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Record W4383342103 · doi:10.1080/14693062.2023.2231398

Left behind: emerging oil and gas producers in a warming world

2023· article· en· W4383342103 on OpenAlexaff
Valérie Marcel, Deborah Gordon, Naadira Ogeer, Ekpen James Omonbude

Bibliographic record

VenueClimate Policy · 2023
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsInternational Institute for Sustainable Development
FundersDirektoratet for Utviklingssamarbeid
KeywordsGreenhouse gasNatural resource economicsEquity (law)Fossil fuelPetroleumBusinessPetroleum industryEconomicsResource (disambiguation)Economic policyEnvironmental scienceEngineeringWaste managementPolitical scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

The push for decarbonization is dampening resource prospects in nations with undeveloped oil and gas. It is critical to reduce greenhouse gas (GHG) emissions from the petroleum sector, but there are equity issues related to requiring a shift away from oil and gas before development gains are made, especially in countries that have contributed very little to historical emissions. We review the prospects for five emerging producers to produce oil and gas at the lowest emissions intensity while achieving their economic and environmental goals. We find they lack the required capacity for stringent emissions management and to manage transition risks. The low-carbon pathway presents its own challenges with plans that lack national specificity and offer no substitute to the fiscal potential of the petroleum sector, and a lack of supportive technical assistance and finance. A just transition (JT) approach in these countries will not be about reskilling as they move away from a petroleum dependent economy, but instead about engaging with citizens to break the mould of petroleum-led development expectations and defining the new pathway for development. These countries will require support for transition planning that ensures that any oil and gas production minimizes GHG emissions, and limits the risk of economic lock-in, to invest in broad-based benefits and in a credible shift to a low-carbon economy. Inadequate international support risks leaving some countries behind, or to essential changes being contested in the transition.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.330
Teacher spread0.308 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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