Cryptocurrency energy consumption: Analysis, global trends and interaction

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The rapid spread of cryptocurrencies is one of the most relevant trends today. One of the significant risks of their spread is the increase in energy consumption, which has a negative impact on the environment due to carbon emissions. This requires the development of a scientific toolkit for assessing relationships and predicting the impact of cryptocurrencies on energy consumption, which is the aim of this paper.
With the correlational regression analysis, the model of the dependence of spending on IT sector, energy consumption of Bitcoin, Ethereum and global capitalization of the cryptocurrency market was conducted, based on statistical data from Statista.com, Сoinmarketcap.com and International Data Corporation. To check the possible relationship, tests for the adequacy of the results obtained (Fisher’s test, Student’s t-test) confirmed the correctness of coefficients for independent variables.
The results showed a significant direct correlation (Multiple R is 95%) of spending on IT sector, energy consumption and global capitalization of the cryptocurrency market. The established relationships allowed predicting that Bitcoin energy consumption may reach 142 Terawatt hours per year in 2026. And its impact on environment by mining in 2022 was at least 27.4 Mt of CO2 emission.
As a proposal, a conclusion was made on the expediency of linking mining to the use of certain sources of electricity production, such as “residual” natural gas, nuclear power, renewable energy sources. The obtained results and conclusions may be used as a basis for political decisions in the field of energy efficiency and climate change mitigation.

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    • Figure 1. Bitcoin energy consumption index
    • Figure 2. Comparison of real and predicted data of spending on digital transformation technologies and services worldwide
    • Figure 3. Prediction of Bitcoin energy consumption worldwide in 2023–2026 (tWh)
    • Table 1. CO2 emission in main Bitcoin mining centers
    • Table 2. Input data for regression analysis
    • Table 3. Collinearity checking
    • Table 4. Summary output for the model
    • Table 5. Fisher’s test checking
    • Table 6. Coefficients for the model and the T-test checking
    • Conceptualization
      Yevhen Bublyk, Anna Hlazova
    • Formal Analysis
      Yevhen Bublyk, Olena Borzenko
    • Project administration
      Yevhen Bublyk
    • Supervision
      Yevhen Bublyk
    • Writing – original draft
      Yevhen Bublyk, Anna Hlazova
    • Investigation
      Olena Borzenko
    • Methodology
      Olena Borzenko, Anna Hlazova
    • Software
      Olena Borzenko
    • Validation
      Olena Borzenko
    • Visualization
      Olena Borzenko, Anna Hlazova
    • Writing – review & editing
      Olena Borzenko
    • Data curation
      Anna Hlazova