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Energy consumption of cryptocurrency mining: A study of electricity consumption in mining cryptocurrencies

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Listed:
  • Li, Jingming
  • Li, Nianping
  • Peng, Jinqing
  • Cui, Haijiao
  • Wu, Zhibin

Abstract

Cryptocurrency is a relatively new combination of cryptology and currency in financial areas and is increasingly frequently used worldwide. Blockchain applications are expected to reshape the renewable energy market. However, there is a lack of studies covering the power usage of digital currencies. Therefore, this study ran experiments on mining efficiency of nine kinds of cryptocurrencies and ten algorithms. A comparison of statistical analysis of data in a benchmark and experiment results of Monero mining was conducted. Thereafter, this study provided an estimation of global electricity consumption of the Monero mining activity. The results indicated that the hashing algorithm mainly determines the mining efficiency. Data analysis and experiments and estimated Monero mining electricity consumption in the world and its carbon emission in China as a case study. In 2018, Monero mining may consume 645.62 GWh of electricity in the world after its hard fork. The Monero mining in China may consume 30.34 GWh and contribute a carbon emission of 19.12–19.42 thousand tons from April to December in 2018. Although cryptocurrency mining and blockchain technology are promising, their influence on energy conversation and sustainable development should be further studied.

Suggested Citation

  • Li, Jingming & Li, Nianping & Peng, Jinqing & Cui, Haijiao & Wu, Zhibin, 2019. "Energy consumption of cryptocurrency mining: A study of electricity consumption in mining cryptocurrencies," Energy, Elsevier, vol. 168(C), pages 160-168.
  • Handle: RePEc:eee:energy:v:168:y:2019:i:c:p:160-168
    DOI: 10.1016/j.energy.2018.11.046
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    References listed on IDEAS

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    1. David Garcia & Claudio Juan Tessone & Pavlin Mavrodiev & Nicolas Perony, 2014. "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy," Papers 1408.1494, arXiv.org.
    2. Gkillas, Konstantinos & Katsiampa, Paraskevi, 2018. "An application of extreme value theory to cryptocurrencies," Economics Letters, Elsevier, vol. 164(C), pages 109-111.
    3. David Garcia & Claudio Tessone & Pavlin Mavrodiev & Nicolas Perony, "undated". "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy," Working Papers ETH-RC-14-001, ETH Zurich, Chair of Systems Design.
    4. Evan G. R. Davies, 2018. "Breaking the spell," Nature Energy, Nature, vol. 3(9), pages 716-717, September.
    5. Mattila, Juri, 2016. "The Blockchain Phenomenon – The Disruptive Potential of Distributed Consensus Architectures," ETLA Working Papers 38, The Research Institute of the Finnish Economy.
    6. Adam Hayes, 2015. "A Cost of Production Model for Bitcoin," Working Papers 1505, New School for Social Research, Department of Economics.
    7. Phillip, Andrew & Chan, Jennifer S.K. & Peiris, Shelton, 2018. "A new look at Cryptocurrencies," Economics Letters, Elsevier, vol. 163(C), pages 6-9.
    8. Qunli Wu & Chenyang Peng, 2016. "Scenario Analysis of Carbon Emissions of China’s Electric Power Industry Up to 2030," Energies, MDPI, vol. 9(12), pages 1-18, November.
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