Charting the electric vehicle battery reuse and recycling network in North America
Bibliographic record
Abstract
As electric vehicle (EV) sales grow across the world, a common question arises: "what happens to the batteries?" Using expert elicitation, this study identifies the current pathways for retired EV batteries in the United States and Canada and anticipates how the network might evolve in the future. The majority of end-of-life (EOL) EVs are currently managed within the manufacturer and dealership network, but more will enter the independent afterlife market as growing volumes reach EOL out-of-warranty. The interviews indicate that safety, transportation, and accessible information about battery composition and remaining capacity are critical issues across sectors. Participants demonstrated a strong commitment to creating a closed-loop value chain, motivating novel partnerships between recyclers and producers. At the same time, the value of EOL batteries as a material supply source may create competition between recycling and repurposing in the short term. State and federal governments are implementing policies to facilitate access to information and incentivize domestic manufacturing, but compared to other countries, the US lacks a mechanism to ensure that batteries will be collected and recycled. In addition, there is no national tracking system that would provide more robust data on LIB management. Multiple participants noted that the network handles the majority of EOL batteries without significant policy intervention. However, at present, the system depends the economics of reuse and recycling when accounting for the cost of collection and processing, which creates a risk of stranded batteries and/or wasted materials for packs that are lower-value or difficult to access.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".