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Rizaldi Boer and Team Geomet FMIPA-IPB e-mail: rboer@fmipa.ipb.ac.id

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.

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Rizaldi Boer and Team Geomet FMIPA-IPB e-mail: rboer@fmipa.ipb.ac.id

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  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.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. Forest Community Group-B (CGB) Used for C-sinks Project by CGB Community Group-A (CGA) Land belong to CGB which are used for crop cultivation by CGA for long-time CGA moves their Activities to forest~ forest is opened 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 factorsthat 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(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

  9. Predictors • 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)

  10. Baseline + leakage Project case Possible change due to project Possible change due to project P1 P2 A LP1 LP2 B1 B2 LN1 LN2 Carbon Stock (Mt) - leakage 2001 2008 2012 2020 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 in Batanghari in 1999 Land Use Prediction in Batanghari 1999 Land Use Prediction in Jambi Province 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. 1999 BS 2008 BS 2012 BS 1999 MIT1 2008 MIT1 2012 MIT1 1999 MIT2 2008 MIT2 2012 MIT2 Predicted LULUCF (Land use, land use change and forest) in the period of 1999-2012. Circles in the maps are location of the projects

  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)

  19. THANK YOU

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