N-ETS 2020* Ireland’s Non-Traded Sector Target

An analysis of abatement potentials and costs in respect of Ireland’s 2020 Non-Traded Sector Target using the GAINS Ireland modelling system

Summer, 2011

*Extended Version

N-ETS 2020* Ireland’s Non-Traded Sector Target

*Extended Version

An analysis of abatement potentials and costs in respect of Ireland’s 2020 Non-Traded Sector Target using the GAINS Ireland modelling system

Summer, 2011

AP EnvEcon IMP Ireland Team

Dr Andrew Kelly Dr Luke Redmond Dr Fearghal King

UCD IMP Ireland Team

Dr Miao Fu

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Acknowledgements

This piece has been compiled by AP EnvEcon as part of the IMP Ireland project. The IMP Ireland project

is funded by the Environmental Protection Agency with co-funding from AP EnvEcon. The Environmental

Protection Agency funding is provided as part of the Science, Technology, Research and Innovation for

the Environment (STRIVE) Programme 2007-2013. The programme is financed by the Irish Government

under the National Development Plan 2007-2013. It is administered on behalf of the Department of the

Environment, Heritage and Local Government by the Environmental Protection Agency which has the

statutory function of co-ordinating and promoting environmental research. The authors are extremely

grateful to the Environmental Protection Agency and the Department of Environment, Heritage and Local

Government for their support, without which this work would not be possible.

In addition, the authors are extremely grateful to a number of individuals and their organisations for their

input and thoughts in regard to the development of this piece. At IIASA, the team would like to thank

Fabian for his support in setting up the optimisations, Lena for support on CH4, Wilfried on N2O, Jens on

transport and Zig on agriculture generally. Also thanks go to Janusz on energy calibration queries, and

Robert for all the technical support in adjusting the GAINS Ireland system in new ways. In Ireland, the

team would like to thank Frank McGovern and Gemma O’ Reilly of the EPA for their thoughts on the early

development of the piece. With particular thanks for technical input to Martin Howley of SEAI, Matt

Clancy and Jim Scheer of SEAI’s energy modelling group, Liam Kinsella of DAFF and both Bernard Hyde

and Stephan Leinert of the EPA for their many replies and valuable insights. This report advances the

GAINS Ireland work in the context of climate policy analysis considerably. The exercise has also identified

many parameters and components where further research or evidence is required to facilitate or validate

national measurements and thereafter to enhance national representation in the GAINS Ireland system.

Views expressed are those of the authors alone.

Table of Contents

Executive Summary ................................................................................................................. 4

1. Introduction ............................................................................................................................. 6

Methodology and Report Overview ......................................................................................... 8

2. The NETS Challenge ............................................................................................................ 11

The Traded Sector (ETS) and Non-Traded Sector (NETS) ..................................................... 11

Ireland’s NETS Target ............................................................................................................. 12

3. The GAINS Model Framework ............................................................................................ 15

Overview of GAINS framework ............................................................................................... 15

Emissions in GAINS ................................................................................................................ 16

Abatement options in GAINS .................................................................................................. 18

Cost in GAINS ........................................................................................................................ 20

ETS and NETS Sectors in GAINS ........................................................................................... 24

4. The GAINS Model Setup for this Analysis .......................................................................... 25

Baseline Scenario ................................................................................................................... 26

Sector: Agriculture ................................................................................................................. 28

Sector: Commercial, Residential, Heat .................................................................................. 29

Sector: Waste.......................................................................................................................... 32

Sector: Process ....................................................................................................................... 33

Sector: Transport ................................................................................................................... 33

5. Results .................................................................................................................................... 35

Sensitivity ............................................................................................................................... 40

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6. Note on Additional Mitigation Options and LULUCF ........................................................ 40

Additional technical potential of the menu of measures ........................................................ 41

Non-technical and behavioural policy measures ................................................................... 42

LULUCF – Carbon Sinks in NETS process ............................................................................ 43

7. Conclusions ............................................................................................................................ 46

8. References and Bibliography .............................................................................................. 49

9. Appendix – Marginal Abatement Cost Measures ............................................................... 53

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Acknowledgements

This piece has been compiled by AP EnvEcon as part of the IMP Ireland project. The IMP Ireland project

is funded by the Environmental Protection Agency with co-funding from AP EnvEcon. The Environmental

Protection Agency funding is provided as part of the Science, Technology, Research and Innovation for

the Environment (STRIVE) Programme 2007-2013. The programme is financed by the Irish Government

under the National Development Plan 2007-2013. It is administered on behalf of the Department of the

Environment, Heritage and Local Government by the Environmental Protection Agency which has the

statutory function of co-ordinating and promoting environmental research. The authors are extremely

grateful to the Environmental Protection Agency and the Department of Environment, Heritage and Local

Government for their support, without which this work would not be possible.

In addition, the authors are extremely grateful to a number of individuals and their organisations for their

input and thoughts in regard to the development of this piece. At IIASA, the team would like to thank

Fabian for his support in setting up the optimisations, Lena for support on CH4, Wilfried on N2O, Jens on

transport and Zig on agriculture generally. Also thanks go to Janusz on energy calibration queries, and

Robert for all the technical support in adjusting the GAINS Ireland system in new ways. In Ireland, the

team would like to thank Frank McGovern and Gemma O’ Reilly of the EPA for their input on the

development of the piece. With particular thanks for technical input to Martin Howley of SEAI, Matt

Clancy and Jim Scheer of SEAI’s energy modelling group, Liam Kinsella of DAFF and both Bernard Hyde

and Stephan Leinert of the EPA for their many replies and valuable insights. This report advances the

GAINS Ireland work in the context of climate policy analysis considerably. The exercise has also identified

parameters and components where further research or evidence is required to facilitate or validate

national measurements and thereafter to enhance national representation in the GAINS Ireland system.

Views expressed are those of the authors alone.

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Executive Summary

This report presents an analysis of Ireland’s Non-ETS (NETS) target challenge. The original NETS targets

were principally established on the basis of ability to pay in 2005, specifically considering the GDP per

capita of a member state as an indicator of the capacity to invest in further abatement and the likely rate

of future growth. The recent international economic turmoil has altered the backdrop to the NETS target

significantly. In particular the Irish economic outlook in 2011 is far removed from the expectations held

just a few years ago. On the environmental front, the latest official inventory figures from the

Environmental Protection Agency in 2011 confirm notable falls in emission levels. The continuance of

various policy measures and initiatives has certainly contributed to these achievements. However, the

persistent global economic issues and Ireland’s position to the fore of the ongoing European crisis have

played a major role in reducing economic activity, stalling growth and thereby curtailing emission levels

in Ireland. This ‘silver lining’ to the crisis is however set against a further cloud in the sense that

investment and finance is constrained and the need to grow business, economic activity and create jobs

will remain the priority at national and individual levels. This is not to suggest that core objectives of

economic growth, energy efficiency and increased abatement may not be achieved in parallel. However,

the case for efficiency and proposed abatement policies and actions, particularly in regard to cost, must be

made clear with the means of action facilitated in terms of finance, access and information.

Evidence is required now that informs the choice of abatement options and identifies pathways towards

compliance. In presenting this information we must also reconcile the cost of investments and actions

with the combined savings from efficiency, abatement and contribution towards compliance with

environmental objectives. This report engages the GAINS Ireland model to evaluate a pathway to NETS

compliance in 2020. The official 2011 ‘with measures’ energy scenario has been paired with official

agricultural forecasts of 2011 to provide a baseline activity forecast in the GAINS Ireland model. The

principal calibration challenge in this process lay in configuring the ‘menu’ of abatement options in the

model prior to analysis. This involved adjusting the existing, expected and possible abatement measures

in the modelling system so as to reflect what is, and what else might be done in Ireland and at what cost.

The abatement menu calibration is a blend of own in-house estimations, official national estimations, and

internationally defined estimates for measures. The type of measure captured is broad but not exhaustive,

and it is important to acknowledge that non-technical or behavioural measures (e.g. carbon tax) generally

remain exogenous to the model. The outcomes of such policies can be integrated, but the measures

themselves are not included formally in the abatement menu due to the complexity associated with

determining their impact, feasibility and cost for the optimisation process used in this analysis.

The primary outcome of the analysis suggests that from the ‘With Measures’ (WM) baseline starting point

and excluding LULUCF, the NETS target of 37.4M tonnes of CO2e in 2020 cannot be met via the defined

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menu of options where measures up to a marginal cost limit of €502005 per tonne of CO2e are taken. Nor

can it be met where all of the non-exclusive menu options1 are taken, at marginal costs up to €2252005 per

tonne of CO2e. The results show that the €50 cap on marginal abatement cost delivers NETS emissions of

42.2M tonnes, whereas the unrestricted marginal cost cap scenario delivers a NETS emissions outcome of

40.4M tonnes. The inability of the optimisations to achieve the target in 2020 also signals additional

concern for the interim targets from 2013 to 2020 which are not analysed in this report. As part of

extended assessments it was found that only where we substitute the national ‘With Additional Measures’

(WAM) scenario into the model setup and allow a marginal abatement cost of €200 per tonne of CO2e can

we achieve the 37.4M tonnes target.

However, whilst the headline results are not particularly encouraging, there are four key conclusions from

this report in respect of Ireland’s efforts to address this challenge. Firstly, the analyses do not represent all

that can be done. The model excludes certain policy interventions (e.g. revisions to the carbon tax) that

could also contribute to progress on the target, and there remains additional extension and calibration of

the abatement menu to be conducted over time. Furthermore, there is certainly untapped potential in the

transport sector that has not been adequately captured in this first calibration of the model. Secondly,

whilst the target is not met under the WM analysis, the cost assessment from a social planner perspective

indicates no net annual cost, due to cumulative cost savings on certain measures, where the package of

measures up to a marginal cost of €150 per tonne are taken. This is encouraging, but highlights that whilst

social cost analyses indicate worthwhile actions, barriers such as information asymmetry and financing

persist from a private investment perspective that require innovative solutions. Thirdly, whilst the WM

optimisations fail to achieve the NETS target, the impact on over compliance for the ETS sector is notable.

Similarly there would be strong co-benefits with transboundary air pollution policy objectives where

significant progress is made on the NETS target. Finally, whilst the process has identified many areas

where additional data and evidence are required; there is cause for optimism in respect of this calibration

challenge. A number of potential sources for these data are identified, and the progressive collating and

integration of this information into the model framework will offer a still stronger analytical tool for

navigating a pathway to compliance with NETS from 2013 to 2020. In summary, the GAINS Ireland

model offers an excellent framework into which developed research and evidence may be integrated, and

will thereby provide a robust methodological platform on which to build both dynamic compliance

strategies and sound negotiation positions for both climate and transboundary air pollution

commitments. Specifically in the climate context, GAINS Ireland is being developed to provide robust

technical support in regard to the NETS annual target challenges, including the requisite ‘corrective plans’

that will be necessary where an annual target is missed in the 2013 to 2020 period.

1 In other words measures that may be combined – not all controls are additive. For example, if we already have 100% of a stage 2 control, we cannot also add more of a stage 1 control to obtain further emission reductions in that situation.

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1. Introduction

It is a globally shared goal to prevent dangerous climate change. The EU has adopted a position

whereby global temperature rise would be held as far as possible under a 2°C rise beyond pre-

industrial levels. This target has been clearly communicated to the UNFCCC and sets the

European Union apart as a global leader in climate protection. The actions necessary within

Europe to contribute towards attainment of this goal are defined under EU climate change

policy, whereby member states are currently required to collectively reduce the EU’s greenhouse

gas emissions by 20% relative to 1990 by 2020 over the period 2013-2020. The effort could be

increased to 30% should there be a significant commitment in terms of climate ambition from

other major developed nations, however, currently the target remains as 20%. In order to

achieve the objective of a 20% reduction in greenhouse gas emissions in a cost effective manner

the European Commission emphasised a need for all sectors of the economy to contribute to

achieving these emission reductions2. The EU agreed specific targets for the ETS (traded) and

NETS (non-traded) sectors as follows:

• A 21% reduction in ETS sector emissions by 2020 compared to 20053. This reduction

will be achieved through the allocation of an annually declining single EU wide

allowance cap across all ETS sectors between 2013 and 2020. There are no country

specific targets for ETS emissions.

• An overall 10% reduction in NETS emissions by 2020 compared to 2005 levels. Each

Member State has agreed a specific emissions limit for the 2013-2020 period.

2 Point 1 of “Position of the European Parliament adopted at first reading on 17 December 2008 with a view to the adoption of Decision No .../2009/EC of the European Parliament and of the Council on the effort of Member States to reduce their greenhouse gas emissions to meet the Community’s greenhouse gas emission reduction commitments up to 2020”; European Parliament Climate & Energy Package Text 3 2005 was used as the reference year as it was the most recent year for which reliable data was available. It includes verified emissions at installation level within the EU ETS, as well as the overall GHG emissions of Member States as officially reported to the United Nations Framework Convention on Climate Change.

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This report is focused on Ireland’s NETS challenge. The Irish ETS sector is also evaluated as

important interactions exist between the ETS and NETS sectors. However, as the ETS is closely

regulated by Europe, with the established participation of market players, it presents less of a

direct concern for member state policymakers. In contrast, the non-traded sector targets

comprise a challenging set of responsibilities for many individual Member State policy makers

who must manage these complex multi-agent sectors. Co-benefits for other areas (e.g.

transboundary air pollution) that are captured within the GAINS modelling system are not

presented in this analysis in order to maintain the focus on the NETS challenge.

The principal objective of this report is to undertake an analysis of Ireland’s NETS target

challenge, and to identify potential pathways ‘towards’ compliance. The approach is to utilise a

configuration of the ‘full4’ capacities of the GAINS Ireland model5,6 to determine the scope and

costs of potential abatement and mitigation options from the relevant NETS sectors in regard to

greenhouse gas emissions. Optimisation analysis is used to constrain the system to the specific

targeted outcome. Specifically, the optimisation focuses on identifying a least cost pathway to

attainment of the NETS target in Ireland in 2020. The principal analysis is derived from the

GAINS Ireland model framework, calibrated with the most recent national ‘with measures’

energy forecast (SEAI, 2010) and agricultural data provided in 2011 from the EPA projections

unit. A sensitivity calibrated on the ‘with additional measures’ energy forecast7 is also run, with

summary results discussed. Under the following subheading we provide a brief up front

introduction to the key methodological concepts that must be understood from the outset in

reading through the report.

4 To date the GAINS model has rarely been run in a full mode whereby the model engages all abatement options (e.g. fuel switching and efficiency changes, in addition to technical options) and pollutants in an assessment. This mode, in a climate context, is a comparatively more recent development of the system and yet requires a considerable effort for refined calibration to individual member states. For the purpose of this report, the IMP team have engaged closely with IIASA and national experts in order to establish an initial calibration platform for analyses. Further national research and effort will be required over time to develop and sustain this system for ongoing usage. 5 See Policymeasures.com Literature and Guides for GAINS Ireland by the IMP Ireland team 6 Also see Reports and Publications by the IIASA team 7 Also known as the NEEAP/NREAP scenario

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Methodology and Report Overview

The analysis in this report draws principally on the GAINS Ireland Model and associated

methodologies of the GAINS modelling framework.8 The GAINS model is a techno-economic

integrated assessment model focused on climate and transboundary air pollution policy. In the

context of this report there are three principal components to the model operations that are

particularly relevant to understanding and interpreting the analysis presented in this work.

These are activities, controls and optimisation.

Activities

This defines the activities in regard to energy use, agricultural data (e.g. herd, fertiliser use) and

processes such as waste treatment or cement manufacture. These data represent the major

drivers of emission levels. In this report, activity levels have been based upon recent official

national data where available. Specifically, the activity component of the model has been

calibrated with the most recently available national data on agriculture (direct correspondence

Spring 2011, EPA) and energy activity (SEAI, 20109). Supplemental information on other

emission drivers such as population, waste generation and cement production have also been

sourced from official data and included as available. All data in the model are forecast out to a

2020 time horizon. The principal energy scenario choice is the ‘With Measures’ or Baseline

scenario. It includes defined measures in place, and does not presume success with a number of

relevant targets e.g. national energy efficiency targets or renewable target. A sensitivity using the

‘With Additional Measures10’ scenario is also run to indicate the estimated cost and abatement

potentials from a more advanced starting point in terms of renewable deployment and energy

efficiency progress.

Controls

The control component relates to the ‘menu’ of abatement controls or other actions available

within the calibrated model to control emissions. These controls are detailed in the system in

terms of what is currently in place, what is expected in 2020, and what is believed technically

feasible by 2020. Each individual control is linked to a specific abatement potential per unit of

relevant activity, a corresponding abatement cost function and also the associated synergies or

8 For specific reference material on GAINS Ireland approaches see AP EnvEcon (2008 & 2010), and for information on the general model framework, see for example Hoglund-Isaksson et al. (2009) and Klaassen et al. (2004). 9 Actual data files for energy forecasts were sourced from the EPA directly 10 Defined also as the NEEAP/NREAP scenario in the national projections (SEAI, 2010).

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trade-offs with other measures and pollutants. The GAINS Ireland model benefits from a core of

international research in its design and default calibration of this menu. National specific

studies have also been used where available to refine the model in line with national research

and evidence. Though there are many analytical gaps in the national research of ‘options’ and

their cost and potential. Specifically the preparatory work for this analysis has focused upon:

a) Reflecting those measures implicit within the national activity scenarios mentioned11

So as to limit the risk of double counting of abatement potential in the analysis

b) Researching and defining appropriate boundaries for the potentials of abatement

controls

So as to more appropriately reflect what can be done in Ireland

c) Refining and adjusting the cost, efficiency and abatement potentials of measures

So as to improve confidence in the cost and abatement results generated by the model

Optimisations

The linked structure of activities, costs and controls in GAINS, is what allows the optimisations

to determine outcomes such as a cost-effective pathway to a given emission constraint12 or the

maximum abatement progress that can be achieved for a given investment. The analytical

approach within this report is to run three optimisations over the 2011 ‘with measures’ scenario

as follows:

• Cost-Optimal Baseline (COB)

An optimised baseline which selects measures with negative or zero marginal abatement cost to

reconfigure the baseline to a ‘no regret’ starting point where savings would be greater than cost.

• Maximum Feasible Reduction (MFR)

This run presents the maximum level of emission reductions that may be achieved from the

analytical perspective of the model. Whilst the title suggests no limit on cost, in practice

measures range up to €2502005 per tonne of CO2. No measures above this cost are included in

the menu of abatement options.

11 For example, accounting for the effect of implicit energy efficiency progress in the two scenarios used. Reflecting the energy change is straight forward, reflecting the controls used to achieve this is more complex. 12 Alternatively, in cases where the target cannot be met with all measures the optimisation will return inter alia the remaining gap that must be closed.

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• Least Cost Optimisation (LCO)

The LCO is an optimised scenario constrained to find the least cost pathway to achieving the

NETS target with a cap imposed on the marginal cost of measures of €502005 per tonne of CO2.

The focus for the optimisations above is the with measures scenario, however, summary results

from a sensitivity analysis that runs the same optimisations over the more ambitious13 ‘with

additional measures’ scenario for 2011 are also presented. These indicate a more advanced

starting point in terms of closing the gap to the NETS target, given higher renewable

penetrations, achievement of national efficiency targets and so forth. Whilst additional

measures are captured in the model calibration for the WAM scenario, the outcomes still deliver

a lower level of emissions in 2020 as the WAM includes certain options (e.g. high EV

penetration) and rates of progress (e.g. energy efficiency) beyond the current setup of the model.

The report is structured as follows. Section 2 provides the contextual setting for the report,

describing the climate change challenge facing the EU and Ireland and how this challenge has

been distributed between the traded and non-traded sectors. Section 3 describes the modalities

of the GAINS model. Detailing how the model handles emissions, cost calculations and the split

and interactions between the traded and non-traded sectors in the model framework. Section 4

describes the research and setup of the GAINS Ireland model for the analyses. Sections 5

presents modelled optimisation results, and section 6 offers a brief discussion of further

abatement potential. The latter includes areas where additional data is required to refine the

national abatement potential in the model (e.g. transport and electric vehicles), and areas where

measures that lie outside the methodological framework of the model exist (e.g. carbon taxation)

that can support further progress towards the NETS targets. These are discussed with reference

to related research nationally by the IMP Team and others. Section 7 concludes.

13 In terms of energy efficiency and renewable penetration

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2. The NETS Challenge

Ireland must comply with legally binding greenhouse gas (GHG) emissions reduction

commitments established under the European Union’s (EU) Climate and Energy (C&E)

package and subsequent Effort Sharing Decision (ESD).14 The C&E package requires the EU

Emissions Trading Scheme15 (ETS) sectors (principally the power sector and heavy industry) to

reduce emissions levels in 2020 by 21% relative to 2005. The ESD requires Ireland to reduce the

GHG emissions of its non-ETS (NETS) sectors (e.g. agriculture, transport, waste, residential &

commercial, heat) in 2020 by 20% relative to 2005 levels. The European Commission has

however stated that Member State NETS sector targets could be further increased should a

suitably ambitious international agreement to replace the Kyoto Protocol be reached. For now

however, Ireland’s NETS challenge remains at a level of 20% below 2005 levels in 2020. In

quantitative terms this amounts to a threshold on NETS emissions in 2020 of 37.4 Mt CO2e

(EPA 2011a) with annually declining, and still legally binding, NETS emission limits from 2013

to 2020 to support the drive to a compliance trajectory. We acknowledge the significance and

immediacy of the challenge posed by these inter-annual targets from 2013, however, in this

analysis we focus on the end point of 2020.

The Traded Sector (ETS) and Non-Traded Sector (NETS)

At present the ETS covers CO2 emissions from large emitters in the heat and power generation

industry and in selected energy intensive industrial sectors.16 A size threshold based on

production capacity or output was used to determine which installations in the covered sectors

participated in the trading scheme. This process resulted in the ETS being confined to CO2

emissions from combustion installations with a rated thermal input in excess of 20 MW (except

for municipal or hazardous waste incinerators), oil refineries, production and processing of

ferrous metals, manufacture of cement (capacity > 500 tonnes/day), manufacture of lime

(capacity > 50 tonnes/day), ceramics including brick, glass, and pulp, paper and board (>20

tonnes per day). The ETS sector covers approximately 50% of the EU’s CO2 emissions and 40%

14 Effort Sharing Decision (ESD) adopted jointly by the European Parliament and the Council - Effort Sharing Decision 15 See IMP Ireland Report on ETS at www.policymeasures.com 16 The Netherlands is the only Member State to utilise the ETS directive’s provision allowing Member States to include additional national greenhouse gases in the trading scheme. The Netherlands has ‘opted in’ emissions from nitrous oxide.

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of total greenhouse gas emissions.17 The aviation sector is scheduled to join the ETS in 2012. The

entry of the ETS into a third phase in 2013 will see further expansion of the scheme to include

additional sectors (petrochemicals, ammonia and aluminum) and gases (nitrous oxide and

perfluorocarbons).

The NETS sector essentially captures what remains. Specifically, it encompasses the agriculture,

transport, residential & commercial heat, waste and ‘light’ industry sectors. The Commission

identified non-ETS sector targets for Member States on the basis of GDP per capita, with some

acknowledgement of abatement potential18. The underlying principle being one of solidarity

between Member States and the need to allow for balanced and sustainable economic growth

across the EU. Member States with relatively low per capita GDP and high per capita GDP

growth expectations were permitted to increase their emissions relative to 1990 while those with

relatively high GDP per capita must reduce their emissions.

Ireland’s NETS Target

In determining emissions targets for Member State NETS and ETS sectors, the European Union

assigned Ireland a target requiring it to reduce its non-traded sector greenhouse gas emissions

by 20% relative to 2005 by 2020. As noted, Ireland’s 20% NETS target equates to a 2020

emissions level of 37.4Mt CO2e.

Emission reductions for the non-ETS sector will take place between 2013 and 2020. Under the

Climate and Energy package the European Commission foresee a linear emissions reduction

path for the national targets for the NETS sector over the period from 2013 to 2020. As a result

of the “effort sharing” approach under the Climate and Energy package Member States annual

binding emission budgets were determined in accordance with the Commission’s emission

reduction path. Member State emissions will be subject to annual monitoring and compliance

checks to ensure EU greenhouse gas emissions gradually move towards agreed 2020 targets

(European Commission, 2008a). The European Commission have warned that if in any given

17 See European Commission - ETS Description & Statistics 18 Specifically the GAINS model was used to inform the abatement capacities in respect of the agricultural sector

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year a Member States’ NETS emissions are greater than those permitted under the “effort

sharing” emission budgets then it will be forced to take corrective action. Underachieved

emission reductions will have to be realised in the following year with a deduction from a

Member State’s emission allocation budget in the following year equal to the amount in tonnes

of the emissions reduction underachievement multiplied by a penalty factor of 1.08. In addition,

Member States will have to submit a corrective plan to the Commission detailing the measures

and timeframe for getting back on track with a view to meeting their 2020 target (European

Commission, 2008a). For the analysis in this paper we focus on the final year performance in

2020. However, we acknowledge the relevance of establishing an appropriate compliance

trajectory from 2013 onwards, and the potential for penalties and credible revision plans to

affect the challenge in this area into the future.

Figure 1 Ireland’s NETS Sector 2013 – 2020 Greenhouse Emissions Pathways (EPA 2012)

Figure 1 illustrates the NETS challenge facing Ireland. The data presented are based on the

official national emissions forecast (EPA 2012). From Figure 1 it is evident that the NETS sector

is forecast to exceed its emissions target (37.4MtCO2e) from 2016 onwards. According to the

EPA (2012) ‘With Measures’ NETS emissions are forecast to be 45.3MtCO2e in 2020, thus

exceeding the 2020 target by 7.8MtCO2e. These projections exclude carbon sinks19 (which would

19 Carbon sinks, encompassing the storage and removal of greenhouse gas emissions associated with land use, land use change and forestry (LULUCF), are currently excluded from use by Member States as part of their abatement strategy to comply with the 2020

0.00

10.00

20.00

30.00

40.00

50.00

2013 2014 2015 2016 2017 2018 2019 2020

Mto

nn

es C

O2e

NETS Target Estimate*

NETS Emissions (WAM) 2012 ex sinks

NETS Emissions (WM) 2012 ex sinks

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amount to approximately 4.8Mt in 2020 (EPA 2011a,b, 2012). The with measures scenario

reports higher emissions than would be found under the more ambitious ‘with additional

measures’ scenario. By way of international comparison, it is clear from Figure 2 that the

relative challenge faced by Ireland within the non-traded sector is amongst the greatest in

Europe, with a required reduction in emissions of 20% on 2005 levels.

Figure 2: Official Non-traded sector 2020 emissions targets, relative to 2005 levels

When combined the ETS and non-ETS targets will in 2020, result in an overall reduction in

emission levels by 14% compared to 2005. This is equivalent to a reduction of 20% compared to

1990. Failure to agree on a new legally binding international climate agreement at Cancun

meant that the EU 20% emissions reduction target for 2020 remains unchanged for the time

being. At least until the next COP gathering in Durban 2011, or pending some alternative policy

development of significance such as the approach to be taken to carbon sinks in Europe and

targets. The European Commission is currently engaged in a consultation process to determine how carbon sinks might be incorporated into Member States’ emissions target compliance strategies.

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

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connected revisions to ambition levels. By association then the emissions targets assigned by the

EU to individual Member States to help the Union collectively achieve its emissions target have

also remained unchanged following COP16.

3. The GAINS Model Framework

Section 3 is structured into five headings describing a number of aspects of the GAINS model

framework in brief that are of particular importance in regard to the analysis presented in this

report20. These are:

• A general overview of the model framework

• The model handling of emissions

• The model handling of abatement options

• The model approach to cost estimation

• The model approach to ETS and NETS disaggregation and interaction

Overview of GAINS framework

The GAINS model provides an analytical framework to evaluate scenarios in respect of

emissions, abatement options, costs and impacts (Klaassen et al., 2005 and Amann et al.,

200921). The model incorporates exogenous information on energy and agricultural activity as

well as an internationally researched and evolving menu of abatement options and their costs.

The model evaluates multiple pollutants, effects and their interactions, simultaneously offering

decision support in respect of climate and transboundary air pollution policy negotiation and

strategy. Hoglund-Isaksson et al. (2009) provide a concise ‘4-step’ description of the GAINS

methodology as used with a focus on GHG and climate. They describe how the GAINS model:

20 As detailed previously, additional documentation and literature from the IMP Ireland work and IIASA can be sourced online as linked in footnote 6 and 7. 21 The GAINS model includes all 6 greenhouse gases covered under the Kyoto Protocol (CO2, CH4, N2O, HFCs, PFCs, SF6) and covers all anthropogenic sources that are included in the emission reporting of Annex I countries to UNFCCC (Energy, Industrial Processes, Agriculture, Waste, and from LULUCF). GHG pollutants are presented in millions of tonnes of CO2 equivalents. Covered air pollutants within GAINS include SO2, NOx, NMVOC, NH3, CO, PM.

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I. Adopts exogenous projections of future economic development and implied activity

levels in terms of energy consumption, transport demand, industrial production and

agricultural activities as a starting point.

II. Develops a corresponding baseline projection of greenhouse gas emissions for 2020/30

with information derived from national GHG inventories and in collaboration with

national expert teams to validate country specific model input data assumptions.

III. Estimates, with a bottom-up approach, for each economic sector in each country the

potential emission reductions that could be achieved through to 2030 as a result of the

application of the available mitigation measures.22

IV. Quantifies the associated costs that would emerge for these measures under the specific

national conditions.

In addition to these steps, a non-linear optimisation process has also been developed to work in

conjunction with the GAINS model framework. The optimisation process affords the capacity to

solve modelled scenarios to defined constraints such as emission limits, effect limits or cost

limits. This is achieved by not only utilising the information mentioned in the ‘4-step’ process

above, but also taking account of further abatement potentials and constraints, pollutant and

effect interaction, and costs and applicability that are captured in the wider modelling system. It

is this scope of the GAINS model that allows the optimisation process to deliver such valuable

decision support for the development of cost-effective multi-pollutant, multi-effect pollution

control strategies.23

Emissions in GAINS

At a basic level, the individual processes for emissions estimation in GAINS entail a straight

forward calculation involving a number of key parameters. These parameters are described

below in Box 1. Effectively the process takes account of the level of energy used for a given

activity, the default emissions associated with that activity, and the presence and performance of

any abatement controls.

22 GAINS model analysis presently covers a 40 year period (1990 - 2030) in five year intervals. Our focus in this report is on 2020. 23 A description of the optimisation component of the model and its application within the context of this paper is presented in section 6.

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Box 1: Primary elements of emission calculation

Activity Level24 The amount of energy used for a particular activity25

Fuel Type The fuel type providing the energy for the activity

Unabated Emission Factor26 The emission factors for the activity assuming no abatement technology

Technology The abatement technology in place for a given activity

Capacity Controlled

The proportion of an activity covered by a given “control measure”. This can be

a technological measure or a fuel switching measure

Abated Emission Factor The emissions factor for all pollutants after abatement

A more elegant presentation of the emission estimation form is provided by Wagner et al. (2010)

who describe how the calculations are performed at the micro-level in the GAINS modelling

system. Equation 1 details the function used in GAINS to estimate emission levels:27

Emissions𝑐,𝑝 = ∑𝑏∈𝑆∑𝑓∈𝐹𝑐,𝑏∑𝑝∈𝑃𝑏,𝑓∑𝑡∈𝑇𝑏,𝑓,𝑝 EF𝑐,𝑏,𝑓,𝑡,𝑝 ∗ 𝑥𝑐,𝑏,𝑓,𝑡 + em𝑐,𝑝𝑜 ….Equation 1

Equation 1 is composed of three components: (1) EFi, s, f, t, p; (2) xi, s, f, t; and (3) emoi ,p. Within

equation 1 the EFi, s, f, t, p parameter represents the pollutant (p) specific emission factor (EF) of

technology t, applied in sector s with activity f, in country i. For a respective greenhouse gas (p)

the third term - emoi ,p - is a constant term that describes residual emissions in country i in the

24 There is often confusion regarding the dual use of the word activity in the GAINS modelling context. Activity in the model is used to describe the fuel type involved in a given process. Thus activity level would be the petajoules of fuel used. However, activity is sometimes also used in the more common sense to describe a polluting activity e.g. 4 stroke passenger cars are a subsectoral polluting activity. 25 In the case of agriculture, the activity level often refers to animal numbers, and the activity type relates to the type of animal e.g. dairy cattle or poultry. 26 Emission factors for a given activity are reported as kilotons of CO2 per unit of fuel. In the GAINS model, fuels and energy sources are reported in petajoules. For the F Gases, emission factors are reported in units of CO2 equivalents.

27 Wagner et al. (2010) provide a detailed description of all mathematical formulae used in the formulation of the GAINS methodology. Equation 1 represents the emissions calculation formula from Wagner et al. (2010). Details of the components comprising equation 1 are sourced from Wagner et al. (2010).

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base year 2005 and is used for calibration against national greenhouse gas inventories. The

second component of equation 1, xi, s, f, t, represents the technology specific activity data

parameter of the GAINS emissions calculation process. The technology specific activity data

parameter is the product of two model variables:

xi, s, f, t = qi, s, f, t * xai, s, f ......Equation 2

qi, s, f, t is the application rate or control strategy variable. This variable represents the rate of

application (q) of technology t, applied in sector s with activity f, in country i. The xai, s, f variable

provides activity data on activity f in sector s in country i.

Abatement options in GAINS

The GAINS model covers multiple pollutants and incorporates multiple abatement options of

relevance to both air pollutants and greenhouse gases. Details of the options are evolving

regularly, however, Klaassen et al., 2005 offers a useful reference for the scope of abatement

options within the model. Prior to discussing the types of measures in the GAINS menu, we

define five categories of generic abatement measures for GHG reduction.

1. Technical measures (e.g. application of carbon capture and storage)

2. Energy efficiency measures (e.g. application of insulation to homes)

3. Lower carbon substitution measures (e.g. swapping away from coal to gas)

4. Technology deployment measures (e.g. displacing petrol cars with electric vehicles)

5. Behavioural change measures (e.g. carbon taxation, behavioural regulation)

In this list, the full GAINS model can capture the role of a broad set of measures in categories 1

to 4. However, behavioural change measures, or non-technical measures, remain apart from the

model and require exogenous analysis if their role is to be incorporated into a given scenario.

Whilst the outcomes of such analysis can be fed back into the process, this can often present a

number of challenges (AP EnvEcon, 2010d) and such measures are unlikely to feature in the

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GAINS optimisation process in the foreseeable future for these reasons. The important point of

this however, is that whilst the GAINS model can reflect behavioural change, it does not

incorporate all the possible behavioural change options into its considerations of potential

independently, and therefore policymakers should remain aware of the potential offered by such

non-technical or behavioural measures where seeking to drive a path towards compliance. We

revisit these points in the results and conclusion sections.

In terms of further considerations on the type of options that are incorporated as options into

this analysis then, there are a few points to make. Many measures are considered in the GAINS

model, and detail of efforts to calibrate individual sectors is presented in section 4. The main

categories of measures in this analysis are briefly outlined in Box 2 and an extended summary

can be found in Amann et al., 2009 with additional detail on scope available via the online

GAINS glossary. However, there are also some specific notes in regard to certain measures for

this analysis. These are that:

1. Fuel switching to biofuels is included as an option in the analysis, allowing 10% higher

for first generation than the baseline.

2. Switching to wind power is included as an option up to 8.75pj beyond baseline

3. Solar and geothermal potentials are not included at this point

4. CHP for domestic heating and cooling is not included at this point

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Box 2 : Main categories of abatement measures used in the NETS analysis

Sector Types of Measure

Power Plants Fuel switching, CHP, efficiency improvements, IGCC

Residential and Commercial 3 stage energy saving packages for HVAC and appliance use in new and old

houses, apartments, commercial buildings

Industry Fuel switching, good practices, 3 stage energy saving packages, various N2O

and F-gas controls

Transport Advanced engines, efficiency improvements, hybrids, plug-ins, electrics

Waste Waste diversion and treatment options, flaring and utilisation of gas,

wastewater management, waste burning regulation

Agriculture Fertiliser application controls, animal feed, anaerobic digestion, advanced

agro-chemicals, precision farming

Cost in GAINS28

Principally the GAINS mitigation cost methodology operates on the basis of two general

assumptions. In the first instance GAINS estimates abatement values by approximating

mitigation costs at production level as opposed to consumer price level. Specifically, GAINS

focuses on pure technology, investment and operational costs, ignoring transaction costs.

Wagner et al. (2010) highlight that by employing such an approach GAINS takes the social

planner’s perspective in that all costs are taken net of transfer costs, such as taxes, subsidies and

profits. The reason for ignoring such transfer costs is that they do not represent actual resource

use costs. Secondly, the GAINS methodology assumes the existence of a free market for

abatement equipment. Winiwarter (2005) and Tohka (2005) note how the free market must be

accessible to all countries under the same conditions so that capital investments for a specific

technology can be specified as being independent of the country29. A further important choice in

28 A detailed description of the handling and role of cost data in the GAINS model is provided in AP EnvEcon, 2010a. 29 The accessibility of options does not preclude the possibility in the model of restricting certain options as inapplicable in a given country for a defined reason. This is also known as applicability in the context of the GAINS model.

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the model is the specific interest rate used in respect of investment costs. By default, the GAINS

model uses a social interest rate of 4% €2005, in order to reflect societal costs and returns30.

In regard to what is counted when it comes to cost, GAINS differentiates expenditure costs

associated with individual abatement options into three cost components:

1. Investment Costs

2. Fixed Operating Costs

3. Variable Operating Costs

For each of these categories the model utilises a broad range of data parameters in the cost

calculation process with data parameters generally classed as either “common” or “country

specific”. Klaassen et al. (2005) point out how some parameters are considered common to all

countries with country specific parameters characterising the type of capacity operated in a

given country and its respective operating regime.31

The model calculates annual mitigation costs per unit of activity level. Costs are then expressed

per tonne of pollutant abated.32 However, Hoglund-Isaksson and Mechler (2005) emphasise

that although based on the same principles, due to sectoral differences (and the structure of

parameters) the methodologies for estimating costs can vary by sectoral source and pollutant

type. As a result the GAINS cost methodology differs for specific cases. Here we present two

examples of cost estimation – one for a stationary combustion source (Box 3) and one for a

mobile source (Box 4). A more comprehensive review of cost methodology by the IMP Ireland

team is available in a separate guidance report (AP EnvEcon, 2010a) which highlights further

variations when it comes to agriculture, efficiency measures and so on.

30 The system is however flexible and alternative higher rates can be introduced e.g. 20% that would be more representative of private investor cost perspectives. 31 Examples of country specific parameters include: type, size and operating conditions of installations; national fuel consumption trends; vehicle mileage; emissions factors and removal efficiencies; and prices of labour, electricity, fuels and other inputs, and waste disposal/treatment. Common data parameters generally refer to: technology specific data including unit investment costs, fixed operation and maintenance costs, removal efficiencies; and variable cost data relating to energy, labour and materials demand. 32 Costs are generally expressed in constant € values of a given year (e.g. 2000, 2005).

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Box 3 : Costs from Stationary Combustion Sources33

33 Stationary combustion sources refer to immobile pollution sources such as power plants or industrial boilers.

Investment Cost

The investment module equation first calculates all costs accumulated prior to the operationalisation of an abatement

technology. In equation 3 the form of the function is described by coefficients cif and civ, with bs representing boiler

size, v flue gas volume, and r as the retrofitting cost factor.

𝐼 = �𝑐𝑐𝑓 + 𝑐𝑐𝑣

𝑏𝑏� ∗ 𝑣 ∗ (1 + 𝑟) .......Equation 3

Investments are then annualised over the technical lifetime of the plant lt at a real interest rate of q.

𝐼𝑎𝑎 = 𝐼 ∗ (𝐼+𝑞)𝑙𝑙∗𝑞(𝐼+𝑞)𝑙𝑙− 𝐼

.......Equation 4

Operating Cost

Operating costs incorporate both fixed expenditures as well as variable operating costs. Fixed operation (OMfix) costs

refer to maintenance, repair and administrative expenditures. Since these costs are not related to combustion plant

use GAINS simplifies these into a form where they are a percentage (f) of the total investment I:

𝑂𝑂𝑓𝑐𝑓 = 𝐼 ∗ 𝑓 .......Equation 5

Variable costs (OMvar) incorporate a broader set of parameters and can be pollutant or technology specific.

Unit Mitigation Costs1

Unit mitigation costs are determined on the basis of previously calculated investment and operation costs (Equation

6). Klaassen et al. (2004) note that all mitigation expenditures are related to an activity unit and in the case of

stationary sources this unit is a unit of fuel input (measured in PJ). As part of the unit cost calculation process GAINS

converts all investment related costs to fuel input by applying a capacity utilisation factor (pf):1

𝐶𝑃𝑃 = 𝐼𝑎𝑎+ 𝑂𝑂𝑓𝑓𝑓

𝑝𝑓+ 𝑂𝑂𝑣𝑎𝑣 .......Equation 6

Once GAINS calculates costs per unit of activity (cPJ) the model can then estimate cost effectiveness of an abatement

option (cPMk) by estimating the unit cost per tonne of abated emissions:1.

𝐶𝑃𝑂k = 𝐶𝑃𝑃(𝑒𝑓∗ 𝛽) .......Equation 7

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Box 4 Costs from Mobile Sources34

34 Mobile sources include all forms of emission sources which are mobile. These sources thus include trucks, cars, planes, trains and ships.

Investment Cost Module

Calculation of mobile source investment costs broadly follows the same basic approach as for stationary sources.

However, unlike stationary sources where investment costs are calculated per unit of production capacity mobile

source investment costs are estimated per vehicle. The key parameters in the mobile investment equation are the

control technology (t), transport sector/vehicle category (j) and the implementation country (i). Klaassen et al.

(2004) identify that the costs of applying a given control strategy in the transport sector includes added

investment cost, the cost of any change in fuel consumption as a result of the control, and the increase in

maintenance costs as a proportion of the total investment. Investment costs are annualised in accordance with

equation 8:1 using the following function,

𝐼𝑐,𝑗𝑎𝑎 = 𝐼𝑗,𝑡 ∗ (1+𝑞)𝑙𝑡𝑓,𝑗,𝑘∗𝑞

(1+𝑞)𝑙𝑡𝑓,𝑗,𝑘− 1 .......Equation 8

Operating Costs

GAINS treats mobile operating costs in the same fashion as stationary sources. Operating costs account for a few

potential issues with a new technology for a mobile source. The fixed cost component reflects a potential increase

in maintenance costs.1 The variable cost component includes parameters detailing changes in fuel costs as a result

of the implementation of a given control measure.

Unit Mitigation Costs

Mobile source unit mitigation costs follow the same approach as stationary sources. The distinction between both

source types is that mobile unit costs are time dependent (t). Mobile activity unit costs (cePJ) are estimated on the

basis of equation 9:

𝑐𝑐𝑃𝑃,𝑐,𝑗 = 𝐼𝑓,𝑗𝑎𝑎+ 𝑂𝑂𝑓,𝑗

𝑓𝑓𝑓

𝑓𝑓𝑒𝑙𝑓,𝑗 (𝑡)+ 𝑂𝑂𝑐,𝑗

𝑒 (𝑡) .......Equation 9

From equation 9 unit abatement costs (cni, j) are calculated on the basis of equation 10:

𝑐𝑐𝑐,𝑗 = 𝑐𝑒𝑓,𝑗 (𝑡)𝑒𝑓𝑓,𝑗∗ 𝛽

.......Equation 10

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ETS and NETS Sectors in GAINS

The ETS applies only to CO2 emissions from certain covered sectors with coverage presently not

extending to a number of significant economic polluting sectors, e.g. transport or agriculture.

From an emissions perspective Ireland’s non-traded sector covers all CH4, N2O and FGAS

emissions across all economic sectors as well as CO2 emissions from those sectors not covered

by the ETS. Scenario analysis within the GAINS model framework provides detailed information

on emissions trends, activity levels and control strategies for all sectors of the economy, thus

including both ETS and non-ETS sectors. For an evaluation of the ‘NETS’ target, we must define

for the model the points at which to disaggregate the sectors and activities in a binary

distribution of ETS or NETS. The IMP Ireland team, have applied a ‘splitting file’ to the model

which filters results between the two groupings. The specific disaggregation is informed

principally by the EPA’s 2011 Monitoring Mechanism report (EPA, 2011). Ultimately the

splitting file approach enables revisions to the split as necessary down to a sub-sectoral

technology level if necessary.

For now, the split applied assigns all transport, commercial, residential, agricultural and waste

activity to the NETS, and all power generation to the ETS. The ground on which a split occurs is

Industry, wherein broadly the split transfers all large and heavy industry activity to the ETS and

the balance to the NETS. The approach here is not entirely straight forward as the aggregation

and grouping of sectoral activities in GAINS is not the same as used in national forecasting and

reporting. Nonetheless, a review the Monitoring Mechanism Report (MMR) Manufacturing

Industries & Construction (MIC) sector in comparison with the GAINS industrial sector offered

a means of approximating the balance for industrial activity in the ETS and NETS. Specifically

the MMR allocates 55% of MIC CO2 emissions to the traded sector with 45% to the non-traded.

For the purpose of this report we therefore allocate 55% of GAINS industry emissions to the

traded sector and 45% to the non-traded. Splits can be reviewed and adjusted via the splitting

file as necessary into the future.

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4. The GAINS Model Setup for this Analysis

The GAINS Ireland model is used in this report to evaluate abatement options and costs for

Ireland in regard to progressing towards compliance with the NETS target in 2020. This

approach draws on the same international methodology and model used within Europe to

inform prior work in regard to the non-traded sectors for the European Commission. However,

in this instance the approach has been to further refine and recalibrate the model, where

evidence is available nationally, in order to present an interim assessment based on a hybrid of

international model conditions as well as nationally developed parameters and assumptions.

In this section the approach to the general model setup (the baseline scenario used), as well as

calibration for individual sectors is described to provide a clear context for the analytical

configuration. In regard to the latter, the principal sectoral challenges involved defining the

variety of attributes necessary for defining the ‘menu’ of abatement controls (their cost,

potential and expected penetration) for each sector. The choices and decisions here with regard

to calibration are obviously of particular relevance to the ultimate cost and abatement potential

outcomes identified by the GAINS Ireland model in section 5. In a number of cases the work has

highlighted information deficits to be addressed in further research, which will support

increasingly refined and robust estimations of pathways and potentials. Indeed, arguably the

principal interim constraint is in regard to data as opposed to methodology. For example,

measures targeting the domestic sector would benefit from a robust micro level evidence base on

the characteristics and performance of households in terms of heating and appliance use. In this

regard pending initiatives such as smart metering in Ireland, may offer heretofore unavailable

data that dramatically enhances our knowledge of the sector. Similar changes have occurred in

the past and have been of significant benefit to research, modelling and policy analysis. For

example, the interrogation of the national car test data to deliver robust disaggregated annual

vehicle mileage data has fed into a range of policy (e.g. Kelly et al., 2009) and modelling analysis

(Daly and Ó’ Gallachóir, 2011) research pieces.

However, whilst additional data is desirable and something to be pursued progressively over

time, at this point the study represents a significant step forward in this process, being the first

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nationally revised version of the GAINS model to have been developed in collaboration with

IIASA for such a NETS analysis. With sustained effort the system can be iteratively enhanced to

provide regular and more rapid reviews of a given combination of strategy, economic and policy

pathways. A feature that will be of particular value as we plan, negotiate and respond to the

stringent NETS GHG targets from 2013 onwards35.

Baseline Scenario

The energy and agricultural data used for the baseline scenario in this study is developed based

on official “With Measures” (WM) scenario data sourced via the Irish EPA in 2011. The scenario

combines the latest official national energy projections of SEAI (SEAI, 2010), as well as the

latest agricultural activity forecasts from Teagasc and the Department of Agriculture. The EPA

add additional value to these data by formatting them in a manner that is consistent with the in-

house methodologies developed by the IMP Ireland team (AP EnvEcon 2009 and 2010c) to

adopt national data into the GAINS Ireland model.

Whilst the activity data are the same, the methodologies and aggregation of data in the GAINS

Ireland system are distinct from those used in official national projections. For these reasons,

the emissions calculated in the GAINS Ireland model for the WM 2011 scenario differ somewhat

from those in official national projections. The absolute value can vary, and the distribution of

emissions at higher levels of resolution can also vary. The variations are however generally well

understood, which may raise the question as to why these are not corrected. The principal

reasons are:

1. Aggregation and categorisation differences due to the model framework cannot be

corrected without fundamentally altering the GAINS Ireland model structure and

deviating away from the core GAINS Europe structure. As a result it is better to

understand where such variations exist and consider their relevance, if any, in

interpretation of results.

35 This is also not to omit the particular strength of the model in regard to integrated modelling of both climate and transboundary air pollution policy. Thereby informing policy action in two major thematic areas and in a manner that affords insight into the synergies and trade-offs of a given course of action.

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2. Methodological differences in terms of the detail for emission estimation can lead to

variation. For example where one process uses a more detailed bottom-up methodology

as compared with a broader approach of say using population as a driver of emission

outcomes. In some cases the model has not yet been adapted to higher tier analysis, and

in some cases national analysis is on a lower tier pending additional official research and

data. For these reasons variations can exist, that we expect to ultimately reconcile.

3. Source variations can also add to the difference in emission outcomes. For example,

where the model considers a specific source of emissions that may not be required or

officially noted in national forecasts, this can lead to a variation. Once again, over time

we expect these variations will be reconciled, however official changes are complex.

In terms of the key emission variations then to note for this analysis, these are as follows:

1. Official WM CO2e projections for 2020 are 64.05Mt36

2. GAINS Ireland WM CO2e projections for 2020 are 65.10Mt37

Agricultural milk yield emissions from GAINS account for the majority of the variation

(contributing an additional 0.94Mt to the GAINS Ireland estimate) with some remaining

discrepancy with N2O and other sources such as waste incineration contributing the balance.

There are also aggregation variations particularly in areas such as industry and power, where

GAINS combines emission sources in a different manner to national methodologies, and also

transport and agriculture, where the assignment of agricultural fuel use varies. Nonetheless, the

IMP Ireland team conduct regular emission comparison assessments between GAINS Ireland

36 This is the set of 2011 national projections (EPA 2011a) that use comparable data to the GAINS Scenario used 37 Specifically the starting emissions point for the optimisation analyses is 65.10Mt CO2e - porting from the GAINS Ireland model to the GAMS process entails some rounding and other processing which leads to minor variation.

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and official national projections to understand variations, to guide corrections and to record

acknowledged variations38.

In the context of this study we are satisfied with the agreement between the scenarios, and note

that GAINS reports a starting emission level in 2020 of 1.05Mt CO2e more than is reported

nationally. Thus were we to force the use of official national projections, the starting distance to

the NETS target of 37.4Mt CO2e would of course be reduced accordingly.

Sector: Agriculture

In calibrating the GAINS Ireland model for this analysis, some additional efforts were required

to formally reconcile the GAINS model representation of the sector. The principal adjustments

were required in relation to N2O emission levels, N2O control implementation potentials, CH4

abatement control implementation potentials, and CH4 control emission reduction potentials.

Cost data used drew principally on the GAINS Ireland model.

Specifically, in relation to N2O it was noted that the baseline emissions of N2O from indirect

sources were considerably higher (over 2m tonnes of CO2e) in the GAINS system than in

corresponding national forecasts. Following consultation with colleagues nationally and

internationally it was determined that this was attributable to the GAINS model following the

IPCC default of a 30% fraction of nitrogen being leached on pasture and in the paddock. In

Ireland however, national research recommends an equivalent 10% leaching rate and this is

used in official estimations and projections. An exercise was subsequently undertaken to refine

the package of related emission factors and controls for N2O in GAINS Ireland to match more

closely with the national methodology39. Further changes were also made in respect of control

potentials for N2O to approximate evidence provided from the DAFF in respect of N2O emission

control potentials40.

38 The emission comparison reports are delivered exclusively to the IMP Ireland project steering group and are produced subsequent to each new scenario loaded into GAINS Ireland where comparable national projections are available. 39 In practice, correcting for this variation presented a complication as this core value in the GAINS model was somewhat hardcoded into the system. As an interim workaround, the package of reduced emission factors for the N sources were calculated, loaded and initialised in the system to balance the baseline emissions in this case. 40 Specifically work for the preferred policy measures group by DAFF and Teagasc.

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In regard to CH4 the principal challenge was to adjust the performance of certain categories of

CH4 abatement control (e.g. forms of anaerobic digestion) as well as defining their potential

deployment in Ireland. For technical reasons, the approach taken was to allow the potential for

measures to be set at 100% (maximum deployment). However, the abated emission factor for

these controls was revised into a consolidated value which reflected limitations on potential in

discussions with both IIASA and DAFF. At this stage in the model development, the measures

for CH4 have been synthesised into the following two primary categories:

• Feed adjustments (a synthesis of options principally reliant on dietary oils)

• Anaerobic digestion (specifically community scale AD)

The model was further refined to account for updated milk yields, animal housing day estimates

and the scale and eligibility of farms for controls. In respect of agriculture one particular

variation between national forecasts and the GAINS Ireland modelling system, mentioned

earlier, was not reconciled, but was simply acknowledged. This relates to the higher emissions

connected with increased levels of milk yield from the dairy herd.

Sector: Commercial, Residential, Heat

The calibration of the GAINS Ireland model to assess the energy saving potential and associated

cost of measures for the residential, commercial and heat (RESCOM hereafter) sectors posed a

significant challenge. The IMP Ireland team engaged closely with IIASA to conduct this first

nationally focused revision of these parameters for use in the GAINS Ireland model. In order to

describe the calibration, we must first explain the structure of RESCOM in the model with

regard to GHG emissions and controls. Efficiency measures for RESCOM in the GAINS Ireland

model are aggregated under two categories:

• HVAC needs (i.e. heating, ventilation, and air conditioning)

• Other needs (i.e. water heating, cooking, small and large household appliances)

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In terms of sub-sectoral structure, the GAINS Ireland model describes building units as

apartments, houses or commercial buildings, and as either new (2005 onwards) or ‘existing’

(pre 2005) in terms of age. For each sub-category activity (e.g. housing heating, apartment

water heating) the model requires us to define a starting representative energy intensity value

for each relevant parameter from the year 2005.

In terms of abatement and efficiency controls, the model uses a three stage efficiency control

concept as the indicator of progress for each activity. The first stage control delivers a

percentage reduction in the base energy intensity at a defined cost, and so on for the subsequent

stages. In the model we also define the potential for a given stage in a given year. This essentially

restricts the level of abatement to a certain share of the market in a given timeframe. For

example the model may be programmed to limit the amount of insulation retrofitting that may

take place in a given time period.

The efficiency stages themselves should not be interpreted too strictly, but rather should be

viewed as three point representations on a curve of potential efficiency (and associated cost) for

a given activity. In order to calibrate the RESCOM sector for this analysis then we required

starting energy intensities, stock characteristics and controls as well as defined efficiency stages

and costs.

We began the task with independent assessments of energy intensities related to HVAC. Given

the lack of official evidence, two separate approaches were taken to generate a range for

consideration. In the first approach we compiled energy balance data (from SEAI), active

building stock (DOELG, census, CSO, construction data), average floor area (DOELG, planning

applications) and persons per household (CSO) to estimate the heating and other energy

requirements of the sector (e.g. megajoules per M2 for heating). In the second approach we

utilised modified building energy rating (BER) data with expert input (SEAI SSU) to derive an

indicator with which to estimate certain disaggregated parameters, such as spatial energy

intensities, energy used for water heating and so on. .

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Relevant national literature and reports were used to fill other data gaps and to provide context

and broader validation for this work. Specifically sectoral energy reports from the statistical

support unit of the SEAI, building and retrofitting reports from SEAI, statistics and forecasts

from the CSO, SEAI, DOELG and EPA, research papers from Perez-Lombard et al., (2007),

Dineen and O’ Gallachoir (2010) and Regan and O’Gallachoir (2011), and direct correspondence

with the Energy Research Group of University College Dublin. In regard to cost data, appliance

efficiency stage cost data were drawn from the international calibration of GAINS, whereas cost

estimates for retrofitting and new construction were based on broad industry estimates and

insurance prices (e.g. architect estimate of rebuild cost for passive retrofit standard).

These two approaches and the associated literature review offered an indicative basis from

which to decide on a starting point for calibration of the model. In essence compiling a time

series under approach 1 and snapshots under approach 2, offered the means of suggesting how

much energy we should expect our domestic sector to be using in 2020 where we project

parameters (e.g. house numbers, population, house size) on that time horizon.

The second stage however, was to reconcile the outcomes of these independent assessments of

energy intensities and controls in order to finally align the model calibration with national

forecasts of energy. In this process, the associated energy requirements that the model now

estimated would be in use in future years were considered in contrast to the latest official energy

forecasts for the sector from the Sustainable Energy Authority of Ireland. The developed model

parameters were then adjusted (through control efficiency, control share and energy intensity

adjustments) to reconcile values with the baseline (with measures) and white paper (with

additional measures) scenarios of SEAI41 in a consistent manner across both scenarios. Other

factors such as degree day statistics and climate type were also defined in the modelling

framework. The GAINS Ireland model was thereby calibrated to reconcile with national energy

forecasts, but there are two important notes for future research in this area:

41 This work highlighted an area for future research whereby the GAINS Ireland modelling work should work closely with SEAI and others to ensure that the representation of the building stock, energy performance and energy forecasts are consistent across the board.

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1. The extent to which national forecasts adequately capture and represent the expected HVAC,

housing stock and appliance developments needs to be understood more clearly to ensure that

reconciliations are meaningful. Bottom-up methodologies of energy demands and their

relationship to top-down models of national energy demand are important.

2. There are many combinations of measures, intensities and efficiencies that can deliver a given

energy outcome. It is hoped that over time new research and increased penetration of smart

metering and other data sources will provide more detailed evidence on which to base future

calibrations.

Thus, whilst good progress has been made in this process within the GAINS Ireland framework,

it remains clear that there is a paucity of evidence both nationally and indeed internationally

with which to calibrate and estimate costs and potentials in these sectors into the future at an

aggregate scale. The high volumes of heterogeneous units (buildings) operating under different

conditions and for varied purposes within these sectors create much of this challenge, with the

variations in appliances and technology deployment adding a further level of detail. A

conclusion of this initial work is that further efforts should be made in regard to researching new

data and collating and reconciling existing outcomes from different studies and reports.

Sector: Waste

The calibration of the waste sector has drawn on prior efforts to calibrate this sector for analysis

by the authors, the team at IIASA and the Irish EPA in 2009 and again in 201142. This involved

identifying official volumes of waste and projections, and assigning appropriate splits in the type

and source of waste generated. Specific adjustments were made in the recent work to correct the

level of Municipal Solid Waste (MSW) as well as the representation of domestic waste water and

industrial waste water.

42 Specifically those principally involved being, Dr. J.A. Kelly of AP EnvEcon, Dr. L. Hoglund-Isaksson of IIASA, and Dr. B. Hyde of the EPA.

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However, emissions in the calibrated scenario remain approximately 0.2Mtonnes of CO2e below

official estimates43. Much of this is explained by the differing expectations for incineration and

other technologies in the waste market as it develops to 2020. GAINS requires a reasonably

detailed perspective on the volumes, type, source and technologies of waste management into

the future. Therefore expectations on the penetration and performance of specific controls will

inform any future reconciliation. A matter for consideration under further work is the expected

directional development of the waste sector in Ireland, as this will influence the type of activity

and relevant abatement controls into the future. Specifically, a major consideration is the

expected scale of incineration development, and the corresponding development of the waste

sector and associated processing technologies into the future. The international review of waste

management policy44 (2009) offers some insight, however, the authors feel their remains some

particular uncertainty in this market place to be considered for further work.

Sector: Process

The process ‘sector’ in GAINS refers specifically to non-energy processes that contribute

emissions. Principally in Ireland this relates to cement and lime production. The GAINS model

utilises a ‘process’ emission factor to calculate associated emissions, and so the two parameters

required for a process are the controlled emission factor and the amount processed. The

approach nationally differs somewhat, and so for GAINS Ireland a hybrid approach was used for

this analysis. In terms of controlled emission factor, this was determined on the basis of implied

emission factors reported through the national inventory team, and adjusted for expected

improvements over time. In regard to activity, the IMP team projected revised cement

production levels for 2020 to a level of just over 4m tonnes for this analysis. Downward

revisions to this estimate may be warranted in further work on this topic.

Sector: Transport

In regard to transport and GHG emissions, GAINS requires information on three separate areas

as follows:

43 In the context of the overall NETS target, the waste sector is one of the less significant contributors. 44 Report available from DOECLG

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1. Fuel efficiency and investment costs of propulsion technologies

The technologies are classified according to their key propulsion concept. However each

technology represents a whole package of single measures including aerodynamics, rolling

resistance, auxiliaries, vehicle weight, etc. Costs are given as extra manufacturing, operating and

maintenance costs relative to a baseline vehicle.

2. Baseline penetration rates for new technologies in road transport

Penetration rates for each new (propulsion) technology in the road transport sector for the

baseline scenario are required. The penetration rate together with the fuel efficiency of the

respective technology determines the average fuel efficiency of the respective vehicle layer (i.e.

vehicle category per fuel type).

3. Maximum penetration rates for new technologies in road transport

The maximum feasibility is constrained by rate of uptake of new vehicles, which depends on the

turnover of the fleet. It is limited by behaviour, costs, technical feasibility, regulations, etc. The

penetration rate together with the fuel efficiency of the respective technology determines the

average fuel efficiency of the respective vehicle layer (i.e. vehicle category per fuel type). Hence,

the maximally feasible penetration rates determine the lower limit of the fuel efficiency for each

time period.

For this report, estimates of vehicle stock by year are obtained from the TREMOVE Ireland

model, and from CSO figures on vehicle registration, which are now available by CO2 emission

band. By deriving a vehicle depreciation rate, and including the new registration of second hand

vehicles, estimates on vehicle stock are calculable out to 2020. It is worth noting also that these

estimates from TREMOVE Ireland include the effects of several recent tax policies affecting the

transport sector. Namely these include the carbon tax on fuel, the new motor tax, and the

remodelled VRT rates for private vehicles based on CO2 emission bands. Furthermore, in

addition to these vehicle stock level estimates, TREMOVE also contains estimates of vehicle

kilometres and fuel consumption. From the latter, an estimate of fuel efficiency can be derived

when divided by vehicle stock. This has been achieved for all vehicle categories and fuel types as

defined by GAINS.

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However, there were some methodological issues in incorporating this work into GAINS Ireland

at this point, and as a result much of the GAINS model calibration of the transport sector for this

report remains based on a European wide average of abatement potentials and costs for the

vehicle fleet. Whilst this approach serves well for more aggregate analysis, an ex post results

assessment by the IMP team suggests that some considerable further work is necessary to refine

the technical potential of this sector for Ireland. In brief, it is believed that the model analysis as

presented in this report currently underestimates the technical potential for abatement from

this sector. There are also some further research tasks to be undertaken in order to improve this

sector in the model calibration. For example, more refined work in determining the potential

share of transport activity that may be displaced by electric vehicles, or estimating the future

balance of vehicle fleet fuel efficiency, taxes and demand in a manner consistent with national

energy forecasts. A comprehensive review and recalibration of this sector in the model is

planned under further work. In the interim, the estimations draw most heavily on European

average data and are believed by the authors to underestimate potential for abatement.

5. Results

In this section we discuss the baseline and the cost-optimal baseline45 outcomes, followed by the

results of the least cost optimisation (LCO) and maximum feasible reduction (MFR) scenario. In

regard to the baseline, the setup of the GAINS Ireland model for this analysis using the ‘with

measures’ scenario, delivers a total of 65.10 M tonnes of CO2e in 2020 whereas the official

national projections come in at 64.05 M tonnes46. Just under 1M tonnes of the variation is

explained by higher emissions associated with higher milk yield under the GAINS methodology,

with the balance attributable to moderate variations in other sectors such as waste. The varied

categorisation of certain emission sources in GAINS also gives rise to some apparent

distributional variation in the emissions, but these are generally well understood and have little

impact on the results presented. Figure 3 illustrates how the 65.1Mt are distributed across the

sectors in GAINS Ireland.

45 Technically the cost-optimal baseline is an optimisation. 46 Further detail on the setup and sectoral calibration is found in section 4

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Figure 3: 2020 Emission break-down for the With Measures scenario in GAINS Ireland

The cost-optimal baseline (COB) is the first result presented. The COB is effectively a first stage

optimisation whereby all measures with no net annualised cost are engaged and deployed. Thus

measures that deliver an annualised net saving are assumed to be taken under the COB. For this

reason, the model estimates the COB first as it is intended to be representative of what could

and should be done anyway from a societal perspective. The outcome of the COB analysis over

the baseline is captured in Figure 4, the result being a reduction in both ETS and NETS CO2e

emissions of 3.1Mt combined, with NETS emissions coming down to a level of 46.2Mt. The

specific measures taken (more of / less of marginal cost curve format) are presented in

Appendix 1 of the report for all marginal cost levels47. The COB options are captured in the €0

marginal cost row.

In regard to the COB though and the general presentation of abatement costs, it is worth

reiterating the approach to cost in the model and in this specific assessment as described in

section 3. All costs presented here are calculated on a social interest rate of 4% on 2005 price

levels and exclude transfer payments and transaction costs. It is principally these assumptions

47 Some care is required in interpreting the marginal cost curve in the format presented in the appendix. Notes on this point are included within the appendix itself.

0.00

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that give rise to the significant negative costs48. However, the observed potential in this analysis

is by no means unique. Significant negative cost abatement options under comparable

evaluation criteria are in line with the outcomes presented under the Fourth Assessment Report

of the IPCC (Barker et al., 2007).

The next result presented is the least cost optimisation (LCO). The LCO is a cost-minimising

optimisation whereby the scenario seeks the lowest cost package of available measures that can

be engaged to achieve a given target level of emissions. In the case of this report, the LCO target

is the NETS emission target of 37.4Mt CO2e with a marginal cost cap of less than €502005 per

tonne of CO2e imposed. The outcome of the LCO analysis is a total national emissions level of

55.9 Mt CO2e, with the NETS emissions reduced to a level of 42.2 Mt CO2e. Thus in this case we

again fail to meet the NETS target under the prescribed conditions.

The final result presented is the outcome of the maximum feasible reduction (MFR) scenario. In

this case the optimisation applies all available measures to determine the maximum feasible

reductions possible under the model conditions. In this assessment, the listed measures do not

exceed a marginal cost of €250 per tonne of CO2e, however, in time the menu can be extended to

include even higher cost potentials for abatement. The result of the MFR optimisation is a total

national emission level of 51.4 Mt CO2e with NETS emissions of 41.4 Mt CO2e. Therefore even

under the MFR conditions described, Ireland fails to meet the NETS target of 37.4 Mt CO2e. The

summary results of all scenarios and the cumulative cost of different levels of marginal

abatement cost thresholds are presented in Figures 4 and 5. Of note, Figure 4 illustrates the

connected impact of the optimisations on the ETS sector, where the COB, OPT and MFR

scenarios all deliver significant over compliance for the ETS objective. Whilst Figure 5

demonstrates that from a social planner’s perspective, the cumulative annualised net social cost

of options taken remains negative up to the €150 per tonne marginal abatement cost level.

48 That said, it is a common question to ask why, where such measures exist, they have not been taken. Briefly, this can be due to other barriers and failures beyond cost. Whilst we do not discuss these other barriers in this report, we note that it may be of interest to utilise a 20% rate to present a cost outcome representative of the cost investment under a private investment interest rate. This latter analysis would paint a different picture of the abatement options and can be conducted under further research in this area. Specifically it would offer a perspective on the indicative scale of the financial barrier to private investment and would substantially reduce the cost savings relative to the social interest rate analysis presented.

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In terms of specific measures, at the COB level, national GHG emissions are reduced by 3.1Mt

CO2e by taking measures from the zero marginal cost category. These are measures that offer net

cost savings and emission reductions under the defined methodology. In terms of the main

actions at this level, the COB scenario adopts a range of measures principally associated with the

increased efficiency of commercial and residential appliances and space heating, and the

adoption of advanced efficiency and hybrid diesel trucks and buses into the transport fleet.

Further measures in other sectors include the deep injection of manure into soils in the

agriculture sector, and further investment in leak control for gas distribution networks and

compressor stations.

The LCO/OPT scenario selects measures up to a marginal cost of €50 per tonne of CO2e and

develops from the COB. The LCO reduces total national GHG emissions by 9.2 Mt CO2e from the

baseline. At this level we see changes in the power sector, with coal fired generation being

replaced by new gas-fired plants, and further adoption of biofuels and wind power in the sector.

This further decarbonisation of the power generation sector in turn influences the penetration of

efficient appliances and heating services in the domestic and commercial sectors, specifically

slowing some of the change, as higher cost advanced stages of energy efficient appliances and

heating become less significant in terms of associated CO2e reductions. At this level we also see

changes in industry, with the introduction of more industrial CHP, the use of alternative

solvents and refrigerants, and the adoption of more efficient processes in production. In the

agricultural sector we see the introduction of farm scale anaerobic digestion, a mix of feed

changes and improvement of nitrogen efficiency via “precision farming”.

Under the MFR scenario all measures (within the model menu of abatement options) that may

be combined to achieve emission reductions are taken, with a view to identifying the greatest

reduction in emissions possible under the current model framework conditions. In the MFR

some constraints remain in place over the potential change as described in section 3 (e.g. no

more than an additional 8.75pj of wind power beyond the baseline), and the menu of measures

draws on those summarised earlier in Box 2. Under the MFR a large number of “more of” / “less

of” changes take place in terms of abatement options, ultimately delivering a reduction in

national GHG emissions of 13.7 Mt CO2e from the baseline, with NETS emissions at 40.4 Mt

CO2e, some 4Mt CO2e above the target level for 2020.

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Figure 4: ETS & NETS Emission outcomes for WM baseline and defined optimisations in 2020

Figure 5: Net social cost of cumulative marginal cost band measures for WM scenario in 2020

47,962 46,200 42,200 40,400 37,400

17,393 15,800

13,700 11,000 17,690

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Sensitivity

As a matter of interest, the COB, LCO and MFR optimisations were also applied over the

alternative ‘With Additional Measures’ (WAM) scenario, officially now known as the

NEEAP/NREAP scenario. In this scenario the energy data implicitly account for a higher degree

of renewables nationally and the successful attainment of a number of policy targets,

particularly in relation to energy efficiency.

The WAM scenario therefore presents a more advanced (in terms of emission reductions)

starting point for the analysis49. Under the WAM scenario, the baseline emissions from the

calibrated model are 59.2 Mt CO2e. The COB run delivers total national emissions of 56.8 Mt

CO2e with NETS emissions down to 42.9 Mt CO2e. The LCO run delivers a national emissions

total of 51.2 Mt CO2e with a corresponding NETS emission level of 38.7 Mt CO2e – still in excess

of the national NETS target. Finally however, the MFR brings national emissions to a level of

47.2 Mt CO2e, with NETS sector emissions reduced to exactly 37.4 Mt CO2e – the NETS target.

Caution is however required with using the WAM/NEEAP/NREAP scenario given the inherent

ambition of a number of the scenario assumptions, particularly the assumed success of

undefined policies to achieve challenging energy efficiency and renewable targets across the

sectors. As noted at the outset, we feel the WM scenario offers a more rational basis from which

to plan and evaluate strategies and pathways to compliance.

6. Note on Additional Mitigation Options and LULUCF

As a point of information related to the results presented in section 5, it is important to

understand what these results represent in terms of potential. Simply put, they represent the

menu of abatement options as defined in the modelling system used for the analysis. There are a

49 Although the effect of commercial and residential efficiency measures are captured in the model setup process by way of greater penetration of improved efficiency stage controls.

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number of considerations with respect to abatement potentials and compliance with the NETS

target therefore, that should also be discussed. Specifically we will present three brief

discussions in relation to:

Additional technical potential and the core menu of measures

Non-technical and Behavioural measures

The role and relevance of LULUCF and carbon sinks in the NETS process

Additional technical potential of the menu of measures

This report presents the analytical outcomes of a first calibration of the full GAINS Ireland

model for an assessment of GHG abatement potential. This introduces numerous additional

challenges beyond those addressed whilst using the model to inform transboundary air pollution

policy as it extends the system into a new area with new information requirements. We identify

in the results that there is an apparent untapped potential from certain sectors which the

current setup of the model is not identifying. Specifically the transport sector should offer

significantly more emission reduction potential than identified in this first analysis, and this will

be addressed in further work where a blend of model improvements and exogenous research can

be combined to integrate the potentials and costs associated with options such as high levels of

electric vehicle penetration, retrofitting and so forth. Similarly we have earlier acknowledged

that the model has not included the potential for certain options such as CHP in domestic

heating, solar or geothermal, and has constrained certain potentials (e.g. wind energy and

biofuels) to a degree. Of course these constraints can and will be changed in time as the system

is further developed, and as new evidence and research to inform the setup are gathered.

Indeed, on this first calibration exercise a number of areas were identified where national data is

lacking in respect of calibration requirements. This is not exclusively an ‘Irish’ problem, and the

difficulties are quite common at an international level also. The driver of this challenge is

principally that the decision-support demands on the models are requiring increasing levels of

detail in calibration to enable their analysis of complex issues50. However, whilst increased

detail may prompt concern from some quarters, there is cause for optimism in regard to this

50 A major motivation for the development of the www.policymeasures.com resource was to support progress on this ‘calibration challenge’ in a coherent and internationally cooperative manner.

42 | P a g e

‘calibration challenge’, as it is expected that a number of research and technology initiatives,

unknown or untested a decade ago, will be a great support in delivering the requisite data at a

high resolution, that will offer a more robust evidence base into the future (e.g. advanced ICT in

transport, Smart metering in the home). Similarly, the higher level of funded research and

monitoring that prevails nowadays will also facilitate the availability of an evidence base for

calibration that simply did not exist a decade or more ago. For the time being however, we

remain at an earlier stage in this process, and recognise that the calibration of the model

requires additional evidence and research. Improvements in this regard will remain an iterative

process, and we acknowledge that calibration of certain measures in the modelling system (e.g.

the cost and potential for insulation to reduce energy demands in the domestic sector) represent

significant bodies of work in their own right that will take time to address. Nonetheless, the

model framework offers a sound basis on which to integrate evidence for analysis over time.

Non-technical and behavioural policy measures

There are many behavioural measures (e.g. road pricing, carbon taxation) that are not

incorporated into the GAINS methodological framework explicitly. However, in the context of

GHGs and specifically the NETS target, such measures are of particular relevance to national

progress on targets and stimulating the necessary investments and change. Such measures can

be evaluated exogenously and subsequently be incorporated back into the GAINS modelling

framework51 (AP EnvEcon, 2010d).

Analysis to deliver quantified abatement potential estimates of such exogenous measures, as

well as policy interactions have been conducted as part of parallel research under the IMP

Ireland project. Of particular relevance the team have conducted detailed measure analyses and

written papers on topics such as:

The factors influencing vehicle purchase decisions in Ireland (Fu et al., 2011)

Carbon, vehicle registration and road tax policies for transport (Fu and Kelly, 2011a)

The scope for flexible working policies to reduce emissions (Fu et al., 2011b)

51 Though this process remains somewhat complex and challenges persist in regard to accounting for exogenously evaluated measures appropriately in the context of optimisation analyses such as those presented in this report.

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Furthermore, the IMP project has developed and launched a policy and measures resource at

www.Policymeasures.com to serve as an accessible community repository of information

relating to theoretical and empirical environmental policies and measures across all sectors,

both technical and non-technical. Over time these potentials and the evidence from this system

can be integrated into the modelling system and broader model frameworks to offer more

comprehensive and internationally coherent evaluations of abatement potential and policy

synergies and trade-offs.

LULUCF – Carbon Sinks in NETS process

Carbon sinks encompass the net storage and removal of greenhouse gas emissions and are

commonly referred to as the LULUCF (land use, land use change and forestry) sector. The

Environmental Defence Fund (2010) describe LULUCF activities as those covering land use

practices like farming, forestry (afforestation, deforestation & reforestation), ranching, wetland

restoration, and other actions that cause carbon to be absorbed (removed) from the atmosphere

or added to the atmosphere.

The EPA (2011) estimates that carbon sinks have the potential to deliver 4.8Mt of greenhouse

gas emissions reductions in 2020. The ability of the Irish government to use carbon sinks for

emissions compliance would be significant given that Ireland’s carbon sinks equate to

approximately 13% of the NETS target, and the section 5 optimisation results for the WM

scenario has indicated a gap between abatement potential and compliance that must yet be

addressed.

Analysis of LULUCF data contained in Member State Monitoring Mechanism reports submitted

to the EU reveals that other Member States would also significantly benefit from the ability to

use carbon sinks for compliance purposes. Figure 6 provides an illustrative sample of projected

Member State sink levels relative to 2020 NETS targets. At the time of writing carbon sink

projections were available for 16 Member States. Average carbon sink projections are 13Mt with

the largest projected sinks existing in France (50.1Mt), Poland (46.3Mt) and Sweden (29.9Mt).

In relative terms Estonia and Latvia could expect a significant impact on emission inventory,

with respective carbon sinks estimated at 148% and 134% of their NETS target levels. The

median Member State LULUCF share of 2020 NETS targets is 13.2%, thereby placing Ireland on

the European median.

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However, the role of the LULUCF sector in EU climate policy remains undecided and is a hotly

debated topic. At present, Member States cannot use carbon sinks as part of their abatement

strategy to comply with their 2020 (NETS) emissions targets. Until relatively recently EU

climate policy envisioned the 2020 emissions reduction commitment being mainly met through

the ETS and ESD with emissions and removals of GHG in the LULUCF sector excluded as part

of the EU’s reduction commitment. However, since mid-2010 the European Commission has

been actively examining the options for how the LULUCF sector may be included in the policy

framework to contribute to both the EU’s short term (2020) emissions commitments and more

long term (2050) aspirational targets. The Commission’s LULUCF analysis has been driven by

the need to comply with requirements of the Climate and Energy (C&E) Package and the ESD.

The C&E Package indicates that the European Union expects all sectors to contribute to climate

change mitigation in the EU. Articles 8 and 9 of the ESD requiring that if the international

community fails to deliver a new international agreement on climate change by the end of the

2010, that the Commission must assess the modalities for the inclusion of emissions and

removals from activities related to LULUCF in the Community (emissions) reduction

commitment by June 30th 2011.52 53 Following this assessment the Commission should develop,

as appropriate, a legislative proposal for LULUCF inclusion in EU climate policy with the aim

that such legislation will come into force from 2013 onwards.

At the time of writing (July 2011) the Commission has not yet released its LULUCF assessment

report. However, analysis of the assessment report scoping documents indicates that 3 LULUCF

inclusion policy options are considered:54

1. ETS participation

2. ESD participation

3. Development of a separate framework

It would appear that the third option – Separate Framework – is the most likely to feature in a

legislative proposal. According to both the European Commission (2010) and Matthews et al.

(2011) the LULUCF sector should not form part of the ETS because of the existence of

52 Emissions Sharing Directive 53 If an international agreement on climate change had been agreed before December 31st 2010 the terms of the ESD required that the Commission would have to develop a LULUCF assessment report and if appropriate make a legislative proposal within 3 months of the EU’s signing of such an agreement. 54 See European Commission (2010), Matthews et al. (2011), and Entec (2011)

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significant obstacles relating to: emissions monitoring, reporting and verification; the annual

compliance cycle of the ETS; reversibility and variability of emissions removals; allowance

allocation; and price stability.

Figure 6 Selected 2020 Carbon Sink Projections relative to 2020 NETS Emissions Target

The inclusion of LUULCF in the ESD would have both advantages and disadvantages for

Commission policy makers. With the exception of allowance allocation and price stability,

LULUCF inclusion in the ESD would face the same challenges as those presented by ETS

inclusion. The European Climate Change Programme expert group55 did highlight however, that

ESD inclusion would ensure a direct link with related sectors such as agriculture along with

potentially improved scope for a cost efficient achievement of ESD targets.

On balance, it would seem the European Commission (2010) and Matthews et al. (2011) have

both identified establishing a separate policy framework without inclusion in the ETS or ESD as

the preferred option for including the LULUCF sector in the overall EU emissions target. Such

an approach would see the allocation of specific emissions targets to the LULUCF sector while

55 See European Commission 2010.

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providing an opportunity to specifically address the characteristics of forestry and agricultural

land use. The importance of being able to jointly treat forestry and agricultural land use was

recently emphasised by the Commission (European Commission 2011) in its 2011 roadmap for

moving to a competitive low carbon economy when it referred to the need to consider all land

uses in a holistic manner and to formally address land use change and forestry in EU climate

policy. The European Climate Change Programme expert group indicate (European Commission

2011) that in order to incentivise climate change mitigation in the LULUCF sector, policy makers

should give preference to using existing policy instruments rather developing new ones while at

the same time trying to promote coherence between different policy areas. As a result the

expert group has highlighted the potential offered by the Common Agriculture Policy (CAP)

structure, noting how the CAP provides a toolbox of policy instruments that can be used to

incentivise mitigation in agriculture and certain forestry measures.

While the European Commission are reviewing the potential for LULUCF sector inclusion as

part of emissions reduction commitments, it is important to note that the Commission is aware

of the sinks potential (and in some cases substantial potential) that exists in Member States. In

light of the significant ‘sink savings’ identified, it is probable that the European Union will not

want to be seen, at least in an international context, to be offering Member States an exit

strategy with respect to the their existing emission target challenge. As a result, if the EU

approves the use of sinks there exists the potential that they could also decide to increase the

Member States’ NETS target, thereby offsetting some (or potentially all) of the mitigation

benefits that sink inclusion would provide. In such a case, the share of sinks relative to the NETS

target may become relevant.

7. Conclusions

This report presented an analysis of Ireland’s NETS target challenge. Having been established in

2005 with a focus on ability to pay and expected growth, the recent economic turmoil has

altered the backdrop to the NETS target significantly. The recessionary impacts may have

contributed to sectoral emission reductions, but they have also constrained investment

resources and arguably shifted political priorities. As a result, evidence and focused action is

required now that identifies options, and reconciles the cost of investments with the value of

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returns from increased efficiency, abatement and compliance. This report has engaged the

GAINS Ireland model to evaluate a pathway to NETS compliance in 2020. The model was

calibrated with official ‘With Measures’ national energy and activity data, and a hybrid

compilation of information and values for abatement measures, abatement potentials and costs.

The results point to a significant deficit in the defined abatement potentials with respect to

reaching the NETS target of 37.4M tonnes of CO2e in 2020. The LCO optimisation which

introduces measures up to a marginal cost of €50 per tonne left a remaining gap to target for the

NETS of 4.8M tonnes of CO2e in 2020. Other factors to acknowledge include the expected

interim challenges that are posed from 2013 onwards, and the ultimate format of a European

decision in regard to how the role of LULUCF will be addressed in this context.

However, whilst the headline results are not particularly encouraging, there are four additional

conclusions from this report in respect of Ireland’s efforts to address this challenge. Firstly, the

analyses do not represent all that can be done. The model excludes certain policy interventions

(e.g. revisions to the carbon tax) that could also contribute to progress on the target, and there

remains additional extension and calibration of the abatement menu to be conducted over time.

Furthermore, there is certainly untapped potential in the transport sector that has not been

adequately captured in this first calibration of the model and requires further research attention.

Secondly, whilst the target is not met under the WM analysis, the cost assessment from a social

planner perspective indicates no net annual cost, due to cumulative cost savings on certain

measures, where the package of measures up to a marginal cost of €150 per tonne are taken.

This is encouraging, but highlights that whilst social cost analyses indicate worthwhile actions,

barriers such as information asymmetry and financing persist from a private investment

perspective that require innovative solutions. Thirdly, whilst the WM optimisations fail to

achieve the NETS target, the impact on over compliance for the ETS sector is notable. Similarly

there would be strong co-benefits with transboundary air pollution policy objectives where

significant progress is made on the NETS target. Finally, whilst the process has identified many

areas where additional data and evidence are required; there is cause for optimism in respect of

this calibration challenge. A number of potential sources for these data are identified, and the

progressive collating and integration of this information into the model framework will offer a

still stronger analytical tool for navigating a pathway to compliance with NETS from 2013-2020.

Beyond these conclusions, it is also important to map out the direction of further work in this

area. Whilst calls for further research are common to many research reports and may prompt

sentiments ranging from bemusement to exasperation from policy makers, it is important to

48 | P a g e

understand the reasons why further research is required in this area. Specifically, further work is

required as we are dealing with a comparatively new area of research and new policy challenges

where we have yet to establish an adequate foundation of knowledge and evidence on which to

base decisions. A decade ago the monitoring and management of greenhouse gas emissions both

nationally and internationally was at a far less advanced level. The methods, the science and the

driving policy frameworks have all advanced considerably in the intervening years. As one

outcome we find ourselves in a position where we face significant policy challenges such as the

NETS target, which represent high cost and high stakes agreements, and where we must be

appropriately equipped to respond. A platform for integrated modelling of choices across

climate and air pollution policy is one important tool in managing cost-effective responses.

GAINS is used at a European level by the Commission, as well as at national levels in a number

of countries and regions to inform this field of policy. Therefore the GAINS Ireland model is well

placed to contribute to the national efforts on these specific policy challenges.

However, the GAINS Ireland model can only continue to support these policy challenges where

the system is actively managed. In regard to the model methodology, this is evolving and GAINS

Ireland will continue to be based on the best insights from the national and International

research community wherein Ireland is now an active participant. It is instead the data and the

detail for calibration which present arguably the greatest challenge nationally in regard to

applying this decision-support tool in the next phase of work. The level of detail required in

regard to data for calibration may seem onerous, but again this is a function not of the desire to

operate complex models, but rather out of necessity for addressing broad and complex issues. In

practice, the information required is not in fact particularly complex, it is instead simply

detailed and deep. As noted though, there are new sources and new research that will deliver a

more solid foundation of detailed information for all stakeholders and modellers in this area in

the coming years. It is absolutely certain though that the economic outlooks, technological

potentials and policy directions will change, and consequently that the tools which provide

associated decision support, such as GAINS Ireland, must be similarly dynamic. Through the

sustained integration of new research and information the GAINS Ireland model will become an

established and increasingly relevant and valuable analytical tool in this national policy context

as we plot a course to meet our goals.

49 | P a g e

8. References and Bibliography

Amann, M., Bertok, I., Borken, J., Cofala, J., Heyes, C., Hoglund, L., Klimont, Z., Purohit, P., Rafaj, P.,

Schopp, W., Toth, G., Wagner, F. and Winiwarter, W. 2009. Potentials and costs for greenhouse gas

mitigation in annex I countries, Interim Report IR-09-043, Laxenburg, Austria.

Amann, M., Bertok, I., Borken, J., Cofala, J., Heyes, C., Hoglund, L., Klimont, Z., Nguyen, B., Posch, M.,

Sandler, R., Rafaj, P., Schopp, W., Wagner, F. and Winiwarter, W. 2011. Cost-effective control of air

quality and greenhouse gases in Europe: Modelling and Policy Analysis, Forthcoming in Environmental

Modelling & Software.

AP EnvEcon, 2008. GHGs and GAINS – Capacities of the GAINS model with respect to greenhouse

gases. AP EnvEcon Report accessible online at GHG & GAINS Brief 2008, Summer 2008.

AP EnvEcon, 2009. GAINS Agricultural Guide Version 1, AP EnvEcon Report, Spring 2009.

AP EnvEcon, 2010a. GAINS Abatement Cost Guide. AP EnvEcon Report accessible online at Cost Guide

2010, Winter 2010.

AP EnvEcon, 2010b. The NOx National Emission Ceiling for Ireland: A review of progress to date, further

options, and the factors that militate against compliance, AP EnvEcon Report accessible online at NOX

Ireland Report 2010, Autumn 2010.

AP EnvEcon, 2010c. GAINS Ireland Energy Guide Version 1, AP EnvEcon Report accessible online at

Energy Guide 2010, Spring 2010.

AP EnvEcon 2010d. Non-Technical Measures Report I, AP EnvEcon Report accessible online at NTM

Report 2010, Winter 2010.

Barker, T., I. Bashmakov, A. Alharti, M. Amann, L. Cifuentes, J. Drexhage, M. Duan, O. Edenhofer, B.

Flannery, M. Grubb, M. Hoogwijk, F. Ibitoye, C. J. Jepma, W. A. Pizer and K. Yamaji (2007). Mitigation

from a cross-sectoral perspective, Climate Change 2007: Mitigation. Contribution of Working Group III

to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and

New York, USA, Cambridge University Press.

50 | P a g e

Borken-Kleefeld, J., J. Cofala, Z. Klimont, P. Purohit and P. Rafaj (2008). GHG mitigation potentials and

costs in the transport sector of Annex I countries - Methodology. International Institute for Applied

Systems Analysis (IIASA), Laxenburg, Austria.

Cofala, J., Purohit, P., Rafaj, P. and Klimont Z., 2008. Estimating CO2 mitigation potentials and costs

from energy use and industrial sources.

Dineen, D. and O Gallachoir, B (2010) Modelling the impacts of building regulations and a property

bubble on residential space and water heating, Energy and Buildings, (43): 166-178.

Daly, H. and Ó’ Gallachóir, B (2011) Modelling private car energy demand using a technological car stock

model, Transportation Research Part D, (16): 93-101.

Entec, 2011. Report for the European Commission: Public Consultation on the role of agriculture and

forestry in achieving the EU’s climate change commitments – Results. Final Report.

Environmental Defence Fund, 2010. Forestry and Land Use Policy 101 (LULUCF).Washington.

EPA, 2011a. Ireland’s Greenhouse Gas Emissions Projections 2010 – 2020. Ireland.

EPA, 2011b. Monitoring Mechanism. Report developed to comply with emissions reporting obligations

established under Article 3 (2) of the European Commission’s Monitoring Mechanism Decision

(Commission Decision 208/2004/EC) and elaborated in Articles 8, 9 and 10 of the Implementing

Provisions (Commission Decision 2005/166/EC) and UNFCCC reporting guidelines for national

communications (FCCC/CP/1999/7).

EPA, 2012. Ireland’s Greenhouse Gas Emissions Projections 2010 – 2020. Ireland.

51 | P a g e

European Commission, 2010. Summary Report on the work carried out by European Climate Change

Programme (ECCP) expert group on Climate Policy for Land Use, Land Use Change and Forestry

(LULUCF). Final Report. DG Climate Action & DG Environment. Brussels.

European Commission, 2011. A Roadmap for moving to a competitive low carbon economy in 2050.

COM(2011) 112 final. Brussels.

Hoglund-Isaksson, L., Winiwarter,. W., and Tohka, A., 2009. Potential and Costs for Mitigation of Non-

CO2 Greenhouse Gases in Annex I Countries. Version 2. Laxenburg, Austria.

Hoglund-Isaksson, L., and Mechler, R., 2005. The GAINS Model for Greenhouse Gases – Methane (CH4).

IIASA Interim Report IR-05-54. Laxenburg. Austria.

Fu., M., Ahern, A. and Kelly, J.A., 2011. Regional characteristics and the distribution of car engine sizes: A

case study of Ireland, Transportation Research Part D, (16) 509-514.

Fu., M. and Kelly, J.A., 2011a. Carbon related taxation policies for road transport: Efficacy of ownership

and usage taxes, the role of public transport and motorist cost perception on policy outcomes, in

Submission process.

Fu., M., Kelly, J.A., Clinch, J.P. and King, F. 2011b. Environmental policy implications of working from

home: modelling the impacts of land-use, infrastructure and socio-demographics, in Submission process.

Kelly, J.A., Ryan, L., Casey, E. and O’Riordan, N., 2009. Profiling road transport activity: Emissions from

2000-2005 in Ireland using national car test data , in Transport Policy, 16, (4).

Klaassen, G., Amann, M., Berglund, C., Cofala, J., Hoglund-Isaksson, L., Heyes, C., Mechler, R., Tohka,

R., Schopp, W., and Winiwarter, W., 2004. The Extension of the RAINS Model to Greenhouse Gases.

IIASA Interim Report IR-04-015. Laxenburg, Austria.

52 | P a g e

Klaassen, G., Berglund, C., and Wagner, F., 2005. The GAINS Model for Greenhouse Gases – Version 1.0:

Carbon Dioxide (CO2). IIASA Interim Report IR-05-53. Laxenburg. Austria.

Matthews, R., Kuikman, P., & Watterson, J., 2011. Possible ways forward – Preliminary results from a

study on policy options for inclusion of LULUCF in the EU’s greenhouse gas reduction commitments.

Report for the European Commission.

Perez-Lombard, L., Ortiz, J. and Pout, C., 2008, A review on buildings energy consumption information,

Energy and Buildings, (40): 394-398

Regan, F. and O’ Gallachoir, B., 2011, Ex-Post evaluation of a residential energy efficiency policy measure

using empirical data, Conference proceedings of ECEEE 2011 Summer Study

Sustainable Energy Authority of Ireland (2010) Energy Forecasts for Ireland to 2020, 2010 Report,

accessible online at SEAI Energy Forecasts Report 2010

Tohka, A., 2005. The GAINS Model for Greenhouse Gases – HFC, PFC and SF6. IIASA Interim Report IR-

05-56. Laxenburg, Austria.

Wagner, F., Schoepp, W., Amann, M., Cofala, J., Borken, J., Hoglund, L., Winiwarter. W., 2010. GAINS

Cost Curves for Annex 1 Parties: Documentation of the Optimisation Module. Interim Report??

Laxenburg, Austria.

Winiwarter. W., 2005. The GAINS Model for Greenhouse Gases – Nitrous Oxide (N2O). IIASA Interim

Report IR-05-55. Laxenburg. Austria.

53 | P a g e

9. Appendix – Marginal Abatement Cost Measures

The following tables provide the measures at various marginal cost price ranges as selected by the model

under optimisation. Generally measures are transparent and should be easily interpreted, however, in

certain cases, for example certain agricultural measures (e.g. Feed), the specified measure is a proxy for a

package of measures related to that category heading. The list was obtained by comparing GAINS Ireland

model results at consecutive marginal cost values. Thus, this table provides a list of incremental steps of

measures. It takes into account the fact that the combination of mitigation measures may not be as

effective as the sum of the measures taken individually. For example, from a systems perspective reducing

electricity consumption is a less effective mitigation measure per se if it is accompanied with a

decarbonisation of the supply fuel mix.

With regard to interpretation, the column More describes the measures that would be taken in addition to

those taken at the previous marginal cost. Likewise measures under the heading Less are being reduced

relative to the previous marginal cost. At each marginal cost the list also shows:

• The remaining GHG emissions (in Mt CO2eq) for the ETS and NETS, and the NETS alone.

• The total amount of GHG emissions reduced relative to the baseline (in MtCO2eq)

In interpretation of the marginal abatement list it is also important to reiterate that the table has been

shortened to the additional or changed measures at a given marginal cost level so as to avoid

considerable repetition. Therefore we build on the measures from the baseline level, adding or changing

the portfolio at each cost level. Thus for a given marginal cost price level there is generally a larger

portfolio of options than is listed. Furthermore, there are some complexities to interpreting the outcomes.

For example, in a small number of cases the model calls for less of a higher efficiency standard (e.g. stage

2) and more of a lower efficiency standard (e.g. stage 1). Whilst apparently counter intuitive to the task at

hand, the outcome is a function of cost. Specifically, as noted above, at a low carbon price electricity

savings offer a cost-effective GHG mitigation measure. However, as the carbon price rises, Ireland is

decarbonising its power system with the outcome that the actual per tonne cost of carbon is reduced by

the electricity saving measure has now become more expensive.

In regard to the actual marginal cost values, costs are annual marginal cost of abatement per tonne of

CO2e in 2020. As noted in section 5, the cost methodology employed excludes transaction and transfer

costs, and is based on an interest rate of 4% €2005.

54 | P a g e

Marginal

Cost

Sector(s)

affected More of… Less of…

Remaining

emissions

Remaining

NETS

emissions

Cumulative

reductions

relative to

baseline

€0/t

CO2eq

Domestic

(Residential &

Commercial)

Residential: Stage 1 efficiency

measures: HVAC (new houses and

apartments), small appliances,

thermal water heating. Stage 2

efficiency measures: cooking with

electricity, thermal fuels, electric

water heating, thermal water

heating. Commercial: Stage 1

efficiency measures: HVAC

(existing buildings), large

appliances, cooking with

electricity, thermal fuels,

commercial lighting, thermal water

heating. Stage 2: HVAC (new

buildings), small and large

appliances, cooking with

electricity, thermal fuels,

commercial lighting.

Residential: Standard efficiency:

heating and cooling (new

apartments and houses), small

appliances, cooking with

electricity, electric water

heating, thermal water heating.

Efficiency measures stage 1:

cooking with electricity, thermal

fuels, electric water heating.

Commercial: Standard efficiency:

heating and cooling (new and

existing buildings), small and

large appliances, cooking with

electricity, thermal fuels,

lighting, thermal water heating.

Efficiency measures stage 1:

heating and cooling (new

buildings), small appliances.

62.0 Mt

CO2eq

46.2 Mt

CO2eq

-3.1 Mt

CO2eq

Transport

Light duty gasoline and diesel

trucks with advanced internal

combustion engine, highest

efficiency diesel buses and heavy

duty diesel trucks, advanced hybrid

diesel LD trucks, LD hybrid gasoline

and diesel trucks, improved

efficiency diesel buses and HD

diesel trucks.

Heavy and light duty diesel

trucks with standard efficiency,

light duty gasoline trucks with

standard efficiency, diesel buses

with standard efficiency.

Waste Food waste incineration Landfill with gas recovery and

flaring and utilisation.

Agriculture Deep injection of manure into soils

Other

Alternative refrigerant, reduced

gas losses at compressor stations

for gas transmission, doubling of

leak control frequency at gas

distribution networks.

Refrigeration good practice: Leak

control & end-of-life collection,

replacement cast iron gas

distribution networks.

55 | P a g e

Marginal

Cost

Sector(s)

affected More of… Less of…

Remaining

emissions

Remaining

NETS

emissions

Cumulative

reductions

relative to

baseline

€25/t

CO2eq

Power Biofuels, wind energy

New conventional coal fired

power plants, new gas fired

power plants

57.2 Mt

CO2eq

42.6 Mt

CO2eq

-7.9 Mt

CO2eq

Domestic

(Residential)

Standard efficiency: small

appliances, Stage 2 efficiency

measures: thermal water heating

Stage 1 efficiency measures:

small appliances, residential

thermal water heating

Industry

Stage 1 efficiency improvements:

Energy conversions industry, non-

ferrous metals industry

Efficiency improvements: best

current practice (energy

conversions industry, non-

ferrous metals industry)

Waste

Anaerobic digestion with gas

recovery and utilisation (food

industry waste), optimising

wastewater treatment to abate

N2O emissions

Landfill with gas recovery and

flaring and utilisation (food

industry waste).

Agriculture

Farm scale anaerobic digester

(manure management), mix of

feed changes (enteric

fermentation), optimising

agricultural nitrogen efficiency by

"precision farming"

Deep injection of manure into

soils as fertiliser

Other

Alternative propellant (aerosols),

alternative blowing agent (other

foams), process modifications

including alternative refrigerants

(industrial refrigeration), increased

flaring of associated gas (oil and

gas production), flaring (oil

refining), product use as in

anaesthetic abolished by full

replacement.

Industrial refrigeration good

practice - end-of-life recollection,

product use in anaesthetic

reduced by combination therapy.

56 | P a g e

Marginal

Cost

Sector(s)

affected More of… Less of…

Remaining

emissions

Remaining

NETS

emissions

Cumulative

reductions

relative to

baseline

€50/t

CO2eq

Power Biofuels, new gas-fired power

plants

New conventional coal-fired

power plants

55.9 Mt

CO2eq

42.2 Mt

CO2eq

-9.2 Mt

CO2eq

Domestic

(Commercial)

Standard efficiency: cooking with

electricity. Stage 1 efficiency

measures: lighting.

Stage 2 efficiency measures:

cooking with electricity, lighting

Industry

Stage 1 efficiency improvements:

Non-metallic minerals industry.

Stage 2 efficiency improvements:

Non-ferrous metals industry.

Combined heat and power plants

used in industry.

Efficiency improvements: best

current practice (non-ferrous

metals industry, non-metallic

minerals industry). Stage 1

efficiency improvements: Non-

ferrous metals industry.

Other

Alternative refrigerant: pressurised

CO2 (mobile air conditioner),

Alternative solvent: NF3

(semiconductors), industrial

refrigeration: process

modifications including alternative

refrigerants.

Mobile air conditioner: good

practice - end-of-life

recollection. Industrial

refrigeration: good practice -

leak control.

57 | P a g e

Marginal

Cost

Sector(s)

affected More of… Less of…

Remaining

emissions

Remaining

NETS

emissions

Cumulative

reductions

relative to

baseline

€75/t

CO2eq

Power New gas-fired power plants Existing power plants using fossil

fuels, new oil-fired power plants

53.3 Mt

CO2eq

42.1 Mt

CO2eq

-11.8 Mt

CO2eq

Domestic

(Commercial)

Standard efficiency: Large

appliances

Stage 2 efficiency measures:

Large appliances

Industry

Efficiency improvements: best

current practice - non-ferrous

metals industry. Stage 2 efficiency

improvements: Non-ferrous metals

industry.

Stage 1 efficiency improvements:

Non-ferrous metals industry

Other

Commercial refrigeration: Process

modifications including alternative

refrigerants.

Commercial refrigeration: Good

practice - end-of-life recollection

€100/t

CO2eq

Power New gas-fired power plants Existing power plants using fossil

fuels

52.4 Mt

CO2eq

41.2 Mt

CO2eq

-12.7 Mt

CO2eq

Industry

Efficiency improvements: best

current practice - energy

conversions industry. Stage 2

efficiency improvements: Non-

ferrous metals industry.

Efficiency improvements: best

current practice - Non-ferrous

metals industry. Stage 1

efficiency improvements: Energy

conversion industry.

Agriculture Manure management: Farm-scale

anaerobic digester. -

Other

Commercial refrigeration: Process

modifications including alternative

refrigerants.

Commercial refrigeration: Good

practice - leak control.

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Marginal

Cost

Sector(s)

affected More of… Less of…

Remaining

emissions

Remaining

NETS

emissions

€125/t

CO2eq

Domestic

(Residential &

Commercial)

Stage 1 efficiency measures:

electric water heating (residential).

Stage 2 efficiency measures:

Thermal water heating

(commercial).

Standard efficiency: Thermal

water heating (commercial).

Stage 2 efficiency measures:

Electric water heating

(residential).

52.1 Mt

CO2eq

41.0 Mt

CO2eq

Industry

Stage 1 efficiency improvements:

Chemical industry, Energy

conversions industry, Iron and

steel industry, Paper and pulp

industry. Stage 2 efficiency

improvements: Non-ferrous metals

industry.

Efficiency improvements - best

current practice: Chemical

industry, Energy conversions

industry, Iron and steel industry,

Non-ferrous metals industry,

Paper and pulp industry.

Agriculture Enteric fermentation: Mix of feed

changes -

Other

Mobile air conditioning:

Alternative refrigerant (pressurised

CO2).

Mobile air conditioning: Good

practice - leak control

Marginal

Cost

Sector(s)

affected More of… Less of…

Remaining

emissions

Remaining

NETS

emissions

€150/t

CO2eq

Power New gas-fired power plants -

52.1 Mt

CO2eq

41.0 Mt

CO2eq

Industry

Combined heat and power plants

used in industry. Stage 1 efficiency

improvements: Paper and pulp

industry.

Efficiency improvements: Best

current practice - Paper and pulp

industry.

Other

Stationary air conditioning: Process

modification including alternative

refrigerant. Gas distribution

networks: Replacement grey cast

iron networks.

Stationary air conditioning: Good

practice - end-of-life

recollection. Gas distribution

networks: Doubling of leak

control frequency.

Marginal

Cost

Sector(s)

affected More of… Less of…

Remaining

emissions

Remaining

NETS

emissions

59 | P a g e

€175/t

CO2eq

Domestic

(Residential)

Stage 2 efficiency measures:

Heating and cooling (new and

existing houses), electric water

heating.

Standard efficiency: Heating and

cooling (new houses). Stage 1

efficiency measures: Heating and

cooling (existing houses), electric

water heating.

51.5 Mt

CO2eq

40.5 Mt

CO2eq

Industry Stage 2 efficiency improvements:

Chemical industry

Stage 1 efficiency improvements:

Chemical industry

€200/t

CO2eq

Domestic

(Residential &

Commercial)

Stage 1 efficiency measures:

residential electric water heating.

Stage 2 efficiency measures:

Commercial cooking - thermal

fuels.

Stage 1 efficiency measures:

Commercial cooking - thermal

fuels. Stage 2 efficiency

measures: Residential electric

water heating.

51.4 Mt

CO2eq

40.4 Mt

CO2eq Industry

Stage 2 efficiency improvements:

Chemical industry. Stage 3

efficiency improvements: Non-

ferrous metals industry, non-

metallic minerals industry.

Stage 1 efficiency improvements:

Chemical industry, non-metallic

minerals industry. Stage 2

efficiency improvements: Non-

ferrous metals industry.

Waste Food industry waste: Incineration

Food industry waste: Anaerobic

digestion with gas recovery and

utilisation.

€225/t

CO2eq Power New gas-fired power plants

Existing power plants using fossil

fuels

51.4 Mt

CO2eq

40.4 Mt

CO2eq