Journal of Development Economics 97 (2012) 427–436
We estimate neighbor interactions in deforestation in Costa Rica. To address simultaneity and the presence of spatially correlated unobservables, we measure for neighbors’ deforestation using the slopes of neighbors’ and neighbors’ neighbors’ parcels. We find that neighboring deforestation significantly raises the probability of deforestation. Policies for agricultural development or forest conservation in one area will affect deforestation rates in non-targeted neighboring areas. Correct estimation of the interaction reverses the naive estimate’s prediction of multiple equilibria.
B. Rapidel, F. DeClerk, J. LeCoq and J. Beer Eds. “Ecosystem services from Agriculture and Agroforestry: Measurement and Payment”. Chapter 14. Earthscan Press.
in Costa Rica. The first years of implementation set the basis for what the programme has become. Important changes have been made since the beginning, such as the institution in charge of implementing the programme, parcels selection criteria, and new offices that were opened in different areas of the country with the objective of reducing application costs. Using 2003 as the starting point of when these changes took place, we discuss if they had a programme efficiency effect on reducing deforestation. We focus on forest conservation contracts because it is the most important category of the programme in terms of budget and amount of land enrolled. We use matching techniques, geographic information systems (GIS), characterize the areas where payments were implemented in each of the time periods using a long list of variables, and look for similar areas that did not receive payments. We find that, as other studies have found for this period (Robalino et al, 2008; Arriagada, 2008), the impacts are low but significant. While it seems that, overall, institutional changes have not had a significant effect on impact, we also look at the impacts of forest conservation contracts per office. We find that those offices located in areas with high deforestation tend to have higher impacts.
Alexander Pfaff, Juan Robalino, G. Arturo Sanchez-Azofeifa, Kwaw S. Andam, Paul J. Ferraro
The B.E. Journal of Economic Analysis & Policy: Vol. 9: Iss. 2 (Contributions), Article 5.
To support conservation planning, we ask whether a park’s impact on deforestation rates varies with observable land characteristics that planners could use to prioritize sites. Using matching methods to address bias from non-random location, we find deforestation impacts vary greatly due to park lands’ characteristics. Avoided deforestation is greater if parks are closer to the capital city, in sites closer to national roads, and on lower slopes. In allocating scarce conservation resources, policy makers may consider many factors such as the ecosystem services provided by a site and the costs of acquiring the site. Pfaff and Sanchez 2004 claim impact can rise with a focus upon threatened land, all else equal. We provide empirical support in the context of Costa Rica’s renowned park system. This insight, alongside information on eco-services and land costs, should guide investments.
Engel, S. and C. Palmer, editors, “Avoided Deforestation: Prospects for Mitigating Climate Change”, Routledge Explorations in Environmental Economics 2009
This chapter conveys why human choices complicate correct evaluations of impacts. Unobservable land choices, choices affecting policy location and interactions among choices complicate both ex post impact evaluation and ex ante policy planning. Based on application of proper methods to Costa Rica, we then suggest how these hurdles can best be addressed. We provide examples of: how a best practice deforestation baseline rightly conveys the constraints on the impact the pioneering Costa Rican eco-payments programme could have; why it may be critical to have different baselines for different locations to correctly infer the impacts of Costa Rican protected areas; and how choices by conservation agencies and landowners can determine the bias within heretofore typical approaches to impact evaluation.
Kwaw Andam, Paul Ferraro, Alexander Pfaff, Juan Robalino, G. Arturo Sanchez-Azofeifa
PNAS 105(42):16089-16094
Global efforts to reduce tropical deforestation rely heavily on the establishment of protected areas. Measuring the effectiveness of these areas is difficult because the amount of deforestation that would have occurred in the absence of legal protection cannot be directly observed. Conventional methods of evaluating the effectiveness of protected areas can be biased because protection is not randomly assigned and because protection can induce deforestation spillovers (displacement) to neighboring forests. We demonstrate that estimates of effectiveness can be substantially improved by controlling for biases along dimensions that are observable, measuring spatial spillovers, and testing the sensitivity of estimates to potential hidden biases. We apply matching methods to evaluate the impact on deforestation of Costa Rica’s renowned protected-area system between 1960 and 1997. We find that protection reduced deforestation: approximately 10% of the protected forests would have been deforested had they not been protected. Conventional approaches to evaluating conservation impact, which fail to control for observable covariates correlated with both protection and deforestation, substantially overestimate avoided deforestation (by over 65%, based on our estimates). We also find that deforestation spillovers from protected to unprotected forests are negligible. Our conclusions are robust to potential hidden bias, as well as to changes in modeling assumptions. Our results show that, with appropriate empirical methods, conservation scientists and policy makers can better understand the relationships between human and natural systems and can use this to guide their attempts to protect critical ecosystem services.
Alexander Pfaff, Suzi Kerr, Romina Cavatassi, Benjamin Davis, Leslie Lipper, G. Arturo Sanchez-Azofeifa, J. Timmins
Economics of poverty, environment and natural resource use (chapter 6).
We review many theoretical predictions that link poverty to deforestation and then examine poverty’s net impact empirically using multiple observations of all of Costa Rica after 1960. Countrywide disaggregate (district-level) data facilitate analysis of both poverty’s location and its impact on forest. If the characteristics of the places the poor live are not controlled for, then poverty’s impact is confounded with differences between poorer and less poor areas and we find no significant effect of poverty. Using our data over space and time to control for effects of locations’ differing characteristics, we find that the poorer are on land whose relative quality discourages forest clearing, such that with these controls the poorer areas are cleared more. The latter result suggests that poverty reduction aids the forest. For the poorest areas, this result is weaker but another effect is found: deforestation responds less to productivity, i.e., the poorest have less ability to expand or to reduce given land quality.
We estimate neighbor interactions in deforestation in Costa Rica. To address simultaneity and the presence of spatially correlated unobservables, we measure for neighbors’ deforestation using the slopes of neighbors’ and neighbors’ neighbors’ parcels. We find that neighboring deforestation significantly raises the probability of deforestation. Policies for agricultural development or forest conservation in one area will affect deforestation rates in non-targeted neighboring areas. Correct estimation of the interaction reverses the naive estimate’s prediction of multiple equilibria.