Post-Consumer Management of Electric Vehicle Batteries
Bibliographic record
Abstract
The end-of-life management of EV batteries is a significant issue. With the use of high-performance batteries on the rise, they have the potential to become the next global waste management challenge. This Major Research Paper comparatively analyzes the policy structures for managing the end-of-lives for electric vehicle (EV) batteries in Canada, the European Union and the United States. Sociotechnical transition theory is used to understand the \neffects of large-scale technological transitions as they relate to electric vehicles. Emphasis is placed on the downstream consequences of technological transitions, and the lack of discussion in the transitions literature of downstream effects. This paper utilizes a methodological framework that draws inspiration from the work of Dr. Mark Winfield and Hugh Benevides in the Walkerton Water Inquiry. It is used to comparatively analyze the policy structures in Europe and North America for end-of-life EV batteries. I conclude that based on existing policy structures, the European Union has developed a basic framework on this issue through the implementation of the 2006 Battery Directive. The United States and Canada, with the exception of Quebec, are falling behind on the issue. Design for disassembly is explored as a potential method for alleviating the concerns with downstream effects. It also allows for the growth in markets for secondlife applications of end-of-life EV batteries. Second-life applications, where possible, are preferred to direct recycling because of the potential development of undesirable waste streams. Extended producer responsibility (EPR) is explored and chosen as the preferred model for countries to hold producers responsible for the waste they generate. This model, in conjunction with an emphasis on second-life applications, can incentivize producers to design their batteries for easier disassembly, reuse and recycling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".