1. Economic Dynamics and Forest Clearing A Spatial Econometric Analysis for Indonesia David Wheeler Dan Hammer Robin Kraft Susmita Dasgupta Brian Blankespoor Development Research Group World Bank 2012

2. Presentation Outline  Motivation  FORMA: A New Approach to Monitoring Tropical Forest Clearing  Trends in Indonesian Forest Clearing  Model Specification  Data  Econometric Results  Conclusions

3. Motivation  Forest clearing accounts for 15% of annual GHG emissions (WRI 2010).  Most forest clearing occurs in developing countries.  Forest conservation will be difficult as long as forested land has a higher market value in other uses.  Actual success of compensation schemes will depend on program designs tailored to the economic dynamics of forest clearing.  Economic returns to forest clearing vary widely over space and time (RFF 2011).

4. The Case of Indonesia  Forest Clearing in Indonesia is heavily driven by palm-oil and wood-processing exports to fast-changing Asian Markets.  Availability of Monthly database for forest clearing at 1 km resolution since 2005 from FORMA (Forest Monitoring for Action).

5. FORMA  Constructs deforestation indicators from MODIS- derived data on the incidence of fires and changes in vegetation color as identified by the Normalized Difference Vegetation Index.  Calibrates to local deforestation by fitting statistical model to the best available information on actual deforestation in the area.  Incorporates biological, social and economic diversity by monitored territory into blocks and fitting the model to data for the parcels in each block.  FORMA is calibrated using the map of forest cover loss hotspots (FCLH) for 2000- 2005 published by Hansen et al. 2008.  Applies the fitted model to monthly MODIS indicator data for the period after 2005. Output  A predicted forest-clearing probability for each 1 sq km parcel outside of previously-deforested area as identified in the FCLH map- for each month.  Selection of parcels with probabilities exceeding 50%.  An index of forest clearing from the above.

6. Large-Scale Forest Clearing in Indonesia December 2005-December 2010 Indonesia’s natural forest area in 2000 was 951,160 sq. km. (WRI, 2010)

7. Annualized Forest Clearing in Indonesia Top 5 Provinces in January 2007

8. Sumatra FORMA/Indonesia

9. Sumatra FORMA/Indonesia

10. Riau Province FORMA/Indonesia

11. 270 km 167 mi Forest in 2000 Forested in 2000

12. 270 km 167 mi Forest in 2000 Cleared 2000 - 2005 Cleared by 2005 (Hansen)

13. 12/2005 Cleared 2000 - 2005 Forest in 2000 60 – 70% 50 – 60% 70 – 80% 80 – 90% > 90% Probability FORMA

14. 1/2006

15. 3/2006

16. 6/2006

17. 9/2006

18. 12/2006

19. 3/2007

20. 6/2007

21. 9/2007

22. 12/2007

23. 3/2008

24. 6/2008

25. 9/2008

26. 12/2008

27. 3/2009

28. 6/2009

29. 9/2009

30. Cleared 2000 - 2005 Forest in 2000 60 – 70% 50 – 60% 70 – 80% 80 – 90% > 90% Probability 270 km 167 mi Cleared by10/2009

31. By Kabupaten Changes in Forest Clearing, 2006-2011 * Increase No Change Decrease * January - August

32. Model: Building Blocks  Proprietor/ occupant of a forested area considers the relative profitability of maintaining/ clearing the area.  In each period, the agent compares the present-value profitability of sustainably harvested forest products with the clear-cut value of forest products and the cleared land’s present value profitability in its best use (e.g., plantation, pasture, smallholder agriculture, settlement).  Forest clearing dynamics are different in cases where commercial exploitation rights are well- or poorly-defined.  Determinants of forest clearing highlighted in prior research: Population Scale & Density, Distance from Markets, Quality of Transport Infrastructure, Agricultural Input Price, Topography, Precipitation, Soil Quality, Zoning of Land.

33. Model Specification π = Expected relative profitability of forest clearing p e =Vector of expected prices for relevant products (palm oil, sawlog) q e =Vector of expected demands for relevant products (palm oil, sawlog) n=Rupiah-denominated input cost per unit of output t=Transport cost per unit of output (mean travel time to nearest city of 50,000+) c=Communications cost per unit of output (coverage by mobile phone networks) i e =Expected interest rate x e =Expected exchange rate (rupiah/dollar) g=Quality of governance from investors’ perspective (KPPOD index) r=Regulatory quality (KPPOD index) u=Officially-designated use (protected forest, palm oil plantations, timber plantations, logging concessions) h=Population density y=Unskilled wage rate w=Precipitation (forest-burning is more difficult when rainfall is heavier) s=Slope of the terrain (mean slope, std. deviation) Expectations: π’(p e )>0, π’(q e )>0, π’(n) 0, π’(g)>0, π’(r) 0, π’(y)>0, π’(w)<0, π’(s)<0

34. Data Variable Source PriceIMF US GDP DeflatorBureau of Economic Analysis World Palm Oil ProductionUSDA World Production of Saw logsFAO Mobile Phone CoverageGSM World Inc. Index of Opportunity Cost of Forested LandRFF Travel Time to Nearest City of 50,000+Nelson (2008) Poverty Rate, InflationWorld Bank Interest RateBank of Indonesia Exchange RateONADA’s Database Land-Use DataSaxon and Sheppard (2010) Governance QualityKPPOD PrecipitationPREC/L SlopeVerdin, et al.(2002)

37. Findings  Significant roles for lagged (+) changes in product prices, demands, (+) exchange rate and (-) interest rate.  Highly variable lags: < 1 year for product prices. around 1 year for product demands and exchange rate. close to 2 years for real exchange rate.  Significant roles for (+) communication infrastructure, (+) zoning for palm oil plantations, and physical factors: (+) uncleared forest in 2000, (-) terrain slope and (-) rainfall.  Insignificant roles for local governance quality, access time, population density, poverty rate, protected area status and zoning for timber plantations.

38. Conclusions  Forest clearing is an investment highly sensitive to  Expectations about future forest product prices & demand;  Changes in the cost of capital;  Relative cost of local inputs;  Cost of land clearing.  Opportunity cost of forested land fluctuates widely with changes in international markets, local weather conditions and decisions by financial authorities about exchange and interest rates.  Forest conversation programs are unlikely to succeed if they ignore the economic dynamics of forest clearing.

39. FORMA.. For Indonesia Annualized Forest Clearing, 2006-2011