establishing mechanisms for payments for carbon ......encompassing all power stations over 20mw, oil...

The Edinburgh Centre

for Carbon Management

ESD (Energy for Sus tai nable Development) Group

Establishing Mechanisms for Payments for Carbon Environmental Services in the Eastern Arc Mountains, Tanzania. May 2007

ECCM 14/05/2007

1

Table of Contents

Glossary ..........................................................................................................................................3

1. Introduction ............................................................................................................................4

1.1 Objectives of the study.............................................................................................................4

1.2 Conceptual framework for quantifying project carbon benefits................................................5

1.3 What is carbon trading? ...........................................................................................................6

1.4 Forests and land use in the global carbon cycle......................................................................7

1.5 Why carbon is the most practical unit of environmental service..............................................8

1.6 Carbon management using forests and land use....................................................................9

2. Methods ................................................................................................................................10

2.1 Review of Legal and Market Framework ...............................................................................10

2.2 Estimate of Potential Carbon Benefits from Conservation ....................................................10

3. Results and Discussion ......................................................................................................23

3.1 Review of Legal and Market Framework ...............................................................................23

3.2 Voluntary Carbon Market .......................................................................................................24

3.3 Estimate of Potential Carbon Benefits from Conservation ....................................................25

3.4 Alternative approaches to project implementation.................................................................29

3.5 Plan vivo scheme...................................................................................................................30

4. Conclusions and Recommendations.................................................................................33

4.1 Research Priorities.................................................................................................................33

4.2 Action Priorities ......................................................................................................................33

5. References............................................................................................................................34

Appendix 1 ....................................................................................................................................36

Appendix 2 ....................................................................................................................................39

Appendix 3 ....................................................................................................................................41

Appendix 4 ....................................................................................................................................42

Appendix 5 ....................................................................................................................................61

ECCM 14/05/2007

2

Acknowledgements

ECCM would like to thank the following people for their assistance in providing data, information and guidance to this study:

Dr Boniface Mbilinyi, Sokoine University of Agriculture Alfred Kalaghe, Africare Eliakimu Zahabu, FORCONSULT Dos Santos Silayo, FORCONSULT Dr Felician Kilahama CMEAMF Dr Neil Burgess WWF/ UNDP Antje Ahrends and other staff at the Institute for Tropical Ecosystem Dynamics, York University Internal Contributors at ECCM / ESD: Richard Tipper Will Garrett Jeff Felton Jessica Abbott Geoffrey Onyango Murefu Barasa Chris Sherrington, Scottish Agricultural College

ECCM 14/05/2007

3

Glossary

Carbon Used in carbon trading or economic discussion to denote all greenhouse

gases Carbon Dioxide Equivalent

The universal unit of measurement used to indicate the global warming potential (GWP) of each of the 6 Kyoto greenhouse gases. It is used to evaluate the impacts of releasing (or avoiding the release of) different greenhouse gases

CCB Climate Community and Biodiversity

CDM Clean Development Mechanism

CER Certified Emission Reduction

CMEAMF Conservation and Management of the Eastern Arc Mountains Forests

DFID Department for International Development (UK government)

EAMCEF Eastern Arc Mountains Conservation Endowment Fund

FORCONSULT A consultancy unit of the Faculty of Forestry and Conservation at Sokoine University of Agriculture, Tanzania.

GHG Greenhouse gas. The current IPCC inventory includes six major greenhouse gases. These are Carbon dioxide (CO2), Methane (CH4), Nitrous oxide (N2O), Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs), Sulphur hexafluoride (SF6).

GtC Giga tonnes of Carbon

IPCC The Intergovernmental Panel on Climate Change.

ODA Official Development Assistance

UNDP United Nations Development Programme

UNFCCC United Nations Framework Convention on Climate Change

VER Verified Emission Reduction

ECCM 14/05/2007

4

1. Introduction

The development of international carbon markets such as the Clean Development Mechanism (CDM) and voluntary carbon offset schemes can mobilise resources from the private sector and national government compliance funds to help combat deforestation, land degradation and poverty. There is potential for forest conservation and ecological restoration projects to sell carbon services to companies and institutions that wish to offset their CO2 emissions or achieve compliance within national emissions trading schemes.

ECCM have undertaken a study of land use change and deforestation in the Eastern Arc Mountains of Tanzania based on information provided by staff at Sokoine University, FORCONSULT and CMEAMF. Carbon benefit potential from conservation has been estimated and the potential for establishing mechanisms for payments for carbon environmental services has been examined in the context of the current legal and market Framework. Preliminary results were presented at a workshop with stakeholders, held in Dar Es Salaam on 20-21

st February 2007. These results have since been reviewed incorporating feedback from the

workshop.

1.1 Objectives of the study

The Eastern Arc Mountain chain is a series of isolated mountains stretching southwards from the Taita Hills in Kenya through Tanzania to the Udzungwa and Mahenge Mountains. Much of the original forest, especially at the more accessible or lower elevations, has been converted for agricultural crops. Despite the human influence on the natural vegetation, these mountains are recognised as one of 24 globally important "hot spots" for forest biodiversity according to Conservation International. The mountains also serve as water catchments for urban areas such as Dar es Salaam, Tanga, and Morogoro. The forests provide firewood, medicinal plants, lumber, and other forest-related products. Local villagers depend on these forests for their livelihoods and where there were once closed mountain forests, there is now a patchwork of forest fragments and agriculture. The aim of this study is to provide UNDP and CMEAMF with information about the potential for mobilising carbon finance towards the conservation and restoration of forests in the Eastern Arc Mountains (EAM) of Tanzania. Specific objectives are:

• To summarise the status and requirements of different markets for carbon services and how these might relate to activities promoting conservation (and restoration) of forests in the Eastern Arc Mountains.

• To provide an estimate of the potential carbon benefits that could be achieved through conservation of forests in the EAM.

• To recommend ways of developing carbon finance streams towards the conservation of forests in the EAM.

ECCM 14/05/2007

5

1.2 Conceptual framework for quantifying project carbon benefits

The conceptual framework for quantifying the carbon benefits of emission reduction and carbon sequestration projects has been developed over the past 10 years across a wide range of technologies and economic situations. In principle, it should be possible to quantify the carbon impacts of any defined set of actions orr project through the following steps:

1. Definition of the boundary and scope of the activity or project - in the case of a land use project, this would include the physical boundaries, whereas in a renewable electricity project it might include the sources of electricity (power stations) where electrical load is displaced. This will result in a list of all the processes that result in uptake or release of carbon (and other greenhouse gases covered by the Kyoto Protocol) as a result of the project activities.

2. Description of the baseline and additionality - the effect of the project is measured relative to a 'baseline scenario' that represents what would happen in the absence of the project. Additionality is the extent to which the carbon finance is instrumental in making a real difference to the outcome of the project, and the extent to which the project makes a difference to emissions of greenhouse gases.

3. Quantification of baseline emissions and crediting period - the emissions that would occur with the baseline scenario, and the number of years over which the project may take credit. The crediting period varies depending on the type of carbon trading scheme.

4. The emissions and uptake of carbon by the project - in the case of afforestation and reforestation projects, the uptake of carbon will be calculated using forestry growth data. The net benefit of the project is then calculated by subtracting the emissions that would have occurred in the baseline scenario.

5. Adjustment for leakage and risk of reversal - The amount of benefit for which a project will be allowed to take credit must be adjusted to take account of leakage and risks of reversal. Reversal is the unplanned loss of carbon stocks as a result of human activities or natural events (this does not include planned carbon losses resulting from actions such as timber harvesting, which should be taken into account in the project design). Leakage is an unintended loss of carbon benefit outside the project boundary that occurs as a direct result of project activities. For example if timber extraction is displaced from a conservation area to an area outside the project. The specific adjustment procedures applied vary considerably between different schemes but creating a reserve or buffer of carbon offsets is the most straightforward. The best approach to managing leakage is to avoid it in the first place. This is best done at the project design stage, notably by:

a. Consultation and working with local stakeholders –ensuring that food, fuel and timber production is not compromised by project activities;

b. Integration of project design with local, regional and/or national priorities and legislation;

c. Participation of landowners or managers in the project, avoiding their exclusion or displacement;

d. Effective monitoring of project activities and likely sources of leakage.

The ways in which these methods for quantifying carbon benefits for land use and forestry projects are applied will be discussed in more detail in Section 3.

ECCM 14/05/2007

6

1.3 What is carbon trading?

Carbon trading is the transaction of carbon based financial instruments. There are two main types of carbon instruments – allowances (or permits) and credits (or offsets). Allowances are issued by governments (either by grandfathering

1 or auction) to companies that

emit GHG’s who must then surrender (or retire) them against actual emissions on a periodic basis. If a company emits more than its allocation it must purchase allowances from a company that has surplus allowances or else pay a fine. If a company reduces its emissions below the level of its allocation it may sell the excess allowances or (subject to some restrictions) bank these emissions for future use. The major allowance based trading scheme is the European Emissions Trading Scheme (EUETS), that covers about 12,500 major combustion plants in Europe – encompassing all power stations over 20MW, oil refineries, cement works, steel works and major chemical plants. Credits are instruments issued by a crediting authority or, in the case of the voluntary sector by project owners themselves, to denote emission reductions achieved relative to a baseline of “business as usual”. The major crediting scheme is the Clean Development Mechanism (CDM), administered by the CDM Executive Board. To date the CDM has approved (registered) almost 500 projects. For reasons to be explained in Section 4, the vast majority of the CDM projects are large scale industrial project involving the capture of methane, destruction of HFC

2s (and other

gases with high global warming potentials). Credits are issued to projects once they have achieved demonstrated emission reductions. In the case of the CDM the credits are known as CERs and these may be used in the EUETS in place of allowances by companies who need to purchase additional instruments to cover their actual emissions. In the case of voluntary sector offset schemes credits may be purchased to compensate for (or neutralise) emissions on a goodwill basis. Figure 1.1 shows why many businesses favour carbon trading regimes rather than environmental taxes: Figure 1.1 Carbon Trading Regime

It is important to note that carbon trading itself does not reduce GHG emissions; trading merely transfers carbon units from one owner to another. Market theory indicates that the market will

1 Given to companies based upon their historic emissions 2 HFC = Hydroflourocarbon.

Old steel factory (high emission)

Hi tec’ industry (low emission)

Goes bust – production shifts to China

Pays tax (no environmental improvement)

Carbon Trading Regime

Old steel factory (high emission)

Hi tec’ industry (low emission)

Reduced energy cost and emissions

Takes % ownership of emission reduction

Invests in energy efficiency in old factory

ECCM 14/05/2007

7

seek out the most efficient way of reducing emissions, thereby reducing the overall economic cost of meeting a given environmental target.

1.4 Forests and land use in the global carbon cycle

Around 50% of the mass of dry plant matter consists of carbon in the form of carbohydrates and

other organic compounds, derived from atmospheric CO2 through photosynthesis. Recent

surveys indicate that the present stock of carbon in the terrestrial biosphere is around 2000 Gt

(Malhi et al., 1999). While there is considerable uncertainty as to how terrestrial carbon stocks

have changed in the 18,000 years since the last glacial maximum, there is clear evidence that

human activities over the past 500 years—principally the expansion of agriculture and exploitation

of forests for fuel and timber—have led to significant reductions of these stocks.

Figure 1.2 Potential change to annual carbon flows from 2020 to 2080 due to carbon management in forestry and land use sectors. The figures in black represent current annual flows, and those in green represent approximate potential for managed change (both are expressed in GtC yr–1).

According to Houghton (1996), over 100 GtC were released to the atmosphere between 1850

and 1980 through changes in land use. Current concern about rising levels of CO2 in the

atmosphere has now focused considerable attention on both conventional and novel techniques

to increase the carbon stored in terrestrial stocks. The integration of greenhouse gas

considerations into other objectives of land use management has now been termed “terrestrial

carbon management”.

Figure 1.2 shows how some of the main options for increasing carbon storage in terrestrial stocks

relate to the carbon cycle. Forests, which account for about 80% of the flows of carbon between

terrestrial vegetation and the atmosphere, have attracted most attention for potential carbon

management. Intact forests may be managed to increase their uptake of carbon and conserved to

reduce emissions; new forests may be established to act as carbon sinks—so-called carbon

sequestration. Forest products may also provide products that can substitute for fossil fuels and

energy-intensive products such as cement and steel. Other land use measures, such as the

adoption of zero tillage or non-burning agricultural practices, appear to offer considerable

potential for stabilising or increasing durable stocks of soil carbon. Improvements in the efficiency

of fertiliser and irrigation water use can have indirect effects on CO2 emissions by reducing fossil

fuels used in the Haber–Bosch process and for pumping. Non- CO2 greenhouse gas emissions,

5.8 –3?

0.1? –0.05?

0.1? –0.05?

118 120 +4?

2 –2

0.2 +1.0

Intact terrestrial ecosystem exchange

Logging and deforest-ation

Forest regrowth and plantations

Fossil fuel and cement production

CH4 from rice and livestock

CO2 from fertiliser and water pumping

ECCM 14/05/2007

8

including methane and N2O, can be mitigated by changes to rice cultivation, fertilisation practices

and livestock feeding.

The potential of land use and forestry activities to reduce GHG emissions over the next 50–100

years appears considerable, possibly of the order of 60–120 GtC (Brown et al., 1996; Kauppi and

Sedjo, 2001). However, it should be stressed that terrestrial carbon management cannot provide

a comprehensive solution to greenhouse gas emissions and that stabilisation of atmospheric CO2

at levels that avoid serious and irreversible damage will also require significant reductions in

emissions from fossil fuel sources.

1.5 Why carbon is the most practical unit of environmental service

While forests and woodland ecosystems can provide several environmental benefits in addition to

carbon sequestration, carbon is the only environmental service that has a global market, and

which can be objectively quantified using standard methods across a wide range of ecosystems.

In many cases, carbon is therefore likely to be the most practical unit of environmental service. A

comparison between environmental services is made in Table 1.1 below.

Table 1.1 Markets for environmental services

Quantification of Benefit Market for Benefits

Biodiversity Very site specific – difficult to give

a linear measure of value

No standard market. Financial

contributions typically made on a

case-by-case basis.

Water

Difficult to quantify – depends on

hydrology and microclimate

Can establish local markets – user

payment schemes, but practicality

depends on specific local factors

(catchment size and users’ ability to

pay). Soil protection

Depends on soil type and rainfall. Main beneficiaries are land owners

themselves, so limited potential for

transactions.

Carbon Linear quantification of benefit.

Standard methods of

quantification, based on

conventional forest mensuration.

Applicable to all forests.

Large global market. Rising

demand.

ECCM 14/05/2007

9

1.6 Carbon management using forests and land use

One of the main reasons why environmental service payments have been of interest to

organisations seeking to conserve forests and establish sustainable land management is the

frequent ineffectiveness of conventional development aid funding when addressing the underlying

problems of non-sustainable land use. Problem factors identified with development aid include:

• tendency for development funding to be skewed towards interests of donors and/or

central government rather than local needs.

• tendency for development funding to create short-term impacts.

• tendency for development funding to create a dependency attitude (NGOs established

to take advantage of funding streams; recipients often regard aid inputs as

unconditional).

• tendency for the benefits of development funding to be captured by local elites or used

up in administration, monitoring and research.

There has been great interest in using environmental service payments as either a substitute for

development assistance or as a transition from aid to conventional business funding. While the

experience of environmental service payments is too limited to form definite conclusions, there

are a number of factors that indicate why environmental service payments may be more effective

than ODA:

• Environmental service payments are made in relation to objectively verifiable progress

/ improvements, rather than as unconditional support;

• As a service provision the transaction is less “one-sided” than conventional donor

funding (both parties have rights and responsibilities under terms of contract);

• The brokers and project developers are subject to commercial pressures and need to

maintain efficiency and effectiveness;

• Environmental service provision is inherently long-term and tied in with sustainable

resource management.

ECCM 14/05/2007

10

2. Methods

2.1 Review of Legal and Market Framework

A summary of the current state of the Kyoto compliance (CDM) market and the voluntary carbon offset market is given in section 4.1. This review is based on ECCM’s experience as an advisor to a number of project developers, carbon offset purchasing organisations and government bodies on both sectors of the carbon market.

2.2 Estimate of Potential Carbon Benefits from Conservation

To estimate potential carbon benefits from conservation and restoration of EAM forests, ECCM examined land use / deforestation from 1995 to ~2000 and extrapolated this change into the future. The stages in this method were as follows:

• estimation of the above ground carbon stock in each main vegetation type • estimation of changes in land use / vegetation type from 1995 to ~2000 • combination of land use change and carbon stock data to derive historic emissions • examination of “driving factors” by comparison of land use changes in different situations • projection of future emissions

These stages are described below. Stage 1. Estimation of above-ground carbon density for each main forest type Data Sources Given the diversity and human influence on the forests in the Eastern Arc Mountains, forest biomass (and carbon density) will vary considerably within each forest or woodland type depending on the degree of disturbance and local soil / climate conditions. However, it was necessary to estimate the average above ground carbon stocks in order to estimate the gains or losses of carbon associated with transitions between different types of land us (e.g. forest land to non-forest land). ECCM was provided with data from a number of forest surveys and studies from various parts of the EAM (Malimbwi et al., 2005; FORCONSULT 2006). The only data sets that included volumetric measured consistently across a range of forest types and regions were the District Forest Inventory Reports (Malimbwi et al., 2005). While many of the areas inventoried fall outside the core forest areas of the EAM, and some parts of the EAM are not adequately covered, we believe that this is the best of the available data on which to base biomass / carbon density estimates. ECCM calculated average carbon densities for different vegetation types using data from 11 District Forest Inventory Reports (Table 2.1). The degree of overlap between the areas surveyed and the EAM forests is shown in Figure 2.1. ECCM used this data and other descriptive information contained within the District Forest Inventory Reports to estimate average carbon storage for major forest / woodland categories.

ECCM 14/05/2007

11

Table 2.1 Sources of forest inventory data used to calculate carbon density for different vegetation types (Malimbwi et al., 2005) District District area (Ha) Forest area surveyed 2005 (Ha)

Kilombero 655,464 91,614

Handeni 848,567 55,062

Kisarawe 381,021 281,409

Liwale 2,455,050 815,012

Kilwa 725,553 341,821

Mpanda 3,196,744 2,774,429

Mvomero 897,554 353,413

Nachingwea 190,159 102,779

Rufiji 608,794 321,029

Tunduru 1,051,050 915,283

Ulanga 2,493,095 90,396

Total 13,503,051 6,142,246

The methodology used in the forest inventories is summarised in Appendix 1.

Figure 2.1

Vegetation Types The District Forest Inventory Reports provided data of standing volume (m

3/ha) and forest area

(productive and protective) for a large part of Tanzania. Descriptive information was also provided for each forest of location, topography, vegetation, dominant tree species and human impacts. On the basis of the interpretation of this information ECCM placed each forest area into a simplified

ECCM 14/05/2007

12

forest / woodland category (Table 2.2), and recorded the level of disturbance described as high, medium or low (Table 2.3).

Table 2.2 Descriptions of the forest/ woodland categories in the Eastern Arc Mountains

ECCM classification Description (in government inventory reports)

Forest (montane and sub-montane)

Described as either montane, dry montane, submontane, dry submontane or mixed montane and sub-montane forest.

Miombo Described as miombo woodland

Lowland Described as lowland woodland

Mixed woodland categories

The descriptions of mixed woodland are dominated by miombo and lowland woodland though some are also described as having an element of montane and/or submontane forest.

Plantation Described either plantations or as planted with exotic tree species

Other Described as areas where forest has been completely cleared.

Montane forest. Both dry and moist montane forest occur in the Eastern Arc Mountains. This forest type typically occurs at elevations above 1,500 metres above sea level. Upper montane will typically occur above 2,200 m.a.s.l. More endemic tree species are found in moist montane forest than dry (Lovett, 1998). Tree heights will regularly exceed 60 meters. Montane forest is characterised by a high incidence of low level cloud cover. Common tree species include Cordia africana, Albizia petersiana, Symphonia globulifera, Cussonia arborea, Tarenna nigrescens, and Ocotea usambarensis. This is typically a closed canopy forest with evergreen trees and shrubs and little or no ground vegetation. Sub-montane forest. Submontane forests are very rich in endemic tree species. However large areas in the Eastern Arc Mountains have been replaced by plantation or disturbed by logging. Submontane forests are generally found at lower elevations than montane forests between 800 – 1,500 m.a.s.l. Common tree species include Allanblackia stuhlmanni and other Allanblackia species, Maesa lanceolata,.Newtonia buchananii, Pterocarpus rotundifolia, Ocotea usambarensis and Parinari species. This is typically a closed canopy forest with evergreen trees and shrubs and little or no ground vegetation. Miombo woodland. Miombo is dominated by trees in the genera Brachystegia, Julbernardia and Isoberlinia of the family Fabaceae, subfamily Caesalpinioideae. Such woodlands extend across about 2.8 million km

2 of the southern sub-humid tropical zone from Tanzania and Zaire in the

north, through Zambia, Malawi and eastern Angola, to Zimbabwe and Mozambique in the south. Their distribution largely coincides with the flat to gently undulating surfaces that form the Central African plateau. This is a closed canopy deciduous woodland type dominated by a few species of trees. Crowns of trees are typically not interlocking. The shrub layer is also deciduous although as it grades into forest types the shrub layer can become semi-or even fully evergreen. Generally grass grows below the trees making a fire-prone system.

ECCM 14/05/2007

13

Lowland woodland. A closed canopy semi or fully evergreen woody stand dominated by many different species of trees. Crowns of the trees are generally interlocking. The shrub layer is evergreen. This system has very little or no ground vegetation and the system is not fire tolerant (Burgess, N. 2007. Personal communication). Table 2.3 Descriptions of disturbance in the Eastern Arc Mountains Level of disturbance Description

Low

Low levels of disturbance mainly by fires and intrusions for timber, hunting, ritual, medicine, fuel wood and charcoal.

Medium

Medium levels of disturbance mainly for timber, charcoal, fuelwood, honey, medicine, including some shifting cultivation and disturbance caused by fires.

High

High levels of disturbance described by fire, permanent settlements, illegal timber harvesting, encroachment for cultivation and grazing, as well as charcoal and fuel wood

Plantation

Described as plantation or planted with exotic tree species i.e. no natural forest or woodland remaining.

Cleared Cleared of all forest cover

Many of the forest areas reported on in the District Forest Inventory Reports covered very large areas (up to 100,000 hectares). There is likely to be a high variability of land use, disturbance and carbon storage (per hectare) within such large areas.

Calculations of carbon density for vegetation types Above-ground carbon density per hectare was calculated as follows for each vegetation type:

C (t/ha) = (standing volume x dry wood density x carbon content) + (other carbon pools) The standing volume (m

3/ha) for each forest is listed in the Forest Inventory Reports.

The dry wood density (MgDM/m

3) was calculated for each forest area on the basis of average dry

wood densities (where available) for the dominant tree species listed for each forest in the District Forest Inventory Reports. FAO Forestry Paper – 134 (Brown, 1997) was the main source of information for dry wood density data for individual tree species. Dry wood density values for the forest areas covered by the eleven inventory reports ranged from 0.49 to 0.75 MgDM/m

3.

The carbon content (MgC/MgDM) is assumed to be 0.5 for all forest areas. The carbon stored in other pools (branches, foliage and roots) has been calculated by applying default expansion factors (relative to carbon stored in the tree’s main stem) to account for carbon stored in the other parts of the trees (Table 2.4).

ECCM 14/05/2007

14

Table 2.4 Expansion factors used for other carbon pools in the Eastern Arc Mountains

Other carbon pools Expansion factor (relative to standing volume)

Branches 0.24 Foliage 0.05 Roots 0.25

As more information becomes available these expansion factors may be revised. These expansion factors are considered by ECCM to be conservative. The average carbon storage was calculated for the above vegetation categories based on levels of disturbance. The results of these calculations are shown in Table 2.5. It should be recognised that there is a considerable range of carbon stocks within each category. The scatter plots in Figure 3.2 illustrate the range of carbon densities in each vegetation category. For a more detailed picture of the distribution of carbon densities within each vegetation category see Appendix 2.

Table 2.5 Average carbon density for different vegetation types (above and below ground, not including soil) and levels of disturbance in the Eastern Arc Mountains

Level of disturbance

Category

Low (tC/ha) Medium (tC/ha) High (tC/ha)

Forest (montane and sub-montane)

306

83

Miombo

87

63

37

Lowland

157

33

Mixed woodland categories

119

88

57

Plantation

TBA

Other 0

N.B. The categorisation of forest by level of disturbance was done by ECCM on the basis of interpretation of descriptive information in the District Forest Inventory Reports. On the basis of the available data for forest and lowland categories the difference between low and medium disturbance could not be meaningfully distinguished. ECCM therefore decided to group the low and medium level of disturbance categories together. The results for carbon storage in the different forest and woodland categories (Table 2.5) are considered by ECCM to be approximately in line with what would be expected. The advantage of using the data contained within the District Forest Inventory Reports is that the results for carbon storage by vegetation category are based on a very wide survey area (beyond the Eastern Arc Mountains) within the relevant area of Tanzania.

ECCM 14/05/2007

15

The figures for all the forest / woodland categories clearly demonstrate the negative impact that forest degradation has on carbon storage. These results demonstrate the huge potential for carbon storage through the conservation of montane and sub-montane forest (possibly in excess of 200 tonnes of carbon per hectare). Although carbon storage in montane and sub-montane forest is highlighted as being of particular relevance to this study it has not been possible to use the data provided in the District Inventory Reports to distinguish between the two.

Insufficient data was provided to accurately calculate average carbon storage in forest plantations in the Eastern Arc Mountains (Figure 2.2). However, growth data should be freely available for the commonly grown commercial tree species (e.g. Eucalyptus spp.) which could be used to model carbon uptake and storage in plantation forests if required on a case by case basis. For the purposes of this study ECCM have assumed a value of 100 tonnes of carbon per hectare for all plantation areas across all the forest blocks.

0

100

200

300

400

500

600

0 1 2 3 4 5 6 7

Land use catego ry

1 - D isturbed fo rest

2 - F o rest ( low disturbance)

3 - Lowland woodland

4 - M iombo woodland

5 - M ixed woodland and fo rest

6 - P lantatio n fo rest

Tonnes of carbon / hectare

Figure 2.2 Carbon storage per hectare for forest / woodland categories in the Eastern Arc Mountains

ECCM 14/05/2007

16

Stage 2. Estimating and land use and vegetation changes from 1995 to ~2000 Land Use Data All land use data was supplied as ArcGIS (.shp) files. This was converted to MapInfo (.tab) files, using the Universal Transverse Mercator, Zone 37, Southern Hemisphere (WGS 84) projection. All calculations of area were generated from Cartesian rather than Spherical measurements.

o Eastern Arc Mountain block boundaries, based on the 500m contour, and supplied as polylines were generated at the GIS and remote sensing laboratory of the Sokoine University of Agriculture. These were converted to polygons in order to define the areas of analysis.

o 1995 vegetation data was supplied for 17 districts, offering almost complete

coverage of the Eastern Arc Mountain blocks. Figure 2.3 shows the coverage of this and later data. This data was collected for a World Bank sponsored project to produce a vegetation map of Tanzania. The satellite data was made available by the Sokoine University of Agriculture (where a complete dataset is held).

o Dr Boniface Mbilinyi supplied the ArcGIS data that was used to create the land cover

maps in Appendix 1 of the CMEAMF Forest Baseline Report (FORCONSULT, 2006). These relate to Landsat TM images and survey data variously from 1999 through to 2003. Figure 2.3 shows where this coverage overlaps the 1995 vegetation coverage within the Eastern Arc Mountain boundaries. It is within these areas that analysis of change between the 1995 vegetation data and CMEAMF data was undertaken, Table 2.6 shows the dates of Landsat TM data for the ~2000 series.

o Forest reserves, roads and villages were supplied as polygon, polyline, and point

data respectively. Forest Reserves are from the Institute of Resource Assessment in the University of Dar es Salaam, with names updated for the World Database of Protected Areas (UNEP-WCMC). Roads and villages were supplied by the remote sensing laboratory at the Sokoine University of Agriculture and the Institute of Resource Assessment at the University of Dar es Salaam.

Table 2.6 Dates of land cover used to measure land use change in the Eastern Arc Mountains

3

Forest block Land cover 1 Land cover 2 (based on LandSat image) East Usambara 2003 Mahenge 1999 Nguru (includes Nguu) 2003 Rubeho 2000 Udzungwa 1999 Uluguru 2000 Ukaguru 2000 West Usambara

1995

1999

Differences in the classification methods between the earlier and later dates caused significant difficulties for interpretation, (ground truthing would be required to estimate the errors associated with differences in classification). Forest cover change between 1995 and ~2000 was calculated by overlaying the two land cover layers. The detailed processes applied to GIS data are shown in Appendix 1.

3 Land cover 1 (1995) was complete for all the forest blocks. No land cover 2 data was available for North and South Pare (1999). The only complete land cover 2 available for this study was for East Usambara. The results for all the other forest

blocks refer to the areas bound by land cover 1 and land cover 2.

ECCM 14/05/2007

17

The results of this exercise should therefore be interpreted with some caution – especially when considering specific blocks of forest. The output from land use change assessment resulted in maps of land use change for each block of forest: Figure 2.4 provides an example.

ECCM 14/05/2007

18

Figure 2.3 Data availability for 1995 and ~2000 in the EAM

ECCM 14/05/2007

19

Figure 2.4 Example output from overlay of land classification maps

ECCM 14/05/2007

20

Stage 3. Combination of land use change and carbon stock data to derive historic emissions Land use / vegetation change data was combined with data for average carbon stocks for each vegetation type for each of the following 12 EAM areas, to calculate the approximate loss in above ground carbon:

• West Usambara • East Usambara • North Nguru • South Nguru • Ukaguru • Uluguru • Rubeho • Udzungwa • Mahenge

The detailed steps used to estimate the change in carbon stocks in each forest area are described in Appendix 1. The overall results from this analysis are given in Section 4 of this report. Tables containing the results of this process for each block are shown in Appendix 4. Maps showing changes in carbon stocks are shown in Appendix 5. Stage 4. Examination of “driving factors” by comparison of land use changes in different situations The causal or “driving factors” for deforestation appear to vary significantly between regions. According to FAO (2001), changes in tropical forest cover in the 1980’s in Africa were driven mainly by rural population expansion, in Latin America deforestation was driven by centrally planned operations such as resettlement, cattle ranching and hydroelectric schemes; while in Asia deforestation resulted from a combination of both factors. At a national/regional level, changes in land-use may be correlated with more specific factors relating to climate, topography, access, demography and various socio-economic conditions. For example, Harrison (1991) found that population growth in Costa Rica between 1950 and 1984 was strongly correlated with deforestation in some regions but not in others; the effect of population growth on deforestation depended on other factors including the rate of urbanisation and the number of immigrants in the population. Sader and Joyce (1988) found that deforestation in Costa Rica was also strongly related to other factors including distance to roads, slope, eco-region and protection status. The interactions between different social groups, land tenure and access also play important roles. Deforestation may occur through a sequence of processes, for example logging operations followed by ranching and/or clearance by small-scale farmers in which each process is determined by a specific set of causal factors (Walker 1987). Where land is already populated and few large areas of wilderness still exist, deforestation is more likely to occur in a piecemeal fashion as current areas of agricultural land are extended. Where population density is low and settlements are sparse the rate of deforestation may depend more on the accessibility of the forest areas through road construction. Steep terrain may be an impediment to agricultural expansion where land is plentiful but where land is scarce other pressures may eventually drive farmers to clear forests on ever-steeper slopes (Ochoa and González 2000). Other socio-economic factors, which may vary from region to region, will also have an effect; for example where the prevailing land-use is large-scale cattle ranching, large areas of forest may be cleared

ECCM 14/05/2007

21

but population density remains low. In contrast, the population in regions with intensive agricultural systems may increase while natural forest cover is maintained. Skole et al. (1994) stated that regional trends may be influenced by external policy factors such as taxes and market prices, but that these are mediated by local-scale conditions. Further to this Veldkamp and Lambin (2001) point out that variation in explanatory variables of land-use change with scale follow a certain pattern: at the farm level, social and accessibility factors play a role, at the landscape level, topography and agroclimatic potential seem to be the main determinants, while at a regional level, climatic variables as well as macro-economic and demographic factors appear to drive land-use. Castillo-Santiago et al. (2003) applied a geographic analysis of risk factors to an area of approximately three million hectares in southern Mexico. In this study the authors found a strong correlation between the rate of deforestation / degradation and spatial factors such as distance from roads and communities. In this study we examined the effect of driving factors spatially by quantifying the amount of deforestation and carbon losses that occurred during this period in different categories of land that appear to represent different levels of risk. The risk categories (buffers) selected were:

• Whole area • Park areas only • 1km from roads • 2km from roads • 1km from settlements • 2km from settlements

The detailed process by which these buffers were created is described in Appendix 1. The results of this analysis of risk are presented in Section 4, with the detailed outputs shown in the tables in Appendix 4. Stage 5. Projection of future emissions It is recognised that the estimation of future emissions associated with land use change in any developing country context will be uncertain given the dynamic nature of the social drivers. Factors such as increased urbanisation can have complex effects on land use – while rural to urban migration may reduce the demand for land used for subsistence agriculture, as people switch from fuelwood to charcoal for their primary energy consumption this leads to a 3 or 4 –fold increase in the amount of woodfuel used to supply that energy. Furthermore, expansion of urban centres demands material resources such as timber that is likely to be brought in from surrounding areas. The growth of commercial agriculture may increase productivity in selected areas, however, subsistence farmers may have to react by cultivating larger areas to make up for more competitive market prices. While recognising that this level of complexity makes any projection of future emissions uncertain, we have adopted a rather straightforward approach of projecting the recent (1995 to ~2000) rate of land use change in each of the forest areas forwards to the next 20 years. This was done for the areas as a whole and for each of the risk categories listed above.

ECCM 14/05/2007

22

Stage 6. Updating of land use categories as a result of stakeholder meeting in Dar Es Salam (February 2007) It became apparent to ECCM at an early stage in the study that there were several inconsistencies between the land use classification in 1995 and other studies on land use in the EAM conducted by FORCONSULT and the Ministry of Natural resources and Tourism, Forest and Beekeeping Division. During the stakeholder meeting in Dar Es Salam in February 2007 when ECCM presented preliminary results it became clear that the errors in land use classification were due primarily to the 1995 vegetation data used by ECCM as part of this study. Particular problems were identified with the land cover classifications in Mahenge, Uluguru, Udzungwa and West Usambara. At this meeting ECCM agreed with UNDP (Neil Burgess) to visually compare the 1995 land use classification with an image showing Forest and Woodland Cover Change between the 1970’s and 1990’s in The Eastern Arc Mountains (Appendix 3) and to make changes to the 1995 dataset accordingly. The key changes made to the 1995 land use classification are listed in Table 2.7. Table 2.7 Updates to 1995 land use categories as a result of stakeholder meeting in Dar Es Salaam (February 2007)

Name of forest block Target area(s) Land use re-classifications Mahenge Entire block There are only 3 small patches of ‘forest’

in Mahenge. Mahenge has extensive areas of wet Miombo which had incorrectly been classified as ‘forest’ by the 1995 land use survey.

Uluguru South west part of south Uluguru (Mgeta Valley and hills in North Selous e.g. Ngolme)

The land use re-classification was made to the 2000 land cover. An area of ‘wet Miombo’ had been incorrectly classified as ‘forest’ by 2000 survey.

Udzungwa South west of forest block and one small are in centre of block

Areas of plantation / grassland / degraded woodland were incorrectly classified as ‘forest’ according to the 1995 land use survey. For the purposes of calculating carbon densities these areas were reclassified as degraded woodland with average carbon storage of 33 tonnes per hectare.

West Usambara Entire block Many areas re-classified (1995 survey) from ‘forest’ to woodland or other land uses (particularly in the north of this forest block).

ECCM 14/05/2007

23

3. Results and Discussion

3.1 Review of Legal and Market Framework

UNFCCC / Kyoto Protocol Mechanisms The Kyoto Protocol (signed 1997) is an agreement, under the UNFCCC, which places legally binding emission reduction targets on industrialised countries. The Kyoto Protocol has been ratified by over 160 countries and the first commitment period will run from 2008 to 2012. The Clean Development Mechanism (CDM), set out in Article 12 of the Kyoto Protocol, is the mechanism through which developing countries can generate income through the implementation of projects that reduce GHG emissions or sequester CO2. The only type of forestry and land use project activities allowed under the CDM are afforestation and reforestation on lands deforested prior to 1990. Projects designed to combat deforestation and land degradation are not eligible under the CDM. To generate Certified Emission Reductions (CERs) that can be traded on the international carbon market, CDM projects must pass through a set of review and approval processes including: acceptance of a methodology for calculating GHG benefits; approval by the host country government; validation by a “designated operational entity” (auditor); registration by the CDM executive; and verification by a DOE. At present, out of a total 500 approved projects under the CDM there is only one forestry project

4

that has gained registration. The vast majority of projects approved by the CDM involve capture of methane from landfill and other organic or geological sources, or the destruction of HFCs or nitrous oxide. The reasons for what is now widely recognised as a failure of the CDM to provide a basis for afforestation/reforestation activities in developing countries include: uncertainty over the rules and methods to be applied to forestry projects; the high bureaucratic and administrative hurdles for project approval; complex monitoring requirements; the long timescale of projects relative to the short time horizon of the CDM

5; and high level of uncertainty over the value of

CERs from forestry activities6. Given the small number of forestry projects within the CDM and

the small scale of these projects relative to industrial projects it is quite possible that there will be no trade in forestry CERs. Furthermore, given the uncertainty over the future of the CDM beyond 2012 it seems unlikely that many more forestry projects will be submitted to this mechanism, unless for political rather than economic reasons. While the reform of the CDM and the expansion of the Kyoto Protocol beyond 2012, and inclusion of a mechanism to promote avoided deforestation are being considered in UNFCCC negotiations, progress appears to be slow and there is no immediate means by which forest conservation activities can access carbon finance through this mechanism.

4 “Facilitating Reforestation for Guangxi Watershed Management in Pearl River Basin” 5 There is no certainty about the continuation of the CDM beyond 2012, whereas most forestry projects will not

generate significant emission reductions until after 2012. 6 CERs from afforestation and reforestation are classed as “reversible” by the CDM Executive and will therefore trade

at lower prices than conventional CERs. Given the small number of forestry projects within the CDM and the small

scale of these projects relative to industrial projects it is likely that there will be no trade in forestry CERs.

ECCM 14/05/2007

24

Figure 3.1 shows how carbon crediting works for afforestation and reforestation project in the CDM. Credits are generated at periodic intervals corresponding to the commitment periods of the Kyoto protocol, based on the carbon uptake over the previous 5 year period. When a plantation is felled, or if the carbon stock decreases for any other reason the credits that have been previously issued are cancelled and the owner (or the supplier, depending on the contract) must purchase replacement credits. Figure 3.1 Carbon crediting for afforestation and reforestation projects in the CDM

The uncertainty of the value for credits for carbon uptake post-2012, combined with retrospective crediting, and high up-front costs of registration renders the net carbon value for nearly all afforestation / reforestation projects near to zero.

3.2 Voluntary Carbon Market

One of the most significant developments in the area of environmental finance over the past ten years has been the growth of the voluntary carbon market. The voluntary carbon market began in the mid-1990’s, with the primary motivation as a way for many companies to experiment with, demonstrate or learn about emissions trading. Various initiatives by companies such as the US utility AES and Edisson Electric involved a range of forestry and renewable energy based carbon offset schemes under a CDM precursor scheme known as US Initiative in Joint Implementation (USIJI). These early projects led to the development of generic methodologies for quantifying the GHG benefits of various types of projects, including renewable energy, energy efficiency, afforestation and avoided deforestation, now adopted within the CDM. The voluntary carbon market is now based around the idea of companies and individuals providing finance to “offset” (compensate for) emissions associated with their commercial activities or products. This market is currently estimated at approximately $80 million per year and is expanding and changing rapidly (growth of 50 to 100% per year). Forestry and land use projects have been popular within the voluntary carbon market for the following reasons:

Y5 Y10 Y15 Y20

Carbon Stock

Carbon stock of plantation over course of 17 Yr rotation.

Credit

Credit

Credit Debit

ECCM 14/05/2007

25

• Many technology based projects, such as renewable energy are already covered by government legislation or programmes.

• Forestry projects are scaleable by area, allowing investors or purchasers to decide at what level to participate, whereas most technology projects are fixed scale and often rather large (for example establishment of generation of electricity from bagasse – might involve an investment of 10 to 20 million USD; in this context it is difficult for a purchaser to see how their investment makes a clear difference).

• Forest conservation and afforestation appear to be highly cost-effective methods to avoid GHG emissions relative to many other technologies.

• There can be many additional benefits associated with forest conservation and afforestation including rural livelihoods and biodiversity conservation.

However, voluntary carbon offset initiatives have been criticised by a number of observers as being used as an excuse by companies and individuals to carry on emitting greenhouse gases:

“Fossil fuels have to stay in the ground. Carbon offset schemes flatter the fallacy that we can continue pretty much as we are.” R. Newman, 2006.

Forestry projects in particular have come in for considerable criticism, as being poorly designed and not sustainable (Ma’anit 2006).

3.3 Estimate of Potential Carbon Benefits from Conservation

Deforestation and Carbon losses from 1995 to ~2000 For the EAM forests as a whole, our assessment of deforestation found that there was a loss or severe degradation of approximately 35,000 hectares of natural forest; almost 10% of natural montane and sub-montane forests that between 1995 and ~2000. This figure is approximately in line with the losses estimated by FORCONSULT (2006). We estimate the CO2 emissions associated with this loss of vegetation to be approximately 10.6 million tonnes carbon (equivalent to 38.7 million tonnes CO2). See Table 4.1 We recommend that ground-truthing is carried out to clarify the forest classifications and improve biomass estimates for the main forest types within the EAM. While our process of classification may have over-estimated the incidence of forest clearance in some areas, we have not taken into consideration losses of carbon from forest types that remained the same. The distribution graphs of carbon density for each forest type (Appendix 2) show that all forest types exhibit a wide range of carbon densities, much of which is likely to result from processes such as selective extraction of timber trees, lopping of trees for firewood, under-grazing or charcoal pits. In many cases, it appears that vegetation change is not a discrete, one-off process but a gradual process of degradation leading to progressively lower biomass vegetation. Our study found large differences in the loss of forest and associated carbon stocks between different areas within the EAM; from 0 – 21% between 1995 and ~2000. South Nguru and East Usambara had the highest losses over this period – around 20%, but other areas – West Usambabra, Rubeho, North Nguru and Ukaguru appear to have suffered much lower levels of loss 0% to 5%. See Appendix 4 for details and Appendix 5 for maps of carbon stocks in all EAM forest blocks.

ECCM 14/05/2007

26

Carbon losses in different risk categories For the EAM as a whole, the study found significant differences in the rate of deforestation between areas in different risk categories. Loss of carbon stocks from montane and sub-montane forests in protected areas was only 6% relative to an overall average of 9%, whereas losses from these forests within 1 km of roads amounted to 35%, and 28% within 1km of settlements. Just as the overall levels of forest loss vary considerably across the EAM forests, so the effects of risk factors appears to vary. There is however overall a clear trend that shows that rates of deforestation are significantly higher in close proximity to roads and existing settlements. See tables in Appendix 4 for details. These results are similar to findings in other studies, such as Castillo-Santiago et al. (2003) which also found a strong relationship between deforestation and proximity to roads and existing cultivated areas.

ECCM 14/05/2007

27

Table 3.1 Land use change and carbon stock change for all EAM blocks

1995 2000's 1995 2000's % change

Forest (montane and submontane) with a low level of

disturbance 363,756 329,245 111,309,305 100,748,873 -9

Forest (montane and submontane) with a high level

of disturbance 0 3,311 0 274,820 N/A

Plantation 66,363 112,188 6,636,330 11,218,781 69

Other forest / woodland 1,189,918 824,180 63,348,823 50,704,081 -20

Other land uses 969,394 1,471,430 0 0 N/A

Total 2,739,956 2,740,353 193,798,348 162,946,549 -16

Total (excluding plantations & other land uses) 1,704,199 1,156,736 187,162,018 151,727,768 -19


disturbance 270,387 255,470 82,738,324 78,173,921 -6



Plantation 51,150 66,760 5,114,999 6,676,015 31

Other forest / woodland 207,951 188,358 12,820,321 13,378,660 4

Other land uses 173,058 107,992 7,055,082 0 N/A

Total 621,304 621,527 100,668,713 98,473,208 -2

Total (excluding plantations & other land uses) 397,096 446,776 88,498,632 91,797,193 4


disturbance 15,027 9,727 4,598,330 2,976,575 -35



Plantation 8,467 16,080 846,718 1,608,016 90

Other forest / woodland 85,580 54,525 4,203,175 3,058,030 -27

Other land uses 132,303 158,401 0 0 N/A

Total 240,631 240,675 9,654,182 7,803,702 -19

Total (excluding plantations & other land uses) 99,860 66,193 8,807,464 6,195,686 -30


disturbance 35,726 26,033 10,932,172 7,966,115 -27



Plantation 15,963 31,381 1,596,355 3,138,067 97


Other land uses 238,157 301,082 4,183 0 -100

Total 473,222 458,600 21,741,048 16,806,910 -23



disturbance 16,467 11,804 5,038,786 3,611,895 -28


of disturbance 0 278 0 23,093 N/A

Plantation 2,475 2,415 233,368 241,530 3



Total 227,282 227,549 7,931,130 5,926,686 -25



disturbance 60,010 49,586 18,363,003 15,173,260 -17



Plantation 7,974 7,809 797,385 780,874 -2



Total 561,941 562,246 26,905,369 22,052,634 -18


1 km of settlements

2 km of settlements

All blocksArea (ha) Tonnes of carbon

Total area

Forest reserves

1 km of roads

2 km of roads

ECCM 14/05/2007

28

Projected Emissions over next 20 years We projected historic rates of deforestation forward over 20 years to determine the projected carbon losses over 20 years at current rates and also a conservative estimate (0.5 x the projected value). These calculations are shown in Table 4.2. Our projected carbon loss over 20 years is around 69 million tonnes C, with a conservative estimate of 34 million tonnes. (This is equivalent to 125 – 250 million tonnes of CO2) Table 3.2 Estimated carbon losses over next 20 years

Forest Block

1995 tC

(excluding

plantations)

Current tC

(excluding

plantations)

Carbon losses over next 20 years

assuming deforestation and forest

degradation continue at current

rates

Conservative estimate

of carbon losses over

next 20 years (current

rate / 2)

East Usambara 10,632,233 9,398,239 2,481,090 1,240,545

Mahenge 17,546,610 5,443,754 5,428,108 2,714,054

North and South Pare N/A N/A N/A N/ANorth Nguru 12,930,054 12,137,312 1,770,500 885,250

Rubeho 22,531,845 18,646,879 9,900,190 4,950,095

South Nguru 15,329,362 14,438,242 2,002,131 1,001,066

Udzungwa 76,421,411 62,921,439 39,113,784 19,556,892

Ukaguru 10,044,312 7,893,929 4,882,412 2,441,206

Uluguru 8,534,830 8,606,674 -293,473 -146,736West Usambara 13,191,360 12,241,301 3,817,317 1,908,659

Total 187,162,018 151,727,768 69,102,059 34,551,030 It is apparent from Table 3.2 that the greatest losses of carbon are expected in Udzungwa – which also has the largest stock of carbon and a relatively high rates of loss (if our vegetation classification is correct).

ECCM 14/05/2007

29

3.4 Alternative approaches to project implementation

In deciding how to develop practical projects following a feasibility study it is important to consider the alternative approaches. There are two main alternatives: (a) one or a few large, fixed-scale projects; or (b) several smaller pilot projects that can be scaled-up across the region as demand for carbon develops. The strengths and weaknesses of these alternatives are shown in Table 3.3. Table 3.3 Two alternative approaches to project design

Large, Fixed-Scale Model Multi-site, expansion Model

Similar to the traditional model of establishment of protected areas. Matches one or a few large donors (carbon purchasers) with a large, fixed area of forest to be protected. Examples: Noel Kempff Mercado project, Bolivia Rio Bravo Conservation project, Belize Advantages: Can apply rapidly to large priority conservation areas, through land purchase and control. Does not rely on social participation (although good relations may be essential in long-term). Disadvantages: May not deal with underlying causes of land degradation. Depends on achieving a critical up-front carbon deal of sufficient size to match the requirement of the project.

A social model built around local needs for resources; combines restoration of degraded areas, with agroforestry, and designation of protected areas. Starts with small groups and expands and replicates throughout a region. Examples (see box 3.1) Scolel Te project, Mexico N’hambita prioject, Mozambique Advantages: Deals with underlying causes of land degradation and deforestation. Participatory basis may improve long-term prospects for sustainability. Can scale-up on annual basis as carbon purchases are made. Disadvantages: Slower to develop. Relies on social participation. May be difficult to ensure protection of specified, high conservation value forests (e.g. if communities around those areas are not willing to co-operate).

It is worth noting that the examples of large, fixed scale projects are relatively few and none have been established over the past five years, whereas the multi-site, expansion model has grown considerably, over the past three years since it has been able to take advantage of a flexible demand for carbon credits. Taking the Eastern Arc Mountains areas as a whole, it may be the case that there are some good candidate areas for a fixed scale approach. However, our recommendation is to focus attention on the underlying causes of land degradation and to put most efforts into a multi-site expansion model.

ECCM 14/05/2007

30

3.5 Plan vivo scheme

The Plan Vivo scheme for carbon management and rural livelihoods was developed in Mexico during the 1990’s through a DFID supported collaboration between researchers at the University of Edinburgh, El Colegio de la Frontera Sur (Mexico), and representatives from the Union de Credito Pajal Ya Kac’ Tic (a farmers’ union). The concept developed as a reaction to the disappointing results of centrally managed government / World Bank forestry and agroforestry projects. Farmers were interested in accessing sources of investment that would enable them to plan and implement forestry, forest restoration, agroforestry and conservation activities in a way that met their own needs and capabilities. From a financial perspective, the Plan Vivo system works like a mortgage company – the certificates issued by the Plan Vivo secretariat promise the delivery of future carbon benefits, the delivery commitment and risk of fulfilment is spread across a large number of individual farmers, communities and groups. Funds from the sale of offsets are held in trust funds (escrow accounts) until farmers reach specified milestones. The Plan Vivo system manages the risk of fulfilment by placing entry requirements on participants, local project administration teams and also by requiring each project to retain a reserve “buffer” of unsold carbon. While the Plan Vivo system is still at a relatively early stage of overall development it has gained considerable recognition as a model for good practice in this area. It is noted by the UK’s Carbon Trust (a government-funded executive agency) as one of two credible forest and land use schemes, the other being the CCB standard. The Plan Vivo scheme is in discussion with a range of environmental organisations and financial stakeholders to scale-up its activities and professionalise its risk management methods to the level at which the resultant certificates could be internationally recognised units of environmental benefit. The name of this scale-up initiative is Earth Carbon. The main differences between Plan Vivo and CCB Standard are shown in Table 3.4.

ECCM 14/05/2007

31

Table 3.4 Comparison between Plan Vivo and CCB standard

Plan Vivo CCB Standard

Restricted to projects working with communities and smallholders in developing countries.

No restrictions on eligibility of landowner or landholding.

Provides process for approval by scheme which combines basic design requirements plus an assessment of local capacity and ability to manage.

Sets design standards for projects. No process for development prescribed. No capacity requirements or training requirements.

Recognises land owners’ / communities’ rights of ownership and ensures their consent to carbon service provision.

Ownership of carbon credits not addressed

Includes process for approving carbon calculations, monitoring plans and risk management.

Carbon calculation review not included. Monitoring plan review not included. No risk management requirements.

Plan Vivo certificates issued by independent organisation. Register maintained by independent organisation.

No certification of carbon credits included. No register.

Annual operational check and verification of monitoring assessment.

5-year validation / verification

Governed by independent non-profit organisation

Run by Conservation International

ECCM 14/05/2007

32

Box 3.1 Plan Vivo – a scaleable model for delivery of carbon benefits

Plan Vivo is a system for

managing the delivery of carbon services from land holdings, in a way that promotes rural livelihoods and conserves biodiversity. From a financial perspective the Plan Vivo system works much like a mortgage company. Each project has multiple agreements for long-term delivery of carbon storage (measured against verifiable milestones). Each project (local fund) develops its own internal risk management strategy (to minimise drop-out rates) but must maintain a reserve stock to cover potential losses.

Buyer

Local fund Certifier

There are currently 3 local funds operating (Mozambique, Uganda & Mexico) covering almost 1000 holdings. The model is scalable both by recruitment of new areas into each local fund, and by replication of projects. As the system grows it is possible to generate more effective means of managing delivery risk. There are also administrative economies of scale.

N’hambita:Location: Sofala Province, Gorongosa, Mozambique

Status: Pilot Phase (limited issue of certificates)

Carbon offset potential: 50,000 tCO2/yr by 2007

Open for investment: Yes direct purchase: Limited

Description: working with the Nhambita community and resident subsistence farmers in the buffer zone of the Gorongosa National Park, the project is linked to a suite of activities aimed at slowing deforestation in this area of outstanding importance for biodiversity. Land use activities include:

•Planting of timber and fruit species including Acacia species, amarula and mango.

•Mixed native woodlots on degraded land, agroforestry systems on homesteads, and boundary planting.

•Conversion from slash and burn agriculture and alternative income streams.

Management and Technical Support: Envirotrade Ltd, ECCM, the University of Edinburgh, and ICRAF.

Research & Development Funding: EU – Europaid (2003-2008)

Investors & Purchasers: Envirotrade, The CarbonNuetral Co.

Children in mashamba

Carpentry workshops nr. N’hambita

Scolel Té: Location: Chiapas & Oaxaca, Mexico

Status: Fully Operational

Carbon offset potential: approximately 100,000 tCO2/yr

Open for, investment: Yes Direct purchase of credits: Yes

Description: Scolel Té {‘the tree that grows”} is the original Plan Vivo project. It now includes over 2000 families of indigenous Mayan and mestizo farmers in 30 communities in central and northern Chiapas and communities in the northeast of Oaxaca.

The main activities financed through the project are:

•Establishment of small plantations of high value native timber trees in tropical areas;

•Restoration of degraded pine-oak forest in upland areas;

•Protection and restoration of cloud forest.

Management & Technical Support: Administration and farmer support – AMBIO; Review and capacity building – ECCM; Science & technical specifications - El Colegio de la Frontera Sur; University of Edinburgh.

Research & Development Funding: UK-DFID; Instituto Nacionalde Ecologia; IEA Greenhouse Gas R&D Programme

Current investors and recent purchasers: FIA Foundation; Future Forests (Pink Floyd); World Bank – IBRD.

Farmers participating in biomass survey

Women selecting fruit trees

ECCM 14/05/2007

33

4. Conclusions and Recommendations

ECCM concludes that there is considerable potential for using carbon finance to reduce deforestation pressures and to restore areas of degraded forests in and around the Eastern Arc Mountains of Tanzania. The current CDM does not provide a viable mechanism for obtaining this finance; however the expanding voluntary market for carbon offsets does provide a more promising route.

4.1 Research Priorities

ECCM considers that the following research could help to provide the basis for credible projects in the EAM:

- Investigation of the errors associated with vegetation classification used in this study and to compare the findings of this assessment with those of the FORCONSULT study.

- Further biomass surveys in key forest areas to increase the accuracy of carbon density

estimates for different forest types.

- Further work to determine ways of assessing the degree of disturbance and degradation in EAM forests (within each forest category).

- Improved understanding of specific driving factors in different regions of the EAM.

4.2 Action Priorities

ECCM proposes that UNDP and other stakeholder organisations consider the following points for specific actions:

- Selection of sites and organisations to develop pilot projects that could be scaled-up or replicated across the EAM.

- Examine potential route to market for the services provided by forest carbon projects

(Plan Vivo, Earth Carbon or other schemes). UNDP and other international stakeholders may be able to play a role in supporting the marketing of these services.

- Discuss ways of strengthening protection in forest reserves where the level of protection

appears to be low: particularly in East Usambara but also in North Nguru.

ECCM 14/05/2007

34

5. References

Brown S., Sathaye J., Cannel M. and Kauppi P. (1996), ‘Management of forests for mitigation of greenhouse gas emissions’, in Climate Change 1995, Impacts, Adaptations and Mitigation of Climate Change: Scientific-Technical Analyses. Report of Working Group II, Assessment Report, IPCC, Watson R. T., Zinyowera M. C. and Moss R. H., eds., pp 773–797, Cambridge University Press, Cambridge. Brown, S. (1997). Estimating Biomass and Biomass Change of Tropical Forest: a Primer (FAO

Forestry Paper – 134) Castillo-Santiago, M.A., Hellier, A., Tipper, R. and De Jong, B.H.J. (2003). Carbon emissions from land-use change: an analysis of causal factors in Chiapas, Mexico. Mitigation and Adaptation Strategies for Global Change 30. FAO (2001). State of the World’s Forests 2001, Food and Agriculture Organisation of the United

Nations, Rome, Italy. FORCONSULT (2006). Forest Area Baseline for the Eastern Arc Mountains. The Ministry of

Natural Resources and Tourism, Forestry and Beekeeping Division. Harrison, S. (1991). ‘Population growth, land-use and deforestation in Costa Rica, 1950–1984’, Interciencia 16(2), 83–93. Houghton R.A. (1996), ‘Converting terrestrial ecosystems from sources to sinks of carbon’, Ambio 25, 267–272. Kauppi P. and Sedjo R. (2001). ‘Technological and economic potential of options to enhance, maintain, and manage biological carbon reservoirs and geo-engineering’, in Climate Change 2001, Third Assessment Report of the IPPC. Lovett, J.C. (1998) Importance of the Eastern Arc Mountains for vascular plants. Journal of the

East African Natural History Society. 87, 59-74. Ma’anit, A. 2006, ‘ If You Go Down to the Woods Today..’ New Internationalist, Issue 391 July 2006. http://live.newint.org/features/2006/07/01/keynote/index.php Malhi Y., Baldocchi D.D. and Jarvis P.G. (1999), ‘The carbon balance of tropical, temperate and boreal forests’, Plant, Cell and Environment 22, 715–740. Malimbwi, R.E., Shemwetta, D.T.K., Zahabu, E., Kingazi, S.P., Katani. & J.Z., Silayo, D.A. (2005)

Forest Inventory Report for Kilombero District, Morogoro – Tanzania. The Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division.

Malimbwi, R.E., Shemwetta, D.T.K., Zahabu, E., Kingazi, S.P., Katani. & J.Z., Silayo, D.A. (2005)

Forest Inventory Report for Handeni District. Tanzania. The Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division.


Forest Inventory Report for Kisware District. Tanzania. The Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division.

ECCM 14/05/2007

35

Malimbwi, R.E., Shemwetta, D.T.K., Zahabu, E., Kingazi, S.P., Katani. & J.Z., Silayo, D.A. (2005) Forest Inventory Report for Liwale District. Tanzania. The Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division.


Forest Inventory Report for Kilwa District. Tanzania. The Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division.


Forest Inventory Report for Mpanda District. Tanzania. The Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division.


Forest Inventory Report for Mvomero District. Tanzania. The Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division.


Forest Inventory Report for Nachingwea District. Tanzania. The Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division.


Forest Inventory Report for Rufiji District. Tanzania. The Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division.


Forest Inventory Report for Tunduru District. Tanzania. The Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division.


Forest Inventory Report for Ulanga District. Tanzania. The Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division.

Ochoa-Gaona, S. and González-Espinosa, M. (2000). ‘Land use and deforestation in the highlands of Chiapas, Mexico’, Appl. Geogr. 20, 17–42. Sader, S.A. and Joyce, A.T. (1988). ‘Deforestation rates and trends in Costa Rica, 1940 to 1983’, Biotropica 20, 11–19. Skole, D.L., Chomentowski, W.H., Salas, W.A. and Nobre, A.D. (1994). ‘Physical and human dimensions of deforestation in Amazonia’, Bioscience 44(5), 314–322. UNFCCC (2006) Guidelines for completing CDM-AR-PDD and CDM-AR-NM Veldkamp, A. and Lambin, E.F. (2001). ‘Predicting land-use change’, Agric. Ecosyst. & Envir. 85,1–6. Walker,R.T. (1987). ‘Land use transition and deforestation in developing countries’, Geogr. Anal. 19(1), 18–30. World Bank, Mexico: 1995, Resource Conservation and Forest Sector Review, Natural Resources and Rural Poverty Operations Division, Country Department II, Latin America and the Caribbean Regional Office.

ECCM 14/05/2007

36

Appendix 1

1. Data collection methodology for forest Inventories

1.1 Data collected for the District Forest Inventory Reports was based on systematic sampling along transects (Malimbwi et al., 2005). The agreed sampling intensity was 0.01% which would require a distance of approximately 2.6 km between plots. A minimum of four plots was set for each forest whose area was less than 3,000 ha. Sample plots were located on the ground using GPS.

1.2 Each sample plot had a radius of 15 m. Trees with a diameter at breast height (dbh) of 5

– 10 cm were measured within a plot radius of 5 m. Trees with a diameter at breast height (dbh) of 10 – 20 cm were measured within a plot radius of 10 m and all trees with a diameter at breast height (dbh) in excess of 20 cm were measured within a plot radius of 15 m.

1.3 In addition to dbh, total height was recorded by measuring the height of the tree nearest

to the plot centre.

1.4 This data has been used to calculate harvestable timber volume (m3/ha) by species for

each forest area.

1.5 ECCM assume that other information relating to each forest area (vegetation, location, topography, human impacts) is derived from an assessment of the entire area rather than from sample plots.

2. Initial manipulation of data for each mountain block 2.1 1995 vegetation data is held as separate tables relating to district boundaries. Typically

mountain blocks cross a number of districts. Therefore all the1995 vegetation districts intersected by a mountain block are combined into a single table, and then cropped to erase all data outside the mountain block. This is achieved by making the combined table editable, selecting all of it, using Objects>Set Target, then selecting the mountain block polygon, and using Objects>Erase Outside.

2.2 A similar process is undertaken with each of the CMEAMF tables to create new tables that

only hold information for areas within mountain blocks. 2.3 To enable a like-for-like comparison, copies of the newly cropped 1995 vegetation tables are

further cropped by the new CMEAMF boundary. 2.4 In both the 1995 vegetation tables and the CMEAMF tables, new integer columns are

created. These are entitled ‘New Area’ and ‘Total Area’. Using Table>Update Column with CartesianArea(obj, "sq km") the area of each object within the table is given. Using the Σ function, a check can be made that the areas of each table are the same.

2.5 In a number of cases the areas turn out not to be the same. This is usually due to double

plotting in the CMEAMF table that then shows up as having a larger area. An example might be where forest surrounds a few small patches of land which are classified as permanently bare/grass. The forest will have been plotted, and then the permanently bare/grass areas will have been plotted on top of the forest. The solution is to make the table editable, select the

ECCM 14/05/2007

37

forest object, use Objects>Set Target, then select the permanently bare/grass areas, and use Objects>Erase.

2.6 Once areas are the same, the ‘Cover’ classifications in the CMEAMF table need to be visually

cross-referenced against the maps at Appendix 1 in the CMEAMF report, by creating a Thematic map using the ‘Id_’ column (Map>Create Thematic Map>Region IndValue Default). In most cases, while the ‘Id_’ column can clearly be seen to relate to a particular land type, the entries in the ‘Cover’ column can vary (e.g. Wood-degrad, wood-degraded?, wood-deg) and are sometimes blank. These examples would be updated to read ‘Degraded Woodland’.

2.7 Once ‘Cover’ classifications are checked, the ‘Total Area’ columns can be filled. As an

example, this is achieved in the 1995 vegetation tables by using Query>SQL Select to browse the table ordered by Vegtype. Then selecting all rows of similar Vegtype, and using the Σ function, the ‘Total Area’ column for that selection can then be updated with this figure.

2.8 Once the ‘Total Area’ columns are filled, use Query>SQL Select to browse the table ordered

and grouped by Vegtype. Then export this query as a tab delimited .txt file, to then be opened in Excel.

3. Assessing vegetation change near roads and villages 3.1 To create buffers around roads, make the roads layer editable, select all, and then use

Objects>Buffer>One buffer for all objects. Set the buffer radius to be 1km using Cartesian distance. Save the buffer, and repeat the process to create a 2km buffer.

3.2 For villages, follow the same process. 3.3 To create a new table showing vegetation within the buffer, make the desired landcover table

(1995 vegetation or CMEAMF) editable, with the desired buffer table open and selectable as a higher layer, then select all from the landcover table. Use Objects>Set Target, then selecting the buffer, and use Objects>Erase Outside. Save the selection as a new table.

3.4 Re-open this new table, use Table>Update Column to turn every entry in the ‘New Area’ and

‘Total Area’ columns to zero. Then recalculate areas using Table>Update Column ‘New Area’ with CartesianArea(obj, "sq km").

3.5 Then ‘Total Area’ columns can be filled. For example, in a table created from the 1995

vegetation table, use Query>SQL Select to browse the table ordered by Vegtype. Then selecting all rows of similar Vegtype, and using the Σ function, the ‘Total Area’ column for that selection can then be updated with this figure.



4. Assessing vegetation change within forest reserves 4.1 Select all forest reserves that intersect the mountain block of interest, save a copy as a new

table. Open this new table and combine them. 4.2 To create a new table showing vegetation within the forest reserve, make the desired

landcover table (1995 vegetation or CMEAMF) editable, with the combined forest reserve table open and selectable as a higher layer, then select all from the landcover table. Use

ECCM 14/05/2007

38

Objects>Set Target, then select the combined forest reserve table, and use Objects>Erase Outside. Save the selection as a new table.

4.3 Re-open this new table, use Table>Update Column to turn every entry in the ‘New Area’ and

‘Total Area’ columns to zero. Then recalculate areas using Table>Update Column ‘New Area’ with CartesianArea (obj, "sq km").

4.4 Then ‘Total Area’ columns can be filled. For example, in a table created from the 1995

vegetation table, use Query>SQL Select to browse the table ordered by Vegtype. Then selecting all rows of similar Vegtype, and using the Σ function, the ‘Total Area’ column for that selection can then be updated with this figure.



5. ECCM codes 5.1 All landcover types in both 1995 vegetation and CMEAMF tables were allocated an ECCM

code, which grouped them into one of five categories. A new column was added to each table and these were updated manually with the relevant ECCM code.

5.2 Thematic maps were then generated to display these categories.

6. Carbon stocks 6.1 All landcover types in both 1995 vegetation and CMEAMF tables were allocated a carbon

stock value (tC/ha). A new column called ‘carbon stock’was added to each table and these were updated manually with the relevant value.

6.2 Thematic maps were then generated to display these categories.

7. Changes in carbon stocks 7.1 In order to represent changes in carbon stock, a new table was created containing landcover

details for both 1995 vegetation and CMEAMF data. This involved splitting the geographic element of the CMEAMF table by the geographic element of the 1995 vegetation table using the following procedure:

7.2 Have CMEAMF data as the lower layer, 1995 vegetation above. Make CMEAMF layer

editable, and select all. Then Objects>Set Target, then marquee select 1995 vegetation, and use Objects>Split. Save the selection as ‘vegetation changes’.

7.3 Open ‘vegetation changes’ table, then create two new column called ‘carbon 1995’ and

‘carbon change’, both as integers. Then Table>Update Column, and update Column ‘carbon 1995’ in Table ‘vegetation changes’, getting the Value from ‘1995 vegetation’, using the join where ‘Object from 1995 vegetation CONTAINS object from ‘vegetation changes’, with the value of ‘carbon stock’.

7.4 Table ‘vegetation changes’ will now have two columns showing carbon figures for each

individual polygon, called ‘carbon stock’ and ‘carbon 1995’. To get a carbon stock change figure, take the ‘carbon 1995’ figure from the ‘carbon stock’.

ECCM 14/05/2007

39

Appendix 2

Variation of carbon density within each category of vegetation as shown by following graphs:

Area of Montane and Sub Montane forest in each Carbon Density Category

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Below 40 40 to 80 80 to 120 120 to 160 160 to 200 200 to 240 240 to 280 280 to 320 320 to 360 360 to 400 >400

Carbon density Category (tC /ha)

potential restoration?


This stock of exceptionally high C forest is accounted for by 3 blocks: the North & South

Uluguru Forest Reserves and the North Nguru Forest Reserve

Total Area of Montane & Sub-montane Forests in survey = 55,000 ha

Total stock of above ground C = 15.8 million tC

Mean carbon stock = 287 tC /ha

Area of Mixed Woodland Area in Each Carbon Density Category

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Below 20 20 to 40 40 to 60 60 to 80 80 to 100 100 to 120 >120

Carbon Density Category (tC /ha)

Hectares

potential restoration? potential restoration?

Total Area of Mixed Woodlands in Forest Survey = 820,000 ha

Total stock of above ground C = 52 million tC


ECCM 14/05/2007

40

Areas of Lowland Woodland in Each Carbon Density Category

-

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

Below 20 20 to 40 40 to 60 60 to 80 80 to 100 100 to 120 120 to 140 >140

Carbon Density Category (tC /ha)

Hectares

Total Area of Lowland Woodlands = 134,000 ha

Total stock of above ground C = 15.5 million tC



This high C area mainly accounted for by Kilombero and S. Nguru FR.

Area of Miombo Woodland in Each Carbon Density Category

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

0 to 20 20 to 40 40 to 60 60 to 80 >80

Cartbon Density Category (tC /ha)

Hectares

Total area classified as Miombo in survey = 4.9 million ha

Total stock of above ground C = 189 million tC

Mean C stock = 38 tC /ha


ECCM 14/05/2007

41

Appendix 3

ECCM 14/05/2007

42

Appendix 4

ECCM 14/05/2007

43

1995 2003 1995 2003 % change


disturbance 33,095 25,988 10,127,131 7,952,316 -21



Plantation 3,645 8,317 364,541 831,704 128

Other forest / woodland 12,316 20,358 505,102 1,377,726 173

Other land uses 58,396 52,249 0 0 0

Total 107,452 107,733 10,996,774 10,229,943 -7



disturbance 20,357 17,152 6,229,120 5,248,573 -16


of disturbance 0 9 0 768 N/A

Plantation 1,770 3,546 177,033 354,639 100

Other forest / woodland 1,967 3,346 76,866 273,052 255

Other land uses 5,135 5,200 0 0 0

Total 29,228 29,254 6,483,018 5,877,032 -9



disturbance 5,922 4,928 1,812,025 1,508,036 -17



Plantation 2,039 3,645 203,904 364,482 79


Other land uses 20,961 17,684 0 0 0

Total 30,940 30,939 2,089,663 2,134,257 2



disturbance 14,625 11,868 4,475,219 3,631,474 -19



Plantation 3,172 6,534 317,218 653,448 106


Other land uses 37,890 32,359 0 0 0

Total 60,905 60,922 4,983,036 4,843,635 -3



disturbance 2,422 1,673 741,098 511,990 -31



Plantation 273 226 27,321 22,581 -17


Other land uses 6,151 6,770 0 0 0

Total 10,501 10,601 825,553 635,282 -23

Total (excluding plantations & other land uses) 4,076 3,605 798,232 612,701 -23


disturbance 8,368 6,747 2,560,553 2,064,628 -19



Plantation 1,293 1,651 129,346 165,142 28


Other land uses 15,532 15,791 0 0 0

Total 29,772 30,016 2,845,879 2,569,874 -10


East UsambaraArea (ha) Tonnes of carbon

Total area

2 km of settlements

Forest reserves

1 km of roads

2 km of roads

1 km of settlements

ECCM 14/05/2007

44

Key observations of forest cover and carbon storage in forests within the East Usambara Forest Block.

• Between 1995 and 2003 the forested area decreased by 21%. • Net carbon emissions resulting from deforestation (of forest and woodland categories)

and forest / woodland degradation are 3.4 tonnes per hectare per year of the forest / woodland area in 1995 (not including plantations).

• Deforestation (of montane and submontane forest) is observed to have occurred across the entire East Usambara forest block between 1995 – 2003. Decreases in carbon stocks as a result of deforestation range between 16 – 31% for different datasets within this region. Proximity to existing settlements and roads is observed as being a key driver of land use change in East Usambara.

• Net carbon emissions resulting from deforestation (of montane and sub-montane forest) are 8.21 tonnes per hectare per year of the forest area in 1995. The actual carbon emissions may be lower as this figure does not include areas of degraded forest.

• It has not been possible using the data provided to calculate accurately the rate of forest (montane and sub-montane) degradation. However, it is likely that the area of highly disturbed forest is much higher than the figures indicate. It is also likely that the areas of degraded forest have been captured in the other forest / woodland category (when mapped for GIS) rather than being classified as degraded forest.

• Increases in carbon stocks as a result of plantation forestry activities are likely to have occurred across the entire East Usambara forest block.

• Carbon stocks in all forest and woodland categories (excluding plantations) have decreased in all categories (data sub-sets). This grouping factors in carbon losses as a result of forest degradation as far as is possible based upon the data supplied to ECCM. Overall carbon losses range from 11 – 25% across the region. Deforestation within 1 km of existing settlements is calculated to be significantly higher (25%) than in other areas of the East Usambara forest block.

• Decreases in the carbon stocks in Forest Reserves are observed to be only marginally less than forest areas outside of the Forest Reserves.

• A trend towards increased land used for agriculture is observed.

ECCM 14/05/2007

45

1995 1999 1995 1999 % change


disturbance 2,148 2,026 657,288 619,932 -6



Plantation 0 0 3 3 0


Other land uses 20,103 203,111 0 0 0

Total 274,167 274,200 17,546,613 5,443,757 -69



disturbance 1,169 1,146 357,830 350,746 -2




Other forest / woodland 1,917 548 113,495 47,347 -58

Other land uses 319 1,711 0 0 0

Total 3,405 3,405 471,325 398,093 -16



disturbance 0 0 0 0 0




Other forest / woodland 15,188 4,058 1,030,099 279,254 -73

Other land uses 3,991 15,121 0 0 0

Total 19,179 19,179 1,030,099 279,254 -73

Total (excluding plantations & other land uses) 15,188 4,058 1,030,099 279,254 -73


disturbance 0 30 0 9,249 100



Plantation 0 0 0 3 N/A


Other land uses 6,016 28,975 0 0 0

Total 37,109 37,109 2,170,653 561,281 -74








Other land uses 1,089 0 0 3 0

Total 5,349 5,349 515,944 24,297 -95



disturbance 17 36 5,325 10,965 106




Other forest / woodland 15,629 1,862 1,056,855 124,485 0

Other land uses 3,711 17,459 0 0 0

Total 19,357 19,357 1,062,180 135,450 -87


MahengeArea (ha) Tonnes of carbon

Total area

2 km of settlements

Forest reserves

1 km of roads

2 km of roads

1 km of settlements

ECCM 14/05/2007

46

Key observations of forest cover and carbon storage in forests within the Mahenge Forest Block.

• 6% deforestation (montane and submontane) occurred in this forest block during the period from 1995 and 1999.

• The small remaining area of Forest Reserve is observed to be reasonably protected (decreased area of 2%).

• All forest and woodland categories in proximity to roads and exiting settlements suffered significant decreases during this period.

• Net carbon emissions resulting from deforestation (of montane and sub-montane forest) are 3.48 tonnes per hectare per year of the forest area in 1995.

ECCM 14/05/2007

47

1995 2003 1995 2003 % change


disturbance 36,523 29,062 11,176,160 8,893,033 -20



Plantation 0 0 3 0 -100


Other land uses 60,089 61,425 0 0 0

Total 161,736 161,791 15,329,365 14,438,242 -6



disturbance 25,040 23,322 7,662,301 7,136,654 -7






Total 32,319 32,319 8,005,735 7,607,494 -5



disturbance 3,889 1,012 1,190,034 309,547 -74





Other land uses 4,465 10,336 0 0 0

Total 34,751 34,769 2,354,345 1,721,709 -27



disturbance 8,654 4,263 2,648,152 1,304,368 -51





Other land uses 7,324 16,788 0 0 0

Total 63,362 48,696 4,807,810 2,983,013 -38



disturbance 1,346 916 411,943 280,251 -32




Other forest / woodland 6,980 7,011 373,731 210,657 -44

Other land uses 5,216 5,619 0 0 0

Total 13,542 13,546 785,674 490,908 -38



disturbance 6,887 4,412 2,107,269 1,350,201 -36





Other land uses 3,181 14,160 0 0 0

Total 41,596 41,615 3,640,058 2,692,352 -26


NguruArea (ha) Tonnes of carbon

Total area

2 km of settlements

Forest reserves

1 km of roads

2 km of roads

1 km of settlements

ECCM 14/05/2007

48

Key observations of forest cover and carbon storage in forests within the Nguru Forest Block.

• The total forest / woodland area remains relatively consistent during this period. • The area of natural forest (montane and submontane) decreases by 20% during this

period. • Proximity to roads and existing settlements are observed to be drivers of deforestation

and forest degradation. • Net carbon emissions resulting from deforestation (of montane and sub-montane forest)

are 7.81 tonnes per hectare per year of the forest area in 1995.

ECCM 14/05/2007

49

1995 2003 1995 2003 % change


disturbance 26,870 25,728 8,222,251 7,872,829 -4



Plantation 0 0 0 0 #DIV/0!


Other land uses 10,628 47,143 0 0 0

Total 122,118 122,118 12,930,054 12,137,312 -6



disturbance 19,005 17,038 5,815,408 5,213,506 -10





Other land uses 3,383 2,569 0 0 0

Total 29,013 29,013 6,250,473 6,031,871 -3



disturbance 319 0 97,763 0 -100





Other land uses 4,445 7,485 0 0 0

Total 8,750 8,751 297,470 104,500 -65



disturbance 964 97 295,098 29,828 -90





Other land uses 7,463 14,007 0 0 0

Total 17,351 17,352 742,868 304,383 -59



disturbance 1,082 1,127 330,954 344,724 4





Other land uses 2,120 3,682 0 0 0

Total 10,668 10,791 683,115 862,963 26

Total (excluding plantations & other land uses) 8,548 7,108 683,112 862,960 26


disturbance 5,330 4,768 1,630,897 1,459,020 -11





Other land uses 4,609 11,365 0 0 0

Total 31,694 31,695 2,766,181 2,807,299 1


NguuArea (ha) Tonnes of carbon

Total area

2 km of settlements

Forest reserves

1 km of roads

2 km of roads

1 km of settlements

ECCM 14/05/2007

50

Key observations of forest cover and carbon storage in forests within the Nguu Forest Block.

• Proximity to roads is observed as being the key driver to land use change • Net carbon emissions resulting from deforestation (of montane and sub-montane forest)

are 1.63 tonnes per hectare per year of the forest area in 1995.

ECCM 14/05/2007

51

1995 2000 1995 2000 % change


disturbance 43,173 41,166 13,211,030 12,596,888 -5



Plantation 0 168 3 16,830 560,887


Other land uses 35,646 110,592 0 0 0

Total 235,768 235,768 22,531,848 18,663,708 -17



disturbance 24,565 24,148 7,517,012 7,389,319 -2





Other land uses 4,568 9,385 0 0 0

Total 46,385 46,385 8,394,169 8,420,686 0



disturbance 1 60 188 18,365 9,660



Plantation 0 102 3 10,226 340,750


Other land uses 4,685 10,268 0 0 0

Total 13,998 13,998 574,063 293,580 -49



disturbance 49 222 15,012 68,017 353



Plantation 0 168 3 16,830 560,887


Other land uses 8,022 20,088 0 0 0

Total 28,479 28,479 1,253,292 682,331 -46



disturbance 0 27 3 8,380 279,241




Other forest / woodland 580 486 33,404 36,410 9


Total 1,517 1,517 33,404 44,790 34

Total (excluding plantations & other land uses) 580 514 33,401 44,787 34


disturbance 114 131 35,007 40,237 15





Other land uses 3,257 3,818 0 0 0

Total 6,403 6,403 216,269 228,008 5

Total (excluding plantations & other land uses) 3,146 2,585 216,266 228,008 5

RubehoArea (ha) Tonnes of carbon

Total area

2 km of settlements

Forest reserves

1 km of roads

2 km of roads

1 km of settlements

ECCM 14/05/2007

52

Key observations of forest cover and carbon storage in forests within the Rubeho Forest Block.

• Deforestation (Montane and submontane) of 5% over 5 years (i.e. 1% / year) • Net carbon emissions resulting from deforestation (of montane and sub-montane forest)

are 2.85 tonnes per hectare per year of the forest area in 1995. • Observed increases in carbon stocks in proximity to roads and existing settlements are

likely to be shown as a result of inconsistencies in land use classifications rather because of actual land use change.

ECCM 14/05/2007

53

1995 1999 1995 1999 % change

Forest (montane and submontane) with a low

level of disturbance 145,964 131,314 44,664,984 40,182,084 -10

Forest (montane and submontane) with a high

level of disturbance 0 868 0 72,053 N/A

Plantation 54,130 95,564 5,413,000 9,556,350 77


Other land uses 539,427 702,186 0 0 0

Total 1,339,770 1,339,737 81,834,411 72,477,789 -11



level of disturbance 121,277 112,781 37,110,762 34,510,986 -7


level of disturbance 0 2,542 0 211,023 N/A

Plantation 42,319 56,224 4,231,940 5,622,394 33


Other land uses 142,779 73,569 7,055,082 0 0

Total 394,194 394,195 52,125,408 50,715,673 -3



level of disturbance 1,547 1,029 473,431 314,752 -34


level of disturbance 0 996 0 82,686 N/A

Plantation 5,679 11,948 567,851 1,194,834 110


Other land uses 63,123 63,609 0 0 0

Total 88,476 88,476 1,852,855 2,079,541 12



level of disturbance 4,041 2,970 1,236,470 908,915 -26


level of disturbance 0 1,214 0 100,756 N/A

Plantation 10,653 23,365 1,065,270 2,336,535 119


Other land uses 118,343 121,489 0 0 0

Total 173,323 173,323 4,201,894 4,511,121 7



level of disturbance 1,320 601 403,963 183,833 -54


level of disturbance 0 0 0 0 N/A

Plantation 781 466 78,096 46,617 -40


Other land uses 58,993 61,647 0 79 0

Total 66,982 66,982 815,066 480,844 -41



level of disturbance 4,691 3,114 1,435,559 952,921 -34


level of disturbance 0 0 0 0 N/A

Plantation 1,244 835 124,375 83,505 -33


Other land uses 148,045 158,660 0 0 0

Total 176,506 176,506 2,857,336 1,820,517 -36


2 km of settlements

Forest reserves

1 km of roads

2 km of roads

1 km of settlements

UdzungwaArea (ha) Tonnes of carbon

Total area

ECCM 14/05/2007

54

Key observations of forest cover and carbon storage in forests within the Udzungwa Forest Block.

• Deforestation (montane and submontane) of approximately 10% during this period (1995 – 1999).


• Higher carbon emissions are observed to have occurred in proximity to roads and settlements than across the forest block as a whole.

• Significant carbon emissions are observed from forest reserves (7% during this period). These emissions are lower than the total for this forest block.

ECCM 14/05/2007

55

1995 2000 1995 2000 % change


disturbance 25,808 24,297 7,897,370 7,434,943 -6



Plantation 0 443 3 44,261 1,475,250


Other land uses 99,190 97,267 0 0 0

Total 138,963 139,005 8,534,833 8,650,934 1



disturbance 20,276 21,788 6,204,303 6,667,036 7




Other forest / woodland 282 1,786 15,883 114,358 620

Other land uses 7,037 4,021 0 0 0

Total 27,594 27,594 6,220,186 6,781,394 9



disturbance 1,288 530 394,027 162,199 -59



Plantation 0 242 3 24,165 805,407


Other land uses 13,687 14,719 0 0 0

Total 16,808 16,835 458,497 291,060 -37



disturbance 2,206 1,150 674,904 352,050 -48



Plantation 0 443 3 44,261 1,475,250


Other land uses 27,462 29,499 0 0 0

Total 34,593 34,621 869,276 655,775 -25



disturbance 2,305 692 705,471 211,779 -70



Plantation 0 185 3 18,503 616,650


Other land uses 33,159 32,959 0 0 0

Total 37,634 37,676 778,739 503,018 -35



disturbance 9,816 7,258 3,003,705 2,220,960 -26



Plantation 0 368 3 36,794 1,226,380


Other land uses 65,039 62,028 0 0 0

Total 79,334 79,376 3,172,254 2,942,971 -7


UluguruArea (ha) Tonnes of carbon

Total area

2 km of settlements

Forest reserves

1 km of roads

2 km of roads

1 km of settlements

ECCM 14/05/2007

56

Key observations of forest cover and carbon storage in forests within the Uluguru Forest Block.

• Medium level of deforestation (montane and submontane) in this forest block during this period (6% between 1995 – 2000).


• Significantly higher carbon emissions are observed to have occurred in proximity to roads and settlements than across the forest block as a whole.

ECCM 14/05/2007

57

1995 1999 1995 1999 % change


disturbance 18,371 18,774 5,621,587 5,744,875 2



Plantation 1,753 1,683 175,302 168,344 -4


Other land uses 48,010 75,301 0 0 0

Total 129,440 129,458 10,219,614 8,062,273 -21



disturbance 15,493 15,179 4,740,705 4,644,743 -2



Plantation 1,558 1,571 155,787 157,143 1

Other forest / woodland 1,076 10 65,963 324 -100

Other land uses 2,681 3,963 0 0 0

Total 20,658 20,807 4,957,540 4,809,146 -3







Other forest / woodland 0 0 0 0 0

Other land uses 11 11 0 0 0

Total 11 11 0 0 0

Total (excluding plantations & other land uses) 0 0 0 0 0






Other forest / woodland 354 80 30,765 3,537 -89

Other land uses 127 400 4,183 0 0

Total 480 480 34,948 3,537 -90

Total (excluding plantations & other land uses) 354 80 30,765 3,537 -89


disturbance 2,008 2,032 614,445 621,777 1





Other land uses 6,054 7,923 0 0 0

Total 10,655 10,655 847,357 644,870 -24



disturbance 6,492 6,268 1,986,601 1,917,922 -3



Plantation 279 87 27,947 8,675 -69


Other land uses 16,770 23,571 0 0 0

Total 32,573 32,573 2,673,720 2,014,186 -25


UkaguruArea (ha) Tonnes of carbon

Total area

2 km of settlements

Forest reserves

1 km of roads

2 km of roads

1 km of settlements

ECCM 14/05/2007

58

Key observations of forest cover and carbon storage in forests within the Ukaguru Forest Block.

• No deforestation (montane and submontane) in this forest block during this period (1995 – 1999).

• Significant carbon emissions are observed to have occurred as a result of decreases in the area of other forest and woodland (reduced in area by almost 50%).

ECCM 14/05/2007

59

1995 1999 1995 1999 % change


disturbance 31,802 30,889 9,731,504 9,451,973 -3



Plantation 6,835 6,013 683,475 601,290 -12


Other land uses 97,905 122,154 0 0 0

Total 230,542 230,542 13,874,835 12,842,591 -7



disturbance 23,206 22,916 7,100,883 7,012,357 -1



Plantation 5,502 5,418 550,224 541,837 -2


Other land uses 6,791 4,382 0 0 0

Total 38,509 38,557 7,760,859 7,831,819 1



disturbance 2,062 2,169 630,862 663,677 5



Plantation 750 143 74,951 14,310 -81


Other land uses 16,935 19,169 0 0 0

Total 27,718 27,718 997,188 899,801 -10



disturbance 5,187 5,432 1,587,317 1,662,213 5



Plantation 2,139 870 213,855 86,991 -59


Other land uses 25,509 37,478 0 0 0

Total 57,618 57,618 2,677,270 2,261,833 -16



disturbance 5,983 4,736 1,830,908 1,449,161 -21



Plantation 1,279 1,538 127,939 153,818 20


Other land uses 43,223 47,447 0 0 0

Total 70,434 70,434 2,646,277 2,239,714 -15



disturbance 18,294 16,851 5,598,086 5,156,406 -8



Plantation 5,157 4,868 515,705 486,758 -6


Other land uses 77,017 92,079 0 0 0

Total 144,706 144,706 7,671,492 6,841,978 -11


West UsambaraArea (ha) Tonnes of carbon

Total area

2 km of settlements

Forest reserves

1 km of roads

2 km of roads

1 km of settlements

ECCM 14/05/2007

60

Key observations of forest cover and carbon storage in forests within the West Usambara Forest Block.

• Between 1995 and 1999 the total forested area decreased by 3%. • Net carbon emissions resulting from deforestation (of forest and woodland categories)

and forest / woodland degradation are 1.89 tonnes per hectare per year of the forest / woodland area in 1995 (not including plantations).

• On the basis of this data net carbon emissions resulting from deforestation (of montane and sub-montane forest) are 2.2 tonnes per hectare per year of the forest area in 1995. The actual carbon emissions may be lower as this figure does not include areas of degraded forest.

• It has not been possible using the data provided to calculate accurately the rate of forest (montane and sub-montane) degradation. However, it is likely that the area of highly disturbed forest is much higher than the figures indicate. It is also likely that the areas of degraded forest have been captured in the other forest / woodland category (when mapped for GIS) rather than being classified as degraded forest.

• Carbon stocks in all forest and woodland categories (excluding plantations) have decreased in all categories (data sub-sets). This grouping factors in carbon losses as a result of forest degradation as far as is possible based upon the data supplied to ECCM. Overall carbon losses range from 0 – 21% across the region.

• It is not possible to detect that carbon losses are different as a result of proximity to either roads or existing settlements.

• Decreases in the carbon stocks in Forest Reserves are observed to be significantly less than forest areas outside of the Forest Reserves.

ECCM 14/05/2007

61

Appendix 5

ECCM 14/05/2007

62

Edinburgh Centre for Carbon Management Ltd • 18F Liberton Brae • Edinburgh • EH16 6AE • UK• Tel: +44 (0) 131 666 5070

establishing mechanisms for payments for carbon ......encompassing all power stations over 20mw, oil...

Documents