Measuring the effectiveness of protected area networks in reducing deforestation

TLDR

Global efforts to curb tropical deforestation depend on protected areas, yet evaluating their effectiveness is hampered by the unobservable counterfactual, non‑random protection assignment, and potential spillover deforestation to adjacent forests. This study shows that effectiveness estimates can be markedly improved by adjusting for observable biases, quantifying spatial spillovers, and testing sensitivity to hidden biases. We employ matching techniques to assess the impact of Costa Rica’s protected‑area system on deforestation from 1960 to 1997. The analysis indicates that protection prevented about 10% of potential deforestation, conventional methods overestimate avoided loss by more than 65%, spillovers are negligible, and the results are robust to hidden bias, underscoring the value of rigorous empirical methods for conservation policy.

Abstract

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.