Incentives for pollution control - regulation and public disclosure
Bibliographic record
Abstract
An increasing number of regulators have adopted public disclosure programs to create incentives for pollution control. Previous empirical analyses of monitoring and enforcement issues have focused strictly on the impact of such traditional practices as monitoring (inspections) and enforcement (fines and penalties) on polluters'environmental performance. Other analyses have separately focused on the impact of public disclosure programs. But can these programs create incentives in addition to the normal incentives of fines and penalties? The authors study the impact of both traditional enforcement and information strategies in the context of a single program, to gain insights into the relative impact of traditional (fines and penalties) and emerging (public disclosure) enforcement strategies. Their results suggest that the public disclosure strategy adopted by the province of British Columbia, Canada, has a greater impact on both emission levels and compliance status than do orders, fines, and penalties traditionally imposed by the courts and the Ministry of the Environment. But their results also demonstrate that adopting stricter standards and higher penalties also significantly affected emission levels. Policymakers, take note: 1) The presence of strong, clear standards together with a significant, credible penalty system sends appropriate signals to the regulated community, which responds by lowering pollution emissions. 2) The public disclosure of environmental performance creates strong additional incentives to control pollution.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".