Lithium Mining for a Clean Energy Transition: Perspectives and Prescriptions of Local Stakeholders
Bibliographic record
Abstract
There is broad scientific agreement that a transition away from fossil fuels is critical to mitigate the climate crisis. Current government plans for this transition prescribe the proliferation of clean technologies such as electric vehicles. These technologies rely on significant inputs of battery metals, including lithium. The present article asks how local stakeholders in proposed lithium mining projects perceive the clean energy positioning of companies and governments who wish to exploit lithium reserves in Canada. It examines lithium extraction in Abitibi-Témiscamingue, Québec, drawing from documentary analysis and 40 semi-structured interviews. The author finds that local stakeholders do not see lithium’s role in clean energy as a central element within local discussions about proposed lithium mines. Interviewees with various positions and affiliations across Abitibi-Témiscamingue’s lithium industry highlighted other priorities in determining their buy-in for lithium projects; criticized the electrification of transport as a means of addressing climate change; and proposed alternative solutions to address the root issues of environmental crises. This paper asserts that mining-centered approaches to climate change mitigation must be met with caution, consultation of affected stakeholders, and consideration of other solutions that can be implemented instead of or in tandem with metal-intensive clean technologies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".