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The impact of Russian Energy Resources on the Economic Growth of the EU: Using Computational Intelligence Algorithms

Author

Listed:
  • Mohamed F. Abd El-Aal

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

  • Abdelsamiea Tahsin Abdelsamiea

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

Abstract

This study explores the impact of Russian oil and natural gas on the economic growth of the European Union. The Gradient boosting algorithm was relied on to determine this effect because of its high prediction metrics (MSE: 0.002, RMSE: 0.040, MAE: 0.034, R2: 99.9). The study depended on three scenarios. The first scenario is that Russia's exports of both products decline to half the year 2022, then to the quarter of 2023, and this second scenario, then the worst scenario, is to prevent Its exports of both products in 2024. But the result is a decline in the European Union's economic growth in 2022 to (-2.15%), then it turns to 2.85% in 2023, and then to 3.86% in 2024, i.e., in the worst scenario year. The evidence for this is that the economies of these countries reduced their growth rates in 2020 (the Covid-19 crisis) to -5.96%, which turned to positive growth in 2021, amounting to 5.38%. This indicates these economies' ability to adapt in the short term by providing alternatives to the crisis.

Suggested Citation

  • Mohamed F. Abd El-Aal & Abdelsamiea Tahsin Abdelsamiea, 2023. "The impact of Russian Energy Resources on the Economic Growth of the EU: Using Computational Intelligence Algorithms," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 597-602, November.
    • Handle: RePEc:eco:journ2:2023-06-62
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    References listed on IDEAS

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    1. Md. Kausar Alam & Mosab I. Tabash & Mabruk Billah & Sanjeev Kumar & Suhaib Anagreh, 2022. "The Impacts of the Russia–Ukraine Invasion on Global Markets and Commodities: A Dynamic Connectedness among G7 and BRIC Markets," JRFM, MDPI, vol. 15(8), pages 1-20, August.
    2. Thomas Glauben & Miranda Svanidze & Linde Götz & Sören Prehn & Tinoush Jamali Jaghdani & Ivan Đurić & Lena Kuhn, 2022. "The War in Ukraine, Agricultural Trade and Risks to Global Food Security," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 57(3), pages 157-163, May.
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    Cited by:

    1. S. Ya. Dranev & A. D. Melovatsky & Yu. Ya. Dranev, 2026. "A Comparison of the Institutional Conditions of the Largest Oil and Gas Businesses in Russia and Abroad in Response to the Shocks of 2020 and 2022," Studies on Russian Economic Development, Springer, vol. 37(1), pages 73-81, February.

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    JEL classification:

    • 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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