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Machine Learning the Carbon Footprint of Bitcoin Mining

Author

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  • Calvo Pardo, Héctor
  • Olmo, Jose
  • Mancini, Tullio

Abstract

Building on an economic model of rational Bitcoin mining, we measure the carbon footprint of Bitcoin mining power consumption using feedforward neural networks. After reviewing the literature on deep learning methods, we find associated carbon footprints of 3.8038, 23.8313 and 19.83472 MtCOe for 2017, 2018 and 2019, which conform with recent estimates, lie within the economic model bounds while delivering much narrower confidence intervals, and yet raise alarming concerns, given recent evidence from climate-weather integrated models. We demonstrate how machine learning methods can contribute to non-for-profit pressing societal issues, like global warming, where data complexity and availability can be overcome.

Suggested Citation

  • Calvo Pardo, Héctor & Olmo, Jose & Mancini, Tullio, 2021. "Machine Learning the Carbon Footprint of Bitcoin Mining," CEPR Discussion Papers 16267, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:16267
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    Cited by:

    1. is not listed on IDEAS
    2. 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.
    3. Mushtaq Hussain Khan & Shreya Macherla & Angesh Anupam, 2025. "Nonlinear connectedness of conventional crypto-assets and sustainable crypto-assets with climate change: A complex systems modelling approach," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-18, February.

    More about this item

    Keywords

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    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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