Governance gaps and accountability traps in renewables extractivism
Bibliographic record
Abstract
Abstract The global uptake of renewable technology is both a dramatic and insufficient contribution to achieving a 1.5–2° world. However, urgently decarbonizing energy use and systems by shifting to renewables relies on intensifying global supply chains, beginning with the extraction of “critical” minerals, an industry that has a long history of generating significant social and ecological harms. This paper examines the nature of transnational governance initiatives that have emerged to regulate what has been called “renewables extractivism.” We develop a novel database of 44 transnational initiatives for governing minerals for onshore wind, solar PV, and lithium‐ion batteries, which are driving renewable energy uptake. The database reveals “governance gaps” that refer to an absence of rules for many critical minerals and “accountability traps” where actors are held responsible for processes, standards, and sanctions that reflect their own normative logics, rather than the needs of affected communities and ecosystems. Current initiatives are designed in a way that measures, evaluates, and (very rarely) sanctions governance outcomes primarily in relation to supply chain security and energy access, as opposed to mitigating the social and environmental harms of resource extraction. The result is a transnational governance architecture that operates primarily (and systematically) with minimal scrutiny, transparency, and accountability. For stakeholders directly affected by the latest mining boom cycle, the absence of effective and legitimate accountability mechanisms reinforces a pattern of uneven development that shifts the most destructive forms of extraction to the social and ecological margins of the global commodity frontier.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| 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".