Sustainable Finance and AI

References

[1] Ahmad, H., Yaqub, M., & Lee, S. H. (2024). Environmental-, social-, and governance-related factors for business investment and sustainability: A scientometric review of global trends. Environment, Development and Sustainability, 26(2), 2965-2987.

[2] Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834.

[3] Ahmed, S., Alshater, M. M., El Ammari, A., & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646.

[4] Al Qudah, I., Hashem, I., Soufyane, A., Chen, W., & Merabtene, T. (2022, July). Applying latent dirichlet allocation technique to classify topics on sustainability using Arabic text. In Science and Information Conference (pp. 630-638). Cham: Springer International Publishing.

[5] Alonso-Robisco, A., Carbo, J., & Marqués, J.M. (2022). Climate Finance Innovation: The Value of Machine Learning. Atz, U., Van Holt, T., Liu, Z. Z., & Bruno, C. C. (2023). Does sustainability generate better financial performance? review, meta-analysis, and propositions. Journal of Sustainable Finance & Investment, 13(1), 802-825.

[6] Aziz, S., Dowling, M., Hammami, H., & Piepenbrink, A. (2022). Machine learning in finance: A topic modeling approach. European Financial Management, 28(3), 744-770.

[7] Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, 120420.

[8] Boubaker, S., Goodell, J. W., Kumar, S., & Sureka, R. (2023). COVID-19 and finance scholarship: A systematic and bibliometric analysis. International Review of Financial Analysis, 85, 102458.

[9] Buckley, R. P., Zetzsche, D. A., Arner, D. W., & Tang, B. W. (2021). Regulating artificial intelligence in finance: Putting the human in the loop. Sydney Law Review, The, 43(1), 43-81.

[10] Crona, B., & Sundström, E. (2023). Sweet Spots or Dark Corners? An Environmental Sustainability View of Big Data and Artificial Intelligence in ESG. In Handbook of Big Data and Analytics in Accounting and Auditing (pp. 105-131). Singapore: Springer Nature Singapore.

[11] Bui, T. D., Ali, M. H., Tsai, F. M., Iranmanesh, M., Tseng, M. L., & Lim, M. K. (2020). Challenges and trends in sustainable corporate finance: A bibliometric systematic review. Journal of Risk and Financial Management, 13(11), 264.

[12] Ferraro, G., Ramponi, A., & Scarlatti, S. (2024). Fintech meets Industry 4.0: a systematic literature review of recent developments and future trends. Technology Analysis & Strategic Management, 36(8), 1911-1927.

[13] Kashi, A., & Shah, M. E. (2023). Bibliometric review on sustainable finance. Sustainability, 15(9), 7119

[14] Kasmawati, K., Siregar, I. F., Munika, R., & Rahmawati, R. (2024). Growth and Collaboration in Sustainable Finance Literature: Bibliometric Analysis. Journal of Law and Sustainable Development, 12(1), 01-26.

[15] Kasmawati, K., Siregar, I. F., Munika, R., & Rahmawati, R. (2024). Growth and Collaboration in Sustainable Finance Literature: Bibliometric Analysis. Journal of Law and Sustainable Development, 12(1), 01-26.

[16] Kaur, A., & Tanwar, A. (2024). Adoption Potential and Challenges of Artificial Intelligence in Banking. In Sustainable Investments in Green Finance (pp. 271- 293). IGI Global.

[17] Park, J., Choi, W., & Jung, S. U. (2022). Exploring trends in environmental, social, and governance themes and their sentimental value over time. Frontiers in Psychology, 13, 890435

[18] Purnomo, A., Sari, A. K., Susanti, T., Mannan, S. S. A., & Lumentut, T. M. B. A. (2021, April). Sustainable finance study of bibliometric overview. In IOP Conference Series: Earth and Environmental Science (Vol. 729, No. 1, p. 012124). IOP Publishing

[19] Ramadhani, D. (2019). Understanding environment, social and governance (ESG) factors as path toward ASEAN sustainable finance. APMBA (Asia Pacific Management and Business Application), 7(3), 147-162.

[20] Rotaru, C. S. F. (2019). Challenges and Opportunities for Sustainable Finance. Journal of Contemporary Issues in Business & Government, 25(1).

[21] Ryszawska, B. (2016). Sustainability transition needs sustainable finance. Copernican Journal of Finance & Accounting, 5(1), 185-194.

[22] Saxena, A., Singh, R., Gehlot, A., Akram, S. V., Twala, B., Singh, A., ... & Priyadarshi, N. (2022). Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape. Sustainability, 15(1), 309

[23] Sciarelli, M., Cosimato, S., Landi, G., & Iandolo, F. (2021). Socially responsible investment strategies for the transition towards sustainable development: The importance of integrating and communicating ESG. The TQM Journal, 33(7), 39-56.

@article{1708512,
      author = {Raijo Nirmal},
      title = {Sustainable Finance and AI},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {992-1001},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708512.pdf},
      abstract = {Sustainable finance refers to the consideration of environmental, social, and governance (ESG) factors in financial decision-making. This paper proposes to create a scientometric analysis of sustainable finance with the aid of AI and text analytics. The data of the abstract and title text were extracted from a sample of 317 research articles that had been retrieved from the ProQuest database until August 22, 2023. This study applies one of the important AI methods, that is, text mining, to systematically analyze and extract knowledge from unstructured text data. The findings of the study are bifurcated into two aspects: (1) title and abstract text data and (2) author/publication-related information. Based on the title and abstract data, word frequency analysis of the most common words used in these studies is obtained through a word cloud. Opposite to these, the least common words are identified through TF-IDF. Correlations between the words were computed and shown through correlation graphs, along with additional correlations between keywords and other words. Significant themes were generated via LDA graphs for topic modeling. The second part of the results concerns author/publication-related information, such as influential authors through authors' word cloud, collaborating authors through authors' correlation graphs, the origin of countries, how many papers are published each year, the place of publications, and top journals related to sustainable finance. This study provides valuable insights into the current state of research; identifies critical trends, voids, and opportunities in sustainable finance research; and provides insight into the future of sustainable finance research.},
      keywords = {ESG, Text Analytics, Scientometric Analysis, Wordcloud, TF-IDF},
      month = {May},
  }