Machine Learning the Carbon Footprint of Bitcoin Mining
Hector Calvo-Pardo, Tullio Mancini, José Olmo
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 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.