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Regional modelling for REDD+ project development: the case of the Suruí Forest Carbon Project
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Regional modelling for REDD+ project development: the case of the Suruí Forest Carbon Project

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Credible baseline setting and accurate and transparent Measurement, Reporting and Verification (MRV) of results are key conditions for successful REDD+ projects. In this presentation, Gabriel Carrero …

Credible baseline setting and accurate and transparent Measurement, Reporting and Verification (MRV) of results are key conditions for successful REDD+ projects. In this presentation, Gabriel Carrero from IDESAM explains the process of regional spatial and non-spatial modeling for determining a baseline in the Surui Forest Carbon Project.

Gabriel Carrero gave this presentation on 8 March 2012 at a workshop organised by CIFOR, ‘Measurement, Reporting and Verification in Latin American REDD+ Projects’, held in Petropolis, Brazil. The workshop aimed to explore important advances, challenges, pitfalls, and innovations in REDD+ methods — thereby moving towards overcoming barriers to meeting MRV requirements at REDD+ project sites in two of the Amazon’s most important REDD+ candidate countries, Peru and Brazil. For further information about the workshop, please contact Shijo Joseph via s.joseph (at) cgiar.org

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  • 1. Regional modelling for REDD+project development: the case of the Suruí Forest Carbon Project GABRIEL C. CARREROMEASUREMENT, REPORTING AND VERIFICATION IN LATIN AMERICAN REDD+ PROJECTS – CIFOR WORKSHOP PETROPOLIS, MARCH 8TH2012.
  • 2. IDESAM’sClimateChangeandEnvironmentalS ervicesProgram • REDD+ and A/R projects • Juma REDD Project Project • Suruí REDD+ Development • Greener Apui Program • Carbon Neutro Program International •UNFCCC, GCF, etc. Linkage and • Latin American REDD Forum PMC Public • “Articulación Regional Amazonica” (ARA) Policies • National: OC, FBMC, Amazonas State Government • Nesting State Level Programs into National Programs (AM- RO) Capacity • REDD Projects Development Building • Katoomba Group Courses &Training • Amazonas State REDD+ and CC •Others
  • 3. Baseline Approaches• Projecting historical average X modeling rates• Regional modeling – Need local understand, identifying agents, drivers and underlying causes
  • 4. Simamazonia I Soares Filho et al. 2006
  • 5. Baseline construction
  • 6. Project Boundaries 208,039 ha31,994ha 3,416.6 ha
  • 7. Baseline Scenario - Agents Suruí people identified as the sole agents of deforestation. – Control over the Territory – Logging agreements – Lease pastures and sharecropping systems
  • 8. Baseline Scenario - Drivers– Cash income from external actors (logging, sharecropping and pasture leasing)– Population growth– Increased labor available Average:157.4 ha/year
  • 9. SimSurui Modeling• Non-spatial modeling – Vensim software (http://www.vensim.com/index.html)• Spatial modeling – Dinamica Ego platform (http://www.csr.ufmg.br/dinamica/) PhD Student Claudia S. Vitel (Agroparistech& INPA).
  • 10. Non-spatial model: SimSuruiSystem Dynamics>Modeling method of variables of interest based on empirical data for testing and assessing patterns and responses of the system in question. – Conceptual model – Causal Diagram – Selected variables • Profitability scenarios
  • 11. SIMSURUI Causal diagram
  • 12. Datasets Description / Class 2009 2038Suruí Population (individuals) 1142 2504[0-15 years] 518 705[15 - 65] 597 1266[> 65] 27 532Suruí Households 195 428Employed Individuals 62 316Labor available in Surui territory 534 949 Interviews Number families Families (No.) % 2009 2038 Without Productive Group 0 11 9.1 18 39 Activity/Subsistence Group 1 Coffee Growers 53 44 85 187 Coffee Growers and Group 2 48 40 78 170 Ranchers Group 3 Ranchers 9 7.4 14 31 Total 121 100 195 428 Net Livestock Net Number revenue Net income ** Fixed Fixed net revenue of Timber* Handicrafts* own coffee Income * expenses* revenue own Househ (R$/ yr) (R$/ yr) livestock sharecropping (R$/ yr) (R$/ yr) lease ** coffee ** olds ** (R$/ ha) (R$/ ha) (R$/ ha) (R$/ ha) Group 0 11 11,663 8,857 4,840 116 - - - - Group 1 53 6,974 7,026 7,120 148 - - 294.0 121.6 Group 2 48 6,042 9,060 9,984 344 190.8 60.0 294.0 121.6 Group 3 9 5,006 8,423 7,875 12 190.8 60.0 - -
  • 13. Economic dynamics of the productive agent groups
  • 14. Net Household’s financial flows Percentage Net household Percentage Percentage invested in spent on financial invested in productive activities Total consumer balance (R) real estate (livestock, coffee*) goods >10,000 31.2% 59.2% 9.6% (7.91%, 1.69%) = 100% [5000-10000] 46.0% 22% 32% (21.34%, 10.56%) = 100% [0-5000] 47.0 6.2% 46.8% (25.74%, 21.06%) = 100%
  • 15. Model calibration Period comparison 2005-2009 Data 2009 2004 Proportions 2009 Population 1,142 956 Number of individuals Adults 597 500 Adults / total population 0.52 Dependent 518 434 Dependent / total population 0.45 Elderly 27 23 Elderly / total population 0.02 Families Group 1 86 72 Productive area Families Group 2 77 65 Coffee G1/area cleared 0.06 FamiliesGroup 3 15 12 Coffee G2/area cleared 0.11Families subsistence agriculture 154 129.1 G2/areas cleared pastures 0.41 Income wood Group 1* 7,120.3 10,680 G3/area cleared pastures 0.10 Income wood Group 2* 9,984.4 14,977 Subsistence agriculture/deforested area 0.04 Income wood Group 3* 7,875.0 11,813 Groups Areas in use Coffee Group 1 * 2.3 1.26 Group total 1/população 0.44Areas in use Pasture Group 2 * 16.8 9.40 Group 2 / total population 0.40 Areas in use Coffee Group 2* 4.4 2.46 Group 3 / total population 0.07 Areas in use Pasture Group 3* 21.6 12.11 Subsistence agriculture / total population 0.79 Subsistence farming areas * 0.7 0.41 Areas of initial Capoeiras 230 230.3 Initial areas of native forest 240,033 241,748 Deforested initial 3,187 1,498 Areas of non-forest 4,073 4,073 Total area 247,845 247,845 * Mean values per Suruí family
  • 16. Model calibration More 80% Value Less 80% of the parameter Origina of the paramete Payoff Payoff (Payoff • of investment inX historical “cumulative area cleared”Ratio Model productive Parameters l value parameter r min max min)activities to net family income [families • Least squares method for find the1.02 2.16 0.301earning R$5,000-10,000] combined witha ratio of investment in productive 0.492 0.0984 0.8856 best fit minimizing the sum of squares (payoff).activities to net family income [familiesearning more than R$10,000] of 0.094Ratio of investment in productiveactivities to net family income [families 0.492 0.0984 0.8856 4.36 7.86 0.102earning R$5000-10,000]Ratio of investment in productiveactivities to net family income [families 0.228 0.0456 0.4104 1.11 42.45 0.094earning more than R$10,000]Average Surui coffee profitability (R$ / 294 58.8 529.2 3.16 5.3 417.76year)Average return on Surui livestock (R$ / 190.8 38.16 343.44 3.46 5.39 39.59year)Birth rate multiplier 1 0.2 1.8 4.34 4.49 1.79Mortality rate multiplier 1 0.2 1.8 4.34 4.49 1.79Timber income multiplier 1 0.2 1.8 0.712 20.16 0.26
  • 17. After model calibrationCumulative historical deforestation, observed and modeled between 2004 and 2009
  • 18. Sensitivity of the Baseline Scenario Monte Carlo Sensitivity Analysis of Vensim PLE Plus• Average profitability of leasing pasture land (R$/year):[11,133.9-21.145,5 hectares] 10,012.4 hectares,• Birth rate coefficient multiplier [10,256.8- 18,154] 7,897.2 hectares,• Average profitability of Surui-managed ranching (R$/year) [10,330.9-15,896.6] hectares5,565.7 hectares,• Ratio of investment in productive activities to net family income [families earning R$0-5000] [12,372.4- 15,101] 2,728.6 hectares.
  • 19. Baseline Scenario – Deforestation 2038 Accumulated: 13,575 ha Average: 452.5 ha/year
  • 20. Spatial allocation
  • 21. Baseline Projection
  • 22. Integration with the National REDD+ Strategy 50% - Estado de Rondônia 50% - Estado de MatoGrosso Sistema Estadual Sistema EstadualPCFS (tCO2) PCFS (tCO2) REDD+ RO REDD+ MT 0 29.649.843,50 0 81.833.568,17 0 29.649.843,50 0 81.833.568,17 0 29.649.843,50 0 81.833.568,17 176.866,61 29.649.843,50 176.866,61 81.833.568,17 178.855,01 29.649.843,50 178.855,01 81.833.568,17 246.806,97 29.649.843,50 246.806,97 81.833.568,17 238.932,35 29.649.843,50 238.932,35 81.833.568,17 228.469,98 29.649.843,50 228.469,98 81.833.568,17 221.420,10 29.649.843,50 221.420,10 81.833.568,17 212.096,97 29.649.843,50 212.096,97 81.833.568,17 200.232,39 29.649.843,50 200.232,39 81.833.568,17 182.565,87 29.649.843,50 182.565,87 81.833.568,17 154.574,93 29.649.843,50 154.574,93 81.833.568,17 161.547,84 29.649.843,50 161.547,84 81.833.568,17 168.805,49 29.649.843,50 168.805,49 81.833.568,172.371.174,52 444.747.652,50 0,27% 2.371.174,52 1.227.503.522,55 0,10%
  • 23. OBRIGADO! Gabriel C. Carrero gabriel.carrero@idesam.org.br www.idesam.org.br blog.idesam.org.br