Hedging the Planet: The Demand for Global Governance in Sustainable Finance
Bibliographic record
Abstract
Sustainable finance governance, or the rules and processes by which the financial sector navigates issues like climate change, has seen enormous expansion over the last twenty years, with over one hundred related global initiatives launched. Counter to what existing scholarship might predict, financial institutions have been participating, by the thousands, in efforts governed by high-profile environmental authorities with little sway over traditional financial affairs. This dissertation seeks to understand why financial actors participate, and why some more than others. I approach these questions in three movements, first focusing on national contexts, then organizational perspectives, and finally, public policy processes. In this first movement, I deductively derive hypotheses and use quantitative analysis to examine patterns of industry-wide participation at the country level. I find that wealthier liberal market economies with more environmental NGOs tend to precipitate greater initiative involvement, but much of the overall variation is not explained at the national level of analysis. To go beyond the limitations of existing explanations in the literature, the second and third movements of the dissertation proceed abductively in a theory-building exercise, bringing together concepts and qualitative data to make sense of the phenomenon. At the organizational level, I argue that financial actors seek out collective action to navigate the ambiguity and uncertainty that environmental problems create for the economy. Collective action distributes high-fixed costs, brings in external expertise to bolster credibility, and mitigates the risks of being singled out for failure. Whether a given organization seeks to innovate new responses depends on the stakeholder pressures they experience to act on these issues, relative to the adjustment costs that searching for solutions and implementing them entails. I evaluate this argument using four comparative initiative cases: the Paris Aligned Investment Initiative, The Net Zero Asset Managers Initiative, The Net-Zero Banking Alliance, and the Net-Zero Insurance Alliance. In the final empirical movement, I investigate the role of public policy in creating demand for initiatives. I argue that, in a positive feedback dynamic, participation trends increase the likelihood that a governmental response on sustainable finance issues materializes, which in turn can position initiatives as means of compliance, preemption, or venues to assist in navigating new rules. I demonstrate the validity of this argument, and the mechanisms by which such a process occurs, using the case of the European Union’s Action Plan on Financing Sustainable Growth, from 2014-2022. In total, this dissertation contributes to understanding conditions for business power in emerging issue areas, the forces driving the politics of sustainable finance, and the interactions between public and private authorities in evolving regime complexes.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".