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Developing machine learning models to evaluate the environmental impact of financial policies

Year: 2024
Type: article
Abstract: 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.
DOI: 10.30574/ijsra.2024.12.2.1437
Authors: Oladipo Olowe, Iyinoluwa Elizabeth Fatoki, Lucky Oneimu Abolorunke, Elebimoye Oluwatobi Samuel, Daniella Bekobo, Olayinka Mary Omole, Abdullah Tunde Adewuyi, Sunday David Esebre, Victoria Porter
Institutions: Sheffield Hallam University, Western Illinois University, Fisk University, University of Ilorin, Clarkson University, Bournemouth University, Texas Tech University, University of North Carolina at Chapel Hill
Language: English
Subfield: Finance
Open Access status: hybrid
OpenAlex ID: W4401515294
FWCI: 1.824
Cites:
Cited by: 5
Related to: 10