An Institutional Pressure and Adaptive Capacity Framework for Green Bonds: Insights from India’s Emerging Green Bond Market
Bibliographic record
Abstract
Although climate finance tools like green bonds have been gaining popularity in academia, the research has been limited to examining the financial viability and performance of this market. We explore a different research avenue related to institutional dynamics that are driving this market at the country level and shaping its adaptive capacity to climate change. Our paper introduces a new conceptual framework by linking institutional isomorphism with adaptive capacity dimensions in the green bond market. Using a mixed methods exploratory approach, we apply our institutional pressure-adaptive capacity framework to India’s green bond market. Our results show that different social actors, ranging from formal institutions like regulators and investors to informal ones like advocacy groups, can play a key role in shaping the legitimacy of this market. By highlighting ‘invisible’ social norms such as awareness about climate finance, changing regulatory priorities and the institutional strength of social actors, we contribute to the literature on this topic. We also introduce the concept of a high priority social actor and conclude that varying degrees of institutional pressure from such actors will ultimately decide the growth and legitimacy of this integral climate finance market at the country level as well as influence its adaptive capacity response to 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".