1. 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.idrboer@fmipa.ipb.ac.id New Delhi, 23-24 September 2002

2. 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

3. Illustration

4. 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

5. 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

6. 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

7. 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 ?

8. Approach to answer the question ? Logit(P i ) = a +  (b j x j ) –P is probability of land cover change-i, –a intercept and –b j coefficient of independent variable x j P i = e logit(Pi) /(1+e logit(Pi) ) If P i = 0 (no cover change) and 1 (cover change occur) P crit = 0.5 might be used to define whether cover change occur or not

9. The physical predictors: –Distance a pixel to a center of a given land use (X 1 ) –Distance to resettlement area (X 2 ) –Distance to main-river (X 3 ) –Distance to main road (X 4 ) The socio-economic predictors: –Population density (number of people per pixel[1], X 5 )[1] –Ratio between job opportunity and job seeker (X 6 ) –Ratio between total land use for agriculture and plantation and population (X 7 ) –Ratio between income and expenditure of the region (X 8 ) Predictors

10. Baseline Project case Possible change due to project + leakage Possible change due to project - leakage 2001200820122020 Carbon Stock (Mt) A B1 P1 LP1 LN1 B2 P2 LP2 LN2 Positive Leakage = [(P2-B2)-(P1-B1)]+[(LP2-B2)-(LP1-B1)] Negative Leakage = [(P2-B2)-(P1-B1)]-[(B2-LN2)-(B1-LN1)]

11. Location of the study

12. 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%

13. C-Sinks Projects

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

15. 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 MIT12008 MIT12012 MIT1 1999 MIT22008 MIT22012 MIT2 2008 BS 2012 BS 1999 BS

16. Estimated standing C-stock under different scenarios

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

18. 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)