Estimating the environmental impacts of global lithium-ion battery supply chain: A temporal, geographical, and technological perspective
Bibliographic record
Abstract
Abstract A sustainable low-carbon transition via electric vehicles will require a comprehensive understanding of lithium-ion batteries’ global supply chain environmental impacts. Here, we analyze the cradle-to-gate energy use and greenhouse gas emissions of current and future nickel-manganese-cobalt and lithium-iron-phosphate battery technologies. We consider existing battery supply chains and future electricity grid decarbonization prospects for countries involved in material mining and battery production. Currently, around two-thirds of the total global emissions associated with battery production are highly concentrated in three countries as follows: China (45%), Indonesia (13%), and Australia (9%). On a unit basis, projected electricity grid decarbonization could reduce emissions of future battery production by up to 38% by 2050. An aggressive electric vehicle uptake scenario could result in cumulative emissions of 8.1 GtCO2eq by 2050 due to the manufacturing of nickel-based chemistries. However, a switch to lithium iron phosphate-based chemistry could enable emission savings of about 1.5 GtCO2eq. Secondary materials, via recycling, can help reduce primary supply requirements and alleviate the environmental burdens associated with the extraction and processing of materials from primary sources, where direct recycling offers the lowest impacts, followed by hydrometallurgical and pyrometallurgical, reducing greenhouse gas emissions by 61, 51, and 17%, respectively. This study can inform global and regional clean energy strategies to boost technology innovations, decarbonize the electricity grid, and optimize the global supply chain toward a net-zero future.
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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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".