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Optimal Environmental Targeting in the Amazon Rainforest

Juliano Assuncao, Robert McMillan, Joshua Murphy and Eduardo Souza-Rodrigues

Working Papers from University of Toronto, Department of Economics

Abstract: This paper sets out an empirically-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, 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 ‘changes-in-changes’ (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)
New Economics Papers: this item is included in nep-env
Date: 2019-03-20
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