Machine learning the carbon footprint of bitcoin mining

Calvo-Pardo, Hector, Mancini, Tullio and Olmo, Jose (2022) Machine learning the carbon footprint of bitcoin mining. Journal of Risk and Financial Management, 15 (2), [71]. (doi:10.3390/jrfm15020071).

Record type: Article

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 MtCO2e 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.

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Accepted/In Press date: 22 January 2022

Published date: 5 February 2022

Additional Information: Funding Information: Funding: The APC was funded by J.B.O. Author Voucher discount code (df149a0768e7508d) and by the University of Southampton, Hartley Library, Southampton SO171BJ, UK. Funding Information: Acknowledgments: H.C.-P. acknowledges financial support from ESRC grant ES/R009139/1; T.M. acknowledges financial support from the University of Southampton Presidential Scholarship and J.B.O. from “Fundación Agencia Aragonesa para la Investigación y el Desarrollo”. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords: Bitcoin mining, CO, dropout methods, machine learning, neural networks