Emerging Research Trends in Green Finance: A Bibliometric Overview
Bibliographic record
Abstract
Green finance is significant since it is the first organized effort by the financial industry to link financial performance with a positive environmental impact. Green finance products are being developed appropriately to achieve sustainability. The present study employs a fundamental bibliometric methodology to assess the current state and progress of academic research on green finance. 1748 papers are taken for this study. Data are extracted from a scholarly database i.e., SCOPUS and for network analysis, VOSviewer software is used. The present paper is focused on six research questions. Information is gathered to examine the above research questions and network maps are applied. We examined year-wise document publications, types of documents, subject areas, most influential articles, different journal sources, co-authorship of countries, and co-occurrence of keywords of green finance. We categorized keywords into clusters and discovered new trends in green finance. The paper also highlighted the recent issues and challenges. The study has also certain limitations and it is concluded by providing implications and suggestions for future studies. At last, this paper will give more insights to researchers, academicians, and others to discover the research gaps in this field of green finance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.055 | 0.066 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".