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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".