Lithium-ion battery recycling: a critical review of techno-economical and socio-environmental impacts
Bibliographic record
Abstract
The increasing use of lithium-ion batteries (LIBs) in electric vehicles and electronic devices has created a pressing need for sustainable recycling solutions. This study presents a comprehensive analysis of LIB recycling, integrating technological, environmental, economic, and social dimensions. Recycling processes such as hydrometallurgy, pyrometallurgy, and direct recovery are explored for the extraction and reuse of essential battery components, including cathodes, anodes, electrolytes, binders, separators, and current collectors. Life cycle comparisons between recycled and virgin materials, as well as disposal methods like landfilling, are conducted, focusing on greenhouse gas emissions, energy demand, water use, and system costs. Direct recycling emerges as the most environmentally and economically favorable method, demonstrating the lowest emissions (0.6–8.1 kg CO₂/kg), energy consumption (3.5–112.1 MJ/kg), and cost ($0.9–4.1/kg), with minimal water pollution. In contrast, virgin LIB manufacturing and landfilling exhibit significantly higher environmental and economic impacts compared to recycling. Economic analysis further reveals that fiscal incentives, even at modest levels, can markedly enhance the profitability and competitiveness of all recycling routes, particularly direct recycling. The study also explores the role of policy instruments such as subsidies, carbon credits, and extended producer responsibility (EPR) schemes in enhancing recycling viability. Additionally, it identifies key technical and socio-environmental challenges and provides future research directions to guide advancements in sustainable LIB recycling. The findings emphasize the need for policy-driven support to scale direct recycling and close research gaps, offering actionable insights for developing a low-carbon, circular battery economy.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".