Deforestation in the Amazon: A Unified Framework for Estimation and Policy Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Deforestation is a matter of pressing global concern, yet surprisingly little is known about the relative efficacy of various policies designed to combat it. This article sets out a framework for measuring the cost effectiveness of alternative policies—both command-and-control and incentive-based—in the Brazilian Amazon. First, I estimate the demand for deforestation on private properties, exploiting regional variation in transportation costs as a means to recover farmers’ responses to permanent policies. Here, rescaling transportation costs using local yields allows me to express changes in farmers’ valuations in dollars per hectare. I then use the estimated demand to infer farmers’ willingness to deforest under different counterfactual policies, such as payments to avoid deforestation and taxes on land use, along with the corresponding potential farmers’ lost surpluses. The results indicate that payment programmes and land use taxes on agricultural land can be highly effective in preserving the rainforest and also be substantially less expensive than command-and-control policies (approximately 8 times less costly). A carbon tax equal to the social cost of carbon could virtually eliminate all agricultural land in the Amazon, given the low agricultural returns there.
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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 | 0.002 | 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) | 0.000 | 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 it