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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
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    Citations

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    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. Samet Gürsoy & Bartosz Jóźwik & Mesut Dogan & Feyyaz Zeren & Nazligul Gulcan, 2024. "Impact of Climate Policy Uncertainty, Clean Energy Index, and Carbon Emission Allowance Prices on Bitcoin Returns," Sustainability, MDPI, vol. 16(9), pages 1-14, May.
    11. 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).
    12. Hebous, Shafik & Vernon-Lin, Nate, 2024. "Cryptocarbon: How much is the corrective tax?," Energy Economics, Elsevier, vol. 138(C).
    13. 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.
    14. Natkamon Tovanich & Nicolas Soulié & Nicolas Heulot & Petra Isenberg, 2022. "MiningVis: visual analytics of the Bitcoin mining economy," Post-Print hal-03348145, HAL.
    15. 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.

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