1 Department of Computing, Sheffield Hallam University, College of Business, Technology and Engineering, Shefield, UK.
2 Department of Computer Science, Western Illinois University, IL, USA.
3 Department of Computer Science and Mathematics, Fisk University, Nashville, TN, USA.
4 Department of Accounting and Finance, University of Ilorin, Nigeria.
5 Clarkson University, School of Business, NY, USA.
6 Independent Researcher, IT Project Manager, Toronto, Canada.
7 Department of Accounting, Finance and Economics, Bournemouth University, Bournemouth, UK.
8 Department of Mathematics and Statistics, Texas Tech University, College of Art and Science, Texas, USA.
9 Kenan-Flager Business School, University of North Carolina, North Carolina, USA.
Received on 26 June 2024; revised on 04 August 2024; accepted on 07 August 2024
The intersection of financial policies and environmental impact is a critical area of research given the urgent need to address climate change and sustainability challenges. This review article explores the current state of machine learning (ML) models used to evaluate the environmental impact of financial policies. We discuss the methodologies, applications, challenges, and future directions of this interdisciplinary field. Emphasis is placed on the integration of economic and environmental data, model interpretability, and the potential of ML to provide actionable insights for policymakers.
Machine Learning; Financial Policies; Green Technology; Carbon Pricing; Data Analytics
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Oladipo Olowe, Iyinoluwa Elizabeth Fatoki, Lucky Oneimu Abolorunke, Elebimoye Oluwatobi Samuel, Daniella Bekobo, Olayinka Mary Omole, Abdullah Tunde Adewuyi and Sunday David Esebre. Developing machine learning models to evaluate the environmental impact of financial policies. International Journal of Science and Research Archive, 2024, 12(02), 1727–1737. Article DOI: https://doi.org/10.30574/ijsra.2024.12.2.1437
