اقتصاد و سیاست گذاری مالی

اقتصاد و سیاست گذاری مالی

AI-Enabled Decision-Making in Green Finance: A Structured Review of Capabilities, Tensions, and Research Directions

نوع مقاله : مقاله پژوهشی

نویسندگان
Sina Rad
Mahdi Khazaei
Departmant of Managment, Faculty of UNF, University of Niagara Falls Canada, Canada
10.22034/efp.2026.2089218.1048
چکیده
Artificial intelligence (AI) is reshaping how financial institutions identify, evaluate, price, and allocate capital toward environmentally oriented activities. Yet scholarship on AI-enabled green-finance decision-making remains fragmented across finance, accounting, information systems, computer science, and sustainability research, and no prior review has organized this evidence around the decision tasks that financial actors actually perform. This systematic literature review (SLR), conducted in accordance with the PRISMA 2020 guidelines, synthesizes 107 peer-reviewed journal articles published between January 2010 and May 2026, retrieved from Scopus and Web of Science using a pre-specified three-block search strategy and screened by two independent reviewers (Cohen's kappa = 0.81). We develop an integrative framework that links AI capabilities, green-finance decision tasks, governance filters, and outcome criteria, and we organize the corpus around six decision-task clusters: ESG scoring and sustainability assessment, green credit and lending decisions, climate-aware portfolio construction, sustainability-disclosure analysis, green-bond and carbon-market applications, and algorithmic governance and Green AI. Three cross-cutting tensions structure the field: the trade-off between predictive accuracy and interpretability, the problem of contested and inconsistent sustainability data, and the environmental footprint of AI itself. Our central contribution is a re-framing of the debate from whether AI improves prediction to when AI improves green-finance decisions under conditions of data uncertainty, model opacity, and sustainability accountability. We translate the synthesis into a six-item research agenda covering decision-level validation, causal identification, explainable and energy-aware modelling, greenwashing detection, cross-jurisdictional comparability, and Green-AI governance. The review provides scholars, practitioners, and regulators with the first decision-centred map of an interdisciplinary field that has, until now, lacked a unifying frame.

چکیده تصویری

AI-Enabled Decision-Making in Green Finance: A Structured Review of Capabilities, Tensions, and Research Directions
کلیدواژه‌ها
Artificial Intelligence
Machine Learning
Green Finance
Sustainable Finance
ESG
Climate Risk
Green AI
Alonso-Robisco, A., Bas, J., Carbó, J. M., de Juan, A., & Marqués, J. M. (2025). Where and how machine learning plays a role in climate finance research. Journal of Sustainable Finance & Investment, 15(2), 456–497. https://doi.org/10.1080/20430795.2024.2370325.
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.
Beunza, D., & Stark, D. (2004). Tools of the trade: The socio-technology of arbitrage in a Wall Street trading room. Industrial and Corporate Change, 13(2), 369–400. https://doi.org/10.1093/icc/dth015. .
Bingler, J. A., Kraus, M., Leippold, M., & Webersinke, N. (2022). Cheap talk and cherry-picking: What ClimateBERT has to say on corporate climate risk disclosures. Finance Research Letters, 47, 102776. https://doi.org/10.1016/j.frl.2022.102776. .
Bolton, P., & Kacperczyk, M. (2021). Do investors care about carbon risk? Journal of Financial Economics, 142(2), 517–549. https://doi.org/10.1016/j.jfineco.2021.05.008.
Bolton, P., & Kacperczyk, M. (2023). Global pricing of carbon-transition risk. Journal of Finance, 78(6), 3677–3754. https://doi.org/10.1111/jofi.13272.
Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57(1), 203–216. https://doi.org/10.1007/s10614-020-10042-0.
Cao, L. (2022). AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55(3), Article 64. https://doi.org/10.1145/3502289.
Carlei, V., Furia, D., Ceccarelli, A., & Cascioli, P. (2025). Outperforming ESG stocks portfolio: A machine learning ranking model with CatBoost regressor. The North American Journal of Economics and Finance, 80, 102517. https://doi.org/10.1016/j.najef.2025.102517.
Černevičienė, J., & Kabašinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: A systematic literature review. Artificial Intelligence Review, 57, Article 216. https://doi.org/10.1007/s10462-024-10854-8.
D'Amato, V., D'Ecclesia, R., & Levantesi, S. (2022). ESG score prediction through random forest algorithm. Computational Management Science, 19(2), 347–373. https://doi.org/10.1007/s10287-021-00419-3.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 4171–4186). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423.
di Pietro, F., Giráldez-Puig, P., Palos-Sánchez, P. R., & Korzeb, Z. (2026). Artificial intelligence in climate and sustainable finance: A blessing or a curse? Journal of Economic Surveys. Advance online publication. https://doi.org/10.1111/joes.70075.
Elbouknify, I., Machado, M. R., & Iannario, M. (2026). Designing green artificial intelligence (Green AI) models for finance: A novel approach for sustainable and responsible adoption. Financial Innovation, 12, Article 96. https://doi.org/10.1186/s40854-026-00915-y.
Flammer, C. (2021). Corporate green bonds. Journal of Financial Economics, 142(2), 499–516. https://doi.org/10.1016/j.jfineco.2021.01.010.
Giudici, P., & Wu, L. (2025). Sustainable artificial intelligence in finance: Impact of ESG factors. Frontiers in Artificial Intelligence, 8, Article 1566197. https://doi.org/10.3389/frai.2025.1566197.
Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009.
Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226–251. https://doi.org/10.1016/j.eswa.2019.01.012.
Huang, A. H., Wang, H., & Yang, Y. (2023). FinBERT: A large language model for extracting information from financial text. Contemporary Accounting Research, 40(2), 806–841. https://doi.org/10.1111/1911-3846.12832.
Huynh, T. L. D., Hille, E., & Nasir, M. A. (2020). Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies. Technological Forecasting and Social Change, 159, 120188. https://doi.org/10.1016/j.techfore.2020.120188.
Ilhan, E., Krueger, P., Sautner, Z., & Starks, L. T. (2023). Climate risk disclosure and institutional investors. Review of Financial Studies, 36(7), 2617–2650. https://doi.org/10.1093/rfs/hhad002.
Krappel, T., Bogun, A., Borth, D., Schimmelpfennig, T., & Stiehl, S. (2021). Heterogeneous ensemble for ESG ratings prediction. Proceedings of the KDD Workshop on Machine Learning in Finance. arXiv preprint arXiv:2109.10085.
Krueger, P., Sautner, Z., & Starks, L. T. (2020). The importance of climate risks for institutional investors. Review of Financial Studies, 33(3), 1067–1111.https://doi.org/10.1093/rfs/hhz137.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310.
Liaras, E., Nerantzidis, M., & Alexandridis, A. (2024). Machine learning in accounting and finance research: A literature review. Review of Quantitative Finance and Accounting, 63(4), 1431–1471. https://doi.org/10.1007/s11156-024-01306-z.
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, 30, 4765–4774. Curran Associates.
Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372(71). https://doi.org/10.1136/bmj.n71.
Pástor, Ľ., Stambaugh, R. F., & Taylor, L. A. (2021). Sustainable investing in equilibrium. Journal of Financial Economics, 142(2), 550–571. https://doi.org/10.1016/j.jfineco.2020.12.011.
Paul, J., Lim, W. M., O'Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). International Journal of Consumer Studies, 45(4), O1–O16. https://doi.org/10.1111/ijcs.12695.
Pedersen, L. H., Fitzgibbons, S., & Pomorski, L. (2021). Responsible investing: The ESG-efficient frontier. Journal of Financial Economics, 142(2), 572–597. https://doi.org/10.1016/j.jfineco.2020.11.001.
Pollock, N., & Williams, R. (2010). The business of expectations: How promissory organizations shape technology and innovation. Social Studies of Science, 40(4), 525–548. https://doi.org/10.1177/0306312710362275.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). ACM. https://doi.org/10.1145/2939672.2939778.
Sautner, Z., van Lent, L., Vilkov, G., & Zhang, R. (2023). Firm-level climate change exposure. Journal of Finance, 78(3), 1449–1498. https://doi.org/10.1111/jofi.13219.
Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. https://doi.org/10.1145/3381831.
Seow, R. Y. C. (2025). Transforming ESG analytics with machine learning: A systematic literature review using TCCM framework. Corporate Social Responsibility and Environmental Management, 32(6), 7358–7389. https://doi.org/10.1002/csr.70089.
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039.
Starks, L. T. (2023). Presidential address: Sustainable finance and ESG issues — Value versus values. Journal of Finance, 78(4), 1837–1872. https://doi.org/10.1111/jofi.13255.
Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1355.
Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8, Article 45.https://doi.org/10.1186/1471-2288-8-45.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375.
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11, Article 233. https://doi.org/10.1038/s41467-019-14108-y.
Webersinke, N., Kraus, M., Bingler, J. A., & Leippold, M. (2022). ClimateBERT: A pretrained language model for climate-related text. SSRN Working Paper No. 4229146. https://doi.org/10.2139/ssrn.4229146.
Zhang, D., Zhang, Z., & Managi, S. (2019). A bibliometric analysis on green finance: Current status, development, and future directions. Finance Research Letters, 29, 425–430. https://doi.org/10.1016/j.frl.2019.02.003.
Zhang, H., Hu, Z., & Ji, Q. (2024). Green bond and yield spread: Evidence from machine learning. Energy Economics, 132, 107473. https://doi.org/10.1016/j.eneco.2024.107473.
 
اقتصاد و سیاست گذاری مالی
دوره 3، Special Issue
زمستان 1405
صفحه 407-415