Reputational punishment of environmental violations in Canada
Bibliographic record
Abstract
This thesis examines to what extent market-imposed sanctions, i.e. "reputational penalties", impose significant costs on firms that violate environmental regulations in Canada. Determining the presence and size of reputational penalties is important for policy implications as it can be used to determine whether legal fines are adequately punishing firms that violate environmental regulations and, in combination with market-imposed sanctions, become a sufficient deterrent of corporate behavior causing negative externalities on the environment. The empirical method used to determine the changes in public attitudes following an environmental violation is the standard event study methodology. The dependent variable is the abnormal returns on shares of the company, or the difference between the security's expected return and its actual return. The event for this study was the release of the enforcement notification (i.e. the notification of the fine that the firm has to pay). After applying inclusion criteria 28 cases of environmental violations between 2010-2019 were noted. The results suggest that there is some negative reputational effect on the day of the notification. However, the results also suggest that there may be a positive effect of the event for higher fines. This, somewhat unexpected result from a theoretical point of view, may be the result of the fine being smaller than expected by the market or, alternatively, that the market appreciates that uncertainty is removed, along with the risk of a drawn out and costly legal case. Based on the results, the legal penalties in Canada appear to be too low and failing to impose the intended and adequate cost on the firm. Therefore, the main policy recommendation is to increase the explicit costs imposed on firms that violate environmental regulations.
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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) | 0.000 | 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".