Supplementing review strategies with penalties in environmental enforcement
Bibliographic record
Abstract
A framework for more effective and efficient enforcement of environmental regulations is proposed. An operator's comply-violate decision is analysed in the context of the operator's continuing relationship with an environmental agency, permitting an effective enforcement policy to be developed using the theory of repeated games. More specifically, enforcement conflicts between an operator and an agency are modelled using a noncooperative game called the enforcement dilemma that clarifies the causes of enforcement inefficiency. Then a systematic long-term enforcement policy, the review strategy, is introduced and shown to effect substantial improvements in enforcement efficiency. However, as some numerical examples illustrate, the review strategy alone cannot always give the operator the incentive to comply fully, because of exogenous uncertainty in monitoring procedures. A supplementary penalty, which may be quite small, is then introduced into the enforcement framework to strengthen the review strategy's ability to deter violation. In combination with a suitable penalty system, the review strategy can be an effective means for an agency to enforce environmental regulations despite limited resources.
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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) | 0.014 | 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".