Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data
Abstract
:1. Introduction
2. Study Site and Data Sources
2.1. Study Sites
2.2. Data Sources
3. Methodology
3.1. Calibration of NTL Data
3.2. Estimation Model of Quarterly GDP at the Pixel Level
3.3. Predictive Model for Quarterly GDP at Provincial, Prefectural, and County Levels
3.4. Accuracy Assessment Indices
4. Results
4.1. Accuracy Assessment
4.1.1. Regression Results and Accuracy of GDP Estimation
4.1.2. Results of Predictive Model
4.2. Economic Recovery Assessment of Hebei Province
4.2.1. Provincial Economic Recovery
4.2.2. Economic Recovery of Prefecture-Level Cities
4.2.3. Economic Recovery of County-Level Cities
5. Discussion
5.1. Relationship between Pandemics and NTL
5.2. Uncertainty Analysis of Results
5.3. Contributions and Practical Implications
5.4. Future Works
6. Conclusions
- (1)
- At the provincial level, the economy of Hebei province suffered declines in all quarters of 2020 and 2021, but showed a progressive recovery trend. At the prefectural scale, the economy of prefecture-level cities in Hebei province showed large economic fluctuations and a slow recovery process in the affected areas. By the end of 2021, the economies of all prefecture-level cities had recovered, except for Shijiazhuang, Cangzhou, Zhangjiakou, and Hengshui. At the county-level scale, the COVID-19 pandemic caused dramatic economic disturbances for county-level cities in Hebei Province. Although the economies of the county-level cities are recovering rapidly, the economies of 40 county-level cities have not yet recovered.
- (2)
- Overall, the economies of 49.1% of county-level cities in Hebei province were affected by the COVID-19 pandemic lockdown policy during 2020 and 2021. The unaffected, low-impact, moderate-impact, and high-impact areas all contain economically undeveloped, moderately developed, and developed county-level cities, indicating that the impact of the COVID-19 pandemic on the economy of Hebei Province is non-discriminatory.
- (3)
- During the initial and mid-term phases of the COVID-19 outbreak, the number of new infections correlates positively with the total monthly NTL of the city. In contrast, during the later phases of the outbreak, or under conditions where only a few cities suffered from the pandemic, the number of new infections did not correlate with the total monthly NTL of the city.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data | Data Description | Year | Source |
---|---|---|---|
NPP-VIIRS | Version 1 NPP-VIIRS monthly vcmsl NTL data | April 2012–December 2021 | Earth observation group |
MOD13A1 | Version 6 16-day EVI and 16-day pixel reliability index data | 2012–2021 | Atmosphere Archive and Distribution System and Distributed Active Archive Center |
GDP statistics | Quarterly GDP statistics for 31 Chinese provinces | 2012–2021 | National Bureau of Statistics of China |
Administrative boundaries | Administrative boundaries for 11 prefecture-level cities and 167 county-level cities in Hebei Province, and 31 provinces in China | 2015 | National Catalogue Service for Geographic Information |
Prefecture | 2020 Q1 | 2020 Q2 | 2020 Q3 | 2020 Q4 | 2021 Q1 | 2021 Q2 | 2021 Q3 | 2021 Q4 |
---|---|---|---|---|---|---|---|---|
Baoding | 32 | 6 | 0 | 0 | 11 | 0 | 0 | 15 |
Cangzhou | 48 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Chengde | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Handan | 32 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Hengshui | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Langfang | 30 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
Qinhuangdao | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Shijiazhuang | 29 | 0 | 0 | 0 | 896 | 0 | 0 | 136 |
Tangshan | 58 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Xingtai | 23 | 0 | 0 | 0 | 71 | 0 | 0 | 2 |
Zhangjiakou | 41 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
Quarter | 2020GDP | 2021GDP | ||||
---|---|---|---|---|---|---|
Estimated | Predictive | ERI | Estimated | Predictive | ERI | |
Q1 | 7410.13 | 8839.48 | −0.19 | 8750.48 | 9953.40 | −0.14 |
Q2 | 8977.12 | 9707.87 | −0.08 | 9988.86 | 10821.79 | −0.08 |
Q3 | 9417.12 | 10049.21 | −0.07 | 10321.40 | 11163.13 | −0.08 |
Q4 | 10402.52 | 10963.64 | −0.05 | 11330.53 | 12077.56 | −0.07 |
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Li, F.; Liu, J.; Zhang, M.; Liao, S.; Hu, W. Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data. Remote Sens. 2023, 15, 22. https://doi.org/10.3390/rs15010022
Li F, Liu J, Zhang M, Liao S, Hu W. Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data. Remote Sensing. 2023; 15(1):22. https://doi.org/10.3390/rs15010022
Chicago/Turabian StyleLi, Feng, Jun Liu, Meidong Zhang, Shunbao Liao, and Wenjie Hu. 2023. "Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data" Remote Sensing 15, no. 1: 22. https://doi.org/10.3390/rs15010022