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Remote Sensing Evidence for Significant Variations in the Global Gross Domestic Product during the COVID-19 Epidemic

Author

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  • Bin Guo

    (College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
    These authors contributed equally to this work.)

  • Wencai Zhang

    (College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
    These authors contributed equally to this work.)

  • Lin Pei

    (School of Exercise and Health Sciences, Xi’an Physical Education University, Xi’an 710068, China)

  • Xiaowei Zhu

    (Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97207, USA)

  • Pingping Luo

    (School of Water and Environment, Chang’an University, Xi’an 710054, China)

  • Weili Duan

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China)

Abstract

Coronavirus disease 2019 (COVID-19) has been spreading rapidly and is still threatening human health currently. A series of measures for restraining epidemic spreading has been adopted throughout the world, which seriously impacted the gross domestic product (GDP) globally. However, details of the changes in the GDP and its spatial heterogeneity characteristics on a fine scale worldwide during the pandemic are still uncertain. We designed a novel scheme to simulate a 0.1° × 0.1° resolution grid global GDP map during the COVID-19 pandemic. Simulated nighttime-light remotely sensed data (SNTL) was forecasted via a GM(1, 1) model under the assumption that there was no COVID-19 epidemic in 2020. We constructed a geographically weighted regression (GWR) model to determine the quantitative relationship between the variation of nighttime light (ΔNTL) and the variation of GDP (ΔGDP). The scheme can detect and explain the spatial heterogeneity of ΔGDP at the grid scale. It is found that a series of policies played an obvious role in affecting GDP. This work demonstrated that the global GDP, except for in a few countries, represented a remarkably decreasing trend, whereas the ΔGDP exhibited significant differences.

Suggested Citation

  • Bin Guo & Wencai Zhang & Lin Pei & Xiaowei Zhu & Pingping Luo & Weili Duan, 2022. "Remote Sensing Evidence for Significant Variations in the Global Gross Domestic Product during the COVID-19 Epidemic," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15201-:d:974693
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    References listed on IDEAS

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