Machine Learning the Carbon Footprint of Bitcoin Mining

49 Pages Posted: 6 Aug 2020

See all articles by Hector F. Calvo-Pardo

Hector F. Calvo-Pardo

University of Southampton

Tullio Mancini

University of Southampton

Jose Olmo

Universidad de Zaragoza; University of Southampton

Date Written: July 9, 2020

Abstract

Building on an economic model of rational Bitcoin mining, we measure the carbon footprint of Bitcoin mining power consumption using feed-forward 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.

Keywords: Machine Learning, Carbon Footprint, Cryptocurrencies, Now-casting, Feed-forward Neural Networks, Climate Change

JEL Classification: Q47, Q54, C45, C55, F55, F64

Suggested Citation

Calvo-Pardo, Hector F. and Mancini, Tullio and Olmo, Jose, Machine Learning the Carbon Footprint of Bitcoin Mining (July 9, 2020). Available at SSRN: https://ssrn.com/abstract=3647593 or http://dx.doi.org/10.2139/ssrn.3647593

Hector F. Calvo-Pardo (Contact Author)

University of Southampton ( email )

University Rd.
Southampton SO17 1BJ, Hampshire SO17 1LP
United Kingdom

Tullio Mancini

University of Southampton ( email )

University Rd.
Southampton SO17 1BJ, Hampshire SO17 1LP
United Kingdom

Jose Olmo

Universidad de Zaragoza ( email )

Gran Via, 2
50005 Zaragoza, Zaragoza 50005
Spain

University of Southampton ( email )

Southampton
United Kingdom

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