IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v168y2019icp160-168.html
   My bibliography  Save this article

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

Author

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218322503
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.11.046?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Evan G. R. Davies, 2018. "Breaking the spell," Nature Energy, Nature, vol. 3(9), pages 716-717, September.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    2. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    3. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    4. Damian Zięba & Katarzyna Śledziewska, 2018. "Are demand shocks in Bitcoin contagious?," Working Papers 2018-17, Faculty of Economic Sciences, University of Warsaw.
    5. Christie Smith & Aaron Kumar, 2018. "Crypto‐Currencies – An Introduction To Not‐So‐Funny Moneys," Journal of Economic Surveys, Wiley Blackwell, vol. 32(5), pages 1531-1559, December.
    6. Alexandre Bovet & Carlo Campajola & Jorge F. Lazo & Francesco Mottes & Iacopo Pozzana & Valerio Restocchi & Pietro Saggese & Nicol'o Vallarano & Tiziano Squartini & Claudio J. Tessone, 2018. "Network-based indicators of Bitcoin bubbles," Papers 1805.04460, arXiv.org.
    7. Bouri, Elie & Gupta, Rangan & Tiwari, Aviral Kumar & Roubaud, David, 2017. "Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions," Finance Research Letters, Elsevier, vol. 23(C), pages 87-95.
    8. Kajtazi, Anton & Moro, Andrea, 2019. "The role of bitcoin in well diversified portfolios: A comparative global study," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 143-157.
    9. Mehmet Balcilar & Elie Bouri & Rangan Gupta & David Roubaud, 2016. "Can Volume Predict Bitcoin Returns and Volatility? A Nonparametric Causality-in-Quantiles Approach," Working Papers 201662, University of Pretoria, Department of Economics.
    10. Stefano Martinazzi & Daniele Regoli & Andrea Flori, 2020. "A Tale of Two Layers: The Mutual Relationship between Bitcoin and Lightning Network," Risks, MDPI, vol. 8(4), pages 1-18, December.
    11. Omane-Adjepong, Maurice & Alagidede, Paul & Akosah, Nana Kwame, 2019. "Wavelet time-scale persistence analysis of cryptocurrency market returns and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 105-120.
    12. Baumöhl, Eduard, 2019. "Are cryptocurrencies connected to forex? A quantile cross-spectral approach," Finance Research Letters, Elsevier, vol. 29(C), pages 363-372.
    13. Lyócsa, Štefan & Molnár, Peter & Plíhal, Tomáš & Širaňová, Mária, 2020. "Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).
    14. Elie Bouri & Naji Jalkh & Peter Molnár & David Roubaud, 2017. "Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven?," Applied Economics, Taylor & Francis Journals, vol. 49(50), pages 5063-5073, October.
    15. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. I," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 44, pages 5-24.
    16. Zvonko Merkaš & Vlasta Roška, 2021. "The Impact of Unsystematic Factors on Bitcoin Value," JRFM, MDPI, vol. 14(11), pages 1-17, November.
    17. Ji, Qiang & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2018. "Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 203-213.
    18. Yue, Yao & Li, Xuerong & Zhang, Dingxuan & Wang, Shouyang, 2021. "How cryptocurrency affects economy? A network analysis using bibliometric methods," International Review of Financial Analysis, Elsevier, vol. 77(C).
    19. Neveen Ahmed & Omar Farooq & Nidaa Hamed, 2023. "Relation Between Bitcoin and Its Forks: An Empirical Investigation," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 49(2), pages 249-261, April.
    20. Frode Kj rland & Maria Meland & Are Oust & Vilde yen, 2018. "How can Bitcoin Price Fluctuations be Explained?," International Journal of Economics and Financial Issues, Econjournals, vol. 8(3), pages 323-332.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:168:y:2019:i:c:p:160-168. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.