Alternative Approaches to Address Leakage in Carbon Sinks in Indonesia: Methods and Case Study in Sumatra

Rizaldi Boer and Team

Geomet FMIPA-IPB

e-mail: rboer@fmipa.ipb.ac.id

New Delhi, 23-24 September 2002

Background

• Leakage is one of technical problem that should be addressed in carbon-sink project ~ It is to ensure that the increase of carbon stock in project location is real.

• Leakage is as unanticipated loss or gain of net greenhouse gas benefits beyond a project-accounting boundary

Illustration

Forest

Community Group-A (CGA)

Community Group-B (CGB)

Land belong to CGB which are used for crop cultivation by CGA for long-time

Used for C-sinks Project by CGB

CGA moves theirActivities to forest~ forest is opened

Type of Leakage (SGS, 1998; Moura Costa et al., 1997):

• Primary Leakage refers to leakages that occur when the GHG benefits resulted by the project causes an increased or decreased GHG emissions elsewhere.

• Secondary leakage refers to leakages that occur when a project’s outputs create incentives to increase or decrease GHG emissions elsewhere

Leakage Assessment• Need to understand linkage between ‘baseline drivers’,

‘baseline agents’, ‘causes and motivations’, and ‘indicators’– Baseline driver: are defined as activities

predominantly taking place in the absence of the project, and that the project will replace

– Baseline agent: are actors who are engaged in those activities

– Causes and motivations refer to factors that drive the baseline agents to do the activities and these can be represented by indicators

CIFOR (2001) used the following indicators for leakage

• Leakage occurs when one of the following phenomena occurs outside 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

What we should answer ?

What are the likely changes in land use and land use cover change in

the future with and without carbon sink projects ?

Approach to answer the question ?

• Logit(Pi) = a + (bjxj)

– P is probability of land cover change-i, – a intercept and

– bj coefficient of independent variable xj

• Pi = elogit(Pi)/(1+elogit(Pi))

• If Pi = 0 (no cover change) and 1 (cover change occur)

• Pcrit = 0.5 might be used to define whether cover change occur or not

• The physical predictors:– Distance a pixel to a center of a given land use (X1)

– Distance to resettlement area (X2)

– Distance to main-river (X3)

– Distance to main road (X4)

• The socio-economic predictors:– Population density (number of people per pixel[1], X5)

– Ratio between job opportunity and job seeker (X6)

– Ratio between total land use for agriculture and plantation and population (X7)

– Ratio between income and expenditure of the region (X8)

Predictors

BaselineProject casePossible change due to project

+ leakage

Possible change due to project

- leakage

2001 2008 2012 2020

Car

bon

Sto

ck (

Mt)

A B1P1

LP1LN1 B2

P2LP2

LN2

Positive Leakage = [(P2-B2)-(P1-B1)]+[(LP2-B2)-(LP1-B1)]Negative Leakage = [(P2-B2)-(P1-B1)]-[(B2-LN2)-(B1-LN1)]

Location of the study

Land Use in Jambi Province 1999

Land Use Prediction in Jambi Province 1999

Land Use in Batanghari in 1999

Land Use Prediction in Batanghari 1999

Validation of Logit Regresion

Percent Matching: 54% Percent Matching: 57%

C-Sinks Projects

Tree Species Land Allocated for

Scenario-1 Land Allocated for

Scenario-2 Location

of Projects

Rubber / Karet (Hevea braziliensis) 1861 2955 Muara Bulian

Oil palm / Kelapa Sawit 1278 2029 Muara Bulian

Rambutan (Nephelium sp). 971 1542 Mersam

Meranti (Shorea spp.) 668 1061 Pemayung

Durio / Durian (Durio zibethinus) 1232 1956 Muara Bulian

Albizia (Paraserianthes falcataria) 723 1149 Pemayung

Duku (Lansium domesticum) 910 1446 Batin XXIV

Manggo / Mangga (Mangifera indica.) 1162 1846 Mersam

Macang (Mangifera sp.) 952 1513 Batin XXIV

Pinang (Arenga pinanga) 376 597 Muara Tembesi

Candle nut / Kemiri (Aleurites mulluccana) 611 970 Muara Tembesi

Total 10,743 17,065

Mean and Standard Deviation of the Three Predictors Under Baseline and Mitigation Scenarios for the Five Sub-Districts

Socio-Economic Variables

Scenarios 1999 2000 2005 2008 2012 2015

0.214 0.212 0.204 0.199 0.192 0.188 BS 0 0 0 0 0 0

0.214 0,725 0,614 0,557 0,492 0,450 Mit-1 0.000 0,143 0,114 0,100 0,084 0,073 0.214 1,239 1,024 0,916 0,792 0,712

Ratio between job opportunity and job seeker (X6)

Mit-2 0.000 0,286 0,229 0,200 0,167 0,146 0.921 0.921 0.924 0,925 0,928 0,931 BS 0.542 0.542 0.544 0,544 0,546 0,547 0.921 0,923 0,933 0,939 0,947 0,953 Mit-1 0.542 0,542 0,542 0,542 0,542 0,542 0.921 0,924 0,936 0,944 0,953 0,960

Ratio between total land use for agriculture and plantation and population (X7) Mit-2

0.542 0,543 0,547 0,549 0,552 0,554 0.902 0.924 1.044 1.123 1.237 1.331 BS 0.076 0.078 0.088 0.095 0.104 0.112 0.902 0,940 1,050 1,126 1,239 1,332 Mit-1 0.076 0,078 0,088 0,095 0,104 0,112 0.902 0,957 1,056 1,129 1,240 1,332

Ratio between income and expenditure of the region (X8)

Mit-2 0.076 0,079 0,088 0,095 0,104 0,112

Predicted LULUCF (Land use, land use change and forest) in the period of 1999-2012. Circles in the maps are location of the projects

1999 MIT1 2008 MIT1 2012 MIT1

1999 MIT2 2008 MIT2 2012 MIT2

2008 BS 2012 BS1999 BS

Estimated standing C-stock under different scenarios

Baseline Revised

Baseline-1 Mitigation-1 Revised

Baseline-2 Mitigation-2 Tonnes 1999 14539956 14539956 14539956 14539956 14539956 2008 19574996 19723074 19919179 19196940 20121723 2012 21780656 22613314 22296930 22074993 22600746

C-credit of the projects in the period between 1999-2012

Mitigation-2

-500000

0

500000

1000000

1500000

2000000

1999-2008 1999-2012 2008-2012C-S

tock (Tonnes)

Without LeakageWith Leakage

Mitigation-1

0

500000

1000000

1500000

2000000

1999-2008 1999-2012 2008-2012

C-S

tock (tonnes)

Without LeakageWith Leakage

Concluding Remarks• The use of satellite imagery for assessing leakage is

possible ~ what is the acceptable error (?)• The satellite approach may be more efficient and

effective for assessing leakage of multi-projects covering wide area

• The main constraint is data availability• The approach still needs improvement

– Selection of predictors– Projection of the predictors– Estimation of carbon stock– Annual running (check with more satellite data)