IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v186y2020ics0165176519302526.html
   My bibliography  Save this article

Bitcoin’s energy consumption: Is it the Achilles heel to miner’s revenue?

Author

Listed:
  • Das, Debojyoti
  • Dutta, Anupam

Abstract

In this paper, we add a new dimension to the cryptocurrency literature by examining the relationship between Bitcoin’s energy consumption and miner’s revenue. By resorting to quantile and Markov regime switching regression, we report a negative association between these variables. Further, the negative impact is strongly significant when the miner’s revenues are low and volatile. Thus, the higher energy consumption in the wake of escalating global energy costs amid bearish market sentiments impedes the miners to break-even. Hence, it would not be viable to sustain the business unless cheap energy sources and efficient mining hardware are relied upon.

Suggested Citation

  • Das, Debojyoti & Dutta, Anupam, 2020. "Bitcoin’s energy consumption: Is it the Achilles heel to miner’s revenue?," Economics Letters, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:ecolet:v:186:y:2020:i:c:s0165176519302526
    DOI: 10.1016/j.econlet.2019.108530
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176519302526
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2019.108530?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    2. Koutmos, Dimitrios, 2018. "Bitcoin returns and transaction activity," Economics Letters, Elsevier, vol. 167(C), pages 81-85.
    3. Symitsi, Efthymia & Chalvatzis, Konstantinos J., 2018. "Return, volatility and shock spillovers of Bitcoin with energy and technology companies," Economics Letters, Elsevier, vol. 170(C), pages 127-130.
    4. 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.
    5. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Rainer Böhme & Nicolas Christin & Benjamin Edelman & Tyler Moore, 2015. "Bitcoin: Economics, Technology, and Governance," Journal of Economic Perspectives, American Economic Association, vol. 29(2), pages 213-238, Spring.
    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. Michael L. Polemis & Mike G. Tsionas, 2023. "The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1602-1621, April.
    2. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    3. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2022. "Cryptocurrency returns under empirical asset pricing," International Review of Financial Analysis, Elsevier, vol. 82(C).
    4. 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.
    5. 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.
    6. Zhou, Siwen, 2018. "Exploring the Driving Forces of the Bitcoin Exchange Rate Dynamics: An EGARCH Approach," MPRA Paper 89445, University Library of Munich, Germany.
    7. Helder Miguel Correia Virtuoso Sebastião & Paulo José Osório Rupino Da Cunha & Pedro Manuel Cortesão Godinho, 2021. "Cryptocurrencies and blockchain. Overview and future perspectives," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 21(3), pages 305-342.
    8. Muhammad Anas & Syed Jawad Hussain Shahzad & Larisa Yarovaya, 2024. "The use of high-frequency data in cryptocurrency research: a meta-review of literature with bibliometric analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-31, December.
    9. Toan Luu Duc Huynh & Muhammad Shahbaz & Muhammad Ali Nasir & Subhan Ullah, 2024. "Correction to: Financial modelling, risk management of energy instruments and the role of cryptocurrencies," Annals of Operations Research, Springer, vol. 332(1), pages 1273-1273, January.
    10. Francisco Javier García-Corral & José Antonio Cordero-García & Jaime de Pablo-Valenciano & Juan Uribe-Toril, 2022. "A bibliometric review of cryptocurrencies: how have they grown?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
    11. ORĂȘTEAN Ramona & MĂRGINEAN Silvia Cristina & SAVA Raluca, 2019. "Bitcoin In The Scientific Literature – A Bibliometric Study," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 14(3), pages 160-174, December.
    12. Symitsi, Efthymia & Chalvatzis, Konstantinos J., 2019. "The economic value of Bitcoin: A portfolio analysis of currencies, gold, oil and stocks," Research in International Business and Finance, Elsevier, vol. 48(C), pages 97-110.
    13. Matkovskyy, Roman, 2019. "Centralized and decentralized bitcoin markets: Euro vs USD vs GBP," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 270-279.
    14. 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.
    15. Katsiampa, Paraskevi, 2019. "An empirical investigation of volatility dynamics in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 50(C), pages 322-335.
    16. Nikolaos A. Kyriazis, 2019. "A Survey on Empirical Findings about Spillovers in Cryptocurrency Markets," JRFM, MDPI, vol. 12(4), pages 1-17, November.
    17. Dejian Yu & Libo Sheng, 2020. "Knowledge diffusion paths of blockchain domain: the main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 471-497, October.
    18. 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.
    19. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    20. Pal, Debdatta & Mitra, Subrata K., 2019. "Hedging bitcoin with other financial assets," Finance Research Letters, Elsevier, vol. 30(C), pages 30-36.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G1 - Financial Economics - - General Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    Statistics

    Access and download statistics

    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:ecolet:v:186:y:2020:i:c:s0165176519302526. 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.elsevier.com/locate/ecolet .

    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.