Based on the Time-Spatial Power-Based Cryptocurrency Miner Driving Force Model, Establish a Global CO 2 Emission Prediction Framework after China Bans Cryptocurrency

Xuejia Sang, Xiaopeng Leng, Linfu Xue and Xiangjin Ran
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Xuejia Sang: School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Xiaopeng Leng: Computer and Cyber Security College, Chengdu University of Technology, Chengdu 610059, China
Linfu Xue: College of Earth Science, Jilin University, Changchun 130061, China
Xiangjin Ran: College of Earth Science, Jilin University, Changchun 130061, China

Sustainability, 2022, vol. 14, issue 9, 1-18

Abstract: The energy consumption and carbon footprint of cryptocurrencies have always been a popular topic. However, most of the existing studies only focus on one cryptocurrency, Bitcoin, and there is a lack of long-term monitoring studies that summarize all cryptocurrencies. By constructing a time series hash rate/power model, this research obtained the 10-year time series data on energy consumption dataset of global top-25 cryptocurrencies for the first time. Both the temporal coverage and the spatiotemporal resolution of the data exceed previous studies. The results show that Bitcoin’s power consumption only accounts for 58% of the top-25 cryptocurrencies. After China bans cryptocurrencies, the conservative change in global CO 2 emissions from 2020 will be between −0.4% and 4.4%, and Central Asian countries such as Kazakhstan are likely to become areas of rapid growth in carbon emissions from cryptocurrencies.

Keywords: bitcoin; electronic waste; bans cryptocurrency; CO 2 emission (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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