How Effective is ‘Green Regulatory Threat’? Empirical Evidence from Canadian Plant-Level Data
Bibliographic record
Abstract
Governments are increasingly relying on environmental policies predicated on the assumption that the mere threat of regulation will entice companies to reduce their toxic emissions. This paper makes an attempt at identifying the effect of regulatory threat theoretically and empirically. The key to identifying regulatory threat’s effect on the environment is to condition firms ’ response on their characteristics, namely, their environmental exposure that combines the effect of firm size, emission intensity, pollutant toxicity, and population at risk, and their abatement ladder rung that determines whether individual firms will participate in pollution abatement. Using 1993-99 data from the Canadian National Pollutant Release Inventory (NPRI), empirical analysis establishes that the theoretical predictions help identify statistically significant effects of regulatory threat that are, however, very small in magnitude. Green regulatory threat does not appear to be an effective instrument in Canada.
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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 | 0.001 |
| Open science | 0.001 | 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".