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Record W4394619539 · doi:10.1080/13876988.2024.2328605

Do Political Institutions Influence the Dismantling of Fossil Fuel Subsidies? Lessons from the OECD Countries and a Comparative Analysis of Canadian and German Production Subsidies

2024· article· en· W4394619539 on OpenAlexaboutno aff
E. M. Drake, Jakob Skovgaard

Bibliographic record

VenueJournal of Comparative Policy Analysis Research and Practice · 2024
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
FundersEnergimyndigheten
KeywordsSubsidyPoliticsEconomicsGermanProduction (economics)CorporatismFossil fuelEconomic policyMarket economyNatural resource economicsInternational economicsPolitical scienceMacroeconomicsEngineeringLaw

Abstract

fetched live from OpenAlex

Despite a global consensus that fossil fuel subsidies should be reformed, limited progress has been made.The study assesses whether domestic political institutions insulating politicians from backlash and compensating those affected by reforms make subsidies easier to dismantle.It was found that proportional representation and corporatism were correlated with lower levels of fossil fuel subsidies in OECD countries.A comparative case study of coal production subsidies in Germany and gas production subsidies in Canada suggests that political insulation and compensation contributed towards the dismantling of fossil fuel subsidies.The findings provide an understanding of the impact of corporatism and electoral systems on reform.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.641

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.005
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.189
GPT teacher head0.474
Teacher spread0.285 · 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 designObservational
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

Citations5
Published2024
Admission routes1
Has abstractyes

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