Growth and Collaboration in Sustainable Finance Literature: Bibliometric Analysis
Bibliographic record
Abstract
Objective: Research in the field of sustainable finance aims to understand the development and trends of sustainable finance over time and the relationship of keywords related to sustainable finance and research developments with authors who are very influential in further research. This research helps identify projects or sectors that contribute positively to sustainability and identify environmental and social risks that may result from investment activities. Additionally, to encourage innovation and development of financial products that support sustainability goals. Theoritical framework: Sustainable finance promotes sustainable business practices, including transparency, prevention of human rights violations, diversity, and positive societal contributions. The greenwashing phenomenon occurs a lot nowadays, where companies or products claim to have a positive or sustainable environmental impact, but the reality is inconsistent with these claims. Enhancing supervision, transparency, and strict sanctions are crucial to address these issues. Efforts are necessary to increase understanding and education about sustainable finance so that more parties can take relevant actions. Methods: Bibliometric analysis, there are dozens of tools to collect and analyze data. In this research, the tool to measure sustainable finance trends is Scopus, one of the popular academic databases for bibliometric analysis. This tool ensures access to scholarly journals, conferences, and other academic literature. Scopus offers rich information on publications, citations, citation index, and other metrics for bibliometric analysis. VOS viewer is a visualization tool to visualize collaboration networks, keyword clustering, and citation patterns in bibliometric analysis. Result & Conclusion: English is the most widely used language, with 644 total publications or 96.55% of Russian, French, German, Italian, Spanish and Ukrainian. In 2020, the publication trends related to sustainable finance were the most researched at 77 publications. It is identified that in 2022 the emergence of climate risks and opportunities associated with climate change will continue to be the research focus. There is a yellow cluster signifying the novelty associated with sustainable finance, i.e., Nigeria, New Zealand, Greece, and Finland. The second cluster is marked in light green. In 2021, sustainable finance research will be carried out in Italy, Germany, Spain, China, Bahrain, Malaysia and Indonesia. Furthermore, the third cluster marked in solid green in 2020, the United Kingdom dominates research, and the last cluster in purple in 2019 includes Switzerland, Denmark, Brazil, Canada, the United States, and South Africa. Implications: Implications of this study is Sustainable finance entails managing risks and uncertainties associated with environmental and social factors. Measuring and managing these risks involve assumptions and predictions that may have uncertainties. Contribution / Originality: Originality in this research is understanding the development, trends of sustainable finance over time, and understanding the relationship of keywords related to sustainable finance, and the advancement of research with authors who are prominent in further study.
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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 | 0.010 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.209 | 0.269 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".