Growth and Collaboration in Sustainable Finance Literature: Bibliometric Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,037 | 0,067 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».