Feasibility analysis of recycling and repurposing end-of-life vehicle batteries in isolated island areas: A case study in British Columbia, Canada
Bibliographic record
Abstract
The increasing adoption of electric vehicles (EVs) is widely recognized as a pivotal strategy for mitigating climate change. However, the surge in EV usage brings significant challenges regarding the end-of-life management of lithium-ion batteries, particularly in geographically isolated islands, such as Vancouver Island, Canada. This study addresses the urgent need for effective waste management solutions by integrating life cycle assessment (LCA) and multi-criteria decision analysis (MCDA) to evaluate six distinct scenarios for managing spent EV batteries. These scenarios encompass pyrometallurgical and hydrometallurgical recycling, on-island and off-island repurposing for secondary applications, and landfilling. A comprehensive assessment of environmental, social, and economic impacts reveals that all management strategies, except landfilling, positively contribute to environmental performance. Among all scenarios, on-island repurposing emerges as the most feasible option across multiple impact categories, including terrestrial ecotoxicity, human health, and abiotic resources. Furthermore, hydrometallurgical recycling shows promise due to its efficiency and lower environmental burden compared to the pyrometallurgical method. The results offer valuable insights into optimizing waste EV battery management systems in island contexts and underscore the potential for innovative approaches. This research provides a framework for policymakers to optimize waste EV battery management systems in island regions, ensuring sustainable practices that align with growing EV markets. • Repurposed second-life batteries offer considerable economic benefits. • Hydrometallurgical recycling performs better than pyrometallurgical methods. • On islands, shorter transport distances and higher recycling rates are optimal. • Landfills are simpler and more achievable but do not ensure long-term sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".