Optimal Environmental Targeting in the Amazon Rainforest
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".