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Bitcoin price evolution versus energy consumption; trend analysis

Author

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  • Crina Anina Bejan
  • Dominic Bucerzan
  • Mihaela Daciana Crăciun

Abstract

Digital technology developments shape the behaviour, performances, standards of society, organizations and individuals imposing new ways of payments and new forms of money. In this environment in 2008 was developed a new type of currency, namely Bitcoin. Cryptocurrency, as this new form of money has been generically called, puts pressure on the traditional concept of money. Today, the economic value of cryptocurrencies is attested by their circulation and acceptance by user communities for trade. However, establishing this value raises debates in the literature. The research from this paper investigates and analyses if there is a strong enough connectedness between Bitcoin price evolution and energy consumption tendency (for mining), to influence Bitcoin value. Public data from January 2014 to July 2021 is used. An Artificial Neural Network (ANN) was used to study and predict the tendency of Bitcoin price and energy consumption. A comparison between the forecasting trend and the real trend (the evolution of energy consumption and Bitcoin price) was made. The conducted research starts with a quantitative one and ends with a qualitative one (trends). The obtained results show that qualitatively, there is a good correlation between monthly average values of BTC prices and electricity consumption for mining.

Suggested Citation

  • Crina Anina Bejan & Dominic Bucerzan & Mihaela Daciana Crăciun, 2023. "Bitcoin price evolution versus energy consumption; trend analysis," Applied Economics, Taylor & Francis Journals, vol. 55(13), pages 1497-1511, March.
  • Handle: RePEc:taf:applec:v:55:y:2023:i:13:p:1497-1511
    DOI: 10.1080/00036846.2022.2097194
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    Cited by:

    1. 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.

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