MétaCan
Menu
Back to cohort
Record W4417443841 · doi:10.1111/jcms.70079

Carbon Disclosure and Climate Change Mitigation in the European Union: Diffusion and Dominance of Transparency Frames

2025· article· en· W4417443841 on OpenAlexaff
Fábio Moreira Corrêa, Kerem Öge

Bibliographic record

VenueJCMS Journal of Common Market Studies · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsTransparency (behavior)Framing (construction)Corporate governanceLegitimacyClimate changeEuropean unionDominance (genetics)Civil societyStakeholder

Abstract

fetched live from OpenAlex

Abstract This article investigates the rise of governance‐by‐disclosure in the global climate regime, examining how the framing of carbon disclosure evolved into an effective governance norm within the European Union (EU). Employing discourse network analysis, we analyse the evolution of carbon disclosure frames in the EU between 2000 and 2020. We show that debates on carbon disclosure were initiated by non‐state actor coalitions that framed transparency as a risk moderator and an economic opportunity for businesses and investors. These findings confirm the expectations of stakeholder and economic theories of transparency, emphasising privatisation and marketisation as pivotal drivers of the disclosure regimes. Additionally, since the early 2010s, we observe an increasing trend towards institutionalisation/diffusion. Especially after the Paris Agreement, European actors, including the European Commission, have increasingly converged around regulatory frames, in addition to the existing dominant discourse on financial benefits. Reflecting the growing consensus around these frames, this period also saw a significant expansion of policy initiatives within the EU advocating for mandatory disclosures. Beyond carbon disclosure, our findings show how market‐driven frames gain traction, diffuse and inform regulatory frameworks. They also reveal the dominance of private governance logics in EU climate policy and the resulting tensions over democratic legitimacy in disclosure regimes.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.059
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.029
GPT teacher head0.287
Teacher spread0.259 · 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 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJCMS Journal of Common Market StudiesSame topicCorporate Social Responsibility ReportingFrench-language works237,207