Overlap and fragmentation in the global governance complex of sustainable finance
Bibliographic record
Abstract
Global governance initiatives addressing sustainable finance, whether for advancing climate risk disclosure or defining green bond standards, have proliferated for over 20 years. Emblematic of a larger trend of broadening global governance complexity, a key question is whether these proliferating initiatives – developed by public and private actors alike – are producing a division of labor or a duplication of efforts. Moreover, if duplication is occurring, are public initiatives more likely to contribute as compared to private or public-private initiatives? Building on the regime complexity literature, we assess these questions with an original dataset of 111 Sustainable Finance Governance Initiatives (SFGIs) established between 2000 and 2020. We expand on existing measures of institutional overlap and fragmentation by developing an approach that focuses on an initiative’s ‘governance space’, defined by an SFGI’s issue area, governance function, actor target, and time of launch. We find that while there is significant duplication in sustainable finance, the governance landscape is more characterized by a division of labor between issues, functions, and targets. Moreover, we do not find that public initiatives contribute more to duplication than other initiatives. As such, our article offers theoretical and empirical contributions to the study of global governance and the international political economy of finance.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| 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".