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Machine Learning the Carbon Footprint of Bitcoin Mining

Author

Listed:
  • Hector F. Calvo-Pardo

    (Department of Economics, Highfield Campus, University of Southampton, Southampton SO17 1BJ, UK
    Centre for Population Change (CPC), Institut Louis Bachelier (ILB), 75002 Paris, France
    Centre for Economic Policy Research (CEPR), London EC1V 0DX, UK)

  • Tullio Mancini

    (Department of Economics, Highfield Campus, University of Southampton, Southampton SO17 1BJ, UK)

  • Jose Olmo

    (Department of Economics, Highfield Campus, University of Southampton, Southampton SO17 1BJ, UK
    Department of Economic Analysis, Universidad de Zaragoza, 50009 Zaragoza, Spain)

Abstract

Building on an economic model of rational Bitcoin mining, we measured the carbon footprint of Bitcoin mining power consumption using feed-forward neural networks. We found associated carbon footprints of 2.77, 16.08 and 14.99 MtCO 2 e for 2017, 2018 and 2019 based on a novel bottom-up approach, which (i) conform with recent estimates, (ii) lie within the economic model bounds while (iii) delivering much narrower prediction intervals and yet (iv) raise alarming concerns, given recent evidence (e.g., from climate–weather integrated models). We demonstrate how machine learning methods can contribute to not-for-profit pressing societal issues, such as global warming, where data complexity and availability can be overcome.

Suggested Citation

  • Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2022. "Machine Learning the Carbon Footprint of Bitcoin Mining," JRFM, MDPI, vol. 15(2), pages 1-30, February.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:2:p:71-:d:742638
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    References listed on IDEAS

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    Cited by:

    1. Yerushalmi, Erez & Paladini, Stefania, 2023. "Blockchain in Financial Intermediation and Beyond: What are the Main Barriers for Widespread Adoption?," CAFE Working Papers 22, Centre for Accountancy, Finance and Economics (CAFE), Birmingham City Business School, Birmingham City University.

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    More about this item

    Keywords

    machine learning; neural networks; dropout methods; Bitcoin mining; CO 2;
    All these keywords.

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • F55 - International Economics - - International Relations, National Security, and International Political Economy - - - International Institutional Arrangements
    • F64 - International Economics - - Economic Impacts of Globalization - - - Environment

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