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Record W3195539894 · doi:10.1080/14693062.2021.1965524

Fossil fuels, climate change, and the COVID-19 crisis: pathways for a just and green post-pandemic recovery

2021· article· en· W3195539894 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueClimate Policy · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFossil fuelClimate changeNatural resource economicsLeverage (statistics)Energy transitionBusinessRenewable energyEconomicsWaste managementEcologyEngineeringBiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.122
GPT teacher head0.362
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it