Machine learning the carbon footprint of Bitcoin mining

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 15 ; Year: 2022 ; Issue: 2 ; Pages: 1-30 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: machine learning ; neural networks ; dropout methods ; Bitcoin mining ; CO2

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Calvo Pardo, Héctor F. ; Mancini, Tullio ; Olmo, Jose
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2022

Further information

• DOI: doi:10.3390/jrfm15020071
• Handle: http://hdl.handle.net/10419/258794
• Last update: 10.03.2025, 11:43 AM CET