Environmental impacts of lithium supply chains from Australia to China
Bibliographic record
Abstract
Abstract Lithium (Li) has been widely recognized as an essential metal for clean technologies. However, the environmental impacts and emission reduction pathways of the lithium supply chain have not been clearly investigated, especially between Australia and China, where most lithium ore are mined and produced. This study analyzed and compared the environmental and human health implications of six key cross-border Li supply chains from Australia to China through material flow analysis (MFA) and life cycle assessment (LCA) methods. Key findings include: (1) approximately 30% of total Li extraction is lost in the beneficiation stage due to low recovery rates; (2) the Cattlin–Yaan routes exhibit superior environmental and human health performances than other routes attributed to lower diesel consumption, reduced electricity use, and a high chemical conversion rate; (3) the Wodgina production routes have a higher carbon footprint mainly due to low ore grade and significant diesel consumption; (4) the dominant environmental implications in the supply chain are associated with refining battery-grade lithium carbonate, driven by energy use (electricity, coal and natural gas), sulfuric acid, soda ash, and sodium hydroxide. In addition, lithium carbonate refining has the highest water consumption. Overall, the analysis highlights opportunities to improve environmental performance, advance data-poor environmental assessments, and provide insights into sustainable Li extraction.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".