Life cycle carbon footprint of novel technologies for lithium production and potential implications for the supply chain in North America
Bibliographic record
Abstract
• Novel technologies show promise to reduce lithium’s carbon footprint. • Estimated potential carbon footprints of 2–18 kgCO₂e/kg (no allocation). • Carbon footprint is influenced by energy and chemical sources, and LCA methods. • Compared Li and Na-ion batteries carbon footprints with different functional units. • Conclusions may vary based on the functional unit when assessing battery impacts. This study explores unconventional lithium sources in North America for producing lithium hydroxide monohydrate (LiOH.H 2 O) with lower greenhouse gas (GHG) emissions compared to traditional sources. We also estimate how batteries using LiOH.H 2 O would compare with the emerging sodium-ion battery technology. Novel technologies (e.g., direct lithium extraction, DLE, and electrochemical refining) show promise to reduce GHG emissions compared to traditional methods, with carbon footprints from 2 to 18 kgCO 2 eq/kg LiOH.H 2 O (baseline, no allocation). Electricity carbon intensity and methodological choices (e.g., co-product allocation/substitution, boundary definitions) are the most influential factors across pathways, with impacts ranging from -156 % to 130 % in carbon footprints relative to baseline scenarios. Furthermore, while unconventional lithium sources coupled with novel processing technologies may reduce carbon footprints compared to current incumbent pathways, research and development (R&D) and innovation effects should be considered to maintain competitiveness in the face of other emerging technologies, such as sodium-ion batteries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".