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A Machine Learning Process for Examining the Linkage among Disaggregated Energy Consumption, Economic Growth, and Environmental Degradation

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Listed:
  • M. Kahia

    (Qassim University [Kingdom of Saudi Arabia])

  • T. Moulahi

    (Qassim University [Kingdom of Saudi Arabia])

  • S. Mahfoudhi

    (Qassim University [Kingdom of Saudi Arabia])

  • S. Boubaker

    (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie, VNU - Vietnam National University [Hanoï], Swansea University)

  • A. Omri

    (Qassim University [Kingdom of Saudi Arabia], UCAR - Université de Carthage (Tunisie))

Abstract

Improving environmental quality is at the heart of the Saudi Vision 2030. Within this context, this study seeks to extend previous environmental economics literature by examining the relationship between disaggregated energy use, economic growth, and environmental quality in Saudi Arabia using machine learning (ML) techniques. Using data from 1980 to 2020, we found that reducing CO2 emissions cannot be done in Saudi Arabia without a complete transition from fossil to renewable resources and a more viable road to sustainability. ML-based regression and prediction shows that CO2 emissions will continue to grow until 2024. Beginning in 2025 and beyond, the emissions decrease (i.e., reducing CO2 emissions) must be accompanied by an increment use of renewable energies to guarantee stable economic growth. © 2022 Elsevier Ltd

Suggested Citation

  • M. Kahia & T. Moulahi & S. Mahfoudhi & S. Boubaker & A. Omri, 2022. "A Machine Learning Process for Examining the Linkage among Disaggregated Energy Consumption, Economic Growth, and Environmental Degradation," Post-Print hal-04454686, HAL.
    • Handle: RePEc:hal:journl:hal-04454686
    DOI: 10.1016/j.resourpol.2022.103104
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