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Record W4301183375 · doi:10.1162/glep_a_00682

Supply-Side Climate Policies in Major Oil-Producing Countries: Norway’s and Canada’s Struggles to Align Climate Leadership with Fossil Fuel Extraction

2022· article· en· W4301183375 on OpenAlexaffabout
Kathryn Harrison, Guri Bang

Bibliographic record

VenueGlobal Environmental Politics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsSocial Sciences and Humanities Research Council
Fundersnot available
KeywordsProsperityFossil fuelNatural resource economicsGovernment (linguistics)EconomicsGreenhouse gasClimate changeConventionInternational tradeEconomyPolitical scienceEconomic growthLawEcology

Abstract

fetched live from OpenAlex

Abstract This article considers the puzzle of Norway and Canada, two countries that have adopted ambitious Paris Agreement targets yet are also major fossil fuel exporters. To date, both countries have taken full advantage of the international convention that assigns responsibility only for emissions within a country’s borders. However, climate activists, First Nations, and green politicians increasingly have challenged fossil fuel production via campaigns centered on issues salient to voters in nonproducing regions: opposing new exploration licenses in Norway and pipelines in Canada. While supply-side campaigns have sometimes succeeded in ending expansion, neither country has seriously entertained restricting current production. We attribute these outcomes to continued public support for fossil fuel–driven prosperity; institutions that assign responsibility for production and climate to different government agencies; and the success of counternarratives that unilateral supply restrictions are futile, prosperity from petroleum exports will fund domestic clean-energy transitions, and gas exports advance global climate action.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.443

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.220
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations21
Published2022
Admission routes2
Has abstractyes

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