Evolution of Green Finance: Mapping Its Role as a Catalyst for Economic Growth and Innovation
Bibliographic record
Abstract
This scientometric study analyzes the evolving landscape and outlook of green finance as a driver of economic innovation and growth, highlighting key trends and influential research within this critical field. A dataset of 371 publications was compiled from the Scopus and Web of Science databases and analyzed using VOSviewer, Bibliometrix, and Voyant tools to map the research landscape. By systematically reviewing the scientific literature, this research tracks the development of green finance’s role as a catalyst for economic innovation and growth, identifying trending topics, key studies, and major contributors through bibliometric and scientometric methods. The analysis reveals a growing interdisciplinary approach, integrating environmental, social, and political dimensions into green finance research. Keyword analysis identified three primary thematic clusters: (1) green finance and innovation, (2) economic growth, carbon neutrality, and fintech, and (3) renewable energy and urbanization. This study provides a comprehensive overview of the field and aims to guide future research while contributing to ongoing debates on the role of green finance in fostering economic innovation and sustainable growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.071 | 0.125 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".