Analyzing Trends in Green Financial Instrument Issuance for Climate Finance in Capital Markets
Bibliographic record
Abstract
Numerous stakeholders concur that tackling the climate change effect requires massive financial mobilization from the public and private sectors to reduce the climate financing gap. Capital markets are among the key players fostering this mobilization by issuing green financial instruments and facilitating capital flows to green investments. The study aimed to conduct a bibliometric analysis to fill a knowledge gap by evaluating the status and linkages in the literature on capital markets’ green financial instrument issuances. We utilized the Bibliometrix R package and VOS viewer to analyze 314 relevant publications from the Web of Science in 2017–2023 following the Sustainable Stock Exchanges’ green finance voluntary action plan. The analysis entailed mapping the scientific production trends, journal significance, author productivity, keyword linkages, emerging and trending topics, and collaborations within social structures. Further, the study assessed the applicability of Bradford’s, Zipf’s, and Lotka’s bibliometric laws. We highlight six conclusions based on the analysis, their relevance to various stakeholders, and future research directions. The findings are essential in enhancing the decision-making process of policymakers, corporations, responsible investors, and researchers interested in understanding the effectiveness and impact of green financial instruments.
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.075 | 0.113 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".