Whose risk counts? Climate risk frames in global green finance governance complex
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it