Life cycle assessment of nickel, manganese, cobalt critical minerals: lithium hydroxide monohydrate (mine-to-material) in Québec, Canada
Bibliographic record
Abstract
The production of electric vehicles (EVs) is rapidly expanding, particularly in North America, where new lithium-ion battery (LIB) and original equipment manufacturing (OEM) plants are being built. This has increased the demand for critical minerals, especially lithium. As the supply chain shifts from Asia, particularly from China to North America, there is a growing focus on sourcing these minerals locally, especially in Canada and the United States. Thus, establishing a sustainable LIB supply network is essential to minimize the detrimental environmental impacts. A life cycle assessment (LCA) is a key tool in achieving this goal. Québec, Canada, holds one of the world's largest deposits of spodumene ore, a major source of lithium. This study conducted an LCA of battery-grade lithium hydroxide monohydrate (LiOH•H 2 O) produced from Québec spodumene. Results show that producing one ton of LiOH•H 2 O emits approximately 5.46 tons of CO 2 -equivalent. This assessment serves as the foundation for a broader series of LCAs on all critical materials in battery production. Understanding the full environmental impact of EV batteries requires evaluating each stage, from raw material extraction to manufacturing, use, and disposal. Identifying emissions hotspots within the supply chain allows for targeted improvements. By applying this holistic approach, the EV industry can develop strategies to reduce greenhouse gas emissions and ensure the transition to electric mobility is both sustainable and environmentally responsible.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".