Supply-side challenges and research needs on the road to 100% zero-emissions vehicle sales
Bibliographic record
Abstract
Abstract In this review paper, we delve into the supply-side challenges and considerations for transitioning to 100% zero-emission vehicles (ZEVs), weaving together an analysis of batteries, vehicle production, charging infrastructure, and relevant supply-side policies. We begin by examining the innovations and environmental impacts of lithium mining and recycling, highlighting the need for robust frameworks to ensure sustainable battery production. Our exploration of vehicle production reveals important issues regarding labor dynamics and global competitiveness. Our investigation into charging infrastructure reveals complexities in deployment models and access, reflecting broader societal and economic considerations. Lastly, a critical evaluation of policies across various jurisdictions provides insights into the effectiveness and potential improvements needed to support the ZEV transition. We emphasize the need for coordinated efforts and further research, particularly in areas such as end-of-life considerations for batteries and the alignment of international production standards. Our findings contribute to a comprehensive understanding of the supply-side landscape for ZEVs and underscore the essential research directions to ensure a responsible and successful electrification of the transportation system.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".