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Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Author

Listed:
  • Shiqi Ou

    (Energy and Transportation Science Division, Oak Ridge National Laboratory)

  • Xin He

    (Aramco Services Company: Aramco Research Center—Detroit)

  • Weiqi Ji

    (Massachusetts Institute of Technology)

  • Wei Chen

    (Michigan Department of Transportation)

  • Lang Sui

    (Aramco Services Company: Aramco Research Center—Detroit)

  • Yu Gan

    (Energy Systems Division, Argonne National Laboratory)

  • Zifeng Lu

    (Energy Systems Division, Argonne National Laboratory)

  • Zhenhong Lin

    (Energy and Transportation Science Division, Oak Ridge National Laboratory)

  • Sili Deng

    (Massachusetts Institute of Technology)

  • Steven Przesmitzki

    (Aramco Services Company: Aramco Research Center—Detroit)

  • Jessey Bouchard

    (Aramco Services Company: Aramco Research Center—Detroit)

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Suggested Citation

  • Shiqi Ou & Xin He & Weiqi Ji & Wei Chen & Lang Sui & Yu Gan & Zifeng Lu & Zhenhong Lin & Sili Deng & Steven Przesmitzki & Jessey Bouchard, 2020. "Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand," Nature Energy, Nature, vol. 5(12), pages 1051-1052, December.
    • Handle: RePEc:nat:natene:v:5:y:2020:i:12:d:10.1038_s41560-020-00711-7
    DOI: 10.1038/s41560-020-00711-7
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    Citations

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    Cited by:

    1. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    2. Prakash Chandra Mishra & Anand Gupta & Saikat Samanta & Rihana B. Ishaq & Fuad Khoshnaw, 2022. "Framework for Energy-Averaged Emission Mitigation Technique Adopting Gasoline-Methanol Blend Replacement and Piston Design Exchange," Energies, MDPI, vol. 15(19), pages 1-26, September.
    3. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. Francesco Piccialli & Vincenzo Schiano Cola & Fabio Giampaolo & Salvatore Cuomo, 2021. "The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic," Information Systems Frontiers, Springer, vol. 23(6), pages 1467-1497, December.
    5. Bocklet, Johanna, 2020. "The Reformed EU ETS in Times of Economic Crises: the Case of the COVID-19 Pandemic," EWI Working Papers 2020-10, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    6. Nikolaos Apostolopoulos & Panagiotis Liargovas & Nikolaos Rodousakis & George Soklis, 2022. "COVID-19 in US Economy: Structural Analysis and Policy Proposals," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    7. Hanmin Dong & Xiujie Tan & Si Cheng & Yishuang Liu, 2023. "COVID-19, recovery policies and the resilience of EU ETS," Economic Change and Restructuring, Springer, vol. 56(5), pages 2965-2991, October.
    8. Zhang, Xiaokong & Chai, Jian & Tian, Lingyue & Yang, Ying & Zhang, Zhe George & Pan, Yue, 2023. "Forecast and structural characteristics of China's oil product consumption embedded in bottom-line thinking," Energy, Elsevier, vol. 278(PA).
    9. Feng Wang & Min Wu, 2021. "The Impacts of COVID-19 on China’s Economy and Energy in the Context of Trade Protectionism," IJERPH, MDPI, vol. 18(23), pages 1-23, December.
    10. Wang, Qiang & Li, Shuyu & Zhang, Min & Li, Rongrong, 2022. "Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach," Energy, Elsevier, vol. 239(PC).
    11. Hui Zhu, 2023. "Oil Demand Forecasting in Importing and Exporting Countries: AI-Based Analysis of Endogenous and Exogenous Factors," Sustainability, MDPI, vol. 15(18), pages 1-19, September.

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