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.
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.
Chapter in “Amazonia and Global Change” book (American Geophysical Union, linked to the NASA LBA project)
We examine the evidence on Amazonian road impacts with a strong emphasis on context. Impacts of a new road, on either deforestation or socioeconomic outcomes, depend upon the conditions into which roads are placed. Conditions that matter include the biophysical setting, such as slope, rainfall, and soil quality, plus externally determined socioeconomic factors like national policies, exchange rates, and the global prices of beef and soybeans. Influential conditions also include all prior infrastructural investments and clearing rates. Where development has already arrived, with significant economic activity and clearing, roads may decrease forest less and raise output more than where development is arriving, while in pristine areas, short-run clearing may be lower than immense long-run impacts. Such differences suggest careful consideration of where to invest further in transport.