Compliance Strategies under Permits for Emissions
Bibliographic record
Abstract
We characterize the trade‐offs among firms' compliance strategies in a market‐based program where a regulator interested in controlling emissions from a given set of sources auctions off a fixed number of emissions permits. We model a three‐stage game in which firms invest in emissions abatement, participate in a share auction for permits, and produce output. We develop a methodology for a profit‐maximizing firm to derive its marginal value function for permits and translate this value function into an optimal bidding strategy in the auction. We analyze two end‐product market scenarios independent demands and Cournot competition. In both scenarios we find that changing the number of available permits influences abatement to a lesser extent in a dirty industry than in a cleaner one. In addition, abatement levels taper off with increasing industry dirtiness levels. In the presence of competition, firms in a relatively clean industry can, in fact, benefit from a reduction in the number of available permits. Our findings are robust to changes in certain modeling assumptions.
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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) | 0.000 | 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 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".