Machine Learning the Carbon Footprint of Bitcoin Mining

Hector F. Calvo-Pardo, Tullio Mancini and Jose Olmo
Additional contact information
Hector F. Calvo-Pardo: Department of Economics, Highfield Campus, University of Southampton, Southampton SO17 1BJ, UK
Tullio Mancini: Department of Economics, Highfield Campus, University of Southampton, Southampton SO17 1BJ, UK
Authors registered in the RePEc Author Service: Hector Fernando Calvo Pardo

JRFM, 2022, vol. 15, issue 2, 1-30

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 MtCO 2 e 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.

Keywords: machine learning; neural networks; dropout methods; Bitcoin mining; CO 2 (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1911-8074/15/2/71/pdf (application/pdf)
https://www.mdpi.com/1911-8074/15/2/71/ (text/html)

Related works:
Working Paper: Machine Learning the Carbon Footprint of Bitcoin Mining (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:15:y:2022:i:2:p:71-:d:742638

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().