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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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  • Kahia, Montassar
  • Moulahi, Tarek
  • Mahfoudhi, Sami
  • Boubaker, Sabri
  • Omri, Anis

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.

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  • Kahia, Montassar & Moulahi, Tarek & Mahfoudhi, Sami & Boubaker, Sabri & Omri, Anis, 2022. "A machine learning process for examining the linkage among disaggregated energy consumption, economic growth, and environmental degradation," Resources Policy, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:jrpoli:v:79:y:2022:i:c:s0301420722005475
    DOI: 10.1016/j.resourpol.2022.103104
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