From mine to motor: A literature review on environmental assessments of electric vehicle battery supply chains
Bibliographic record
Abstract
Abstract The fast-changing nature of sustainable mobility and the exponential growth of electric vehicles (EVs) have now placed battery supply chains (SCs) at the forefront of environmental concern. This review article examines 84 peer-reviewed studies published between 2008 and 2025, highlighting that 78% of the studies were published from 2020 to 2025, reflecting the rapid acceleration of EV battery sustainability research in recent years. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method, the review has identified significant environmental hotspots and trade-off issues across six phases of the battery SC, as well as inconsistencies regarding methodology, such as functional units of measurement, and missing data in relation to the Global South. Hotspots are most prominent in South America and Central Africa (extraction), China and South Korea (manufacturing) and Southeast Asia (end-of-life). New contributions include a comparison of the life cycle assessment approaches, using new data from 2023 to 2025, adding updated insights on policy evolution, improved recycling efficiencies and digital traceability technologies that enhance supply-chain transparency. Furthermore, this review highlights ignored areas, such as informal recycling of batteries and unfair regulations across borders, and it provides recommendations, which are relevant to policymakers, industry and academia, to improve transparency in the SC, better compliance with environmental, social and governance requirements and sustainability initiatives.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".