The unbearable lightness of lithium governance: Legitimizing extraction for a just and sustainable energy transition
Bibliographic record
Abstract
The electrification of renewable energy systems is fostering a global surge in demand for the “critical” metals that are used in the production of lithium-ion batteries, raising concerns that the latest round of “renewable extractivism” is degrading some of the world's most fragile ecosystems and communities. In the absence of credible and legitimate forms of state regulation, transnational corporations in the mining, battery and auto sectors have used a range of procedures to monitor, report, and verify their performance on environmental, social, and governance indicators. This article examines how transnational governance initiatives seek to regulate the extraction of lithium for lithium-ion batteries and electric vehicles. It starts from the premise that their monitoring, reporting, and verification (MRV) procedures are “rituals of legitimation” or technical routines that frame and define what constitutes responsible mining practice while not mitigating harm. We analyze an original database of 18 public, private, and hybrid governance initiatives to investigate the types of rituals used. In theory, using third-party audits to monitor, report, and verify mining standards and regulations provides an important means of holding powerful mining companies accountable for the social and ecological harms of resource extraction. However, maintaining the autonomy of third-party auditors entails reducing or eliminating the role of mining interests in transnational governance practices. We find that the strongest and most independent forms of governance are ones that are rooted in public institutions with legal mechanisms for enforcing corporate compliance. By contrast, private initiatives place significant responsibility in the hands of subcontractors, offering limited opportunities for including or offering affected communities a means of redress. Finally, hybrid initiatives establish more comprehensive MRV practices, but these too adopt procedures that limit the conditions under which affected communities may question, negotiate, or – indeed – say no to mining. The findings highlight the importance of establishing governance procedures that maintain the autonomy of third parties by institutionalizing and enforcing independent site visits, local participation, grievance mechanisms, and meaningful consequences for non-compliance.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".