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

Taha Zaghdoudi, Kais Tissaoui (), Mohamed Hédi Maâloul, Younès Bahou and Niazi Kammoun
Additional contact information
Taha Zaghdoudi: Management Information Systems Department, Applied College, University of Ha’il, Hail City P.O. Box 2440, Saudi Arabia
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

Energies, 2024, vol. 17, issue 13, 1-15

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.

Keywords: bitcoin energy consumption; economic policy uncertainty; geopolitical risk; energy uncertainty; energy efficiency; machine learning; Shapley additive explanation method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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