Civil society as a quasi‐regulator: Coordination in financial regulation on climate change
Bibliographic record
Abstract
Abstract As early as 2015, financial regulators were developing disclosure frameworks aimed at enabling capital markets to price climate risks. Yet the literature on sustainability disclosure offers little insight into how regulatory agendas change, instead focusing on how nongovernmental organizations drive voluntary disclosure. To address this deficiency, this paper charts how financial regulators came to embrace climate risk, analyzing how an array of non‐state initiatives became coordinated in highlighting climate‐related impairment risks. This coordination is conceptualized via scholarship on decentered regulation, allowing a first, theoretical, contribution by constructing and demonstrating one analytical approach to studying substantive change on sustainability. This paper draws on a 25‐month participant observation of a United Nations standard‐setting project, supported by semi‐structured interviews. This allows a second, empirical, contribution by mapping how an accounting device, the so‐called “carbon budget” (the maximum amount of cumulative greenhouse gas emissions that limits the probability of exceeding 2°C of warming to 20%), coordinated this array of non‐state action toward resolving a core trade‐off: if we burn our current fossil fuel reserves, we will exceed our warming targets. The paper then shows how these coordinated efforts pressured regulatory authorities to intervene on how finance affects and is affected by climate change.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".