Econometric Model of Deforestation in Mexico: Application to REDD+ Policies

Transcription

1 Econometric Model of Deforestation in Mexico: Application to REDD+ Policies Ruben Lubowski A. Javier Miranda, RuohongCai, KalifiFerretti-Gallon, Max Wright, Marc Steininger, Jonah Busch October 23-24, 2014 LEF Biennial Workshop Economics of Carbon, Climate Change and Forests INRA, Nancy, France

2 To help reduce carbon emissions from deforestation Assess vulnerability to future deforestation Spatially explicit analysis of the impact of alternative REDD+ policy structures Econometric analysis of revealed preference land-use choices in response to land conversion benefits Historical counterfactual simulations Future projections OVERVIEW National deforestation study in Mexico Spatially and temporally rich new global data (Hansen et al. 2014) National and local scales of modeling: Indentification versus prediction

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4 General OSIRIS Approach Econometric calibration of historical relationship between deforestation and economic incentives for net benefits of land conversion Assumption: response to carbon or other policy incentives would be same as response to market benefits Simulation model of policies that change incentives for land-use change related to REDD+ WHO is paid HOW MUCH for doing WHAT Actors maximize economic returns Scale of decision-making adjustable Partial equilibrium model of agricultural production at forest frontier Captures deforestation leakage

5 Mexico-REDD (MREDD) Early Action Areas

6 Meta-Analysis of Drivers of Deforestation (Ferretti-Gallon & Busch 2014) National Model / OSIRIS IDRISI- LCM

7 Mexico Regions and AATRs

8 OUTLINE Introduction Literature Review Theoretical & Empirical Models Historical Simulations Future Projections Summary & Future Research 8

9 INTRODUCTION In 2010, Mexico ranked 8th among countries with the largest area of primary forest, but the country maintained an average deforestation rate of 0.24% during (FAO) Potential REDD+ link to California Annual forest loss across regions in Mexico Regional Shares of Total Forest Loss, Source: Based on Hansen et al. (2014) 9

10 METHODOLOGY Data - Forest, agricultural return, other local characteristics Econometric estimation - Poisson model and Negative Binomial model Simulation based on estimated coefficients - Alternative agriculture return scenarios, and land types area scenarios Future projections - Business-as-usual and alternative carbon price scenarios 10

11 LITERATURE REVIEW Based on 23 econometric studies of deforestation in Mexico, we find that driver variables associated with deforestation in Mexico include protected areas, community forestry, and payments for ecosystems services, agricultural activity, population, soil suitability and proximity to urban area. Varies by region. 11 Note: This graph presents regression results from studies on deforestation in Mexico. Results are ordered by ratio of negative to positive association with deforestation. Source: Ferretti-Gallon and Busch 2014.

12 Hansen Lab Data on Global Forest Changes,

13 MODEL Landowners will choose, from a set of potential land uses, the option that brings the highest expected discounted returns. Main empirical challenges: Dependent variable structure Unobserved spatial heterogeneity in potential net returns Capturing spatio-temporal process Potential agricultural revenues per hectare Combine municipality-level data on actual revenues per hectare from SAGARPA scaled with finer-scale data on potential yields (FAO GAE Fixed and variable costs based on proxies, including starting forest area 13

14 Mexico Forest Cover Losses, (Hansen et al. 2014)

15 EMPIRICAL MODEL DEPENDENT VARIABLE Applied 25% tree cover threshold to identify forest pixels in 2000 Consistent with remote sensing procedure to identify losses Aggregated 30m cell observations to 900m (81 ha) for tractability About 1.4 million agrgregated observations per year Forest area values of 0 to 900 for each 900m cell Deforestation measured as annual change in forest cover (discrete count of 30m cells deforested)

16 EMPIRICAL MODEL - VARIABLES Dependent variable: the annual change in forest cover (the number of 30m x 30m cells in the 900m x 900m cell) between 2000 and 2012 Forest cover = mx30m grid cell with 25% as threshold 900 meters A count structure Poisson and Negative Binomial Models 16

17 ROLE OF STARTING FOREST AREA (1/2) Greater absolute amounts of deforestation are (in general) observed when there is more forest available at the start of the period - The absolute physical limits - Conversion costs and revealed information over net returns 17

18 ROLE OF STARTING FOREST AREA (2/2) % forest loss per 900m cell, by starting forest area Total deforestation, by equal total forest area categories

19 EMPIRICAL MODEL - VARIABLES Assumption: land owners prefer the option The with amount the of highest deforestation expected over a discounted given period returnsis Proximity to the non-forest Proximity to closely linked to the cells urban in surrounding area area starting forest area Land management Two-way and three-way interaction terms between forest area category, regions, and above variables are also included to help identify heterogeneity. 19

20 HISTORICAL SIMULATIONS (1/2) These simulations are generated based on the estimated model coefficients 20

21 HISTORICAL SIMULATIONS (2/2) Total forest Difference Difference loss, from factual from factual 12 simulation simulation (Ha) (Ha) (%) Observed 1,996, , % 1) Factual simulation 1,883, % 90% ejido area 1,874,102-9, % 110% ejido area 1,885,727 2, % 90% agrarian community area 1,881,367-2, % 110% agrarian community area 1,883, % 90% National Protected Areas 1,886,809 3, % 110% National Protected Areas 1,883, % Caveat: Correlation is not causation!

22 FUTURE PROJECTIONS FOREST Business as Usual Overall 27% businessas-usual increase in annual deforestation in Mexico over the next ten years, relative to % versus 16% projected increase within versus outside of AATR regions. AATRs do not all have the highest projected deforestation but generally higher versus other lands in region. 22

23 FUTURE PROJECTIONOS FOREST Business as Usual $10/tCO 2 Avoided Deforestation Overall 27% business-as-usual increase in annual deforestation in Mexico over the next ten years, relative to

24 FUTURE PROJECTIONS - CARBON About half of estimated reductions at prices below $10/ton CO 2. More than two thirds below $20/ton and 90% below $100/ton. The most responsive land types (forest fragments, agrarian communities, protected areas) are not that important in absolute terms.

25 Link to local modelling using IDDRISI LCM, Oaxaca Sierra Norte AATR. Soft prediction of deforestation Hard prediction,

26 SUMMARY National econometric analysis of deforestation in Mexico, and associated scenario simulation modeling A critical driver of deforestation is anticipated economic returns from land conversion to agriculture Modulated by land ownership type and initial forest area Deforestation changes by about 0.24% (2-3%) for 1% (10%) change in agricultural returns AATRs not all located in the areas with the highest projected deforestation but have higher projected deforestation increases than other areas and most of potential to cost-effectively avoid deforestation. 26

27 FUTURE RESEARCH Integrate partial equilibrium model for more realistic policy simulations Use other statistical models, such as survival analysis to better capture interplay of spatial and dynamic factors Analyze data at the 30m by 30m resolution using better computational capacity Improve carbon modeling Out of sample predictive validation in combination with local scale modeling Apply methods to other countries with severe deforestation, such as Indonesia, Brazil 27