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Leveraging Machine Learning to Assess the Impact of Energy Consumption on Global GDP Growth: What Actions should be taken Globally toward Environmental Concerns?

Author

Listed:
  • Mohamed F. Abd El-Aal

    (Department of Economics, Faculty of Commerce, Arish University, North Sinai, Egypt)

  • Hasan Amin Mohamed Mahmoud

    (Department of Economics, Faculty of Commerce, Aswan University, Egypt)

  • Abdelsamiea Tahsin Abdelsamiea

    (Department of Economics, Faculty of Commerce, Mansoura University, Egypt)

  • Marwa Samir Hegazy

    (Department of Economics, Faculty of Commerce, Mansoura University, Egypt)

Abstract

The study aims to explore the impact of renewable, nonrenewable, and nuclear energy consumption on global gross domestic product (GDP) growth through machine learning algorithms. The findings reveal that renewable energy consumption is the most influential variable, contributing to a predicted 67.5% global GDP growth. In contrast, nuclear energy consumption contributes 17.8%, and non-renewable energy consumption contributes 14.6%. Notably, the relationship between nuclear energy consumption and global economic growth is positive; there is a negative relation in conjunction with renewable energy consumption. However, the association with non-renewable energy is consistently fixed. These results suggest that an increased reliance on renewable energy may necessitate a trade-off, potentially leading to a reduction in global GDP growth despite the positive contributions from renewable sources.

Suggested Citation

  • Mohamed F. Abd El-Aal & Hasan Amin Mohamed Mahmoud & Abdelsamiea Tahsin Abdelsamiea & Marwa Samir Hegazy, 2024. "Leveraging Machine Learning to Assess the Impact of Energy Consumption on Global GDP Growth: What Actions should be taken Globally toward Environmental Concerns?," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 108-115, July.
    • Handle: RePEc:eco:journ2:2024-04-10
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    References listed on IDEAS

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    Cited by:

    1. Irem Ersöz Kaya & Suna Korkmaz, 2025. "Empirical Analysis of the Energy–Growth Nexus with Machine Learning and Panel Causality: Evidence from Disaggregated Energy Sources," Sustainability, MDPI, vol. 17(19), pages 1-29, September.

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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