1 Independent Researcher, Maryland, USA.
2 Electrical Engineering, The University of Nebraska-Lincoln, USA.
3 FieldCore Canada, part of GE Vernova, Canada.
4 Department of Metallurgical and Materials Engineering, The University of Alabama, USA.
5 Hult International Business School, USA.
6 Niger Delta Power Holding Company, Akure, Nigeria.
Review Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 1791-1801
Article DOI: 10.30574/wjarr.2024.21.2.0620
Received on 14 January 2024; revised on 25 February 2024; accepted on 27 February 2024
As the world grapples with the urgent need for sustainable development, the transition towards green energy stands as a critical imperative. Financing this transition poses significant challenges, requiring innovative approaches that align financial objectives with environmental sustainability goals. This review presents a conceptual framework for leveraging data-driven techniques in sustainable finance to facilitate the transition towards green energy. The proposed framework integrates principles of sustainable finance with advanced data analytics to enhance decision-making processes across the financial ecosystem. At its core, the framework emphasizes the importance of harnessing vast datasets related to energy production, consumption, environmental impact, and financial performance. By leveraging machine learning algorithms and predictive modeling techniques, financial stakeholders can gain deeper insights into the risks and opportunities associated with green energy investments. Key components of the framework include data collection and aggregation, risk assessment, impact measurement, and investment optimization. Data sources range from traditional financial indicators to environmental metrics, social impact assessments, and geopolitical factors. Through comprehensive data analysis, financial institutions can assess the long-term viability and sustainability of green energy projects, while also evaluating potential social and environmental impacts. Risk assessment methodologies within the framework consider both financial risks, such as market volatility and regulatory uncertainty, and non-financial risks, such as climate change impacts and community resilience. By integrating these factors into risk models, investors can make more informed decisions that mitigate potential losses and maximize returns. Furthermore, impact measurement tools enable stakeholders to quantify the environmental and social benefits of green energy investments. By tracking metrics such as carbon emissions reduction, energy efficiency improvements, and job creation, investors can assess the contribution of their portfolios towards broader sustainability objectives. Finally, the framework incorporates investment optimization strategies that align financial goals with environmental objectives. Through portfolio diversification, asset allocation, and innovative financial instruments such as green bonds and impact investing funds, financial institutions can allocate capital more efficiently towards green energy projects. The conceptual framework presented herein offers a systematic approach to integrating data-driven methodologies into sustainable finance practices. By leveraging advanced analytics and comprehensive datasets, financial stakeholders can drive the transition towards green energy while simultaneously achieving financial returns and positive environmental outcomes.
Data-Driven; Finance; Green Energy; Transition; Sustainable; Review
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Omotayo Bukola Adeoye, Emmanuel Chigozie Ani, Nwakamma Ninduwesuor-Ehiobu, Danny Jose Montero, Favour Oluwadamilare Usman and Kehinde Andrew Olu-lawal. A conceptual framework for data-driven sustainable finance in green energy transition. World Journal of Advanced Research and Reviews, 2024, 21(2), 1791-1801. Article DOI: https://doi.org/10.30574/wjarr.2024.21.2.0620