Bibliographic record
Abstract
Abstract Environmental regulation can interact with agricultural markets to produce underappreciated competitiveness and leakage effects. This paper measures effective carbon tax stringency by structurally recovering the domestic supply schedule for a trade‐exposed beef cattle industry such that elasticities and carbon tax rates change with product prices (i.e., due to the curvature of the supply function). Two basic propositions from the economics of taxation—that excess burdens increase in elasticities and tax rates—are shown to cause the stringency of uniform carbon policy to vary nonlinearly with output prices. Based on the domestic supply function, the relationship between marginal excess burden, a measure of policy stringency from the industry's perspective, and product prices is estimated. Several policy‐relevant counterfactual scenarios are explored. Results show that with moderately high output prices, supply elasticities are small and the efficiency cost of a $40/tCO 2 e carbon tax (gross of environmental benefits) is less than $0.01 per dollar tax revenue. As prices decline, supply curves become increasingly elastic and marginal excess burdens grow rapidly.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".