Optimal Environmental Targeting in the Amazon Rainforest.

This article 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" due to Athey and Imbens (2006), finding that it reduced deforestation by 43 |$\%$| and cut emissions by almost 50 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 |$\%$| lower than the actual list, amounting to savings of more than |$ \$ $| 1.2 billion (34 |$\%$| of the total value of the Priority List), with emissions over 23 |$\%$| 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. [ABSTRACT FROM AUTHOR]