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Machine learning the carbon footprint of bitcoin mining

Machine learning the carbon footprint of bitcoin mining
Machine learning the carbon footprint of bitcoin mining
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.
Bitcoin mining, CO, dropout methods, machine learning, neural networks
Calvo-Pardo, Hector
07a586f0-48ec-4049-932e-fb9fc575f59f
Mancini, Tullio
3e5a59a2-e184-4996-a7d6-7b4394bec08c
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
Calvo-Pardo, Hector
07a586f0-48ec-4049-932e-fb9fc575f59f
Mancini, Tullio
3e5a59a2-e184-4996-a7d6-7b4394bec08c
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e

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

Identifiers

Local EPrints ID: 454944
URI: http://eprints.soton.ac.uk/id/eprint/454944
PURE UUID: 2bfd9bab-f162-4de0-952c-e7dcfa9c01e6
ORCID for Hector Calvo-Pardo: ORCID iD orcid.org/0000-0001-6645-4273
ORCID for Jose Olmo: ORCID iD orcid.org/0000-0002-0437-7812

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Date deposited: 02 Mar 2022 17:44
Last modified: 27 Jul 2022 01:46

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Contributors

Author: Tullio Mancini
Author: Jose Olmo ORCID iD

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