Potential Effects of Single Use Plastics Ban on Ontario Manufacturers
Bibliographic record
Abstract
During the past few decades, plastics pollution has become a global concern. Governments are striving to find the best way to reduce plastics consumption and waste. The Government of Canada has proposed a ban on single-use plastics to be implemented in 2021, a potentially disruptive public policy. Many studies have been conducted on the environmental impacts of plastics and the benefits of a plastics ban, but little has been written about the potential effects of these policies on plastics manufacturers. An economic model was developed to analyze the effects on Ontario single use plastics manufacturers. Results of the model show that most plastics manufacturers would be able to recover their investments within three years for the costs of converting to an alternative material. However, there is an aggregated cost on manufacturers of approximately $262 million for the first three years. Additionally, a small number of specialized manufacturers would not be able to recover from the ban, potentially leading to some job losses. Overall, however, the results indicate that manufacturers would be able to adjust to the ban in the longer-term, providing for the environmental benefits of reduced plastics consumption and waste.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".