Addressing the Data Gap in Modeling Bitcoin's Carbon Footprint
Reference Type:
Preprint
Environmental concern over the excessive electricity consumption from bitcoin mining presents a significant obstacle to adopting an innovation that promises to positively impact global financial inclusion and asset diversification. Since the carbon intensity of electricity generation varies widely from region to region and the Bitcoin network design largely conceals the miners’ locations, it is difficult to precisely estimate the CO2 emissions from mining at a regional level. Unsurprisingly then, existing works offer emission estimates based on coarse, self-reported, or overly extrapolated data, presenting a data gap that could thwart meaningful climate action for the industry. To address this, we design and implement an innovative, more precise modeling methodology to uncover the locations of bitcoin miners to infer their CO2 emissions. Our method generates reliable disaggregated information on where the mining activity is taking place and the amount of electricity that is used in each location. We then apply the prevailing CO2 emissions factors for electricity generation at each location to infer the associated CO2 emissions. When fully collected and analyzed, our data provides insight to how the recent Chinese regulations banning cryptocurrency mining have influenced the geographic locations of mining entities, which were once concentrated in China, and the consequence for CO2 emissions. Moreover, our data reveals the degree of mobility within the bitcoin mining industry and how miners respond intensively, in terms of their power usage, and locationally to exogenous oil price shocks and changes in the price of bitcoin. While we focus on bitcoin, our general methodology can be applied to any proof-of-work (PoW) cryptocurrency. Because cryptocurrency mining is an important, yet a nascent and obscure, segment of the digital economy, our research provides real-time information of interest to researchers, governments, and the media to better analyze cryptocurrency mining ecosystems and to assess the need for their regulation.
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