Have a personal or library account? Click to login
Artificial Intelligence in ESG and Sustainable Finance: A Bibliometric Analysis of Research Trends Cover

Artificial Intelligence in ESG and Sustainable Finance: A Bibliometric Analysis of Research Trends

Open Access
|Jul 2025

References

  1. Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716
  2. Alhasnawi, M. Y., Alshdaifat, S. M., Aziz, N. H. A., & Almasoodi, M. F. (2024). Artificial intelligence and environmental, social and governance: a bibliometric analysis review. In International Conference on Explainable Artificial Intelligence in the Digital Sustainability (pp. 123-143). Cham: Springer Nature Switzerland.
  3. Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. Proceedings of the International AAAI Conference on Web and Social Media, 3(1), 361–362. https://doi.org/10.1609/icwsm.v3i1.139.
  4. Berg, F., Kölbel, J. F., & Rigobon, R. (2022). Aggregate confusion: The divergence of ESG ratings. Review of Finance, 26(6), 1315–1344. https://doi.org/10.1093/rof/rfac033
  5. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  6. Briere, M., Keip, M., Le Berthe, T., & Nuriyev, M. (2022). Artificial intelligence for sustainable finance: Why it may help. [SSRN Working Paper No. 4252329]. SSRN. https://doi.org/10.2139/ssrn.4252329
  7. Broadus, R. (1987). Toward a definition of “bibliometrics”. Scientometrics, 12(5-6), 373–379. https://doi.org/10.1007/BF02016680.
  8. Carè, R., Fatima, R., & Boitan, I. A. (2024). Central banks and climate risks: Where we are and where we are going? International Review of Economics & Finance, 92, 1200–1229. https://doi.org/10.1016/j.iref.2024.01.024.
  9. Chytis, E., Eriotis, N., & Mitroulia, M. (2024). ESG in business research: A bibliometric analysis. Journal of Risk and Financial Management, 17(10), 460. https://doi.org/10.3390/jrfm17100460.
  10. Curi, S., Eriotis, N., & Scarpa, M. (2024). A bibliometric review of the literature on ESG performance in banks. Research in International Business and Finance, 62, Article 101686. https://doi.org/10.1016/j.ribaf.2022.101688
  11. De Giuli, M. E., Grechi, D., & Tanda, A. (2023). What do we know about ESG and risk? A systematic and bibliometric review. Corporate Social Responsibility and Environmental Management. https://doi.org/10.1002/csr.2654.
  12. Demolli, H., Dokuz, A. S., Ecemis, A., & Gokcek, M. (2019). Wind power forecasting based on daily wind speed data using machine learning algorithms. Energy Conversion and Management, 198, 111823. https://doi.org/10.1016/j.enconman.2019.111823
  13. Doddipatla, L. (2024). Sustainable finance with AI: Leveraging data-driven insights for green investments. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.
  14. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070.
  15. Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment, 5(4), 210–233. https://doi.org/10.1080/20430795.2015.1118917
  16. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
  17. Freunek, M., & Niggli, M. (2023). Introducing DynaPTI–constructing a dynamic patent technology indicator using text mining and machine learning. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1136846
  18. Galletta, S., Mazzù, S., & Naciti, V. (2022). A bibliometric analysis of ESG performance in the banking industry: From the current status to future directions. Research in International Business and Finance, 62, Article 101684. https://doi.org/10.1016/j.ribaf.2022.101684.
  19. Graetz, G., & Michaels, G. (2018). Robots at work. Review of Economics and Statistics, 100(5), 753–768. https://doi.org/10.1162/rest_a_00754
  20. Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033. https://doi.org/10.1016/j.jafr.2020.100033
  21. Khaw, T. Y., Amran, A., & Teoh, A. P. (2024). Factors influencing ESG performance: A bibliometric analysis, systematic literature review, and future research directions. Journal of Cleaner Production, 448, 141430. https://doi.org/10.1016/j.jclepro.2024.141430
  22. Moosavi, J., Fathollahi-Fard, A. M., & Dulebenets, M. A. (2022). Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies. International journal of disaster risk reduction: IJDRR, 75, 102983. https://doi.org/10.1016/j.ijdrr.2022.102983.
  23. Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl_1), 5200–5205. https://doi.org/10.1073/pnas.0307545100.
  24. Nica, I., Delcea, C., Chiriță, N., & Ionescu, Ș. (2024). Toward quantifying shadow banking and financial contagion dynamics: A bibliometric analysis. Accounting and Management Information Systems, 23(1), 261–290. https://doi.org/10.3390/jrfm17100460.
  25. Petrica, I.-M., Caraiani, C., Lungu, C. I., & Anica-Popa, L.-E. (2024). The interconnectivity of ESG research within the realm of sustainability: A bibliometric analysis. Accounting and Management Information Systems, 23(1), 261–290. https://doi.org/10.24818/jamis.2024.01012.
  26. Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of documentation, 25, 348.
  27. Radclyffe, C., Ribeiro, M., & Wortham, R. H. (2023). The assessment list for trustworthy artificial intelligence: A review and recommendations. Frontiers in Artificial Intelligence, https://doi.org/10.3389/frai.2023.1020592.
  28. Rasul, T., Lim, W. M., Dowling, M., Kumar, S., & Rather, R. A. (2022). Advertising expenditure and stock performance: A bibliometric analysis. Finance Research Letters, 50, Article 103283. https://doi.org/10.1016/j.frl.2022.103283.
  29. Roy, J. K., & Vasa, L. (2025). Financial technology and environmental, social and governance in sustainable finance: a bibliometric and thematic content analysis. Discover Sustainability, 6(1), 1-22.
  30. Tuptuk, N., & Hailes, S. (2018). Security of smart manufacturing systems. Journal of Manufacturing Systems, 47, 93–106. https://doi.org/10.1016/j.jmsy.2018.04.007.
  31. United Nations. (2004). Who Cares Wins: Connecting Financial Markets to a Changing World. United Nations Global Compact.
  32. Wang, X., & Wang, Q. (2021). Research on the impact of green finance on the upgrading of China’s regional industrial structure from the perspective of sustainable development. Resources Policy, 74, 102436. https://doi.org/10.1016/j.resourpol.2021.102436.
Language: English
Page range: 1506 - 1517
Published on: Jul 24, 2025
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Adriana Anamaria Davidescu, Ioana Bîrlan, Eduard Mihai Manta, Cristina Maria Geambaşu, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.