Bibliometrics Analysis of Green Financing Research
Bibliographic record
Abstract
The study investigates the trends and dynamics in green financing using bibliometric analysis techniques.Green financing has gained momentum due to its alignment with global environmental concerns and sustainability goals.It involves funding economic endeavors that offer ecological benefits, particularly in mitigating adverse impacts on environmental sustainability and global warming.The study explores various aspects of green financing research, including its publication trends, geographical distribution, prominent journals, leading institutions, authors, and thematic clusters.The methodology employs bibliometric analysis utilizing Scopus and VOSviewer tools to discern patterns and advancements in Green Financing.The research identifies key clusters of themes in green financing, such as alternative energy, carbon emissions, economic development, and sustainable investment.The study highlights the significance of green financing in addressing environmental challenges, fostering innovation, and driving sustainable economic growth.The top journals, institutions, and authors contributing to the field are also identified, focusing on their affiliations and countries of origin.Through comprehensive analysis, the study aims to provide insights into the trajectory of green financing research, facilitate future research directions, and contribute to advancing sustainable finance practices.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.200 | 0.260 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".