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Renewable energy, innovation, and stock markets: Machine learning perspectives on environmental sustainability

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  • Zhang, Zhenya
  • Chang, Zheren
  • Gan, Yufei
  • Li, Jiayan

Abstract

This study addresses a pressing yet underexplored issue in environmental economics: understanding the complex interactions between environmental sustainability and key economic drivers such as technological innovation, renewable energy, and economic complexity. Using machine learning (ML) and artificial neural networks (ANNs), the study forecasts environmental quality in Bangladesh and Pakistan by analyzing the impacts of economic growth, renewable energy, natural resources, and technological advancements. Results reveal the sensitivity of quarterly load capacity factor fluctuations to changes in stock market development and renewable energy utilization. The coefficient of determination, mean absolute percentage error, and root mean square error demonstrate that ML outperforms ANNs in terms of predictive accuracy. This study offers actionable policy recommendations to promote equitable and sustainable development in Bangladesh and Pakistan and provides new insights into the potential of advanced computational methods for environmental forecasting and policy formulation.

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  • Zhang, Zhenya & Chang, Zheren & Gan, Yufei & Li, Jiayan, 2025. "Renewable energy, innovation, and stock markets: Machine learning perspectives on environmental sustainability," International Review of Financial Analysis, Elsevier, vol. 97(C).
    • Handle: RePEc:eee:finana:v:97:y:2025:i:c:s1057521924008123
    DOI: 10.1016/j.irfa.2024.103880
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