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 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.
Centre for Economic Policy Research
Calvo-Pardo, Hector
07a586f0-48ec-4049-932e-fb9fc575f59f
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
Mancini, Tullio
3e5a59a2-e184-4996-a7d6-7b4394bec08c
16 June 2021
Calvo-Pardo, Hector
07a586f0-48ec-4049-932e-fb9fc575f59f
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
Mancini, Tullio
3e5a59a2-e184-4996-a7d6-7b4394bec08c
Calvo-Pardo, Hector, Olmo, Jose and Mancini, Tullio
(2021)
Machine learning the carbon footprint of bitcoin mining
(Centre for Economic Policy Research, DP16267)
Centre for Economic Policy Research
Record type:
Monograph
(Working Paper)
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.
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Published date: 16 June 2021
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Local EPrints ID: 450503
URI: http://eprints.soton.ac.uk/id/eprint/450503
PURE UUID: 25c48dea-f39a-4ffa-b255-9a390c4affba
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Date deposited: 30 Jul 2021 16:31
Last modified: 17 Mar 2024 03:32
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Author:
Tullio Mancini
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