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Can Economic, Geopolitical and Energy Uncertainty Indices Predict Bitcoin Energy Consumption? New Evidence from a Machine Learning Approach

Author

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  • Taha Zaghdoudi

    (Management Information Systems Department, Applied College, University of Ha’il, Hail City P.O. Box 2440, Saudi Arabia
    Laboratoire de Recherche en Economie et Finance Appliquées, Carthage High Commercial Studies Institute, University of Carthage, Carthage 2085, Tunisia)

  • Kais Tissaoui

    (Management Information Systems Department, Applied College, University of Ha’il, Hail City P.O. Box 2440, Saudi Arabia)

  • Mohamed Hédi Maâloul

    (Department of Computer Science, Applied College, University of Ha’il, Hail City P.O. Box 2440, Saudi Arabia)

  • Younès Bahou

    (Department of Computer Science, Applied College, University of Ha’il, Hail City P.O. Box 2440, Saudi Arabia)

  • Niazi Kammoun

    (Economic Development Laboratory, University of Sfax, Route de l’Aéroport Km 0.5 BP 1169, Sfax 3029, Tunisia)

Abstract

This paper explores the predictive power of economic and energy policy uncertainty indices and geopolitical risks for bitcoin’s energy consumption. Three machine learning tools, SVR (scikit-learn 1.5.0),CatBoost 1.2.5 and XGboost 2.1.0, are used to evaluate the complex relationship between uncertainty indices and bitcoin’s energy consumption. Results reveal that the XGboost model outperforms both SVR and CatBoost in terms of accuracy and convergence. Furthermore, the feature importance analysis performed by the Shapley additive explanation (SHAP) method indicates that all uncertainty indices exhibit a significant capacity to predict bitcoin’s future energy consumption. Moreover, SHAP values suggest that economic policy uncertainty captures valuable predictive information from the energy uncertainty indices and geopolitical risks that affect bitcoin’s energy consumption.

Suggested Citation

  • Taha Zaghdoudi & Kais Tissaoui & Mohamed Hédi Maâloul & Younès Bahou & Niazi Kammoun, 2024. "Can Economic, Geopolitical and Energy Uncertainty Indices Predict Bitcoin Energy Consumption? New Evidence from a Machine Learning Approach," Energies, MDPI, vol. 17(13), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3245-:d:1427213
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    References listed on IDEAS

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