Whose risk counts? Climate risk frames in global green finance governance complex
Bibliographic record
Abstract
In recent decades, global green finance governance institutions (GGFGIs) have developed diverse frames for understanding climate-related risks. Understanding these risk frames is crucial because they lead to distinctive “de-risking” policies, empowering different types of actors. This paper examines how GGFGIs produce different climate risk frames, and what the prevailing climate risk frame is and whose risk it addresses. We investigate these questions by analyzing the current global green finance governance complex applying a constructivist approach emphasizing contestation over normative issues and a Critical Political Economy perspective. Our mapping based on 74 GGFGIs shows exercise that a risk framing focusing on climate impact on business actors became prevalent over other types of climate risks imposed on people and nature. Our finding shows the dominant influence of the Task Force on the Climate-Related Financial Disclosure created by G20's Financial Stability Board. This development reflects broader trends of climate capitalism. • Global Green Finance Governance Institutions (GGFGIs) predominantly frame climate change as risks to business. • This risk framing prevailed as GGFGIs adopted the Task Force on Climate-Related Financial Disclosures’ recommendations. • However, some GGFGIs—such as those under UNEP—reinterpret the risk frame by emphasizing climate risks to people and nature. • The overall evolution of the global green finance governance architecture reflects broader trends of climate capitalism. • This dominant framing, protecting business over people or nature, may hinder just and transformative pathways.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".