Incentives for pollution control - regulation and public disclosure
Bibliographic record
Abstract
An increasing number of regulators have \n adopted public disclosure programs to create incentives for \n pollution control. Previous empirical analyses of monitoring \n and enforcement issues have focused strictly on the impact \n of such traditional practices as monitoring (inspections) \n and enforcement (fines and penalties) on polluters' \n environmental performance. Other analyses have separately \n focused on the impact of public disclosure programs. But can \n these programs create incentives in addition to the normal \n incentives of fines and penalties? The authors study the \n impact of both traditional enforcement and information \n strategies in the context of a single program, to gain \n insights into the relative impact of traditional (fines and \n penalties) and emerging (public disclosure) enforcement \n strategies. Their results suggest that the public disclosure \n strategy adopted by the province of British Columbia, \n Canada, has a greater impact on both emission levels and \n compliance status than do orders, fines, and penalties \n traditionally imposed by the courts and the Ministry of the \n Environment. But their results also demonstrate that \n adopting stricter standards and higher penalties also \n significantly affected emission levels. Policymakers, take \n note: 1) The presence of strong, clear standards together \n with a significant, credible penalty system sends \n appropriate signals to the regulated community, which \n responds by lowering pollution emissions. 2) The public \n disclosure of environmental performance creates strong \n additional incentives to control pollution.
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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 | 0.001 | 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 | 0.001 | 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".