Environmental Impact Assessment of Plastic Waste Management Scenarios in the Canadian Context
Bibliographic record
Abstract
Given the scale of plastic generation, its persistent presence in the environment, and the urgent need to transition to a net-zero emissions paradigm, managing plastic waste has gained increasing attention globally. Developing an effective strategy for plastic waste management requires a comprehensive assessment of the potential benefits offered by different solutions, particularly with respect to their environmental impact. This study employs the life cycle assessment (LCA) methodology to evaluate the environmental impact of two alternative scenarios to the As-Is scenario for managing plastic waste in the province of British Columbia in Canada. The LCA results suggest that the Zero Plastic Waste scenario, which heavily relies on chemical recycling, may not inherently result in a reduced environmental footprint across all impact categories. This is notable when the focus is solely on end-of-life treatment processes, without considering the produced products and energy. The Intermediate scenario reduces the amount of plastic waste sent to landfills by directing more end-of-life plastic to mechanical recycling facilities. This scenario provides immediate benefits for resource conservation, with a minimal increase in the environmental burden resulting from treatment processes. Nonetheless, achieving a net-zero transition requires combining traditional and emerging recycling technologies. The current study could offer some guidance to policymakers on strategies for fostering more sustainable management of plastic waste.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".