Modelling the Economic Effects of a Disruptive Event: Investigating the Implications and Effects of the Proposed Federal Single-Use Plastics Ban on Manufacturing in Ontario
Bibliographic record
Abstract
During the past few decades, plastics pollution has become a global concern. Governments, in particular, are striving to find the best way to control the issues plastics have caused to the environment. The Government of Canada is seeking to phase out harmful single-use plastics by the end of 2021. The announced ban is a potentially disruptive public policy that may have consequences. A myriad of studies has been conducted on the environmental impacts of plastics, but there is a lack of literature on the evaluation of such regulations on manufacturers. This thesis aims to evaluate the economic implications of the proposed single-use plastics ban by generating a private cost-benefit analysis on manufacturers in Ontario and finds the impacts of transitioning from conventional plastics to alternative materials on companies. The model is applied to 139 single-use plastics companies in Ontario. This study assumes that manufacturers will make their decision based on the net present value of their overall benefits of material substitution. The results of the analytical model are then explained, and a series of sensitivity analyses are conducted for some parameters.The novelty of the proposed model lies in evaluating the impacts of the ban on manufacturers from an economic point of view, covering a wide range of single-use plastics products and a one-by-one cost-benefit analysis on companies within Ontario.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".