Machine Learning the Carbon Footprint of Bitcoin Mining

Hector F Calvo-Pardo,Tullio Mancini,Jose Olmo

doi:10.3390/jrfm15020071

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.

Highlights

Does Bitcoin mining contribute to climate change? Participation in the Bitcoin blockchain validation process1 requires specialized hardware and vast amounts of electricity, translating into a significant carbon footprint
Besides the gains in accuracy, here, we argue that machine learning (ML) methods present additional significant advantages for enabling timeless public decision making regarding pressing complex social issues, just as they do in private-sector for-profit decisions, e.g., business analytics, new technology design, improvement or product adaptation and/or marketing
Faced with the unobservability of miners geolocation and actual hardware and source of energy efficiency used, supervised ML is a statistical approach that overcomes the difficulty of providing prediction intervals that are robust to model misspecification mistakes, by automating model selection and estimation under a high-quality approximation constraint given by the class of functions considered

Summary

Introduction

Does Bitcoin mining contribute to climate change? Participation in the Bitcoin blockchain validation process requires specialized hardware and vast amounts of electricity, translating into a significant carbon footprint. Feedforward neural networks, called multilayer perceptrons (MLPs), have been developed since the midtwentieth century, relying on joint advances from computer science, applied mathematics and information and probability theory Their recent success stems from their theoretical ability to approximate unknown data generating processes (Universal Approximation Theorem and its variants), while handling large and complex datasets. They approximate or learn some unknown function of the data (or inputs) that generates an output, such as the CO2 emissions of Bitcoin network energy consumption, assuming that information “feeds forward” from the input, through the unknown function, to the output.. Our main contribution is to provide a robust measure of the carbon footprint associated with producing increasingly popular cryptocurrencies, such as bitcoin (BTC), as well as of the uncertainty associated with that measure currently lacking in the literature, conveying the likelihood of potentially alarming scenarios

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Results

Conclusion