Review of Economic Studies · 2022 · Juliano Assunção、R. S. McMillan、Joshua Murphy、Eduardo Souza-Rodrigues
中文摘要
本文提出一种数据驱动的方法，通过对环境政策进行最优定向来遏制森林砍伐。我们聚焦于世界上面积最大的雨林——亚马孙雨林。2008年，巴西联邦政府发布了一份市镇“优先名单”，即一份将接受更严格环境监测和执法的黑名单。首先，我们采用Athey和Imbens（2006）提出的“变化中之变化”方法，估计优先名单对森林砍伐的因果影响（以及其他相关处理效应），结果发现，该名单使森林砍伐减少了43%，并使碳排放量减少了近5000万吨。其次，我们构建了一个用于计算定向最优黑名单的新框架。该框架利用我们的处理效应估计，将市镇分配至一份反事实名单，在符合现实资源约束的条件下使森林砍伐总量最小化。我们表明，事后最优名单所产生的碳排放量将比实际名单低10%以上，相当于节省逾12亿美元（占优先名单总价值的34%）；与随机选定的名单相比，其碳排放量平均低23%以上。我们提出的方法既适用于评估旨在减少森林砍伐的定向反事实政策，也适用于更一般地量化政策定向的影响。
Abstract
Abstract 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.