Extended producer responsibility’s effect on producers’ electronic waste management practices in Japan and Canada: drivers, barriers, and potential of the urban mine
Bibliographic record
Abstract
Electronic waste is the fastest-growing domestic waste stream globally, continuously outstripping projections. With increasing ubiquity of complex computing, many non-renewables are contained in end-of-life electronics, creating a vast urban mine, potentially hazardous, depending on treatment. The aim of this study is to compare how Extended Producer Responsibility (EPR) policy is applied in two case countries, Japan and Canada, the practical implications of EPR policy design on producer operations, and how EPR affects electronic waste management improvements in each case. These cases share international obligations for electronic waste management but employ contrasting EPR policies. These policies are widespread in both cases, yet are not presided over by larger, regional obligations. Therefore, country-level interviews with electronic waste management stakeholders focusing on how EPR regulation affects producer practice were conducted. The physical application of EPR, as seen in Japan, drives design changes by producers intending to simplify downstream treatment, while financial responsibility in Canada, creates greater concern with cost-savings for producers, complicating end-of-life processing. EPR implementation, along with specific geographical factors, also create contrasting resource recovery results between countries. Regulation primarily drives EPR implementation in both countries, which is consistent with the literature. This study presents new drivers and barriers, namely pre-emptive legislation, and no incentive to improve, classifying the Japanese and Canadian systems as suffering from externalities on an insular system, and lack of harmonization, respectively. This research addresses a gap in comparative studies across regions of physical and financial EPR effects on producer practice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".