Patterns of air pollution enforcement in Canada: Environmental priorities versus enforcement outcomes
Bibliographic record
Abstract
Ambient air pollution is one of the leading health and environmental concerns worldwide, including in Canada. To reduce air pollution impacts, governments create and enforce various laws and regulations. Few studies have examined the particulars of enforcement and fewer still in Canada. To this end, we ask: How does enforcement vary across jurisdictions, time, or other factors? What types of violations or offenders appear to be prioritized for enforcement action in Canada? We created a dataset of air pollution enforcement actions between 2000 and 2020, using data from 8 provinces and the federal government. Through this process, we identified gaps in data sharing and transparency for air-pollution-related enforcement in Canada related to ease-of-access and standardization. Based on these available data, which has acknowledged limitations, we find that regulators appear to employ a cooperative and nonresponsive approach to enforcement, as demonstrated by low fines, a lack of escalating enforcement actions for repeat offenders, frequent use of low-level penalties, and infrequent prosecutions leading to few court convictions. Environmental priorities and enforcement outcomes appear to be misaligned, with few and low penalties for large emitters and repeat offenders. We offer recommendations to better align enforcement strategies with stated environmental policy goals, including focusing enforcement on high-risk offenses, improving data sharing, and strengthening federal environmental laws and agencies.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".