Carbon Disclosure and Climate Change Mitigation in the European Union: Diffusion and Dominance of Transparency Frames
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".