Assessment of carbon leakage in multiple carbon-sink projects: a case study in Jambi Province, Indonesia

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    Assessment of carbon leakage in multiple carbon-sinkprojects: a case study in Jambi Province, Indonesia

    Rizaldi Boer Upik R. Wasrin Perdinan Hendri Bambang D. Dasanto Willy Makundi Julius Hero M. Ridwan Nur Masripatin

    Received: 23 June 2006 / Accepted: 12 August 2006 / Published online: 20 March 2007 Springer Science+Business Media B.V. 2007

    Abstract Rehabilitation of degraded forest land through implementation of carbon-sinkprojects can increase terrestrial carbon (C) stock. However, carbon emissions outside the

    project boundary, which is commonly referred to as leakage, may reduce or negate the

    sequestration benefits. This study assessed leakage from carbon-sink projects that could

    potentially be implemented in the study area comprised of 11 sub-districts in the Batanghari

    District, Jambi Province, Sumatra, Indonesia. The study estimates the probability of a given

    land use/cover being converted into other uses/cover, by applying a logit model. The pre-

    dictor variables were: proximity to the center of the land use area, distance to transportation

    channel (road or river), area of agricultural land, unemployment (number of job seekers), job

    opportunities, population density and income. Leakage was estimated by analyzing with and

    without carbon-sink projects scenarios. Most of the predictors were estimated as being sig-

    nificant in their contribution to land use cover change. The results of the analysis show that

    R. Boer (&) Perdinan Hendri B. D. DasantoClimatology Laboratory, Department of Geophysics and Meteorology, Faculty of Mathematicsand Natural Sciences, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 16680Indonesiae-mail:

    U. R. WasrinLand Management Grand College, Bogor Agricultural University, Kampus IPB Darmaga,Bogor 16680, Indonesia

    J. Hero U. R. WasrinEcology Laboratory, Faculty of Forestry, Bogor Agricultural University, Kampus IPBDarmaga, Bogor 16680, Indonesia

    W. MakundiLawrence Berkeley National Laboratory, Berkeley, CA, USA

    M. RidwanLestari Hutan Indonesia, Jakarta, Indonesia

    N. MasripatinDepartment of Forestry, Jakarta, Republic of Indonesia


    Mitig Adapt Strat Glob Change (2007) 12:11691188DOI 10.1007/s11027-006-9058-1

  • leakage in the study area can be large enough to more than offset the projects carbon

    sequestration benefits during the period 20022012. However, leakage results are very

    sensitive to changes of carbon density of the land uses in the study area. By reducing

    C-density of lowland and hill forest by about 10% for the baseline scenario, the leakage

    becomes positive. Further data collection and refinement is therefore required. Nevertheless,

    this study has demonstrated that regional analysis is a useful approach to assess leakage.

    Keywords Carbon leakage Carbon-sink projects Logistic modeling Mitigation


    In the past few decades, forest cover in Indonesia has declined significantly due to

    increasing rate of deforestation in the larger islands (Kalimantan, Sumatra, Sulawesi and

    Irian Jaya), extensive forest destruction by wild fires and a declining rate of reforestation

    and afforestation. The forest estate is generally classified into several main types (ITTO

    2002): (i) conservation forestfor scientific reserve and nature reserve, wild life sanc-

    tuaries, national parks, grand forest parks and nature recreation parks, (ii) protection

    forestusually on very steep slopes and vulnerable to soil erosion and water degrada-

    tion, and not made available for logging, (iii) production and conversion forestfor

    logging and also for conversion to other land uses, (iv) critical forestformer forest land

    severely damaged by excessive harvesting of wood and/or non-wood forest products,

    poor management, repeated fires, grazing, and disturbances or land uses that damage

    soils and vegetation to a degree that inhibits or severely delays the re-establishment of

    forest after abandonment, (v) degraded forestprimary forest that has been adversely

    affected by the unsustainable harvesting of wood and/or non-wood forest products and

    has lost the structure, function, species composition and/or productivity normally asso-

    ciated with the natural forest type expected at that site, (vi) unproductive landslands

    with reduced capability to produce goods and services that are economically and socially

    viable such as fallow land, bare land, bush and thickets, and (vii) plantation forestsa

    forest stand that has been established by planting or seeding. To illustrate the rate of

    decline of forest cover, in 1997, the area classified as critical land and degraded forest

    was estimated to be about 30 million hectares (Mha) (Boer 2001). By 2000, the area of

    critical and unproductive lands in the state forestland had increased to 54.6 Mha (MOF

    2001), an increase of 82% over 3 years.

    This study is based on analysis done for potential carbon sequestration projects in Jambi

    province. Based on a 1986 vegetation map and 1992 satellite imagery (Landsat TM), the

    mean annual rate of deforestation in the province was estimated at 106,700 ha/year. The

    annual rate of forestation (re-greening, reforestation, and timber estate plantations) was

    significantly lower, estimated at about 14,000 ha per year between 1988 and 2000, with the

    difference representing the annual increase in critical land area. In 1989, the total area of

    critical land inside and outside forest area was 194,000 ha, and by 1999, this area had

    increased to 716,000 ha. Total critical land in Jambi at the end of 2000 was about

    887,500 ha, distributed in four districts, i.e., 77,100 ha in Batanghari 96,400 ha in Kerinci,

    321,400 ha in Bungo Tebo and 329,500 ha in Sarko. About 61% of the critical land is

    grassland, while the remaining is shrubs or fallow or shifting cultivation.

    1170 Mitig Adapt Strat Glob Change (2007) 12:11691188


  • Funding sources for restoring forests are very limited. The Forest Rehabilitation Fund

    (Dana Reboisasi) is only enough for restoring 34 Mha of degraded lands and forests(Boer et al. 2001), while total degraded lands and forest of Indonesia in 2000 reached

    49 Mha (MoE 2003). Thus in order to reforest the remaining degraded lands other sources

    of funds must be sought, including other domestic sources, and bilateral and other inter-

    national funding mechanisms. The clean development mechanism (CDM) of the United

    Nations Framework Convention on Climate Change (UN FCCC) Kyoto Protocol provides

    one likely source of investment for reforesting these areas.

    In addition to carbon benefits from the rehabilitation of degraded lands such projects

    may have other benefits, including biodiversity, quality of life, watershed and water

    quality, and adaptive capacity to climate change. However, accounting for the carbon (C)

    that is actually saved by the projects poses a number of challenges (Brown et al. 1997).

    First, most carbon sequestration projects involve multiple point sources of emissions or

    sequestration and they are spread over a wider geographic area. This leads to complexities

    arising from the variations in data, biomass and soil properties as well as in land-use

    classification. Second, projects that sequester carbon may carry some risk of unintended

    release of the C (e.g. in forest fires) or the duration of C storage may only be temporary.

    Third, the implementation of these projects in a given location may lead to C emission or

    sequestration in another area outside the project locationcommonly referred to as

    leakage. Various suggestions have been put forth on approaches to address leakage (IPCC2000) but so far there has not been clear acceptable methodology which can resolve all the

    major associated technical problems.

    The two key elements in accounting for greenhouse gas (GHG) benefits are the (i) setting

    of a baseline against which a change in GHG emissions or removals are to be measured, and

    (ii) determination of additionality (the additional amount of C stored or emissions reduced by

    the project). In addition, the baseline needs to be adjusted against leakage, i.e., for the loss or

    gain of net GHG benefits beyond the project boundary. This study develops an approach for

    the determination of the baseline and measurement of leakage in multiple potential forestry

    projects in Batanghari District, Jambi Province, Indonesia.

    Carbon leakage

    Leakage is defined as loss or gain of net GHG benefits outside a project boundary.

    According to a COP9 decision, leakage refers only to the increase of all greenhouse gases

    outside the project boundary, measurable and attributable to the project. CIFOR (2001)

    stated that leakage in sinks projects might occur when one of the following phenomena

    occurs outside the project boundary:

    Unallocated forested lands are harvested Protected areas are converted into production forest areas Illegal logging increases in protected and production forests Land is converted to lower C stocking rates due to emissions reductions elsewhere.

    Furthermore, establishment of community woodlots may result from protection of an

    area which previously was the source of timber and woodfuel for a community.

    In order to predict whether leakage will occur or not, Auckland et al. (2001) stated that

    baseline drivers, baseline agents, causes and motivations, and indicators that exist in theproject sites should be understood. Baseline drivers are defined as activities predominantly

    Mitig Adapt Strat Glob Change (2007) 12:11691188 1171


  • taking place in the absence of the project, and that the project will replace. Baseline agentsare actors who are engaged in those activities. Causes and motivations refer to factors thatdrive the baseline agents to do the activities and these can be represented by indicators. Byknowing the interrelationship between these factors, we can predict whether leakage would

    occur or not. The following example illustrates the definitions mentioned above.

    Suppose that the type of activity proposed involves the establishment of timber estate

    plantationHutan Tanaman Industri (HTI). The establishment of HTI in Indonesia nor-

    mally takes place on state-owned land carried out by state enterprises or private forest

    companies. At present only a few of the degraded production forests in Batanghari district

    in Jambi have been converted into HTI. The idle degraded forest-lands are normally left as

    unmanaged land (fallow) or used by local community for ranching, agricultural activities

    or as a source of fuel wood. Fallow, ranching or agricultural activities are baseline drivers,while local communities that engage in these activities are the baseline agents. One of themain reasons for the local community to engage in these activities on this land is to get

    additional income, and this factor is taken as cause and motivation. The next question is,what indicator can be used to measure the leakage?

    To answer the above question, further information from related stakeholders in the

    project site needs to be sought (see Fig. 1). The responses to the questions in Fig. 1 help to

    determine whether leakage is likely to occur or not.

    There are two main types of leakageprimary and secondary leakage (Moura-Costa

    et al. 1997; SGS 1998). Primary leakage occurs when the GHG benefits of the project

    cause an increase or decrease of GHG emissions elsewhere. For example, if the degraded

    forest-land allocated for HTI is already used by the local community for agriculture, the

    implementation of the HTI project may displace the agricultural activities to other areas or

    may cause the community to engage in other income generating activities such as logging,

    which would increase emissions elsewhere (negative leakage). Secondary leakage occurs

    when a projects outputs create incentives to increase or to decrease GHG emissions

    elsewhere. For example, the project increases economic activity in the project area that

    creates additional income for the local community thus leading to a reduction in defor-

    estation or illegal logging outside the project area (positive leakage). The project can also

    lead to negative leakage if the increase in income leads to activities that increase GHG

    emissions such as conversion of forest areas to rice cultivation. Thus, both primary and

    secondary leakage can be positive or negative depending on the nature of their causes, and

    the agents involved.

    The above examples show that change in forest cover and C density outside the project

    area can be an indicator of leakage. In order to know whether the deforestation rate is

    altered by the project activities, we may need to track historical series of deforestation

    surrounding such projects, before and after the initiation of the project. Other external

    factors that may affect deforestation such as rate of population growth, agricultural prices,

    demand for timber/fiber/fire wood, road density, change in forest law, and enforcement

    policies also need to be assessed, as well as agents involved in baseline activities

    throughout the project timeframe and the activities they engage in.

    Considering that leakage may cover very wide areas away from the project area, the use

    of satellite imagery for assessing the leakage can be very useful (e.g. Chomitz and Gray

    1995; Hall et al. 1995). The potential extensive area of leakage impact is one reason put

    forth advocating the use of regional baselines (IPCC 2000). In this study, we utilized

    satellite imagery for assessing leakage and setting up a regional baseline for future sinks


    1172 Mitig Adapt Strat Glob Change (2007) 12:11691188


  • Project site characteristics

    Location of carbon-sink projects

    The available maps could not be used to identify the critical lands, as such the analysis

    assumed that the critical/degraded lands are generally to be found in the lowland logged-

    over forest and secondary re-growth areas.

    Satellite images.

    In this study, satellite images for the analysis were from Landsat TM 1986 and 1992, which

    were obtained from Wasrin et al. (2000). The study area in Batanghari district has 12 sub-

    districts, and it is assumed that carbon sequestration or avoidance projects will be

    implemented in 11 sub-districts excluding the sub-district of Kodya Jambi.



    What is the status of allocated degraded forest-land

    Is the allocated land already planned for HTI by

    government or companies?

    Is the allocated land already degraded before


    Is the allocated land used by local community for income


    Does the project provide alternative livelihood


    Do the baseline agents engage in the livelihood

    programs ?

    It may not meet additionality rule

    It may not meet Kyoto rule

    Will the project increase wood supply or create new

    job opportunities?

    No leakage expected

    It may reduce deforestation

    (positive leakage) It may increase deforestation

    (negative leakage)

    No leakage expected













    Will the livelihood programs provide equal income with or more than the replaced ones?

    Will the increase in income change the attitude...