ABSTRACT
The COVID-19 prevention and control measures are taken by China's government, especially traffic restrictions and production suspension, had spillover effects on air quality improvement. These effects differed among cities, but these differences have not been adequately studied. To provide more knowledge, we studied the air quality index (AQI) and five air pollutants (PM2.5, PM10, SO2, NO2, and O3) before and after the COVID-19 outbreak in Shanghai, Wuhan, and Tangshan. The pollution data from two types of monitoring stations (traffic and non-traffic stations) were separately compared and evaluated. We used monitoring data from the traffic stations to study the emission reduction caused by traffic restrictions. Based on monitoring data from the non-traffic stations, we established a difference-in-difference model to study the emission reduction caused by production suspension. The COVID-19 control measures reduced AQI and the concentrations of all pollutants except O3 (which increased greatly), but the magnitude of the changes differed among the three cities. The control measures improved air quality most in Wuhan, followed by Shanghai and then Tangshan. We investigated the reasons for these differences and found that differences in the characteristics of these three types of cities could explain these differences in spillover effects. Understanding these differences could provide some guidance and support for formulating differentiated air pollution control measures in different cities. For example, whole-process emission reduction technology should be adopted in cities with the concentrated distribution of continuous process enterprises, whereas vehicles that use cleaner energy and public transport should be vigorously promoted in cities with high traffic development level.
ABSTRACT
Under the background of normalized COVID-19 prevention and control, regional epidemics occur frequently in China. How to quantify the impact of COVID-19 prevention and control measures on economic operation and passenger and freight transportation has become an urgent problem. To this end, we design a calculation method for expressway transportation indicators, propose the level and stage division process of COVID-19 prevention and control measures, and then establish a difference-in-difference model to further analyze their impact on expressway transportation indicators. Taking major cities in the Guangdong-Hong Kong-Macao Greater Bay Area as an example, case studies are conducted based on the expressway toll data and COVID-19 prevention and control information from May 2020 to April 2022. The results show that in the level I (strengthened) stage, the passenger vehicle flow has dropped significantly, the drop in each case is between 8% and 27%, and the freight indicators have not changed significantly. In Shenzhen and Dongguan, both passenger and freight indicators dropped sharply in the level II (strict) stage. Passenger vehicle flow in the two cities dropped by 46.3% and 33.7%, and truck flow by 42.7% and 27.6%, respectively, and cargo and turnover decreased as much as truck flow. The average inter- city distance of expressway passenger cars has a downward trend under the level I stage, but under the level II stage, the average inter-city distance of passenger cars and trucks has increased significantly. This study can provide a certain reference value for the formulation and implementation of COVID-19 prevention and control measures in cities and urban agglomerations. © 2022 Science Press. All rights reserved.
ABSTRACT
After the pandemic, China's fiscal and monetary authorities implemented macroeconomic restructuring measures to combat the pandemic. Using a difference-in-difference model based on data collected during the COVID-19 phase, this study attempted to determine the economic recovery in China using the pandemic means for economic growth and energy consumption in other economies. A 0.21 percent increase in the western region's economic growth is comparable to a 0.15 percent increase in the growth of the southern central and northern regions during the pandemic period. Accordingly, we found evidence of actual provincial spillover effects in the clustering of high- and poor-performing regions. The impact of China's economic resurgence beyond the pandemic phase plays an important role in expanding power consumption in different regions. Since headwinds hamper economic development to aggregate output, fiscal policy is the sole option for maintaining pollution levels while simultaneously improving household well-being in terms of demand and employment.
ABSTRACT
This quasi-natural experimental study examined an online teaching intervention implemented in response to COVID-19 in China in 2020. It applied the difference-in-difference model to examine the impact and path of the intervention on students' learning performance of a college foreign language (LPCFL). Based on data from records of withdrawing and changing courses, classroom learning, and teaching evaluations; a questionnaire survey of teachers and students; and relevant school documents during the last seven terms, the results indicated that the online teaching intervention could significantly improve students' LPCFL. This finding remained robust after adopting a placebo test approach to mitigate possible endogeneity issues. Additionally, this study also conducted a group test through sub-sample regression based on students' discipline characteristics and intervention organization methods. The results showed that the students who participated in the intervention significantly improved in the three disciplines: humanities was most significantly affected, science and engineering were least significantly affected, and economics and management were in the middle. A range effect was observed for organizational methods. The two downward transmission methods by college teaching management terms had significant positive effects, whereas the other two methods of downward transmission by college student management had significant negative effects. An analysis of the action mechanism indicated that the online teaching intervention mostly improved LPCFL through two channels: students' learning input and learning support. Overall, these findings not only help expand the research framework on macro environmental intervention policy and micro-learning behavior but also have implications for the in-depth understanding of the real learning effect of online learning interventions for college students and their design in the post-COVID-19 era.
ABSTRACT
At the beginning of 2020, the novel coronavirus disease (COVID-19) became an outbreak in China. On January 23, China raised its national public health response to the highest level. As part of the emergency response, a series of public social distancing interventions were implemented to reduce the transmission rate of COVID-19. In this article, we explored the feasibility of using mobile terminal positioning data to study the impact of some nonpharmaceutical public health interventions implemented by China. First, this article introduced a hybrid method for measuring the number of people in public places based on anonymized mobile terminal positioning data. Additionally, the difference-in-difference (DID) model was used to estimate the effect of the interventions on reducing public gatherings in different provinces and during different stages. The data-driven experimental results showed that the interventions that China implemented reduced the number of people in public places by approximately 60% between January 24 and February 28. Among the 31 provinces in the Chinese mainland, some provinces, such as Tianjin and Chongqing, were more affected by the interventions, while other provinces, such as Gansu, were less affected. In terms of the stages, the phase with the greatest intervention effect was from February 3 to 14, during which the number of daily confirmed cases in China showed a turning point. In conclusion, the interventions significantly reduced public gatherings, and the effects of interventions varied with provinces and time.