This paper sets out a data-driven approach for targeting environmental policies optimally in order to combat deforestation. We focus on the Amazon, the world's most extensive rainforest, where Brazil's federal government issued a `Priority List' of municipalities in 2008 -- a blacklist to be targeted with more intense environmental monitoring and enforcement. First, we estimate the causal impact of the Priority List on deforestation (along with other relevant treatment effects) using `changes-in-changes' (Athey and Imbens, 2006), finding that it reduced deforestation by 43 percent and cut emissions by 49 million tons of carbon. Second, we develop a novel framework for computing targeted optimal blacklists that draws on our treatment effect estimates, assigning municipalities to a counterfactual list that minimizes total deforestation subject to realistic resource constraints. We show that the ex-post optimal list would result in carbon emissions over 10 percent lower than the actual list, amounting to savings of more than $1.29 billion (36% of the total value of the Priority List), with emissions over 23 percent lower on average than a randomly selected list. The approach we propose is relevant both for assessing targeted counterfactual policies to reduce deforestation and for quantifying the impacts of policy targeting more generally.
About the Speaker
Eduardo Souza-Rodrigues is an associate professor of Economics at the University of Toronto. He obtained his PhD degree in Economics at Yale University in 2012. After that, he became a post-doc fellow at Harvard University for one year. Eduardo Souza-Rodrigues’ research agenda lies at the intersection of Environmental Economics and Industrial Organization, with an emphasis on Structural Dynamic Models (i.e., on models in which economic agents are forward looking). His research focuses on problems related to tropical deforestation, especially on the Amazon rainforest, and on the performance of existing and yet-to-be-implemented conservation policies. Evaluating yet-to-be-implemented policies involves counterfactual analysis based on economic behavioral models. Eduardo’s second research area is dedicated to the questions of when, and under what conditions, counterfactual predictions are identified in structural dynamic models (which have been extensively used in applied work).