Optimal Environmental Targeting in the Amazon Rainforest.

This article analyzes the effectiveness and optimization of Brazil’s 2008 “Priority List,” a blacklist of Amazon municipalities targeted for intensified environmental monitoring to reduce deforestation. Using the changes-in-changes (CIC) econometric model, the study finds that the Priority List causally reduced deforestation by 43% and avoided nearly 50 million tons of carbon emissions during 2009–10, including significant spillover effects in neighboring untreated municipalities. The authors develop a novel framework to compute ex post optimal blacklists under realistic resource constraints and partial identification of treatment effects, showing that such optimized targeting could reduce carbon emissions by over 10% compared to the actual list, yielding additional social benefits exceeding $1.2 billion. The findings highlight the potential for improved policy design that accounts for heterogeneous treatment effects, spillovers, and monitoring costs, offering a quantitative approach applicable to targeted environmental regulations beyond the Amazon context.