Fossil Fuel Industry Phase-Out and Just Transition: Designing Policies to Protect Workers’ Living Standards
Bibliographic record
Abstract
This paper focuses on transition policies targeted at supporting workers now employed in the fossil fuel industries and ancillary sectors within high-income economies. As a general normative principle, I argue that the overarching aim of such policies should be to protect workers against major losses in their living standards resulting through the fossil fuel industry phase-out. The impacted workers should be provided with guarantees to accomplish this, in the areas of jobs, compensation and pensions. Just transition policies should also include job search, retraining and relocation programs, but these forms of support should be recognized as supplementary. The overall set of just transition policies is fully aligned with the Energy Justice and Capabilities Approach as well as the UN's Sustainable Development Goals. Within this framework, the paper first reviews experiences with transitional policies in Germany, the UK, the EU and, more briefly, Japan and Canada. The policies either implemented or discussed in these cases do not provide the needed guarantees. The paper then presents an illustrative robust just transition program for the heavily fossil fuel-dependent U.S. state of West Virginia. This program will cost, as an annual average, about $42,000 per impacted worker, or about 0.2 percent of West Virginia's current GDP. I briefly summarize results for seven other U.S. states and for the overall U.S. economy. For the U.S. economy overall, the just transition program's costs would total to about 0.015 percent of GDP. These findings demonstrate the financial viability of robust just transition programs for high-income economies.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".