Review of the global evolution of regulations on single-use plastics and lessons drawn for Canada
Bibliographic record
Abstract
Plastic pollution is a global problem and many countries are strengthening their regulations to mitigate the related environmental degradation and health risks and to support the development and deployment of circular economy for various types of plastics. As Canada also develops its strategy for regulating single-use plastic as one element of the plastic pollution, aligned federal and provincial policies are essential. This study presents an analysis of existing and emerging policies to provide guidance on Canada's future regulations. Qualitative and quantitative data regarding plastic regulations were gathered from similar countries including Australia, the United Kingdom, the European Union, the United States and relevant scientific articles. Analysis was also conducted of current Canadian regulations that both impact and guide the path for plastic regulation, international examples provided guidance for future Canadian regulations. The analysis found that there is a need for public education on the gravity of plastic pollution to gain their support; for establishing pioneering provinces or cities in plastic regulations to learn from and provide other cities with support; and to start with banning items with available alternatives, to be followed by phasing out other items that are more difficult to replace. The study also showed potential areas of improvement in impact data. The need for reliable regulatory performance data against a baseline scenario; consistency in methodology; and proper scoping to reduce the risk of displacement or exclusivity in policy were identified.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".