Assessment of Barriers and Drivers for the Second-Life Electric Vehicle Battery Supply Chain
Bibliographic record
Abstract
As the adoption of electric vehicles (EVs) scales up around the world, the management of end-of-life batteries has emerged as a significant challenge - and chance - towards more sustainable use of resources. Second-life applications, which refer to the reuse of EV batteries for stationary energy storage systems or other applications, can provide both environmental and economic benefits. However, while theoretically viable, the development and deployment of second-life battery supply chains are limited due to a multiplicity of technical, regulatory, economic, and social considerations overall. This paper provides a holistic assessment of the main barriers and drivers affecting the growth of second-life EV battery supply chains. The study builds on a comprehensive literature review, and initial industry input, to present key challenges including: the lack of standard testing and evaluation protocols, uncertainty about used battery performance, regulatory frameworks unaligned with circular economy principles, and high costs of collection and repurposing. The authors identify key drivers for the adoption of second-life batteries including: increasing demand for stationary storage, developments in battery diagnostics, the growth of the circular economy agenda and supporting policies. By representing the barriers and drivers, this study presents a cohesive and comprehensive starting point for future policy and strategic decision making relating to the supply chain. The study will also provide the authors to develop priorities for action and identify next steps to encourage collaboration across the sector.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".