The duty rate for both bio-ethanol and biodiesel utilised for road use was

set at 0.5795p / Litre, and 0.1114p / Litre for non-road use [429].

C. Logistical Barriers

A further major barrier to the growth of biomass trade markets is the logistics of transporting

the resource around the World. A large proportion of resources being harvested and sourced

from regions of the World that have infrastructure that lags behind ambition [425].

The fuel characteristics required by bioenergy systems are often highly specific. Therefore,

the nature and properties of the resources traded is a vital element of the global trade market.

Thus a major barrier to trade is the maturity of technical and pre-treatment technologies. To

ensure cost-efficient transportation and favourable energy characteristics to satisfy bioenergy

importation legislations, compacting and densification technologies are vital to ensure that

biomass trade becomes increasingly viable; the final density per volume of transported solid

biomass resources being far less than liquid biofuel resources; albeit this ratio is continually

improving. [17].

D. Regulatory Barriers

Regulatory systems are also highly influential in determining both the types and extent that

different resources may or may not be imported, for a given country’s bioenergy sector.

Changing regulations that incentivise or prioritise different bioenergy pathways, and thus the

resources required to fuel them; can strongly influence the resources traded on the global

markets. For example, changing the technology focus of feed-in-tariffs either incentivises the

utilisation of particular resources in bioenergy pathways, or may otherwise render them


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uneconomical. Regulatory systems therefore represent a major barrier for the whole

bioenergy sector and throughout supply chains [424].

Further potential regulatory influences are represented in the form of sanitary or

phytosanitary measures. These are technical regulations that can place potential constraints

on the trade and movements of resources across international borders. These are typically

implemented to target the reduction risks associated with biological impacts, such as the

spread of pests or pathogens around the World [17].

Meeting these requirements is often straightforward through chain-of-custody tracking and

assessments; although on occasion imported resources especially from developing regions to

demand regions, are rejected [428]. For imports brought into the EU, thorough inspections

are widespread, especially for under-bark untreated category imports [428].

Sustainability criteria represent further regulatory requirements and minimum standards

placed on imported resources. These regulations typically presenting a framework of

sustainability performance compliance criteria such as: ecological, land-use, competing

market and food system impacts; as well as embodied energy and greenhouse gas emission

thresholds [425]. The sustainability barriers and implications of the global trade of biomass

are discussed further in Section 8.1.5 of this Chapter.

E. Geopolitical Barriers

Geopolitical instabilities present risks and barriers to all international trade, with biomass

being no different. These instabilities can highly influence the price and supply security of all

energy commodities, most pronouncedly for the fossil fuels. Although these potential risks to

biomass supply are thought to be less extreme and more manageable, they still present major

potential uncertainties and limitations for the future biomass commodity markets [65].

Also, many potential biomass exporting regions of the World are often those with higher risk

of political instability. Thus trading with these regions and investment in enabling-

infrastructure also carries risks [424].

8.1.5 Sustainability of Global Biomass Resource Production

This next section provides an overview of some of the key sustainability issues that result

from the increased productivity and development of a global biomass trade market.

Bioenergy differs from all other renewable and conventional energy pathways, as it is largely

directly tied to the farms, forests, and ecosystems; from which biomass resources and

feedstocks are extracted. Thus this close association within bioenergy systems and supply

http://www.eesi.org/feedstocks


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chains creates the potential for wide-ranging environmental and social impacts that can be

both positive and negative [430]. Many of these major potential impacts are discussed in the

following sections.

A. Potential Biomass Cultivation Impacts

Increased production of biomass for the bioenergy sector may incur negative impacts on a

wide range of issues including: water systems, air quality, social impacts, biodiversity, and

soil systems [15], [431].

i. Ecosystem & Biodiversity Impacts

The potential impacts of increased biomass cultivation on biodiversity and ecosystems stem

from stresses on land availability and use. The impacts from the production of biomass range

from the complete transformation and potential destruction of ecosystems, to the selective

extraction of resources resulting in a potential gradual degradation in the health of

ecosystems [432], [433].

The loss of valuable ecosystems continues to be the key factor linked to biomass production

in certain regions around the World. Increased agriculture and unsustainable forest

management practices are the key drivers of this risk [434].

It is internationally acknowledged that protecting biodiversity in zones, is an insufficient

mechanism aimed at halting the reduction of global biodiversity. Protection zones often

experience continued exploitation of border regions, with gradual continual degradation

infiltrating the whole ecosystem. Thus specific activities for cultivating biomass resource for

the bioenergy sector have to be addressed in terms of their compatibility with biodiversity

protection [275], [434]. However, it is also acknowledged that biomass resource cultivation

may also present scope for providing positive biodiversity benefits through the appropriate

planning and management of production systems. [275].

A further related impact of biomass resource production is that to soil systems. Unsuitable

and unmanaged cultivation practices potentially leading to loss of topsoil through erosion, as

well as the risk of soil compaction. Soil systems represent vast sinks of organic carbon that if

released through degradation or erosion of the soil structures, will create large carbon

emission impacts [434].

ii. Water Impacts

The impact of biomass resource cultivation on water is often overlooked in discussions.

Agriculture around the World already places significant demands on water resources.


254

Increased production of biomass places further stresses, impacting both water quantity and

quality. With water stresses are already a highly relevant issue in some of the regions with the

greatest focus on resource production for energy end-uses. The sustainable production of

biomass ensuring that cultivation remains in-line with water availability is essential if conflict

between biomass growth and other water demands are to be averted [275].

iii. Social Impacts

Potential sustainability impacts resulting from increased biomass resource production that can

impact on society are frequently overlooked; none being more important than issues relating

to food security. The land-used for biomass production is always a limited resource, and

therefore the production of biomass itself may reduce a region’s food productivity. In some

regions of the World this may present major implications for local populations [434].

Further localised impacts on food availability may have the influence of driving up the price

of food commodities. Negative food-security impacts are especially prevalent for net-food-

importing countries, and specifically net-food-purchasing households. The prices of key

staple food commodities related to income levels, determining ability of households to

affordably meet their food needs [435].

B. Potential Air Quality Impacts

A further and often overlooked sustainability impact of bioenergy sector, are its potential

impacts on air quality. Significant air pollution can potentially occur, when any biological

materials are combusted without the utilisation of appropriate technologies to limit these

[275].

Particulate emissions are a potential major air quality issue particularly associated with

bioenergy systems without appropriate treatment technologies. Although particulate controls

have improved over recent years in many places around the World, small particulates can

remain a concern for local air quality, especially where small-scale bioenergy systems or

practices are utilised [77].

Bioenergy combustion systems can also produce sulphur (SOx) emissions. However, these

are of relatively minor concern especially when the bioenergy systems are replacing oil and

coal-based combustion systems that would otherwise generate large concentrations of SOx

emissions [77].

NOx emissions are a further category of pollutants which are more difficult to control as they

result from all combustion processes regardless of the fuel types. Advanced combustion


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systems can reduce NOx emissions to minimal levels, but the investment requirements for

these technological advances render them unsuitable for small bioenergy applications [77].

C. Potential Land-Use Change Impacts

A close relationships exists between bioenergy and demand for finite land supplies. Thus the

increased use of bioenergy and the requirement to produce more resources to balance

demands; represents a growing area of stress for land. Direct and indirect land-use-change

impacts can potentially result in increased greenhouse gas emissions that may reverse any

savings offered through bioenergy utilisation in the first place [275].

The greatest perceived biomass land-use issue is the conflict between the production of food

commodities and biomass resources for energy; direct conflicts arising as the focus for

agricultural lands shift from producing food crops to dedicated energy crops. Many of the

traditional food commodities such as: cereal, sugar, and oil-based crops, also being widely

used as bioenergy feedstocks [430].

A further issue that is heavily associated with the production of biomass are GHG emissions

that may result from changing land-use and land management practices. GHG emissions

predominantly contributed by CO2, CH4, and N2O; result from agricultural inputs such as

fertilisers and the releasing of carbon emissions locked within soil and flora systems.

Further indirect emissions may also arise from market-driven land-use change; as forests,

grasslands, and / or other ecosystems, are cleared in order to produce crops to compensate for

lands diverted to produce bioenergy bound resources [430].

As such, the impacts attributed to indirect land-use can be difficult to quantify and are

sometimes a controversial subject [430]; as the accounting of impacts and GHG emissions

from displaced lands, may occur outside the country or region in which the direct land-use

change occurred [434].

Methods for estimating indirect land-use change are highly complex [15], and include

approaches that use macro-economic, econometric, and biophysical models [436]. A review

undertaken by Croezen et al (2010) [437], concluded that the increased demand for resources

for the bioenergy sector, will likely accelerate intensification of agricultural productivity and

the widespread conversion of forestry and grassland systems to arable lands; sometimes far

away from where the direct land-use change impacts occurred.


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D. Potential Greenhouse Gas Emission Impacts

Bioenergy is widely promoted as a low carbon source of energy. The combustion of biomass

releases carbon emissions, with the carbon-neutral logic stemming from the fact that the

carbon released is once again taken up by the growth of further biomass over the short-term.

However, the initial uptake of carbon during biomass growth stages and released during the

combustion stage, are not the full story. The accounting and reduction of GHG emissions

across the whole life cycle of bioenergy systems should be a minimum pre-requisite for any

bioenergy development [275].

Bioenergy systems create a ‘carbon-debt’ that represents a time gap between the release of

carbon emissions during the extraction, transportation, and combustion of biomass; and the

time required to rebuild stores of carbon through new resource growth. This ‘payback period’

can vary drastically depending on the types of biomass utilised, the land types, and the land-

use changes occurring; as well as further influences from a long list of other variables. In

some scenarios this payback period may represent many decades or even centuries, before

new biomass growth is able to absorb the equivalent carbon initially released [431].

Research carried out by the UK Environment Agency (2009) [438], [439], demonstrated the

importance of developing and adhering to best practices in terms of cultivation, processing,

and the bioenergy conversion processes; if significant GHG emission reductions are to be

achieved through bioenergy. Also emphasising that GHG emissions resulting from land-use

change may negate all savings achieved through the utilisation of biomass, if best practices

and guidelines are not adhered to. Although they also found that the utilisation of waste and

residue resources in bioenergy pathways may result in significantly greater carbon emission

savings; than the utilisation of grown resource for the bioenergy sector that may have

incurred land-use change emission debts.

Land-use change emissions are highly important as existing forests and other flora systems

represent large carbon sinks. They also represent vast mechanisms that sequester further

carbon emissions. Therefore, removing these systems either as feedstocks to directly to

produce bioenergy, or with the aim to free up land for crop cultivation, will result in large

GHG emission impacts [275].

It is widely argued that current regulations around the World, and relevant to this research in

the EU; fail to guarantee that resources utilised in bioenergy systems take account of these

whole supply chain emissions, and therefore many bioenergy systems and supply chains are


257

not truly carbon neutral [431]. Also, it is very difficult to ensure that resources with

unfavourable lifecycle balances do not enter EU supply chains, given the nature of

international commodity markets [275].

E. Sustainability Schemes & Certification

With the growth of the biomass and biofuel industry, the need to ensure sustainability at each

step within supply chains becomes increasingly important. Biomass sustainability and

certification frameworks and tools are the chosen strategy to ensure sustainability, and

demonstrate chains of custody to account for the impact of different resources. The remit of

these, cover supply chain characteristics, and features ranging from land-use impacts, social

issues, and agricultural practices; to whole life-cycle assessments of embedded energy and

carbon, as discussed previously in this Chapter [440].

Studies carried out by van Dam et al (2010) [441], and Scarlat & Dallemand (2011) [442];

review and describe a wide range of sustainability targets, schemes, and certification

frameworks from around the World that target enhanced sustainability of biomass supply

chains. A summary of some of the key applicable schemes relevant to both the UK and Brazil

are listed as listed as follows:

European Commission & Parliament – Biomass & Bioenergy Requirements [13],

[443]

European Committee for Standardisation – Bioenergy [444]

International Organisation for Standardisation – Bioenergy [445]

Fair Trade Labelling Organisations (FLO) [446]

United Nations Energy – Bioenergy Framework [447]

United Nations Environment Program – Bioenergy Framework [448]

Global Bioenergy Partnership (GBEP) – Bioenergy Framework [449]

Round Table on Sustainable Palm Oil (RSPO) – Framework [450]

Round Table on Responsible Soy (RTRS) – Framework [451]

Better Sugarcane Initiative Ltd (BSI) – Framework [452]

Rainforest Alliance – Forestry Framework [453]

Forest Stewardship Council (FSC) – Forestry Framework [454]

Program for Endorsement of Forest Certification (PEFC) – Forestry Framework [446]

United Kingdom Renewable Transport Fuels Obligation (RTFO) – Biofuels Transport

[54]

Brazil Social Fuel Seal – Biofuel Framework [455]


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International Sustainability & Carbon Certification (ISCC) – Biofuels Framework

[456]

Drax Power Ltd – Sustainability Policy [457]

The Council on Sustainable Biomass Production (CSBP) – Framework [458]

World Wildlife Fund – Bioenergy Framework [459]

However, as this brief summary highlights, the number and range of sustainability

frameworks that have been developed to address bioenergy issues is extremely broad. Thus

there are doubts and uncertainties as to whether the protocols and requirements of respective

frameworks may be followed accurately [15]; especially within countries and regions with

existing weak regulation and enforcement [460]. There are also concerns regarding the great

variance within sustainability schemes, with both linked and differing understandings of

sustainable systems and issues. This results in a potential reduction in the overall creditability

and influence of the underling sustainability goals they aim to achieve [461].

8.1.6 Chapter Conclusions & Consequences for the UK Bioenergy Sector

Chapter 8 has provided discussions and descriptions of the current and potential future global

biomass trade markets. In relevance to this research these descriptions have highlighted the

nature of the global markets that the UK will increasingly have to operate within, if it is to

balance its resource demands. The Chapter has also identified some potential barriers and

limitations that are likely to continue or emerge, that may influence the ability of the UK

bioenergy sector to meet its resource demands. Some of the key conclusions and

consequences for the UK bioenergy sector can be summarised as follows:

A. Expanding Biomass Resource Trade

The Chapter determined that the global biomass trade markets are growing rapidly yet are

still far from maturity. Although it is clear which regions will represent the future major

exporters and importers of resources, and where and what the major flows of resources will

be. One certainty has emerged, this being that the volumes of resources currently traded are

likely to vastly increase. However, a major uncertainty remains as to how the major trade

flows may change as the energy aspirations of developing countries continue to evolve.

B. Trade Hub Europe

For each of the major traded biomass resource categories, principally the wood-based and

biofuel resources; Europe has been found to be the key trading hub and demand region for all

biomass resources. Driven by renewable energy and GHG reduction targets Europe’s

demands for all biomass resources as highlighted throughout the literature, comes across as


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being insatiable. All major biomass trade flows appear to be headed towards the EU. This

may represent both positive and negative issues for the UK bioenergy sector. The UK being a

part of the central hub of biomass trade, will likely present opportunities. On the other hand

the UK bioenergy sector is competing within the global market for biomass resource against

its neighbouring EU Member States, who have their own equally large(r) and growing

demands for resources. Resources destined for the UK can be easily diverted to any number

of EU ports as a result of any number of influencing factors – a practice already typical

within the oil and gas energy global trade market [462]. A further conclusion drawn from the

EU’s position in the global market is potentially a premonition of what global demands may

resemble if other regions continue to develop and direct greater focus on their respective

bioenergy sectors. This placing unforeseen stresses on the resources available for trade [463].

C. Trade Limitations

A series of potential barriers were identified that may represent to varying degrees,

significant limitations to the future growth of the global biomass trade markets. A concluding

theme developed from these particular discussions, is that the presence of a wide range of

international and even geopolitical drivers, will influence the global biomass markets; as with

they do with any other internationally traded commodity. These limitations highlighting

potential risks for the UK in the future, in having little control over these drivers, and having

an increasing proportion of its energy generation mix dependent on imported resources.

D. Sustainability Implications

A wide range of sustainability issues were also highlighted as being associated with the

increased production of biomass resources around the World. The major conclusions from

these discussions within this Chapter highlighting the irrelevance of pursuing bioenergy

pathways to reduce GHG emissions, if the supply chains and / or related land-use and

cultivation practices, are not undertaken to ‘best practice’. The potential impacts of both

direct and indirect land-use change and ecosystem degradation, negating any benefits

associated with applying bioenergy pathways over those of conventional fuels. A further

social-responsibility conclusion can are be derived, highlighting the importance of ensuring

that supply chains also respect and are developed to prevent negative impacts within the

regions where the resources are cultivated.


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Chapter 9 - Case Study: Brazilian Biomass Resource Analyses


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9.1 Case Study – Brazil’s Biomass Resource

Analyses

Chapter 9 provides further analyses of the global biomass trade markets, through focusing on

Brazil. Brazil represents a case study of a country that has a leading role to play in both the

current and long-term future of the global biomass trade markets. As Chapter 8 concluded,

Brazil is a dominant producer and the top exporter of bio-ethanol and related feedstocks. This

was found to be a trend that is forecast to continue, and potentially intensify. Brazil is also

identified as a key country where there may be great opportunities for the future exportation

of wood-based biomass resources for the bioenergy sector.

The aim of Chapter 9 is to gain a greater understanding of the biomass supply chains and

resource opportunities that Brazil may provide in the future. The analyses are undertaken

through the adaptation of the Biomass Resource Model to reflect the biomass resource

dynamics within Brazil (Brazil BRM), and analysing how these may change in the future.

Further important analyses are undertaken to evaluate how levels of biomass resources

exported from Brazil may change in the future, if Brazil’s domestic energy focus were to

change so that a greater proportion of its indigenous resources were utilised to meet domestic

energy demands, rather than being exported. This analysis is undertaken through the

development of a series of bioenergy scenarios that represent different pathways, in which

bioenergy may increasingly contribute to Brazil’s energy mix; allowing the potential impacts

on resource exports to be evaluated and forecast.

The relevance and importance of the Brazilian analyses to the research project and

specifically to the UK, is the evaluation of a further currently unforeseen dynamic that may

potentially influence the global biomass resource markets – on which the UK is likely to be

reliant in order balance its future biomass resource demands.

Brazil is the ideal case study for this analysis as Brazil represents a major contributor to the

global biofuel markets, and more relevantly the biomass resource global market. Thus any

influences impacting Brazil’s contribution of resources available for global trade will likely

have large impacts across the global market.

Chapter 9 starts with a discussion of how the BRM is adapted to reflect the biomass supply

dynamics in Brazil (Brazil BRM). This section provides descriptions of Brazil’s supply chain

characteristics and lists all the references utilised to adapt the BRM. This section does not


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discuss the methodologies of the Brazil BRM, as these reflect those developed and presented

within Chapter 4 for the UK BRM.

Chapter 9 goes on to provide an initial evaluation of biomass resources potentially available

within Brazil, and evaluates how these are likely to change to 2050. This is undertaken

through the development of a Brazil Baseline Scenario within the BRM, similar to that

developed for the UK within Chapter 5.

A series of Brazil bioenergy scenarios are then developed, reflecting directions that Brazil

could potentially take to 2050; utilising varying increased levels of their indigenous resources

to meet their domestic energy demands, rather than exporting them.

A resource balance analysis is then undertaken reflecting that carried out for the UK in

Chapter 7. This analysis enables an evaluation of the extent that Brazil may export biomass

resources in the future, in reflection of each of the developed bioenergy scenarios.

Finally Chapter 9 draws conclusions from the analyses that may help to predict the future

state of global biomass trade market, and the relevance of this specifically for the UK.


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9.2 The Brazil Biomass Resource Model –

Stage One Analyses

The following section discusses the development of the Brazil BRM’s Stage One analyses.

The analyses undertaken in stage one of the Brazil BRM, focuses on the land-use dynamics in

Brazil that influence the availability of resources for the bioenergy sector. Rather than

provide a detailed discussion of the methodologies of this analyses, this section introduces the

land-use characteristics in Brazil and provides an overview of the data sources and references

utilised in adapting the BRM for Brazil. The methodologies and applied calculation equations

utilised within the Brazil BRM are the same as those developed for the UK BRM, as

discussed within Chapter 4. A step-by-step walk through of the Stage One analysis for Brazil

is provided, focusing on the following key analysis areas:

Population Dynamics Food & Agriculture Systems

Built-Up Land Area Agriculture Land

Land Availability Growing Biomass

Food Commodity Demands Agriculture & Biomass Yields

Woodlands, Forests & Plantations

9.2.1 Brazil Population Dynamics

The confirmed official population data for Brazil in 2010 has been taken to provide baseline

figures for the Brazil BRM’s analysis. High, Medium and Low population change forecasts

have been sourced from the United Nations’ ‘Population Division’ [145]. Highlighted within

Table 9.1, these provide the population change scenarios for the analysis timeframes within

the Brazil BRM. The Medium Forecast is applied as the default where population dynamics

are utilised.

Table 9.1: Brazil Population Forecasts Table 41) Table 9.1: Brazil Population Forecasts


Brazil

High 195,210,000 205,687,000 216,394,000 236,202,000 267,574,000

Medium 195,210,000 203,657,000 211,102,002 222,748,000 231,120,000

Low 195,210,000 201,628,000 205,809,000 209,384,000 198,877,000


9.2.2 Brazil Built-Up Land Area

The Brazil BRM utilises FAO-Stat data (2011) [146], to quantify the area of Brazil’s built-up

land area. This area of land includes: residential, commercial, industrial, and infrastructure,


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built-up land area classifications. Within the Brazil BRM this land is identified as being

unsuitable for any form of agricultural or biomass resource growth.

Future forecasts of the expansion of developed land area in the Brazil BRM have been

derived from work carried out at the ‘Lincoln Institute of Land Policy’, undertaken by Angel

et al (2010) [464]. High, Medium, and Low development forecasts are utilised reflecting the

data shown within Table 9.2; the Medium Forecast again being applied as the default within

the Brazil BRM. Expansion of built-up land area is assumed in part, to take up land that

would otherwise be potentially available for biomass resource growth.

Table 9.2: Brazil Urban Development Land Area Forecasts (Hectares) Table 42) Table 9.2: Brazil Ur ban Development Land Area Forecasts (Hectares)


Brazil

High 5,445,873 8,406,565 11,237,530 14,590,725 18,496,063

Medium 5,445,873 6,882,713 8,324,967 9,780,456 11,218,429

Low 5,445,873 5,635,089 6,167,287 6,556,035 6,804,321


9.2.3 Brazil Forests, Woodlands & Plantations

The characteristics of Brazil’s forests and plantations are highly dynamic in comparison to

those of the UK. The extent, types, productivity, and characteristics of Brazilian forestry are

described and analysed within a wide range of reports, studies, and research that were

reviewed when developing the Brazil BRM [416], [450], [465]–[474]. Within Stage One of

the Model, the current forested land area and forecast trends for how this may change are the

key focus; the Brazil BRM using forest characteristic descriptions and data from the FAO’s

‘Global Forest Resources Assessment Report’ (2010) [474]. Forecast scenarios for how

Brazilian forested area may change to 2050, are utilised from the Forestry Stewardship

Council (2012) [469]; these being discussed further later in this Chapter.

9.2.4 Brazil Food & Agriculture Systems

A. Food Commodity Demands

FAO data from the ‘FAO Stat’ website and database [146], provides the food commodity

data for the Brazil BRM.

B. Forecasting Changing Food Demands

Again reflecting the methodologies of Smeets et al (2004) [140], and Fischer et al (2007)

[139], the future food commodity demand within the Brazil BRM is linked to the changing

population scenarios [145], (Chapter 6).


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C. Animal Based Food Feed Demands

The Brazil BRM’s analysis of animal feed demands again utilises the data and assumptions

largely developed and applied by Bouwman et al (2004) [150]. Specific data for Brazil

reflecting: the applied animal based agriculture production systems [150], feeding practices

[150], feed compositions [475], and animal feed conversion efficiencies [150], [153], [209],

[476]–[478]; that are utilised. The key data values and assumptions utilised for these being

listed within Appendix 10.

The total land area required to produce animal based products to meet demand, is again based

on the area of pasture and arable land required to produce feed for animal growth.

9.2.5 Agriculture & Biomass Productivity Yields

A. Current Brazil Productivity Yields

A Brazil focused literature review was undertaken to develop a database of agricultural yield

data for all the food and biomass resource commodities analysed within the Brazil BRM

[140], [146], [150], [158], [159], [163], [165]–[167], [479]–[481]. Production yields for each

commodity and grown resource analysed within the Brazil BRM are listed within Appendix

11.0.

B. Future Brazil Productivity Yields

A further Brazil focused literature review was undertaken to analyse how agricultural

productivity yields may change to 2050. A review of a broad range of studies [21], [116],

[158], [159], [163], [175], [177]–[181], [184], [187]–[189], [481]–[486], concluded that

global agricultural yields are expected to increase by 20-200% by 2050. Agricultural

productivity yield trends for South America and Brazil range from +60% to 160% by 2050.

In reflection of these studies a default increased productivity of +120% is assumed for

increased commodity productivity, within the Brazil BRM.

9.2.6 Land Area to Meet Food Commodity Demands

Once the demand for each food commodity is determined, the Brazil BRM forecasts the land

area that may be required to produce the volumes of food commodities to balance demands.

This land area being a function of: the arable and pastoral food commodity demands, the

animal feed commodity demands, arable and pastoral agricultural productivity yields, and

population change scenarios.


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A. Prioritising Land for Food Production

The Brazil BRM prioritises the area of land to meet food demands before it is identified as

being potentially available for the production of biomass resources. This reflects the

methodologies developed by Smeets et al (2004) [140], and Fischer et al (2007) [139].

9.2.7 Brazil Land Availability

The final analysis step within Stage One of the Brazil BRM is again to forecast the total area

of land that may be available and suitable for the production of biomass resources. This land

area being calculated as a function of: the land area required to meet food commodity

demands, the area of built-up land, the forested land area, and the area of land unsuitable for

biomass production.


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Stage Two Analyses

This next section discusses the development of the Brazil BRM’s Stage Two analyses. The

analyses undertaken in stage two of the Brazil BRM, focuses on quantifying and forecasting

the availability of the different biomass resources analysed within the BRM. Once again,

rather than providing a detailed discussion of the methodologies of this analysis, this section

provides an overview of the data sources and references utilised; so that the BRM is adapted

to reflect Brazil’s biomass supply chains. The methodologies and applied calculation

equations are the same as those developed for the UK BRM, as discussed within Chapter 4. A

step-by-step walk through of the Stage Two analyses for the Brazil BRM is provided,

focusing on the following key analysis areas:

Forest Productivity Forestry Residues Industry – Forestry Dynamics

Industrial Residues Agricultural Residues Arboriculture Residues

Waste Generation Waste Management Biomass Planting Strategies

9.3.1 Forestry System Productivity & Characteristics

This section discusses the extents and characteristics of Brazil’s forestry industry, also

forecasting how productivity may change through to 2050. The productivity of forests within

the Brazil BRM drives industry and also provides resources directly to the bioenergy sector.

A. The Brazilian Forestry System

The extent (forestry area and standing volume) and characteristics (types and uses) of

Brazil’s forests are described within a large number of studies. Although the Brazil BRM

focuses on reports carried out by the FAO (2010), ‘Global Forest Resources Assessment’

[474], to evaluate the current status of Brazilian forests; and Buongiorno et al (2012)

‘Outlook to 2060 for World Forests & Forest Industries’ [470], to analyse how these may

change to 2050.

Current Brazilian forestry systems are quantified and categorised to reflect the way in which

they are used. These categories reflect those developed by the FAO as described below [474],

[487]; the ‘Production’ designated category representing those forests linked to the bioenergy

sector.

Production - Forest areas within this designation have the primary use of producing

wood fibre for industry and the bioenergy sector. This classification of forestry also


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includes activities of subsistence collection of wood, for both energy and wood-based

product use.

Protection of Soil & Water - These forested areas are designated for the primary use

of protecting soil and water systems. These areas include those subject to national

regulations that restrict the use of wood in order to maintain tree cover and vegetation

to protect soils.

Conservation of Biodiversity - The primary use of forests within this designation are

to conserve and maintain the biodiversity and the health of ecosystems. These areas

include forests with protection status.

Social Services - These forests are designated primarily for social services such as:

recreation, tourism, education, research, conservation and / or sites of cultural or

spiritual value. Forests utilised for subsistence wood fuel and wood product

requirements are not included in this category.

Multiple Use – Multiple-use designated forests have more than one purpose, where

none of the above categories are considered as the sole predominant designated

function.

Other - Forestry areas that have utilisation designations where the primary function is

other than that of production, conservation, social services, or multiple use.

None / Unknown Use - Forestry area currently with no known utilisation

designations.

i. Changing Forestry Systems to 2050

The Brazil BRM integrates a series of scenarios that forecast how forestry may change to

2050, as developed by Buongiorno et al (2012) [470]. The overall forestry area will change

over the analysis period, as will the designations of ‘forest category’ also evolve. These

scenarios are described in Table 9.3. Scenario 3 is applied as the default option within the

Brazil BRM. Figure 9.1 demonstrates a stacked area chart, reflecting changes to ‘overall

forest area and designations’ within Scenario 3, to 2050.


269

Table 9.3: Brazil BRM Forestry Growth Scenarios Table 43) Table 9.3: Brazil BRM Forestry Growth Scenarios

Forestry Growth Scenarios Description

Scenario 1

Increase in Forest Area Under this scenario forested area increases over the analysis timeframe.

Scenario 2

Large Decline In Forest Area

Under this scenario forested area experiences large decreases over the

analysis timeframe.

Scenario 3

Gradual Decline In Forest Area

Under this scenario forested area experiences gradual decreases over the

analysis timeframe.

Scenario 4

Low Fuel wood Scenario

Under this scenario there is projected to be reduced levels of fuel wood

that result in further degradation of existing prime productive forestry.

Adapted from [470]

67) Figure 9.1: Brazil Forestry Area & Designations to 2050 within the Scenario 3 Pat hway

Figure 9.1: Brazil Forestry Area & Designations to 2050 within the Scenario 3 Pathway

B. Brazilian Forestry System Productivity

The resource productivity of Brazil’s forests is a further dynamic, modelled using the

scenarios within the Brazil BRM. The resource productivity of Brazil’s current forests is

again highlighted by the FAO [474]. Further studies [469], [488], were analysed to gain a

greater understanding of how forest productivity may change to 2050. Scenarios developed

for the Forestry Stewardship Council [469], analysing future forests and plantation

productivities; are utilised within the Brazil BRM and described in Table 9.4. Scenario 2 is

applied as the default option within the Brazil BRM.

Forecast Areas & Classifications of Forestry in Brazil to 2050


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Table 9.4: Brazil BRM Forestry Productivity Scenarios Table 44) Table 9.4: Brazil BRM Forestry Productivity Scenarios

Forestry Growth Scenarios Description

Scenario 1

Continuation of Current

This scenario represents a steady continuation of present harvest and

productivity levels.

Scenario 2

Steady Productivity Increase

This scenario represents a steady increase in harvest and productivity

levels. These resulting from technological improvements and increased

managements of forestry systems

Scenario 3

Large Productivity Increase

This scenario represents the theoretical upper limits of harvest and

productivity levels.

Adapted from [469]

9.3.2 Forestry Residues

A. Current Resource

Forestry residues are utilised extensively throughout Brazil, albeit predominantly as wood

fuel for small scale and domestic uses [488]. The extent and potential of forestry residues

from Brazil’s extensive forests has been analysed to be significant [489].


The Brazil BRM calculates the availability of forestry residues for the bioenergy sector as a

function of the area of managed and plantation forests, and the harvest proportion

capabilities.

C. Future Resource

A review of studies and literature [251], [377], [488]–[490], highlights that by 2050 up to

45% of available forestry residues may be harvested and may potentially be available for

utilisation by the bioenergy sector. The Brazil BRM also takes account of the forestry and

plantation growth scenarios, discussed further within Chapter 4 and Section 9.3.9.

9.3.3 Industrial Residues

A. Current Resource

The current levels of biomass resources produced from Brazilian industries are not well

documented beyond the information provided by larger companies. Although Brazil does

have an extensive number of industries reliant and using forestry resources [469], [488].


The Brazil BRM calculates the availability of industrial residues for the bioenergy sector as a

function of the growth of wood based industries, and the extent that residues are not utilised

by competing industries.


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C. Future Resource

The future resource opportunities from industrial residues are modelled within the Brazil

BRM, utilising forecasts of current resource levels [469]; with growth calculated as a

function of both resource growth and trade dynamics [470].

9.3.4 Straw Agricultural Residues

A. Current Resource

The Brazil BRM utilises trend data for the production of straw and other related agricultural

residues from the Brazilian Ministry of Agriculture, Livestock & Food Supply [411].

Forster-Carneiro et al (2013) [491], provide data on the current end-uses and competing

markets for straws in Brazil; predominantly reutilisation of resources by the agricultural

sector.


The predominant factor determining the availability of straw resources within the Brazil

BRM is the limitation extent that it can be harvested. The Brazil BRM’s analysis reflecting

the work carried out within a series of reports which estimate Brazilian harvesting

capabilities [411], [488], [491]–[494].

C. Future Resource

It is estimated that up to 40% of all straws may be harvestable by 2050 through the

development of technological and practical advances [488]. A series of harvest potential

forecasts and evaluations of competing markets [411], [488], [491]–[494]; are reflected

within the Brazil BRM. The mean average of these values derived from this literature, is set

as the default within the Brazil BRM.

9.3.5 Slurry Agricultural Residues

A. Current Resource

The agriculture residue resource produced by animals within the Brazil BRM is again

calculated as a function of: the number and types of livestock, the excreta rates, housing

practices, and the manure management methods applied.

The Brazil BRM’s analysis utilises the animal based food commodity data discussed in

Section 9.2.4, to determine the number and type of livestock farmed. A literature review was

undertaken [208]–[212], to evaluate the ‘manure factor per animal per year’, to determine the

total levels of resource that may be produced. Data sourced from Bouwman et al (2004)

[150], and Schmidt (2009) [495], is applied to evaluate typical farming practices and to


272

determine the duration that different animal species are housed – allowing the estimation of

the maximum levels of resource that may be collectable.


The extent that resources may be available to the bioenergy sector, are determined following

a further literature review [150], [208], [495]–[500]. The key factors in determining

availability being: the extent that the resource can be collected, and the volume required by

competing markets – predominantly used onsite.

C. Future Resource

The future potential of slurry resource is analysed within the Brazil BRM utilising the

methodology developed by E4tech (2009) [138]. This focuses on forecasts of the number of

animals that will be farmed (Section 9.2.4), and changing farming practices such as the extent

that animals are housed, and manure extraction rates over time.

9.3.6 Arboriculture Residues

There is very little reporting of arboricultural residues in Brazil aside from that for large

municipal areas; the majority of resources collected from urban areas and urban forests being

distributed to landfill sites [501], [502]. There is no literature or accounts describing

utilisation of these resources within bioenergy pathways.

A. Current Resource

As such the Brazil BRM assumes that no arboricultural arisings are currently utilised within

bioenergy pathways.


As the large majority of arboricultural arising are currently diverted to landfill sites [501],

[502], the Brazil BRM assumes that the current availability for the bioenergy sector is

negligible.

C. Future Resource

The Brazil BRM makes the assumption that the potential extent of the arboriculture resources

will expand in line with urban development [464], this reflecting the same general

assumption made for the UK BRM [199]. The Brazil BRM also assumes that in the future the

Brazilian bioenergy sector may start to utilise the resources previously diverted to landfill.

These assumptions being: in 2015 - 1% of resources, in 2020 - 5% of resources, in 2030 -

10% of resources, and in 2050 - 20% of resources. These reflect the modest assumptions of

increased waste management practices [503].


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9.3.7 Wastes

Brazil is a rapidly expanding economy and thus it has been forecast that a Brazilian waste-

generation ‘population boom’ is still to come [504]. Waste generation and management data

in Brazil does not appear to reflect the whole picture of what is actually happening but is

continually improving. Current waste generation and especially waste management practices

in Brazil are reflective of the country’s rapid on-going growth, and poorly developed waste

management policies. Energy recovery from waste applications is currently not widely

utilised, however the Brazilian Government has ambitions for increased energy recovery

applications [505].

A. Brazil BRM Waste Resource Categories

The Brazil BRM categorises different waste resources in reflection of the statistics and

methodologies used by the Brazilian Business Commitment for Recycling (CEMPRE) [506],

[507]. Table 9.5 demonstrates the predominant waste categories as accounted for in Brazil,

also highlighting their availability for the bioenergy sector as reflected within the Brazil

BRM.

Table 9.5: Waste Streams & Availability for the Bioenergy Sector Table 45) Table 9.5: Waste Streams & Ava ilabil ity for the Bioenergy Sector

Waste Categories Hazard Classification Availability to Bioenergy Sector

Metallic Wastes Non-Hazardous X Metallic Wastes Hazardous X

Glass Wastes Mixed X Paper & Cardboard Wastes Non-Hazardous

Rubber Wastes Non-Hazardous X Wood Wastes Mixed Textile Wastes Non-Hazardous

Animal & Vegetable Wastes Non-Hazardous


B. Waste Generation Forecasts Scenarios

There are a series of reports and studies [504], [507]–[510], that develop scenarios

forecasting waste generation trends in Brazil. The Brazil BRM is developed integrating

Brazilian waste generation forecast scenarios, as produced by the University of Sao Paulo

[506]. These scenarios reflect both upper and lower limit forecasts for how waste may be

generated in the future. A reference rate scenario is also developed, representing a ‘business

as usual’ future forecast. The waste generated trends data relevant to these scenarios as

utilised within the Brazil BRM, is listed in Appendix 12.0.


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C. Waste Management Scenarios

Brazilian waste management practices remain far behind those of many of the more

developed countries, such as the UK. The majority of Brazil’s waste streams are disposed of

through landfill sites. However Brazil has high ambitions to increase its waste management

practices; principally to increase the proportion of wastes it recycles, reuses, and utilises

within energy recovery pathways.

The Brazil BRM is developed to reflect scenarios for the management of Brazil’s future

wastes, as researched by the University of Utrecht in consultancy MWH for the Brazilian

Ministry of Infrastructure & the Environment [505]. The data from this research will help to

determine the extent and distribution of wastes within the following scenarios: Reference

Rate, Waste Law, and Recycling+. A further description of these scenarios is provided

below, and the corresponding waste distribution proportions reflected within the Brazil BRM

are documented in Appendix 12.0.

Reference Rate Scenario: Represents a business-as-usual continuation of waste

distribution within Brazil. This reflects large proportions of waste being distributed to

landfill sites, with marginal reuse and recycling practices. No waste within this

scenario is made available to the bioenergy sector for energy recovery processes

[505].

Waste Law Scenario: Is based on Brazilian waste land and targets set within the

Brazilian National Waste Plan [511]. This scenario favours the reuse and recycling of

wastes rather than energy recovery options. The reduction of the proportion of wastes

distributed to landfill being the prime objective [505].

Recycling + Scenario: Greater focus is placed on the recycling and reuse of resources

rather than distribution to landfill sites. This scenario also focuses on increasing the

anaerobic digestion of organic wastes thereby providing great opportunities for the

Brazilian bioenergy sector [505].

9.3.8 Sewage Waste

Currently only about 50% of the Brazilian population is connected to public sewage grid

systems; with only 28% these having sewage treatment infrastructure in place [512]. The

majority of the remaining sewage wastes are discharged into rivers, the sea, and other

dumping sites [513].


275

The lack of a universal sewage system and treatment works in Brazil has large health and

well-being implications; with the missed opportunities for the bioenergy sector being a

distant priority [514].

Of the sewage that is treated in Brazil, the most common treatments are based on stabilisation

lagoons and sludge processing systems. In rural areas individual systems and septic tanks are

widely utilised with wastes being filtered, treated and percolated through soils [514].

A. Current Resource

The current sewage resource is reflective of the current sewage system networks and

infrastructure in Brazil; the infrastructure determining the extent that sewage resource may be

collected, and its availability as a potential bioenergy resource. The current utilisation of

sewage resources within Brazilian energy pathways is limited and restricted to large

municipal areas [512]–[514].


As with the UK BRM the overall sewage resource for the Brazil BRM is calculated, and

forecast to change in-step with population growth [145]. The availability of sewage waste

resources identified within the Brazil BRM are also determined as a function of the current

and future development of the sewage infrastructure; this representing a percentage of the

overall resource that may be available for energy pathways [512]–[514].

C. Future Resource

Government and Industry have developed a roadmap for developing sewage and sanitation

networks to cover the whole of Brazil. At the current and planned rate of investment, it may

take up to 60 years for this level of coverage to be achieved [515], [516]. Thus the Brazil

BRM utilises these trends when modelling the future total availability of sewage resources,

and their potential availability for the Brazilian bioenergy sector.

9.3.9 Grown Biomass & Energy Crops

The final major group of resources evaluated within Stage Two of the BRM’s analyses, are

the biomass resources and energy crops grown specifically for the bioenergy sector. This

analysis utilises the area of land identified as being available and suitable for crop growth,

from Stage One of the Model’s analysis, and allows the evaluation of different planting

strategies for the utilisation of this land.


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A. Dedicating Available Land for Resource Growth & Plantation Strategies

The characteristics of Brazil’s current and forecast future plantations are well discussed in

literature [146], [416], [450], [465]–[467], [469], [471]–[473], [517]–[519]. The extent that

available land is utilised within the Brazil BRM, for the cultivation of resources dedicated for

the bioenergy sector; reflects these studies. The combined view showing the potential extent

of Brazilian plantations to 2050 are: >52.8 MHa by 2015, >61.5 MHa by 2020, >79.0 MHa

by 2030, and >114.0 MHa by 2050. Further details documenting the proportion of land with

respect to other land-uses in Brazil, are described in Section 9.5.1 later in this Chapter; where

a Brazil Baseline Scenario is developed.

Literature confirms that Brazilian plantation resources, potentially grown specifically for the

bioenergy sector will focus on the following key crops:

Poplar [465], [469], [517] Sugarcane [146], [416], [465], [467], [469], [471], [473], [518]

Palm [450], [465] Soya [146], [416], [465], [469], [519]

Pine [465], [469], [517] Eucalyptus [465], [469], [472], [517]

Jatropha [465], [469], [517]

A literature review was undertaken focusing on each of these key plantation species, and a

‘baseline planting strategy’ was developed to reflect the extent that available land is

dedicated for the growth of each. Figure 9.2, highlights the land dedicated to each of the key

plantation crops, when the Brazil BRM is calibrated to reflect the default Brazil Baseline

Scenario (discussed later in Section 9.5.1). When analysing Figure 9.2, it is important to

consider the respective land productivity yields for each specific crop, as the extent that land

is dedicated to each crop does not necessarily reflect the overall standing of resource

produced.


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68) Figure 9.2: Brazil P lantation Strategy to 2050 w ithin the Brazil Baseline Scenarios

Figure 9.2: Brazil Plantation Strategy to 2050 within the Brazil Baseline Scenarios

Biomass Plantation Crop Planting Strategy to 2050 within

the Brazil Baseline Scenario

Sugarcane

Soya


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Stage Three Analyses

The Stage Three analyses undertaken within the Brazil BRM follow the same pathways as

that developed for the UK BRM (Chapter 4). However, further unique analysis is undertaken

with respect to accounting for differing bioenergy conversion priorities, and the energy target

requirements applicable to Brazil.

The Brazil BRM utilises the same BRM parameters, values, and analysis methodologies as

those applied within the UK BRM for determining the bioenergy potential of the resources

accounted for within Stage Two. Where the analysis methodologies diverge however, is

instead of comparing these bioenergy potential values against static, energy, renewable

energy, and bioenergy targets defined by legislation; the Brazil BRM compares the bioenergy

potential calculations against a series of scenarios of future bioenergy demands. These

scenarios are developed to reflect varying levels of targeted contributions from bioenergy.

Thus when undertaking the resource balance analysis for the Brazil BRM, these scenarios

allow evaluation of the extent to which Brazilian biomass resources may be available for

exportation to the global biomass trade markets; in line with forecasts of the varying levels of

indigenous utilisation of resources.


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9.5 Brazil Biomass Resource Availability

This section of the Chapter includes the results and discussions of the Brazilian biomass

resource availability, and the bioenergy potential analyses undertaken. The section starts with

the development of a Brazil Baseline Scenario, to determine the potential availability of

different resources to 2030. Bioenergy scenarios are then developed to evaluate how

Brazilian biomass resource exports may be impacted, if a greater proportion of indigenous

resources are utilised to meet domestic bioenergy demands.

The analysis timeframe is undertaken to 2030 instead of 2050 for the Brazil bioenergy

scenarios. The reasoning behind this analysis timeframe for the Brazil BRM research is that

2030 reflects the mid-term timeframe, when the UK bioenergy utilisation forecasts are

predicted to potentially peak. Beyond 2030 there is increasingly greater uncertainty (Chapter

7). Therefore, restricting the analysis to 2030 allows the analysis conclusions relating the

UK’s potential importation of resources; to be regarded with greater realisation.

9.5.1 Brazil Biomass Supply Chain Dynamics to 2030

The first step within the analysis is to develop a Baseline Scenario for Brazil, in order to

evaluate the extent that different biomass resources may be available to 2030.

A. Developing a Brazil Baseline Scenario

Reflecting the methodology developed and discussed in Chapter 5, a database was produced

collating the range of values that literature and studies forecast for how Brazilian biomass

supply chains may vary to 2030. This ‘literature informed’ mean or ‘Baseline Scenario’,

enables the evaluation of the ‘average’ availability of each resource to 2030.

A summary of the reports, studies and research contributing to the development of the Brazil

Baseline Scenario are listed in Table 9.6, and the key information describing how the Brazil

BRM is calibrated to reflect this scenario, is documented within Appendix 13.0.


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Table 9.6: Reports Studies and Research Influencing the Brazil Baseline Scenarios Table 46) Table 9.6: Reports St udies and Research Influencing the Brazil Baseline Scenarios

Categories Specific Drivers Descriptions

Development Drivers Population Change [145]

Changes in Built-Up Land Area [146], [464]

Food Production

System Drivers


[21], [116], [140], [146], [150], [153], [158], [159],

[163], [165]–[167], [175], [177]–[181], [184], [187]–[189], [209], [475]–[486]

Food Waste Generation [79]

Food Commodity Imports [468], [470], [520], [521]


Utilisation of Agricultural Wastes & Residues [138], [150], [208]–[212], [411], [488], [491]–[500]

Forestry & Wood-

based Industry Drivers

Forestry Expansion & Productivity [416], [450], [465]–[474], [481]

Wood-based Industry Productivity [468], [469]

Imports of Forestry Product [470], [520], [521]


Biomass Residue &

Waste Utilisation

Drivers

Utilisation of Forestry Residues [251], [377], [488]–[490]

Utilisation of Industrial Residues [468]–[470], [488]

Utilisation of Arboriculture Arisings [199], [464], [501]–[503]

Waste Generation Trends [504]–[510], [512]–[516]

Waste Management Strategies. [505]

Biomass & Energy

Crop Strategy Drivers

Land Dedicated for Energy Crop Growth [146], [416], [450], [465]–[467], [469], [471]–[473],

[517]–[519] Biomass Resource Planting Strategies

9.5.2 Brazil Baseline Scenario – Land-Use Analysis

Reflecting the analysis undertaken in Chapter 6 for the UK BRM scenarios, this first step in

analysing the Brazil Baseline Scenario evaluates how land-use, changes over the analysis

timeframe. Land-use change trends, providing a first indication of the dynamics taking place

that may influence the availability of different biomass resources.

A. Land-Use Analysis – Results & Discussions

Figure 9.3 documents the Brazil Baseline Scenario land-use change trends, to 2030. The

stacked columns of Figure 9.3 each reflect a breakdown of Brazil’s land-use classifications.

The total land area analysed, representing Brazil’s total area of suitable land that could

otherwise potentially be utilised to grow resources for the bioenergy sector.

Reflected by the ‘Forestry and Woodland’ category, the proportion of forested area in Brazil

is forecast to decline to 2030. This trend is potentially reflective of exploitation of forestry

systems beyond any replanting rates.

At the same time an increase in the proportion of ‘Land Dedicated for Biomass & Energy

Crops’ is documented. This reflects the trend towards increased utilisation of available land

within the Baseline Scenario, for growth of resources for the bioenergy sector.


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The proportional area of ‘Agricultural Land’ is shown to decrease from 10.0% to 8.2% over

the analysis timeframe. As discussed throughout Chapter 4, one of the principal design

features of the BRM is to ensure that land requirements are designated to balance domestic

food demands, and to allow a continuation of food commodity export levels. The BRM

automatically calculates the land area required to produce food commodities to balance food

demands. Thus a trend of decreased land dedicated for agriculture systems as shown in

Figure 9.3, is likely a reflection of increasing land efficiencies and increasing agricultural

productivity yields in Brazil, to 2030.

The developed area of land in Brazil is reflected by the ‘Built-Up Land Area’ category within

Figure 9.3. The area of developed land is shown to increase over the analysis timeframe, but

still represents a minor proportion of the overall land area in Brazil.

The ‘Other Land’ category of Figure 9.3 represents land that has been identified which could

potentially be utilised for resource growth, but its current application rests outside the

categories analysed within the BRM. This land area is shown to increase from 22.8% to

27.1% over the analysis timeframe. This additional land is assumed to represent previous

forestry systems or land previously categorised as agricultural land within the BRM – land

‘freed-up’ through overall improvements in land and agricultural productivities. This

category represents the second largest proportion of overall land.

69) Figure 9.3: Brazil Land U se within the Basel ine Scenario to 2030

Figure 9.3: Brazil Land-Use within the Baseline Scenario to 2030

Brazilian Land-Use Classifications within the Brazil

Baseline Scenario to 2030


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9.5.3 Brazil Baseline Scenario – Biomass Resource Availability Analysis

The next section presents the results of the Brazil Baseline Scenario analysis, determining the

availability of different biomass resources to 2030.

A. Biomass Resource Availability Analysis – Results & Discussions

Figure 9.4, documents the biomass resource availability analysis for the Brazil Baseline

Scenario to 2030. The stacked bar charts of Figure 9.4 represent the extent of resource

availability for the given year in, the analysis timeframe. The stacked line charts linking the

bars allow further visual analysis, by providing a breakdown of the different resource within

each of the resource categories of the BRM: Grown Resources, Residue Resources, and

Waste Resources (further described in Table 4.1).

Figure 9.4 does not have to be examined closely to identify the clear dominance in

availability of a few key resources in comparison to the overall totals. The ‘Energy Crops’

category reflects the availability of all oil, sugar, and cereal-based crop species. They are

forecast to contribute >71.5% of the resource total by 2015; rising to >74.8% by 2030. The

production and availability of sugarcane resource, is by far the dominant contributor to this

resource group. This analysis outcome aligns with Brazil’s standing as the World’s second

largest sugarcane producer, and largest exporter [416], [488].

Other significant resources with large availabilities reflected within Figure 9.4, are: the

‘Biomass Crops’, ‘Forestry Resources’, and ‘Agricultural Residues’. ‘Biomass Crops’

consisting of plantation species such as eucalyptus and poplar, are forecast to contribute

>5.9% of the resource total by 2015, falling to >4.2% by 2030. ‘Forestry Resources’

reflecting resources harvested directly from forestry systems, are forecast to contribute

>14.1% of the total resource by 2015, falling to 10.94% by 2030. ‘Agricultural Residues’

consisting both of straws and slurry resources, are forecast to contribute >7.5% of the

resource total by 2015, rising to >8.6% in 2030.

The production dominance and increasing focus on the growth of ‘Energy Crops’

(sugarcane), overshadows that of the other main biomass resource categories. Despite the

availabilities of both ‘Forestry Resources’ and ‘Biomass Resources’ increasing over the

analysis timeframe, their overall proportional contribution to the biomass resource total

decreases – such is the rapid rate of increase in the forecast for sugarcane production. A

further ‘beneficiary’ within the resources category, resulting from this rapid increase in

energy crops, is the linked increase in agricultural residues; the proportional contribution of


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agricultural residues and specifically straw resources, are forecast to increase over the

analysis timeframe.

A further area of analysis demonstrated in Figure 9.4, is the relative contribution made by

each of the resource categories. The ‘Grown Resources’ category is by far the most dominant

contributor to biomass resource availability in Brazil. ‘Residue Resources’ driven largely by

contributions from agricultural residues are shown to represent further resource opportunities;

especially towards the end of the analysis timeframe. The availability and potential of ‘Waste

Resources’ for the bioenergy sector within the Brazil Baseline Scenario, are shown to be

negligible in comparison to that of the other resources. This characteristic is likely a result of

the limitations to the infrastructure and extent of Brazilian waste management systems.

70) Figure 9.4: Brazil Biomass Resource Availabil ity within t he Baseline Scenario to 2030

Figure 9.4: Brazil Biomass Resource Availability within the Baseline Scenario to 2030

A. Brazil BRM Validation

This section presents a validation exercise, comparing the biomass resource availability

forecasts from the Brazil Baseline Scenario to those highlighted within other studies, reports,

and literature. This analysis is undertaken to test the credibility of the Brazil BRM’s analysis

outputs.

A comparative analysis is undertaken, focusing on the forecast availabilities for both forestry

resources and energy crops. These resource categories are chosen as they represent the two

most dominant resource categories highlighted by the Brazil Baseline Scenarios results.

Forecast Biomass Resource Availabilities within

the Brazil Baseline Scenario to 2030

Sugarcane

Resource

Forestry

Resource


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A wide literature review was undertaken to evaluate the range of existing forecasts which

estimate availabilities of forestry resources and wood-based resources [470], [489], [522],

[523], and also energy crop resources [123], [124], [180], [251], [524]–[533]; produced by

Brazil in the year 2030. The Brazil Baseline Scenario’s forecasts for these resources in 2030

are then compared to the forecasts from the relevant studies. These comparisons are presented

in the graphs of Figures 9.5 and 9.6.

71) Figure 9.5: Brazil Baseline Scenario – Va lidation of Wood Resource Availability Forecasture

Figure 9.5: Brazil Baseline Scenario Validation of Wood Resource Availability Forecast

Range of Brazilian Wood

Resource Availability

Forecasts by 2030

According to Literature,

and Forecast Availability

within the Brazil Baseline

Scenario


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72) Figure 9.6: Brazil Baseline Scenario – Va lidation of Energy Crop A vailability Forecast

Figure 9.6: Brazil Baseline Scenario Validation of Energy Crop Availability Forecast

Figures 9.5 and 9.6, demonstrate that the forecasts of the Brazil BRM’s Baseline Scenario for

the availability of both Wood Resources, and Grown Energy Crop Resources; fall within the

range of existing forecasts for Brazil in 2030. Figure 9.5, demonstrates that the Baseline

Scenario Wood Resource availability forecast is placed in the lower third of the range of

forecasts, demonstrated within existing relevant studies. Figure 9.6, highlights that the

Energy Crop Resource availability forecast is placed towards the middle of the range of

forecasts, demonstrated within existing relevant studies. Therefore, this exercise and analysis

supports the credibility of the forecasts generated by the Brazil BRM, and specifically from

the Brazil Baseline Scenario.

Range of Brazilian Energy

Crop Resource Availability

Forecasts by 2030

According to Literature,

and Forecast Availability

within the Brazil Baseline

Scenario


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9.6 Brazil Bioenergy Scenarios

This next section introduces the bioenergy scenarios analyses, undertaken using the Brazil

BRM Baseline Scenario. The aim of this section is to analyse how Brazil’s potential biomass

resource balance may change, should the Brazilian Government decide in the future to

implement strategies that vary their domestic consumption of biomass resources, to meet

their own domestic energy demands.

The key output from this section is the evaluation of the extent to which global biomass

markets may be impacted, should the actual future exports from countries such as Brazil

differ from, the expected forecasts widely documented by literature (Chapter 8).

This section is structured by initially undertaking an analysis of Brazil’s current energy

systems, and highlighting the strategies and policies directing Brazil’s current energy

pathway. Brazil’s energy strategies and related targets are then compared to those of other

countries from around the World. This comparative analysis will provide further context, and

allow the Brazilian Government’s energy ambitions to be benchmarked against those of other

countries. The comparison may also provide some useful insight into how Brazil’s energy

strategies and targets may evolve in the future.

A series of ‘Brazil Bioenergy Scenarios’ are then developed to reflect varying energy

strategies that Brazil may pursue. These scenarios are then applied to the Brazil BRM

Baseline Scenario to evaluate how Brazil’s biomass resource balance and potential resource

exports, may be impacted under the influence of varying energy strategies and targets.

9.6.1 Brazil Current Energy System

As highlighted the first step in this section is to undertake an analysis outlining the nature of

the Brazil’s current energy system. This is best reflected by data and reports provided by the

International Energy Agency (IEA) within their ‘Energy Balance Reports’ [534]. Figures 9.7,

9.8, and 9.9, introduce pie-charts presenting the IEA’s data which reflects: energy

technologies contributing to Brazil’s primary energy supply in 2011 (Figure 9.7), the fuels

and resources utilised to generate Brazil’s power in 2011 (Figure 9.8), and the fuels and

resources utilised within Brazil’s transport sector in 2011 (Figure 9.9). A similar graph is not

presented describing the fuels and resources utilised to generate Brazil’s heat energy, as

dedicated heat and power is generated almost entirely utilising charcoal biomass resources

[132].


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Figure 9.7: Technologies Contributing to Brazil’s Total Primary Energy Supply (2011) 73) Figure 9.7: Energy Technolog ies Contributing to Brazil’s Total Primary Energy Supply (2011)

Data from, [534]

Contribution of Resources to Brazil’s

Primary Energy Supply (2011)


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74) Figure 9.8: Fuels & Resources Contribut ing to Generate Brazil’s P ower (2011)

Figure 9.8: Fuels & Resources Contributing to Generate Brazil’s Power (2011)

Data from, [534]

Contribution of Resources to

Brazil’s Power Generation (2011)


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75) Figure 9.9: Fuels & Resources Utilised wit hin Brazil’s Transport Sector (2011)

Figure 9.9: Fuels & Resources Utilised within Brazil’s Transport Sector (2011)

Data from, [534]

A. Brazil’s Total Primary Energy Supply

Figure 9.7 presents the data related to Brazil’s total energy supply in 2011[534]. The

proportions within the pie chart reflect the contribution from different inland energy

technologies; taking account of all energy processes and the utilisation of all fuels and

resources for energy generation [535].

This analysis highlights the balance in contributions from both renewable and conventional

fossil-based energy pathways. Crude oil represents the largest single contributor (45%), with

hydroelectric (31%), and bioenergy pathways (15%), providing further notable contributions

in 2011.

B. Brazil’s Power Sector

The characteristics of Brazil’s power generation sector in 2011 are documented by the data

presented in Figure 9.8. The outstanding feature highlighted by the pie-chart is the large

power generation contribution from hydroelectric power (81%). Biofuels (6%) represent the

Contribution of Resources to

Brazil’s Transport Energy (2011)


290

second largest contributor in 2011, with the remaining power generated from a broad range of

other energy technologies.

The current dominance of hydroelectric power stems from the 1,025 Hydro Power Plants

presently operating as of 2012; the Itaipú Hydro Power Plant alone generating 14,000 MW,

equivalent to approximately 20% of Brazil’s electricity consumption [536].

The key output from this analysis most relevant to this Thesis section, is the dominance of

hydropower and the likely future trend in further hydropower development; determining the

future direction and nature of Brazil’s power sector.

C. Brazil’s Transport Energy Sector

Figure 9.9 presents the energy data for Brazil’s transport sector in 2011[534]. Once again this

pie-chart demonstrates a balance of the fuels and resources utilised in Brazil’s transport

energy sector. Biofuels are highlighted as the largest contributor (47%) to energy demand,

almost balanced with oil-based transport fuels (46%) in 2011. The large contribution from

biofuels reflects Brazil’s focus [488], and mandates [537], requiring the widespread

utilisation of blended fuels across its transport sector.

The key output from the analysis relevant to this Thesis section, is the engrained biofuel

demands of the Brazilian transport sector. Brazil’s future domestic requirements for biofuels

and related feedstocks are likely to be highly linked to future trends within the transport

sector.

D. Brazil’s Heat Energy Sector

Biomass and specifically charcoal, is the current predominant fuel of choice for both

domestic and industrial heat generation. Heavy industries such as Brazil’s steel plants are also

notably reliant on charcoal instead of coal to drive their heat generation and production

processes [538]. Although coal consumption by industry is growing, the dominance of

charcoal is encouraged by the Government within the ‘National Plan on Climate Change’

[539], and remains an abundantly available resource.

Trends influencing the continued and further utilisation of biomass (charcoal) within Brazil’s

heating sector will therefore be a further key factor influencing Brazil future domestic

biomass demands; and thus may potentially influence the availability of resources for export.


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9.6.2 Brazil’s Future Energy Strategy & Targets

This section progresses through evaluating Brazil’s relevant energy initiatives, targets,

strategies, and mandates. Discussions are provided highlighting how each may drive the

future direction of Brazil’s energy sector.

A. Brazil Energy Sector – Strategies

The Strategies listed in this section represent Brazilian Governmental current targets and

broad objectives, for the development of the future Brazilian energy sector.

i. ‘Brazil’s ‘2010 to 2019 Decennial Plan for Energy Expansion’ Plan (translated) [540]

Brazil’s Decennial Plan sets out a series of energy targets to aim towards by 2019. The key

themes of these include plans to start the phasing-out of fossil fuel plants, replaced by the

following:

Hydro-Power : Target from 83.1 GW in 2010 to 116.7 GW by 2019

Small Scale Hydro-Power: Target from 4 GW in 2010 to 7GW by 2019

Bioenergy: Target from 5.4 GW in 2010 to 8.5 GW by 2019

Wind-Power: Target from 1.4 GW in 2010 to 6 GW by 2019

ii. Brazil’s ‘National Climate Change Plan’ (translated) [539]

The National Climate Change Plan is Brazil’s key strategy that focuses on reducing GHG

emissions, energy efficiency, renewable energy generation, rural electrification, and the

potential climate change impacts from deforestation. Specific key targets of the plan are:

Target for electricity produced through co-firing pathways with sugarcane based

biofuels to be at least 11.4% by 2030.

Stimulate the use of thermal solar domestic heating applications.

Investigate how to facilitate energy from waste pathways.

Increase the utilisation of biofuels.

Encourage the industrial sector to increase their utilisation of biofuels.

iii. ‘India-Brazil-South Africa Declaration on Clean Energy’ [541]

This Declaration reflects a joint Strategy for the Governments of Brazil, India, and South

Africa to pool resources and research knowledge, relating to renewable energy technologies.

The objective is to promote and stimulate growth of the renewable energy sector through

collaboration. This includes cooperation in research on clean coal technologies, innovative

technologies, advanced bioenergy pathways, and the commercialisation of clean energy.


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iv. Brazil’s ‘2030 National Energy Plan’ (translated) [533]

Brazil’s 2030 National Energy Plan lays out further targets for the Brazilian energy sector to

work towards by 2030. The Plan sets out broad objectives within the following themes:

Large focus on developing Brazil’s natural gas power sector over the timeframe.

Further focus on the development of Brazil’s nuclear sector.

Increased utilisation of biomass and biofuels.

Focus on energy efficiency measures.

B. Brazil Energy Sector – Targets & Mandates

This next section presents some the specific energy related mandates and targets currently

active within Brazil.

i. Brazil’s ‘Renewable Energy Auction Requirements’ [542], [543]

Brazilian energy distributors are required to sign up to long-term contracts when auctioning

their electricity to meet demands. This is undertaken through a reverse auction system that

favours low carbon and renewable energy generation.

The structure of this auction creates incentives for the developers of low carbon and

renewable energy plants as it enhances their position in the market. An example being

Brazil’s first biomass-only reserve auction in 2008, where 2,379 MW of power from a series

of 31 sugarcane and Napier Grass feedstock powers plants, were auctioned with contracts

extending to 15 years [543].

ii. Brazil’s ‘Biodiesel Mandates’ [537]

Brazil’s Mines & Energy Ministry have enacted laws that mandate the requirement for

biodiesel to have minimum blend inclusions of a mix of plant, crop, or reused oils.

In 2008, the ANP Resolution 7/2008 established new minimum biodiesel specification

requirements, setting limits on many of the parameters that were previously only

reported. It was mandated that biodiesel blend should be set at 5% (B5 Biodiesel).

In 2011, a biodiesel blend mandate of 10% (B10 Biodiesel) was proposed by the

Energy Ministry for Presidential approval.

In 2012, a consultation was undertaken aimed at enabling the use of B100 biodiesel

and to expand the number of possible raw materials that may be included in the blend.


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iii. Brazil’s ‘Bio-ethanol Mandates’ [377], [544], [545]

Since 1976 the Brazilian Government has mandated the blending of bio-ethanol with

conventional fossil fuels. This initially required a blend range of 10-22% bio-ethanol content,

although this has progressed to the present day where there are no longer any light vehicles in

Brazil, operating on pure petroleum based fuels [545].

In 1993, the mandatory blend was fixed by law at 22% anhydrous ethanol (E22) by

volume in the entire country, but with leeway to the Executive to set different

percentages of ethanol within pre-established boundaries.

In 2003, the blend range was amended within set limits: at 20% minimum, and a

maximum of 25%.

In 2007, a blend of 25% bio-ethanol (E25 Blend) was mandated.

In 2011, the lower limit was reduced to 18% as a result of recurring ethanol shortages

and periodic high prices between harvest seasons [546].

C. Brazil Future Energy Strategy & Targets - Discussion

The Brazilian Energy Strategies discussed within this section, provide insight into the future

potential directions of the Brazilian energy sector. Brazil’s Decennial Plan [540], sets out

strong targets for increased renewable energy generation, especially for large-scale

hydroelectric power. Whilst the analysis of Brazil’s key energy mandates highlighted Brazil’s

strong links and further aspirations for the role of biofuels within the transport sector.

These suggest that Brazil has fixed targets and a desired direction involving the increased

utilisation of biofuels and hydroelectric power.

Brazil has large resources and favourable conditions to match its strong aspirations for

increased hydropower and biofuels. However, further reports [536], [547], highlight that

regardless of Brazil’s various energy strategies, the majority of the near-term major

developments within the Brazilian energy sector, are /or have been focused on the

development of the fossil fuel sectors; specifically natural gas plants. Much of this impetus

linked to the hosting of major sporting events; the 2014 Football World Cup, and the 2016

Olympics. Szklo et al (2013) [547], forecast that the contribution of renewable energy

generation to Brazil’s energy mix will be much diminished in 2030 compared to current

levels, unless the pace of renewable technology development keeps up with that of

conventional fuels.


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The analysis within Chapter 8 found that Brazil is a global giant in terms of its productivity

of biofuel feedstocks, and has great potential to vastly increase its productivity levels further.

Brazil is also a potential sleeping giant in relation to future wood-based biomass resources,

especially pellet production, and has large existing [132] and vast potential hydropower

opportunities [533]. In reflection of these themes, the current ambitions, targets, and

aspirations for Brazil’s energy sector could be described as modest at best compared to the

targets of some other countries with less fortunate resource backing.

9.6.3 Global Comparisons – Leading Energy Targets & Strategies

Building on the theme developed in the previous section, the following discussions provide

some comparisons and analyses of Case Study Countries, who currently lead the way in

terms of energy strategies, renewable energy contribution targets, GHG emission reductions,

and energy efficiency.

The aims of this section are to move the discussion towards identifying the potential

directions that the Brazilian Government could take, if it were to decide to utilise a greater

proportion of its natural resources to generate its future energy.

A. Case Study Countries

The Governments of many Nations are currently discussing and implementing targets and

strategies that have varying levels of ambition: to reduce their respective GHG emissions,

increase the development of renewable energy technologies, and enhance energy efficiencies

[548]. Various databases are available online [133], [134], [396], that allow the search and

comparison of energy related policies and targets for different countries; with more than 60

countries currently having national targets or policies supporting renewable energy

technologies [396].

Three Case Study Countries have been strategically identified, each implementing different

approaches in working towards their respective ambitious energy system objectives:

Germany, Norway, and Denmark. Although at first thought, the similarities between each of

these countries and Brazil may seem slim in terms of comparisons of their stages of

development, resource availability and land characteristics; it is Brazil who has the land area,

the natural resources and the development momentum. As the World Resources Institute

stated [549], Brazil is placed amongst the top GHG emitting countries, but looking forward to

2030 and beyond, it also has great potential and the capability to achieve large reductions in

emissions.


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i. Case Study - Germany

Germany is chosen as the first Cast Study Country as it appears to have juxtaposed and

contradictory characteristics; being a country with a strong and growing industrial base as

well as having strong renewable energy and carbon reduction ambitions. Therefore, the

energy strategy and targets developed by the German Government, focusing on increasing

renewable energy contributions and GHG emissions reductions whilst also maintaining this

industrial base; present an example of a case study that could appear to be reflective of a

large number of other countries.

The German Government’s ‘Energy Transition Strategy’ (translated name) [550], sets out

aims and objectives for “making German industries stronger through making them more

efficient for a greener future”. The roadmap developed to achieve these targets focuses on

increasing energy efficiency through mandating a series of progressively stringent energy

reduction waypoints, accompanied by the increasing contributions from the range of

renewable energy technologies [551]. Germany’s key mandated energy targets are as follows:

Energy Demand Reduction Targets - Reduce energy demand below 2008 baseline

levels by: 20% by 2020, and by 50% by 2050.

Renewable Energy Contributions Targets – Achieve contribution levels of: 18% by

2020, 30% by 2030, and 60% by 2050.

Electricity Reduction Targets – Reduce electricity consumption below 2008

baseline levels by: 10% by 2020, and 25% by 2050.

Renewable Electricity Contribution Targets – Achieve renewable electricity levels

of: 35% by 2020, 50% by 2030, 65% by 2040, and 80% by 2050.

ii. Case Study - Norway

Norway is chosen as the second Case Study Country as Norway represents a highly

developed country that has vast fossil fuel resource reserves, an economy heavily linked to

the oil and gas industry, but also a country with strong renewable energy and GHG reduction

ambitions. Norway’s relationship with fossil fuels makes it an interesting case study of a

country whose ambitious energy strategies stand out; representing a potential reference for

countries with similar characteristic contradictions.

Norway is ranked 62nd in terms of its land area [146], 118th in terms of its population size

[145], but in 2011 was the World’s 8th largest exporter of crude oil and the third largest

exporter of natural gas, with significant reserves still untapped under the North Sea [552]. In


296

addition to having these strong linkages with the oil and gas industry, Norway has set itself

the target of becoming carbon neutral by 2050 [553].

The key element to Norway’s strategy for achieving this goal is the provision for trading

international carbon credits [554] as part of its energy policies. Norway’s ‘Energy Road Map

2050’ [555], confirming that all existing and new policy instruments relating to Norway’s

petroleum, energy and transport sectors, will target GHG emission reductions. These will be

achieved principally through increasing energy efficiencies, increasing the contributions from

renewable energy technologies, and progressively implementing technological advances that

result in carbon or energy savings. The utilisation of fossil fuel based energy systems being

minimised, reflecting a low baseline contribution to the Norwegian energy mix.

iii. Case Study - Denmark

Denmark is the third Case Study country chosen, as it also has unique characteristics that

make it stand-out as a leading country in terms of renewable energy and GHG emission

reductions.

The Danish Government has taken the stance that it is no longer possible to build prosperity

on the back of finite fossil fuel resources, and thus future Danish prosperity should be

founded on energy efficiency and renewable energy technologies [556]. As such, Denmark

has developed a series of ambitious energy-policy targets that will lead towards Denmark’s

whole energy and transport systems, being based 100% on renewable energy technologies by

2050 [557].

The Case Study of Denmark represents the upper limits of potential that any given country

could aspire to, in order to reduce the impacts of its energy system. Therefore, Denmark’s

energy targets and strategies are relevant to all countries.

9.6.4 Developing Brazil Bioenergy Scenarios

This next section develops a series of Bioenergy Scenarios that represent potential directions

the Brazilian bioenergy sector could take in the future. Drawing on the themes provided by

the Case Study Countries in the previous section, these Bioenergy Scenarios will reflect

ambitious targets that Brazil could potentially develop, if it were to move towards greater

utilisation of its renewable resources.

The key element of the analysis relevant to this section of the research is the identification of

how the potential demands of the future Brazilian bioenergy sector may change. The biomass

resource demands from each of the Bioenergy Scenarios are compared to the potential


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availability of resources, forecast within the Brazil BRM Baseline Scenario. The target of this

analysis is to identify how Brazil’s resource balance may change, and essentially the extent to

which potential Brazilian biomass exports may be impacted, if Brazil were to utilise a greater

proportion of its resources for its domestic energy demands.

A. Brazil Bioenergy Scenario - Structures

The developed Bioenergy Scenarios reflect trajectories of how demand for energy from

different technologies may evolve in Brazil. This methodology reflects other research [280],

[558]–[561], where energy-modelling scenarios have been applied to provide instruments to

inform policymakers on decisions relating to GHG reduction and renewable energy targets.

The Bioenergy Scenarios are developed utilising the IEA’s ‘World Energy Outlook’

Scenarios [132], to inform a baseline of trends for how the Brazilian energy sector may

evolve. The IEA Scenarios are utilised as follows:

i. IEA’s ‘Current Policies Scenario’ [132]

The IEA’s Current Policies Scenarios are developed to reflect how the energy systems of

different countries may evolve, based on the assumption that all energy strategies and polices

that are current and formally adopted, will be realised through to maturity.

The Brazilian Current Policies Scenario developed by the IEA, therefore reflects what the

Brazilian energy system may look like if all of Brazil’s current energy policies and strategies

were realised.

ii. IEA’s ‘450 Scenario’ [132]

The IEA’s 450 Scenarios are developed reflecting how the energy systems of different

countries may evolve, assuming implementation of their high-end national pledges and strong

policies beyond 2020. The overarching aims of the 450 Scenarios includes the near-universal

reduction of fossil-fuel based subsidies, to achieve the objective of limiting the global

concentration of GHG’s in the atmosphere to 450 parts per million of CO2, and global

temperature increase to 2°C.

The Brazilian 450 Scenario developed by the IEA, therefore reflects what the Brazilian

energy system may look like if increased focus is placed on renewable technologies, and the

utilisation of fossil fuel systems are reduced.


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B. Introducing the Brazil Bioenergy Scenarios

The developed Brazil Bioenergy Scenarios are the: ‘Current Policy Scenario’, ‘Energy

Efficiency Scenario’, ‘Carbon Trading Scenario’, and the ‘100% / 2050 Scenario’. These are

developed taking inspiration from the energy targets and strategies from the Case Study

Countries discussed in Section 9.6.3.

The specific targets and policies modelled within the scenarios progress to a 2050 time limit.

However, as the Brazil BRM’s analysis runs to the year 2030, the Brazil Bioenergy Scenario

analysis represents a reflection of progress to 2030.

Figure 9.10, is developed to reflect the contribution of different energy technologies towards

Brazil’s energy system to 2030, within each of the Bioenergy Scenarios. The extent of the

stacked bar charts represents the total energy demand for Brazil through the timeframe, for

each scenario. The proportion of the bars dedicated to each energy technology, reflects the

contribution of these energy technologies to the total demand. The first bar within the chart

reflects historic data for 2008; providing a reference year against which the scenario

trajectories may be compared.

76) Figure 9.10: Brazil Bioe nergy Scenarios – Contribution from Energy Technologies

Figure 9.10: Brazil Bioenergy Scenarios – Contribution from Energy Technologies

The following sections introduce the Bioenergy Scenarios developed for the Brazil BRM, and

provide further discussion for how they are developed, and their linkages with the IEA

Contribution of Energy Technologies within the

Brazilian Bioenergy Scenarios


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Scenarios and other inspiring literature. The dynamics of Figure 9.10 with respect to each

Bioenergy Scenario are also discussed.

C. Brazil Bioenergy Scenario – Current Policy Scenario

The Current Policy Scenario (C-P Scenario) is developed to reflect how the contribution of

different energy technologies may evolve, if current Brazilian energy targets and strategies

are realised. The characteristics of this scenario reflect those of the IEA’s Current Policies

Scenario [132].

This scenario is important to the analysis as it provides a baseline indication of how Brazil’s

bioenergy sector may evolve, and the potential extent of Brazil’s domestic biomass resource

requirements by 2030, without any policy changes. The range of variances of the other three

scenarios compared with the C-P Scenario thus provides an indication of the levels of

ambition that they represent.

Figure 9.10, highlights that within the C-P Scenario there is a gradual increase in energy

produced from both renewable and fossil fuel energy technologies, to 2030. The energy

generated by each of the energy technologies, apart from the coal energy pathways; are

forecast to increase. Energy contributions from both biomass and hydropower technologies

are shown to increase but only in-line with those from other technologies.

Overall, there is a gradual but significant increase in total energy demand over this period.

This being especially evident when compared to Brazil’s energy demands for the historic

data-reference year (2008). Few significant trends can be identified to indicate that

prioritisation is focused towards any given energy technology.

An overview of the themes and targets of the C-P Scenario are summarised within Table 9.7.

D. Brazil Bioenergy Scenario – Energy Efficiency Scenario

The Energy Efficiency Scenario (E-E Scenario) has been developed drawing influences from

the Case Study of Germany. Within this scenario, energy reduction is the key target achieved

by increased efficiency. Focus is also placed on key renewable technologies over fossil fuels.

This E-E Scenario adopts ambitious energy reduction targets reflecting those developed by

Germany. This includes a target for a 20% reduction in primary energy demand by 2020

based on 2008 levels, and a 50% reduction by 2050. A further reduction in electricity demand

is targeted to reflect a 10% reduction in consumption by 2020 based on 2008 levels, and a

25% reduction by 2050.


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The E-E Scenario makes the assumptions that energy generation trends from hydroelectric,

nuclear power and other renewable technologies (wind & solar etc.), will continue to increase

in-line with those forecast within the IEA inspired C-P Scenario. The C-P Scenario also

assumes that energy from natural gas plants and bioenergy technologies will be maintained;

as reductions are prioritised from oil and coal technology pathways. It is assumed that energy

contributions from oil, and coal-energy technologies, will reduce over the analysis timeframe

in-line with falling energy demand.

The overall energy and specific technology contribution trends, for the E-E Scenario are

clearly highlighted within Figure 9.10. The standout observation from this analysis is the

forecast showing an overall reduction in energy demand over the analysis time frame; the

historic data of the reference year (2008) putting this trend in perspective. As stipulated, the

levels of energy generated from hydroelectric, nuclear, and other renewable technologies are

forecast to increase over the analysis timeframe. The energy contribution from natural gas

and bioenergy technologies are shown to be maintained as the contributions from oil and gas

technologies falls.

An overview of the themes and targets of the E-E Scenario are summarised within Table 9.7.

E. Brazil Bioenergy Scenario – Carbon Trading Scenario

The Carbon Trading Scenario (C-T Scenario) has been developed drawing influences from

the Norway Case Study. The focus of this scenario is the reduction in contribution of fossil

fuel energy systems to minimum baseline levels by 2050, a reduction of energy demands

through enhanced efficiencies, and the increased focus on renewable technologies.

The specific characteristics of the C-T Scenario are developed through utilising assumptions

reflective of the IEA’s ‘450 Scenario’ for Brazil [132], and also from the ‘Pathways to a

Low-Carbon Economy for Brazil’ Report [548], produced by McKinsey & Company, aimed

at informing Brazilian and global policy makers.

McKinsey & Company (2011) [548], concluded that Brazil could potentially continue to

develop within an evolved energy mix, that included contributions from fossil fuel energy

technologies that reflected 14% of total demands. Therefore, the C-T Scenario utilises this

14% fossil fuel technology contribution as the potential baseline level. The C-T Scenario is

therefore developed to assume that the contributions from all fossil fuel technologies will

reduce at a steady rate, to achieve 14% minimum contribution levels by of 2050


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The C-T Scenario is developed on the assumption that total energy demands and the energy

contributions from hydroelectric, nuclear, and other renewable technologies will continue to

increase in-line with levels forecast within the IEA’s 450 Scenario [132]; as the contribution

from fossil fuel technologies fall. The C-T Scenario assumes that bioenergy technologies will

be increasingly utilised to balance the overall demands.

The trends and characteristics of the C-T Scenario are also reflected within Figure 9.10. The

overall energy demands of the Scenario are shown to increase, reflecting the forecasts of the

IEA ‘450 Scenario’. The contributions from hydroelectric, nuclear, and other renewable

technologies, increase to the limits also forecast within the IEA’ ‘450 Scenario’. The

contributions from fossil fuel based technologies can be seen to fall at a steady rate, whilst

the contributions from bioenergy technologies increase to balance the demand.

An overview of the themes and targets of the C-T Scenario are summarised within Table 9.7.

F. Brazil Bioenergy Scenario – The 100% / 2050 Scenario

Finally, the 100% / 2050 Scenario (100-50 Scenario) has been developed drawing influence

from the Denmark Case Study. This scenario is designed to analyse the potential dynamics if

Brazil were to adopt a similar ultimate target; to generate 100% of its energy from renewable

and low carbon technologies by 2050.

The 100-50 Scenario is developed reflecting a similar structure to that of the C-T Scenario.

Increases in total energy demands and contributions from hydroelectric, nuclear, and other

renewable technologies, reflect the forecast of the IEA’s ‘450 Scenario’ [132]. The

contribution of fossil fuel technologies are assumed to gradually reduce, in reflection of

trends that would see their contributions reduced to 0% by 2050, if realised. The contribution

from bioenergy technologies are assumed to increase at rates required to balance the energy

demands, thus largely taking over from the energy demands previously provided by fossil

fuel technologies.

The nature and characteristics of the 100-50 Scenarios are also reflected within Figure 9.10.

Here, the overall energy demands of the Scenario and the contributions from hydroelectric,

nuclear, and other renewable technologies, are shown to increase to the limits also forecast

within the IEA’s ‘450 Scenario’. The contribution from fossil fuel technologies are shown to

fall, notably at a greater rate than those demonstrated within the C-T Scenario. Likewise the

contributions from bioenergy technologies are shown to increase, again notably greater than

levels reflected within the C-T Scenario.


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An overview of the themes and targets of the 100-50 Scenario are summarised within Table

9.7.

Table 9.7: Overview of Brazil Bioenergy Scenario Themes and Targets Table 47) Table 9.7: Overview of Brazil Bioenergy Scenario The mes and Targets

Bioenergy Scenario Scenario Key Targets & Themes

Current Policy Scenario Energy efficiency & renewable contribution levels reflect those of the IEA’s ‘Current Policy

Scenarios’ [132].

Energy Efficiency Scenario Energy demand reduction targets below 2008 levels: 20% by 2020 / 50% by 2050.

Electricity reduction targets below 2008 levels: 10% by 2020 / 25% by 2050.

Carbon Trading Scenario

Fossil fuel energy contribution decrease to 14% minimal levels by 2050, reflecting the ‘McKinsey & Company’ Scenario [548]

Renewable energy technology contribution increases, and energy efficiency increases

reflecting the IEA’s ‘450 Scenario’ [132].

Bioenergy pathways further contributing to balance demand.

100% / 2050 Scenario

Renewable energy contribution target: 100% by 2050.

Renewable energy technology contribution increases and energy efficiency increases

reflecting the IEA’s ‘450 Scenario’ [132].

Energy from biomass pathways further contributing to achieve 100% renewable generation.

9.6.5 Brazil Bioenergy Scenarios – Bioenergy Potentials

The next step in the Bioenergy Scenarios analyses is to evaluate the energy-demand

dynamics of the scenarios, and compare and place them in the context of the forecasts of the

Brazil BRM Baseline Scenario.

A. Brazil Bioenergy Potentials - Results

The key output data allowing this analysis is presented within the graph of Figure 9.11, where

the energy data and trends are highlighted. At the top of Figure 9.11 the forecast range of

Brazil’s future primary energy demands are shown. This range of data reflects that of the

energy scenario forecasts developed by the IEA [132]. The primary energy demand trend line

shown to be reducing over the analysis timeframe, further reflects the potential trajectory of

primary energy demand, if Brazil were to adopt and realise energy reduction targets as

described for the E-E Scenario.

The series of solid coloured lines towards the bottom of Figure 9.11, each represent the

various bioenergy demands that would be required if any of the developed Bioenergy

Scenarios were realised. These demands reflecting the proportional contribution of bioenergy

technologies to the overall energy mix within each scenario.

The dashed coloured line presented within Figure 9.11, represents the bioenergy potential of

the resources forecast as being available within the Brazil BRM Baseline Scenario. These

bioenergy values are calculated reflecting the quantities of different resources forecast as

being available and converted to bioenergy; utilising the ‘preferred’ energy conversion

pathways applicable to each resource. The details of the chosen pre-treatment and conversion


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pathways relevant to each resource are discussed further within Chapter 4, and are

documented in Appendix 1.0.

77) Figure 9.11: Brazil ian Primary Energy & Bioenergy Demand Forecasts

Figure 9.11: Brazilian Primary Energy & Bioenergy Demand Forecasts

B. Brazil Bioenergy Potentials - Discussion

Figure 9.11 demonstrates a broad range of trends. The standout observation is the extent of

the bioenergy potential of resources, forecast as being available within the Brazil BRM

Scenario. These are shown to have the capacity to potentially contribute >47% of Brazils

total primary energy demand by 2015, rising to levels that may exceed their total energy

demand by 2030. This analysis once again highlights and reaffirms Brazil’s position as a

major current producer, and also a major potential future producer of biomass resources. The

analysis also significantly validates the concept that Brazil has large biomass resource

capacity and the potential capability if it chooses in the future, to redirect its energy sector to

make greater use of these – potentially reflecting any of the developed Bioenergy Scenarios.

The distance within Figure 9.11 between the Baseline Scenario’s Bioenergy Potential and the

bioenergy demands of the respective scenarios, also provides an indication of the extent of

potential surplus biomass resources that Brazil may have available, if any of the scenarios

Forecast Bioenergy Potential, Primary

Energy Demands & Bioenergy Demands

within the Brazilian Bioenergy Scenarios


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were realised. The bioenergy potential of resources, forecast as being available within the

Baseline Scenario, far exceeding the forecast demands for each of the developed scenarios.

These ‘surplus’ resources are in theory potentially available for trade on the global biomass

markets.

Figure 9.11 also highlights that if Brazil’s current energy policies and strategies were to be

realised as reflected by the C-P Scenario, the contribution of bioenergy to Brazil’s energy

mix would remain relatively steady. Figure 9.11 highlights that the energy contribution from

bioenergy technologies within the C-P Scenario reflects >31% of total energy demand in

2015, 32% in 2020, and 33% in 2030. This analysis reaffirms some of the previous

discussions, further highlighting that Brazil’s current energy strategies and targets are

relatively conservative in terms of growing the contribution of the bioenergy sector. The

analysis also highlights that there may be potential in the future for the Brazilian Government

to increase its focus on bioenergy technologies using Brazilian resources.

9.6.6 Brazil Bioenergy Scenarios – Resource Balance Analysis

The final analysis step, focusing on the Brazil Bioenergy Scenarios is to undertake a resource

balance analysis. Reflecting the methodologies developed in Chapter 7 for the similar

analysis undertaken for the UK, this section evaluates the types, and the extent to which

Brazil would need to use its available biomass resources, if it were to balance its bioenergy

demands in reflection of any of the developed Bioenergy Scenarios being realised. Likewise,

the analysis also allows an assessment of the ‘surpluses’ of biomass resources that may

potentially be available for trade on the global biomass markets.

A. Resource Balance Analysis – Methodology

As already highlighted, the resource balance analysis methodology undertaken for Brazil

reflects that previously developed for the UK in Chapter 7. The key dynamics required are:

an assessment of Brazil’s bioenergy demands (Section 9.6.2), an analysis of the type and

extent that resources may be available, their suitability for utilisation to contribute towards

meeting the bioenergy demands (Section 9.5), and unique to the Brazil analysis; identifying

the specific resources that may be suitable or prioritised for the export market, if identified as

being surplus to domestics demands.

i. Suitability of Brazilian Biomass Resources for Potential Exportation

Table 9.8 provides a summary of Brazilian resources marked as being potentially suitable for

exportation within the resource balance analysis. These have been identified in reflection of


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wide literature consensus [17], [403], [407], [421], [463], [488], [562], of the different

resources that are currently or are expected to be widely traded by Brazil.

The quantities of these specific resources identified within the biomass resource balance

analysis to be surplus to the demand-requirements of the Brazilian bioenergy sector, will be

highlighted as being potentially available for exportation.

Table 9.8: Brazil BRM Resources Identified as Potentially Suitable for Exportation Table 48) Table 9.8: Brazil BRM Resources Identifie d as P otentially Suitable for Exportation

Brazilian Resources Suitable for Exportation

Grasses

Short Rotation Coppices

Short Rotation Forestry Resources

Cereal Crops

Oil Crops

Sugar Crops

Forestry Residue Resources

Industry Residues Resources

Resources Directly from Forestry Systems

B. Resource Balance Analysis – Results

The results of the Brazil resource balance analysis are documented within Figure 9.12. Here

the stacked-bar charts represent the total availability of resources over the analysis timeframe.

The proportions of resources that are required to meet the Brazilian bioenergy sectors

demands, and those identified as being potentially suitable and available for export, are

highlighted for each bioenergy scenario.

A further dynamic highlighted by Figure 9.12 is the trend demonstrating the change in extent

that resources are available and suitable for exportation in 2030. Identified as the ‘Trend’ bars

within Figure 9.12, these highlight the differential availability of resources for exportation for

each of the bioenergy scenarios, in comparison to the Current Policy Scenario. These trend

values represent the forecast reductions or increases in biomass resource quantities that may

be available for trade on the global markets, if Brazil were to adopt and realise any of the

developed bioenergy scenarios.

Another characteristic highlighted within Figure 9.12 that needs further explanation, is the

difference in the resource totals quantified within each of the columns. The resources

analysed for each Bioenergy Scenario reflect the same forecasts of the Brazil BRM Baseline

Scenario. The different resource totals are a consequence of the process of identifying

resources as being either potentially suitable or unsuitable for exportation. Only those

resources deemed suitable for exportation, and which show a surplus are quantified within the

analysis of Figure 9.12.


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78) Figure 9.12: Forecast Brazilian Bioenergy Sector Resource Demands & Potential Surplus Resources Available for Export

Figure 9.12: Forecast Brazilian Bioenergy Sector Resource Demands & Potential Surplus

Resources Available for Export

C. Resource Balance Analysis – Discussion

The key analysis outputs from Figure 9.12 are the highlighted trends in surplus resources that

may potentially be available for exportation. This analysis shows that if Brazil were to adopt

and realise energy strategies and targets reflective of the E-E Scenario, there would likely be

the greatest resource availability for exportation. However, if Brazil were to adopt and realise

energy strategies and targets reflective of either the C-T or 100-50 Scenarios, there would

likely be less resource available for exportation.

The analysis for the E-E Scenario shows that if Brazil were to pursue and realise strategies

and policies that resulted in reduced energy demands, and selected increases in renewable

energy technologies, there may be >22% more biomass resources available for export in

2030, compared to the future pathway where Brazil’s current energy strategies and targets are

realised (C-P Scenario).

The analysis shows that if Brazil were to pursue and realise strategies of increased bioenergy

generation that reduces fossil fuel technology utilisation to minimum levels (C-T Scenario),

or as a result of a 100% renewable technology target (100-50 Scenario); there may be >18-

29% less biomass resources available for export in 2030, compared to the future pathway

where Brazil’s current energy strategies and targets are realised (C-P Scenario).

Biomass Resource Demands & Surplus Resources Potentially Available for

Export within the Brazilian Bioenergy Scenarios


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These analysis findings are highly significant as Brazil is currently the top global exporter of

ethanol biofuels and is forecast to increase its exports (Chapter 8). To add further context to

this potential scenario, the International Energy Agency state that a ≥7% fall in the global

supply of oil to 2030 [563]–[565]is classified as an energy ‘disruption’ (crisis).

Therefore, with high-demand resources such as biofuels being predominantly produced in a

few key countries [410], if Brazil or any other large exporter were to reduce their exports by

anywhere near >29%, there would likely be significant ramifications for the global biomass

resource markets. The implications are most significant for those countries whose current

energy and bioenergy strategies, plan to rely heavily on imported biomass resources to

balance their future demands.

Therefore, this analysis should raise significant concerns for those countries planning

strategies that may be heavily reliant on global biomass trade markets, to balance their

demands. Aside from taking on the multiple risks, limitations, and impacts associated with

trading biomass resources (Chapter 7), those countries who adopt such strategies risk

becoming dependent on the major biomass resource exporting Nations for the continued

availability of resources, in highly a competitive market.

9.6.7 Chapter Conclusions & Consequences for the UK Bioenergy Sector

Chapter 9 has provided discussions and further analyses of Brazil’s current and future

biomass resource potentials. Through the process of adapting the Biomass Resource Model to

reflect the dynamics and characteristics of Brazil’s biomass resource supply chains (Brazil

BRM), a greater understanding has been gained regarding the extent of Brazil’s existing

biomass resource availability, and the dynamics affecting their potential availability, within

possible future energy scenarios. The Brazil BRM was applied to develop forecasts of the

extent and types of biomass resources that may be available to Brazil’s domestic bioenergy

sector, to the year 2030. A further literature-review driven analysis was undertaken to review

Brazil’s current bioenergy sector, and to further understand how this may evolve.

A series of potential Bioenergy Scenarios were developed to evaluate how Brazil may

potentially utilise its biomass resources in the future. These developed scenarios drew

influences and themes from a series of Case Study Countries, who currently lead the way in

terms of energy and GHG reduction strategies. The key output from this section’s analyses

was the evaluation of how Brazil’s biomass resource potential exports may be impacted, if


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Brazil were to vary its future utilisation of biomass for its domestic energy demands rather

than exporting it.

The key conclusions and the relevant potential consequences for the UK bioenergy sector can

be summarised as follows:

A. Brazilian Biomass Resource Availability

The first key conclusion of this Chapter’s analysis, results from the Brazil BRM’s literature-

informed Baseline Scenario. Reflecting the general consensus of a wide range of literature

highlighted in Chapter 8, Brazil was found to have vast biomass resources that were forecast

to potentially increase over the analysis timeframe, to 2030. The analyses highlighted Brazil’s

large potential to increase its production of the crops and feedstocks, required to produce

biofuels. Brazil’s current and potential future large wood-based biomass resources were also

identified. The consequences of these results for the global biomass trade markets, and

relevantly for the UK bioenergy sector; are that Brazil has the potential to continue to be a

dominant player in exporting resources for global trade in the future.

B. Brazilian Biomass Resource Export Caution

The second key conclusion from Chapter 9 flags a cautionary note, and contradicts other

findings within this Chapter. Although Brazil was identified as having large current and

future potential biomass resources, and at first-sight Brazil can be identified as a renewable

energy-focused country; Brazil’s current energy strategies and targets were found to be

relatively conservative and modest with respect their planned future ambitions. The analysis

found that Brazil may have enough biomass resources to meet is entire forecast primary

energy demands, and the literature also determined that Brazil has the potential to increases

its already large hydroelectric generation capacity. Therefore, with such a current focus on

renewable energy generation and bio-fuel production, it is entirely possible that the Brazilian

Government may decide in future, to utilise a greater proportion its biomass resources to

meet its own domestic renewable energy demands, and for bio-fuel exports, rather than focus

on exporting them (progress towards the 100–50 and CT- Scenarios).

This Chapter’s analysis tested a series of ambitious potential bioenergy strategies that the

Brazilian Government could potentially adopt, resulting in Brazil using a larger proportion of

its biomass resources. The analysis found that Brazil could export up to >29% less biomass if

it were to adopt and realise more ambitious domestic energy strategies.


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This key conclusion presents a strong cautionary notice for countries such as the UK who are

developing bioenergy strategies that will require large biomass resource imports to balance

their future demands.


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Chapter 10 - An Alternative UK Bioenergy Strategy


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10.1 An Alternative UK Bioenergy Strategy

Chapter 10 draws together the key conclusions and analysis results from across the Thesis. A

widespread study of the UK’s biomass resource supply chains and bioenergy sector has been

undertaken, and a further analysis has focused on the global biomass trade markets; including

a case study of a large biomass exporting country, Brazil. For each of these key analysis

themes covered within the Thesis, a series of conclusions and potential consequences for the

UK’s bioenergy sector were developed and identified.

In summary, the Thesis so far has found that:

The UK’s biomass resource supply chains are highly complex, although the research

found that a few key drivers provide great influence in determining the potential

availability of indigenous biomass resources for the UK bioenergy sector (Chapter 5).

A few key specific UK biomass resource types were identified which demonstrated

the greatest potential for the UK bioenergy sector, in terms of the extent of their

potential availability and the bioenergy that they could potentially generate (Chapters

5 and 6).

If the UK were to implement strategies with varying levels of ambition, aimed at

mobilising indigenous biomass resources for its bioenergy sector, the potential

bioenergy generated was forecast to be capable of making large contributions towards

meeting the UK’s energy, renewable energy, and bioenergy targets (Chapter 6).

The current direction of the UK bioenergy sector is driven predominantly by the UK’s

energy and bioenergy strategies and policies, and is moving towards a future where a

large proportion of the biomass resources required to balance demand will have to be

imported. As the UK has neither the extent, nor the specific forms of biomass

resources required to fuel a bioenergy sector within its planned future strategy, the

UK is headed towards a future increasingly reliant on imported biomass resources

(Chapter 7).

The global biomass resource trade markets were also found to be highly dynamic; the

majority of trade being driven by the growing demands of a few key regions, and the

flow of the majority of resources arising from a few key dominant regions. The global

biomass markets were found to be relatively immature and still developing; whilst a


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number of important limitations, uncertainties, and sustainability impacts were

highlighted as being linked with the global trade of biomass resources (Chapter 8).

Focusing on Brazil as a dominant biomass exporting country, the potential availability

of Brazilian biomass resources were found to be vast, with much potential for further

productivity growth. However, the research also found that if Brazil were to utilise

greater proportions of its available biomass to meet domestic future energy demands,

Brazilian biomass exports could potentially fall resulting in large potential deficits for

the global biomass trade markets, thus negatively impacting those countries reliant on

imported biomass to balance their demands (Chapter 9).

Chapter 10 brings the focus of the Thesis back to the UK. In reflection of the potential

uncertainties associated with increasing reliance on imported biomass resources, Chapter 10

aims to evaluate potential future strategies that the UK could adopt to mobilise and utilise a

greater proportion of its indigenous resources for its domestic bioenergy sector.

The Chapter is structured by first re-highlighting the specific analyses conclusions of

Chapters 5, 6, and 7; these providing a framework of key potential areas that an ‘alternative

UK bioenergy strategy’ could focus on.

The Chapter then progresses through a discussion of the potential shortfalls within the current

UK energy and bioenergy strategies and polices; and goes on to highlight potential

opportunities for change. Barriers restricting change are identified, existing and proposed

policies, and case studies from both the UK and other countries are highlighted in suggesting

potential mechanisms for reducing these barriers.

The themes and concepts of this Chapter represent an alternative UK bioenergy strategy, one

in which the UK could potentially mobilise and utilise a greater proportion of its biomass

resources for its bioenergy sector to reduce its future dependence on imported resources.

10.1.1 Thesis Analysis Key Conclusions

The first step in Chapter 10 is to re-highlight some of the key analysis conclusions identified

within previous chapters; predominantly from the UK focused analyses of Chapters 5 to 7.

The following section documents how the analyses conclusions have been grouped and

structured within this Chapter, and highlights the discussion areas that will be covered when

proposing potential directions for an alternative UK bioenergy strategy:


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A. UK High Potential Resources & Key Supply Chain Drivers

The analyses of Chapters 5 and 6 found that particular UK biomass resources demonstrated

significantly greater availability and bioenergy potential than others. Chapter 5 found that

specific supply chain drivers are highly influential in determining the availability of different

forms of biomass, and therefore a targeted approach should be developed to increase the

availability and utilisation of UK indigenous resources.

Both Chapters 5 and 6 developed a consensus concluding that the UK biomass resources that

demonstrate the greatest potential in terms of resource availability and bioenergy potential

are: UK grown biomass resources and energy crops, agricultural residues, and household and

organic wastes. The sensitivity analysis undertaken within Chapter 5 found that the following

supply chain drivers represented key influences, and would therefore need to be appropriately

targeted and managed to maximise the availability of these resources:

UK Grown Biomass & Energy Crops - the standout driver influencing the

availability of these resources was identified as the extent that available land was

utilised for their growth.

Agricultural Residues - the availability of all residues were found to be

comparatively robust to influencing drivers; the extent to which resources can be

harvested / collected being the most influential factor.

Household & Organic Wastes – all waste resources were found to be highly

influenced by one key driver; the nature of the waste management system adopted.

The analyses of Chapter 6 also highlighted the key relationships between both biomass and

food systems. Strong, efficient, and productive food systems were found to provide many

positive feed-back benefits for the production of biomass for the bioenergy sector.

i. Approach for Developing an Alternative UK Bioenergy Strategy

The barriers representing the main obstacles preventing the UK from growing and utilising

more of its indigenous biomass resources, energy crops, agricultural residues, household and

organic wastes, are discussed. A series of potential ‘enabling mechanisms’ are identified that

could be applied to increase the utilisation of these resources. These mechanisms are drawn

from ideas and concepts of existing biomass applications, policies, and case studies, from

both the UK and around the World.


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B. Current UK Bioenergy Strategies & Policies

The analysis from Chapter 6 highlighted that the prioritisation of heat bioenergy conversion

pathways with suitable resources, resulted in the greatest levels of bioenergy generation from

the resources available. The research conclusions of Chapter 6 suggest that the UK could gain

the greatest potential from its indigenous biomass resources, if specific selected resources

were utilised within the bio-refinery industries, with all remaining suitable resources being

dedicated for heat generation pathways. Chapter 6 also concluded that the generation of

renewable electricity could potentially be better achieved through alternative technology

pathways.

However, the analysis of Chapter 7 highlighted that the UK’s current and planned future

bioenergy sector strategies predominantly focus on bio-power bioenergy pathways, and

biofuels for the transport sector. This strategy is found likely to lead to an increasingly large

UK biomass resource deficit, greater resource imports, and trade within increasingly

competitive global biomass resource markets, to balance future demand.

A further key conclusion from Chapter 7 highlighted the on-going uncertainties relating to

the future of the UK bioenergy sector; there being limited consensus between ‘UK bioenergy

experts’ as to what the future UK bioenergy sector will look like. Chapter 7 highlighting that

this level of uncertainty and clarity, undoubtedly provides a significant barrier to increased

growth of the UK bioenergy sector.

i. Approach for Developing an Alternative UK Bioenergy Strategy

This Chapter works through the conflicting themes evident between the analysis conclusions,

and the current directions and strategies for the future UK bioenergy sector. It focuses on

identifying the current prospects and barriers for the UK bio-heat sector. This includes

highlighting the UK’s current stance toward bio-heat generation and identifying ‘enabling

mechanisms’ in the form of policy suggestions that could be implemented to steer the UK

towards increased bio-heat generation.

The Chapter goes on to discuss the impact of uncertainty on the future development of the

UK bioenergy sector. Discussions highlight the key reasons for this uncertainty and identify

potential policies and mechanisms that may be implemented to reduce uncertainty.


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10.2 Alternative Policy & Strategy Options

The following discussions in this section highlight the current barriers preventing the greater

growth and utilisation of biomass and energy crops, agricultural residues, and household and

organic wastes; by the UK bioenergy sector. In each case potential enabling mechanisms, and

bioenergy strategy and policy options are highlighted that may increase the utilisation of

these resources. Particular focus is placed on addressing and potentially managing the key

supply chain drivers as highlighted within Chapter 5.

The section then goes on to highlight the perceived barriers of the UK’s bioenergy strategies

and policies, to increased development of the UK bio-heat sector. Potential alternative policy

and strategy options are highlighted that may increase focus on development of the bio-heat

sector.

10.2.1 UK Grown Biomass & Energy Crops

Biomass and energy crops grown in the UK were identified within both Chapters 5 and 6, as

resources representing high potential for the UK bioenergy sector. Chapter 5 also identified

that the area of available land dedicated for crop growth, was the defining driver influencing

the availability of these resources for the bioenergy sector. The UK Bioenergy Strategy [8],

recognises that production of energy crops on unused lands, or lands of low ecological value

is essential in ensuring that growth in the bioenergy sector is achieved. The area of available

land dedicated to grow these resources is essentially reliant on UK farmers utilising their

lands to grow crops for the bioenergy sector, rather than for food. The UK’s primary

incentive mechanism for promoting farmers to grow biomass resources and energy crops has

been the ‘Energy Crops Scheme’ [566]. This provides financial incentives to farmers for

establishing Miscanthus, and short-rotation coppice biomass crops, either for their own

utilisation or to supply the wider bioenergy industry. The main incentive focuses on

providing subsidies equivalent to approximately 50% of actual costs (supplies / materials),

contractor costs, and / or 50% of on-farm costs – such as for labour and machinery; where

applicable

Despite such Government incentive schemes and investment, widespread dedication of lands

to grow biomass and energy crops has not materialised across the UK. This being partially

due to the lack of promotion [566] of these schemes, as well as a series of further barriers as

discussed in the next section.


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A. Barriers for Further UK Utilisation

Through consultation exercises and analyses by Natural England (2014) [566], and the

NNFCC (2012a, 2012b) [235], [567], identified educational, economic, legislative, and

technical barriers that are perceived to prevent the wider utilisation of agricultural lands for

biomass resource and energy crop production. These are summarised as follows:

i. Educational Barriers:

Awareness – promotion of the Energy Crop Scheme has been relatively poor with

little or no promotion of the scheme [235], [567].

Tradition - key crops such as Miscanthus and SRC Willow, differ from the typical

range of crops produced by farmers. These therefore require a break from traditional

agricultural practices, which can present a daunting prospect [235].

Practice - farmers were typically found to have a poor understanding of the best

practices for establishment and management of energy crops [235].

ii. Economic:

Cash Flow – the period between planting and the first harvests of biomass and energy

crops can be a critical financially factor. Support for cash flow during this period is

lacking under present conditions [235].

Margins – the current profit margins associated with small-scale production of

resources means there is little room for error [235], [567].

Market - the link between farmers and the end-use markets for their energy crops

needs to be supported. [235].

iii. Legislative:

Inflexibility - a legislative barrier exists in that new ‘innovative’ crops such as

Switchgrass are not classified as energy crops under the Renewables Obligation (RO).

The RO needing to be further developed and updated to enable access to finance for

energy-crop supply chains, and infrastructure [235], [566], [567].

iv. Technical:

Compatibility – the specific fuel requirements of many Bioenergy Plants, especially

the smaller bioenergy systems, are not always compatible with the widely varying

characteristic of the crops produced [235], [566], [567].


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Infrastructure - further financial support is required to develop processing

infrastructure such as pelleting equipment. This would facilitate greater transportation

of resources and an opening-up of markets [235], [566], [567].

B. Proposed Enabling Mechanisms to Increase UK Utilisation

There is strong demand for biomass resources and energy crops grown in the UK, not only

for large-scale co-firing power plants, but also for local use within smaller-scale pellet

systems [568]. Therefore, it is important to develop mechanisms to reduce the above barriers

and allow the markets to develop naturally.

As confirmed by the UK Forestry Commission (2013) [569], producing biomass resources

and energy crops may not only reduce energy generation GHG emissions, but also offer great

opportunity for diversification within the agricultural sector; whilst also providing potential

environmental and biodiversity benefits. Thus the wider benefits of growing resources for the

energy sector need to be better promoted.

The fundamental barriers relating to the stability of policy and financial packages, especially

in respect of: the Energy Crop Scheme, the Renewable Heat Incentive, and Feed-in-Tariff

schemes [235], need to be addressed. Good examples already taking place in the UK are the

policies and incentives developed for the promotion of wood fuels. The Forestry

Commission’s 2011 ‘Wood fuel Strategy’ [570], lays out a framework of targets and a

roadmap for how to increase the availability and use of wood fuels. This strategy is backed-

up by various national policies and incentives; notably fronted by the Renewable Heat

Incentive [571]. With further grants and subsidies available, such as the ‘Wood fuel

Woodland Improvement Grant’ and the ‘English Woodland Grant Scheme’ that specifically

target development of wood-fuel supply chains and production bases [572]. This UK example

represents a clear and concise strategy, and an incentive framework that could ideally be

replicated and focused on biomass resource and energy crops.

A report carried out for the European Commission (2012) [573], summarised some of the

leading incentive schemes currently being applied across the EU to promote biomass and

energy crop growth. These provide insight into further potential directions that the UK

Government could take in developing policies.

Finland – bioenergy plants have access to investment support (~15 – 30 % of capital

costs), and emission trading feeds are not applicable where biofuels are utilised.


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Italy – the ‘green certificate’ mechanism only supports power generation from

biofuels.

Spain – the ‘regulated tariff’ for using energy crops in biopower systems is set for 15

years, providing greater subsidies the greater the generation capacity.

Austria – biopower production feed-in tariffs are fixed for 15 years with incentives

increasing with generating capacity. A further energy crop-utilisation premium

subsidy is applicable aimed at promoting energy usage and markets.

10.2.2 Plant Based Agricultural Residues

Plant based agricultural residues, and specifically straws were identified within both Chapters

5 and 6 as representing resources with great potential for the UK bioenergy sector.

Table 10.1 presents farming statistic for 2010 released by DEFRA [574]. These document the

utilisation of renewable energy and specifically the use of plant based agricultural residues

for renewable energy, by the UK’s crop focused farms in 2010. This data highlights that on

average <6% of the UK’s plant-focused farms utilise renewable energy, and of those that do,

less than 45% utilise renewable energy systems using feedstock such as straw. As the farms

represented in the data are all crop focused and thus likely have plant based agricultural

residues, the small utilisation of these resources highlights the large bioenergy opportunity

currently being missed by both UK farms and the wider bioenergy sector.

Table 10.1: UK Plant Based Farming Renewable Energy Characteristics Table 49) Table 10 .1: UK P lant Base d Farming Renewable Energy Characteristics

Types of Plant

Focused UK Farms

UK Farms Producing Renewable Energy UK Farms Producing Renewable Energy from

Plant Based Agricultural Residue Resources

Proportion (%) Number of Farms Proportion (%) Number of Farms

Cereals 4.3 662

44 2247

General Cropping 4.4 754

Horticulture 7.3 339

Mixed 6.7 564

Unclassified 4.8 76


The following sections discuss the current perceived barriers that may explain why the UK

isn’t generating more bioenergy from crop-based agricultural residues such as straw.


Despite there being interest from farmers to develop a market for straws as a feedstock for

bioenergy processes, as well as strong demand for straws from the bioenergy sector; a

number of key barriers persist preventing the growth of UK straw supply chains. A 2012


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Report by the Institute for European Environmental Policy [575], identifies 5 key barriers to

enhancing the supply and sourcing of straw.

Underdeveloped Markets – with the exception of Denmark, the lack of supply

chains for straw for bioenergy purposes across Europe is identified as being linked to

underdevelopment of the markets.

Competing Uses – straws represent an agricultural residue with a long list of

alternative uses. Straw having important roles in maintaining the health of soils but

also with many other farming applications. Any straw sourced for the bioenergy

sector having to compete with these agricultural applications.

Inaccurate Guidance – incorrect and improper use of straw to maintain soil health

can lead to large unnecessary usage that impacts the availability of straws for other

purposes.

Undeveloped Infrastructure – the inaccessibility and lack of appropriate agricultural

machinery and infrastructure for the handling and processing of straws, represents a

major issue in terms of supplying this resource to the bioenergy sector.

Resource Variability – as a result of varying climatic conditions and fluctuating

straw-harvest yields, variability in the quantity and quality of straws has large

implications for the bioenergy sector that typically requires specific fuel characteristic

specifications.


The following section presents a series of ideas, policy options, and potential strategies that

could be implemented to encourage the greater utilisation of plant based residues by the UK

bioenergy sector. Figure 10.1 presents screenshots from the European Commission’s

EUROSTAT Statistical Atlas 2013 [576]. The annotated map on the left demonstrates the

density of plant based farming within each region of the UK. Whilst the annotated map on the

right demonstrates the development of roads and transport networks the UK’s major regions.

In each case the darker the colour, corresponds to the greater density of farming or transport

networks, respectfully. Figure 10.1 further informs the discussions in the following sections.


320

79) Figure 10.1: Density of Plant Based Agriculture Compared to Transport Network Development

Figure 10.1: Density of Plant based Agriculture Compared to Transport Network

Development

Taken from [576]

i. Market Development

The immaturity of the market for straw agricultural residues is identified as the primary

barrier limiting the development of the bioenergy sector. Despite some companies investing

heavily in establishing bioenergy plants, the supply chains are still lagging behind [575]. The

NNFCC [567], also identify the lack of investment in equipment and infrastructure as a

further fundamental obstacle to the future development of the UK’s straw bioenergy sector.

Figure 10.1 demonstrates that the UK regions with highest density of crop-based agriculture

are not those with the most developed transportation infrastructure. This adds to the problems

of the high transportation costs associated with the typically high dispersion, bulk, and low-

value associated with straws. Appropriate investment in processing machinery and

infrastructure would lower the economic costs of transporting straw resource [577].

A market-driven innovation taking place between the Sleaford Straw CHP Plant operated by

Eco2 energy [578], and the resource supplying farmers, is an agreement that the farmers will

receive free delivery of ‘process residues’ that can in-turn be reused to supplement fertilisers

to stimulate further growth. This incentive was developed to further encourage the supply of

straw for the plant, whilst also raising the profile of the technology. This case study of an

industry innovation, provides an example of the types of mechanisms that may organically


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develop as markets mature; and is something that needs to be encouraged across the UK

where applicable [575].

When it comes to the straw bioenergy sector, Denmark is by far the leading country in both

the development of their supply chains, and bioenergy plant infrastructure. This was

recognised in the UK Department for Trade & Industry’s 2004 Report [579]; ‘Co-operative

Energy Lessons from Denmark & Sweden’. However, in a further industry report undertaken

by Tybirk et al (2010) [580], that compared the biomass supply chain development

characteristics of different North Sea Region countries, including that of the UK; they

concluded that the UK’s often rural supply chains lacked Government support, and the UK

has no major manufacturers of bioenergy systems; placing it at a distinct disadvantage.

Therefore, drawing lessons from Denmark, the key reasons why Danish straw harvesting

infrastructure and market development are so strong; can be linked to a few key policy

initiatives [575]:

Policy Driver - the use of straw in Denmark has been historically policy-driven with

the aim of increasing Denmark’s energy security, through maximising use of their


Financial Support – Danish Government mandates have required that a higher price

is paid for energy generated from straw resources.

Collaboration – contracts made between the bioenergy sector, individual farmers and

intermediary contractors, allows high specification straw harvesting and processing

equipment to be lent to farmers during harvest period, in order to provide the

bioenergy sector with the fuels they require.

Price Control – farmers producing straw submit bids to energy companies stating

their price and volumes, and the energy companies are at liberty to buy the resource

they require but at the prices demanded by farmers.

Scale – contracts between farmers and energy companies are the same regardless of

whether the producer is an individual farmer, or a large farming association. This

includes contracts with large associations, but also small co-operative contracts that

combine the limited straw resources from small farms.


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Each of these Danish policies represents a potential opportunity for the UK Government to

further develop UK policy; through the emulation of policy initiatives that have been

successfully implemented elsewhere.

ii. The Role of the Common Agricultural Policy

The European Common Agricultural Policy (CAP) has also been identified as potentially

providing mechanisms for increasing the utilisation of straw resources.

A 2013 Report by the Institute for European Environmental Policy [577], identifies key

influences that the CAP could provide in supporting the increased use of straw as a feedstock:

Soil Safeguards - the inclusion of environmental requirements and safeguards for the

protection of soils, may provide accurate guidance to farmers confirming the levels of

straw resources be returned to the soil. Thus reducing circumstances where excessive

straw is utilised.

Supply Chain Guidance - develop initiatives to help improve the efficiency and

functionality of agricultural-residue supply chains.

Encourage Relationships - drawing on the CAP’S broad range of institutional, local,

and individual farm partners and relationships. The CAP may provide the opportunity

to grow relationships to encourage the growth of supply chains, and to link producers

with processors.

10.2.3 Animal Based Agricultural Residues

Animal based agricultural residues and specifically manures and slurries, were also identified

within both Chapters 5 and 6 as representing resources with great potential for the UK

bioenergy sector.

The utilisation of these resources within anaerobic digestion bioenergy pathways may

represent large opportunities for the UK, as these pathways if applied appropriately could: be

highly profitable, create jobs, lead to further energy generation, and greatly reduce GHG

emissions [581]

Table 10.2, presents farming statistic for 2010 released by DEFRA [574]. These document

the utilisation of renewable energy and specifically the use of animal based agricultural

residues for renewable energy, by the UK’s animal-focused farms in 2010. This data

highlights that on average <5% of the UK’s animal-focused farms utilise renewable energy,

and of those that do, less than 50% utilise renewable energy systems using feedstock such as


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slurries and manures. As the farms represented in the data are all animal-focused and are thus

likely to be actively managing slurries and manure, the small utilisation of these resources

highlights the large bioenergy opportunity currently being missed by both UK farms and the

wider bioenergy sector.

Table 10.2: UK Animal Based Farming Renewable Energy Characteristics Table 50) Table 10 .2: UK A nimal Based Far ming Renewable Energy C haracteristics

Types of Animal Focused

UK Farms

UK Farms Producing Renewable Energy UK Farms Producing Renewable Energy from

Animal Based Agricultural Residue Resources

Proportion (%) Number of Farms Proportion (%) Number of Farms

Specialist Pig 3.1 50

46 2343

Specialist Poultry 4.8 103

Dairy 2.7 205

Cattle & Sheep (upland) 5.0 634

Cattle & Sheep (lowland) 4.9 1,667

Mixed 6.7 564

Unclassified 4.8 76


The following sections discuss the currently perceived barriers that may explain why the UK

isn’t generating more bioenergy from animal based agricultural residues such as slurries and

manures.


Reports produced in 2012 for the Royal Agricultural Society of England [582], and in 2013

by The Netherlands’ Wageningen Livestock Research Institute [583], provide comprehensive

summaries of the key barriers preventing the wider utilisation of slurries and manures in

bioenergy systems both across the UK [582], and within many other European countries

[583]. These are presented as follows:

Transportation – The nature and bioenergy characteristics of slurry and manure

resources render them impractical, uneconomical, and energy inefficient, to be

transported any great distance. Therefore, it is necessary to utilise them on-site or

within relative close proximity of the resource sources [583].

Resource Availability – Again due to the nature and characteristics of animal-

focused farming, manure and slurry resources are for most farms, typically only

available (collectable) for a limited number of months. This reflects the period for

which animals are typically held within controlled and manageable environments

(housed), allowing slurries and manures to be collected [583].


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Spatial Constraint – Anaerobic digestion (AD) systems, the most suitable bioenergy

systems for the use of manure and slurry resources; require physical space. The

economics of AD systems are also largely improved through the addition of energy

crop feedstocks. These require potentially large areas for growth of feedstocks and

their production is often incompatible with the nature of farms which have the large

animal based biomass resources [582].

Capital Costs – The initial costs of digesters and associated infrastructure are high,

and are unlikely to come down in cost significantly in the near-term, until there is a

more active market, and regulations are refined. Access to capital to meet these high

initial costs can be difficult [582].

Incentives – Current incentives in the UK are the Feed-in-Tariffs and also potentially

the Renewable Heat Incentives. However, the current incentive bandings in addition

to the small-scale of the average AD system makes them debatably uneconomical and

unattractive options, in comparison to simply building or continuing to use a slurry

store [582].

Collaboration Complexity – The time and costs associated with developing large

community or district systems that pool the resources from a number of local sites,

can be highly complex. These systems requiring larger capital costs, extra annual

maintenance, present reliability issues, have increased digester complexity, and can

often be hampered by infrastructure and wider grid-connection issues [582], [583].


The following section presents a series of ideas, policy options, and potential strategies that

could be implemented to encourage the greater utilisation of animal based residues by the UK

bioenergy sector.

Figure 10.2 presents screenshots from the European Commission’s EUROSTAT Statistical

Atlas 2013 [576]. The annotated map on the left demonstrates the density of animal based

farming within each major region of the UK. Whilst the annotated map on the right

demonstrates the development of roads and transport networks within the UK’s major

regions. In each case the darker the colour corresponds to the greater density of farming or

development of transport networks, respectfully. Figure 10.2 informs discussions in the

following sections.


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80) Figure 10.2: Density of Animal Based A gricult ure Compared to Transport Networ k Development

Figure 10.2: Density of Animal based Agriculture Compared to Transport Network

Development

Taken from [576]

i. Addressing Transport Barriers

A transportation barrier was identified in that it is impractical and uneconomical to transport

animal based agricultural residues any great distances from the resource source. Also, as a

result of the very nature of most farms, these resources are located in rural areas away from

nodes where energy is most required. Therefore, slurries and manures are typically most

suited for use on-site or within a localised area [582]. This transport barrier is also hinted

within Figure 10.2, where the regions with the greatest density of animal based farms are in

contrast to the regions with the most developed transport networks.

Enabling strategies to reduce transport barriers may include the greater utilisation of these

resources on-site where the resources are on or within localised district systems.

However, there is a further option that involves the processing of raw slurries and manure

into resource forms that are more suitable for transport as well as representing higher-value

energy fuels. Mechanical separation is the process of converting slurries and manures into

solid organic fuels; the resulting materials being highly stable and suitable for composting,

drying, pelletising, and anaerobic digestion [583].

The Baltic Manure Project [584] funded by the European Commission, has demonstrated that

mechanical separation technologies have been commercially available and viable for a long


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time. ‘Screw press filter separation systems’ were found to represent, cheap, highly efficient,

and low-maintenance machinery that provide a valuable addition to farms to increase their

utilisation of slurry and manure resources. The European Commission’s ‘Bio-Energy Farm’

project [585], demonstrates further projects across Europe (but not in the UK) that have

successfully applied these technologies. This highlights a further potential opportunity for

UK farmers and the bioenergy sector.

ii. Bioenergy System Scales

The barriers and opportunities affecting the development of anaerobic digestion (AD)

systems can be very variable, depending on the scale of the system. As highlighted in the

previous section the transportation of slurries and manures can present problems. Therefore,

the primary bioenergy option for utilising these resources is likely to be within small-scale

systems on or near to the resource sources.

A large number of environmental and agricultural benefits have been highlighted to further

support the development of small-scale AD systems such as, potentially reducing the

exposure of farms to rising fossil fuel costs, providing sources of renewable energy and

fertiliser; whilst also improving slurry storage, handling, hygiene, and distribution processes

[582].

Aside from designing financial incentives and mechanisms to promote the development of

small-scale AD systems, the Royal Agricultural Society of England [582] have highlighted a

series of further important actions that should be fostered in order to promote AD

technologies to the relevant farmers and stakeholders. These include:

Education – it is important to inform and educate farmers, bankers, and regulators

about the potentials of small-scale AD technologies.

Collaboration – promote industry and relevant bodies such as WRAP and the

NNFCC, to sit down and work with farmers, regulators, financiers, and planners; to

develop sufficient policies and enabling mechanisms to extend this technology’s

application.

Demonstration – it is important to make the technology accessible so that

prospective farmers and stakeholders are given the opportunity to visit AD sites to

gain a greater understanding of the technology.


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At the other end of the technological scale, large and district AD plants represent further

options for the greater utilisation of slurries and manures. As Figure 10.2 highlights, many of

the UK’s animal focused farms are clustered in high densities regions. In Counties such as

Devon, Cheshire, North Yorkshire, and Cumbria where there are large cattle and dairy herds,

there are also significant opportunities for the development of large-scale AD facilities [581].

Large systems may require significant capital costs in the region of one to five million

pounds, rendering them unaffordable for individual farmers; although this may become a

more viable option for farms located in the close proximity to each other and able to work

together in cooperatives, pooling their resources [581].

The community owned ‘NW 1000 kW Energy Farm’, in Silloth, West Cumbria; being an

excellent UK example. Here, eight farms in Cumbria located within 2.5 miles of the central

AD plant, generate energy from around 30,000 tonnes of slurry and silage per year [586].

This represents an excellent example of the potential of what can be achieved, and should be

further promoted to stimulate interest from potential future participants in similar schemes

[582].

iii. Incentives

Potentially the most important barriers to the development of AD or any renewable

technologies are financial. The 2011 joint DEFRA and DECC ‘Anaerobic Digestion Strategy

& Action Plan’ [587], and updated by DEFRA’s Annual Report (2013) [588]; presented the

array of financial mechanisms and incentives designed to promote the development of AD

plants in the UK. In summary these included:

Incentives - the design of incentives so that the options to develop AD plants, are as

at least as attractive as simply building slurry storage tanks.

Feed-in-Tariffs - the development of a more advantageous Feed-in-Tariff band for

smaller-scale AD systems.

Renewable Heat Incentive - the design of the Renewable Heat Incentive to

incentivise small-scale systems where the generation of electricity is not possible and

subsequently the systems are applicable for Feed-in-Tariffs.

Capital Funding - the introduction of funding models for renewable technologies

with options for lower interest rates and initial payment holidays.


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However, the AD sector in the UK is still highly underdeveloped, especially when compared

against other countries such as Germany [589]. In Germany, the AD Sector has been

supported by the German Renewable Energies Act that has paved-the-way for widespread

uptake of the technology through a combination of financial incentives, in addition to

Permitting and Regulatory support [590].

A specific policy that was developed to provide strong support for AD technologies [589],

was the ‘2009 German Gas Network Ordinance’(GasNZV) [591], that placed an obligation

on all gas-grid operators to allow preferred grid access to AD plants that have requested

access. Also, the additional requirement for the grid operators to fund 75% of grid access

costs for all distances less than 1 km; the AD operator then funds the remaining 25% up to a

maximum of €250,000. This represents an example of a constructive and innovative policy

that supports the development of the AD Sector by reducing the financial barriers of

development.

10.2.4 Household & Organic Wastes

Household and organic wastes were also identified within both Chapters 5, and 6, as

representing resources with vast potential for the UK Bioenergy Sector. Although Chapter 5

found that realising this potential was strongly reliant on the UK developing waste

management strategies that complement the bioenergy sector.

In the UK energy from waste has historically had a poor image, with landfill distribution and

early incinerators favoured to simply reduce volumes. However, with the introduction of

Land-Fill Diversion Targets and the development of new energy from waste technologies;

bioenergy pathways are back on the UK’s agenda. Although the UK’s waste strategies and

policies focus on waste reduction and recycling, efficient energy recovery remains an

important element of the strategy to both generate energy, and reduce land-filled waste

volumes [592].

The following section discusses the barriers to the wider uptake of energy from waste

technologies in the UK, and those that prevent waste management strategies from currently

maximising energy recovery.


There are a broad range of issues that represent barriers to the further utilisation of energy

from waste systems in the UK. These include social implications, regulation hurdles, and

technical and economic barriers as summarised:


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Incentive – the cost comparison of energy from waste systems compared to landfill

represents a strong barrier against the further development of this sector. The 1999

introduction of the ‘Landfill Tax Escalator’ has reduced this barrier, but near-term and

medium-term cost analysis, still favours landfill distribution over energy recovery

[593].

Waste Hierarchy – the supply of biomass-waste-feedstocks is restricted by the waste

hierarchy that is in turn driven by the UK’s waste policy’s primary focus; to reduce

landfill and recycle. Thus a further barrier exists in that plants are not necessarily able

to access the types and extent of waste-feedstocks that they require [567].

Opposition – by far the greatest barrier to the development of energy from all waste-

systems is the social opposition led by individual local communities and the lobbying

by regional and national environmental-action-groups; social opposition contributing

to the planning rejection / withdrawal of almost 25% of proposed plants on the UK

Planning Register in 2010 [594].

Finances – the Government’s definition of biomass wastes can vary between different

departments and thus the available subsidy regimes for particular wastes for bioenergy

generation also varies. This represents a barrier preventing developers from accessing

the finances required to grow the sector [567].


i. Development of Waste Management Strategies

There are limitations as to how far the UK can develop its waste management strategies as

across all EU Member States, these are almost entirely regulated by EU Directives. These

Directives define both the political framework and the national regulations in all the EU

countries. However, the reality is that most EU countries are not yet complaint with these

standards, with New EU Member States having defined dates for achieving compliance goals

[595].

The extent that energy recovery technologies are utilised across the EU are strongly

controlled by the ‘Waste Framework Directive’ [596]. Whilst the EU’s ‘Landfill Directive’

[597], sets targets for land-filled waste proportions, and also influence the extent that waste

resources are potentially available to the bioenergy sector. The EU’s ‘Waste Incineration

Directive’ [598], defines the legal framework, sets incineration emission standards, and


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widely encourages Member States to use wastes for energy. The EU ‘Renewable Energy

(Electricity) Directive [599], also strongly promotes energy from waste pathways.

However, adaptations of EU Directives into national laws, allows room for manoeuvre; with

the definitions of wastes in the context of bioenergy being a key variable that differs with

each Member State. This manoeuvrability is a key area where different Member States can

adjust the parameters of their waste management strategies to variably favour energy from

waste generation, through determining the extent of the resource that may be eligible for

subsidisation [595]. A number of key variances between Member States that should

potentially be reviewed and considered by the United Kingdom are as follows:

Germany – energy from waste is not favoured, with wastes being classified as having

no biomass origin and waste incineration plants are exempted from carbon trading.

Sweden - 50% of the power generated from wastes are classified as bioenergy, the

remaining allocated to fossil fuels where CO2 Certificates are applied.

The Netherlands – classify 50% of power from waste incineration as bioenergy,

although only if the conversion efficiency exceeds 30%.

Finland - 60% of the energy generated from wastes is classified as bioenergy.

ii. Addressing Public Perception Barriers

In relation to addressing the large barriers associated with the opposition to energy from

waste technologies by the public and environmental groups; the UK could draw influence

from scenarios around the World and specifically within other EU Member States, where

public opinions are far less hostile. A review undertaken by WMW (2014) [600], compared

the perspectives and opinions of different relevant stakeholders linked to the energy-from-

waste sectors both in the UK and within countries with strong energy-from-waste traditions.

As summarised below, there are many themes that the UK could focus on and develop to help

promote the UK’s energy-from-waste sector, and ultimately utilise the UK’s potentially large

biomass waste resources.

Denmark & Sweden - have a long tradition of waste incineration, with the

Scandinavian population sympathetic of the technologies. This results from the cheap

heating provided by waste powered district heating systems. Also, the close

relationship that energy-from-waste operators develop with local populations, starting

at a young age where school children and the public are invited to tour energy-from-

waste plants.


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European Commission – the ‘Waste Framework Directive’ raises awareness that

pollutants are highly regulated and restricted from energy-from-waste plants.

UK Consultation Consultant – The key energy-from-waste barrier that topped

people’s list of priorities during a broad consultation exercise was the proximity of the

Plants to populations. Public perceptions were largely softened with discussion linked

energy-from-waste systems with: energy security, waste minimisation, and climate

change themes.

UK Waste Management Company – research carried out by SITA UK (2011) [601],

found that 79% of 1000 participants interviewed in the UK favoured energy-from-

waste pathways. Their research concluding that the voices of minority groups often

overshadow the opinions of the majority.

10.2.5 Promoting the UK Bio-Heat Sector

The research conclusions from Chapter 6 highlighted the motion that the UK should utilise its

biomass resources to generate heat, as bioenergy heat conversion pathways were found to

provide the greatest energy for the resources available; Chapter 6 also stating that the UK

should potentially focus on generating its renewable power from alternative technologies

other than biomass, to allow biomass resource distribution to the bio-heat sector.

However, the analysis of the UK Bioenergy Strategy undertaken in Chapter 7 provided

contrasting conclusions – in the near to medium-term the UK bioenergy sector is headed

towards a future with increased focus on bio-power generation pathways with bio-heat

favoured for small-scale and selected industrial applications.

Therefore, with the UK’s policy-focus seemingly prioritising the use of its domestic

resources for bio-power systems (and specifically co-firing plants) rather than for bio-heat,

the UK’s strategy and policies themselves may represent the greatest barriers to the broader

development of the UK bio-heat sector.

DEFRA’s 2007 Biomass Strategy [77], acknowledged that biomass was under-utilised within

UK heating applications, although it would have to play a central role in the UK meeting its

renewable heat energy targets. The key mechanism that has since further developed to

encourage and enable the development of bio-heat applications, is the UK’s Renewable Heat

Incentive. Despite this scheme, there are many currently perceived barriers that are

preventing the wider development of UK bio-heat applications. The following discussions


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within this section go on to highlight these barriers, and also to identify and present potential

enabling mechanisms and alternative strategies that may reduce these barriers.

A. Barriers for the UK Bio-Heat Sector

The following section presents a series of identified barriers that are perceived to be

preventing the wider development of the UK’s Bio-Heat Sector. These range from social,

practical, technical, and financial issues preventing the development of both small-scale, and

large and district-scale systems.

i. Broad Bio-Heat Sector Barriers:

Heat Demand – with respect to heat generated through CHP systems, the lack of

continuous heat demands required for optimal use can significantly reduce the

efficiency of the systems, and as such degrade the appeal of bio-heat systems [602].

Economics – the financial considerations of bio-heat-systems even when Renewable

Heat Incentive subsidies are applicable, can still be uneconomical in comparison to

alternative conventional heating technologies [567], [602], [603].

Resource Chains – with the development of large-scale bio-power and co-firing

plants in the UK, there is extreme competition for biomass resources. As many bio-

heat-systems are small and domestic-scale, the resource supply chains can be / appear

inaccessible. Therefore, a major barrier exists as to where biomass resource will come

from to fuel the UK’s Bio-Heat Sector if it is to continue to grow [567], [603].

ii. Small Scale Bio-Heat System Barriers

Capital Cost - Installation costs of small-scale biomass boiler system are typically

three times the cost of an oil or gas system equivalent [603]. Also, the conversion of

any existing infrastructure to adapt to biomass systems can also be expensive [567].

Application – biomass boiler systems are unfamiliar to the majority of the UK

population. This presents barriers to new prospective users as the typical requirement

to load the systems, and also source unfamiliar fuel supplies, can represent social

obstacles [567].

Space – biomass boiler and fuel stores require much more space than alternative fossil

fuel based systems; this presenting a barrier or even making the technologies

impractical for certain applications [567].


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Air Quality - as many dense UK urban areas already exceed the required threshold

values for Particulate and Nitrogen Oxides emissions, all new sources receive large

scrutiny. The Air Quality legislation needs updating to recognise the characteristics of

biomass systems. Until this issue is addressed, it will remain a barrier to the wider

adoption of bio-heat system applications [567], [603], [604].

iii. Large Scale Bio-Heat System Barriers:

Capital Cost - costs associated with the infrastructure connecting district-systems to

the grid can be large. The current comparatively low-load factors of UK’s district

networks further reduce the economics [603].

Consumer Resistance – consumers, especially within urban areas may resist the

long-term contracts necessary to secure the development of district heating

infrastructure [603] [567].

Disruption – urban networks require extensive excavation and the laying of pipe-

work which will likely result in disruptions to transport systems and utilities [602],

[603].

B. Proposed Enabling Mechanisms to Encourage Further UK Bio-Heat Generation

A wide range of studies have been undertaken that focus on identifying the barriers and

potential solutions [7], [257], [604]–[613], for further developing the Bioenergy Sector and

specifically for bio-heat applications [567], [603].

This section presents some potential strategies or innovations that the UK Government could

consider to break-down the barriers preventing the development of both small and large-scale

bio-heat applications.

i. Promotion of Small Scale Bio-Heat

The UK’s Renewable Heat Incentive has so far proved to be relatively successful with a large

number of businesses taking up the opportunity to become ‘Greener’ predominantly through

the application of bioenergy heating systems. The UK’s Government have also confirmed

[57] that the RHI will be further supplemented in spring of 2014, with an additional scheme

aimed specifically at households.

However, to supplement the UK’s future planned policies there are a range of further

initiatives that have been undertaken across other EU Member States, from which the UK

Government could draw inspiration. For example, the European Commission funded ‘Bio-

Housing Project’ [614], was undertaken across: Finland, Austria, France, Italy, and Spain;

with the prime aim of identifying and removing the barriers for the widespread utilisation of


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bio-heat-systems across European homes. This project included collaborative work involving:

house builders / owners, bio-heat and housing-industry engineers, boiler manufacturers, fuel

specialists, and whole range of other relevant housing and heating stakeholders. The key

project outcomes were the design and production of universal bioenergy systems, and

associated supporting information tools and guidance materials; these addressing each of the

technical, practicality, and social barriers that were identified as holding back the domestic

bio-heat industry. This represents the kind of project that the United Kingdom should be

aiming to participate in; to develop the UK markets and learn from other Member States who

have far-more advanced bio-heat sectors.

Sweden represents the EU Member State with the greatest bio-heat utilisation level. The

growth of Sweden’s bio-heat sector has gradually taken place over more than a century [615].

Sweden therefore represents an ideal case study of a country that the UK should be looking

at, with regard to developing a regulatory and financial support framework to help the UK

Bio-Heat Sector to grow. A summary of Sweden’s standout policies promoting bio-heat are

as follows:

Project Approval - the approval of solid biomass plants by Swedish authorities is not

perceived as a barrier. Sweden has highly stringent noise, emission, and other impact-

thresholds required to achieve planning approval. However, the advanced bio-heat

market and authorities well versed in bioenergy technologies; smooth the application

process [615].

Public Perception – the Swedish public are also highly knowledgeable of bioenergy

technologies as a result of their widespread application. They are also widely

consulted within each project, and therefore public opposition to bioenergy

developments is not considered a typical barrier [615].

Financing – Swedish banks are familiar with financing bioenergy projects. As a result

of Sweden’s carbon and energy taxation schemes that favour bioenergy systems;

financing bioenergy projects is typically straightforward [615].

Stability – the market perspectives, political framework and bioenergy supporting

financial structures are well known and stable in Sweden, providing high level of

certainty for potential investors [615].


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Supply - special focus is placed on ensuring reliable, sustainable, and long-term

feedstock supplies within all bioenergy projects. The market entry requirement for all

new potential Swedish resource suppliers being highly formalised [615].

ii. Promotion of Large Scale Bio-Heat

When it comes to addressing the barriers typically associated with large and district-scale

bio-heat applications, many European countries, again can be used as best practice case

studies, as a result of their long-standing traditions and their gradual development of the

technologies. Denmark and Sweden again lead the way and represent countries that the UK

could learn from, in terms of both financing and promoting larger-scale bio-heat applications.

Ericsson (2009) [616], highlights the key themes that make the Swedish and Danish bio-heat

markets so strong:

Ownership – municipalities play an important role in establishing district-heating

systems as they are already responsible for local electricity distribution. This eases the

infrastructure planning development process and automatically includes all public

building into the network.

Public Perception – the cost effective, reliable, and high reputations associated with

district-heating systems within both Denmark and Sweden, ease public perspectives of

further proposed developments.

Subsidies – a range of schemes have been developed for both Denmark and Sweden

that have encouraged the development of large-scale systems in preference to

conventional networks.


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Chapter 11 - Thesis Conclusions


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11.1 Thesis Conclusions

Chapter 11 of the Thesis presents the conclusions; bringing together and summarising the

various analyses results and individual Chapter conclusions, from across the Thesis.

Deductions are arrived at from the results and their potential wider implications are

discussed. The Chapter also highlights specific methodologies, results, and conclusions that

represent original research, and will therefore contribute towards advancing or strengthening

existing knowledge in their respective research themes.

Chapter 11 also highlights and acknowledges the various limitations that relate to the

research undertaken. Recommendations are provided of further work that could be

undertaken to address perceived limitations, and to engage further research of interesting and

important themes. The Chapter concludes with a final concluding statement that summarises

the Thesis.

11.1.1 Summary of Thesis Conclusions

This first section of the Conclusion Chapter provides a recapping of the key analysis results

and conclusions from each of the Thesis Chapters where analysis was undertaken:

A. Chapter 5 – Drivers Influencing Biomass Resource Availability & Bioenergy

High Potential UK Resources - the research highlights that particular resources

demonstrate significantly greater availability and bioenergy potential than others.

Household wastes, agricultural residues, and UK grown biomass resources and energy

crops, demonstrating the greatest potential for the UK’s bioenergy sector.

Availability of UK Grown Biomass Resources & Energy Crops - the utilisation of

available suitable lands for resource growth, is the key driver influencing the

availability of these resources. UK grown resources are found to demonstrate

relatively low near-term resource availabilities, thus concerted planning may be

required if anywhere near the upper-levels of the resource forecasts are to be realised.

Availability of Agricultural Residues - availability of all residues was found to be

comparatively robust to influencing drivers; the extent that resources are harvested /

collected being the most influential factor. The availabilities of agricultural-resource

residues are forecast to steadily increase without any major influences from drivers.

Availability of Household Wastes - found to be highly influenced by one key driver;

the waste management system adopted. The availability of waste resources was found


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to be much diminished when the adopted waste management strategy was

uncomplimentary to the bioenergy sector.

B. Chapter 6 - UK Biomass Resource Scenarios

High Potential Availability of Indigenous Biomass – high-levels of indigenous

resources within the UK could be mobilised without impacting food or industry

systems; including the growth of biomass resources and energy crops within the UK,

without adversely impacting food production.

Biomass Residue Resources - again found to represent continuous and robust

resources that maintained a high-availability regardless of the scenario or duration

within the analysis timeframe.

Biomass Waste Resources - availability and promise of waste resources for the UK’s

bioenergy sector were again found to hinge on the focus of implemented waste

strategies.

Food – Biomass Positive Feedback Relationship - a future scenario pathway that

emphasised, increasing productivity and reducing wastes from food-systems resulted

in large future feedback benefits for the bioenergy sector. Increasing the productivity

of the land not only resulted in increased food-security and self-sufficiency, but

ultimately resulted in less land being required to produce more food; potentially

freeing up additional land for biomass resource and bio-energy crop growth.

Prioritisation of Bio-Heat Conversion Pathways - the prioritisation of heat

bioenergy conversion pathways with suitable resources resulted in the greatest levels

of bioenergy generation. This suggests that the best option for the UK to make the

most of its indigenous biomass resources may potentially be, for selected resources to

be utilised by industries involved in bio-refinery to produce specialised products, with

all remaining suitable resources being dedicated for heat generation pathways. The

generation of renewable electricity, potentially being best achieved through

alternative technology pathways.

C. Chapter 7 – The Future UK Bioenergy Sector

Future Bioenergy Sector Resource Demands – current and planned bioenergy

sector directions reflect an increasing focus towards bio-power bioenergy pathways,

and like many other countries the UK will require large quantities of wood-type

biomass resources for this energy generation pathway. Biofuels and the suitable


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feedstocks to produce them are also widely sought for the current and future transport

sector.

Future Uncertainties – there are increasing uncertainties relating to the future of the

UK bioenergy sector, especially for medium to long-term forecasts.

Indigenous Resource Deficits - large potential resource deficits are forecast of the

specific types of biomass that the planned UK bioenergy sector will require.

UK Biomass Trade Requirements - if the UK’s current bioenergy plans mature, it is

forecast that the UK will likely become increasingly dependent on imported

resources.

D. Chapter 8 - Global Biomass Trade: Supply, Demand, Limitations & Sustainability

Growing Biomass Resource Trade - the global biomass trade markets are growing

fast yet are still far from maturity. A certainty that has emerged is that the volumes of

resources currently traded, are likely to vastly increase. However, a major uncertainty

remains as to how the biomass resource trade flow may change, as the energy

aspirations of developing countries continue to evolve.

Trade Hub Europe - Europe is found to be the key trading hub and demand region

for all forms of biomass resources. Driven by renewable energy and GHG reduction

targets, Europe’s demands for all biomass resources as highlighted throughout all

literature, comes across as being insatiable.

Trade Limitations - there are a wide range of international and even geopolitical

drivers that will influence the global biomass markets, as with any other

internationally traded commodity. These limitations may represent a potential risk for

the UK, as it will have limited control over these drivers.

Sustainability Implications – the process of pursuing bioenergy pathways to reduce

GHG emission becomes irrelevant, if the supply chains, and / or related land-use and

cultivation practices; are not undertaken to ‘best practice’ to reduce sustainability and

climate change risk impacts.

E. Chapter 8 - Global Biomass Trade: Supply, Demand, Limitations & Sustainability

Brazilian Biomass Resource Availability - Brazil was found to have vast biomass

resources that were forecast to potentially increase to 2030 and beyond. Brazil


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therefore has the potential to continue to be a dominant player, in exporting resources

for global trade in the future.

Brazilian Biomass Resource Export Caution – if the Brazilian Government were to

make future change to their energy strategies and polices, and utilise a greater

proportion of their resources for domestic use, there may be large impacts on the

levels of resource that Brazil would export; and thus less available within the global

trade markets.

F. Chapter 10 - An Alternative UK Bioenergy Strategy

Case Studies & Innovation – A broad range of potential strategy and policy options

were highlighted that the UK Government could potentially consider implementing, in

order to enhance the mobilisation and utilisation of UK indigenous resources by the

UK bioenergy sector.


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11.2 Deductions & Implications

It is possible to draw one bold overarching deduction from the research findings and

conclusions; that the UK Government should potentially consider re-evaluating its current

biomass strategies and policies.

11.2.1 Conclusion – Develop a Bioenergy Sector Compatible with UK

Resources

Throughout the Research Project themes have developed from the analyses results that go

against the paradigm of the UK’s current biomass resource and bioenergy sector landscape.

The analyses and results of Chapters 5 and 6, found that the UK had the potential to mobilise

large levels of indigenous resources, with large resource availability potentials represented by

waste resources, residues from on-going activities and resources grown specifically for the

bioenergy sector. It was found that if these resources were mobilised in reflection of any of

the biomass resource scenarios developed in Chapter 6, UK indigenous resources could make

large contributions towards the UK meeting its energy, renewable energy and bioenergy

targets.

However, in the analysis undertaken in Chapter 7 these potentially available UK indigenous

resources are compared against the specific demands forecast for the UK bioenergy sector, if

current UK policies and strategies progress through to maturity. The types of resource that

the UK’s planned future bioenergy sector would demand, were found to be out of balance

with the types of indigenous biomass resource that were found to be potentially available in

the UK. This suggests that the UK’s Bioenergy Sector development plans don’t necessarily

take appropriate consideration of the UK’s indigenous resources. Thus the UK is likely to

have to import increasing levels of biomass resource in the future, to balance its demands.

The research analysis undertaken in Chapter 6, also highlighted that the prioritisation of bio-

heat energy pathways would represent the best use of the UK’s indigenous biomass

resources, as this pathway would provide the highest energy levels for the resources

available. However, this conclusion is contradicted by the research analysis undertaken in

Chapter 7, where an evaluation of the UK’s bioenergy strategies and policies highlighted that

in the near to medium-term especially, large-scale bio-power systems are highly favoured in

addition to increasing requirements for biofuels and related feedstocks for the transport

sector. This further highlights that UK’s bioenergy strategy and policies may not have been

developed in reflection of the resources available.


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11.2.2 Conclusion - Developing Mobilisation & Utilisation Strategies

This Research Project has found that the UK Government’s stance of “not picking winners”

[8], potentially goes against the themes developed in the conclusions of this research. The

analysis results suggest that the UK should be picking winners when it comes to focusing on

UK indigenous biomass, and according to this research those winners are: household and

organic wastes, agricultural residues, and biomass resources and energy crops grown on UK

land.

The research results from Chapters 5 and 6, both highlight that these resources demonstrate

significantly greater potential for the UK’s bioenergy sector in terms of volumes of resource

that could potentially be produced / harvested / collected. The supply chain sensitivity

analyses undertaken in Chapter 5 also highlighted that if the UK wants to increase the

availability of these domestic resources, it should be focusing on managing the precise supply

chain drivers that demonstrate the greatest influence on their availabilities; not focusing on

promoting a broad range of initiatives.

The resource types that were found to potentially provide the UK Bioenergy Sector with the

greatest extent of biomass resource are wastes. The supply chain sensitivity analysis

highlighted that the development of a complementary waste management system is the single

most important issue to address, if the UK is to realise the potential of these resources. The

discussions in Chapter 10 highlighted that European Union Directives are the key influencing

factors that determine how the UK can manage its wastes. However, lessons from other EU

Member States should teach the UK that EU legislation should be used as a tool to help

develop the energy from its waste sector; not hinder it. The strict waste categorisation criteria,

emission thresholds, and technology-impact requirements, required by EU legislation; should

be embraced to aid the application processes. Drawing on influences from Scandinavia, the

UK public should be integrated into the planning processes and familiarised with energy-

from-waste technologies, in order to reduce opposition.

Agricultural residues were also found to be resources that if sufficiently mobilised, may also

provide significant volumes of resource for the UK bioenergy sector. The supply chain

sensitivity analysis highlighted that these resources are robust to variances, are largely

available in reflection of the extent that they can be harvested and the ability of supply chains

to deliver the resource to bioenergy plants; or in the case of manure and slurries,

predominantly used on-site or within the local area. Chapter 10, presented research that

highlighted the proportion of UK farms that currently utilise their agricultural biomass


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residues within bioenergy pathways; and demonstrated that there is large potential for growth

in the availability and utilisation of this resource.

Chapter 10 again highlighted that the United Kingdom can learn from Scandinavian examples

of best-practice, where Denmark is the European leader in terms of agricultural straw residue

utilisation in bioenergy processes. Potential lessons learned should include the concept that

once a market has matured beyond a certain threshold, market forces will take over and

supply chains will naturally strengthen as the result of innovation and competition. Denmark

lends a perfect example to highlight this concept. In order to provide stimulation and to get

markets to initiate development, Chapter 10 highlighted policies implemented by the Danish

Government that the UK Government could potentially emulate; through a reverse-auctioning

systems and universal contract agreement between the bioenergy sector and farmers, a market

for straw resource will develop.

Chapter 10 also highlighted the barriers that exist to prospective individuals or groups of

farmers wishing to invest in anaerobic digestion systems to generate bioenergy from their

animal based agricultural residues. The lack of finance and subsidy-economics were

identified as major issues preventing such investments in the UK, especially when compared

against the alternative costs for simply storing or spreading the collected agricultural

residues.

The research conclusions from Chapters 5 and 6 also found that the resource opportunity

provided by UK grown biomass resource and energy crops for the UK Bioenergy Sector was

also large; although heavily dependent on encouraging farmers to use their lands to grow

these resources. The research thus highlighted a major missed opportunity and a large barrier

that the UK Government should surely work to address.

11.2.3 Conclusion – Placing Greater Focus on Indigenous Resources

The UK should ideally be making more of its potential indigenous resources, not locking

itself into strategies that are reliant on the types and extent of biomass resources that the UK

does not have. The research suggests that the UK should potentially import biomass to

supplement its indigenous biomass resources, to support its bioenergy sector; not plan for

these imported resources to form the majority resource base.

Analysis undertaken within Chapter 8 highlighted that the global biomass trade markets are

growing strongly, and there will most likely be adequate biomass resources available until at

least the medium-term future (2030). However, Chapter 8 also highlighted the potential


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limitations, and highly significant sustainability impacts and risks associated with the future

global trade in biomass. Biomass resources that have been produced utilising less than best-

practice and with associated impacts such as, land-use change, and / or deforestation, are

likely to be far more negative for climate change, than any positive benefit gained from

switching to bioenergy in place of conventional fuels. A future UK that becomes reliant on

imported biomass resources will have to deal with these uncertainties.

Further potential opportunities and / or risks were identified in Chapter 8, in that Europe is

clearly becoming the global trade-hub for all types of biomass, with all major trade-flows

headed into the EU. Although this may place the United Kingdom at the centre of biomass

trade, having the UK’s major competitors for the same resources, in such close proximity in

times of market uncertainties, may lead to steeply rising costs.

However, the most revealing research theme developed in Chapter 9 results from the

bioenergy scenarios analysis, where it was determined that Brazil’s biomass resource exports

may be impacted, if it were to increase the ambitions of its current energy strategies and

policies - to utilise great proportions of its resources for domestic energy generation. The

results found that almost a 30% reduction in export levels may be realised if the Brazilian

Government were to strengthen its policies. As Brazil is the World’s largest exporter of bio-

ethanol and sugarcane feedstocks, a reduction in exports below expected future anticipated

levels, of anywhere near this extent would likely have major implications on the future

biomass markets, and on those countries heavily reliant on imported resources. Although

Brazil does not presently shows signs of aiming to increase the ambitions of its domestic bio-

energy policies, it does highlight a broader theme; that if a major exporter or a combination

of multiple smaller exporters were to marginally increase the use of their own biomass

resources rather than exporting them; the collective impact on global markets could be

significant. This therefore presents a further cautionary note for the UK Government to

consider, before fully committing to future development pathways that could potentially lock

the UK into biomass resource deficits, and reliance on global markets over which it has

limited influence.


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11.3 Original Contributions to Knowledge

This section highlights some of the original methodologies and analyses techniques

developed and applied through the Research Project. The section also identifies the key

analyses results and conclusions that contribute to strengthening or furthering knowledge

within the related research themes.

11.3.1 Biomass Resource Model

The Biomass Resource Model developed as the Research Project’s key analysis tool,

represents a series of unique methodologies, and allows the development of unique analyses.

A. Whole System Analysis

As widely discussed within the methodologies of Chapter 4 the BRM was developed drawing

influence from a wide range of existing models, reports, and research. The BRM represents a

unique tool as it is capable of modelling whole-system supply chains for any land-based

biomass resources, and also provides the capability of analysing the bioenergy potential of

resources analysed.

Alternative models including those that influenced the development of the BRM, largely

focus on modelling a few key resources for a specific country, or are highly broad;

developing global-level forecasts. As such, those alternative models which are also capable of

analysing bioenergy potentials are restricted to forecasts focusing on a limited number of

resources, or for making broad-ranging global predictions.

B. Structural Flexibility

The BRM also represents a unique biomass modelling tool which is highly flexible, in that it

can be amended for application to any country of region; restricted only by the availability of

data. A large part of the BRM’s flexibility stems from its design, and its utilisation of data

from universal sources such as the FAO, IEA, and UN; who publish data relevant to almost

all countries.

This flexibility allows the BRM to generate biomass resource forecasts for different countries

such as the UK (Chapters 5 and 6), and Brazil (Chapter 9). In each case the methodological

structure of the BRM is maintained, and thus the BRM’s forecasts for each country can be

compared without having to account for the increased variances in assumptions and

limitations, if they were separate Models.


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C. Supply Chain Sensitivity Analyses

As Chapter 5 confirms, the influences and drivers that may impact biomass resource supply

chains have previously been analysed and addressed by a wide range of previous studies.

Each of these studies focused either on biomass supply chains as hypothetical concepts, or

focused on the influences to a specific resource, within a specific country, or within a specific

supply chain. The BRM’s supply chain sensitivity analysis undertaken in Chapter 5 focused

on the UK, and provided a unique analysis of how UK biomass supply chains impact the

range of land-based biomass resources.

Further recognition of the BRM’s flexibility is highlighted by the concept that a further

sensitivity analysis exercise could just as easily have been undertaken for the Brazil case

study.

D. Link between Biomass & Bioenergy

As already hinted at, the link provided within the BRM between available biomass resources

and their bioenergy potential, provides a further original area of analysis. The majority of

biomass resource models focus predominantly on determining the availability of specific

resources, and stop short of undertaking further analysis to determine the bioenergy potential

forecasts [103].

As demonstrated by the Power, Heat, and Transport Fuel, Sub-Scenarios developed within

Chapter 6, the BRM’s link between resources and bioenergy, as well as the BRM’s capability

to ‘prioritise’ different conversion pathways if desired; enables opportunities for advanced

scenario analysis. This being a further analysis dynamic that stands the BRM apart from the

majority of alternative tools.

E. Ultimate Realistic Biomass Resource Forecasts

As introduced in Chapter 3 and described in the methodologies of Chapter 4, the BRM is

bottom-up resource focused and produces forecasts of the ultimate realistic potential of

biomass resource availability. Then through the calibration of the BRM’s drivers, practical

biomass availability forecasts are developed. The large majority of models applicable to the

UK are demand-driven, in that they first focus on the energy required before calculating the

resource required to balance this demand. The majority UK models are also designed to

generate economic focused biomass resource forecasts – determining the extent to which

resources may affordably be mobilised.


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This is a further differentiation that sets the BRM apart from the majority of other UK

models. Thus this allows the BRM to undertaken analyses different from that of most other

UK focused models.

11.3.2 Food & Industry Biomass Demands

The BRM’s key design characteristic to first allocate land and resource requirements to

balance the demands of both the food and biomass linked industries, is a further feature of the

BRM that allows unique analyses.

All the biomass resource analyses produced by the BRM and presented in this Thesis reflect

the design assumption that food and industry will not be impacted by the increased utilisation

of biomass for energy pathways.

11.3.3 UK Indigenous Biomass Resource Analyses

UK biomass research knowledge focusing on the analysis of different biomass resources is a

relatively well covered research theme. However, there are particular areas of UK focused

analysis undertaken in this Research Project that may fill gaps in existing knowledge, or at

least strengthen existing knowledge.

A. Biomass Resource Scenarios

The development of the biomass resource scenarios and the bioenergy potential forecasts

within Chapter 6; contribute further results and data towards the existing knowledge base in

the relevant research areas. The scenario resource availability and bioenergy potential

forecasts developed in this Research Project can be defined from those of the majority of

existing studies, in that they present analyses for the full range of land-based biomass

resources.

In addition, the choice of developing the four scenarios that focus on, Economic,

Conservation, Food, and Energy prioritised pathways, sets these results apart from the

majority of existing biomass scenario research focused on the UK. Most previous UK

biomass scenario analyses have focused on evaluating the economic affordability and

technical potential of biomass and bioenergy. The Food focused and Conservation focused

biomass resource scenarios developed for the UK, were found to be especially unique in

focus, compared to those of other existing studies reviewed when developing the research

theme.


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B. UK Biomass Supply Balance

When developing the research themes within Chapter 7, a series of existing UK focused

reports were identified that discussed how UK biomass resource should potentially be best

used [617]. However, no specific research was found that links biomass resource availability

forecasts, with forecast scenarios of the specific types and quantities of biomass resources

required by the UK’s demand sectors.

Therefore, the resource balance analysis undertaken in Chapter 7 represents an area of

research that will contribute to a gap in UK biomass research knowledge.

11.3.4 Brazil Biomass Resource Forecasts & Bioenergy Scenarios

Despite Brazil having vast biomass resources and being a dominant participant in the global

biomass markets, there was a real scarcity of in-depth reporting regarding Brazil’s biomass

resources, and the extent of the data and information available for development of the Brazil

BRM was considerably less than that available when developing the UK BRM. A significant

number of Brazil focused reports were examined, these having been compiled by

international institutes and companies, and by Universities in the United States and here in

Europe. Thus the Brazil focused research carried out in Chapters 8 and 9 should contribute

further to the existing knowledge base.

A. Brazilian Biomass Resource Availability

As discussed in Chapters 8 and 9, there are a number of studies and reports that attempt to

quantify Brazil’s current biomass resource, and a much smaller number of studies and reports

that attempt to forecast how these may change in the future. The majority the reports provide

either broad-range, or highly specific estimates of how much biomass resource,

(predominantly focusing on Sugarcane feedstock) Brazil is capable of producing, or will

produce.

Applying the BRM to Brazil and developing a literature informed Brazil Baseline Scenario,

enabled forecasts to be made that reflected Brazil’s biomass supply chains, allowing an

assessment of all land-based resources to be undertaken. Based on the literature reviews, and

as part of the process of developing this theme within the Research Project, it was not

possible to identify any resource assessments that covered the complete spectrum of biomass

resources. Thus, the results of Brazil Biomass Resource Availability analyses presented in

Chapter 9, add to the limited knowledge base currently available in this research area.


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B. Bioenergy Scenarios Analyses & Resource Export Implications

All of the existing literature reviewed when developing the Brazil research theme focused on

Brazil as a dominant current and future exporter of biomass. No studies were found that

considered the potential dynamic that the Brazilian Government or that of any other major

biomass exporting country may increase the ambitions of their energy strategies and policies.

As such the resource balance analyses linked to the Bioenergy Scenarios undertaken for

Brazil in Chapter 9, represents an original theme of biomass research that is absent from

reference in current publications.


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11.4 Limitations & Further Work

This next section of the Conclusion Chapter highlights some of the key limitations attributed

to the adopted research methodologies implemented in the Research Project. In each case

there are discussions regarding the potential impacts of the perceived limitation, and

justification is provided where appropriate, explaining why the limitation was not addressed

further within the Research Project. In addition, this section highlights further proposed work

that could be undertaken in the future to reduce these limitations.

11.4.1 Changing Food Diets

Large aspects of the BRM’s analyses relate strongly to the food system, and the demands for

food commodities. The methodologies reflect previous studies [123], [140], where growth in

food commodity demand has been linked to population change. However, these previous

studies and also the BRM do not allow analyses relating to changes in dietary trends. Trends

relating to increased or reduced meat consumption, or changes in the consumption of specific

crop-based food commodities, will likely have implications influencing the availability of

resources for the bioenergy sector.

Therefore, this represents a limitation of the BRM and is something that could potentially be

updated as a further project. For example, a report carried out by Bow-Larkin et al (2012)

[618], developed potential scenarios where dietary trends and food systems adapted as a

result of potential climate change drivers. Integrating such research with analyses from the

BRM could potentially provide some interesting results.

11.4.2 Climate Change Impacts

Within the BRM, the potential impacts of climate change on biomass supply chain dynamics

is modelled largely through the integration of assumption onto the land and crop productivity

yields. However, by their very nature climate change impacts are forecast to be highly broad,

influencing all aspects and levels within countries, societies, and systems etc. As such, the

accurate modelling of climate change impacts represents a highly complex process, and

represents a potential limitation of the applied methodologies.

A further limitation to the research, relates to addressing sustainability, land-use change, and

other climate change related impacts associated with biomass resource production and trade.

Chapter 8 highlighted the broad themes and impacts that may be linked to the production and

trade of biomass. However, the Research Project and wider BRM methodology could have


351

gone much further, potentially undertaking life cycle analysis assessments to measure the

potential impacts linked to biomass trade flows.

This represents further work that is currently planned as part of future projects involving: The

Supergen Bioenergy Hub, The Department of Energy & Climate Change, The University of

Manchester, The Imperial College London, Southampton University, and Rothamsted

Research. The BRM and the analysis outputs and conclusions from this Research Project will

filter into these planned future works where appropriate.

11.4.3 Spatial Scale & Distribution of Modelling

A large limitation of the developed modelling approach, relates to the spatial resolution of the

capable analysis. The BRM was designed to model the overall and average, dynamics and

distribution associated with biomass resource supply chains of the country or region in

question. Single values being developed to represent this desired geography.

This represents a limitation, as in reality biomass resources may be highly distributed or

concentrated in a few key nodes. This also applies when modelling the productivity and

characteristics of land, as all the land within a country is obviously not all the same. The

BRM assumes that all land identified as being suitable and potentially available for the

production of biomass and energy crops, has the same (averaged) potential productivity

characteristics, which will clearly not be the case.

A series of initial attempts were made to increase the spatial resolution capability of the

BRM, although these clashed with the further requirement for the BRM to be highly flexible

and to be amended to reflect the supply change dynamics of different countries. When

developing the UK BRM acquiring reflective data sets that accurately reflected the dynamics

of each UK Region or even UK County was not possible, due to the number of dynamics

measured and lack of appropriate data at the County resolution.

Ideally the BRM would have a higher analysis resolution, and as such this is a key area that

further work could explore especially if specifically focusing on a single country or region.

11.4.4 Energy Conversion Modelling

The BRM’s developed methodology for calculating bioenergy potentials, based on the

developed ‘preferred energy conversion’ pathways methodology (Appendix 1.0). This

presents a limitation, as this premise relies on the major assumption that all resources

potentially available for the bioenergy sector would be processed using preferred conversion

pathways to generate the forms of energy that are most ‘suitable’ to those resources.


352

Although this may be the case with the majority of the resources it is unlikely to fully reflect

reality.

This therefore represents a limitation in the way that the bioenergy potentials are calculated

throughout the Research Project. Further work could be undertaken to further understand how

resource use within bioenergy pathways may develop, however these forecasts will also be

based on their own assumptions.

11.4.5 Chemical Industry

The chemical industry is a further dynamic that has not received emphasis within the adopted

modelling approach.

The chemical industry is closely linked with biomass with some projections for 2030

showing that up to 35% of global chemical production could to some extent, be based on

biomass resources [619]. Whether the chemical sector uses more biomass resources will

depend on whether energy, feedstocks, or alternative chemicals will result in net GHG

emission savings, in comparison to the fuels being replaced [620].

As the chemical industry plays an important role in the UK economy, as both a major

exporter and employer, there is high potential that the growth and development of this

industry will be prioritised. This may potentially lead to further increasing demands for

biomass resources [621].

This research does not widely consider the demands and the growth dynamic of the Chemical

Industry, and this highlights a further limitation, and a potential focus for further work.

11.4.6 Supply Chain Drivers

Chapter 5, documents the range of supply chain drivers that are identified by a large number

of studies (Table 5.1), as potentially providing influence in determining the extent that

biomass resource may be available. Chapter 5 goes on to list the specific drivers which can or

cannot be analysed within the BRM. Thus, there is a series of further drivers that were not

considered directly within the methodology and as such represent a limitation. These are

listed as follows:

Gross Domestic Product

Rural Economy Development

Water Availability

Support Policies & Mechanisms

Soil Degradation Drivers

Flood Protection Land Requirements

Supply Change Resilience

Diet Change

Environmental Protection Policies

Calorie Consumption


353

Many of these supply chain drivers and influences could potentially be analysed within the

existing BRM, either through developing assumptions (for example dedicating specific areas

of land for flood defenses), or through the development of specific scenarios (such as to test

the influence of changing calorie consumption and diets). These were not carried in the

Research Project as they were deemed to present research direction that didn’t align with the

key aims and objectives.

However, other drivers such as water availability or financial support mechanisms cannot

currently be directly modelled within the BRM, and therefore represent further limitations of

the Research Project.


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11.5 Concluding Statement

This Thesis presents the work undertaken during a Three Year PhD Research Project within

the Tyndall Centre for Climate Change Research, at the University of Manchester.

The themes of the work focused on the analyses of biomass resource supply chains, and how

they influence the availability of biomass resources. These themes were placed within the

context of the United Kingdom’s high bioenergy aspirations, having ambitious renewable

energy and climate change targets, to which biomass is expected to make large contributions.

Through the development and application of a Biomass Resource Model, the research

focused on analysing the extent that UK indigenous biomass resources could contribute to

these targets.

Analyses were undertaken to evaluate how the UK’s biomass supply chain dynamics

influence the availability of different biomass resources. A selection of biomass resource

scenarios were then developed, placing emphasis on different United Kingdom future energy

pathways. The potential availability of various indigenous resources was evaluated within the

context of the future development of the UK bioenergy sector, and its associated resource

supply chains.

The analyses found that the United Kingdom has potentially large availabilities of specific

indigenous biomass resources; predominantly, household and organic wastes, and agricultural

residues. In addition, it also shows significant potential for the growth of biomass resources

and energy crops specifically for the bioenergy sector.

Further analyses compared these scenario results with the forecast demands of the future UK

bioenergy Sector based on current UK bioenergy strategies, found that the types and the

extent of biomass resources that the UK will require, are out of balance with those identified

as being predominantly available. Thus the key conclusions from the research, focus on the

theme that the future development and direction of the United Kingdom’s bioenergy sector,

will not be driven by the potential availability of the UK’s domestic and indigenous biomass

resources, but is likely be become highly dependent on imported biomass resources to

balance future demands; with significant inherent risks being identified with such a strategy.

A further area of research was undertaken which identified that the global biomass resource

markets have embedded uncertainties, and potentially major sustainability implications linked

to the trade of biomass.


355

Therefore, the Thesis Conclusions deduce that the UK Government should consider

refocusing its Bioenergy Strategies and Polices, so that they are to a larger extent driven by

the development and exploitation of the UK’s indigenous resources, rather than the premise

of future availability of imported biomass resource.


356

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380

Thesis Appendices


381

Appendix 1.0

Appendix 1.0 includes a summary of all the key bioenergy conversion data utilised within the BRM

calculations. These are collated from a wide range of reports, studies and research as discussed within the Thesis

text.

Appendix 1.1

Biomass Resource &

Feedstock Calorific

Values

This appendix includes a summary of the specific calorific values of the biomass

resource and feedstocks analysed within the BRM. These have been sourced

from a wide range of reports, studies and literature as discussed within the Thesis

text.

Appendix 1.2

Grown Biomass

Resource Bioenergy

Conversion Efficiencies

This appendix includes a summary of the bioenergy conversion efficiencies for

each of the grown biomass resources analysed within the BRM. These have been

sourced from a wide range of reports, studies and literature as discussed within

the Thesis text.

Appendix 1.3

Residue Resource


Efficiencies


each of the residue biomass resource and feedstocks analysed within the BRM.

These have been sourced from a wide range of reports, studies and literature as

discussed within the Thesis text.

Appendix 1.4

Waste Resource


Efficiencies


each of the waste biomass resource and feedstocks analysed within the BRM.

These have been sourced from a wide range of reports, studies and literature as

discussed within the Thesis text.

Appendix 1.5

Biomass Resource BRM

Preferred Bioenergy

Pathways

This appendix includes a summary of the preferred bioenergy conversion

pathways set as the default scenario within the BRM. These have been inspired

from a wide range of reports, studies and literature as discussed within the Thesis

text.

Appendix 1.6

Wood Resource

Conversion Factors

A summary of wood resource conversion factors applied within the BRM’s

biomass resource conversion calculations.

Appendix 1.7

Specific Wood Densities A summary of specific wood density values applied within the BRM’s biomass

resource conversion calculations.


382

Appendix 1.1: Biomass Resource & Feedstock Calorific Values

Resource

Category Resources Post Pre-Treatment Resource

Higher

Heating

Value

(MJ/Tonne)

Lower

Heating

Value

(MJ/Tonne)

Biomass

Crops

Grasses

Switch Grass

Switch Grass 18024 16767

Pellets 18950 -

Char 16337 12799

Char & Pellets 18950 -

Char 16337 12799

Switch Grass Chips (10% MC) 18024 16767

Switch Grass Pellets (10% MC) 18950 -

Reed Canary

Grass

Reed Canary Grass 18370 17126

Pellets 18950 -

Char 16337 12799


Char 16337 12799

Reed Canary Grass Chips (10% MC) 18024 16767

Reed Canary Grass Pellets (10% MC) 18950 -

Miscanthus

Miscanthus 19070 17860

Pellets 15081 -

Char - 17300


Char 17300

Miscanthus Chips (10% MC) 15081 -

Miscanthus Pellets (10% MC) 15081 -

Short

Rotation

Coppices

Poplar

Poplar 18255 17000

Pellets 17640 -

Char - -


Char - -

SRC Chips (25% MC) 11323 -

SRC Pellets (10% MC) 17640 -

Willow

Willow 18255 17000

Pellets 17640 -

Char 26700 25805


Char 26700 25805

SRC Chips (25% MC) 19600 18326

SRC Pellets (10% MC) 17640 -

Forestry

Eucalyptus Wood Pellets (10% MC) 16280 17700

Wood Chips (25% MC) 16280 17700

Beech

Raw Material 18800 17582

Pellets 16780 18200

Char 19530 20710

Char & Pellets 19530 20710

Char 19530 20710

Ash


Pellets 16780 18200

Char 19530 20710


Char 19530 20710

Birch


Pellets 16780 18200

Char 19530 20710


Char 19530 20710

Sycamore

Raw Material 19080 -

Pellets 17280 -

Char 17500 -


Char 17500 -

Pine


Pellets 17280 -

Char - -


Char - -


383

Spruce


Pellets 17280 -

Char 17500 -


Char 17500 -

Fir


Pellets 18980 16630

Char 17500 -


Char 17500 -

Energy

Crops

Cereal

Crops

Barley

Barley 18780 17563

Pellets 17370 17980

Char 18000 17389


Char 18000 17389

Oats

Oats 18780 17563

Pellets 17370 17980

Char 18000 17389


Char 18000 17389

Wheat

Wheat 14500 15910

Pellets 17370 17980

Char 16306 16000


Char 16306 16000

Oil Crops

Oilseed Rape

Rapeseed 20190 21600

Pellets 17370 17980

Char 18000 17389


Char 18000 17389

Palm Oil

Palm 20190 21600

Pellets 17370 17980

Char 18000 17389


Char 18000 17389

Palm Kernel

Palm 17140 18550

Pellets 17370 17980

Char 18000 17389


Char 18000 17389

Jatropha

Jatropha 43000 -

Pellets - -

Char - -

Char & Pellets - -

Char - -

Soya

Soyabean 20190 21600

Pellets 17370 17980

Char 23870 -


Char 23870 -

Sunflower

Sunflower 20800 19300

Pellets 17263 15799

Char 23870 -


Char 23870 -

Sugar Crops

Sugarcane

Sugar Cane 15620 17020

Pellets 15620 17020

Char - -


Sweet Sorghum

Sweet Sorghum 14260 15990

Pellets 14260 15990

Char 14260 15990


Char 14260 15990

Sugar Beet

Sugar Beet 17700 16600

Pellets 17700 16600

Char 17700 16600



384

Char 17700 16600

Forestry

Resource

Direct

Forestry

Production

Softwood -

Wood Fuel

Softwood 18645 -

Pellets 17280 -

Char 17500 -


Char 17500 -

Hardwood -

Wood Fuel

Hardwood 19080 -

Pellets 17280 -

Char 17500 -


Char 17500 -

Agricultural

Residues

Straws

Wheat

Wheat 19363 18028

Pellets 17370 17980

Char 25176 24587


Char 25176 24587

Barley (Spring

+ Winter)

Barley 18780 17563

Pellets 17370 17980

Char 18000 17389


Char 18000 17389

Oats

Oats 18089 17011

Pellets 17370 17980

Char 18000 17389


Char 18000 17389

Other Cereals

Other 16306 16000

Pellets 17370 17980

Char 18000 17389


Char 18000 17389

Oilseed Rape

Oilseed Rape 21604 20187

Pellets 17370 17980

Char 18000 17389


Char 18000 17389

Other Oil Seeds

Other Oil Seed 21604 20187

Pellets 17370 17980

Char 18000 17389


Char 18000 17389

Slurry's &

Manure

Cow Manure Manure 11230 18600

Pig Manure Manure 13930 18680

Poultry Litter Manure 19350 18120

Sheep Manure Manure 16020 14910

Forestry

Residues Wood

Residues

Total Forest Residues

Forestry Residues 18100 16791

Pellets 17280 -

Char 17500 -


Char 17500 -

Arboriculture

Arisings Residues & Arisings

Wood Chips (25% MC) 20396 19062

Wood Pellets (10% MC) 17280 -

Industry

Residues

Residues

from

Industry

Wood Chips

Wood Chips 20396 19062

Pellets 17280 -

Char 17500 -


Char 17500 -

Bark

Bark 16900 15500

Pellets 17280 -

Char 17500 -


Char 17500 -

Sawdust &

Other

Sawdust 19410 18115

Pellets 17280 -

Char 17500 -


Char 17500 -

Household, Wastes Chemical Wastes - -


385

Industry &

Other

Wastes

Used oils Wastes - -

Healthcare &

Biological Wastes - -

Metallic Wastes - -

Glass Wastes - -

Paper &

Cardboard Wastes 16900 15661

Rubber Wastes - -

Plastic Wastes - -

Wood Wastes 11000 15000

Textile Wastes 12199 11990

Waste containing PCB

Wastes - -

Animal &

Vegetal Wastes 4200 -

Food Prep &

Products Wastes 4200 -

Animal Faeces, Urine &

Manure

Wastes 17136 14772

Household &

Similar Wastes 17300 15900

Mixed Unsorted Materials

Wastes 15700 -

Sorting Wastes 15700 -

Common

Sludges Wastes 12100 10600

Mineral Wastes - -

Other Wastes - -

Sewage Sewage Sludge Waste 15100 14031

Appendix 1.2: Grown Biomass Resource Bioenergy Conversion Efficiencies

Bioenergy Pathway Grasses

Short

Rotation

Coppices

Forestry Cereal

Crops Oil Crops

Sugar

Crops

Direct

Forestry

Production

Bio-Syngas

Base 65% 65% - 65% 65% 65% 65%

2015 66% 66% - 66% 66% 66% 66%

2020 67% 67% - 67% 67% 67% 67%

2030 68% 68% - 68% 68% 68% 68%

2050 71% 71% - 71% 71% 71% 71%

BtL

Base 45% 45% - 45% 45% 45% 45%

2015 45% 45% - 45% 45% 45% 45%

2020 46% 46% - 46% 46% 46% 46%

2030 47% 47% - 47% 47% 47% 47%

2050 48% 48% - 48% 48% 48% 48%

Ethanol

Base 34% 34% - 54% 0% 36% 34%

2015 34% 34% - 55% - 37% 34%

2020 35% 35% - 56% - 38% 35%

2030 37% 37% - 57% - 39% 37%

2050 40% 40% - 60% - 42% 40%

Biodiesel

Base - - - - 58% - -

2015 - - - - 59% - -

2020 - - - - 59% - -

2030 - - - - 60% - -

2050 - - - - 62% - -

Heat from

Biomass

Combustion

Base 85% 85% 85% 85% 85% 85% 85%

2015 86% 86% 86% 86% 86% 86% 86%

2020 88% 88% 88% 88% 88% 88% 88%

2030 90% 90% 90% 90% 90% 90% 90%

2050 95% 95% 95% 95% 95% 95% 95%

Dedicated

Biopower

Base 34% 34% 34% 34% 34% 34% 34%

2015 35% 35% 35% 35% 35% 35% 35%

2020 36% 36% 36% 36% 36% 36% 36%

2030 39% 39% 39% 39% 39% 39% 39%

2050 43% 43% 43% 43% 43% 43% 43%

Dedicated Base 23% 23% 23% 23% 23% 23% 23%


386

Biopower

with CCS

2015 24% 24% 24% 24% 24% 24% 24%

2020 25% 25% 25% 25% 25% 25% 25%

2030 27% 27% 27% 27% 27% 27% 27%

2050 30% 30% 30% 30% 30% 30% 30%

Energy from

Pyrolysis

Base - 35% - - - - -

2015 - 36% - - - - -

2020 - 38% - - - - -

2030 - 40% - - - - -

2050 - 45% - - - - -

Anaerobic

Digestion

(Biogas)

Base - - - - - - -

2015 - - - - - - -

2020 - - - - - - -

2030 - - - - - - -

2050 - - - - - - -

Co-Firing

with Fossil

Fuels

Base 36% 36% 36% - - - 36%

2015 37% 37% 37% - - - 37%

2020 38% 38% 38% - - - 38%

2030 40% 40% 40% - - - 40%

2050 43% 43% 43% - - - 43%

Overall CHP

Efficiency

Base 85% 85% 85% - - - 85%

2015 86% 86% 86% - - - 86%

2020 87% 87% 87% - - - 87%

2030 90% 90% 90% - - - 90%

2050 94% 94% 94% - - - 94%

CHP

Heat Output

Efficiency

Base 57% 57% 57% - - - 57%

2015 57% 57% 57% - - - 57%

2020 58% 58% 58% - - - 58%

2030 60% 60% 60% - - - 60%

2050 63% 63% 63% - - - 63%

CHP

Power

Output

Efficiency

Base 28% 28% 28% - - - 28%

2015 29% 29% 29% - - - 29%

2020 29% 29% 29% - - - 29%

2030 30% 30% 30% - - - 30%

2050 31% 31% 31% - - - 31%

Dedicated

Transport

Fuel

Base - - 85% - - - 85%

2015 - - 86% - - - 86%

2020 - - 88% - - - 88%

2030 - - 90% - - - 90%

2050 - - 95% - - - 95%

Appendix 1.3: Residue Resource Bioenergy Conversion Efficiencies

Bioenergy Pathway Straws Slurry's &

Manure

Forestry

Residues

Arisings

Collected from

Built-Up Areas

Residues from

Industry

Bio-Syngas

Base 65% - 65% - 65%

2015 66% - 66% - 66%

2020 67% - 67% - 67%

2030 68% - 68% - 68%

2050 71% - 71% - 71%

BtL

Base 45% - 45% - 45%

2015 45% - 45% - 45%

2020 46% - 46% - 46%

2030 47% - 47% - 47%

2050 48% - 48% - 48%

Ethanol

Base 54% 34% 34% - 34%

2015 55% 34% 34% - 34%

2020 56% 35% 35% - 35%

2030 57% 37% 37% - 37%

2050 60% 40% 40% - 40%

Biodiesel

Base - - - - -

2015 - - - - -

2020 - - - - -

2030 - - - - -

2050 - - - - -

Heat from

Biomass

Base 85% 85% 85% 85% 85%

2015 86% 86% 86% 86% 86%


387

Combustion 2020 88% 88% 88% 88% 88%

2030 90% 90% 90% 90% 90%

2050 95% 95% 95% 95% 95%

Dedicated

Biopower

Base 34% - 34% 34% 34%

2015 35% - 35% 35% 35%

2020 36% - 36% 36% 36%

2030 39% - 39% 39% 39%

2050 43% - 43% 43% 43%

Dedicated

Biopower

with CCS

Base 23% - 23% 23% 23%

2015 24% - 24% 24% 24%

2020 25% - 25% 25% 25%

2030 27% - 27% 27% 27%

2050 30% - 30% 30% 30%

Energy from

Pyrolysis

Base - 35% 35% - 35%

2015 - 36% 36% - 36%

2020 - 38% 38% - 38%

2030 - 40% 40% - 40%

2050 - 45% 45% - 45%

Anaerobic

Digestion

(Biogas)

Base - 75% - - -

2015 - 76% - - -

2020 - 78% - - -

2030 - 80% - - -

2050 - 85% - - -

Co-Firing

with Fossil

Fuels

Base - - 36% 36% 36%

2015 - - 37% 37% 37%

2020 - - 38% 38% 38%

2030 - - 40% 40% 40%

2050 - - 43% 43% 43%

Overall CHP

Efficiency

Base - - 85% 85% 85%

2015 - - 86% 86% 86%

2020 - - 87% 87% 87%

2030 - - 90% 90% 90%

2050 - - 94% 94% 94%

CHP

Heat Output

Efficiency

Base - - 57% 57% 57%

2015 - - 57% 57% 57%

2020 - - 58% 58% 58%

2030 - - 60% 60% 60%

2050 - - 63% 63% 63%

CHP

Power

Output

Efficiency

Base - - 28% 28% 28%

2015 - - 29% 29% 29%

2020 - - 29% 29% 29%

2030 - - 30% 30% 30%

2050 - - 31% 31% 31%

Dedicated

Transport

Fuel

Base - - - 85% -

2015 - - - 86% -

2020 - - - 88% -

2030 - - - 90% -

2050 - - - 95% -

Appendix 1.4: Waste Resource Bioenergy Conversion Efficiencies

Bioenergy Pathway Chemical Healthcare &

Biological

Paper &

Cardboard Wood Textile

Animal &

Vegetal

Bio-Syngas

Base - 65% 65% 65% 65% -

2015 - 66% 66% 66% 66% -

2020 - 67% 67% 67% 67% -

2030 - 68% 68% 68% 68% -

2050 - 71% 71% 71% 71% -

BtL

Base - 45% 45% 45% 45% -

2015 - 45% - 45% 45% -

2020 - 46% - 46% 46% -

2030 - 47% - 47% 47% -

2050 - 48% - 48% 48% -

Ethanol

Base - 34% 34% 34% 34% 34%

2015 - 34% 34% 34% 34% 34%

2020 - 35% 35% 35% 35% 35%

2030 - 37% 37% 37% 37% 37%


388

2050 - 40% 40% 40% 40% 40%

Biodiesel

Base - - - - - -

2015 - - - - - -

2020 - - - - - -

2030 - - - - - -

2050 - - - - - -

Heat from

Biomass

Combustion

Base 85% 85% 85% 85% 85% 85%

2015 86% 86% 86% 86% 86% 86%

2020 88% 88% 88% 88% 88% 88%

2030 90% 90% 90% 90% 90% 90%

2050 95% 95% 95% 95% 95% 95%

Dedicated

Biopower

Base - 34% 34% 34% 34% -

2015 - 35% 35% 35% 35% -

2020 - 36% 36% 36% 36% -

2030 - 39% 39% 39% 39% -

2050 - 43% 43% 43% 43% -

Dedicated

Biopower

with CCS

Base - 23% 23% 23% 23% -

2015 - 24% - 24% 24% -

2020 - 25% - 25% 25% -

2030 - 27% - 27% 27% -

2050 - 30% - 30% 30% -

Energy from

Pyrolysis

Base - 35% 35% 35% 35% 35%

2015 - 36% 36% 36% 36% 36%

2020 - 38% 38% 38% 38% 38%

2030 - 40% 40% 40% 40% 40%

2050 - 45% 45% 45% 45% 45%

Anaerobic

Digestion

(Biogas)

Base - - - - - 55%

2015 - - - - - 56%

2020 - - - - - 58%

2030 - - - - - 61%

2050 - - - - - 66%

Co-Firing

with Fossil

Fuels

Base - 36% 36% 36% 36% -

2015 - 37% 37% 37% 37% -

2020 - 38% 38% 38% 38% -

2030 - 40% 40% 40% 40% -

2050 - 43% 43% 43% 43% -

Overall CHP

Efficiency

Base - 85% 85% 85% 85% -

2015 - 86% 86% 86% 86% -

2020 - 87% 87% 87% 87% -

2030 - 90% 90% 90% 90% -

2050 - 94% 94% 94% 94% -

CHP

Heat Output

Efficiency

Base - 57% 57% 57% 57% -

2015 - 57% 57% 57% 57% -

2020 - 58% 58% 58% 58% -

2030 - 60% 60% 60% 60% -

2050 - 63% 63% 63% 63% -

CHP

Power

Output

Efficiency

Base - 28% 28% 28% 28% -

2015 - 29% 29% 29% 29% -

2020 - 29% 29% 29% 29% -

2030 - 30% 30% 30% 30% -

2050 - 31% 31% 31% 31% -

Dedicated

Transport

Fuel

Base - - - - - -

2015 - - - - - -

2020 - - - - - -

2030 - - - - - -

2050 - - - - - -


389

Appendix 1.4: Waste Resource Bioenergy Conversion Efficiencies (Continued)

Bioenergy Pathway

Food

Preparation

& Products

Animal

Faeces, Urine

& Manure

Household &

Similar

Common

Sludges Other

Sewage

Sludge

Bio-Syngas

Base - - - - 65% -

2015 - - - - 66% -

2020 - - - - 67% -

2030 - - - - 68% -

2050 - - - - 71% -

BtL

Base - - - - 45% -

2015 - - - - 45% -

2020 - - - - 46% -

2030 - - - - 47% -

2050 - - - - 48% -

Ethanol

Base 34% 34% 34% - 34% 34%

2015 34% 34% 34% - 34% 34%

2020 35% 35% 35% - 35% 35%

2030 37% 37% 37% - 37% 37%

2050 40% 40% 40% - 40% 40%

Biodiesel

Base - - - - - -

2015 - - - - - -

2020 - - - - - -

2030 - - - - - -

2050 - - - - - -

Heat from

Biomass

Combustion

Base 85% 85% 85% 85% 85% 85%

2015 86% 86% 86% 86% 86% 86%

2020 88% 88% 88% 88% 88% 88%

2030 90% 90% 90% 90% 90% 90%

2050 95% 95% 95% 95% 95% 95%

Dedicated

Biopower

Base - - - - 34% -

2015 - - - - 35% -

2020 - - - - 36% -

2030 - - - - 39% -

2050 - - - - 43% -

Dedicated

Biopower

with CCS

Base - - - - 23% -

2015 - - - - 24% -

2020 - - - - 25% -

2030 - - - - 27% -

2050 - - - - 30% -

Energy from

Pyrolysis

Base 35% 35% 35% - 35% 35%

2015 36% 36% 36% - 36% 36%

2020 38% 38% 38% - 38% 38%

2030 40% 40% 40% - 40% 40%

2050 45% 45% 45% - 45% 45%

Anaerobic

Digestion

(Biogas)

Base 55% 55% 55% - - 55%

2015 56% 56% 56% - - 56%

2020 58% 58% 58% - - 58%

2030 61% 61% 61% - - 61%

2050 66% 66% 66% - - 66%

Co-Firing

with Fossil

Fuels

Base - - - - 36% -

2015 - - - - 37% -

2020 - - - - 38% -

2030 - - - - 40% -

2050 - - - - 43% -

Overall CHP

Efficiency

Base - - - - 85% -

2015 - - - - 86% -

2020 - - - - 87% -

2030 - - - - 90% -

2050 - - - - 94% -

CHP

Heat Output

Efficiency

Base - - - - 57% -

2015 - - - - 57% -

2020 - - - - 58% -

2030 - - - - 60% -

2050 - - - - 63% -

CHP

Power

Output

Base - - - - 28% -

2015 - - - - 29% -

2020 - - - - 29% -


390

Efficiency 2030 - - - - 30% -

2050 - - - - 31% -

Dedicated

Transport

Fuel

Base - - - - - -

2015 - - - - - -

2020 - - - - - -

2030 - - - - - -

2050 - - - - - -

Appendix 1.5: Biomass Resource BRM Preferred Bioenergy Pathways

Resources

Base Year 2015 2020 2030 2050

Bioenergy

Pathways %

Bioenergy

Pathways %

Bioenergy

Pathways %

Bioenergy

Pathways %

Bioenergy

Pathways %

Grasses H-BC 100 H-BC 100 H-BC 100 H-BC 100 H-BC 100

Short

Rotation

Coppices

H-BC 50 H-BC 50 H-BC 50 H-BC 50 H-BC 50

D-BP 50 D-BP 50 D-BP 50 D-BP 50 D-BP 50

Forestry H-BC 25 H-BC 25 H-BC 25 H-BC 25 H-BC 25


Cereal Crops Eth 100 Eth 100 Eth 100 Eth 100 Eth 100

Oil Crops BioDl 100 BioDl 100 BioDl 100 BioDl 100 BioDl 100

Sugar Crops Eth 100 Eth 100 Eth 100 Eth 100 Eth 100

Direct

Forestry

Production



Straws D-BP 80 D-BP 80 D-BP 80 D-BP 80 D-BP 80


Slurry's &

Manure

AD-H 30 AD-H 30 AD-H 30 AD-H 30 AD-H 30

AD-P 70 AD-P 70 AD-P 70 AD-P 70 AD-P 70

Wood

Residues



Residues &

Arisings



Residues

from

Industry



Healthcare

& Biological H-BC 100 H-BC 100 H-BC 100 H-BC 100 H-BC 100

Paper &

Cardboard



Wood H-BC 30 H-BC 30 H-BC 30 H-BC 30 H-BC 30


Textile H-BC 30 H-BC 30 H-BC 30 H-BC 30 H-BC 30


Animal &

Vegetal



Food Prep &

Products



Animal

Faeces,

Urine &

Manure



Household &

Similar



Mixed

Unsorted

Materials



Sorting H-BC 30 H-BC 30 H-BC 30 H-BC 30 H-BC 30


Mineral H-BC 30 H-BC 30 H-BC 30 H-BC 30 H-BC 30


Other H-BC 30 H-BC 30 H-BC 30 H-BC 30 H-BC 30


391


Sewage

Sludge AD-P 100 AD-P 100 AD-P 100 AD-P 100 AD-P 100

Key

H-BC Heating from Biomass Combustion D-BP Dedicated Biopower

Eth Ethanol Production BioD Biodiesel Production

AD-H Anaerobic Digestion – Heat AD-P Anaerobic Digestion – Power

Appendix 1.6: Wood Resource Conversion Factors

Initial Wood Resource Converted Wood Resource References

1 Green Tonne

0.982 m3 under-bark softwood [622]

0.875 m3 under-bark hardwood [622]

1.100 m3 over-bark softwood [622]

1.000 m3 over-bark hardwood [622]

1.222 m3 over-bark standing softwood [622]

1.111 m3 over-bark standing hardwood [622]

1 m3 under-bark

1.018 green tonnes softwood [622]

1.143 green tonnes hardwood [622]

1.120 m3 over-bark softwood [622]

1.143 m3 over-bark hardwood [622]

1.244 m3 over-bark standing softwood [622]

1.270 m3 over-bark standing hardwood [622]

1 m3 over-bark





1.111 m3 over-bark standing (softwood or hardwood) [622]

1 m3 over-bark

standing





0.900 m3 over-bark (softwood or hardwood) [622]

Appendix 1.7: Specific Wood Densities

Wood Species

Volume /

Green Tonne Green Density MC (Green)

Oven Dry

Density References

m3/tonne kg/m3 % kg/m3

Scots pine 0.980 1020 60% 461 [623]

Corsican pine 1.000 1000 60% 450 [623]

Lodgepole pine 1.050 950 60% 439 [623]

Sitka Spruce 1.080 920 62% 391 [623]

Norway Spruce 1.040 960 65% 380 [623]

European Larch 1.110 900 50% 501 [623]

Douglas Fir 1.150 870 51% 479 [623]

Western Hemlock 1.070 930 62% 403 [623]

Mean Softwood 1.087 921 60% 417 [623]

Oak 0.940 1060 47% 638 [623]

Beech 0.970 1030 47% 645 [623]

Sycamore 1.200 830 41% 554 [623]

Birch 1.070 930 43% 603 [623]

Elm 0.970 1030 58% 486 [623]

Ash 1.280 780 33% 600 [623]

Poplar 1.11 900 60% 408 [623]

Mean Hardwood 1.077 937 47% 562 [623]


392

Appendix 2.0

Appendix 2.0 includes a list of all the specific food commodities categorised by the FAO and analysed within

the BRM.

Appendix 2.1

Crop Based Food

Commodities

A full list of the crop based food commodities categorised by the FAO and


Appendix 2.2

Animal Based Food

Commodities

A full list of the animal based food commodities categorised by the FAO. And



393

Appendix 2.1: Crop Based Commodities Categorised by the FAO

Category Food Commodities

Fruits &

Berries

Apples

Apricots Bananas

Berries Nes

Blueberries Buckwheat

Cherries

Citrus fruit - Lemons + Limes Citrus fruit - Other

Citrus Juice Concentrated

Cranberries Currants

Dates

Figs Flour of Fruits

Fruit Fresh - Other

Fruit Tropical Dried Nes Gooseberries

Grapefruit

Grapefruit Juice Concentrated Grapes

Grapes - Wine

Cooked Fruit Prep Kiwi fruit

Lemons and limes

Mangoes Mangosteens

Guavas

Marc of Grapes Maté

Orange Juice Concentrated

Oranges Oranges

Mandarines

Other Melons (inc. Cantaloupes Papayas

Peaches

Nectarines Pears

Persimmons

Pineapples Plantains

Plums

Sloes Quinces

Raspberries

Sour Cherries Stone Fruit

Strawberries

Tangerines Mandarins

Clementines

Tomatoes Watermelons

Cereals &

Grains

Alcohol -Non-Food

Barley

Beer Beverages, Alcoholic

Beverages, Fermented

Brans Cereals, Other

Fonio

Maize

Maize for Forage & Silage Maize - Green

Millet

Mixed Grain Oats

Popcorn

Quinoa

Rice (Milled Equivalent) Rice (Paddy Equivalent)

Rye

Triticale Wheat

Nuts &

Seeds

Almonds with Shell

Arecanuts

Brazil Nuts with Shell Canary Seed

Cashew nuts with Shell

Chestnuts Coconut - Oil

Coconuts

Coconuts – incl. Copra Copra Cake

Flax Fibre & Tow Groundnut Cake

Groundnut Oil

Groundnuts (in Shell Eq)

Groundnuts (Shelled Eq)

Groundnuts, with Shell

Hazelnuts, with Shell Hemp Tow Waste

Hempseed

Kapokseed in Shell Karite Nuts (Sheanuts)

Kolanuts

Manila Fibre (Abaca) Melonseed

Mustard Seed Nuts

Pistachios

Poppy Seed

Rape & Mustard Cake

Rape & Mustard Oil

Rape & Mustardseed Rapeseed

Safflower Seed

Sesameseed Sesameseed Cake

Sesameseed Oil

Sunflower Seed Sunflowerseed Cake

Sunflowerseed Oil Tung Nuts

Walnuts, with Shell

Vegetables

Asparagus Cabbage for Fodder

Cabbages & other Brassicas

Carrots & Turnips Cow Peas, dry

Cucumbers

Gherkins Eggplants (aubergines)

Leeks, other Alliaceous Veg.

Leguminous for Silage Leguminous Vegetables, nes

Lettuce

Chicory

Lupins Mushrooms T

ruffles

Okra Onions (inc. Shallots), green

Peas, dry

Peas, green Pepper (Piper spp.)

Pigeon Peas

Pimento

Potatoes

Pulses Other Pumpkins for Fodder

Pumpkins, Squash and Gourds

Roots & Tuber Dry Equiv Roots, Other

Sweet Potatoes

Turnips for Fodder Vetches

Vegetables – Other

Yams

Plants

Agave Fibres Nes

Anise Badian

Fennel

Corian Artichokes

Avocados

Beets for Fodder Carobs

Casava

Chicory Roots Chillies & Peppers, dry

Chillies & Peppers, green

Coffee, green Coir

Hard Fibres

Hops Jute

Kapok Fibre

Other Bastfibres Pyrethrum dried

Ramie

Sisal Soft-Fibres, Other

Sorghum

Tobacco, unmanufactured Yautia (cocoyam)

Beans

Bambara Beans

Beans, dry

Beans, green Broad Beans,

Horse Beans, dry

Chick Peas

Cocoa Beans Lentils

Soyabean Cake

Soyabean Oil

Soyabeans String beans

Herbs

Spices &

Leaves

Cinnamon (canella)

Cloves

Garlic Ginger

Nutmeg, Mace & Cardamoms

Peppermint

Spices Spinach

Spices - Other

Sweeteners, Other

Tea Vanilla


394

Oil Plants

Cotton Lint

Cottonseed

Cottonseed Cake Cottonseed Oil

Gums Natural

Linseed Natural rubber

Oil Palm Fruit

Oilcrops Oil, Other

Oilcrops, Other Oilseed Cakes, Other

Oilseeds

Olive Oil Olives

Maize Germ Oil Rubber

Palm Oil

Palmkernel Cake Palmkernel Oil

Palmkernels

Sugar

Crops

Molasses

Sugar Beet

Sugar cane

Sugar, Raw Equivalent Sugar, Refined Equiv

Appendix 2.2: Animal Based Commodities Categorised by the FAO

Category Food Commodities

Meats

Bird

Buffalo Camel

Cattle (bovine)

Chicken (poultry) Duck

Game

Goose

Guinea Fowl Horse

Meat Meal

Meat of Asses Meat of Mules

Mutton & Goat Meat

Other Meats

Pig Rabbit

Sheep

Sheep Milk, whole, fresh Meat - Other

Turkey

Dairy

Buffalo Milk, whole, fresh

Butter, Ghee Camel Milk, whole, fresh

Cheese

Cow Milk, whole, fresh

Cream Goat Milk, whole, fresh

Milk – excl. Butter

Milk, skimmed

Milk, whole

Whey

Offals,

Fats,

Skins &

Hides

Buffalo Hide

Fats, Animals, Raw

Goatskins Hair of Horses

Hides & Skins Offals

Sheepskins

Skin Furs Skins With Wool Sheep

Wool, greasy

Eggs Hen Eggs, in shell Other Bird Eggs, in shell

Others Beeswax

Honey Silk-Worm Silk Snails, Not Sea


395

Appendix 3.0

Appendix 3.0 includes an overview of the datasets within the BRM’s calculations analysing UK feed

commodity dynamics. These are utilised within the Model to analyse the amount of land and resources required

to produce animal based food commodities to meet demand.

Appendix 3.1

UK Agriculture

Production Systems

Data within this table reflects the proportions of different types of agricultural

practices applied within the UK to produce the respective animal based products.

Appendix 3.2

Feed Composition Data within this table demonstrates the typical agricultural practices and nature

of feed that is required to produce different animal based products in the UK.

Appendix 3.3

Animal Feed Raw

Material Content

Data within this table demonstrates the raw material content of animal feed

utilised within the UK.

Appendix 3.4

Feed Conversion

Efficiencies

Data within this table demonstrates the typical feed conversion efficiencies for

different animal categories in the UK. These values representing the amount of

feed (kg) that is required to produce an equivalent mass (kg) of animal product.


396

Appendix 3.1: UK Agriculture Production Systems

Agriculture Production Pastoral Practices (%) Mixed + Landless Systems (%)

Beef Meat Based Products 0 100

Milk & Dairy Based Products 0 100

Mutton + Goat Based Products 0 100

Pork Based Products 0 100

Poultry Based Products 0 100

Other Products with Mixed + Landless Land Requirement 100 0

Other Products with Pastoral Land Requirement 100 100

Appendix 3.2: Feed Composition

Agriculture Production

Pastoral Agricultural Practices (%) Mixed + Landless Systems (%)

Food

Crops

Residues

&

Fodder

Animal

Products Grass Scavenging

Food

Crops

Residues

&

Fodder

Animal


Beef Meat Based Products 0 0 0 100 0 12 38 0 50 0

Milk & Dairy Based Products 0 0 0 100 0 15 45 0 40 0

Mutton + Goat Based Products 0 0 0 100 0 2 8 0 90 0

Pork Based Products - - - - - 59 40 1 0 0

Poultry Based Products - - - - - 74 25 1 0 0

Other Products with Mixed +

Landless Feed Land Requirement - - - - - 2 8 0 90 0

Other Products with Pastoral

Land Requirement 0 0 0 100 0 - - - - -

Appendix 3.3: Animal Feed Raw Material Content

Raw Materials Content Demand of each Commodity Required per annum

‘000 Tonnes %

Wheat 2,815.8 25.24%

Barley 757.8 6.79%

Oats 69.5 0.62%

Whole & Flaked Maize 109.1 0.98%

Rice Bran Extractions 17.9 0.16%

Maize Gluten Feed 201.0 1.80%

Wheat Feed 837.5 7.51%

Other Cereals By-Products 148.4 1.33%

Distillery By-Products 358.6 3.21%

Cereal By-Products 1,344.4 12.05%

Whole Oilseeds 53.2 0.48%

Oilseed Rape Cake and Meal 667.0 5.98%

Soya Cake & Meal 1,134.8 10.17%

Sunflower Cake & Meal 278.6 2.50%

Other Oilseed Cake & Meal 435.6 3.90%

Field Beans 71.4 0.64%

Field Peas 35.9 0.32%

Dried Sugar Beet Pulp 242.7 2.18%

Molasses 286.8 2.57%

Citrus & Other Fruit Pulp 56.0 0.50%

Meat & Bone Meal 0.0 0.00%

Other Meal 0.0 0.00%

Fish Meal 0.0 0.00%

All Meal (Fish, Poultry & Other) 110.2 0.99%

Minerals 397.2 3.56%

Oil & Fat 189.7 1.70%

Protein Concentrates 12.7 0.11%

Other Materials 289.3 2.59%

Confectionery By-Products 234.4 2.10%

Totals 11155.5 100%


397

Appendix A3.4: Feed Conversion Efficiencies


Pastoral Agricultural Practices (feed kg/product kg)

Mixed + Landless Systems (%)

(feed kg/product kg)

Low Range Mean High Range Low Range Mean High Range

Beef Meat Based Products 8.0 16.7 33.0 8.0 10.5 15.0

Milk & Dairy Based Products 1.7 1.7 1.7 0.7 0.9 1.2

Mutton + Goat Based Products 29.0 29.0 29.0 5.4 9.0 17.0

Pork Based Products 3.3 4.6 6.2 2.2 4.4 6.2

Poultry Based Products 2.5 3.1 3.6 1.7 2.8 4.0


Landless Feed Land Requirement - - - 8.5 8.5 8.5


Land Requirement 14.6 14.6 14.6 - - -


398

Appendix 4.0

Appendix 4.0 includes a summary of the range of food crop and biomass resource productivity yield data

utilised within the BRM’s calculations.

Appendix 4.1

Crop Commodity

Productivity Yields

A summary of the productivity yield data of all crop commodities analysed

within the BRM.

Appendix 4.2

Biomass Resource

Productivity Yields

A summary of the productivity yield data of all biomass resources analysed

within the BRM.


399

Appendix 4.1: Crop Based Commodities Categorised by the FAO

Biomass Resources Productivity Yield Range

Low 1st 1/4 Mean 3rd 1/4 High

Fruits

&

Berries

Apples 14.6 15.0 15.4 15.8 16.3

Apricots 14.6 14.6 14.6 14.6 14.6

Bananas 15.6 15.6 15.6 15.6 15.6

Berries Nes 9.0 10.4 11.8 13.2 14.6

Blueberries 15.6 15.6 15.6 15.6 15.6

Buckwheat 15.6 15.6 15.6 15.6 15.6

Cherries 2.7 5.7 8.7 11.6 14.6

Citrus fruit - Lemons + Limes 15.6 15.6 15.6 15.6 15.6

Citrus fruit - Other 14.6 16.6 18.7 20.8 22.8

Citrus juice, concentrated 15.6 15.6 15.6 15.6 15.6

Cranberries 15.6 15.6 15.6 15.6 15.6

Currants 4.9 7.3 9.7 12.2 14.6

Dates 15.6 15.6 15.6 15.6 15.6

Figs 15.6 15.6 15.6 15.6 15.6

Flour of Fruits 15.6 15.6 15.6 15.6 15.6

Fruit Fresh - Other 14.6 18.2 21.7 25.3 28.9

Fruit Tropical Dried Nes 15.6 15.6 15.6 15.6 15.6

Gooseberries 8.6 10.1 11.6 13.1 14.6

Grapefruit 14.6 21.5 28.5 35.4 42.3

Grapefruit - concentrated juice 14.6 21.5 28.5 35.4 42.3

Grapes 1.4 4.7 8.0 11.3 14.6

Grapes - Wine 1.4 4.7 8.0 11.3 14.6

Homogen. Cooked Fruit Prep 15.6 15.6 15.6 15.6 15.6

Kiwi fruit 15.6 15.6 15.6 15.6 15.6

Lemons and limes 15.6 15.6 15.6 15.6 15.6

Mangoes, mangosteens, guavas 15.6 15.6 15.6 15.6 15.6

Marc of Grapes 15.6 15.6 15.6 15.6 15.6

Maté 15.6 15.6 15.6 15.6 15.6

Orange juice, concentrated 15.6 15.6 15.6 15.6 15.6

Oranges 15.6 15.6 15.6 15.6 15.6

Oranges, Mandarines 15.6 15.6 15.6 15.6 15.6

Other melons (inc. cantaloupes) 15.6 15.6 15.6 15.6 15.6

Papayas 15.6 15.6 15.6 15.6 15.6

Peaches and nectarines 15.6 15.6 15.6 15.6 15.6

Pears 13.4 13.4 13.4 13.4 13.4

Persimmons 15.6 15.6 15.6 15.6 15.6

Pineapples 23.2 23.2 23.2 23.2 23.2

Plantains 15.6 15.6 15.6 15.6 15.6

Plums and sloes 15.5 15.5 15.5 15.5 15.5

Quinces 15.6 15.6 15.6 15.6 15.6

Raspberries 9.4 9.4 9.4 9.4 9.4

Sour cherries 15.6 15.6 15.6 15.6 15.6

Stone fruit, nes 15.6 15.6 15.6 15.6 15.6

Strawberries 19.8 19.8 19.8 19.8 19.8

Tangerines, mandarins, clem. 15.6 15.6 15.6 15.6 15.6

Tomatoes 401.9 401.9 401.9 401.9 401.9

Watermelons 15.6 15.6 15.6 15.6 15.6

Cereals

&

Grains

Alcohol, Non-Food 5.7 5.7 5.7 5.7 5.7

Barley 5.7 5.7 5.7 5.7 5.8

Beer 5.7 5.7 5.7 5.7 5.7

Beverages, Alcoholic 5.7 5.7 5.7 5.7 5.7

Beverages, Fermented 7.9 7.9 7.9 7.9 7.9

Brans 4.3 4.3 4.3 4.3 4.3

Cereals, Other 4.3 5.6 6.1 7.0 7.0

Fonio 7.9 7.9 7.9 7.9 7.9

Maize 9.5 9.5 9.5 9.5 9.5

Maize for forage and silage 9.5 9.5 9.5 9.5 9.5

Maize, green 9.5 17.2 24.8 32.5 40.2

Millet 7.9 7.9 7.9 7.9 7.9

Mixed grain 4.3 4.3 4.3 4.3 4.3

Oats 5.5 5.6 5.6 5.6 5.7


400

Popcorn 7.9 7.9 7.9 7.9 7.9

Quinoa 7.9 7.9 7.9 7.9 7.9

Rice (Milled Equivalent) 7.9 7.9 7.9 7.9 7.9

Rice (Paddy Equivalent) 7.9 7.9 7.9 7.9 7.9

Rye 5.7 5.7 5.7 5.7 5.7

Triticale 3.8 3.8 3.8 3.8 3.8

Wheat 7.2 7.4 7.6 7.7 7.8

Nuts &

Seeds

Almonds, with shell 2.5 2.5 2.5 2.5 2.5

Arecanuts 2.5 2.5 2.5 2.5 2.5

Brazil nuts, with shell 2.5 2.5 2.5 2.5 2.5

Canary seed 2.5 2.5 2.5 2.5 2.5

Cashew nuts, with shell 2.5 2.5 2.5 2.5 2.5

Chestnuts 2.5 2.5 2.5 2.5 2.5

Coconut - Oil 2.5 2.5 2.5 2.5 2.5

Coconuts 2.5 2.5 2.5 2.5 2.5

Coconuts – inc. Copra 2.5 2.5 2.5 2.5 2.5

Copra Cake 2.5 2.5 2.5 2.5 2.5

Flax fibre and tow 1.5 1.5 1.5 1.5 1.5

Groundnut Cake 3.5 3.5 3.5 3.5 3.5

Groundnut Oil 3.5 3.5 3.5 3.5 3.5

Groundnuts (in Shell Eq) 3.5 3.5 3.5 3.5 3.5

Groundnuts (Shelled Eq) 3.5 3.5 3.5 3.5 3.5

Groundnuts, with shell 3.5 3.5 3.5 3.5 3.5

Hazelnuts, with shell 2.5 2.5 2.5 2.5 2.5

Hemp Tow Waste 2.5 2.5 2.5 2.5 2.5

Hempseed 2.5 2.5 2.5 2.5 2.5

Kapokseed in Shell 2.5 2.5 2.5 2.5 2.5

Karite Nuts (Sheanuts) 2.5 2.5 2.5 2.5 2.5

Kolanuts 2.5 2.5 2.5 2.5 2.5

Manila Fibre (Abaca) 2.5 2.5 2.5 2.5 2.5

Melonseed 2.5 2.5 2.5 2.5 2.5

Mustard seed 2.5 2.5 2.5 2.5 2.5

Nuts 2.6 2.6 2.6 2.6 2.6

Pistachios 2.5 2.5 2.5 2.5 2.5

Poppy seed 2.5 2.5 2.5 2.5 2.5

Rape and Mustard Cake 0.7 0.7 0.7 0.7 0.7

Rape and Mustard Oil 0.7 0.7 0.7 0.7 0.7

Rape and Mustardseed 0.7 0.7 0.7 0.7 0.7

Rapeseed 3.1 3.1 3.2 3.3 3.3

Safflower seed 2.5 2.5 2.5 2.5 2.5

Sesameseed 2.5 2.5 2.5 2.5 2.5

Sesameseed Cake 2.5 2.5 2.5 2.5 2.5

Sesameseed Oil 2.5 2.5 2.5 2.5 2.5

Sunflower seed 2.5 2.5 2.5 2.5 2.5

Sunflowerseed Cake 2.5 2.5 2.5 2.5 2.5

Sunflowerseed Oil 2.5 2.5 2.5 2.5 2.5

Tung Nuts 2.5 2.5 2.5 2.5 2.5

Walnuts, with shell 2.5 2.5 2.5 2.5 2.5

Veg.

Asparagus 2.1 2.1 2.1 2.1 2.1

Cabbage for Fodder 43.5 43.5 43.5 43.5 43.5

Cabbages and other brassicas 21.6 22.6 23.5 24.4 25.3

Carrots and turnips 66.9 66.9 66.9 66.9 66.9

Cow peas, dry 43.5 43.5 43.5 43.5 43.5

Cucumbers and gherkins 479.6 479.6 479.6 479.6 479.6

Eggplants (aubergines) 43.5 43.5 43.5 43.5 43.5

Leeks, other alliaceous veg 43.5 43.5 43.5 43.5 43.5

Leguminous for Silage 43.5 43.5 43.5 43.5 43.5

Leguminous vegetables, nes 11.3 13.9 16.4 19.0 21.6

Lettuce and chicory 18.4 19.2 20.0 20.8 21.6

Lupins 43.5 43.5 43.5 43.5 43.5

Mushrooms and truffles 43.5 43.5 43.5 43.5 43.5

Okra 43.5 43.5 43.5 43.5 43.5

Onions (inc. shallots), green 9.6 12.6 15.6 18.6 21.6

Peas, dry 3.5 3.6 3.8 3.9 4.0

Peas, green 4.0 4.8 5.6 6.4 7.2

Pepper (Piper spp.) 149.0 149.0 149.0 149.0 149.0

Pigeon peas 43.5 43.5 43.5 43.5 43.5

Pimento 43.5 43.5 43.5 43.5 43.5


401

Potatoes 40.2 41.4 42.2 43.2 43.8

Pulses Other 2.8 3.0 3.1 3.3 3.5

Pumpkins for Fodder 43.5 43.5 43.5 43.5 43.5

Pumpkins, squash and gourds 43.5 43.5 43.5 43.5 43.5

Roots & Tuber Dry Equiv 9.0 9.0 9.0 9.0 9.0

Roots, Other 43.5 43.5 43.5 43.5 43.5

Sweet potatoes 43.5 43.5 43.5 43.5 43.5

Turnips for Fodder 43.5 43.5 43.5 43.5 43.5

Vetches 43.5 43.5 43.5 43.5 43.5

Vegetables - Other 12.5 14.8 17.1 19.3 21.6

Yams 43.5 43.5 43.5 43.5 43.5

Plants

Agave Fibres Nes 38.2 38.2 38.2 38.2 38.2

Anise, badian, fennel, corian. 38.2 38.2 38.2 38.2 38.2

Artichokes 38.2 38.2 38.2 38.2 38.2

Avocados 38.2 38.2 38.2 38.2 38.2

Beets for Fodder 38.2 38.2 38.2 38.2 38.2

Carobs 38.2 38.2 38.2 38.2 38.2

Casava 1.3 1.3 1.3 1.3 1.3

Chicory roots 38.2 38.2 38.2 38.2 38.2

Chillies and peppers, dry 38.2 38.2 38.2 38.2 38.2

Chillies and peppers, green 149.0 149.0 149.0 149.0 149.0

Coffee, green 38.2 38.2 38.2 38.2 38.2

Coir 38.2 38.2 38.2 38.2 38.2

Hard Fibres, Other 38.2 38.2 38.2 38.2 38.2

Hops 1.3 1.3 1.3 1.3 1.3

Jute 38.2 38.2 38.2 38.2 38.2

Kapok Fibre 38.2 38.2 38.2 38.2 38.2

Other Bastfibres 38.2 38.2 38.2 38.2 38.2

Pyrethrum, Dried 38.2 38.2 38.2 38.2 38.2

Ramie 38.2 38.2 38.2 38.2 38.2

Sisal 38.2 38.2 38.2 38.2 38.2

Soft-Fibres, Other 1.3 1.3 1.3 1.3 1.3

Sorghum 38.2 38.2 38.2 38.2 38.2

Tobacco, unmanufactured 38.2 38.2 38.2 38.2 38.2

Yautia (cocoyam) 38.2 38.2 38.2 38.2 38.2

Beans

Bambara beans 4.4 4.4 4.4 4.4 4.4

Beans, dry 3.7 5.0 6.3 7.6 8.9

Beans, green 3.7 5.0 6.2 7.5 8.8

Broad beans, horse beans, dry 3.7 3.8 4.0 4.1 4.2

Chick peas 4.4 4.4 4.4 4.4 4.4

Cocoa beans 4.4 4.4 4.4 4.4 4.4

Lentils 4.4 4.4 4.4 4.4 4.4

Soyabean Cake 2.8 3.0 3.3 3.5 3.7

Soyabean Oil 2.8 3.0 3.3 3.5 3.7

Soyabeans 2.8 3.0 3.3 3.5 3.7

String beans 4.4 4.4 4.4 4.4 4.4

Herbs

Spices

&

Leaves

Cinnamon (canella) 39.7 39.7 39.7 39.7 39.7

Cloves 39.7 39.7 39.7 39.7 39.7

Garlic 39.7 39.7 39.7 39.7 39.7

Ginger 39.7 39.7 39.7 39.7 39.7

Nutmeg, mace and cardamoms 39.7 39.7 39.7 39.7 39.7

Peppermint 39.7 39.7 39.7 39.7 39.7

Spices, nes 39.7 39.7 39.7 39.7 39.7

Spinach 39.7 39.7 39.7 39.7 39.7

Spices - Other 39.7 39.7 39.7 39.7 39.7

Sweeteners, Other 39.7 39.7 39.7 39.7 39.7

Tea 39.7 39.7 39.7 39.7 39.7

Vanilla 39.7 39.7 39.7 39.7 39.7

Oil

Plants

Cotton Lint 1.6 1.7 1.8 1.9 2.0

Cottonseed 1.6 1.7 1.8 1.9 2.0

Cottonseed Cake 1.6 1.7 1.8 1.9 2.0

Cottonseed Oil 1.6 1.7 1.8 1.9 2.0

Gums Natural 2.0 2.0 2.0 2.0 2.0

Linseed 1.6 1.7 1.8 1.9 2.0

Natural rubber 2.0 2.0 2.0 2.0 2.0

Oil palm fruit 2.0 2.0 2.0 2.0 2.0

Oilcrops Oil, Other 1.6 1.7 2.3 2.6 3.3

Oilcrops, Other 1.6 1.7 2.3 2.6 3.3


402

Oilseed Cakes, Other 1.6 1.7 2.3 2.6 3.3

Oilseeds, Nes 1.6 1.7 2.3 2.6 3.3

Olive Oil 2.0 2.0 2.0 2.0 2.0

Olives 2.0 2.0 2.0 2.0 2.0

Maise Germ Oil 1.6 1.7 1.8 1.9 2.0

Rubber 2.0 2.0 2.0 2.0 2.0

Palm Oil 2.0 2.0 2.0 2.0 2.0

Palmkernel Cake 2.0 2.0 2.0 2.0 2.0

Palmkernel Oil 2.0 2.0 2.0 2.0 2.0

Palmkernels 2.0 2.0 2.0 2.0 2.0

Sugar

Crops

Molasses 53.9 54.2 54.6 54.9 55.3

Sugar Beet 9.5 31.7 39.6 54.6 55.3

Sugar cane 49.6 49.6 49.6 49.6 49.6

Sugar, Raw Equivalent 53.9 54.2 54.6 54.9 55.3

Sugar, Refined Equiv 53.9 54.2 54.6 54.9 55.3

Appendix 4.2: Biomass Resource Productivity Yields

Category Biomass Resources Productivity Yield Range


Biomass

Resources

Miscanthus 5.0 6.9 12.8 18.0 24.1

Willow 7.0 8.0 8.6 9.0 10.0

Poplar 5.6 6.5 7.7 8.8 10.0

Beech 8.0 9.0 9.9 10.9 11.8

Birch 5.0 6.5 7.7 9.0 10.0

Eucalyptus 9.0 10.5 12.0 13.5 15.0

Fir 8.0 10.5 13.0 15.5 18.0

Pine 8.0 8.5 9.0 9.5 10.0

Spruce 8.0 9.5 11.0 12.5 14.0

Sycamore 6.0 6.8 7.0 7.3 8.0

Sunflower 1.5 1.8 2.0 2.3 2.5

Reed Canary Grass 2.9 5.8 8.9 11.9 15.0

Switch Grass 6.0 6.7 9.6 11.4 15.4

Ash 10.0 10.0 10.0 10.0 10.0

Pasture Grass 10.5 11.3 12.0 12.8 13.5


403

Appendix 5.0

Appendix 5.0 includes a summary of the applicable data utilised within the UK BRM for the waste generation

and waste management scenario calculations.

Appendix 5.1 Waste Generation

Scenarios – Household

Waste

Household waste generation scenario data reflecting the DEFRA forecast

scenarios as described within the Thesis text.

Appendix 5.2

Waste Generation

Scenarios – Other

Waste

Other waste generation scenario data reflecting the DEFRA forecast scenarios as

described within the Thesis text.

Appendix 5.3 Waste Management

Scenarios – Current

Rate + Resource

Recovery

Waste management strategy data reflecting DEFRA’s Current Rate and Resource

Recovery scenarios as described within the Thesis text.

Appendix 5.4 Waste Management

Scenarios – Energy

Recovery + Combined

Recovery

Waste management strategy data reflecting DEFRA’s Energy Recovery and

Combined Recovery scenarios as described within the Thesis text.


404

Appendix 5.1: Waste Generation Scenarios – Household Waste

Year

Waste Generation Scenarios

Reference Rate Green Rate High Tech Rate Unlimited Rate

Waste

(Mt) Change

(%) Waste

(Mt) Change

(%) Waste

(Mt) Change

(%) Waste

(Mt) Change

(%)

2010 28.20 - 28.20 - 28.20 - 28.20 -

2015 28.59 1.10 26.25 -5.65 29.15 2.67 28.80 1.69

2020 28.98 1.09 24.30 -6.08 30.10 2.58 29.40 1.66

2030 29.10 0.37 21.90 -9.26 31.20 3.24 32.60 9.39

2050 29.99 3.02 17.46 -22.52 34.43 9.87 37.62 14.38

Appendix 5.2: Waste Generation Scenarios – Other Waste

Year


Reference Rate Green Rate High Tech Rate Unlimited Rate

Waste

(Mt) Change

(%) Waste

(Mt) Change

(%) Waste

(Mt) Change

(%) Waste

(Mt) Change

(%)

2010 61.10 - 61.10 - 61.10 - 61.10 -

2015 64.50 4.38% 61.00 -0.13% 64.30 4.13% 62.60 1.95%

2020 67.90 4.15% 60.90 -0.13% 67.50 3.92% 64.10 1.90%

2030 71.60 4.80% 56.90 -6.07% 74.60 9.09% 77.30 17.16%

2050 83.76 15.75% 53.29 -6.55% 90.97 19.94% 98.34 24.23%

Appendix 5.3: Waste Management Scenarios – Current Rate + Resource Recovery

Waste

Categories

Waste

Management 2010

Waste Management Scenarios

Current Rate Resource Recovery

2015 2020 2030 2050 2015 2020 2030 2050

Chemical

Wastes

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 63.0% 72.1% 90.4% 95.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 32.6% 24.7% 8.9% 5.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Chemical

Wastes excl. Used Oils

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 63.0% 72.1% 90.4% 95.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 32.6% 24.7% 8.9% 5.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Used Oils

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 63.0% 72.1% 90.4% 95.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 32.6% 24.7% 8.9% 5.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Healthcare &

Biological Wastes

Recycling & Reuse 32.8% 32.8% 32.9% 33.0% 33.0% 38.7% 50.5% 74.1% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 15.7% 15.6% 15.4% 15.1% 15.0% 13.7% 9.8% 2.0% 0.0%

Landspread 51.5% 51.6% 51.7% 51.9% 52.0% 47.6% 39.7% 23.9% 20.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Healthcare &

Biological

Wastes

Recycling & Reuse 32.8% 32.8% 32.9% 33.0% 33.0% 38.7% 50.5% 74.1% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 15.7% 15.6% 15.4% 15.1% 15.0% 13.7% 9.8% 2.0% 0.0%

Landspread 51.5% 51.6% 51.7% 51.9% 52.0% 47.6% 39.7% 23.9% 20.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Metallic Wastes

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 63.0% 72.1% 90.4% 95.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 32.6% 24.7% 8.9% 5.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Metallic Wastes

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 63.0% 72.1% 90.4% 95.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 4.5% 3.2% 0.6% 0.0%


405

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 32.6% 24.7% 8.9% 5.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Glass Wastes

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 63.0% 72.1% 90.4% 95.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 32.6% 24.7% 8.9% 5.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Glass Wastes

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 63.0% 72.1% 90.4% 95.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 32.6% 24.7% 8.9% 5.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Paper & Cardboard

Wastes

Recycling & Reuse 46.6% 46.6% 46.6% 46.6% 46.6% 51.4% 61.0% 80.2% 85.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 6.8% 6.8% 6.8% 6.8% 6.8% 6.0% 4.3% 0.9% 0.0%

Landspread 46.6% 46.6% 46.6% 46.6% 46.6% 42.6% 34.7% 18.9% 15.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Rubber Wastes

Recycling & Reuse 3.7% 3.7% 3.8% 4.0% 4.0% 10.7% 24.8% 53.0% 60.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 7.7% 7.9% 8.2% 8.8% 9.0% 6.8% 4.8% 1.0% 0.0%

Landspread 88.6% 88.4% 88.0% 87.2% 87.0% 82.5% 70.4% 46.1% 40.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Plastic Wastes

Recycling & Reuse 3.7% 3.7% 3.8% 4.0% 4.0% 10.7% 24.8% 53.0% 60.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 7.7% 7.9% 8.2% 8.8% 9.0% 6.8% 4.8% 1.0% 0.0%

Landspread 88.6% 88.4% 88.0% 87.2% 87.0% 82.5% 70.4% 46.1% 40.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Wood Wastes

Recycling & Reuse 16.0% 16.0% 16.0% 16.0% 16.0% 20.2% 28.7% 45.7% 50.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 4.1% 4.1% 4.1% 4.0% 4.0% 3.6% 2.6% 0.5% 0.0%

Landspread 79.9% 79.9% 79.9% 80.0% 80.0% 76.2% 68.7% 53.7% 50.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Wood Wastes

Recycling & Reuse 16.0% 16.0% 16.0% 16.0% 16.0% 20.2% 28.7% 45.7% 50.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 4.1% 4.1% 4.1% 4.0% 4.0% 3.6% 2.6% 0.5% 0.0%

Landspread 79.9% 79.9% 79.9% 80.0% 80.0% 76.2% 68.7% 53.7% 50.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Textile Wastes

Recycling & Reuse 14.2% 14.0% 13.7% 13.1% 13.0% 18.7% 27.6% 45.5% 50.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 7.8% 8.2% 9.0% 10.6% 11.0% 6.8% 4.9% 1.0% 0.0%

Landspread 78.0% 77.8% 77.3% 76.3% 76.0% 74.5% 67.5% 53.5% 50.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Waste Containing PCB

Recycling & Reuse 58.4% 55.2% 48.9% 36.2% 33.0% 61.1% 66.5% 77.3% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 6.3% 8.8% 13.8% 15.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 38.4% 42.3% 50.1% 52.0% 34.4% 30.3% 22.1% 20.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Animal & Vegetal Wastes

Recycling & Reuse 0.3% 0.4% 0.6% 0.9% 1.0% 0.3% 0.3% 0.3% 0.3%

Composting 16.1% 16.1% 16.0% 16.0% 16.0% 25.3% 43.8% 80.8% 90.0%

Energy Recovery 9.0% 9.0% 9.0% 9.0% 9.0% 7.9% 5.6% 1.1% 0.0%

Landspread 74.7% 74.6% 74.4% 74.1% 74.0% 66.5% 50.2% 17.5% 9.3%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Animal Waste

of Food Preparation &

Products

Recycling & Reuse 1.7% 1.7% 1.8% 2.0% 2.0% 1.7% 1.8% 2.0% 2.0%

Composting 0.0% 0.6% 1.9% 4.4% 5.0% 9.1% 27.4% 63.9% 73.0%

Energy Recovery 19.6% 18.9% 17.5% 14.7% 14.0% 17.2% 12.3% 2.5% 0.0%

Landspread 78.7% 78.7% 78.8% 79.0% 79.0% 72.0% 58.6% 31.7% 25.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Animal Faeces, Urine & Manure

Recycling & Reuse 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 6.3% 18.8% 43.8% 50.0%

Energy Recovery 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.2% 0.0% 0.0%

Landspread 99.7% 99.7% 99.7% 99.7% 99.7% 93.5% 81.1% 56.2% 50.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Household & Similar Wastes

Recycling & Reuse 58.4% 55.2% 48.9% 36.2% 33.0% 61.1% 66.5% 77.3% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 6.3% 8.8% 13.8% 15.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 38.4% 42.3% 50.1% 52.0% 34.4% 30.3% 22.1% 20.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Mixed & Recycling & Reuse 58.4% 55.2% 48.9% 36.2% 33.0% 61.1% 66.5% 77.3% 80.0%


406

Undifferentiated

Materials

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 6.3% 8.8% 13.8% 15.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 38.4% 42.3% 50.1% 52.0% 34.4% 30.3% 22.1% 20.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Mixed & Undifferentiated

Materials

Recycling & Reuse 58.4% 55.2% 48.9% 36.2% 33.0% 61.1% 66.5% 77.3% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 6.3% 8.8% 13.8% 15.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 38.4% 42.3% 50.1% 52.0% 34.4% 30.3% 22.1% 20.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Sorting

Residues

Recycling & Reuse 84.1% 84.2% 84.5% 84.9% 85.0% 85.5% 88.2% 93.6% 95.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 0.2% 0.3% 0.5% 0.9% 1.0% 0.2% 0.1% 0.0% 0.0%

Landspread 16.0% 15.8% 15.3% 14.3% 14.0% 14.6% 11.9% 6.4% 5.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Sorting

Residues

Recycling & Reuse 84.1% 84.2% 84.5% 84.9% 85.0% 85.5% 88.2% 93.6% 95.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 0.2% 0.3% 0.5% 0.9% 1.0% 0.2% 0.1% 0.0% 0.0%

Landspread 16.0% 15.8% 15.3% 14.3% 14.0% 14.6% 11.9% 6.4% 5.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Common

Sludges

Recycling & Reuse 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 6.3% 18.8% 43.8% 50.0%

Energy Recovery 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.2% 0.0% 0.0%

Landspread 99.7% 99.7% 99.7% 99.7% 99.7% 93.5% 81.1% 56.2% 50.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Mineral Wastes

Recycling & Reuse 2.0% 2.0% 2.0% 2.0% 2.0% 13.6% 36.9% 83.4% 95.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Landspread 98.0% 94.6% 87.9% 74.4% 71.0% 86.4% 63.1% 16.6% 5.0%

Disposal at Sea 0.0% 3.4% 10.1% 23.6% 27.0% 0.0% 0.0% 0.0% 0.0%

Mineral Wastes

Recycling & Reuse 2.0% 2.0% 2.0% 2.0% 2.0% 13.6% 36.9% 83.4% 95.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Landspread 98.0% 94.6% 87.9% 74.4% 71.0% 86.4% 63.1% 16.6% 5.0%

Disposal at Sea 0.0% 3.4% 10.1% 23.6% 27.0% 0.0% 0.0% 0.0% 0.0%

Other Wastes

Recycling & Reuse 58.4% 55.2% 48.9% 36.2% 33.0% 61.1% 66.5% 77.3% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 6.3% 8.8% 13.8% 15.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 38.4% 42.3% 50.1% 52.0% 34.4% 30.3% 22.1% 20.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Other Wastes

Recycling & Reuse 58.4% 55.2% 48.9% 36.2% 33.0% 61.1% 66.5% 77.3% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 6.3% 8.8% 13.8% 15.0% 4.5% 3.2% 0.6% 0.0%

Landspread 36.5% 38.4% 42.3% 50.1% 52.0% 34.4% 30.3% 22.1% 20.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Appendix A5.4: Waste Management Scenarios – Energy Recovery + Combined Recovery

Waste

Categories

Waste

Management 2010


Current Rate Resource Recovery

2015 2020 2030 2050 2015 2020 2030 2050

Chemical

Wastes

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 58.5% 58.6% 58.9% 59.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 5.1% 5.1% 5.0% 5.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 36.4% 36.3% 36.1% 36.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Chemical

Wastes excl.

Used Oils

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 58.5% 58.6% 58.9% 59.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 5.1% 5.1% 5.0% 5.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 36.4% 36.3% 36.1% 36.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Used Oils

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 58.5% 58.6% 58.9% 59.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 5.1% 5.1% 5.0% 5.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 36.4% 36.3% 36.1% 36.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Healthcare & Recycling & Reuse 32.8% 28.7% 20.5% 4.1% 0.0% 38.7% 50.5% 74.1% 80.0%


407

Biological

Wastes

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 15.7% 25.0% 43.6% 80.7% 90.0% 15.0% 13.6% 10.7% 10.0%

Landspread 51.5% 46.3% 36.0% 15.2% 10.0% 46.3% 36.0% 15.2% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Healthcare & Biological

Wastes

Recycling & Reuse 32.8% 28.7% 20.5% 4.1% 0.0% 38.7% 50.5% 74.1% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 15.7% 25.0% 43.6% 80.7% 90.0% 15.0% 13.6% 10.7% 10.0%

Landspread 51.5% 46.3% 36.0% 15.2% 10.0% 46.3% 36.0% 15.2% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Metallic Wastes

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 58.5% 58.6% 58.9% 59.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 5.1% 5.1% 5.0% 5.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 36.4% 36.3% 36.1% 36.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Metallic Wastes

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 58.5% 58.6% 58.9% 59.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 5.1% 5.1% 5.0% 5.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 36.4% 36.3% 36.1% 36.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Glass Wastes

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 58.5% 58.6% 58.9% 59.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 5.1% 5.1% 5.0% 5.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 36.4% 36.3% 36.1% 36.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Glass Wastes

Recycling & Reuse 58.4% 58.5% 58.6% 58.9% 59.0% 58.5% 58.6% 58.9% 59.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 5.1% 5.1% 5.0% 5.0% 5.1% 5.1% 5.0% 5.0%

Landspread 36.5% 36.4% 36.3% 36.1% 36.0% 36.4% 36.3% 36.1% 36.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Paper &

Cardboard

Wastes

Recycling & Reuse 46.6% 40.8% 29.1% 5.8% 0.0% 51.4% 61.0% 80.2% 85.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 6.8% 17.2% 38.0% 79.6% 90.0% 6.6% 6.2% 5.2% 5.0%

Landspread 46.6% 42.0% 32.9% 14.6% 10.0% 42.0% 32.9% 14.6% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Rubber Wastes

Recycling & Reuse 3.7% 3.2% 2.3% 0.5% 0.0% 10.7% 24.8% 53.0% 60.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 7.7% 18.0% 38.6% 79.7% 90.0% 10.5% 16.1% 27.2% 30.0%

Landspread 88.6% 78.7% 59.1% 19.8% 10.0% 78.7% 59.1% 19.8% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Plastic Wastes

Recycling & Reuse 3.7% 3.2% 2.3% 0.5% 0.0% 10.7% 24.8% 53.0% 60.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 7.7% 18.0% 38.6% 79.7% 90.0% 10.5% 16.1% 27.2% 30.0%

Landspread 88.6% 78.7% 59.1% 19.8% 10.0% 78.7% 59.1% 19.8% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Wood Wastes

Recycling & Reuse 16.0% 14.0% 10.0% 2.0% 0.0% 20.2% 28.7% 45.7% 50.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 5.0% 15.0% 35.0% 40.0%

Energy Recovery 4.1% 14.9% 36.3% 79.3% 90.0% 3.6% 2.6% 0.5% 0.0%

Landspread 79.9% 71.2% 53.7% 18.7% 10.0% 71.2% 53.7% 18.7% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Wood Wastes

Recycling & Reuse 16.0% 14.0% 10.0% 2.0% 0.0% 20.2% 28.7% 45.7% 50.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 5.0% 15.0% 35.0% 40.0%

Energy Recovery 4.1% 14.9% 36.3% 79.3% 90.0% 3.6% 2.6% 0.5% 0.0%

Landspread 79.9% 71.2% 53.7% 18.7% 10.0% 71.2% 53.7% 18.7% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Textile Wastes

Recycling & Reuse 14.2% 12.4% 8.9% 1.8% 0.0% 18.7% 27.6% 45.5% 50.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 7.8% 18.1% 38.6% 79.7% 90.0% 11.8% 19.9% 36.0% 40.0%

Landspread 78.0% 69.5% 52.5% 18.5% 10.0% 69.5% 52.5% 18.5% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%


Recycling & Reuse 58.4% 51.1% 36.5% 7.3% 0.0% 61.1% 66.5% 77.3% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 15.7% 36.9% 79.4% 90.0% 5.7% 6.9% 9.4% 10.0%

Landspread 36.5% 33.2% 26.6% 13.3% 10.0% 33.2% 26.6% 13.3% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Animal &

Vegetal Wastes

Recycling & Reuse 0.3% 0.3% 0.3% 0.3% 0.3% 0.4% 0.6% 0.9% 1.0%

Composting 16.1% 14.1% 10.0% 2.0% 0.0% 16.1% 16.0% 16.0% 16.0%

Energy Recovery 9.0% 19.1% 39.4% 79.9% 90.0% 9.0% 9.0% 9.0% 9.0%

Landspread 74.7% 66.6% 50.4% 18.1% 10.0% 74.6% 74.4% 74.1% 74.0%


408

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Animal Waste

of Food

Preparation & Products

Recycling & Reuse 1.7% 1.7% 1.8% 2.0% 2.0% 1.7% 1.8% 2.0% 2.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 9.1% 27.4% 63.9% 73.0%

Energy Recovery 19.6% 28.2% 45.3% 79.5% 88.0% 19.0% 17.9% 15.6% 15.0%

Landspread 78.7% 70.1% 52.9% 18.6% 10.0% 70.1% 52.9% 18.6% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Animal Faeces,

Urine & Manure

Recycling & Reuse 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 0.3% 6.5% 18.9% 43.8% 50.0% 0.3% 0.3% 0.3% 0.3%

Landspread 99.7% 93.5% 81.1% 56.2% 50.0% 99.7% 99.7% 99.7% 99.7%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Household &

Similar Wastes

Recycling & Reuse 58.4% 51.1% 36.5% 7.3% 0.0% 61.1% 66.5% 77.3% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 15.7% 36.9% 79.4% 90.0% 5.7% 6.9% 9.4% 10.0%

Landspread 36.5% 33.2% 26.6% 13.3% 10.0% 33.2% 26.6% 13.3% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%


Materials

Recycling & Reuse 58.4% 51.1% 36.5% 7.3% 0.0% 61.1% 66.5% 77.3% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 15.7% 36.9% 79.4% 90.0% 5.7% 6.9% 9.4% 10.0%

Landspread 36.5% 33.2% 26.6% 13.3% 10.0% 33.2% 26.6% 13.3% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%


Materials

Recycling & Reuse 58.4% 51.1% 36.5% 7.3% 0.0% 61.1% 66.5% 77.3% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 15.7% 36.9% 79.4% 90.0% 5.7% 6.9% 9.4% 10.0%

Landspread 36.5% 33.2% 26.6% 13.3% 10.0% 33.2% 26.6% 13.3% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Sorting Residues

Recycling & Reuse 84.1% 84.2% 84.5% 84.9% 85.0% 84.2% 84.5% 84.9% 85.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 0.2% 0.3% 0.5% 0.9% 1.0% 0.3% 0.5% 0.9% 1.0%

Landspread 16.0% 15.8% 15.3% 14.3% 14.0% 15.8% 15.3% 14.3% 14.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Sorting Residues

Recycling & Reuse 84.1% 84.2% 84.5% 84.9% 85.0% 84.2% 84.5% 84.9% 85.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 0.2% 0.3% 0.5% 0.9% 1.0% 0.3% 0.5% 0.9% 1.0%

Landspread 16.0% 15.8% 15.3% 14.3% 14.0% 15.8% 15.3% 14.3% 14.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Common Sludges

Recycling & Reuse 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 0.3% 6.5% 18.9% 43.8% 50.0% 0.3% 0.3% 0.3% 0.3%

Landspread 99.7% 93.5% 81.1% 56.2% 50.0% 99.7% 99.7% 99.7% 99.7%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Mineral Wastes

Recycling & Reuse 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Landspread 98.0% 94.6% 87.9% 74.4% 71.0% 94.6% 87.9% 74.4% 71.0%

Disposal at Sea 0.0% 3.4% 10.1% 23.6% 27.0% 3.4% 10.1% 23.6% 27.0%

Mineral Wastes

Recycling & Reuse 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Landspread 98.0% 94.6% 87.9% 74.4% 71.0% 94.6% 87.9% 74.4% 71.0%

Disposal at Sea 0.0% 3.4% 10.1% 23.6% 27.0% 3.4% 10.1% 23.6% 27.0%

Other Wastes

Recycling & Reuse 58.4% 51.1% 36.5% 7.3% 0.0% 61.1% 66.5% 77.3% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 15.7% 36.9% 79.4% 90.0% 5.7% 6.9% 9.4% 10.0%

Landspread 36.5% 33.2% 26.6% 13.3% 10.0% 33.2% 26.6% 13.3% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Other Wastes

Recycling & Reuse 58.4% 51.1% 36.5% 7.3% 0.0% 61.1% 66.5% 77.3% 80.0%

Composting 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Energy Recovery 5.1% 15.7% 36.9% 79.4% 90.0% 5.7% 6.9% 9.4% 10.0%

Landspread 36.5% 33.2% 26.6% 13.3% 10.0% 33.2% 26.6% 13.3% 10.0%

Disposal at Sea 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Appendix 6.0


409

Appendix 6.0 includes a summary of the data utilised in developing the UK BRM Baseline Scenario.

Appendix 6.1 Literature Informed

UK BRM Characteristic

Values

A summary of the upper limit, lower limit and average driver characteristic

values from the UK values-database as described within the Thesis text.

Appendix 6.2

UK Baseline Scenario

Resource Availability &

Bioenergy Potential

Forecasts

A summary of the output data from the UK Baseline Scenario developed within

the UK BRM Driver Sensitivity Analysis. This includes forecasts of biomass

resource availability (‘000 Tonnes) and bioenergy potential (TWh) over the

analysis timeframe to 2050.


410

Appendix 6.1: Literature Informed UK BRM Characteristic Values

Analysis

Year

Driver

Characteristics

UK Forecast Range Informed by Literature

Population

Change

Changes in

Built-Up Land

Area

Crop &

Agriculture

Productivity

Food Waste

Generation

Food

Commodity

Imports

BRM’s

population

change scenarios

BRM’s built-up

land area

scenarios

Change in

agriculture

productivity

Change in food waste generation

Change in food import levels

2015

Lower Limit Low Forecast Low Forecast +0.00% +4.32% +0.00%

Average Medium Forecast Medium Forecast +18.00% -2.34% +2.25%

Upper Limit High Forecast High Forecast +36.00% -9.00% +4.50%

2020




2030




2050




Range Informed by Li

Appendix 6.1: Literature Informed UK BRM Characteristic Values (continued)

Analysis

Year

Driver

Characteristics


Food

Commodity

Exports

Utilisation of

Agricultural

Wastes &

Residues

Forestry

Expansion &

Productivity

Wood-based

Industry

Productivity

Imports of

Forestry

Product

Change in food

export levels

Proportion of

total available resources utilised

Forestry

Commission Scenarios

Change in

industry productivity

Change in

forestry raw material imports

2015

Lower Limit +1.80% 0.00%

Evaluation of each of the

forestry

productivity scenarios

discussed within

the Thesis

+0.00% -25.00%

Average +3.15% 9.00% +1.80% +0.00%

Upper Limit +4.50% 18.00% +5.00% +140.00%

2020

Lower Limit +4.00% 0.00% +0.00% -10.00%

Average +7.00% 20.00% +4.00% +0.00%

Upper Limit +10.00% 40.00% +5.00% +15.00%

2030

Lower Limit +6.00% 0.00% +0.00% -5.00%

Average +10.50% 30.00% +6.00% +0.00%

Upper Limit +15.00% 60.00% +7.00% +5.00%

2050

Lower Limit +10.00% 0.00% +0.00% -5.00%

Average +17.50% 50.00% +10.00% +0.00%

Upper Limit +25.00% 100.00% +20.00% +5.00%


Analysis

Year

Driver

Characteristics


Exports of

Forestry

Product

Utilisation of

Forestry

Residues

Utilisation of

Industrial

Residues

Utilisation of

Arboriculture

Arisings

Waste

Generation

Trends

Change in

forestry raw

material exports

Proportion of

total available

resources utilised

Proportion of

total available

resources utilised

Proportion of

total available

resources utilised

DEFRA Scenarios

2015

Lower Limit -25.00% 0.00% 0.00% 65.00%

Evaluation of

each of the waste

generation scenarios

discussed within

the Thesis

Average +0.00% 9.00% 9.00% 65.00%

Upper Limit +10.00% 20.00% 20.00% 65.00%

2020

Lower Limit -10.00% 0.00% 0.00% 70.00%

Average +0.00% 20.00% 20.00% 100.00%

Upper Limit +25.00% 75.00% 75.00% 100.00%

2030

Lower Limit -5.00% 0.00% 0.00% 85.00%

Average +0.00% 30.00% 30.00% 100.00%

Upper Limit +5.00% 100.00% 100.00% 100.00%

2050

Lower Limit -5.00% 0.00% 0.00% 100.00%

Average +0.00% 50.00% 50.00% 100.00%

Upper Limit +5.00% 100.00% 100.00% 100.00%

Informed by Li


411


Analysis

Year

Driver

Characteristics


Waste

Management

Strategies.

Land Dedicated

for Energy Crop

Growth

- - -

DEFRA Scenarios

Proportion of

total available

land utilised

- - -

2015

Lower Limit

Evaluation of

each of the waste management

scenarios

discussed within the Thesis

0.00% - - -

Average 4.28% - - -

Upper Limit 5.58% - - -

2020

Lower Limit 0.00% - - -

Average 9.50% - - -


2030


Average 14.25% - - -


2050


Average 23.75% - - -


Appendix 6.2: UK Baseline Scenario Biomass Resource Availability & Bioenergy

Potential Forecasts

Biomass Resources

Resource Availability for Bioenergy Sector

(‘000 Tonnes) Bioenergy Potential

(TWh)

2015 2020 2030 2050 2015 2020 2030 2050

Biomass & Energy Crops 873 2,908 7,316 24,416 1.76 6.39 17.59 65.55

Dedicated Forestry Resources 1,172 1,901 3,039 883 2.07 3.69 6.56 2.17

Plant Agricultural Residues 392 948 1,591 3,168 0.42 1.14 2.16 5.09

Animal Agricultural Residues 9,846 10,133 14,519 19,256 25.93 29.03 45.61 68.04

Arboricultural Residues 18 39 53 52 0.30 0.72 0.84 1.00

Forestry Residues 126 278 293 308 0.04 0.10 0.14 0.16

Industry Residues 547 569 603 664 1.35 1.53 1.79 2.24

Household Wastes 6,780 16,149 34,848 40,716 13.84 36.19 86.92 117.30

Food & Organic Wastes 2,626 5,379 11,188 14,700 1.30 2.93 6.79 10.30

Other Wastes 4,166 10,707 24,624 32,705 8.14 20.88 50.74 75.06

Sewage Wastes 1,649 1,697 1,788 1,878 2.83 3.19 3.74 4.54


412

Appendix 7.0

Appendix 7.0 includes a summary of the data utilised within the UK BRM driver sensitivities analysis.

Appendix 7.1

UK BRM Sensitivity

Analysis – Resource

Availability within the

Grown Resources

Category

A summary of the biomass resource availability forecasts from the grown

biomass resources. These represent the maximum availability of resources

forecast (‘000 Tonnes) when the UK BRM drivers are calibrated to reflect the

range of characteristics documented within the values-database as described

within the Thesis text.

Appendix 7.2

UK BRM Sensitivity



Residue Resources

Category

A summary of the biomass resource availability forecasts from the residue

biomass resources. These represent the maximum availability of resources

forecast (‘000 Tonnes) when the UK BRM drivers are calibrated to reflect the

range of characteristics documented within the values-database as described

within the Thesis text.

Appendix 7.3

UK BRM Sensitivity



Waste Resources

Category

A summary of the biomass resource availability forecasts from the waste biomass

resources. These represent the maximum availability of resources forecast (‘000

Tonnes) when the UK BRM drivers are calibrated to reflect the range of

characteristics documented within the values-database as described within the

Thesis text.

Appendix 7.4

UK BRM Sensitivity


Availability of

Individual Resources

A summary of the biomass resource availability forecasts for each individual

biomass resource analysed within the BRM. These represent the maximum

availability of resources forecast (‘000 Tonnes) when the UK BRM drivers are

calibrated to reflect the range of characteristics documented within the values-

database and as described within the Thesis text.


413

Appendix 7.1: UK BRM Sensitivity Analysis – Resource Availability within the Grown

Resource Category

UK BRM Drivers

Maximum Grown Biomass Resource Availability with Variation of BRM

Drivers (‘000 Tonnes)

2015 2020 2030 2050

Population Change 1,018 3,002 9,620 18,339

Changes in Built-Up Land Area 1,002 2,938 9,259 16,729

Crop & Agriculture Productivity 1,173 3,359 10,865 21,857

Food Waste Generation 1,003 2,940 9,265 16,744

Food Commodity Imports 1,001 2,935 9,248 16,697

Food Commodity Exports 1,001 2,935 9,248 16,697

Utilisation of Agricultural Wastes & Residues 1,001 2,935 9,248 16,697

Forestry Expansion & Productivity 1,939 3,449 9,341 18,636

Wood-based Industry Productivity 1,467 3,504 9,817 17,266

Imports of Forestry Product 1,001 2,935 9,248 16,697

Exports of Forestry Product 1,001 2,935 9,248 16,697

Utilisation of Forestry Residues 861 2,680 8,993 16,442

Utilisation of Industrial Residues 1,001 2,935 9,248 16,697

Utilisation of Arboriculture Arisings 1,001 2,935 9,248 16,697

Waste Generation Trends 861 2,680 8,993 16,442

Waste Management Strategies. 861 2,680 8,993 16,442

Land Dedicated for Energy Crop Growth 1,610 4,165 13,844 31,081

Appendix 7.2: UK BRM Sensitivity Analysis – Resource Availability within the Residue

Resource Category

UK BRM Drivers

Maximum Residue Biomass Resource Availability with Variation of BRM


2015 2020 2030 2050












Utilisation of Forestry Residues 11,615 13,072 18,506 24,269



Waste Generation Trends 11,371 13,366 19,572 24,269

Waste Management Strategies. 11,371 13,366 19,572 24,269


Appendix 7.3: UK BRM Sensitivity Analysis – Resource Availability within the Waste

Resource Category

UK BRM Drivers

Maximum Waste Biomass Resource Availability with Variation of BRM


2015 2020 2030 2050












414


Utilisation of Forestry Residues 6,916 8,182 10,530 11,803



Waste Generation Trends 6,969 8,331 11,472 14,076

Waste Management Strategies. 15,220 33,933 72,447 90,000


Appendix 7.4: UK BRM Sensitivity Analysis – Resource Availability of the Individual

Resources

UK BRM Drivers

Resource Availability with Variation of BRM Drivers (‘000 Tonnes)

Biomass & Energy Crops Dedicated Forestry Resources Plant Agricultural Residues

2015 2020 2030 2050 2015 2020 2030 2050 2015 2020 2030 2050

Population Change 862 1,756 5,892 18,300 156 1,245 3,728 390 10,305 11,215 16,541 24,105

Changes in Built-Up Land

Area 846 1,693 5,531 16,689 156 1,245 3,728 390 10,223 10,991 15,763 21,415

Crop & Agriculture

Productivity 1,017 2,114 7,137 21,818 156 1,245 3,728 390 10,223 10,991 15,766 21,425

Food Waste Generation 847 1,695 5,536 16,705 156 1,245 3,728 390 10,223 10,991 15,763 21,415

Food Commodity Imports 845 1,690 5,520 16,658 156 1,245 3,728 390 10,223 10,991 15,763 21,415

Food Commodity Exports 845 1,690 5,520 16,658 156 1,245 3,728 390 10,223 10,991 15,763 21,415

Utilisation of Agricultural

Wastes & Residues 845 1,690 5,520 16,658 156 1,245 3,728 390 10,600 11,849 17,147 23,892

Forestry Expansion &

Productivity 855 1,729 5,613 16,658 1,085 1,720 3,728 1,978 10,223 10,991 15,763 21,415

Wood-based Industry

Productivity 845 1,690 5,520 16,658 622 1,814 4,297 608 10,223 10,991 15,763 21,415

Imports of Forestry Product 845 1,690 5,520 16,658 156 1,245 3,728 390 10,223 10,991 15,763 21,415

Exports of Forestry Product 845 1,690 5,520 16,658 156 1,245 3,728 390 10,223 10,991 15,763 21,415

Utilisation of Forestry

Residues 845 1,690 5,520 16,658 160 990 3,473 216 10,223 10,991 15,763 21,415

Utilisation of Industrial

Residues 845 1,690 5,520 16,658 156 1,245 3,728 390 10,223 10,991 15,763 21,415

Utilisation of Arboriculture

Arisings 845 1,690 5,520 16,658 156 1,245 3,728 390 10,223 10,991 15,763 21,415

Waste Generation Trends 845 1,690 5,520 16,658 160 990 3,473 216 10,223 10,991 15,763 21,415

Waste Management

Strategies. 845 1,690 5,520 16,658 160 990 3,473 216 10,223 10,991 15,763 21,415

Land Dedicated for Energy

Crop Growth 1,594 3,175 10,371 31,297 160 990 3,473 216 10,225 10,991 15,787 21,506


Resources Continued

UK BRM Drivers


Animal Agricultural Residues Forestry Residues Arboriculture Residues

2015 2020 2030 2050 2015 2020 2030 2050 2015 2020 2030 2050

Population Change 9,924 10,339 15,091 21,337 153 735 1,333 818 127 154 284 347

Changes in Built-Up Land Area 9,846 10,133 14,379 18,937 153 735 1,333 818 126 151 270 322

Crop & Agriculture

Productivity 9,846 10,133 14,379 18,937 153 735 1,333 818 126 151 270 308

Food Waste Generation 9,846 10,133 14,379 18,937 153 735 1,333 818 126 151 270 308

Food Commodity Imports 9,846 10,133 14,379 19,234 153 735 1,333 818 126 151 270 308

Food Commodity Exports 9,846 10,133 14,379 18,937 153 735 1,333 818 126 151 270 308


Wastes & Residues 9,846 10,133 14,379 18,937 153 735 1,333 818 126 151 270 308


Productivity 9,846 10,133 14,379 18,937 181 1,011 2,553 3,538 126 151 270 308

Wood-based Industry

Productivity 9,846 10,133 14,379 18,937 153 735 1,333 818 126 151 270 308


415

Imports of Forestry Product 9,846 10,133 14,379 18,989 153 735 1,333 818 126 151 270 308

Exports of Forestry Product 9,846 10,133 14,379 18,937 153 735 1,333 818 126 151 270 308

Utilisation of Forestry Residues 9,846 10,133 14,379 18,937 275 588 800 1,532 126 151 270 308


Residues 9,846 10,133 14,379 18,937 153 735 1,333 818 126 278 293 308


Arisings 9,846 10,133 14,379 18,937 153 735 1,333 818 126 278 293 308

Waste Generation Trends 9,846 10,133 14,379 18,937 153 735 1,333 818 126 151 270 308

Waste Management Strategies. 9,846 10,133 14,379 18,937 153 735 1,333 818 126 151 270 308


Crop Growth 9,846 10,133 14,379 18,937 153 735 1,333 818 126 151 270 308


Resources Continued

UK BRM Drivers


Industry Residues Household Wastes Food & Organic Wastes

2015 2020 2030 2050 2015 2020 2030 2050 2015 2020 2030 2050

Population Change 871 1,492 2,220 1,768 2,737 3,855 6,042 8,546 1,367 1,404 1,408 2,326

Changes in Built-Up Land Area 870 1,489 2,206 1,729 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625

Crop & Agriculture

Productivity 870 1,489 2,206 1,729 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625

Food Waste Generation 870 1,489 2,206 1,729 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,898

Food Commodity Imports 870 1,489 2,206 1,729 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625

Food Commodity Exports 870 1,489 2,206 1,729 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625


Wastes & Residues 870 1,489 2,206 1,729 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625


Productivity 899 1,765 3,427 4,449 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625

Wood-based Industry

Productivity 835 1,487 2,277 1,934 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625

Imports of Forestry Product 870 1,489 2,206 1,729 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625

Exports of Forestry Product 870 1,489 2,206 1,729 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625

Utilisation of Forestry Residues 992 1,342 1,673 1,729 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625


Residues 870 1,616 2,229 1,854 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625


Arisings 870 1,616 2,229 1,729 2,737 3,855 6,042 6,786 1,367 1,404 1,408 1,625

Waste Generation Trends 870 1,489 2,206 1,729 2,790 4,004 6,768 8,513 1,367 1,404 1,520 1,908

Waste Management Strategies 870 1,489 2,206 1,729 6,780 16,149 34,848 40,716 2,626 5,379 11,188 14,700


Crop Growth 870 1,489 2,206 1,729 2,737 3,855 6,042 6,786 1,370 1,414 1,431 1,656


416


Resources Continued

UK BRM Drivers


Other Wastes Sewage Wastes -

2015 2020 2030 2050 2015 2020 2030 2050 2015 2020 2030 2050

Population Change 1,163 1,225 1,293 1,732 1,662 1,732 1,876 2,116 - - - -

Changes in Built-Up Land Area 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -

Crop & Agriculture

Productivity 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -

Food Waste Generation 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -

Food Commodity Imports 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -

Food Commodity Exports 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -


Wastes & Residues 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -


Productivity 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -

Wood-based Industry

Productivity 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -

Imports of Forestry Product 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -

Exports of Forestry Product 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -

Utilisation of Forestry Residues 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -


Residues 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -


Arisings 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -

Waste Generation Trends 1,163 1,225 1,396 1,777 1,649 1,697 1,788 1,878 - - - -

Waste Management Strategies 4,166 10,707 24,624 32,705 1,649 1,697 1,788 1,878 - - - -


Crop Growth 1,163 1,225 1,293 1,513 1,649 1,697 1,788 1,878 - - - -


417

Appendix 8.0

Appendix 8.0 includes a summary of the data utilised within the UK biomass resource scenarios analyses.

Appendix 8.1 Land Utilisation within

the Biomass Resource

Scenarios

A summary of the land utilisation data reflecting the Biomass Resource

Scenarios.

Appendix 8.2 Biomass Resource

Availability Forecast

within the Biomass

Resource Scenarios

A summary of the biomass resource availability (‘000 Tonnes) forecasts within

the Biomass Resource Scenarios discussed within the Thesis text.

Appendix 8.3 Bioenergy Potential

Forecast within the

Biomass Resource

Scenarios

A summary of the bioenergy potential (TWh) forecasts within the Biomass

Resource Scenarios discussed within the Thesis text.

Appendix 8.4 UK BRM

Characteristics

Reflecting Biomass

Resource Scenarios

A summary of the UK BRM driver characteristics developed to reflect the

Biomass Resource Scenarios.


418

Appendix 8.1: Utilisation of UK Land within the Biomass Resource Scenarios Range Informed by Li

Land Food Focus Economic Focus

2015 2020 2030 2050 2015 2020 2030 2050

Total UK Land Area 24,193 24,193

Forestry & Woodland 2,439 2,357 2,235 1,340 2,375 2,188 1,985 1,553

Agriculture Land 10,952 9,626 7,851 5,683 11,756 10,648 9,045 6,850

Built-Up Land Area 2,512 2,612 2,821 3,188 2,512 2,612 2,821 3,188

Land Dedicated for

Biomass & Energy Crops 1,031 1,642 2,991 6,448 917 1,461 2,671 5,581

Other Land Area 7,259 7,956 8,295 7,534 6,634 7,284 7,671 7,021

Appendix 8.1: Utilisation of UK Land within the Biomass Resource Scenarios (continued)

Land Conservation Focus Energy Focus

2015 2020 2030 2050 2015 2020 2030 2050

Total UK Land Area 24,193 24,193

Forestry & Woodland 2,548 2,489 2,354 2,081 2,265 2,057 1,867 1,553

Agriculture Land 11,748 10,628 9,017 6,820 11,756 10,648 9,045 6,850

Built-Up Land Area 2,463 2,514 2,562 2,562 2,512 2,612 2,821 3,188

Land Dedicated for

Biomass & Energy Crops 900 711 2,514 5,714 937 2,246 3,657 6,909

Other Land Area 6,535 7,851 7,747 8,497 6,724 6,630 6,802 5,693

Appendix 8.2: UK Biomass Resource Scenarios – Resource Availability Informed by Li

Biomass Resources Food Focus (‘000 Tonnes) Economic Focus (‘000 Tonnes)

2015 2020 2030 2050 2015 2020 2030 2050

Biomass & Energy Crops 5,148 6,422 21,479 66,137 4,303 5,208 16,793 47,965

Dedicated Forestry Resources 676 521 3,117 1,586 744 689 3,285 1,753

Plant Agricultural Residues 2,152 4,290 4,839 5,439 2,144 4,290 4,805 5,346

Animal Agricultural Residues 9,846 10,133 14,379 18,937 9,846 10,133 14,379 18,937

Forestry Residues 152 736 1,396 837 19 86 156 122

Arboriculture Residues 126 151 270 308 127 154 283 346

Industry Residues 591 603 603 603 645 690 725 761

Household Wastes 2,737 3,855 6,042 6,786 2,515 3,153 4,419 5,193

Food & Organic Wastes 1,367 1,404 1,408 1,625 1,366 1,405 1,491 1,799

Other Wastes 3,900 5,080 7,335 8,299 3,635 4,244 5,440 6,382

Sewage Wastes 1,649 1,697 1,788 1,878 1,649 1,697 1,788 1,878

Category Totals:

Grown Biomass Resources 5,824 6,943 24,596 67,723 5,047 5,897 20,078 49,718

Residue Biomass Resources 12,867 15,913 21,488 26,124 12,781 15,353 20,348 25,512

Waste Biomass Resources 9,653 12,036 16,572 18,589 9,165 10,499 13,137 15,253

Appendix 8.2: UK Biomass Resource Scenarios – Resource Availability (continued)

Biomass Resources Food Focus (‘000 Tonnes) Economic Focus (‘000 Tonnes)

2015 2020 2030 2050 2015 2020 2030 2050

Biomass & Energy Crops 4,220 5,080 16,641 51,697 4,397 8,007 22,994 59,378

Dedicated Forestry Resources 248 1,217 2,765 1,523 5,537 12,489 16,677 20,656

Plant Agricultural Residues 1,072 2,145 3,602 5,392 3,218 4,290 4,911 5,488

Animal Agricultural Residues 9,846 10,133 14,379 18,937 9,846 10,133 14,379 18,937

Forestry Residues 17 79 143 121 283 963 1,166 970

Arboriculture Residues 124 148 257 278 126 278 293 308

Industry Residues 565 565 565 565 565 565 565 565


419

Household Wastes 1,770 1,171 211 0 6,780 16,149 34,848 40,716

Food & Organic Wastes 1,145 816 153 0 2,626 5,379 11,188 14,700

Other Wastes 963 686 128 0 10,945 26,857 59,472 73,421

Sewage Wastes 1,649 1,697 1,788 1,878 1,649 1,697 1,788 1,878

Category Totals:

Grown Biomass Resources 4,468 6,297 19,405 53,220 9,934 20,496 39,671 80,035

Residue Biomass Resources 11,607 12,991 18,802 25,172 14,038 16,229 21,313 26,267

Waste Biomass Resources 5,526 4,370 2,279 1,878 22,000 50,082 107,295 130,716

Appendix 8.3: UK Resource Scenarios – Bioenergy Potential

Year

Biomass Resource Scenarios Bioenergy Potential (TWh)

Food Focus Economic

Focus

Conservation

Focus

Energy Focus (Balanced

Conversion)

Energy Focus (Heat Prioritised)

Energy Focus (Power

Prioritised)

Energy Focus (Transport Fuel

Prioritised)

2015 54.66 49.09 49.51 79.31 91.45 72.53 59.70

2020 75.36 58.83 59.87 151.59 178.27 131.00 98.25

2030 154.20 113.25 114.10 312.62 349.90 263.30 181.63

2050 338.18 251.25 261.15 541.48 593.00 440.74 289.06

Appendix 8.4: UK BRM Characteristics Reflecting Biomass Resource Scenarios

UK BRM Drivers

UK BRM Characteristics Reflecting Biomass Resource Scenarios

Food Focus Scenario Economic Focus Scenario

2015 2020 2030 2050 2015 2020 2030 2050

Population Change Medium Forecast

Medium Forecast

Medium Forecast

Medium Forecast

Medium Forecast

Medium Forecast

Medium Forecast

Medium Forecast

Changes in Built-Up Land Area Medium

Forecast

Medium

Forecast

Medium

Forecast

Medium

Forecast

High

Forecast

High

Forecast

High

Forecast

High

Forecast

Crop & Agriculture

Productivity +36.00% +80.00% 120% 200% +18.00% +40.00% +60.00% +100%

Food Waste Generation -9.00% -20.00% -30.00% -50.00% -2.34% -5.20% -7.80% -13.00%

Food Commodity Imports +2.25 +5.00% +7.50% +12.50% +2.25 +5.00% +7.50% +12.50%

Food Commodity Exports +3.15% +7.00% +10.50% +17.50% +4.50% +10.00% +15.00% +25.00%


Wastes & Residues +9.00% +20.00% +30.00% +50.00% +9.00% +20.00% +30.00% +50.00%


Productivity Forestry Commission’s MB and BP-1 Scenarios Forestry Commission’s MB and IV Scenarios

Wood-based Industry

Productivity +1.80% +4.00% - - +5.00% +5.00% +7.00% +20.00%

Imports of Forestry Product - - - - - - - -

Exports of Forestry Product -20.00% - - - +10.00% +25.00% +5.00% +5.00%

Utilisation of Forestry Residues 9.00% 20.00% 30.00% 50.00% 9.00% 20.00% 30.00% 50.00%


Residues 9.00% 20.00% 30.00% 50.00% 20.00% 75.00% 100% 100%


Arisings 65.00% 70.00% 95.00% 100% 65.00% 70.00% 95.00% 100%

Waste Generation Trends DEFRA’s Current Rate Scenario DEFRA’s High-Tech Rate Scenario

Waste Management Strategies. DEFRA’s Current Rate Scenario DEFRA’s Combined Recovery Scenario


Crop Growth 4.28% 9.5% 14.25% 23.75% 4.28% 9.5% 14.25% 23.75%


420

Appendix 8.4: UK BRM Characteristics Reflecting Biomass Resource Scenarios

(continued)

UK BRM Drivers

UK BRM Characteristics Reflecting Biomass Resource Scenarios

Conservation Focus Scenario Energy Focus Scenario

2015 2020 2030 2050 2015 2020 2030 2050

Population Change Medium

Forecast

Medium

Forecast

Medium

Forecast

Medium

Forecast

Medium

Forecast

Medium

Forecast

Medium

Forecast

Medium

Forecast

Changes in Built-Up Land Area Medium

Forecast

Medium

Forecast

Medium

Forecast

Medium

Forecast

Medium

Forecast

Medium

Forecast

Medium

Forecast

Medium

Forecast

Crop & Agriculture

Productivity +18.00% +40.00% +60.00% +100% +18.00% +40.00% +60.00% +100%

Food Waste Generation -9.00% -20.00% -30.00% -50.00% -2.34% -5.20% -7.80% -13.00%

Food Commodity Imports +2.25 +5.00% +7.50% +12.50% +2.25 +5.00% +7.50% +12.50%

Food Commodity Exports +3.15% +7.00% +10.50% +17.50% +3.15% +7.00% +10.50% +17.50%


Wastes & Residues - - - - +18.00% +40.00% +60.00% +100.00%


Productivity Forestry Commission’s BP and BP-2 Scenarios Forestry Commission’s MB and IV Scenarios

Wood-based Industry

Productivity +1.80% +4.00% - - +1.80% +4.00% - -

Imports of Forestry Product - - - - - - - -

Exports of Forestry Product +10.00% +25.00% +5.00% +5.00% +10.00% +25.00% +5.00% +5.00%

Utilisation of Forestry Residues - - - - 9.00% 20.00% 30.00% 50.00%


Residues 9.00% 20.00% 30.00% 50.00% 20.00% 75.00% 100% 100%


Arisings 65.00% 70.00% 95.00% 100% 65.00% 100.% 100.% 100%

Waste Generation Trends DEFRA’s Green Rate Scenario DEFRA’s Current Rate Scenario

Waste Management Strategies. DEFRA’s Resource Recovery Scenario DEFRA’s Energy Recovery Scenario


Crop Growth 4.28% 9.5% 14.25% 23.75% 5.58% 12.40% 18.60% 31.00%


421

Appendix 9.0

Appendix 9.0 includes a summary of the data utilised within the UK biomass resource balance analyses.

Appendix 9.1 UK Indigenous Wood

Fibre Resource

Availability & Future

Demand

A summary of the wood fibre resource demand data utilised within the resource

balance analysis described in the Thesis text. Also the respective suitable

resource availability data for the UK BRM scenarios developed in the Thesis.

Appendix 9.2

UK Indigenous Biofuel

Energy from Suitable

Resources in 2030

A summary of the bioenergy values of biofuels produced from UK indigenous

resources in 2030 for each of the respective UK BRM analysis scenarios. This

data utilised within the resource balance analysis.


422

Appendix 9.1: UK Indigenous Wood Fibre Resource Availability & Future Demand (‘000

Tonnes)

Year

Forecast UK

Wood Fibre

Demand

Scenarios

Food Focus Economic Focus Conservation

Focus Energy Focus

(Balanced Conversion)

2010 15,542 1,276 1,276 1,276 1,276

2015 47,508 3,593 1,475 3,690 9,378

2020 50,263 8,433 1,828 7,009 22,302

2025 50,263 14,307 4,504 11,042 28,439

Appendix 9.2: UK Indigenous Biofuel Energy from Suitable Resources in 2030 (TWh)

2030 Scenarios

Food Focus Economic Focus Conservation Focus Energy Focus (Balanced Conversion)

Biofuels Energy (TWh) 20.62 16.05 15.88 21.95


423

Appendix 10.0

Appendix 10.0 includes an overview of the datasets utilised within the BRM to evaluate Brazilian feed

commodity dynamics. These are utilised within the Model to analyse the amount of land and resources required

to produce animal based food commodities to meet demand.

Appendix 10.1

Brazilian Agriculture

Production Systems

Data within this table reflects the usage proportions of different types of

agricultural practices within Brazil to produce the respective animal based

products.

Appendix 10.2

Feed Composition

Data within this table demonstrates the typically agricultural practices and nature

of feed that is required to produce different types of animal based products in

Brazil.

Appendix 10.3

Animal Feed Raw

Material Content

Data within this table demonstrates the raw material content of animal feeds

within Brazil, where agricultural practices require specific feed products.

Appendix 10.4

Feed Conversion

Efficiencies

Data within this table demonstrates the typical feed conversion efficiencies for

different animal categories in Brazil. The values representing the amount of feed

(kg) that is required to produce an equivalent mass (kg) of animal product.


424

Appendix 10.1: Brazilian Agriculture Production Systems

Agriculture Production Pastoral Practices (%) Mixed + Landless (%)

Beef Meat Based Products 29 71

Milk & Dairy Based Products 13 87

Mutton + Goat Based Products 33 67

Pork Based Products 0 100

Poultry Based Products 0 100

Other Products with Mixed + Landless Feed Land Requirement 100 0

Other Products with Pastoral Land Requirement 100 100

Appendix 10.2: Feed Composition


Pastoral Agricultural Practices (%) Mixed + Landless Systems (%)

Food

Crops

Residues

&

Fodder

Animal


Food

Crops

Residues

&

Fodder

Animal


Beef Meat Based Products 0 0 0 95 5 5 0 0 70 5

Milk & Dairy Based Products 0 0 0 95 5 9 41 0 45 5

Mutton + Goat Based Products 0 0 0 95 5 1 4 0 0 5

Pork Based Products - - - - - 60 40 1 0 0

Poultry Based Products - - - - - 59 41 1 0 0


Landless Feed Land Requirement - - - - - 1 4 0 90 5


Land Requirement 0 0 0 95 5 - - - - -

Appendix 10.3: Animal Feed Raw Material Content

Raw Materials Content Demand of each Commodity Required per annum

‘000 Tonnes %

Wheat 2,469 2.83%

Barley - -

Oats - -

Whole & Flaked Maize - -

Rice Bran Extractions 2,707 3.10%

Maize Gluten Feed - -

Wheat Feed - -

Other Cereals By-Products 69,550 79.70%

Distillery By-Products - -

Cereal By-Products - -

Whole Oilseeds - -

Oilseed Rape Cake and Meal - -

Soya Cake & Meal - -

Sunflower Cake & Meal - -

Other Oilseed Cake & Meal 256 0.29%

Field Beans - -

Field Peas - -

Dried Sugar Beet Pulp - -

Molasses - -

Citrus & Other Fruit Pulp - -

Meat & Bone Meal 5,777 6.62%

Other Meal 1,158 1.33%

Fish Meal - -

All Meal (Fish, Poultry & Other) - -

Minerals 2,709 3.10%

Oil & Fat - -

Protein Concentrates - -

Other Materials 2,643 3.03%

Confectionery By-Products - -

Totals 289,939 100%


425

Appendix 10.4: Feed Conversion Efficiencies


Pastoral Agricultural Practices (feed kg/product kg)

Mixed + Landless Systems (%)

(feed kg/product kg)

Low Range Mean High Range Low Range Mean High Range

Beef Meat Based Products 10.0 37.0 64.0 25.0 37.0 49.0

Milk & Dairy Based Products 0.7 2.1 3.5 0.7 1.7 2.6

Mutton + Goat Based Products 65.0 65.0 65.0 45.0 45.0 45.0

Pork Based Products 2.5 5.6 9.4 5.5 5.5 5.5

Poultry Based Products 2.0 3.5 4.5 2.0 3.5 4.5


Landless Feed Land Requirement - - - 21.3 21.3 21.3


Land Requirement 28.5 28.5 28.5 - - -


426

Appendix A11.0

Appendix 11.0 includes a summary of the range of the food crop and biomass resource productivity yield data

utilised within the Brazil BRM.

Appendix 11.1

Crop Commodity

Productivity Yields

A summary of the productivity yield data of all food crop commodities analysed

within the Brazil BRM.

Appendix 11.2

Biomass Resource

Productivity Yields

A summary of the productivity yield data of all biomass resources analysed

within the Brazil BRM.


427

Appendix A11.1: Crop Based Commodities Categorised by the FAO

Biomass Resources Productivity Yield Range


Fruits

&

Berries

Apples 32.0 32.0 32.0 32.0 32.0

Apricots 20.1 20.1 20.1 20.1 20.1

Bananas 14.1 14.1 14.1 14.1 14.1

Berries Nes 20.1 20.1 20.1 20.1 20.1

Blueberries 20.1 20.1 20.1 20.1 20.1

Buckwheat 1.1 1.1 1.1 1.1 1.1

Cherries 20.1 20.1 20.1 20.1 20.1

Citrus fruit - Lemons + Limes 22.2 22.2 22.2 22.2 22.2

Citrus fruit - Other 20.1 20.1 20.1 20.1 20.1

Citrus juice, concentrated 20.1 20.1 20.1 20.1 20.1

Cranberries 20.1 20.1 20.1 20.1 20.1

Currants 20.1 20.1 20.1 20.1 20.1

Dates 20.1 20.1 20.1 20.1 20.1

Figs 8.4 8.4 8.4 8.4 8.4

Flour of Fruits 20.1 20.1 20.1 20.1 20.1

Fruit Fresh - Other 20.1 20.1 20.1 20.1 20.1

Fruit Tropical Dried Nes 14.0 14.0 14.0 14.0 14.0

Gooseberries 20.1 20.1 20.1 20.1 20.1

Grapefruit 16.7 16.7 16.7 16.7 16.7

Grapefruit - concentrated juice 16.7 16.7 16.7 16.7 16.7

Grapes 16.8 16.8 16.8 16.8 16.8

Grapes - Wine 16.8 16.8 16.8 16.8 16.8

Homogen. Cooked Fruit Prp 20.1 20.1 20.1 20.1 20.1

Kiwi fruit 20.1 20.1 20.1 20.1 20.1

Lemons and limes 21.9 21.9 21.9 21.9 21.9

Mangoes, mangosteens, guavas 15.9 15.9 15.9 15.9 15.9

Marc of Grapes 20.1 20.1 20.1 20.1 20.1

Maté 6.3 6.3 6.3 6.3 6.3

Orange juice, concentrated 22.4 22.4 22.4 22.4 22.4

Oranges 22.4 22.4 22.4 22.4 22.4

Oranges, Mandarines 22.4 22.4 22.4 22.4 22.4

Other melons (inc. cantaloupes) 23.0 23.0 23.0 23.0 23.0

Papayas 52.4 52.4 52.4 52.4 52.4

Peaches and nectarines 11.4 11.4 11.4 11.4 11.4

Pears 10.7 10.7 10.7 10.7 10.7

Persimmons 19.9 19.9 19.9 19.9 19.9

Pineapples 36.7 36.7 36.7 36.7 36.7

Plantains 20.1 20.1 20.1 20.1 20.1

Plums and sloes 20.1 20.1 20.1 20.1 20.1

Quinces 4.6 4.6 4.6 4.6 4.6

Raspberries 20.1 20.1 20.1 20.1 20.1

Sour cherries 20.1 20.1 20.1 20.1 20.1

Stone fruit, nes 20.1 20.1 20.1 20.1 20.1

Strawberries 7.6 7.6 7.6 7.6 7.6

Tangerines, mandarins, clem. 20.0 20.0 20.0 20.0 20.0

Tomatoes 63.8 63.8 63.8 63.8 63.8

Watermelons 22.0 22.0 22.0 22.0 22.0

Cereals

&

Grains

Alcohol, Non-Food 0.0 0.0 1.1 1.7 3.4

Barley 3.4 3.4 3.4 3.4 3.4

Beer 2.6 2.7 2.8 2.9 3.0

Beverages, Alcoholic 3.4 3.4 3.4 3.4 3.4

Beverages, Fermented 3.4 3.4 3.4 3.4 3.4

Brans 3.4 3.4 3.4 3.4 3.4

Cereals, Other 3.4 3.4 3.4 3.4 3.4

Fonio 3.4 3.4 3.4 3.4 3.4

Maize 3.4 3.4 3.4 3.4 3.4

Maize for forage and silage 3.7 4.0 4.4 4.7 5.0

Maize, green 3.7 4.0 4.4 4.7 5.0

Millet 3.4 3.8 4.2 4.6 5.0

Mixed grain 3.4 3.4 3.4 3.4 3.4

Oats 3.4 3.4 3.4 3.4 3.4

Popcorn 1.9 1.9 1.9 2.0 2.0


428

Quinoa 3.4 3.4 3.4 3.4 3.4

Rice (Milled Equivalent) 3.4 3.4 3.4 3.4 3.4

Rice (Paddy Equivalent) 3.6 4.0 4.3 4.7 5.0

Rye 3.6 4.0 4.3 4.7 5.0

Triticale 1.1 1.3 1.5 1.8 2.0

Wheat 1.9 1.9 1.9 1.9 1.9

Nuts &

Seeds

Almonds, with shell 2.1 2.3 2.6 2.8 3.0

Arecanuts 0.0 0.0 0.9 1.4 2.8

Brazil nuts, with shell 2.8 2.8 2.8 2.8 2.8

Canary seed 2.8 2.8 2.8 2.8 2.8

Cashew nuts, with shell 2.8 2.8 2.8 2.8 2.8

Chestnuts 2.8 2.8 2.8 2.8 2.8

Coconut - Oil 0.3 0.3 0.3 0.3 0.3

Coconuts 2.8 2.8 2.8 2.8 2.8

Coconuts – inc. Copra 10.4 10.4 10.4 10.4 10.4

Copra Cake 10.4 10.4 10.4 10.4 10.4

Flax fibre and tow 10.4 10.4 10.4 10.4 10.4

Groundnut Cake 2.8 2.8 2.8 2.8 2.8

Groundnut Oil 2.8 2.8 2.8 2.8 2.8

Groundnuts (in Shell Eq) 2.7 2.7 2.7 2.7 2.7

Groundnuts (Shelled Eq) 2.7 2.7 2.7 2.7 2.7

Groundnuts, with shell 2.7 2.7 2.7 2.7 2.7

Hazelnuts, with shell 2.7 2.7 2.7 2.7 2.7

Hemp Tow Waste 2.7 2.7 2.7 2.7 2.7

Hempseed 2.8 2.8 2.8 2.8 2.8

Kapokseed in Shell 2.8 2.8 2.8 2.8 2.8

Karite Nuts (Sheanuts) 2.8 2.8 2.8 2.8 2.8

Kolanuts 2.8 2.8 2.8 2.8 2.8

Manila Fibre (Abaca) 2.8 2.8 2.8 2.8 2.8

Melonseed 2.8 2.8 2.8 2.8 2.8

Mustard seed 2.8 2.8 2.8 2.8 2.8

Nuts 2.8 2.8 2.8 2.8 2.8

Pistachios 2.8 2.8 2.8 2.8 2.8

Poppy seed 0.9 0.9 0.9 0.9 0.9

Rape and Mustard Cake 2.8 2.8 2.8 2.8 2.8

Rape and Mustard Oil 2.8 2.8 2.8 2.8 2.8

Rape and Mustardseed 1.4 1.4 1.4 1.4 1.4

Rapeseed 1.4 1.4 1.4 1.4 1.4

Safflower seed 1.4 1.4 1.4 1.4 1.4

Sesameseed 1.4 1.4 1.4 1.4 1.4

Sesameseed Cake 2.8 2.8 2.8 2.8 2.8

Sesameseed Oil 0.7 0.7 0.7 0.7 0.7

Sunflower seed 0.7 0.7 0.7 0.7 0.7

Sunflowerseed Cake 0.7 0.7 0.7 0.7 0.7

Sunflowerseed Oil 1.2 1.2 1.2 1.2 1.2

Tung Nuts 1.2 1.2 1.2 1.2 1.2

Walnuts, with shell 1.2 1.2 1.2 1.2 1.2

Veg.

Asparagus 2.6 2.6 2.6 2.6 2.6

Cabbage for Fodder 2.1 2.1 2.1 2.1 2.1

Cabbages and other brassicas 0.0 0.0 3.6 5.4 10.8

Carrots and turnips 10.8 10.8 10.8 10.8 10.8

Cow peas, dry 10.8 10.8 10.8 10.8 10.8

Cucumbers and gherkins 10.8 10.8 10.8 10.8 10.8

Eggplants (aubergines) 10.8 10.8 10.8 10.8 10.8

Leeks, other alliaceous veg 10.8 10.8 10.8 10.8 10.8

Leguminous for Silage 10.8 10.8 10.8 10.8 10.8

Leguminous vegetables, nes 10.8 10.8 10.8 10.8 10.8

Lettuce and chicory 10.8 10.8 10.8 10.8 10.8

Lupins 10.8 10.8 10.8 10.8 10.8

Mushrooms and truffles 10.8 10.8 10.8 10.8 10.8

Okra 10.8 10.8 10.8 10.8 10.8

Onions (inc. shallots), green 10.8 10.8 10.8 10.8 10.8

Peas, dry 10.8 10.8 10.8 10.8 10.8

Peas, green 10.8 10.8 10.8 10.8 10.8

Pepper (Piper spp.) 22.9 22.9 22.9 22.9 22.9

Pigeon peas 2.3 2.3 2.3 2.3 2.3

Pimento 2.3 2.3 2.3 2.3 2.3

Potatoes 2.4 2.4 2.4 2.4 2.4


429

Pulses Other 10.8 10.8 10.8 10.8 10.8

Pumpkins for Fodder 10.8 10.8 10.8 10.8 10.8

Pumpkins, squash and gourds 24.8 24.8 24.8 24.8 24.8

Roots & Tuber Dry Equiv 10.8 10.8 10.8 10.8 10.8

Roots, Other 10.8 10.8 10.8 10.8 10.8

Sweet potatoes 10.8 10.8 10.8 10.8 10.8

Turnips for Fodder 10.8 10.8 10.8 10.8 10.8

Vetches 10.8 10.8 10.8 10.8 10.8

Vegetables - Other 11.3 11.3 11.3 11.3 11.3

Yams 10.8 10.8 10.8 10.8 10.8

Plants

Agave Fibres Nes 10.8 10.8 10.8 10.8 10.8

Anise, badian, fennel, corian. 11.5 11.5 11.5 11.5 11.5

Artichokes 9.2 9.2 9.2 9.2 9.2

Avocados 0.0 0.0 1.5 2.3 4.6

Beets for Fodder 4.6 4.6 4.6 4.6 4.6

Carobs 4.6 4.6 4.6 4.6 4.6

Casava 4.6 4.6 4.6 4.6 4.6

Chicory roots 16.5 16.5 16.5 16.5 16.5

Chillies and peppers, dry 4.6 4.6 4.6 4.6 4.6

Chillies and peppers, green 4.6 4.6 4.6 4.6 4.6

Coffee, green 13.9 13.9 13.9 13.9 13.9

Coir 4.6 4.6 4.6 4.6 4.6

Hard Fibres, Other 4.6 4.6 4.6 4.6 4.6

Hops 4.6 4.6 4.6 4.6 4.6

Jute 1.1 1.1 1.1 1.1 1.1

Kapok Fibre 4.6 4.6 4.6 4.6 4.6

Other Bastfibres 4.6 4.6 4.6 4.6 4.6

Pyrethrum, Dried 4.6 4.6 4.6 4.6 4.6

Ramie 1.3 1.3 1.3 1.3 1.3

Sisal 4.6 4.6 4.6 4.6 4.6

Soft-Fibres, Other 1.5 1.5 1.5 1.5 1.5

Sorghum 4.6 4.6 4.6 4.6 4.6

Tobacco, unmanufactured 3.3 3.3 3.3 3.3 3.3

Yautia (cocoyam) 1.0 1.0 1.0 1.0 1.0

Beans

Bambara beans 4.6 4.6 4.6 4.6 4.6

Beans, dry 2.3 2.5 2.7 2.8 3.0

Beans, green 2.0 2.0 2.0 2.0 2.0

Broad beans, horse beans, dry 4.6 4.6 4.6 4.6 4.6

Chick peas 0.0 0.0 0.5 0.7 1.5

Cocoa beans 1.5 1.5 1.5 1.5 1.5

Lentils 0.9 0.9 0.9 0.9 0.9

Soyabean Cake 0.9 0.9 0.9 0.9 0.9

Soyabean Oil 0.5 0.5 0.5 0.5 0.5

Soyabeans 1.5 1.5 1.5 1.5 1.5

String beans 0.3 0.3 0.3 0.3 0.3

Herbs

Spices

&

Leaves

Cinnamon (canella) 1.5 1.5 1.5 1.5 1.5

Cloves 2.6 2.6 2.6 2.6 2.6

Garlic 2.6 2.6 2.6 2.6 2.6

Ginger 2.6 2.6 2.6 2.6 2.6

Nutmeg, mace and cardamoms 1.5 1.5 1.5 1.5 1.5

Peppermint 0.0 0.0 1.7 2.6 5.2

Spices, nes 5.2 5.2 5.2 5.2 5.2

Spinach 5.2 5.2 5.2 5.2 5.2

Spices - Other 8.6 8.6 8.6 8.6 8.6

Sweeteners, Other 5.2 5.2 5.2 5.2 5.2

Tea 5.2 5.2 5.2 5.2 5.2

Vanilla 5.2 5.2 5.2 5.2 5.2

Oil

Plants

Cotton Lint 5.2 5.2 5.2 5.2 5.2

Cottonseed 5.2 5.2 5.2 5.2 5.2

Cottonseed Cake 5.2 5.2 5.2 5.2 5.2

Cottonseed Oil 5.2 5.2 5.2 5.2 5.2

Gums Natural 1.8 1.8 1.8 1.8 1.8

Linseed 5.2 5.2 5.2 5.2 5.2

Natural rubber 0.0 0.0 0.5 0.7 1.4

Oil palm fruit 1.4 1.4 1.5 1.5 1.5

Oilcrops Oil, Other 1.4 1.4 1.5 1.5 1.5

Oilcrops, Other 1.4 1.4 1.5 1.5 1.5

Oilseed Cakes, Other 1.4 1.4 1.5 1.5 1.5


430

Oilseeds, Nes 1.4 1.4 1.4 1.4 1.4

Olive Oil 1.4 1.4 1.4 1.4 1.4

Olives 1.4 1.4 1.4 1.4 1.4

Maise Germ Oil 1.4 1.4 1.4 1.4 1.4

Rubber 1.4 1.4 1.4 1.4 1.4

Palm Oil 1.4 1.4 1.4 1.4 1.4

Palmkernel Cake 1.4 1.8 2.2 2.6 3.0

Palmkernel Oil 1.4 1.8 2.2 2.6 3.0

Palmkernels 0.2 0.2 0.2 0.2 0.2

Sugar

Crops

Molasses 0.2 0.2 0.2 0.2 0.2

Sugar Beet 0.2 0.2 0.2 0.2 0.2

Sugar cane 1.4 1.4 1.4 1.4 1.4

Sugar, Raw Equivalent 1.4 1.4 1.4 1.4 1.4

Sugar, Refined Equiv 1.4 1.4 1.4 1.4 1.4

Appendix A11.2: Biomass Resource Productivity Yields

Category Biomass Resources Productivity Yield Range


Biomass

Resources

Miscanthus 5.0 6.9 12.8 18.0 24.1

Willow 7.0 8.0 8.6 9.0 10.0

Poplar 5.6 6.5 7.7 8.8 10.0

Beech 8.0 9.0 9.9 10.9 11.8

Birch 5.0 6.5 7.7 9.0 10.0

Casuarina 0.0 0.0 0.0 0.0 0.0

Eucalyptus 9.0 10.5 12.0 13.5 15.0

Fir 8.0 10.5 13.0 15.5 18.0

Oak 0.0 0.0 0.0 0.0 0.0

Pine 8.0 8.5 9.0 9.5 10.0

Redwood 0.0 0.0 0.0 0.0 0.0

Spruce 8.0 9.5 11.0 12.5 14.0

Sycamore 6.0 6.8 7.0 7.3 8.0

Subabul 0.0 0.0 0.0 0.0 0.0

Sunflower 1.5 1.8 2.0 2.3 2.5

Reed Canary Grass 2.9 5.8 8.9 11.9 15.0

Jatropha 4.5 5.6 6.8 7.9 9.0

Switch Grass 6.0 6.7 9.6 11.4 15.4

Ash 10.0 10.0 10.0 10.0 10.0

Pasture Grass 10.5 11.3 12.0 12.8 13.5


431

Appendix 12.0

Appendix 12.0 includes a summary of the waste generation and waste management strategies utilised within the

Brazil BRM and described within the Thesis text.

Appendix 12.1

Waste Generation

Scenarios

Brazil waste generation scenarios developed by the University of Sao Paulo and

utilised in the Brazil BRM as described within the Thesis text.

Appendix 12.2

Waste Management

Scenarios

Brazil waste management strategy scenarios developed by the University of

Utrecht and MWH as described within the Thesis text.


432

Appendix 12.1: Brazil Waste Generation Scenarios

Year


Reference Rate Upper Limit Rate Lower Limit Rate

Waste (Mt) Change (%) Waste (Mt) Change (%) Waste (Mt) Change (%)

2010 59.8 - 59.8 - 59.8 -

2015 66.2 10.7% 68.3 14.2% 64.0 6.9%

2020 72.5 9.5% 76.9 12.5% 68.1 6.5%

2030 84.4 16.4% 93.0 21.0% 76.0 11.5%

Appendix 12.2: Waste Management Scenarios – Reference Rate + Waste Law

Waste

Categories

Waste

Management 2010


Reference Rate Waste Law

2015 2020 2030 2050 2015 2020 2030 2050

Chemical

Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Chemical Wastes excl.

Used Oils

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Used Oils

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%


Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 6.13% 18.38% 42.88% 49%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 93.88% 81.63% 57.13% 51%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%


Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 6.13% 18.38% 42.88% 49%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 93.88% 81.63% 57.13% 51%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Metallic Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 9.00% 27.00% 63.00% 72%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 91.00% 73.00% 37.00% 28%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Metallic Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 9.00% 27.00% 63.00% 72%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 91.00% 73.00% 37.00% 28%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Glass Wastes

Recycling & Reuse 23% 23.00% 23.00% 23.00% 23% 26.25% 32.75% 45.75% 49%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 77% 77.00% 77.00% 77.00% 77% 73.75% 67.25% 54.25% 51%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Glass Wastes

Recycling & Reuse 23% 23.00% 23.00% 23.00% 23% 26.25% 32.75% 45.75% 49%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 77% 77.00% 77.00% 77.00% 77% 73.75% 67.25% 54.25% 51%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Paper &

Cardboard

Wastes

Recycling & Reuse 23% 23.00% 23.00% 23.00% 23% 26.25% 32.75% 45.75% 49%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%


433

Landspread 77% 77.00% 77.00% 77.00% 77% 73.75% 67.25% 54.25% 51%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Rubber Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Plastic Wastes

Recycling & Reuse 20% 20.00% 20.00% 20.00% 20% 24.00% 32.00% 48.00% 52%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 80% 80.00% 80.00% 80.00% 80% 76.00% 68.00% 52.00% 48%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Wood Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Wood Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Textile Wastes

Recycling & Reuse 30% 30.00% 30.00% 30.00% 30% 30.00% 30.00% 30.00% 30%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 70% 70.00% 70.00% 70.00% 70% 70.00% 70.00% 70.00% 70%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%


Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%


Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 12.50% 37.50% 87.50% 100%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 87.50% 62.50% 12.50% 0%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Animal Waste

of Food

Preparation &

Products

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 12.50% 37.50% 87.50% 100%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 87.50% 62.50% 12.50% 0%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%


Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 12.50% 37.50% 87.50% 100%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 87.50% 62.50% 12.50% 0%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Household & Similar Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 12.50% 37.50% 87.50% 100%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 87.50% 62.50% 12.50% 0%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Mixed &

Undifferentiated

Materials

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Mixed &

Undifferentiated

Materials

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Sorting Residues

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Sorting Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%


434

Residues Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Common

Sludges

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Mineral Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Mineral Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Other Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Other Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Landspread 100% 100% 100% 100% 100% 100% 100% 100% 100%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% 0.00% 0.00% 0.00% 0%

Appendix 12.2: Waste Management Scenarios – Recycling +

Waste

Categories

Waste

Management 2010


Recycling+ Resource Recovery

2015 2020 2030 2050 2015 2020 2030 2050

Chemical

Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Chemical

Wastes excl.

Used Oils

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Used Oils

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Healthcare &

Biological

Wastes

Recycling & Reuse 0% 10.00% 30.00% 70.00% 80% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 2.50% 7.50% 17.50% 20% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Healthcare &

Biological

Wastes

Recycling & Reuse 0% 10.00% 30.00% 70.00% 80% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 2.50% 7.50% 17.50% 20% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Metallic Wastes

Recycling & Reuse 0% 9.63% 28.88% 67.38% 77% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 2.88% 8.63% 20.13% 23% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Metallic Wastes Recycling & Reuse 0% 9.63% 28.88% 67.38% 77% - - - -


435

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 2.88% 8.63% 20.13% 23% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Glass Wastes

Recycling & Reuse 23% 23.00% 23.00% 23.00% 23% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 9.13% 27.38% 63.88% 73% - - - -

Landspread 77% 67.38% 48.13% 9.63% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Glass Wastes

Recycling & Reuse 23% 23.00% 23.00% 23.00% 23% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 9.13% 27.38% 63.88% 73% - - - -

Landspread 77% 67.38% 48.13% 9.63% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Paper & Cardboard

Wastes

Recycling & Reuse 23% 25.13% 29.38% 37.88% 40% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 7.50% 22.50% 52.50% 60% - - - -

Landspread 77% 67.38% 48.13% 9.63% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Rubber Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Plastic Wastes

Recycling & Reuse 20% 26.88% 40.63% 68.13% 75% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 3.13% 9.38% 21.88% 25% - - - -

Landspread 80% 70.00% 50.00% 10.00% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Wood Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Wood Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Textile Wastes

Recycling & Reuse 30% 32.50% 37.50% 47.50% 50% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 6.25% 18.75% 43.75% 50% - - - -

Landspread 70% 61.25% 43.75% 8.75% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -


Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -


Recycling & Reuse 0% 10.00% 30.00% 70.00% 80% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 2.50% 7.50% 17.50% 20% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Animal Waste

of Food Preparation &

Products

Recycling & Reuse 0% 10.00% 30.00% 70.00% 80% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 2.50% 7.50% 17.50% 20% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -


Recycling & Reuse 0% 10.00% 30.00% 70.00% 80% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 2.50% 7.50% 17.50% 20% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Household &

Similar Wastes

Recycling & Reuse 0% 10.00% 30.00% 70.00% 80% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 2.50% 7.50% 17.50% 20% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -


436

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -


Materials

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -


Materials

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Sorting

Residues

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Sorting

Residues

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Common

Sludges

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Mineral Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0% - - - -

Disposal at Sea 0% 0.00% 0.00% 0.00% 0% - - - -

Mineral Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0% - - - -

Composting 0% 0.00% 0.00% 0.00% 0% - - - -

Energy Recovery 0% 12.50% 37.50% 87.50% 100% - - - -

Landspread 100% 87.50% 62.50% 12.50% 0%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0%

Other Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 12.50% 37.50% 87.50% 100%

Landspread 100% 87.50% 62.50% 12.50% 0%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0%

Other Wastes

Recycling & Reuse 0% 0.00% 0.00% 0.00% 0%

Composting 0% 0.00% 0.00% 0.00% 0%

Energy Recovery 0% 12.50% 37.50% 87.50% 100%

Landspread 100% 87.50% 62.50% 12.50% 0%

Disposal at Sea 0% 0.00% 0.00% 0.00% 0%


437

Appendix 13.0

Appendix 13.0 includes a summary of the data utilised in developing the Brazil BRM Baseline Scenario.

Appendix 13.1 Literature Informed

Brazil BRM

Characteristic Values

A summary of the upper limit, lower limit and average driver characteristic

values from the Brazil values-database described within the Thesis text.

Appendix 13.2

Brazil Baseline Scenario

Resource Availability

Forecasts

A summary of the output data from the Brazil Baseline Scenario developed

within the Chapter 9 Case Study of Brazil. This includes forecasts of biomass

resource availability (‘000 Tonnes) over the analysis timeframe to 2030.


438

Appendix A13.1: Literature Informed Brazil BRM Characteristic Values

Analysis

Year

Driver

Characteristics

Brazil Forecast Range Informed by Literature

Population

Change

Changes in

Built-Up Land

Area

Crop &

Agriculture

Productivity

Food Waste

Generation

Food

Commodity

Imports

BRM’s

population

change scenarios

BRM’s built-up

land area

scenarios

Change in

agriculture

productivity

Change in food waste generation

Change in food import levels

2015




2020




2030




Range Informed by Li

Appendix A13.1: Literature Informed Brazil BRM Characteristic Values (continued)

Analysis

Year

Driver

Characteristics


Food

Commodity

Exports

Utilisation of

Agricultural

Wastes &

Residues

Forestry

Expansion &

Productivity

Wood-based

Industry

Productivity

Imports of

Forestry

Product

Change in food

export levels

Proportion of total available

resources utilised

Forestry

Scenarios

Change in industry

productivity

Change in

forestry raw

material import levels

2015

Lower Limit +1.80% 0.00%

Evaluation of

each of the

forestry productivity

scenarios

discussed within the Thesis

+1.00% -25.00%

Average +3.15% 9.00% +7.00% +0.00%

Upper Limit +4.50% 18.00% +8.00% +140.00%

2020

Lower Limit +4.00% 0.00% +0.00% -10.00%

Average +7.00% 20.00% +7.00% +0.00%

Upper Limit +10.00% 40.00% +9.00% +15.00%

2030

Lower Limit +6.00% 0.00% +0.00% -5.00%

Average +10.50% 30.00% +6.00% +0.00%

Upper Limit +15.00% 60.00% +10.00% +5.00%


Analysis

Year

Driver

Characteristics


Exports of

Forestry

Product

Utilisation of

Forestry

Residues

Utilisation of

Industrial

Residues

Utilisation of

Arboriculture

Arisings

Waste

Generation

Trends

Change in

forestry raw

material exports levels


resources utilised


resources utilised


resources utilised

Waste Scenarios

2015

Lower Limit -25.00% 0.00% 0.00% 65.00%

Evaluation of each of the waste

generation

scenarios discussed within

the Thesis

Average +0.00% 9.00% 9.00% 65.00%

Upper Limit +10.00% 20.00% 20.00% 65.00%

2020

Lower Limit -10.00% 0.00% 0.00% 70.00%

Average +0.00% 20.00% 20.00% 100.00%

Upper Limit +25.00% 75.00% 75.00% 100.00%

2030

Lower Limit -5.00% 0.00% 0.00% 85.00%

Average +0.00% 30.00% 30.00% 100.00%

Upper Limit +5.00% 100.00% 100.00% 100.00%


439


Analysis

Year

Driver

Characteristics


Waste

Management

Strategies.

Land Dedicated

for Energy Crop

Growth

- - -

Scenarios

Proportion of

total available

land utilised

- - -

2015

Lower Limit

Evaluation of

each of the waste

management scenarios

discussed within

the Thesis

- - -

Average 29.22% - - -

Upper Limit - - -

2020

Lower Limit - - -

Average 28.45% - - -

Upper Limit - - -

2030

Lower Limit - - -

Average 32.93% - - -

Upper Limit - - -

Appendix A13.2: Brazil Baseline Scenario Biomass Resource Availability

Biomass Resources Resource Availability for Bioenergy Sector (‘000 Tonnes)

2015 2020 2030

Biomass & Energy Crops 716,610 1,072,339 1,945,331

Dedicated Forestry Resources 129,894 162,059 129,894

Plant Agricultural Residues 35,483 76,383 137,522

Animal Agricultural Residues 34,056 44,658 75,561

Arboricultural Residues - 24 64

Forestry Residues 4,011 9,416 14,446

Industry Residues 2,586 2,767 2,933

Food & Organic Wastes 924 3,036 8,247

Other Wastes 583 1,917 5,206

Sewage Wastes 175 181 191


440

Appendix 14.0 Appendix 14.0 provides a series of screenshots from the BRM Excel Spreadsheet. These demonstrate the

structure and layout of the Excel Spreadsheet Tabs that makes up the BRM. Supporting descriptions are

provided that summarise the modelling methodology and calculations for each analyses step.

BRM Excel Spreadsheet Screenshots

Appendix 14.1 Output Summary Page

Appendix 14.2 Import Deficit Analysis

Appendix 14.3 Main BRM Control Panel

Appendix 14.4 Yields

Appendix 14.5 Food Demand by Commodity

Appendix 14.6 Waste Food Scenarios

Appendix 14.7 Feed Conversion Rates

Appendix 14.8 Processed Feed Data

Appendix 14.9 Crop Commodity Land

Appendix 14.10 Animal Feed Land

Appendix 14.11 Pre-Treatment Pathways

Appendix 14.12 Bioenergy-Conversion Matrix

Appendix 14.13 Energy Conversion Efficiencies

Appendix 14.14 Feedstock Calorific Values

Appendix 14.15 Population Data

Appendix 14.16 Land-Use Data

Appendix 14.17 MSW & Waste Wood

Appendix 14.18 Waste Strategies

Appendix 14.19 Industry-Forestry Dynamics

Appendix 14.20 Energy Crop Scenarios

Appendix 14.21 Straw Calculations

Appendix 14.22 Manure & Slurry Calculations

Appendix 14.23 Sewage Sludge


441

Appendix 14.1: BRM Excel Spreadsheet Screenshots – Output Summary Page

The screenshots below demonstrates the key outputs from the Stage One analysis within the BRM. The first

screenshot documents the crop and animal-based food commodity demands over the analysis timeframe, for the

given scenario. The second screenshot presents a Table documenting the outputs from the analyses - the area of

land required to produce the food commodities to balance demands, for the given scenario. The third screenshot

presents a Table documenting the output of the analyses, of the area of land forecast as being potentially

available and suitable for biomass resource and energy crop growth, for the given scenario.

The screenshot below demonstrates a segment of the BRM’s Stage Two analyses ‘output results’. This Table

lists all the resources analysed within the BRM, documents details of the specific resource such as its physical

state and moisture content, and presents the analysed availability quantities for each resource over the analysis

timeframe; reflective of the given scenario being modelled.


442

The screenshot below demonstrates a further segment of the BRM’s Stage Two analyses ‘output results’. The

Table lists the combined availabilities (Tonnes eqv.) of all Grown, Residue, and Waste Biomass Resources, over

the analysis timeframe; for the given scenario being modelled. The Figure also visually documents the ‘active

data’ being used within the BRM’s calculations.

The two screenshots below demonstrate different segments of the BRM’s Stage Three analyses ‘output results’.

The Tables provide various levels of analyses, including the bioenergy potential of the resources forecast as

being potentially available for the bioenergy sector over the analysis timeframe; for the given scenario being

modelled. The Figure provides further visual presentation of these output results.


443

Appendix 14.2: BRM Excel Spreadsheet Screenshots – Import Deficit Analysis

The screenshots below demonstrate segments of the BRM’s ‘Resource Deficit and Import’ analyses. The Table

within the first screenshot shows the control panel, where each of the resources identified within the BRM can

be highlighted, as being either compatible or incompatible with the specific resource demands of the indigenous

bioenergy sector. The Table at the top of the first screenshot provides a summary of the total availability of the

indigenous resources that are deemed to be available and suitable for the specific demands of the indigenous

bioenergy sector.

The Table and Figure within the second screenshot, document the ‘Resource-Balance’ analysis where the BRM

evaluates the indigenous resource surplus or deficit, in comparison to demands.


444

Appendix 14.3: BRM Excel Spreadsheet Screenshots – Main BRM Control Panel

The following screenshots provide an insight of the BRM’s Main Control Panel interface. The key control panel

variables are listed on the left, with drop-down menus providing the ‘control mechanism’ for each time segment

of the analysis timeframe. The text to the right of the control drop-down menus provides information and

descriptions for ‘best use’ of the controls, including details of the BRM’s default settings. Figures on the right of

the control panel also provide ‘active-data’ updates; allowing the BRM user to visualise how respective

variables within the BRM, change as the control panels are adjusted.

Appendix 14.4: BRM Excel Spreadsheet Screenshots – Yields

The screenshot below demonstrates segments of the ‘Agricultural and Biomass Productivity-Yield’ analyses

within the BRM. Each of the resources and agricultural commodities analysed within the BRM are listed. Yield

data sourced from a broad range of sources are also collated within the ‘Range of Values’ Table for each

commodity. The relevant data source references are placed to the right, adjacent to the data. An ‘active yield

value’ is calculated for each commodity in accordance with instructions from the control panel. For example,

the mean yield value of the collated data is taken as the default scenario.


445

The Table at the top of the screenshot shows the control panel for forecasting how yields may change over the

BRM’s analysis timeframe. Depending on the nature of the analysis required, a default yield improvement

scenario can be selected from a series of default options, or a custom designed scenario can be applied that

controls how future yields may change. The ‘yellow highlighted’ cells to the left of the screenshot demonstrate

the ‘active productivity yields’ for each commodity, over the analysis timeframe for the given scenario.

Appendix 14.5: BRM Excel Spreadsheet Screenshots – Food Demand by Commodity

The screenshot below demonstrates segments of the ‘Food Commodity Demand’ analyses for the Base Year

within the BRM. Each food commodity analysed within the BRM is listed and the corresponding food

commodity utilisation data is collated. These Tabs and their associated equations calculate forecasts of the

‘Total Demand’ for each commodity. Successive analysis Tabs within the BRM forecast how specific food

commodity demands may change for each the successive analysis time period up to 2050. Changing commodity

production demands are driven by: population change, food waste dynamics, and food import / export scenarios,

reflected within the specific parameters of any given scenario developed within the BRM.


446

Appendix 14.6: BRM Excel Spreadsheet Screenshots – Waste Food Scenarios

The screenshot below demonstrates segments of the ‘Waste Food Scenarios’ analyses module within the BRM.

The analysis undertaken within this Tab links the BRM’s control panel with each of the food commodity

demand Tabs. Reflective of the food waste parameters for the given scenario, the analysis calculates in the left

columns of the Table, the quantity of each food commodity that is required to balance demand, if food waste

generation trends change.

Appendix 14.7: BRM Excel Spreadsheet Screenshots – Feed Conversion Rates

The screenshot below demonstrates segments of the BRM’s database, and analyses of the ‘Feed Conversion

Rates’ for specific animal food products. The Tables within this Tab list the data for quantities of feed required

to produce specific volumes of animal food product. Highlighted by the upper Table within the screenshot, data

is utilised relevant to the specific livestock and agricultural practices applied to produce them. The segment of

the Table at the bottom of the screenshot, demonstrates that the BRM utilises data collated from various regions

of the World with their respective agricultural practices. In this screenshot, the applied scenario reflects Western

European agriculture.


447

Appendix 14.8: BRM Excel Spreadsheet Screenshots – Processed Feed Data

The following screenshot demonstrates a segment of the BRM’s database which lists the specific composition of

various livestock feeds. The Table documents a large list of grown crops that are used within livestock feeds.

Reflecting how animal feed compositions have evolved, how it may change; and how the feeds differ depending

on the type of livestock.

Appendix 14.9: BRM Excel Spreadsheet Screenshots – Crop Commodity Land

The following screenshot demonstrates the BRM’s analyses calculations of the ‘Land Area Requirement’, to

produce the quantities of crop commodities required to balance demands. The left columns of the Table

highlight all the crop commodities analysed within the BRM. The ‘yellow cells’ within the Table document the

quantities of food commodities required to balance demand - linked to the BRM’s Food Commodity Demand

Tabs. The ‘light blue’ cells document the relevant productivity yields for each crop commodity linked to the

BRM’s Yield Tabs. The final section of this calculation Table, documents the analysis undertaken to determine

the areas of land forecast as being required to grow the quantities of crops, to balance demands.

Appendix 14.10: BRM Excel Spreadsheet Screenshots – Animal Feed Land

The following screenshot demonstrates the BRM’s analysis calculations for the ‘Land Area Requirement’ to

produce the animal product commodities, required to balance demands.

The upper Table within the screenshot demonstrates that feed demands are allocated based on the agricultural

processes applied within the given scenario. Land is allocated either for the growth of food commodities for

feed, or as pasture land where appropriate. The lower Table within the screenshot demonstrates a segment of the

analysis that calculates the land area necessary to produce the quantities and types of crop commodities to

balance feed demands.


448

Appendix 14.11: BRM Excel Spreadsheet Screenshots – Pre-Treatment Pathways

The screenshots below document the BRM’s ‘Resource Pre-Treatment Pathway’ analyses. The screenshots

show segments of the calculation Table with all the resources analysed within the BRM listed in the left column.

Linked to all the BRM’s analysis Tabs, the quantities of resources forecast as being available for the bioenergy

sector within the given scenario, are listed within the ‘yellow cells’ on the left of the first screenshot. The ‘green

cells’ within the first screenshot Table list all of the relevant pre-treatment pathways that can potentially be

applied to each given resource, within the analysis. The Table’s controls are represented by the ‘red cells’ of the

first screenshot, where the BRM user is able to allocate varying proportions of the available resource to different

pre-treatment processes. The ‘blue cells’ highlighted in the second screenshot, document the quantities of

available resource subjected to each pre-treatment pathway, for the given scenario. The ‘yellow cells’ within the

second screenshot document the net quantities of post pre-treatment resource available for conversion to

bioenergy; any losses being the result of the applied pre-treatment processes.


449

Appendix 14.12: BRM Excel Spreadsheet Screenshots – Bioenergy Conversion Matrix

The screenshots below document the BRM’s ‘Resource Bioenergy Conversion Matrix’ analyses module. These

show segments of the calculation Table where all of the various resources analysed within the BRM are listed in

the left column. Linked to the BRM’s Resource Pre-treatment analyses Tab, the quantities of resource ready for

bioenergy conversion following pre-treatment processes, are listed in the ‘light blue cells’.

The bioenergy conversion pathway options applicable to each category of biomass resource are listed within the

cells highlighted by the ‘Adjust Energy Conversion’ sign. These also represent the Table’s controls, where a

series of drop-down menus provide cells into which quantities can be entered. The BRM user is then able to

allocate different proportions of the available resources, for conversion by the different bioenergy conversion

pathways.

The ‘energy debt’ of any pre-treatment processes is accounted for within the ‘white cells’, and the ‘active

bioenergy conversion pathway efficiencies’ relevant to the chosen scenarios are listed within the ‘purple cells’.

The remaining sections of this Table’s analysis focus on calculating and forecasting the extent of, and the forms

of bioenergy that may be generated from the available resources; via the conversion pathways chosen for the

given scenario.


450

Appendix 14.13: BRM Excel Spreadsheet Screenshots – Conversion Efficiencies

The screenshot below demonstrates a segment of the ‘Bioenergy Conversion Efficiency’ database, built within

the BRM. This lists each of the biomass resources analysed, the different forms that these resources could take

post pre-treatment, and the relevant conversion efficiency of each bioenergy pathway.

Appendix 14.14: BRM Excel Spreadsheet Screenshots – Feedstock Calorific Values

The screenshot below demonstrates a segment of the ‘Biomass Resource Calorific Value’ database, built within

the BRM. This lists each of the biomass resources analysed within the BRM with their respective calorific value

data. The data source references for each applied value are also listed in the database, and shown to the right of

the screenshot.


451

Appendix 14.15: BRM Excel Spreadsheet Screenshots – Population Data

The screenshot below demonstrates the modelling of ‘Population Change Dynamics’ within the UK BRM.

Population change forecast scenario data is shown at the top. The ‘active data’ currently being utilised within

the given BRM scenario are shown within the labelled table. The Figure also visually demonstrates the active

data for the given UK scenario modelled.

Appendix 14.16: BRM Excel Spreadsheet Screenshots – Land-Use Data

This screenshot below demonstrates the ‘Changing Built-Up Land Area’ analyses within the UK BRM. The

Table on the left contains data reflecting the UK’s land-use area classifications. The Table at the upper right of

the screenshot documents data for each of the forecast scenarios of the changing UK built-up land area, linked to

the BRM’s Main Control Panel. The yellow column within this table reflects the ‘active data’ currently being

applied within the given BRM scenario. The Figure visually documents the active data reflecting the changing

UK built-up land area, over the analysis timeframe.


452

Appendix 14.17: BRM Excel Spreadsheet Screenshots – MSW & Waste Wood

The following screenshots document segments of the ‘Waste Resource’ analyses within the BRM. The first

screenshot shows the list of waste resource categories analysed within the BRM. This Table highlights a ‘control

capability’ by way of drop-down menus. Each resource can be identified as being either suitable or unsuitable

for utilisation by the bioenergy sector.

The ‘yellow highlighted’ cells and the Figure to the right of the screenshot below demonstrate the total potential

availability of waste resources for the bioenergy sector, for the given scenario.


453

Appendix 14.18: BRM Excel Spreadsheet Screenshots – Waste Strategies

The following screenshot documents segments of the BRM’s built-in databases of the values utilised within

waste generation and waste management scenarios. The screenshot highlights Tables that list data relevant for

each given scenario, over the analysis timeframe. The ‘yellow highlighted’ cells within the screenshot, and the

Figures demonstrate the ‘active data’ for the given scenario modelled.

Appendix 14.19: BRM Excel Spreadsheet Screenshots – Industry-Forestry Dynamics

The following screenshots document segments of the industry-forestry dynamics analyses module within the

BRM. These highlight the complex interactions and linkages between different datasets within the analyses.

The first screenshot provides an overview of these datasets and analyses. The following series of screenshots

provide further focus on each of the relevant Tables within the BRM.

The analysis progresses through applying different datasets dependent on the specific scenarios developed

within the BRM’s controls. The screenshots below provide a high-level overview of the analyses undertaken in

each stage, with the Tables and Figures presenting the ‘active data’ for the given scenario modelled. Different

datasets are utilised reflecting the various scenario themes over the analysis timeframe: forestry expansion,

forestry productivity, productivity of wood-based industries, wood product and raw resource import and export

levels, wood-based industry efficiencies, and scenarios of potential wood residue and waste utilisation by the

bioenergy sector.


454


455

The next screenshot below provides an overview of the layout, interface, and analyses progression of the BRM’s

‘Forestry-Industry Dynamic’ module. The screenshot shows how forest resources and wood products are filtered

through the analysis in reflection of wood and wood products being bought, sold, and produced by different

industries - from the forest to the end product. The levels of resource flow from one industry to another, and the

potential resource availability at each interface for the bioenergy sector, are controlled through the specific

dynamics of a given scenario. The Figures shown within the screenshot in addition to the Table values,

demonstrate to the BRM user the ‘active data’ of these flows for the developed scenario.


456

The next series of screenshots provide a greater focus on the different datasets and analyses processes within the

BRM’s ‘Forestry-Industry Dynamic’ module.


457


458


459

Appendix 14.20: BRM Excel Spreadsheet Screenshots – Energy Crop Scenarios

The following screenshots demonstrate the ‘Biomass Resource and Energy Crop Planting Scenario’ analyses

module, within the BRM. The upper control within the first screenshot can be varied through a drop-down

menu, allowing the BRM user, either to apply one of the BRM’s default planting scenarios, or to opt to develop

a custom scenario. The bottom controls within the first screenshot, allows the BRM user to vary the proportion

of available land (identified through the BRM’s Stage One analyses), to be dedicated for the production of

resources, that will potentially be destined for the bioenergy sector. The Figure to the right of these controls

provides ‘active data’ of the area of land that will be dedicated to the planting scenarios; for the given BRM

scenario.


460

The ‘Planting Matrix’ demonstrated in the next screenshot shows the control panel for developing custom

planting strategies within the BRM. This allows the BRM user to dedicate either specific areas, or proportions of

the available land; for the planting of specific crop species. The Figure to the right of these controls in the

screenshot provides ‘active data’ of the production quantities of the resource that will potentially be produced

from the given planting scenario.

Appendix 14.21: BRM Excel Spreadsheet Screenshots – Straw Calculations

The screenshots below demonstrate segments of the ‘Straw Resource’ analyses within the BRM. The Table

shown within both the first and second screenshots, document the calculation progression; in this case for the

analysis Base Year. The crop commodity species analysed within the BRM that may potentially produce straw-

residue resources for the bioenergy sector, are listed. The production quantities of these crops are linked to the

BRM’s food commodity demand Tabs. The analysis calculations are based on straw recoverability, harvest

rates, and the competition for the specific resource within other applications and industries.

The ‘yellow highlighted’ cells and the Figure within the third screenshot provides the ‘active data’ of the

resources potentially available for the bioenergy sector, within the given scenario modelled.


461

Appendix 14.22: BRM Excel Spreadsheet Screenshots – Animal Waste Calculations

The screenshot below demonstrates a segment of the ‘Manure and Slurry’ analyses within the BRM. The lower

Table within this screenshot documents the calculation progression; in this case for the analysis Base Year. The

livestock species analysed within the BRM, that may potentially produce manure and slurry resources for the

bioenergy sector, are listed. Linked to the food commodity demand Tabs, the production (tonnes) and livestock

quantity values are applied alongside manure-factor data, indoor-occupancy proportions, resource-competition

factors, and harvest / collection potentials.

The ‘yellow highlighted’ cells within the screenshot, and the Figure, provide the ‘active data’ of resource

potentially available for the bioenergy sector, within the given scenario modelled.


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Appendix 14.23: BRM Excel Spreadsheet Screenshots – Sewage Sludge

The screenshot below demonstrates the ‘Sewage Sludge’ analysis within the BRM. The Table and Figure

demonstrate the availability of this resource over the analysis timeframe, for the given scenario modelled. The

key active data (highlighted yellow) within this analysis is the population change scenario data, applied for the

given scenario.


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Appendix 15.0

Appendix 15.0 includes copies of the Journal Papers written during the PhD Programme. The titles and details

of the Papers are introduced below:

Appendix A15.1

Journal Paper 1 ‘Securing a Bioenergy Future without Imports’

Submitted to and accepted by Energy Policy Journal.

Appendix A15.2

Journal Paper 2 ‘Increasing Biomass Resource Availability through Supply Chain Analysis’

Submitted to and accepted by Biomass & Bioenergy Journal.


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Appendix A15.1: Energy Policy Journal Paper

Securing a Bioenergy Future without Imports

Reference:

Welfle A, Gilbert P, Thornley P. Securing a Bioenergy Future without Imports. Energy Policy. 2014; 68: 1-14.

Abstract

The UK has legally binding renewable energy and greenhouse gas targets. Energy from biomass is anticipated to

make major contributions to these. However there are concerns about the availability and sustainability of

biomass for the bioenergy sector. A Biomass Resource Model has been developed that reflects the key biomass

supply-chain dynamics and interactions determining resource availability, taking into account climate, food,

land and other constraints. The Model has been applied to the UK, developing four biomass resource scenarios

to analyse resource availability and energy generation potential within different contexts. The Model shows that

indigenous biomass resources and energy crops could service up to 44% of UK energy demand by 2050 without

impacting food systems. The scenarios show, residues from agriculture, forestry and industry provide the most

robust resource, potentially providing up to 6.5% of primary energy demand by 2050. Waste resources are found

to potentially provide up to 15.4% and specifically grown biomass and energy crops up to 22% of demand. The

UK is therefore projected to have significant indigenous biomass resources to meet its targets. However the

dominant biomass resource opportunities identified in the paper are not consistent with current UK bioenergy

strategies, risking biomass deficit despite resource abundance.

Introduction

European Governments have greenhouse gas emission and renewable energy targets that are bound by the

baseline requirements of the Kyoto Protocol [1], and the European Commission’s renewable energy

requirements [2-3]. In addition, the UK is legally bound by the 2008 Climate Change Act [4], to achieve a

mandatory 80% cut in the UK’s carbon emissions by 2050 and a benchmark 35% reduction by 2020, below

1990 levels [5]. The aim; to encourage a transition towards a low-carbon UK economy through unilateral

binding emissions reduction targets [6].

A key route to achieving these targets is to replace fossil fuel based energy with renewable and low carbon

energy technologies. It is becoming increasingly accepted that having a broad energy mix is likely to be the best

method to achieving energy and climate change targets [7]. Biomass as a renewable energy source contributes

towards reducing greenhouse gas emissions, decarbonisation of energy systems, diversification of fuel supplies,

and the development of long-term replacements for fossil fuels [8]. Despite some concerns over the level of

biofuels deployment, bioenergy remains a key component of European energy strategies [9]. The European

Commission estimates that two-thirds of EU’s 2020 target for 20% contribution by renewable energy resource

may be from biomass [10]. The UK’s Renewable Energy Strategy does not propose targets for individual

technologies, but confirms that bioenergy systems will likely contribute significantly to the UK’s future energy

portfolio [5].

However, biomass pathways are being assumed in many national energy strategies globally [11], so critical

assessment of the biomass resource availability is essential. Most energy strategies of European States assume

the use of non-EU sourced biomass to meet their forecast demands [12], so there is likely to be increased

demand (competition) for globally traded biomass in the future and there are also concerns about ensuring its

sustainability.

The UK provides a case study of a nation with strong bioenergy aspirations but uncertain biomass resource

availability. This paper analyses the UK’s projected biomass resource availability under different future contexts

and constraints. A Biomass Resource Model has been developed that: allows the analysis of forecast scenarios

of biomass resource availability to the year 2050; compares indigenous biomass availability against prescribed

biomass and renewable energy targets; and enables an evaluation of bioenergy strategies in terms of indigenous

resource availability and deficits.

The Model has been developed to reflect a wide range of interacting variables that influence biomass resource

availability. The Model can be calibrated to capture the potential range of these variables in different possible

futures. This paper explores four forecasts of potential pathways that the UK could take to 2050, and measures

the biomass resource availability and potential for the bioenergy sector. The scenarios analysed are:


465

Food Focus Scenario - where emphasis on UK food security and productivity is prioritised;

Economic Focus Scenario - where the UK places future emphasis on economic development and resource

competition with the bioenergy sector occurs;

Conservation Focus Scenario - where the conservation of land, biodiversity and resources are prioritised;

Energy Focus Scenario - where the UK places future emphasis on developing the bioenergy sector and

mobilising biomass resource to meet energy/bioenergy targets.

Within the Energy Focus Scenario different biomass conversion pathways have also been explored via a series

of sub-Scenarios. These analyse the bioenergy potential when the biomass resource is converted to power, heat

and transport fuels according to their most efficient or ‘preferred pathways’ [13–17]. In the Heat Conversion

Pathway Scenario, heat energy generation is prioritised where possible; in the Power Conversion Pathway

Scenario, electrical energy generation is prioritised where possible; in the Transport Fuel Conversion Pathway

Scenario, all suitable resources are utilised to produce biofuels and a hybrid “Balanced Conversion Pathway” is

also considered.

The Biomass Resource Model - Methodology

2.1 Biomass Resource Modelling

The vast array of dynamics that impact the availability of any resource means that no set modelling

methodology can be universally applied. However, a constant applicable to the modelling of any resource is that

it is essentially the science of estimating supply versus demand and attempting to quantify resource reserves

[18]. As such biomass resource modelling typically follows one of two pathways, ‘Resource Focused’ models

aim to quantify the extent of each biomass resource category to determine the resulting energy potential [15,

19], whilst ‘Demand Driven’ models analyse the bioenergy contribution targeted [20], and measure the resource

quantity required to meet this demand [21].

Figure 1: Biomass Modelling Potentials

Furthermore, the outputs from biomass resource models then fall into a series of categories (Figure 1 - Adapted

from Batidzirai et al, 2012 [22]) dependent on the adopted approach and desired output. Theoretical or Ultimate

Potentials – represent the biomass resource potentially grown/harvested/collected limited only by physical and

biological barriers. Technical/Geographic Potentials – reflect biomass resource extent taking into consideration

technical constraints such as land area, ecological impacts and agro-technological constraints. Economic

Potentials – demonstrate biomass resource that reflects economic considerations, fundamentally driven by

supply-demand curves. Implementation/Realistic Potentials – represent biomass resource availability without

inducing detrimental environmental, social or economic impacts [23].

2.2 The UK Biomass Resource Model

The Biomass Resource Model (BRM) is resource focused, analysing the indigenous theoretical potential of each

specific biomass resource within the UK. The BRM is then calibrated in line with the scenario assumptions to

produce more realistic resource availability forecasts (Figure 1).

Various previous studies have been undertaken, aimed at analysing biomass resource levels at different

geographic and regional levels. Many of these studies were included in a review carried out by the UK Energy

Research Centre [24]. As part of the process when developing the concept design for the BRM, the merits and

limitations of each of these previous studies were assessed. A summary of the BRM’s high level methodology is

shown in Figure 2. The BRM’s analysis methodology progresses in three distinct stages that collectively reflect

the dynamics of biomass supply chains. The research influences and descriptions of these analyses stages are

provided below:


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Figure 2: The Biomass Resource Model Methodology Architecture

2.2.1 Stage One: Land-Use & Availability Analysis

Analysis Stage One calculates the area of UK land utilised to meet various demands, including; food production,

further urban development and forestry to the year 2050. The remaining UK land area potentially suitable for

crop growth is then analysed to determine the availability for biomass and energy crop growth dedicated for the

bioenergy sector. This land-use analysis methodology builds on approaches developed within similar studies

[25-26] for wider geographic/regional analysis, and focuses them on the UK in this instance. The BRM goes

further than previous studies in that it provides the facility to analyse land for biomass versus land for food

dynamics.

Table 1: Biomass Resource Categories & Analysis

Categories Resources

Grown Resource

from UK Land

Energy Crops Cereal Crops, Oil Crops, Sugar Crops

Biomass Crops

Grasses, Short Rotation Forestry & Coppices, Other Forestry

Residues

Resource

from UK Forestry, Industries &

Processes

Forestry Residues

Crop Residues

Straws

Animal Residues

Manures & Slurries

Arboriculture Arisings

Sawmill, Pulpmill & Industry Residues

Waste Resource

from UK Industries &

Processes

Waste Wood Packaging, Industrial, Construction, Demolition, Municipal

Tertiary Organic Waste

Household, Commercial, Industrial Papers, Cardboards, Textiles, Foods, Organic & Kitchen, Garden etc

Sewage - Waste Treatment

2.2.2 Stage Two: Biomass Resource Availability

Analysis Stage Two quantifies and forecasts the extent, availability and competing markets for different biomass

resource categories indigenous to the UK. Taking into consideration factors such as changes in levels of arisings

linked to industrial activity or agricultural residues. This analysis stage within the Model has been developed

building on the methodologies of a series studies [14-15, 19-20] carried out for the UK’s Department for Energy

& Climate Change. The analysis within the BRM differs from previous and existing research for the UK in that

the resource availabilities analysed are linked to land-use dynamics (Analysis Stage One) and are not driven by

economic feasibility bias. Table 1 provides an overview of the specific biomass resources and categories

analysed within the Model.


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2.2.3 Stage Three: Indigenous Bioenergy Potential

This analysis stage calculates the energy potential of the specific resource quantities calculated within Stage

Two. The wide range of pre-treatment and energy conversion pathways applicable to different types of biomass

are considered. Within the analysis the resources calculated within Stage Two are ‘filtered’ through an ‘energy

pathway’ as summarised by Figure 3. This includes, a potential pre-treatment process where the resource’s mass

may be reduced and an energy debt incurred [17], followed by an energy conversion pathway to produce heat,

power or transport fuels energy. The energy generated reflects the resource’s calorific value [27] and the energy

conversion efficiency [28–35] of the applicable process. The specific pre-treatment and energy conversion

pathway applied for each resource are reflective of the desired energy output, as discussed further in the

Introduction of this paper. Once the energy potentials of the available resources have been calculated, these are

then compared against the UK’s renewable energy and bioenergy targets.

Figure 3: Biomass Resource Model – Energy Generation Pathways

In summary, the key features of the BRM are the ability to investigate different variables and drivers that

collectively reflect the whole system influences to biomass resource availability. This includes forestry,

agriculture and market resource competition, allowing assessment of “land for food” vs. “land for biomass”

dynamics. A further discussion of the methodology is described by Welfle et al, 2013 [36].

UK Biomass Resource Scenarios

3.1 Scenario Analysis

Scenario analysis is the evaluation of potential future events through the consideration of alternative plausible,

although not equally likely states of the world (scenarios) [37]. Scenarios are a dynamic view of future potential

pathways based on the chosen trajectory variables. Scenario analysis provides an advantage of illustrating

potential directions and illuminating events that may otherwise be missed. This can be particularly instructive

for short and long term coordinated decision making and actions [38].

3.2 Developing Biomass Resource Scenarios

Many variables influencing the UK’s biomass resource availability to 2050 are uncertain and so a scenario

approach has been used to explore the potential indigenous resource availability under alternative assumptions.

Comparative biomass resource scenario approaches have been utilised by existing studies. These develop

scenarios that forecast global biomass resource potential [39-40], or specifically focus on the potential of

specific resources within a set geography [41-42].

The flexibility of the BRM allows scenarios to be analysed that represent realistic future conditions. The chosen

scenarios represent different trajectories that are likely to influence biomass availability in the UK and potential

contributions to the bioenergy sector. Within each scenario it is also important to highlight that the ability for the

UK to continue to meet its food demands are always placed ahead of ability to produce resource for the

bioenergy sector. Therefore these scenarios represent potential biomass resource futures that will not conflict

with requirements for food production.

The parameters within each scenario are built up through the manipulation of a series of key drivers that

collectively control the BRM. These drivers reflect the core dynamics that influence biomass resource

availability and potential for the energy sector. The extent and direction in which these drivers are varied within

each scenario is reflective of a wide range of previous research and studies that provide forecasts. Table 2

provides an overview of these forecast drivers, and a summary of the literature that has informed how these

variables may change to 2050. Table 2 also highlights specific drivers targeted and trade-offs between the

scenarios.


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Table 2: Summary of Scenario Drivers & Forecast Assumptions

Drivers within the Biomass Resource Model

Forecasts

Informing the

Scenario

Characteristics

Focus Placed on the Drivers within

each Scenario

Foo-F Eco-F Con-F Ene-F

UK

Development

Population [43] ●● ●● ●● ●●

Changes in Built-Up Land Area [44] ●● ●●● ● ●●

Food

Production

Systems

Food Production Yields [14, 15, 26, 29, 45-

48] ●●● ●● ●● ●●

Food Waste Generation [45, 49-51] ● ●● ● ●●

Food Commodity Import & Exports [51-52] ● ●● ●● ●●

Utilisation of Agricultural Wastes & Residues [16, 19, 53-57] ●● ●● ● ●●●

Forestry &

Wood Based

Industries

Forestry Expansion & Productivity [58-65] ●● ●●● ●●● ●●●

Wood Based Industry Productivity [52, 66-67]

●● ●●● ●● ●●

Imports & Export of Forestry Product ●● ●●● ●● ●●

Biomass

Wastes &

Residues

Utilisation of Forestry Residues [19, 68-71] ●● ●● ● ●●●

Utilisation of Industry Residues [52, 66-67] ●● ●● ●● ●●●

Utilisation of Arboriculture Arising [19, 67] ●● ●● ●● ●●●

Waste Generation Forecasts [55, 72-75]

●● ●● ● ●●

Waste Management Strategies ●● ●● ● ●●●

Biomass &

Energy Crop

Strategy

Land Strategies Dedicated to Crop Growth [14-15, 76]

● ●● ● ●●●

Biomass & Energy Crop Species Planting Strategies ●● ●● ●● ●●

●●● Future supply chain characteristics within the scenario reflect upper limits of forecasts within the literature*.

●● Future supply chain characteristics within the scenario reflect average values forecasts within the literature*.

● Future supply chain characteristics within the scenario reflect lower limits of forecasts within the literature*.

In summary the developed research scenarios reflect the collective variations of a series of forecasts. This

methodology is designed to be prospective, quantitative and normative as described by Anderson et al [77-78],

in that the scenarios explore probable futures through modelling, based on the extension of a number of key

drivers. The key themes, assumptions that characterise each scenario are summarised within Table 3.

Table 3: Summary of the Key Focus Areas within each Biomass Resource Scenario

Scenario Theme Future Pathway – Key Focus Areas

Food Focus

Scenario

Focus on

enhancing food

security &

increasing self-sufficiency.

Increasing crop yield productivity.

Decreasing food waste.

Reduced food imports, replaced by domestic growth.

Emphasis on agriculture over forestry expansion. Dedication of available land for agriculture ahead of bioenergy crop growth.

Economic

Focus

Scenario

Economic

development is

the prime target.

Reduced restrictions on built-up area expansion.

Increased focus on forestry expansion and productivity.

Utilisation of forestry residues.

Increased exportation rates of food commodities & forestry products. Waste generation rates driven by economic growth and technological advancement.

Conservation Focus

Scenario

Increased

emphasis on conservation &

resource

protection.

Restricted expansion of built-up land area.

Increased focus on forestry expansion & preservation. Lower limit utilisation of forestry & agricultural residues for energy.

Decreased levels of waste generation. Waste management strategies focusing on resource recovery.

Reduced dedication of available land for bioenergy crop growth.

Energy Focus

Scenario

Focus on

enhancing & expanding the

bioenergy sector.

Increased dedication of available land for bioenergy crop growth.

Increasing focus on forestry expansion & productivity.

Increased utilisation of forestry residues, agricultural residues and arboriculture arising by

the bioenergy sector. Waste management focusing on energy recovery.


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3.2.1 Food Focus (Foo-F) Scenario

The Foo-F Scenario has been developed to analyse forecasts of biomass resource availability for the energy

sector, within a future pathway where prime focus has been placed on improving food security and self-

sufficiency.

Scenario Context

Since World War Two European agricultural policy has focused on enhancing food self-sufficiency for the

European population and, as demonstrated by recent overproduction of food, has been highly successful [79].

However an enormous future challenge looms - having to feed up to 9-10 billion people by 2050 globally [51].

Agricultural systems are highly sensitive to climate fluctuations, and a 2°C rise in mean global temperature

reflecting the Intergovernmental Panel on Climate Change’s lowest emission scenarios is predicted to result in

widespread destabilization of farming systems across the world [80]. In addition to the uncertainty regarding

food systems, the large scale production of biofuels is becoming a significant competitor for agricultural land –

and whilst energy security concerns may justify the production of biofuels, the proposed scale of production

raises questions about the trade-offs between biofuels and food crops [81]. The key issue relating to future food

systems remains whether they can keep pace with steep growing demand and dietary transitions in an

environment of climate change and numerous other drivers [82]. This strain will put pressure on Europe’s future

supply chains. Enhancing food security and self-sufficiency may re-emerge as prominent areas of concern for

future governments.

A Future Pathway with Food Focus

Although complete food self-sufficiency is not a current target for the UK, it is important that food systems

adapt so that the UK is able to cope with future stresses in the food system [49-50]. The UK currently produces

about half of the food it consumes, and is ~60% ‘self-sufficient’ [47]. Recognised strategies to address future

food issues include: closing the yield gap – the difference between attainable yields and realised yield;

increasing agricultural productivity through technologies, research and investment; reducing wastes from food

systems; changing diets; and expanding aquaculture opportunities [51].

UK agricultural productivity has been increasing at a steady trajectory through time, and increased research,

development and investment in the sector is likely to see this trend continue [83]. Estimates also suggest that 30-

50% of food grown worldwide may be lost or wasted before and after it reaches the consumer. Therefore future

emphasis should be placed on addressing wastes – the UK Government Office for Sciences suggesting that food

waste could be realistically halved by 2050, equivalent to as much as 25% of current productivity [49-50].

All actions should be realised through coordinated and multifaceted strategies where sustainability is key. This

‘sustainable intensification’ involves an enhancement of current business-as-usual trends. Where agricultural

systems remain largely unchanged and demands follow current projections, but agricultural productivity

becomes increasingly efficient [84].

A summary of the key focus areas and actions within the Foo-F Scenario future pathway are shown in Table 3.

3.2.2 Economic Focus (Eco-F) Scenario

The Eco-F Scenario has been developed to analyse forecasts of biomass resource availability for the energy

sector, in a future where emphasis is placed on economic growth over all other considerations.

Scenario Context

Following the 2008/9 financial problems, an agenda aimed at encouraging economic growth is currently at the

forefront driving the majority of UK policy. In the UK, timber and wood based industries are well established

and contribute about 1.5% of UK export, equivalent to 2.5% of the global share [52]. The flow of materials

between the economy and the environment constitutes the physical foundations of economies – this ‘economic

metabolism’ being a key indicator of economic health [85-86]. It is the growth and dynamics associated with the

wood based industry that will be a key influence in determining in the availability of biomass resources

available for the bioenergy sector within this scenario.

In terms of development of the bioenergy sector, many countries are showing considerable interest in bioenergy

from an economic basis because of the value added (income) and employment opportunities that bioenergy can

bring, especially in the rural areas where the resources are produced/collected [87]. However studies such as

Marques & Fuinhas (2012) [88] have concluded that the high costs associated with supporting renewable energy

options are actually an economic burden, as polices such as increasing tariffs for electricity results in an

economically counterproductive effect and deceleration in economic activity. As things stand European

countries have energy systems and infrastructures that are deeply grounded in fossil fuel provision [88].


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Therefore if future policy, finance and focus is not directed towards renewable energy pathways, it is unlikely

that there will be a widespread move away from conventional fossil fuel generation. A future pathway focused

on economic growth may not specifically focus on the development of the bioenergy sector through the

mobilisation of resource or building of energy infrastructure. But through increasing the on-going activities of

wood based industries, there will still be opportunities for the bioenergy sector.

A Pathway with Economic Focus

A future pathway with economic focus will reflect policies designed to encourage the growth of industry, which

in turn may compete for biomass resource but also provide new opportunities for the bioenergy sector. The UK

Wood Panel Industry Federation (WPIF) identifies the growth of the bioenergy sector as a major concern for

resource, “as subsidised energy generators can afford to out-pay the wood panel industry for primary raw

material” [66]. Therefore a future pathway with economic focus would ensure that the wood industry’s resource

demands are set over those of the bioenergy sector. Industry’s future resource demands have been forecast by

the WPIF [67] and forestry expansion and productivity scenarios are forecast by the Forestry Commission [58-

65] to reflect market behaviour, these forecasts are utilised within the Eco-F Scenario.

Meta-analysis of a series of studies [89] concludes that there is statistical significance between economic growth

and aggregate export levels, especially relating to both manufactured and energy based export categories.

Therefore, it should be expected that a future pathway with economic focus may reflect increases in export

levels, particularly wood products with relevance to the bioenergy sector.

Greyson, (2007) states that, realising zero waste and sustainability with continued economic growth may not be

achievable within the scope of current practices [90]. Therefore the future patterns of waste generation and

management within this scenario may reflect variations of continuing trends. To model this the waste strategies

within this scenario utilise the UK Department of Environment, Food & Rural Affair’s (DEFRA)

technologically driven forecasts [55, 74-75]. These predict that large-scale solutions and technology will be key

to dealing with waste continuing issues.

A summary of the key focus areas and actions within the Eco-F Scenario future pathway are shown in Table 3.

3.2.3 Conservation Focus (Con-F) Scenario

The Con-F Scenario has been developed to analyse forecasts of biomass resource availability for the energy

sector, within a future pathway where emphasis is placed on a paradigm of enhanced conservation and

preservation of biodiversity and resources.

Scenario Context

A century ago forestry cover in the UK was at an all-time low, although following a series of phases of forestry

focus this has increased by two and a half times to the ~13% cover present today. However planting rates in

recent years have once again stagnated, leading to recognition that it is time to regain focus and ‘up the game’,

particularly when measured against the context of having to mitigate and adapt to climate change [91].

The UK’s approach to conservation relies on a series of partnerships between statutory, voluntary, academic and

business sectors at both the National and local scale. The prime focus being to maintain and create habitats and

ecosystems, halt the decline of biodiversity and enhance the robustness of sites to environmental change [92].

This is backed up by a wide spectrum of legislative requirements that aim at safeguarding forestry, biodiversity

and conservation [93].

At the same time: the UK has well established wood based industries that rely on forestry productivity, there is

increasing inclusion of forestry resources within renewable energy strategies [70], and awareness of forestry

resources and ancient woodlands in terms of ecological value is increasing [94]. Collectively these three

competing demands and priorities will shape the pathway for utilisation of forestry in the future.

A further increasingly prominent conservation issue is resource availability and scarcity - a theme motivating

new waste management strategies at both the European and UK level [95]. There is great scope for

improvement in the UK, where recycling levels stand at about 39% of municipal waste compared to >60% in

leading places such as Austria and Germany [96]. To help develop innovative and exemplary practices that drive

behaviour towards enhanced sustainability, the UK Government has on-going ‘Zero Waste Places’ initiatives

[97] - “A simple way of encapsulating the aim to go as far as possible in reducing the environmental impact of

waste. It is a visionary goal which seeks to prevent waste occurring, conserves resources and recovers all value

from materials” [98].


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A Pathway with Conservation Focus

Trade-offs exist between biodiversity, conservation and optimal biomass resource production for the bioenergy

sector. Erb, et al [99] found that estimates of global biomass crop potential are lowered by 9-32% when land

areas of wilderness, biodiversity importance and with protection status are excluded from assessments. The

German Advisory Council on Global Change also found that a minimum of 10-20% of global land should be

protected if the biosphere’s functions such as climate regulation and biodiversity are to be preserved [100]. 14%

of land is currently protected globally [101], meaning a further 6% equivalent to 540,000 km² is required [102].

In summary a future pathway with conservation focus will undoubtedly result in lower levels of biomass

resource being available for the bioenergy sector.

In the UK, the Forestry Commission have a wide range of forestry expansion and productivity scenarios that

reflect varying levels of forest growth and utilisation [58-65]. A future pathway with conservation focus will

reflect the upper projections for forestry expansion. The management of forests including felling is

The approach of the UK forestry industry has progressively shifted primarily from timber production to

increasingly multi-purpose values that include conservation [103]. Forestry industries having an important role

to play in conservation, as the industries long-term sustainability depends on the resource [104]. Therefore

industry within a future pathway with conservation focus would therefore continue to utilise forests, albeit

strongly abiding to the requirement of the ‘UK Forestry Standard’ [105].

Research [106-107] also highlights that the extraction of residues from both forestry and agricultural systems

may pose a risk for the maintenance of soil fertility. It being important that residue removals don’t exceed levels

required to maintain food and habitats for organisms, provide protection against soil compaction or for

maintenance of soil fertility [69, 71]. Therefore a future pathway with conservation focus will likely avoid the

upper limits utilisation of both forestry and agricultural residues.

Waste generation and management strategies will reflect future pathways of reduced waste generation and

increased levels of resource recovery from waste streams. Scenarios reflecting these pathways have been

forecast by the DEFRA [55, 74-75], and will form the basis of future waste analysis within the Con-F Scenario.

A summary of the key focus areas and actions within the Con-F Scenario future pathway are shown in Table 3.

3.2.4 Energy Focus (Ene-F) Scenario

The Ene-F Scenario has been developed to analyse forecasts of biomass resource availability for the energy

sector, in a future pathway where prime focus has been placed on expanding the UK bioenergy sector.

Scenario Context

As already discussed the UK has legally binding energy, carbon and renewable energy targets, including a series

of bioenergy targets relating to heat, power and transport fuel, and overall generation [6, 108-109]. The Energy

Focus Scenario sets out a future pathway where the maximum achievable levels of bioenergy are generated from

indigenous biomass resources. The strategy being for the UK to maximise it’s bioenergy generation potential

through the utilisation of indigenous resources, and reduce potential reliance on imported imported resources.

A Pathway with Energy Focus

The concept behind this scenario is to explore the upper limits of indigenous biomass resources that could

realistically be mobilised for the bioenergy sector to 2050. This involves mobilising and pushing the limits on

resource availability across the range of biomass categories.

Within this future pathway the upper limits of available and suitable land (after food demands are met) is

dedicated for the potential growth of energy and biomass crops [14, 76]. The energy focus scenario will reflect

the Forestry Commission’s forest expansion and productivity forecasts [58-65] that provide the greatest resource

potential for the bioenergy sector. A further future opportunity explored is highlighted by The Independent

Panel on Forestry [110], “only 52% of UK forests and woodland are currently actively managed, so major

resource use opportunities may exist if progress is made in this area”.

There are also notable biomass resource opportunities potentially available for the bioenergy sector in the form

of wastes and residues from on-going activites in the UK [111]. As such a future pathway with energy focus will

work towards achieving increased harvest and collection (biological & realistic) limits for biomass residues,

from forestry, agricultural and industrial processes. The waste generation and management strategies adopted

also reflect DEFRA’s forecast pathways where energy recovery is the focus [55, 74-75].

A summary of the key focus areas and actions within the Ene-F Scenario future pathway are shown in Table 3.


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Biomass Resource, Energy & Land-Use Forecasts

The following section provides a series of figures that allow analysis of the biomass resource availability,

bioenergy potential and land utilisation within each of the scenarios when calibrated using the UK BRM.

4.1 Land Utilisation Analysis

Figure 4 and the data within Appendix A1 summarises how UK land is utilised within each scenario. This is in

accordance with the UK meeting it’s food production demands, any changes in built-up land area and forestry

within each scenario. The output to this analysis is the area of land identified as being potentially available and

suitable for biomass and energy crop growth. The extent to which this land is utilised for this purpose differs

depending on the focus of each scenario. Areas of land with characteristics unsuitable for crop growth such as,

rivers, mountains, coasts and lakes are excluded from the analysis.

The area of land left free and un-utilised within the scenarios is highlighted as ‘Other Land’ within Figure 4.

The ‘Land Dedicated for Biomass & Energy Crops’ category reflects the area of land within each scenario that

has been specifically dedicated for growth for the bioenergy sector. The ‘Built-Up Land Area’ category reflects

land that is utilised for buildings, roads and infrastructure etc. ‘Agriculture Land’ represents the land area within

each scenario dedicated to both pastoral and arable food productivity. The ‘Forestry and Woodland’ category

reflects the area of both managed and unmanaged forests/woodlands within each scenario.

Figure 4: Utilisation of UK Land within the Scenarios

4.2 Indigenous Biomass Resource Analysis Forecasts

One of the key aims of this research is to analyse the UK’s indigenous biomass resource potential to 2050 within

each of the analysis scenarios. Figure 5 and the data within Appendix A2 presents this analysis, documenting the

potential availability of each resource for the bioenergy sector to 2050. Within Figure 5 the stacked columns for

each scenario reflect the availability of each specific biomass resource. The stacked lines joining the columns

within Figure 5 provide segregation, to highlight the different resources within each of the biomass categories

(Table 1) - waste resources, residue resource and grown resources. Grouping the resources into these three

categories allows higher level insight into changing trends of resource availability to 2050. To allow ease of

comparison and analysis of the data, resource quantities within Figure 5 have been converted to a single unit

(tonnes equivalent).


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Figure 5: UK Biomass Resource Scenarios – Resource Availability Analysis

4.3 Forecasting the Bioenergy Potential of the Available Indigenous Resources

The bioenergy generation potential of the available resources shown in Figure 5 are analysed and represented in

both Figure 6, and by the data within Appendix A3. Figure 6 highlights the bioenergy generation potential for

each of the scenarios and also for the Energy Focus sub-scenarios. As discussed in Section 1 the Energy Focus

sub-scenarios allow an evaluation of strategies that focus on either a balanced, heat, power or transport fuel

bioenergy strategy. These are measured against the forecast range [20] of future UK energy demand - shown at

the top of Figure 6. Also the UK’s renewable energy and bioenergy targets [6, 99-100] represented by the

triangle and diamond markers within Figure 6.

Figure 6: Scenario Bioenergy Potentials vs. Forecast UK Energy Demands & Targets

4.4 Biomass Import Deficit Forecasts

In the UK current and planned biomass projects are focusing predominantly on power bioenergy pathways, and

like most European countries the UK will require largely woody biomass (primarily wood pellets and to a lesser

extent wood chips) for this energy generation pathway. Liquid biofuels (biodiesel and bio-ethanol) are also

sought for transport [112]. The international market for biomass resource is still relatively immature, and

therefore projections are uncertain. The UK Department for Transport reported that around 25% of feedstocks

purchased for current bioenergy plants was indigenous resource, the remaining imported from the EU, North

America, Russia, South Africa and New Zealand (predominantly wood pellets), Brazil (biofuels) and Malaysia

and Indonesia (palm oil) [109]. Therefore if the UK’s bioenergy plans mature, the country could become

increasingly dependent on these categories of imported resource [15].

Figure 7 and the data within Appendix A4 highlights a forecast of the UK’s potential wood fibre demand up to

2025 [67]. It also shows the UK’s indigenous resource availability within each of the scenarios when restricted

to woody and liquid biofuels resources, (the resource categories currently forecast as being required by the UK

bioenergy sector if current plans mature). The shaded area under the ‘Forecast UK Wood Fibre Demand’ line

within Figure 7 represents the UK’s potential indigenous resource deficit, of the types of biomass resource that

will be required if current UK’s bioenergy plans mature and biomass demand forecasts are realised.

Figure 7: Analysis of UK Indigenous Biomass Resource Availability & Future Resource Deficits

Discussion

This section discusses the key outputs from the results documented within Section 4, highlighting the

importance of the findings in the context of the UK’s future energy and bioenergy strategies.


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5.1 Land Utilisation Forecasts

The key finding within the land utilisation analysis is that when all the scenarios are compared, the Food Focus

Scenario actually has the lowest land area allocation for agricultural productivity over the analysis period. This

reflects the key themes reflected within this scenario, where research, development and technology for

improving the productivity of agricultural systems are emphasised. Through increasing the productivity of the

land, an overall reduction in land area is required to deliver the increased food quantities required to enhance the

UK’s food security and self-sufficiency. This feedback benefit within the Foo-F Scenario also has a further

positive impact since larger areas of the ‘freed-up’ agricultural land can be dedicated for biomass and energy

crop growth, whilst also allowing over 30% of land to be free from use.

5.2 Indigenous Biomass Resource Availability Forecasts

The ‘Grown Resource’ category within this analysis includes both biomass and energy crops and also dedicated

resource from forests that are available to the bioenergy sector. These are represented at the bottom of the

graphs within Figure 5. Overall, grown resources represent a potentially large opportunity for the bioenergy

sector. Especially towards the latter stages of the analysis where by 2050 in the Foo-F, Eco-F and Con-F

Scenarios, they represent over half of the total resources potentially available to the bioenergy sector. Further

analysis of the Grown Resource category within Figure 5 highlights that the available resource direct from

forestry is relatively small compared to that from dedicated biomass and energy crops. Although, the Ene-F

Scenario does highlight that resources available direct from forestry when mobilised represent a relatively

constant and significant resource opportunity for the bioenergy sector to 2050.

All scenarios assume that an increasing proportion of un-utilised land will be set aside for growth of biomass

and energy crops. The overall proportion of land dedicated being dependent on the focus of the particular

scenario. The analysis highlights that regardless of scenario, UK grown biomass and energy crops are shown to

represent significant potential for the UK bioenergy sector.

As documented in Section 5.1, a positive feedback effect can be seen within the Foo-F Scenario. Towards the

end of the analysis period shown in Figure 5, areas of agricultural land are ‘freed-up’ as a result of sustainable

intensification and improved production yields. This allows larger areas of land to be utilised for the growth of

dedicated biomass and energy crops for the bioenergy sector.

The ‘Residue Resource’ category represents resources from on-going activities such as agriculture, forestry and

industrial processes. Within Figure 5 these represent the middle analysis band of the columns. Figure 5

demonstrates that the resources within this category have relatively continuous and stable availability for all

scenarios and throughout the analysis period to 2050. The resource potential of both plant (straw) and animal

(slurry) based agricultural residues is shown to have significant availability for bioenergy. Even within the Con-

F Scenario where the lower limits of agricultural residues are collected/harvested, the collective availability of

this resource is still significant.

Residue resource from industrial processes are also documented as having relatively constant availability

regardless of scenario or progress of time – slight increases are documented within the Eco-Focus Scenario

where industrial activities are increased.

The ‘Waste Resource’ category represents the broad range of waste streams that may be utilised by the

bioenergy sector (Table 1). These are represented by the top segment of resources highlighted within Figure 5.

Significant potential from the waste category resources is highlighted. While the availability of the waste

resources varies substantially across the scenarios, the Ene-F Scenario highlights the maximum realistic

potential for the bioenergy sector. Household, food and organic wastes represent a large portion of the available

waste resources in this scenario, while other waste streams including woods, textiles, sludge’s and oils

(represented as ‘Other Wastes’ within Figure 5) also highlight significant potential.

5.3 Bioenergy Potential Forecasts

This analysis highlights that if any of the developed scenarios and forecasts of future UK energy demand were

realised, between 19-44% of the UK’s primary energy demand could be delivered from indigenous biomass

resources. Within the Foo-F and Ene-F Scenarios, the UK could potentially meet it’s 2050 bioenergy targets and

a large portion of its 2050 renewable energy targets from indigenous biomass resource (Figure 6). Also if the

UK were to pursue a pathway that focused on developing it’s bioenergy sector, the ‘Heat Prioritised’ Energy

Focus Sub-Scenario demonstrates that heat conversion pathways will likely provide the highest energy

generation for the resources available. Even if future scenarios prioritised conservation or economic themes as

much as 19% of the UK energy demand could still be met from indigenous resources, as long as there is some

focus on increasing resource mobilisation.


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5.4 Indigenous Resource Deficits and Imports

Figure 7 shows the results in the context of the UK’s renewable energy and bioenergy strategies. Even though

the UK has potentially large indigenous biomass resource availability (Figure 5) and a large portion of the UK’s

energy targets could be met through the utilisation of these indigenous resources (Figure 6), the UK does not

have an abundance of the ‘woody’ and transport fuel compatible biomass resources required to meet future

demand, if the UK’s current bioenergy plans mature. Figure 7 shows that even within the Ene-F Scenario where

the upper limits of indigenous biomass resource are mobilised, a large resource deficit will still exist when

compared to the forecast resource demand. In summary this analysis describes an uncomfortable nexus between

the high potential availability of various indigenous resource categories, and the current and future bioenergy

strategies that are potentially steering the UK towards reliance on biomass resource imports. Highlighting the

concept that the UK is not potentially investing in the appropriate biomass technologies and infrastructure to

match the indigenous opportunities.

Conclusions - Scenario Lessons

The scenarios developed in this study were designed to represent four potential pathways that could be realised

in the UK, in order to determine the biomass resource potentials and implications for the bioenergy sector. They

allow an assessment of the potential impacts of different variables and contexts on potential biomass resource

supply. The key policy conclusions arising from the analysis are highlighted below.

High Potential Availability of Biomass & Energy Crops without Impacting Food Systems - The analysis shows

that the potential availability of biomass and energy crops for the bioenergy sector remains high, even when

land is ‘ring-fenced’ for the UK to maintain it’s food production requirements (and even enhanced food

production within the Food Focus Scenario).

Robust & Continuous Resource Availability from On-going UK Activities – Biomass residue resources,

including agricultural, forestry, industrial and arboriculture residues were found to represent a continuous and

robust resource that maintained a high availability regardless of the scenario or time within the analysis.

Agricultural residues, particularly both straw and slurry resources represent a major opportunity for the

bioenergy sector due to their high abundance, availability robustness and current under-utilisation [113].

Large Potential from Waste Resources – Within the Energy Focus Scenario where the adopted waste

management strategy emphasised energy recovery, the potential waste resource availability for the bioenergy

sector was shown to be substantial (>1308 million tonnes equivalent per year in 2050). Likewise within the

Conservation Focus Scenario where a strategy of reduced waste generation and resource recovery was adopted,

the potential for the bioenergy sector was much less attractive (>1.8 million tonnes equivalent per year in 2050).

The abundance of both household and food/plant based waste streams were identified as showing particular

potential for the bioenergy sector.

A Food Focus Positive Feedback Benefit – Analysis within the Food Focus Scenario found that a future

pathway that emphasised increasing the productivity and reduction of wastes from food systems, resulted in

future benefits for the bioenergy sector. Increasing the productivity of the land not only resulted in increased

food security and self-sufficiency, but ultimately resulted in less land being required to produce more food –

freeing up additional land for biomass growth.

Heat Conversion Pathways Providing Most Energy Efficient Use of Resources – The analysis of the energy

conversion sub-scenarios (Figure 6), highlighted that the prioritisation of heat energy conversion pathways with

suitable resources, resulted in the greatest levels of bioenergy generation. This suggests that the best option for

the UK to make the most of its indigenous biomass resource is potentially for, selected resources to be utilised

by the industries in bio-refineries with all remaining suitable resources being dedicated for heat generation

pathways. The generation of renewable electricity being achieved through alternative technologies.

Indigenous Resource Abundance & Our Pathway Towards Increased Resource Deficit – Overall the results

identified the high potential availability of indigenous resources for the bioenergy sector (Figure 5), the large

contributions that indigenous resource could make towards the UK achieving it’s energy targets (Figure 6), but

the forecasts deficits in biomass resources that the UK will need to import in order to supply the current and

planned bioenergy sector (Figure 7). In summary this analysis highlights the non and under-utilisation of

indigenous biomass resources in the UK, and the major currently missed opportunities that are contrary to the

current direction of the UK bioenergy sector.


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Appendix A15.2: Biomass & Bioenergy Journal Paper

Increasing Biomass Resource Availability through Supply Chain Analysis

Reference:

Welfle A, Gilbert P, Thornley P. Increasing Biomass Resource Availability through Supply Chain Analysis.

Biomass & Bioenergy. 2014; 70: 249-226.

Abstract

Increased inclusion of biomass in energy strategies all over the world means that greater mobilisation of

biomass resources will be required to meet demand. Strategies of many EU countries assume the future use of

non-EU sourced biomass. An increasing number of studies call for the UK to consider alternative options,

principally to better utilise indigenous resources. This research identifies the indigenous biomass resources that

demonstrate the greatest promise for the UK bioenergy sector and evaluates the extent that different supply

chain drivers influence resource availability.

The analysis finds that the UK’s resources with greatest primary bioenergy potential are household wastes

(>115TWh by 2050), energy crops (>100TWh by 2050) and agricultural residues (>80TWh by 2050). The

availability of biomass waste resources were found to demonstrate great promise for the bioenergy sector,

although are highly susceptible to influences, most notably by the focus of adopted waste management

strategies. Biomass residue resources were found to be the resource category least susceptible to influence, with

relatively high near-term availability that is forecast to increase - therefore representing a potentially robust

resource for the bioenergy sector. The near-term availability of UK energy crops was found to be much less

significant compared to other resource categories. Energy crops represent long-term potential for the bioenergy

sector, although achieving higher limits of availability will be dependent on the successful management of key

influencing drivers. The research highlights that the availability of indigenous resources are largely influenced

by a few key drivers, this contradicting areas of consensus of current UK bioenergy policy.

1) Introduction

The UK energy sector is facing it’s greatest challenges for at least a generation. The sector is expected to renew

its energy generation portfolio, whilst providing secure, reliable, affordable and low carbon energy to its

customers [1]. Energy from biomass provides options for the energy sector that can provide parts of the solution

to each of these challenges.

Despite some concerns over the extent of biofuels deployment, bioenergy is key to many European energy

strategies [2]; the European Commission estimates that energy from biomass may contribute up to two-thirds of

the EU’s 2020 target for 20% renewable energy contribution [3]. The UK’s Renewable Energy Strategy also

confirms that energy from biomass will significantly contribute towards the UK’s energy portfolio [4].

Inclusion of energy from biomass pathways in both national and global energy strategies [5] means that

increased mobilisation of biomass resource will be required to meet demand. The energy strategies of many EU

countries currently assume the extensive use of non-EU sourced biomass [6], which will increase competition

for suitable feedstocks [7].

The UK faces urgent choices regarding the future direction of its bioenergy sector. If current plans mature the

sector will be increasingly dominated by large scale biopower co-firing systems that will lock the UK into

indigenous deficits of the feedstocks required to keep these plants running [8-9]. There are an increasing number

of studies and calls [9–13] for the UK to consider alternative biomass options, principally to make better use of

the indigenous resources available. Welfle et al [9] showed, through the development of a series of UK biomass

resource scenarios, that the UK could potentially deliver 22% of its primary energy demand in 2050 through

indigenously grown biomass and energy crops, 6.5% through the utilisation of indigenous residue resources

from on-going activities and a further 15.4% from waste resources.

The UK has many potential sources of biomass suitable for energy options. If indigenous resources are to be

increasingly utilised, it is important that a greater understanding is achieved, of how different influencing drivers

determine the extent that biomass resources become available to the bioenergy sector. Assessing the availability

of any given resource being a matter of evaluating how much it is realistically, environmentally and

economically viable to be made available to the energy market [14]. Some of these key drivers can be

categorised as follows [15-16]:

Policy Drivers – energy and environmental themed policies are particularly important in determining a

secure long-term energy strategy. Waste, agricultural and forestry policies have great influence in

determining the potential availability of specific resources.


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Market Drivers – biomass is a relatively immature market in the UK. The level of understanding that

potential resource suppliers/buyers have of the UK market, determines the levels of uncertainty and

likelihood of commitments to long term contracts.

Technical Drivers – are the influences and barriers that may influence the actually processes of energy

generation. These may include issues such as the availability of fuel standards or the ability to integrate

biomass resources with the existing fossil fuel dominated network.

Infrastructure Drivers - influences relating to the performance of all facilities required for the bioenergy

sector to operate, including the, harvesting, collection, storage and transport of feedstocks.

The aims of this Paper are to identify and evaluate the most significant drivers within supply chains that

influence the availability of UK indigenous biomass resource for potential utilisation by the bioenergy sector.

The objective is to inform the developers of bioenergy strategy and policy, and the wider bioenergy sector of

opportunities to increase biomass resource availability. This is enabled through: highlighting specific indigenous

resources that represent robust and continuous options for the bioenergy sector; identifying specific supply chain

drivers that are found to command the greatest influence in determining the availability of biomass resources;

identification of the resources whose availabilities are found to be most and least susceptible to variances within

supply chains; and highlighting areas where policy measures should potentially focus in order to maximise the

availability of indigenous resource.

Although this research is focused on the UK, the analysis is also applicable to similar case studies where a

greater understanding of indigenous biomass availability is sought.

The research analysis is undertaking utilising a Biomass Resource Model (BRM), developed to simulate the

whole system dynamics of biomass supply chains. The BRM brings together and allows the calibration of a

wide range of drivers and variables that collectively determine the potential indigenous resource availability to

2050. Within this research the BRM is utilised in undertaking a sensitivity analysis to evaluate the influence of

how supply chain drivers influences the availability of different categories of biomass.

2) Methodology

The following section introduces and discusses the analysis methodologies applied within this Paper. This

includes an overview of the methodology developed within the BRM, and also that for measuring the extent that

different supply drivers influence indigenous resource availability.

2.1 The Biomass Resource Model

The Biomass Resource Model is a resource focused modelling tool that enables the bottom up analysis of the

practical potential of indigenous biomass resources, in this case within the UK. The drivers that control the

BRM collectively reflect the variances and dynamics that influence biomass supply chains. Calibration of these

drivers within the BRM allows the generation of realistic resource availability forecasts up to 2050. These

drivers are discussed further and listed in Section 3 of this Paper.

A summary of the BRM’s high level design is shown in Figure 1. This highlights that the BRM’s analysis

methodology progresses in three distinct stages as described below. A greater depth discussion of the BRM’s

methodologies including an overview of the key research influences are described in Welfle et al [9, 17].

Figure 1: The Biomass Resource Model Methodology Architecture


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2.1.1 BRM Analysis Stage One: Land-Use & Availability Analysis

Analysis stage one evaluates the area of UK land utilised to meet various demands, including; food production,

further urban development and forestry to 2050. The remaining UK land area potentially suitable for crop

production is then analysed to determine the potential availability for biomass and energy crop growth dedicated

for the bioenergy sector.

2.1.2 BRM Analysis Stage Two: Biomass Resource Availability

The second analysis stage quantifies and forecasts the extent, availability and competing markets for different

biomass resources indigenous to the UK. This takes into consideration factors such as the potential for resource

collection/harvest, changes in the levels of arisings linked to industrial activity and agricultural residue

utilisation. The biomass categories and specific resources analysed within the BRM to reflect the range of UK

resources are shown in Table 1. These also represent the biomass categories analysed within this paper.

Table 1: Summary of the Analysed Biomass Categories & Specific Resources

Categories Biomass Resources

Grown Resource

from UK Land

Energy Crops (food species)

Cereal Crops, Oil Crops, Sugar Crops

Biomass Crops (non-food species)

Grasses, Short Rotation Forestry & Coppices, Other Forestry

Residues Resource

from UK Forestry,

Industries &

Processes

Forestry Residues

Crop Residues

Straws

Animal Residues

Manures & Slurries

Arboriculture Arisings

Industry Residues

Sawmill, Pulpmill & Industry Residues

Waste Resource

from UK Industries

& Processes

Waste Wood

Packaging, Industrial, Construction, Demolition, Municipal

Tertiary Organic Waste

Household, Commercial, Industrial Papers, Cardboards,

Textiles, Foods, Organic & Kitchen, Garden etc

Sewage - Waste Treatment

2.1.3 BRM Analysis Stage Three: Indigenous Bioenergy Potential

The third analysis stage calculates the bioenergy potential of the specific resource quantities calculated within

Stage Two. The range of pre-treatment and energy conversion pathways applicable to different types of biomass

are considered. Resource bioenergy potentials are calculated taking account of the resource and energy

efficiencies reflective of each bioenergy generation pathway. Once the energy potentials of the available

resources have been calculated, these can then be compared against respective renewable energy and bioenergy

targets.

In summary the key features of the BRM important to this analysis are the ability to investigate the different

supply chain drivers that influence biomass resource availability. Also to evaluate food-fuel interfaces by

simultaneously considering the land requirements for food production, biomass production and other uses.

2.2 Developing a Methodology for Analysing Influences to Biomass Resource Availability

This section describes the methodology developed for analysing the extent that different drivers influence

resource availability. The aim was to undertake an assessment of the maximum practical availability of different

indigenous resources to 2050, determine the drivers that most influence resource availability, evaluate the

‘availability robustness’ of each resource, and identify any notable trends through time.

2.2.1 Developing a Baseline

For each of the drivers discussed in Section 3 that control the BRM, a literature review was carried out to

analyse how these currently stand in the UK, and to develop an idea of how these may change to 2050. A

database was produced that collated the range of values that literature and studies forecast for these. This

database was then analysed to develop a series of average or mean values for each of the drivers to 2050. These

values therefore represent a ‘literature informed’ mean or ‘baseline scenario’ of how the UK’s biomass supply


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chains may function to 2050. Calibrating the BRM to reflect this baseline enabled an evaluation of the ‘average’

availability and bioenergy potential of each indigenous resource to 2050. Identifying which resources may be

most abundant, and which resources may provide the greatest bioenergy potential being fundamental to this

analysis.

2.2.2 Evaluating how Different Drivers Influence Biomass Resource Availability

The key element of the Paper’s analysis is evaluating the extent that each supply chain driver influences

resource availability. A sensitivity analysis methodology was developed so that the influence of each driver

could be analysed in isolation of the others. This was achieved through calibrating the BRM to reflect the

baseline scenario (discussed in Section 2.2.1). The BRM was then progressively run to reflect the performance

range of variances forecast by literature for each individual driver, whilst keeping all the other drivers set at the

baseline. Undertaking this methodology for all drivers allowed an assessment to the extent that each influenced

the availability of different biomass resource to 2050.

3) Drivers Influencing Biomass Availability This next section introduces and provides further context to the supply chain drivers that make up the BRM. It

also provides a review of selected literature and discussion for how they are deemed to influence the availability

of different biomass resources in the UK.

All biomass resource models and assessments revolve around analysing the influence of different drivers. As

such the range of drivers listed within literature that are identified as being influential of biomass resource

availability is extremely broad. Table 2 presents an overview of many of these and highlights the capabilities

and limitations of the BRM in analysing each.

Table 2: Summary of Supply Chain Drivers that Influence Biomass Resource Availability

Categories Supply Chain Drivers References BRM Analysis

Capability

Economic &

Development

Drivers

Population Change [18–25] Resource Import / Export [18, 23] Economic & Technical Development [20, 26] - Industry Productivity [20, 23, 26] Gross Domestic Product [19, 24-25] - Rural Economy Development [27-28] X

Infrastructure

Targets

Energy System Structure [23, 27–29] Energy Generation Plant [27-28]

Supply Chain Development [27-28]

Physical &

Climate

Drivers

Land-Use Change [20, 22–25] Water Availability [21, 25] X Climate Change [18, 20-21, 25] - Flood Protection Land Requirements [18] X Nature Conservation Land Requirements [18, 21] - Soil Degradation [18, 21] X

Food Drivers

Per-capita Food Demand & Consumption [18-19, 21] Calorie Consumption [19] X Diet Change [19] X Agriculture Productivity Yields [18-19, 21-22]

Resource

Mobilisation

Technical

Drivers

Technological Advances [22–26, 29] Forest System Productivity [22–26, 29]

Industry & Process Residue Generation [22–26, 29]

Forestry Residues Collection [22–26, 29]

Resource

Demand

Drivers

Resource Use by Industry [18-19, 22, 24–26, 29] Demand for Round Wood [19, 22, 24-25, 29] Demand for Wood Fuel [22, 24–26, 29] Demand for Other Resources [19, 22, 24-25, 29]

Policy Drivers

Greenhouse Gas Emission Targets [23, 25, 27, 29] - Energy Efficiency & Consumption Targets [23–25, 27, 29] Renewable & Bioenergy Targets [23–25, 27, 29] Fuel Security Drivers [23, 27] Support Policies & Mechanisms [23–25, 27–29] X


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Key

The BRM allows the analysis of these drivers in terms of their influence on biomass resource availability and

bioenergy potential.

- The BRM allows the analysis of partial aspects of these drivers. Or can provide an indirect evaluation the drivers

influence on biomass resource availability and bioenergy potential.

X The BRM current design and outputs do not allow the analysis of these drivers.

From this list, a series of key supply chain drivers are identified from literature and form the basis of analysis

within the BRM and this research. The drivers analysed within this research therefore represent a non-

exhaustive reflection of all the drivers that may influence biomass resource within supply chains. These BRM

drivers are listed and categorised within Table 3, and their respective influences on biomass resource availability

are discussed below.

Table 3: Summary of Key Supply Chain Drivers Analysed within the BRM

Category Drivers

UK Development Drivers 1) Population Change

2) Changes in Built-Up Land Area

Food Production System Drivers

3) Crop & Agriculture Productivity

4) Food Waste Generation

5) Food Commodity Imports

6) Food Commodity Exports

7) Utilisation of Agricultural Wastes & Residues

Forestry & Wood-based Industry Drivers

8) Forestry Expansion & Productivity

9) Wood-based Industry Productivity

10) Imports of Forestry Product

11) Exports of Forestry Product

Biomass Residue & Waste Utilisation

Drivers

12) Utilisation of Forestry Residues

13) Utilisation of Industrial Residues

14) Utilisation of Arboriculture Arisings

15) Waste Generation Trends

16) Waste Management Strategies.

Biomass & Energy Crop Strategy Drivers 17) Land Dedicated for Energy Crop Growth

3.1 UK Development Drivers

3.1.1 Population Change Population growth is the fundamental influence for all long term outlooks relating to food and agriculture [30].

The expected large increases in global food demand 2030-2050 are based on forecasts of increasing population

[31]. Food and agricultural systems are closely linked to many biomass resource supply chains, therefore

population is a driver with likely influence on biomass availability.

Within the BRM population forecasts reflect the United Nations Population Division’s forecast variants for the

UK [32].

3.1.2 Built-Up Land Area

Urbanisation is a further driver that influences food and agriculture systems [33]. Changes in the extent of built-

up land area directly influences the potential availability of biomass through reducing the area of land that could

otherwise be dedicated for biomass production.

The BRM utilises forecasts of current and future built-up land areas for the UK, as developed within the

MOSUS Project (Modelling Opportunities & Limits For Restructuring Europe Towards Sustainability) [34].

3.2 Food Production System Drivers

3.2.1 Crop and Agriculture Productivity

The productivity of land and agricultural yields are important drivers that directly influence the production of

biomass. Where crop yields can be increased, agricultural land may be freed for growth of biomass and energy


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crops [9]. Also where biomass and energy crop yields can be enhanced, more resource can be produced from the

land available.

Improvements and variances in food and crop systems productivity results from the collective influence of a

range of manageable and external inputs. The UK has great strength in crop science, including increasing

understanding of responses to global climate change [35]. Mueller et al [36] suggest that the ‘yield gap’ - the

difference between attainable & actual yields, will continue to be reduced. Other forecasts suggest that yield

increases of 70% by 2050 are possible for most crops through improved nutrient management, irrigation and

productivity techniques [36-37].

Harberl et al [30] found that Western European yields could experience further mean increases of >16% from

CO2 fertilisation by 2050, resulting from climate change forces (>2% without CO2 fertilisation).

Although whilst the main northern hemisphere producers may experience favourable conditions from climate

change in the next 40 years, regions where rising food demand is most pronounced will likely see production

hindered. This may lead to a greater number of countries relying on fewer high latitude producers – increasing

vulnerability to extreme weather events in these regions [38].

Current and forecast crop and agricultural yields analysed within the BRM reflects those documented in a wide

range of studies and literature, including predicted climate change impacts [15-16, 38–44].

3.2.2 Food Waste Generation

Food waste influences the availability of biomass resource in multiple ways. Food waste itself is a plausible

resource for bioenergy generation pathways. At the same time food waste is a factor that reduces the supply

chain efficiency – the greater waste from the system, the more land is required to produce food commodity

quantities to meet demand.

Research estimates that 25-50% of food produced is wasted along the supply chain [45–47]. 50% of the UK’s

food waste comes from households, where at some point at least 60% of this waste could have been consumed

[48]. The European Commission is targeting a 50% reduction in food wastes by 2020 [49], and the UK

Government Office for Science suggests that halving food waste by 2050 may be equivalent to 25% of current

productivity [50-51].

These waste reduction targets are considered in the analysis, through the BRM utilising a series of forecasts

[39], [50–53] to quantify UK food waste trends.

3.2.3 Food Commodity Imports and Exports

Food commodity import and export trends are drivers that can influence biomass availability, as they contribute

towards determining the area of UK land that is required to produce the food quantities to meet demand. Any

land dedicated for food production is therefore unavailable for biomass or energy crop growth.

The majority of the UK’s imports come from the EU, with the Common Agricultural Policy and EU Directives

strongly influencing the shape of the UK food system [54]. The UK currently produces about half of the food it

consumes, and is ~60% ‘self-sufficient’ [55]. The UK Government’s stance is that it “sees no economic or

environmental rationale for Government to set targets to raise UK output of particular food products in step with

changes in global food demand” [54].

The analysis takes into consideration these stances of future food import/export trends, the BRM utilising data

from a series of studies [39, 52, 56] to reflect the UK’s path.

3.2.4 Utilisation of Agricultural Wastes and Residues

Agricultural wastes and residues reflect a resource category with sizeable potential for the bioenergy sector [10].

Welfle et al [9] found that this category of biomass resource could deliver up to 80 TWh of bioenergy by 2050.

The key drivers determining the availability of this resource for the bioenergy sector is the extent to which it is

harvested/collected and the competition for the resource. The BRM and analysis reflects the wide range of

research and studies that forecast the extent and timeframes to which these resources could be utilised for energy

generation: 20%-100% of total resource could be utilised, with typically half of this being available for the

energy sector [57–65]. The UK Department for Food & Rural Affairs (DEFRA) provide sustainability guidance

on the extent that agricultural residues should be returned to the soil to protect and enhance soil and biodiversity

(10% Lower Limit, 50% Higher Limit) [66].


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3.3 Forestry and Wood-based Industry Drivers

3.3.1 Forestry Expansion and Productivity

The extent and productivity of forestry systems directly influences the availability of resources for the bioenergy

sector. Forests provide energy generation opportunities either through specifically harvested resources, or via

the collection of residues. Forests also provide indirect opportunities for the bioenergy sector through supplying

resource to wood-based industries, that in turn produce wastes and residues that can be utilised by the bioenergy

sector.

The BRM utilises the UK Forestry Commission’s expansion and productivity forecasts [67–74].

3.3.2 Wood-based Industry Productivity

The on-going activities of wood-based industries produce wastes and residues that provide an opportunity for

the bioenergy sector. At the same time wood-based industries require raw forestry products, of which it

competes directly with the bioenergy sector for the lower grades of resource.

The BRM utilises existing data [56, 75, 76] and forecasts [76] that predict the trends and directions that UK

wood industries may take.

3.3.3 Imports and Exports of Forestry Product.

Forestry product import and export trends can influence the availability of biomass resource through

determining the extent that the indigenous forestry systems are utilised. Where imports are increased and

exports are reduced, there will be less strain on indigenous forestry systems to produce the wood resource

required to meet demand. This may in turn provide increased opportunities for the bioenergy sector. Likewise

reduced imports and increased exports would have the counter influence, putting greater strain on indigenous

forests.

The BRM again utilises existing data [56, 75-76] and forecasts [76] that predict the trends and directions that

UK forestry products imports/exports may follow.

3.4 Biomass Residue & Waste Utilisation Drivers

3.4.1 Utilisation of Forestry Residues

Forestry residues represent an opportunity for the bioenergy sector that is currently un-utilised in the UK [58].

The availability extent of this resource is dependent on the proportion extracted from forestry systems and the

proportions left in-situ to maintain the health of the habitat.

The BRM reflects the full range of residue extraction levels recommended by research and studies [29, 58, 77–

79], from 10% to as much as 100% by 2020 [58].

Forest certification standards set by the Forestry Stewardship Commission (FSC Criterion 5.3 & 6.3),

Ministerial Conference on the Protection of Forests in Europe (MCPFE Criterion 2 & 3) and Programme for the

Endorsement of Forest Certification (PEFC Criterion 4) all provide details for the minimisation of on-site

harvesting and residue processing, maintenance of ecosystem health and function and protection of biodiversity

[78]

3.4.2 Utilisation of Industrial Residues

Biomass residues from on-going industrial processes represent a potential opportunity for the bioenergy sector

[9]. The key drivers influencing the availability of this resource category are the extent to which it can be

collected/processed, and productivity of the UK wood-based industry.

The BRM utilises data that reflects current and forecast productivity trends for the UK’s wood-based industries

[56, 75-76], and also forecasts of potential industry residue utilisation for energy [58, 80-81].

3.4.3 Utilisation of Arboricultural Arisings

UK Local Authorities and tree surgeons produce thousands of tonnes of arboriculture arisings. The majority of

this is currently land-filled, stored for landscaping applications or burnt onsite. Although with correct

processing, handling, grading and storing, these residues provide an opportunity for the bioenergy sector [82].

The key drivers determining resource availability are the extent to which the resource is harvested/collected and

the competition for the resource.

The BRM utilises forecasts from a series of research and studies that forecast that up to 100% of arboriculture

arising could be utilised by the bioenergy sector [58, 76-77].


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3.4.4 Waste Generation Trends & Waste Management Strategies

The potential availability of waste resources for the bioenergy sector is influenced by two key drivers: The

amount of waste being generated, and the strategy implemented for how the waste is managed. Welfle et al [9]

found that there is both potentially high variability and availability of this resource, forecasts ranging from 1.8-

130.7Mt by 2050 dependent on the waste generation and management strategies.

The BRM utilises a series of data sets [48, 61, 83–86] that reflect the UK’s current waste system, and applies

DEFRA forecast scenarios [61, 85] to analyse how the implementation of alternative waste strategies may

influence potential availability for the bioenergy sector.

3.5 Biomass & Energy Crop Strategy Drivers

3.5.1 Land Dedicated for Energy Crop Growth

The area of land dedicated for biomass and energy crop growth is a fundamental driver in determining the

potential availability of grown resource. Energy crops have an important role to play in helping to achieve the

UK’s renewable energy targets [66, 87]. The UK Department for Energy & Climate Change (DECC) estimate

that for the UK to meet these targets, approximately 3,500 km2 of land needs to be dedicated for energy crops –

a large increase from the current 250km2 utilised. Although 3,500 km

2 seems large it currently reflects <2% of

UK agricultural land - an area that could be easily realised through farmers utilising un-used/marginal lands [66,

87].

A large number of reports and studies estimate that varying amounts of the UK’s >170,000 km2 of agriculture

land could be dedicated for biomass resource growth [88-89]. Potential land dedication estimates range from

3,500-10,000 km2 [15-16, 35, 59, 90–93], whilst the theoretical maximum available land for short rotation

coppices and Miscanthus without impacting food systems have been estimated to be between 9,300-36,300 km2

[66, 87].

The European Environment Agency (EEA) also reported that between 8,000-34,000 km2 of land could be

released in the UK by 2030 by reform of the Common Agricultural Policy [92]. Fischer et al [65] estimating that

half of this released land would be former grassland.

The BRM takes into consideration these estimates when determining the proportion of free land to be dedicated

for biomass resource growth.

4) Results – UK Biomass Availability & Supply Chain

Influences The following section provides the results, presented in the form of figures and supplementary tables. These

document the availability and bioenergy potential of different biomass resources to 2050. They also outline the

results of the sensitivity analysis that evaluates the extent that different supply chain drivers influence biomass

resource availability.

4.1 The Potential Availability of UK Biomass for the Bioenergy Sector

Figure 2 documents the results of the analysis undertaken to determine the maximum availability of each

category of UK biomass (Table 1), when the BRM is calibrated to reflect the literature informed baseline

scenario to 2050. The resources availabilities in the research are presented in million tonnes (Mt) of dry basis

biomass resource. This analysis reflects the range of forecast supply chain characteristics for each driver (Table

3) as informed by literature. The trends represented document the resource potential if the most influential

drivers are managed so that maximum levels of resource availability are achieved. Through highlighting the

range in resource availability between 2015-2050, Figure 2 also provides an indication of the extent of actions

that may be required to achieve the higher level forecasts.

UK ‘Grown Resources’ are shown to have relatively low availability in 2015 (>1.9Mt), but this

potentially increases by >1503% by 2050 (to >31Mt).

UK ‘Residue Resources’ in 2015 are shown to have availability of >11.7Mt, potentially increasing by

>152% by 2050 (to >29.7Mt).

UK ‘Waste Resources’ in 2015 are shown to have availability of >15.2Mt, potentially increasing by

>491% by 2050 (to >90.0Mt).


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Figure 2: The Potential Availability of Biomass Resources for the Bioenergy Sector

4.2 Analysing the Influence of Supply Chain Drivers on Biomass Resource Availability

Figures 3, 4 and 5 present radar graphs that document the results of the supply chain driver sensitivity analyses.

These show the extent that the different drivers influence resource availability, with each numbered spoke of the

radar graphs reflecting the corresponding analysis for each of the numbered supply chain drivers (Table 3). The

Figures highlight the maximum availability of the each respective category of biomass resource to 2050 when

the characteristics of each driver reflect the ranges informed by literature.

Figure 3 highlights that the key supply chain drivers influencing the availability of UK ‘Grown

Biomass Resources’ are ‘Population Change’ (Driver 1), ‘Crop & Agriculture Productivity’ (Driver 3),

‘Forestry Expansion & Productivity’ (Driver 8) and ‘Land Dedicated for Energy Crop Growth’ (Driver

17).

Figure 4 highlights that the key supply chain drivers influencing the availability of UK ‘Residue

Biomass Resources’ are ‘Population Change’ (Driver 1), ‘Utilisation of Agricultural Wastes &

Residues’ (Driver 7) and the ‘Forestry Expansion & Productivity’ (Driver 8).

Figure 5 highlights that the key supply chain drivers influencing the availability of UK ‘Waste Biomass

Resources’ are ‘Waste Generation Trends’ (Driver 15), and most notably by ‘Waste Management

Strategies’ (Driver 16).

Figure 3: Analysis of Drivers Influencing the Availability of Grown Biomass Resources (Mt)


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Figure 4: Analysis of Drivers Influencing the Availability of Residue Biomass Resources (Mt)

Figure 5: Analysis of Drivers Influencing the Availability of Waste Biomass Resources (Mt)

4.3 UK Resources Demonstrating the Greatest Potential for the Bioenergy Sector

Further analysis was carried out to determine which specific biomass resources may demonstrate the greatest

potential for the bioenergy sector. The data from this analysis is documented within Appendix A1. This

highlights the availability and bioenergy potential of different biomass resources, when supply chain

characteristics reflect the literature informed baseline scenario to 2050. Further details describing the

methodology for calculating bioenergy potential, including the applied conversion and pre-treatment pathways

and efficiencies can be found in Welfle et al [9, 17]. From this analysis the following UK resources are shown to

demonstrate particular availability for the bioenergy sector:

UK ‘Biomass & Energy Crops’ from the Grown Resources Category (>31.2Mt resource, equivalent to

>104 TWh by 2050),

UK ‘Agricultural Residues’ from the Residue Resources Category (>26.2Mt resource, equivalent to

>83 TWh by 2050),

UK ‘Household Wastes’ from the Waste Resources Category (>40.7Mt resource, equivalent to >117

TWh by 2050),

UK ‘Other Wastes’ from the Waste Resources Category (>32.7Mt resource, equivalent to >75 TWh by

2050).

4.4 Supply Chain Drivers with the Greatest Influence on Biomass Resource Availability

This section provides further discussion of the results of the sensitivity analysis. Evaluating the extent that the

different drivers (Table 2) influence the biomass resources found to demonstrate the greatest potential

availability in the UK: ‘Biomass & Energy Crops’, ‘Agricultural Residues’ and ‘Household Wastes’. ‘Other

Wastes’ are excluded from this further analysis, as this resource category represents a collection of all other

wastes that are not classified as either ‘Household’ or ‘Food or Organic’ (Table 1). The data reflecting this

analysis is included in Appendix A2.

4.4.1 Drivers Influencing the Availability of UK Biomass & Energy Crop Resources

Three drivers are shown to have significant influence in determining the availability of Biomass & Energy Crop

resources. ‘Population Change’ (Driver 1) and ‘Crop & Agricultural Productivity’ (Driver 3) demonstrate


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marginal influence in determining the potential availability of this resource. However the ‘Land Dedicated for

Energy Crop Growth’ (Driver 17) is shown to be the key influence. The results shows that if the upper limits of

land are made available for biomass and energy crop growth (Driver 17) as forecast by literature, the availability

of this resource may be >87% greater in 2050 compared to scenarios where lower limits of land are utilised.

4.4.2 Drivers Influencing the Availability of UK Agricultural Residue Resources

The availability of Agricultural Residue resources is demonstrated to be influenced by ‘Population Change’

(Driver 1) and the ‘Utilisation of Agricultural Residues’ (Driver 7). The analysis shows that realisation of higher

population forecasts (Driver 1) may potentially increase the availability of this resource in 2050 by >12.6%.

Whilst realising upper limits of agricultural residue collection/harvests and utilisation (Driver 7) as forecast by

literature, may result in >11.6% greater resource availability by 2050.

4.4.3 Drivers Influencing the Availability of UK Household Waste Resources

The potential availability of Household Waste resources for the bioenergy sector is demonstrated to be

influenced by both ‘Waste Generation Trends’ (Driver 15) and ‘Waste Management Strategies’ (Driver 16).

Forecast trends of waste generation (Driver 15) are shown to have a minor influence on the availability of this

resource. In contrast the results confirm that the waste management strategy adopted (Driver 16) represents a

major influence. A waste management strategy complementing the bioenergy sector as forecast by literature

may increase the availability of this resource: >318% by 2020, >476% by 2030 and >500% by 2050, compared

to forecasts where waste is less utilised by the bioenergy sector.

5) Discussion - Maximising the Potential of UK Biomass This next section provides a discussion of the results highlighted within Section 4. Identifying which of the

UK’s indigenous biomass resources may provide the best opportunities for the bioenergy sector, and how these

relate to current UK policy.

5.1 The Potential of UK Biomass Resources for the Bioenergy Sector to 2050

For UK resources to substantially contribute towards meeting bioenergy targets, it is important to highlight

which of the broad range of resources may provide the greatest potential and opportunities for the bioenergy

sector.

The results presented within Figure 2 represent three levels of analysis: the maximum potential availability of

different categories of UK biomass; the extent that each resource category may be available in the near-term (by

2015); and also the range in potential resource increment between 2015 and 2050. The maximum availability

potential is important, as it identifies how much resource could be mobilised for the bioenergy sector if

influencing supply chain drivers are effectively managed. The near-term forecast and 2015-2050 increment

ranges are important as they provide an insight into how much resource will be available without extensive

further actions and management of drivers, and likewise provide an indication of the effort that may be required

to achieve the higher levels of forecast availability.

Using this premise to evaluate the result for the three analysed biomass categories: Figure 2 shows that UK

‘Grown Biomass Resources’ are forecast to have relatively low near-term availability, but large potential by

2050. This suggests that these resources may be highly influenced by supply chain drivers, and substantial effort

may be required to manage these in order to increase the resource availability from the low base. The research

highlights that potentially UK grown biomass and energy crops represents the greatest resource opportunities for

the bioenergy sector. Figure 3 confirms that the availability of land dedicated for the growth of these resources

is the key driver requiring appropriate management if higher levels of resource availability are to be realised.

The research highlights that UK agricultural residues represent large resource opportunities for the bioenergy

sector. Figure 2 demonstrates that UK’s ‘Residue Biomass Resources’ are shown to have ‘medium’ near-term

availability compared to the other two resource categories. This increases at a steady rate to 2050 suggesting

that residue resources are relatively robust to supply chain influences and less effort may be required to increase

residue availability in comparison to the other resources categories. The relatively continuous increment in

resource availability demonstrated by the spacing of the analysis time-lines within Figure 4 also highlights that

the availability of residue resources shows robustness to supply chain influences.

Household wastes are also found to represent large resource opportunities for the bioenergy sector. Figure 2

shows that UK ‘Waste Biomass Resources’ have near-term availability that exceeds the other two categories and

the potential maximum increase in waste resource availability to 2050 is significant. This large increment

suggests that waste resources are highly susceptible to supply chain influences, and significant effort may be

required to manage these if the higher forecasts of resource availability are to be realised. This is reaffirmed


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within Figure 5 where the influence of implemented waste management strategies is shown to be key. This

research therefore highlights that in the long-term, wastes may represent resource options with significant

potential for the bioenergy sector, albeit reliant on the implementation of complementary waste management

strategies.

5.2 Increasing the Focus of Bioenergy Strategies

The UK Bioenergy Strategy aims to maximise the opportunities for improving the availability of all biomass

resources through policies aimed at managing a broad range of supply chain drivers [87].

This research has analysed a wide range of supply chain drivers, finding large variances in their influence in

determining biomass availability for the bioenergy sector. The research also highlights that particular resources

demonstrate significantly greater availability and bioenergy potential than others. Therefore if the contribution

of UK resources is to be maximised, the research suggests that bioenergy policies and strategies should become

increasingly focused and targeted.

Table 4 summarises the research findings: ranking the UK’s biomass resources based on their availability and

bioenergy potential; also ranking the analysed supply chain drivers based on their influence in increasing UK

biomass availability for the bioenergy sector.

Table 4: Analysis Summary Ranking UK Biomass Availability, Bioenergy Potential & Supply Chain Influences

Ranking Influencing Drivers Resource Availability & Bioenergy Potential

for Bioenergy Sector

High Ranking


the Greatest

Influence/Potential

Waste Management Strategies

Land Dedicated for Energy Crop

Growth


Household Wastes

Biomass & Energy Crops

Other Wastes

Medium Ranking


Medium Influence/Potential


Population Change

Changes in Built-Up Land Area

Food Waste Generation

Utilisation of Agricultural Wastes & Residues

Forestry Expansion & Productivity

Waste Generation Trends

Dedicated Forestry Resources

Forestry Residues

Food & Organic Wastes

Low Ranking


the Least

Influence/Potential

Food Commodity Imports


Wood-based Industry Productivity

Imports of Forestry Product


Utilisation of Forestry Residues

Utilisation of Industrial Residues

Utilisation of Arboriculture Arisings

Sewage Wastes

Industry Residues

Arboricultural Residues

5.3 Potential Strategies for Increasing UK Resource Availability for the Bioenergy Sector

The following section discusses the current UK context, barriers and potential strategies for increasing the

availability of the UK’s resources in the context of the research findings.

5.3.1 Strategies for Increasing the Availability of UK Resources Grown for the Bioenergy Sector

Research Outputs

The research identifies ‘Crop and Agricultural Productivity’ and the area of ‘Land Dedicated for Energy Crop

Growth’ as the drivers that most significantly influence the availability of UK grown biomass resources such as

energy crops.

The influence of realising higher limits of crop and agricultural productivity is shown to potentially increase the

availability of this resource by >30% by 2050. This is an unsurprising trend, as greater crop yields will also

benefit the production of crops dedicated for the energy sector. Although the standout driver with key influence

on this resource is utilisation of available land dedicated for growth. Realising maximum levels of available land

utilisation demonstrates a potential >87% improvement in resource availability in 2050, compared to conditions


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with reduced land-use. Highlighting that if the UK wants to increase its biomass and energy crop resource,

focusing on anything other than increasing land availability is unlikely to deliver the same scale of results.

Current UK Policy & Strategy Context

The UK Bioenergy Strategy [87] states that the increased growth of resources on unused or low ecosystem value

lands is essential for producing resources for the bioenergy sector. Although the area of available land dedicated

to grow these resources is essentially reliant on UK farmers utilising their lands to grow crops for the energy

sector rather than foods. To promote this the UK’s primary incentive mechanism to promote farmers to grow

biomass and energy crops has been the ‘Energy Crops Scheme’. Although over the lifetime of the scheme

widespread dedication of lands to grow biomass and energy crops has not materialised [94]. A summary of key

barriers preventing land owner from producing resources for the bioenergy sector are presented in Table 5.

Pathways for Increasing Resource Availability

The demand for biomass and energy crops is growing fast [95], whilst their production offers environmental and

economic benefits much wider than for just the energy sector [96]. Thus developing a policy framework and

financial packages especially with respect to the Renewable Heat Incentive, Feed-in-Tariffs and a reworked

Energy Crop Scheme are essential to reduce barriers and allow markets to drive progress [91].

The UK’s already has good comparative examples of policies and incentives in the form of the Forestry

Commission’s ‘Woodfuel Strategy’ [97], where a roadmap and framework of targets backed by incentives are

increasing the availability and use of woodfuels. There are also many examples of leading incentive schemes

currently being applied across the EU to promote the bioenergy sector and incentivise the growth of resources

[98]. These provide insights into further potential directions that the Government could go in developing UK

policies.

5.3.2 Strategies for Increasing the Utilisation of Agricultural Residues by the Bioenergy Sector

Research Outputs

‘Population Change’ and the ‘Utilisation of Agricultural Residues’ are the two drivers identified by the research

as providing the greatest influencing the availability of this resource. These linkages appear to be self-evident,

higher levels of population growth means that more food will need to be produced, resulting in greater

availability of agricultural residues. At the same time the greater extent that agricultural residues are

collected/harvested, the greater availability for the bioenergy sector.

However the more valuable analysis highlighted by Figure 2 and also reflected within Figure 4, is the near-term

and continuous availability of agricultural residues - shown to be relatively constant and robust to major

fluctuations caused by supply chain influences. The resource availability in 2015 is also forecast to exceed

10.3Mt and steadily increase by >109% by 2050. Based on this analysis, agricultural residues should be

highlighted and targeted within bioenergy strategies as reliable and robust opportunities for the bioenergy sector.

Current UK Policy & Strategy Policy Context

There are currently comparatively low levels of agricultural residues utilisation by the bioenergy sector in the

UK. This trend is reflected in UK farming statistics [99] documenting that: <5% of the UK’s livestock focused

farms generate renewable energy, and of these <50% utilise manures and slurry feedstocks. Whilst <6% of

arable agriculture focused farms generate renewable energy, and of these <45% utilise feedstocks such as

straws. The UK Bioenergy Strategy [87] recognises the need to work to improve the economics of respective

supply chains and bioenergy pathways, although many barriers remain as summarised in Table 5.

Potential Mechanisms for Increasing Resource Availability

There are many case studies that the UK could consider where agricultural residues are widely utilised by the

bioenergy sector. Within Europe, Denmark represents the leading example of straw residue utilisation.

Denmark’s established harvesting infrastructure and market development is the consequence of targeted policy

driven initiatives, such as: mandates requiring that higher prices are paid for energy from straws; collaborations

between the bioenergy sector, individual farmer and specialised contractors enabling the shared utilisation of

high specification harvesting and processing equipment; and a market structure that provides farmers with

enhanced controls over their pricing demands, and standard contracts between produces and generators

regardless of resource scale [100].

The utilisation of slurries and manures within anaerobic digestion bioenergy systems from large scale farms or

localised farming cooperatives, represents key opportunities for the UK bioenergy sector. Raising awareness

[101] and financial support [102] for these systems is key. The UK has an array of existing financial

mechanisms and incentives [103-104] designed to promote this sector, although it remains highly undeveloped


489

[105]. Again the UK could learn from successful policy case studies from across the EU, such as the German

Renewable Energy Act [102] and related policies [105-106] that reduce the financial barriers of AD

development schemes through directing increased financial responsibilities onto grid operators.

The European Common Agricultural Policy (CAP) is also widely identified as potential mechanism for

increasing the utilisation of agricultural residues by the bioenergy sector. Potential reform areas being: further

guidance of the quantities of resources to be returned to soils; initiatives to support residue supply chains; and

the broadening of existing institutional and local partnerships to support the bioenergy sector [107].

5.3.3 Strategies for Increasing the Utilisation of Household Waste Resources by the Bioenergy Sector Research

Outputs

The research finds that ‘Waste Generation Trends’ and ‘Waste Management Strategies’ are the key drivers

determining the availability of household wastes. The analysis highlights that implementation of a waste

management strategy that focuses on energy from waste pathways could provide over 40Mt of household waste

resource for the bioenergy sector by 2050. Household wastes therefore representing a substantial opportunity for

the bioenergy sector, albeit highly reliant on the development of complementary waste management strategies.

Current UK Policy & Strategy Policy Context

Energy from waste in the UK has historically had a poor image with landfill distribution and early incinerators

favoured. However the introduction of landfill diversion targets and the development of new technologies have

placed energy from waste back on the UK’s agenda. Although the prime focus of the UK’s waste management

strategies is to reduce and recycle, efficient energy recovery remains an important element of the strategy to

both generate energy and reduce land-filled waste volumes [108]. A summary of key barriers preventing the

wider utilisation of wastes and growth of the sector are presented in Table 5.

Potential Mechanisms for Increasing Resource Availability

The UK’s scope for developing waste management strategies is highly restricted and defined by EU Directives

[109]. However when adapting applicable EU Directives into national laws there is room for manoeuvre, with

the definitions of wastes in the context of bioenergy being a key variable differing between Member States.

Adjusting these categorisation parameters allows varying subsidisation and favourability of energy from waste

pathways [109]. Reviewing these key policy variances between Member States presents a series of case studies

for the UK to potentially consider if aiming to support the energy from waste sector.

With respect to addressing the large barriers associated with the social opposition to energy from waste

technologies, the UK could draw influence from scenarios around the world and specifically other EU Member

States, where public opinions are far less hostile. A review undertaken by WMW (2014) [110] found that:

educating local populations of benefits, linking arguments to climate change, reassuring communities of air

pollutant regulations and providing direct local energy benefits such as cheap district heat can vastly soften

opposition. Also being mindful in planning processes that the voices of minority groups opposing energy from

waste plant, often overshadow the opinions of the majority [111].


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Table 5: Key Barriers to the Greater Production & Utilisation of UK Resources by the Bioenergy Sector

Biomass

Resources Barriers to Increasing Resource Availability for the Bioenergy Sector

Grown

Biomass &

Energy

Crops

[91, 94, 112]

Educational – awareness to incentive schemes, reluctance to move away from producing

traditional agricultural crops, and poor understanding of energy crop establishment and

management best practice.

Economic – cash flow problems between planting and harvests, current margins associated with

small scale productions, and the lack of links between biomass producers and markets.

Legislative – lack of recognition of certain ‘innovative’ crops by inventive schemes.

Technical – specific fuel requirements of bioenergy systems place increased demands on

resources produced, and lack of processing infrastructure that would increase the economic

viabilities.

Plant Based

Agricultural

Residues

(straws)

[100]

Underdeveloped Markets – the lack of established supply chains for straw for bioenergy

purposes.

Competing Uses – straws are extensively used by existing markets with which the bioenergy

sector will compete for resource.

Inaccurate Guidance – overuse of the resource beyond best practice to maintain soil health can

lead to large unnecessary impacts on resource availability.

Undeveloped Infrastructure – the inaccessibility and lack of appropriate machinery and

infrastructure for the handling and processing of straw residues.

Resource Variability – due to varying climatic conditions and fluctuating harvest yields, the

variability in the quantity and quality of straws has large implications for the bioenergy sector

that typically requires specific fuel specifications.

Animal

Based

Agricultural

Residues

(slurries &

manures)

[101, 113]

Transportation – The nature and bioenergy characteristics of slurries and manures makes them

impractical, uneconomical and energy inefficient to be transported any great distance.

Resource Availability – as a result of UK farming practices, slurry and manure can only

typically by collected for a limited number of months, reflecting livestock housing regimes.

Spatial Constraint – anaerobic digestion (AD) systems, the most suitable bioenergy systems for

the use of manure and slurry resources require physical space. The economics of AD systems are

also largely improved through the addition of energy crops feedstocks, which may require

additional (potentially large) planting areas that are typically incompatible with the nature of the

farms with the large animal based biomass resources.

Capital Costs – the capital costs of digesters and associated infrastructure is high and are

unlikely to fall significantly in the near-term.

Collaboration Complexity – The time and costs associated with developing large community

or district systems that pool resources from a number of local sites can be highly complex and

costly.

Waste

Resources

[112, 114,

115]

Incentive – the cost comparison of energy from waste systems compared to landfill represents a

strong barrier against the further development of this sector.

Waste Hierarchy – the supply of the specific waste feedstocks required by bioenergy systems is

restricted by the waste hierarchy and the UK’s waste policies primary focus to reduce and

recycle.

Opposition – social opposition led by local communities and the lobbying of environmental

action groups are by far the greatest barrier to the development of the UK energy from waste

sector.

Finances – the varying definitions of biomass wastes and their respective subsidy regimes can

prevent developers from accessing the finances required to grow the sector.

6) Conclusions A Biomass Resource Model (BRM) was developed reflecting the UK’s indigenous biomass supply chains. The

drivers controlling the BRM were calibrated to 2050 to analyse current and forecast parameters in reflection of a

wide literature review. The analysis focused on the development of a baseline scenario to determine the specific

indigenous biomass resources that demonstrate the greatest potential for the UK bioenergy sector. Systematic

analysis of the BRM’s drivers allowed the evaluation of the extent that they influence indigenous resource

availability to 2050. Key policy conclusions for increasing the availability of UK indigenous resource for the

bioenergy sector are highlighted below.


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Biomass and Energy Crops, Agricultural Residues and Household Wastes - are identified as the

biomass resources that demonstrate the greatest promise for the UK bioenergy sector, in terms of their

availability quantity and bioenergy potential.

Potential and Mobilisation of Grown Biomass Resource – UK grown biomass and energy crop

resources have been identified as potentially providing >31Mt for the bioenergy sector by 2050. The

standout driver influencing the availability of these resources was identified as the uptake of available

land dedicated for its growth. However the analysis also highlighted that this resource currently has a

relatively low starting base, with >1.9Mt forecast by 2015. Therefore concerted efforts will be required

in managing the drivers that influence availability, if anywhere near the upper levels of resource

forecasts are to be realised. These should include the implementation of policies that

encourage/incentivise the utilisation of available land for the growth of resource dedicated for the

bioenergy sector.

Potential and Mobilisation of Biomass Residue Resource – Residue biomass resources were identified

as potentially providing up to >29.8Mt of resource for the bioenergy sector by 2050. Agricultural

residues (straws & slurries) make up the majority of this quantity, whilst also continuing to be utilised

to maintain soil systems. The availability of residues was forecast to steadily increase and be

comparatively robust to supply chain influences. Biomass residues therefore representing a potentially

continuous and reliable near and long-term indigenous resource option for the bioenergy sector.

Potential and Mobilisation of Biomass Waste Resource – Waste biomass in the UK was identified as

potentially providing up to >89Mt of resource for the bioenergy sector by 2050. Household wastes

being the largest waste contributor. Wastes were found to be highly influenced by one key driver, the

waste management system adopted. The availability of waste resources was found to be much

diminished when the adopted waste management strategy was uncomplimentary to the bioenergy

sector. Therefore if wastes are to be increasingly utilised by the bioenergy sector, the analysis confirms

the importance of implementing policies for effective development of waste management strategies.

Refocusing Bioenergy Strategies to Increase the Availability of Indigenous Resources - The paper

highlights the importance of applying a targeted approach for increasing the potential of indigenous

resources. This is contrary to the broad policy focus approach currently being implemented in the UK.

The analysis has identified that there are multiple biomass resource opportunities in the UK, but

realisation of the upper levels of resource availability forecasts is highly dependent on the

implementation of effective policies that target and manage the specific supply chain drivers most

influential for each respective biomass resources.


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Appendix 16.0

Appendix 16.0 includes copies of the Poster Presentations delivered during the PhD Programme. The titles and

details of the relevant events for each presentation are introduced below:

Appendix A16.1

Event Tyndall Centre PhD Conference 2012

Title ‘Knowledge Gaps’

Date 11th

-13th

April 2012

Location University of East Anglia

Presentation Title ‘Modelling the UK’s Indigenous Biomass Resource Potential’


Appendix A16.2

Event Tyndall Centre PhD Conference 2013

Title ‘Climate Transitions’

Date 03rd

-05th

April 2013

Location University of Cardiff

Presentation Title ‘UK Biomass Resource Scenarios’


Appendix A16.3

Event International Bioenergy Conference 2014

Date 11th

-13th

March 2013

Location Manchester Central Convention Complex

Presentation Title ‘Refocusing Biomass Strategies to Maximise Indigenous Resource Potential’



493

Appendix A16.1: Modelling the UK’s Indigenous Biomass Resource Potential


494

Appendix A16.2: UK Biomass Resource Scenarios


495

Appendix A16.3: Refocusing Biomass Strategies to Maximise Indigenous Resource Potential

Appendix 17.0


496

Appendix 17.0 includes summaries of the oral presentations delivered, notable events attended and other notable

activities completed during the PhD Programme.

Appendix A17.1 – Oral Presentations

11-13th

March

2013

International

Bioenergy

Conference 2014

Manchester Central

Convention

Complex

‘Refocusing Biomass

Strategies to Maximise

Indigenous Resource

Potential’

Primary

Presenter

3-7th

June

2013

European Biomass

Conference 2013

Bella Centre,

Copenhagen

‘Meeting Bioenergy Targets

with Reduced Imports’

Primary

Presenter

16-18th

April

2013

Sustainability Live

Conference 2013 NEC, Birmingham

‘Availability &

Characteristics of the Future

UK Biomass Resource’

Contributed

Sections to

Presentation

12th

March

2013

UK Department for

Energy & Climate

Change

Whitehall London ‘Introducing the Biomass

Resource Model’

Primary

Presenter

21st September

2011

IMechE Warming

to Biomass

Conference 2011

London

‘Availability & Sustainability

of Biomass for Heating in the

UK’

Contributed

Sections to

Presentation

Appendix A17.2 – Notable Events Attended

19-24th

January 2014

Global Young

Scientists Summit

2014

Nanyang

University,

Singapore

‘Excite, Engage Enable’ Attended

10-13th

September

2013

Tyndall Centre

Annual Assembly

2013

University of East

Anglia, Norwich

‘Climate Change Scales of

Action’ Attended

11-14th

September

2012

Tyndall Centre

Annual Assembly

2012

Cardiff University ‘Behaviour Change &

Innovation’ Attended

23rd

November

2011

UK Energy

Research Council

Technology &

Policy Assessment

Report Launch

The Royal Society,

London

‘Energy from Biomass: the

Size of the Global Resource’ Attended





497

Appendix A17.3 – Notable Activities

20th

February

2014

BBC Radio Manchester Live

Interview

Live interview on BBC Radio Manchester discussing the

content of the first journal paper published from this PhD

Research ‘Securing a Bioenergy Future Without Imports’

08th

March

2013

Contribution to DECC’s

Carbon Assessment of

Biomass Feedstock Workshop

Attended DECC’s workshop and contributed to the

consultation process for the development of their Biomass

Carbon Calculator Tool.

January 2013

Consultancy work for DECC,

validating their Biomass

Carbon Calculator Tool

Carried out a validation exercise checking and advising the

development of DECC’s Biomass Carbon Calculator Tool.

2012-Present

Student Representative on the

University of Manchester’s

Carbon Leadership Group

Contributed and held the Carbon Leadership Group to

account. The body responsible for accounting the

University of Manchester’s carbon emissions, and

developing strategy to meet carbon emission reduction

targets.

2011-2013

Postgraduate President of the

University of Manchester’s

Student Sustainability Forum

The Students Sustainability Forum provided the link

between the University and the student population for all

sustainability and climate change issues. This role

involved organising and running open meetings and

presentations with the University of Manchester

management team, academics, Students Union and the

student population.

October 2011

Knowledge Gathering at the

European Bioenergy Research

Institute, Aston University

Spent a week within the labs at the European Bioenergy

Research Institute. This placement was carried out to attain

knowledge of biomass feedstock characterisation, and

conversion pathways. Also to build relationships with the

Aston University Supergen members and gain feedback on

early PhD research directions.