Machine Learning the Carbon Footprint of Bitcoin Mining

Hector Calvo Pardo, Jose Olmo and Tullio Mancini

No 16267, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Keywords: Machine learning; Carbon footprint; Cryptocurrencies; Nowcasting; Feed- forward neural networks; Climate change (search for similar items in EconPapers)
JEL-codes: C45 C55 F55 F64 Q47 Q54 (search for similar items in EconPapers)
Date: 2021-06
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