“Green means good:” challenging the absence of international justice in just transition plans
Bibliographic record
Abstract
Advocates for a green energy transition in advanced economies argue that decarbonization of the economy will maintain or improve quality of life for most people. However, the upstream systems necessary to supply raw resources for a transition of this scale are deeply entangled with human rights infractions and catastrophic environmental harms. To ensure that a transition is truly ‘just’, the need for a significant reduction in greenhouse gas emissions must be reconciled with the impacts that the extraction of raw materials will have. In this paper, we analyze just energy transition policies from core and semi-periphery countries. We demonstrate how these policies focus on minimizing impacts to local labour and domestic companies, failing to address how materials necessary for a transition will be acquired ethically. We note the concerning lack of international justice in energy transition planning. We conclude with recommendations to align global colonial extraction systems with the values of just transitions. We advance the belief that an ethical energy transition is achievable but must move beyond global green capitalism and the colonial systems that uphold it.
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.054 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| 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".