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

Bin Guo (), Wencai Zhang, Lin Pei (), Xiaowei Zhu, Pingping Luo and Weili Duan
Additional contact information
Bin Guo: College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
Wencai Zhang: College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
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

Sustainability, 2022, vol. 14, issue 22, 1-21

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.

Keywords: GDP prediction; GM(1, 1); GWR; NPP-VIIRS; COVID-19 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/22/15201/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/22/15201/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:22:p:15201-:d:974693

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().