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

Davidescu, Adriana Anamaria; Bîrlan, Ioana; Manta, Eduard Mihai; Geambaşu, Cristina Maria

doi:10.2478/picbe-2025-0117

Abstract

The integration of Artificial Intelligence (AI), including Machine Learning (ML) and Natural Language Processing (NLP), into Environmental, Social, and Governance (ESG) frameworks and sustainable finance has gained significant academic and industry attention. This study presents a bibliometric analysis of research at the intersection of AI, ESG, and sustainable finance, using Web of Science data from 2004 to 2025. Our analysis maps the evolution of research trends, identifies key authors, influential publications, and collaboration networks, and explores the intellectual structure of the field. Using co-citation analysis, keyword co-occurrence mapping, and thematic clustering, we reveal how AI-driven methodologies have shaped ESG assessments, sustainable investment strategies, and financial decision-making. Findings highlight a sharp rise in AI applications in ESG post-2015, particularly in ESG risk modelling, climate analytics, and AI-driven reporting. This study provides a comprehensive overview of the evolving academic discourse, identifying emerging research directions and challenges related to data standardization, transparency, and ethical AI applications in sustainability.

References

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
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
Broadus, R. (1987). Toward a definition of “bibliometrics”. Scientometrics, 12(5-6), 373–379. https://doi.org/10.1007/BF02016680.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
Doddipatla, L. (2024). Sustainable finance with AI: Leveraging data-driven insights for green investments. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of documentation, 25, 348.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
United Nations. (2004). Who Cares Wins: Connecting Financial Markets to a Changing World. United Nations Global Compact.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article

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

Abstract

Paradigm

My account