Charting the electric vehicle battery reuse and recycling network in North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it