Governance gaps and accountability traps in renewables extractivism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it