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

Author

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 = EM Normandie Business School, VNU - Vietnam National University [Hanoï], Swansea University)

  • A. Omri

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

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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    Cited by:

    1. Mohammed Alhashim & Mohd Ziaur Rehman & Shoaib Ansari & Parvez Ahmed, 2024. "Examining the Influence of Renewable Energy Consumption, Technological Innovation, and Export Diversification on Economic Growth: Empirical Insights from E-7 Nations," Sustainability, MDPI, vol. 16(21), pages 1-19, October.
    2. Mohamad Taghvaee, Vahid & Saboori, Behnaz & Soretz, Susanne & Magazzino, Cosimo & Tatar, Moosa, 2024. "Renewable energy, energy efficiency, and economic complexity in the middle East and North Africa: A panel data analysis," Energy, Elsevier, vol. 311(C).
    3. Omri, Henda & Jarraya, Bilel & Kahia, Montassar, 2025. "Green finance for achieving environmental sustainability in G7 countries: Effects and transmission channels," Research in International Business and Finance, Elsevier, vol. 74(C).
    4. Wang, Xiaoyi & Chen, Guanqun & Afshan, Sahar & Awosusi, Abraham Ayobamiji & Abbas, Shujaat, 2023. "Transition towards sustainable energy: The role of economic complexity, financial liberalization and natural resources management in China," Resources Policy, Elsevier, vol. 83(C).
    5. Anis Omri & Montassar Kahia, 2024. "Natural Resources Abundance and Human Well-Being: the Role of Institutional Quality," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 173(3), pages 607-644, July.
    6. Adebayo, Tomiwa Sunday & Saeed Meo, Muhammad & Özkan, Oktay, 2024. "Scrutinizing the impact of energy transition on GHG emissions in G7 countries via a novel green quality of energy mix index," Renewable Energy, Elsevier, vol. 226(C).
    7. 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.
    8. Tissaoui, Kais & Zaghdoudi, Taha, 2025. "Against a background of energy uncertainty and climate change, is there a substitution effect between fossil fuels in OECD countries?," Energy, Elsevier, vol. 320(C).

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