Future electric vehicle battery waste flows and recycling infrastructure capacity needs in Canada
Bibliographic record
Abstract
Many countries including Canada are pursuing rapid electric vehicle (EV) adoption as a climate mitigation strategy. However, many uncertainties exist over waste management of EV components at the End-of-Life (EoL) phase, especially battery disposal. Discarded batteries can be processed using different strategies, which can have benefits including saving the resource costs of producing new batteries, and delaying the production of new waste streams. These strategies can reduce the economic and environmental impacts over the entire life-cycle of the battery. However, given ambitious EV deployment policies around the world, an important consideration is the timing and scale required to build additional waste management infrastructure capacity. Based on Canada’s current EV policy targets, our scenarios estimate that by 2050, there could be 0.5 to 1 million tonnes of EV batteries being disposed per year. Assuming that the current waste management capacity remains constant, Canada’s recycling capacity will be exhausted between 2034 and 2038 indicating a major shortfall. Our analysis shows that Canada requires significant and rapid investment in waste infrastructure including collection, transportation, processing and disposal of battery waste to prevent environmental pollution and potential human health impacts, which would offset the decarbonization benefits of mass EV adoption.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".