Optimal Environmental Targeting in the Amazon Rainforest
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".