Evaluating economic and environmental viability of recycling lithium-ion battery for electric vehicles in the middle east: a case study in the UAE
Bibliographic record
Abstract
The environmental impacts caused by the manufacturing of spent lithium-ion batteries (LIBs) and the supply risks of the valuable metals in electric vehicle (EV) batteries can be mitigated by recycling used LIBs. This study employed a Cradle-to-Gate life cycle assessment (LCA) and cost-benefit analysis, to evaluate the environmental and economic advantages of recycling and remanufacturing Nickel Manganese Cobalt (NMC) cells using hydrometallurgical and pyrometallurgical processes, compared to manufacturing with virgin materials. In the regional context, hydrometallurgy-based LIB remanufacturing has the potential to reduce energy consumption and greenhouse gas (GHG) emissions by 10.7%, accompanied by 11.3% cost savings, compared to virgin material manufacturing. However, despite its potential for GHG emission reduction, pyrometallurgy-based remanufacturing is economically unviable across all scenarios considered. The economics of LIB recycling are significantly influenced by spent LIB costs, requiring careful management of market trends and purchase expenses for feasibility. Challenges have arisen from the increasing volume of spent LIBs and production cost fluctuations. Various LIB chemical compositions yield nuanced outcomes, with NMC111 and NMC811 showing promise for economic and environmental aspects, respectively, while NMC532 and NMC111 demonstrate relative suitability for a cell remanufacturing. Preliminary analysis of Lithium Iron Phosphate (LFP) batteries, an emerging chemistry with growing adoption, highlights unique challenges underscoring the need for further research. This study marks the first economic and environmental evaluation of LIB recycling in the oil-rich Middle East, emphasizing the need for an assessment tailored to the UAE to inform regional policy development. This framework could be adapted for use in other Middle Eastern countries, aiding the formulation of effective regional policies and regulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".