Optimal Environmental Targeting in the Amazon Rainforest
Joshua Murphy and
Working Papers from University of Toronto, Department of Economics
This paper sets out an empirically-driven approach for targeting environmental policies optimally in order to combat deforestation. We focus on the Amazon, the worldâ€™s most extensive rainforest, where Brazilâ€™s federal government issued a â€˜Priority Listâ€™ of municipalities in 2008, to be targeted with more intense environmental monitoring and enforcement. In this setting, we first estimate the causal impact of the Priority List on deforestation using â€˜changes-in-changesâ€™ (Athey and Imbens, 2006), a flexible treatment effects estimation method, finding that it reduced deforestation by 40 percent and cut emissions by 39.5 million tons of carbon. Second, we develop a novel framework for computing targeted ex-post optimal blacklists. This involves a procedure for assigning municipalities to a counterfactual list that minimizes total deforestation subject to realistic resource constraints, drawing on the ex-post treatment effect estimates from the first part of the analysis. Accounting for spillovers, we show that the ex-post optimal list resulted in carbon emissions over 7.4 percent lower than the actual list, amounting to savings of more than $900 million, and emissions over 25 percent lower (on average) than a randomly selected list. The approach we propose is relevant for assessing both targeted counterfactual policies to reduce deforestation and quantifying the impacts of policy targeting more generally.
Keywords: Policy Targeting; Optimal Regulation; Monitoring; Deforestation; Amazon; Carbon Emissions; Changes-in-Changes; Difference-in-Differences; Spillovers; Resource Constraints; Partial Identification; Minimax Ambiguity (search for similar items in EconPapers)
JEL-codes: H11 H23 L73 L78 Q23 Q57 (search for similar items in EconPapers)
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