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Is no net loss possible? Evaluating policy instruments for reducing deforestation with a growing economy
This document discusses evaluating the impact of policy instruments on reducing deforestation in Australia. It summarizes a study that aims to: 1) Quantify forest loss across Australia from 1972-2011 using high resolution spatial data; 2) Develop a model of deforestation drivers at national/regional scales; and 3) Evaluate the impact of deforestation policies, accounting for macroeconomic and climatic factors. The challenges of evaluation are discussed, as well as approaches using spatially disaggregated data at the local government area level and a flexible "bent-cable" regression model to attempt to detect policy impacts, though the authors note detection remains difficult even with advanced methods.
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Is no net loss possible? Evaluating policy instruments for reducing deforestation with a growing economy
- 1. Is no netloss possible? Evaluating policy instruments for reducing deforestation with a growing economy Megan C Evans1, Grace Chiu2, Andrew K Macintosh3, Philip Gibbons1 1 Fenner School of Environment and Society, The Australian National University, Canberra 2 Research School of Finance, Actuarial Studies and Applied Statistics, The Australian National University, Canberra 3 ANU Centre for Climate Law and Policy, The Australian National University, Canberra Symposium: Conservation trade-offs in a resource-limited world ICCB-ECCB 2015, August 2-6 2015, Montpellier - France @megcevans
- 2. Trading off theenvironment Image: Ben Phalan Map: Lorenzo Catterino Graph: Martina di Fonzo Map: Roman Carrasco
- 3. Deforestation • 2.3 millionkm2 forest lost 2000-2012 Hansen et al. 2013 Science
- 4. • 170 millionha could be lost 2010-2030 • Eastern Australia a global deforestation hotspot 1980-2000 (Lepers et al. 2005) and over next two decades (WWF 2015) WWF International, 2015. Living Forests Report: Saving Forests at Risk.
- 5. Australian policy context Native Vegetation Regulation 2005(NSW) Native Vegetation Retention (NVR) controls under the Planning and Environment Act 1987 (Vic) Vegetation Management Act 1999 Native Vegetation Act 1991 (SA) 1987: Soil and Land Conservation Act (SALCA) 1945 Vegetation Management and Other Legislation Bill 2004
- 6. “Land clearing inQueensland triples after policy ping pong” Maron et al. 2015 The Conversation What was the policy impact?
- 7. Evaluating deforestation policyinstruments • Extensive work on drivers of deforestation • Many cross-country, sometimes spatial, rarely temporal FAO. State of the World’s Forests 2012. Ewers et al 2008 Env Cons Kuznet’s curve plot Laurence et al. 2002 J BiogeographyRudel et al. 2005 Global Env Change
- 8. Evaluating deforestation policyinstruments • Few rigorous evaluations of policy impact • C.f protected area effectiveness FAO. State of the World’s Forests 2012.
- 9. Evaluating deforestation policyinstruments Miteva et al. 2012 Oxford Review of Economic Policy
- 10. Evaluating deforestation policyinstruments • Few rigorous evaluations of policy impact • C.f protected area effectiveness • Very few evaluate impact of policy timing • Arima et al. 2014 Land Use Policy FAO. State of the World’s Forests 2012.
- 11. Evaluating deforestation policyinstruments FAO. State of the World’s Forests 2012. Arima et al. 2014 Land Use Policy
- 12. Evaluating deforestation policyinstruments Australian case study • Developed, wealthy nation with strong governance and high institutional capacity – we would expect policies to be effective • Forest transitions theory; Kuznet’s curve hypothesis • Many candidate policies for evaluation • High resolution spatial data now available
- 13. This study 1. Spatiallyquantify forest loss across Australia from 1972-2011 2. Develop a credible model of deforestation at national/regional scale 3. Evaluate the impact of deforestation policy instruments after removing macroeconomic and climatic effects
- 14. Deforestation imagery Source: AustralianGreenhouse Emissions Information System (AGEIS) • Deforestation events attributed to human intervention • Woody vegetation only (“Kyoto forest”) • 20 epochs, 1972-2011 • 19 tiles • ~ 380 tiles total
- 15. Deforestation in Australia:Theory of change Deforestation Sources of deforestation Agents and their land use decisions Underlying (ultimate) causes Macroeconomic variables and policy instruments Immediate (proximate) causes Factors that influence agent decisions (institutions, infrastructure, markets, technology) Angelsen & Kaimowitz 1999 World Bank Res Obs
- 16. Deforestation in Australia:Theory of change Deforestation Sources of deforestation Agents and their land use decisions Immediate (proximate) causes Factors that influence agent decisions (institutions, infrastructure, markets, technology) Angelsen & Kaimowitz 1999 World Bank Res Obs Terms of trade Farmer terms of trade Real GDP Policy instruments
- 17. Deforestation in Australia:Theory of change Deforestation Sources of deforestation Agents and their land use decisions Angelsen & Kaimowitz 1999 World Bank Res Obs Terms of trade Farmer terms of trade Commodity prices Real GDP Rainfall Forest remaining Policy instruments
- 18. Deforestation in Australia:Theory of change Deforestation Angelsen & Kaimowitz 1999 World Bank Res Obs Terms of trade Farmer terms of trade Commodity prices Real GDP Policy instruments Rainfall AgricultureMining Urban Forest remaining
- 19. Deforestation in Australia:Theory of change Deforestation Angelsen & Kaimowitz 1999 World Bank Res Obs Terms of trade Farmer terms of trade Commodity prices Real GDP Policy instruments Rainfall Agriculture Forest remaining
- 20. Spatial analysis Annual rainfall 0.01deg Landuse Local Government Areas (LGAs) n=564 Human induced deforestation 1972-2011 0.0002deg (25m) Analysed using R raster package
- 21. Spatial analysis Annual rainfall 0.01deg Landuse Local Government Areas (LGAs) n=564 Human induced deforestation 1972-2011 0.0002deg (25m) Analysed using ARGGH raster package
- 22. Statistical approach 𝑦𝑡𝑠 =𝑓𝜃 𝒙 𝐸𝑡 , 𝒙 𝐶𝑡𝑠 + 𝜀𝑡𝑠 𝑦𝑡𝑠where is deforestation at time t in LGA s relative to amount of primary forest remaining Macroeconomic variables at time t (national scale) GDP, ToT, FTOT, CPI, inflation, agric commodity prices Climatic variables at time t and LGA s annual rainfall, rainfall in driest month
- 23. Crude statistical approach Piecewiselinear regression • aka segmented or broken-stick regression, interrupted time series • Used to evaluate impact of policy timing when difficult to isolate a control (counterfactual) • pharmaceutical health , medicine, ecotoxicology, political science
- 24. Crude statistical approach Piecewiselinear regression • aka segmented or broken-stick regression, interrupted time series • Used to evaluate impact of policy timing when difficult to isolate a control (counterfactual) • pharmaceutical health , medicine, ecotoxicology, political science
- 25. Crude statistical approach Piecewiselinear regression • aka segmented or broken-stick regression, interrupted time series • Used to evaluate impact of policy timing when difficult to isolate a control (counterfactual) • pharmaceutical health , medicine, ecotoxicology, political science
- 26. Improved statistical approach Bent-cableregression model • Generalisation of piecewise regression Chiu et al. 2006 J. Amer. Stat. Assoc. • More flexible model for describing system change • Greater capacity to capture potential effect of regulation
- 27. Improved statistical approach Bent-cableregression model • Generalisation of piecewise regression Chiu et al. 2006 J. Amer. Stat. Assoc. • More flexible model for describing system change • Greater capacity to capture potential effect of regulation
- 28. Improved statistical approach Bent-cableregression model Khan et al. 2012 Int J Statistics and Probability • Generalisation of piecewise regression Chiu et al. 2006 J. Amer. Stat. Assoc. • More flexible model for describing system change • Greater capacity to capture potential effect of regulation
- 29. Results from exploratorydata analysis Total deforestation (National)
- 30. Results from exploratorydata analysis Total deforestation (by State)
- 31. Evaluation challenges • Unableto isolate control or synthetic control • Data resolution (spatial and temporal) • Few data points relative to number of interventions; some missing years • Data aggregated at State level too sparse for detecting policy impact • Try to overcome with i) spatially disaggregated data points (n=564 LGAs) ii) bent-cable model (still might not work!)
- 32. Deforestation over time(n = 156 LGAs) Queenslandonly
- 33. Crude linear modelresiduals, temporally detrended Queenslandonly
- 34. 2000: Control of remnantvege 2006: End of broadscale clearing 2009:Regrowth protections Crude linear model residuals, temporally detrended Queenslandonly
- 35. Crude linear modelresiduals, temporally detrended Chiu et al. 2006 J. Amer. Stat. Assoc.
- 36. Next steps • Developholistic model (similar to Khan et al. 2009; 2012) and apply to LGA data • “holistic” in that all n=564 LGAs are modelled collectively to maximize statistical power of a single model • Obtain most recent data to try and detect recent relaxation of laws in Queensland and NSW
- 37. Concluding comments • Evaluationis hard! • Detection of policy impacts can still be difficult with fancy methods and high resolution data • Answering “what” is the impact won’t necessarily tell you “why” need for qualitative data & clear theory of change
- 38. Thank-you P.S – cometo SCB Oceania 2016! @megcevans mcevansresearch.wordpress.com