Extended producer responsibility: An empirical investigation into municipalities' contributions to and perspectives on e‐waste management
Bibliographic record
Abstract
Abstract The development and implementation of extended producer responsibility (EPR) policies to manage e‐waste provide multilevel governance frameworks for achieving greater material circularity. However, the roles and responsibilities that are allocated to various stakeholders under these policies, which are crucial for program effectiveness, often vary across jurisdictions, and consensus is lacking about the best types of relationships and collaboration that should govern municipalities' contributions to EPR programs. Against this backdrop, and since this issue is poorly researched, we conducted an empirical investigation to identify the main drivers and barriers influencing municipalities' collaboration with an e‐waste EPR program in a Canadian province where municipalities are free to decide whether or not to engage with the program. Based on our study, we explore policy implications for similar programs in other jurisdictions, and propose questions for further research. Our findings identify key motivations for collaboration, including perceived program legitimacy, program funding, and logistical efficiencies. Conversely, a lack of program transparency, failure to support local employment, a focus on recycling instead of reuse, and limited program scope are identified as disincentives to program participation. Policymaking for e‐waste management and circularity need to consider municipalities' interests and contributions to ensure successful implementation.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".