IDEAS home Printed from https://ideas.repec.org/a/nat/natcli/v9y2019i9d10.1038_s41558-019-0535-4.html
   My bibliography  Save this article

Implausible projections overestimate near-term Bitcoin CO2 emissions

Author

Listed:
  • Eric Masanet

    (Northwestern University
    Northwestern University)

  • Arman Shehabi

    (Lawrence Berkeley National Laboratory)

  • Nuoa Lei

    (Northwestern University)

  • Harald Vranken

    (Open University of the Netherlands and Radboud University)

  • Jonathan Koomey

    (Rocky Mountain Institute)

  • Jens Malmodin

    (Ericsson Research, Ericsson AB)

Abstract

No abstract is available for this item.

Suggested Citation

  • Eric Masanet & Arman Shehabi & Nuoa Lei & Harald Vranken & Jonathan Koomey & Jens Malmodin, 2019. "Implausible projections overestimate near-term Bitcoin CO2 emissions," Nature Climate Change, Nature, vol. 9(9), pages 653-654, September.
  • Handle: RePEc:nat:natcli:v:9:y:2019:i:9:d:10.1038_s41558-019-0535-4
    DOI: 10.1038/s41558-019-0535-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41558-019-0535-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41558-019-0535-4?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Agur, Itai & Lavayssière, Xavier & Villegas Bauer, Germán & Deodoro, Jose & Martinez Peria, Soledad & Sandri, Damiano & Tourpe, Hervé, 2023. "Lessons from crypto assets for the design of energy efficient digital currencies," Ecological Economics, Elsevier, vol. 212(C).
    2. Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2022. "Machine Learning the Carbon Footprint of Bitcoin Mining," JRFM, MDPI, vol. 15(2), pages 1-30, February.
    3. Baur, Dirk G. & Oll, Josua, 2022. "Bitcoin investments and climate change: A financial and carbon intensity perspective," Finance Research Letters, Elsevier, vol. 47(PA).
    4. Murray A. Rudd, 2022. "100 Important Questions about Bitcoin’s Energy Use and ESG Impacts," Challenges, MDPI, vol. 14(1), pages 1-16, December.
    5. Schinckus, Christophe, 2021. "Proof-of-work based blockchain technology and Anthropocene: An undermined situation?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    6. Sergio Luis Náñez Alonso & Javier Jorge-Vázquez & Miguel Ángel Echarte Fernández & Ricardo Francisco Reier Forradellas, 2021. "Cryptocurrency Mining from an Economic and Environmental Perspective. Analysis of the Most and Least Sustainable Countries," Energies, MDPI, vol. 14(14), pages 1-22, July.
    7. Sharif, Arshian & Brahim, Mariem & Dogan, Eyup & Tzeremes, Panayiotis, 2023. "Analysis of the spillover effects between green economy, clean and dirty cryptocurrencies," Energy Economics, Elsevier, vol. 120(C).
    8. Łęt Blanka & Sobański Konrad & Świder Wojciech & Włosik Katarzyna, 2022. "Is the cryptocurrency market efficient? Evidence from an analysis of fundamental factors for Bitcoin and Ethereum," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 58(4), pages 351-370, December.
    9. Xuejia Sang & Xiaopeng Leng & Linfu Xue & Xiangjin Ran, 2022. "Based on the Time-Spatial Power-Based Cryptocurrency Miner Driving Force Model, Establish a Global CO 2 Emission Prediction Framework after China Bans Cryptocurrency," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    10. Lei, Nuoa & Masanet, Eric & Koomey, Jonathan, 2021. "Best practices for analyzing the direct energy use of blockchain technology systems: Review and policy recommendations," Energy Policy, Elsevier, vol. 156(C).
    11. Shize Qin & Lena Klaa{ss}en & Ulrich Gallersdorfer & Christian Stoll & Da Zhang, 2020. "Bitcoin's future carbon footprint," Papers 2011.02612, arXiv.org, revised Jan 2021.
    12. Natkamon Tovanich & Nicolas Soulié & Nicolas Heulot & Petra Isenberg, 2022. "MiningVis: visual analytics of the Bitcoin mining economy," Post-Print hal-03348145, HAL.
    13. Yuze Li & Shangrong Jiang & Xuerong Li & Shouyang Wang, 2022. "Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.

    More about this item

    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:nat:natcli:v:9:y:2019:i:9:d:10.1038_s41558-019-0535-4. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.