Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data

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.

Assessment of Earthquake Damage and Economic Recovery in Hatay Province, Turkey Using Nighttime Light Data

“…If the slopes of the post-earthquake SVANUI trajectories (k1 and k3) are greater than the slope of the pre-earthquake SVANUI trajectory (k0), it is reasonable to assume that the regional economy will experience a rebound; If the slope of the post-earthquake SVANUI trajectory (k2) is first greater than k0 and then less than or equal to k0, it can be concluded that the regional economy will not recover within a specified period. Similarly, if the slope of the postearthquake SVANUI trajectory (k4) is less than 0, it can be inferred that the regional economy is in recession and is not expected to recover in the short term [60].…”

“…The annual NTL data were also adopted to develop a resilience model for disaster recovery after the 2008 Wenchuan earthquake and to analyze potential factors that influence the economic recovery process [28,29]. Li et al [30] used monthly NTL data to estimate the quarterly gross domestic product (GDP) at the prefecture and county levels in Hebei Province, China. In addition, they designed an economic recovery intensity model to assess the extent of economic recovery in Hebei Province during the pandemic.…”

Human activities along southwest border of China: Findings based on DMSP/OLS Nighttime light data

“…Because of its reliance only on endogenous variable and not requiring assistance from other exogenous variables, it can be effectively combined with NTL data. The ARIMA model has not been widely combined with NTL data, so it has only been applied to predict the GDP of various provinces in China [ 52 , 53 ]. In other fields, the combination of the ARIMA model and NTL data still holds great potential for application, so we are applying it to predict human activities along border areas.…”

From Agricultural Green Production to Farmers’ Happiness: A Case Study of Kiwi Growers in China

“…Data were collected using questionnaires that assessed the happiness of farmers, farmers' adoption of AGP, mediation variables, and control variables. As the COVID-19 pandemic has been going on in China for more than three years [84], the current study may have been limited in terms of the depth and breadth of data. Panel data were not used for the study.…”