DP16267 Machine Learning the Carbon Footprint of Bitcoin Mining
|Author(s):||Hector F. Calvo Pardo, Tullio Mancini, Jose Olmo|
|Publication Date:||June 2021|
|Keyword(s):||carbon footprint, climate change, cryptocurrencies, Feed- forward Neural Networks, Machine Learning, Nowcasting|
|JEL(s):||C45, C55, F55, F64, Q47, Q54|
|Programme Areas:||Public Economics|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=16267|
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.