Abstract
The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response to the pandemic have directly and drastically decreased human socioeconomic activities. To quantify and assess the extent of the pandemic’s impact on the economy of Hebei Province, China, nighttime light (NTL) data, vegetation information, and provincial quarterly gross domestic product (GDP) data were jointly utilized to estimate the quarterly GDP for prefecture-level cities and county-level cities. Next, an autoregressive integrated moving average model (ARIMA) model was applied to predict the quarterly GDP for 2020 and 2021. Finally, economic recovery intensity (ERI) was used to assess the extent of economic recovery in Hebei Province during the pandemic. The results show that, at the provincial level, the economy of Hebei Province had not yet recovered; at the prefectural and county levels, three prefectures and forty counties were still struggling to restore their economies by the end of 2021, even though these economies, as a whole, were gradually recovering. In addition, the number of new infected cases correlated positively with the urban NTL during the pandemic period, but not during the post-pandemic period. The study results are informative for local government’s strategies and policies for allocating financial resources for urban economic recovery in the short- and long-term.
Funder
the Fundamental Research Funds for the Central Universities of China
Subject
General Earth and Planetary Sciences
Reference56 articles.
1. (2022, August 14). WHO Coronavirus (COVID-19) Dashboard. Available online: https://covid19.who.int/.
2. International Monetary Fund (2022). World Economic Outlook: War Sets Back the Global Recovery, International Monetary Fund.
3. Li, F., Liu, X., Liao, S., and Jia, P. (2021). The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas. Remote Sens., 13.
4. Luqman, M., Rayner, P.J., and Gurney, K.R. (2019). Combining Measurements of Built-up Area, Nighttime Light, and Travel Time Distance for Detecting Changes in Urban Boundaries: Introducing the BUNTUS Algorithm. Remote Sens., 11.
5. Measurement of Urban Expansion and Spatial Correlation of Central Yunnan Urban Agglomeration Using Nighttime Light Data;Zhang;Math. Probl. Eng.,2021
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