1. Does Collective Action Sequester Carbon? The Case of the Nepal Community Forestry Programme Randy Bluffstone Department of Economics Portland State University Portland, Oregon USA In Partnership with ForestAction Nepal and the World Bank World Bank Land and Poverty Conference 2016 16 March 2016

2. Co-Authors Randy Bluffstone (Portland State) Eswaran Somanathan (Indian Statistical Institute) Prakash Jha (ForestAction Nepal) Harisharan Luintel (ForestAction Nepal) Rajesh Bista (ForestAction Nepal) Michael Toman (World Bank) Naya Paudel (ForestAction Nepal) Bhim Adhikari (IDRC With GIS assistance from Charles Maxwell

3. The Climate is A-Changin Earth is on average hotter than at least the last 11,000 years (Marcott et al., 2013) 1983-2012 hotter than last 1400 years – medium confidence (IPCC, 2014) Developing country net CO 2 e emissions to make up 2/3 of global emissions (Stern, 2013) Forests store a lot of carbon! Estimated 638 – 861 gigatons (total anthropogenic emissions since 1750 about 375 gigatons) (Pan et al., 2011)

4. Forests are therefore a Big Part of Climate Change Mitigation 12% - 20% of world carbon emissions from forests =» more than all air, sea, land transport emissions combined. Virtually all net deforestation is in developing countries Unsustainably harvested fuelwood, which (with grazing) is a key driver of forest degradation in lower income countries, is especially important

5. CO 2 e Emissions from Land Use, Land Use Changes and Forestry (LULUCF) Source: USEPA

6. REDD+

7. Community Forests and REDD+ About 25% of developing country forests are community owned, managed or controlled (as in Nepal) Reaching climate stabilization goals (e.g. 550 ppm) likely will require community forest input.

8. Community Forest Quality/Carbon Sequestration Likely Depends on Forest Collective Action Activities group members undertake together to improve forests when forests are common resources Does forest collective action sequester carbon? Is it already doing so as a byproduct of other goals?

9. Measuring Forest Collective Action Contributing to Group Despite Private Costs – Hours spent patrolling forests – Participating in meetings – Planting trees in forests

10. Measuring Forest Collective Action

11. RESEARCH QUESTIONS In Nepal, does the Nepal Community Forestry Programme sequester more above-ground carbon than Non-CFs? What institutional arrangements support non-carbon aspects of forest quality?

12. Nepal

13. Population about 30 million

14. ≈ $2300 GNI/capita PPP=> low income

15. Formalized Collective Action: The Nepal Community Forestry Programme (CF) Motivated by open access and serious forest degradation Developed in 1980s and finalized with Forest Act of 1993 that devolved legal control and shared ownership to community forest user groups (CFUGs) Now about 18,000 CFUGs involving about 35% of the population Believed to have delivered improvements

16. But…. CFs are not the only governance structure that shows evidence of forest –based collective action!

17. Governance-Based Definitions of Collective Action Used in Paper Narrow definition: Forest and community are registered as official CFs Modest definition: Forest and community are registered or proposed as official CFs Broad definition: User group leaders are able to report the year groups were established “Open Access”: Government forest. None of the above

18. Governance-Based Definitions of Forest Collective Action in Nepal Gov’t Forest

19. Governance-Based Definitions of Forest Collective Action in Nepal Gov’t Forest Official CF

20. Governance-Based Definitions of Forest Collective Action in Nepal Gov’t Forest Official CF Proposed or Official CF

21. Governance-Based Definitions of Forest Collective Action in Nepal Gov’t Forest Official CF Proposed or Official CF Leaders can ID Group Formation Year

22. Nationally Representative Random Sampling of CFs (MOFSC, 2013) Matched with Observationally Equivalent Non-CFs 130 forests (65 CF and 65 Non-CFs) in hills and Terai along with their communities 15 Terai CFs and 50 hill CFs, reflecting population Non-CFs sampled to be similar to CFs Plot measurements scaled up to per hectare – 620 randomly chosen 250 m 2 plots for estimating carbon/ha and counting trees/ha. – Concentric 100 m 2 plots for estimating sapling carbon/ha. – Concentric 1 m 2 plots for counting seedlings (i.e. regeneration)

23. Forest Quality Dependent Variables Measure Different Aspects of Quality 1.Estimated sequestered carbon in tons per hectare 2.Counted number of trees per hectare 3.Canopy cover in percent (fieldworker estimated) 4.Counted tree seedlings per hectare

24. Forest Quality Dependent Variables Measure Different Aspects of Quality 1.Estimated sequestered carbon in tons per hectare 2.Counted number of trees per hectare 3.Canopy cover in percent (fieldworker estimated) 4.Counted tree seedlings per hectare Forest Ecology Literature Suggests Little Reason to Expect Similar Effects Across Forest Quality Measures (Stephenson et al., 2014; West et al., 2009; Coomes et al., 2012; Enquist et al., 2009)

25. 130 Forests and 620 Forest Plots Sampled Green = CF Red = Non-CF

26. Carbon Estimation Above ground biomass estimated using allometric equations (Chave, 2005) based on DBH and tree height Sapling biomass Carbon estimated using IPCC (2006) default value of 50% of biomass

27. Forest Size in Hectares by CF Status and Physiographic Region

31. Forest Level Empirical Methods and Identification Strategy 2 Forest Level Models – Semi-log specification OLS with robust standard errors – Propensity score matching based on observables

32. Plot Level Empirical Methods

33. Most Important Independent Variables Collective action measures (official CF, CF or proposed CF, leaders identify year of group formation) Forest characteristics (1990 NDVI, size, altitude, natural or plantation, average slope, hill or Terai) Plot characteristics for plot level models (aspect, soil color, sal dummy) Community characteristics (number of households in group, migration rate, households per hectare forest)

34. Forest Level OLS Results Dependent Variables in Logs

35. Forest Level Propensity Score Matching (ATT in tons/hectare)

36. Plot Level Random Effects (tons/hectare)

37. Plot Level Propensity Score Matching Results (tons/hectare)

38. Estimated Additional Carbon/Ha. (tons) by Collective Action Definition Compared with Counterfactual (Propensity Score Matching Models) Blue= Based on Plot Level Models Red= Based on Forest Level Models Not Significant

39. Summary of Key Findings Collective action has generally positive effects on forest quality, including carbon sequestration. CA does no harm, but subjectively assessed crown cover an outlier Carbon sequestration effects empirically large. Plot level results most robust to technique, scale and quality measure. Non-CF collective action exists and is improving forest quality.

40. Other Results Larger forests generally higher quality by various measures Population pressure (households/hectare forest) has no effect Natural forests better quality in almost all cases Forests governed by bigger user groups generally higher quality Out-migration does not matter

41. REDD+ Policy Conjectures Derived from the Research Non-CF collective action should be formalized as CFs so sequestration can be credited under REDD+ REDD+ may be important for incentivizing formal collective action, as well as assuring that existing carbon sequestration is maintained Low-cost ways must be found to credit climate- friendly community efforts

42. Thank You Very Much! bluffsto@pdx.edu bluffsto@pdx.edu