Fossil fuels, climate change, and the COVID-19 crisis: pathways for a just and green post-pandemic recovery
Bibliographic record
Abstract
A climate-positive COVID-19 recovery can accelerate the energy transition away from fossil fuels. Yet, current assessments of recovery stimulus programs suggest that the most fossil fuel producers are more likely to take on a ‘dirty’ recovery path out of the pandemic than a ‘green’ one. Such a path will postpone climate action and entrench fossil fuel dependence. To change course, fossil fuel producers have to get on board of a 'green recovery'. For this, cooperative international efforts mobilizing both fossil fuel consumers and producers need to promote ‘just transition’ policies that increase support for a green shift among fossil fuel companies and producing countries, including fossil fuel exporters. In turn, fossil fuel producers should leverage the opportunity of stimulus packages to reduce their fossil fuel production dependence and help accelerate an energy transition through supply-side measures. A combination of ‘green’ investments and ‘just’ transition reforms could help enroll fossil fuel producers into a climate-friendly post-COVID recovery.Key policy insights Fossil fuel producers have mostly promoted ‘dirty’ rather than ‘green’ recovery paths from the COVID-19 pandemicA green recovery agenda requires a ‘just’ transition component to entice and support fossil fuel producersBoth demand and supply-side strategies are required to advance a ‘just transition’ agenda throughout the post-COVID 19 recoveryThe post-COVID-19 transition requires energy policies responding to the constraints of consumer and producer countries to address climate and equity challenges
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".