Spatio-Temporal Distribution Characteristics and Drivers of PM2.5 Pollution in Henan Province, Central China, before and during the COVID-19 Epidemic.

PM2.5 is the main cause of haze pollution, and studying its spatio-temporal distribution and driving factors can provide a scientific basis for prevention and control policies. Therefore, this study uses air quality monitoring information and socioeconomic data before and during the COVID-19 outbreak in 18 prefecture-level cities in Henan Province from 2017 to 2020, using spatial autocorrelation analysis, ArcGIS mapping, and the spatial autocorrelation analysis. ArcGIS mapping and the Durbin model were used to reveal the characteristics of PM2.5 pollution in Henan Province in terms of spatial and temporal distribution characteristics and analyze its causes. The results show that: (1) The annual average PM2.5 concentration in Henan Province fluctuates, but decreases from 2017 to 2020, and is higher in the north and lower in the south. (2) The PM2.5 concentrations in Henan Province in 2017-2020 are positively autocorrelated spatially, with an obvious spatial spillover effect. Areas characterized by a high concentration saw an increase between 2017 and 2019, and a decrease in 2020; values in low-concentration areas remained stable, and the spatial range showed a decreasing trend. (3) The coefficients of socio-economic factors that increased the PM2.5 concentration were construction output value > industrial electricity consumption > energy intensity; those with negative effects were: environmental regulation > green space coverage ratio > population density. Lastly, PM2.5 concentrations were negatively correlated with precipitation and temperature, and positively correlated with humidity. Traffic and production restrictions during the COVID-19 epidemic also improved air quality.

Can Internet penetration curb the spread of infectious diseases among regions?-Analysis based on spatial spillover perspective.

Based on the outbreak of COVID-19, this paper empirically studied the impact of internet penetration on the incidence of class A and B infectious diseases among regions in spatial Dubin model, by using health panel data from 31 provinces in China from 2009 to 2018. The findings showed that: (1) The regional spillover effect of incidence of class A and B infectious diseases was significantly positive, and that is most obvious in the central regions. (2) Internet penetration not only has a positive effect on curbing the spread of infectious diseases within the local region but also help to inhibits the proximity spread of infectious diseases in neighborhood, showing the synergistic effect of "neighbor as a partner" in joint prevention and control mechanism. (3) The "digital gap" between regions, urban and rural areas, and user structures had led to significant group differences in the effect of the Internet on suppressing the spread of Class A and B infectious diseases. The findings of this paper provide a reference for understanding the potential role of the Internet in the COVID-19 and also provide policy support for the construction of Internet-based inter-regional "joint prevention and control mechanism" in public health events.

A Study on the Spatial-Temporal Evolution and Driving Factors of Non-Grain Production in China's Major Grain-Producing Provinces.

Food self-sufficiency in a large country with 1.4 billion people is very important for the Chinese government, especially in the context of COVID-19 and the Russian-Ukrainian conflict. The objective of this paper is to explore the spatial-temporal evolution and driving factors of non-grain production in thirteen major grain-producing provinces in China, which account for more than 75% of China's grain production, using 2011-2020 prefecture-level statistics. In the present study, the research methodology included GIS spatial analysis, hot spot analysis, and spatial Durbin model (SDM). The findings of this study are as follows: (1) The regions with a higher level of non-grain production were mainly concentrated in the central and western regions of Inner Mongolia, the middle and lower reaches of Yangtze River and Sichuan, while the regions with a low level of non-grain production were mainly distributed in the Northeast Plain. The regions with a higher proportion of grain production to the national total grain production were concentrated in the Northeast Plain, the North China Plain, and the Middle and Lower Yangtze River Plain of China. The hot spot regions with changes in non-grain production levels were mainly distributed in the Sichuan region and Alashan League City in Inner Mongolia, and the cold spot regions were mainly distributed in Hebei, Shandong, Henan, and other regions. (2) An analysis of the SDM indicated that the average air temperature among the natural environment factors, the ratio of the sum of gross secondary and tertiary industries to GDP, the ratio of gross primary industry to the GDP of economic development level, the urbanization rate of social development, and the difference in disposable income per capita between urban and rural residents of the urban-rural gap showed positive spatial spillover effects. The grain yield per unit of grain crop sown area of grain production resource endowment, the total population of social development, and the area sown to grain crops per capita of grain production resource endowment all showed negative spatial spillover effects. The research results of this paper can provide a reference for the country to carry out the governance of non-grain production and provide a reference for China's food security guarantee.

Spatial Effect of Digital Economy on Particulate Matter 2.5 in the Process of Smart Cities: Evidence from Prefecture-Level Cities in China.

During the COVID-19 pandemic, the digital economy has developed rapidly. The airborne nature of COVID-19 viruses has attracted worldwide attention. Therefore, it is of great significance to analyze the impact of the digital economy on particulate matter 2.5 (PM2.5) emissions. The research sample of this paper include 283 prefecture-level cities in China from 2011 to 2019 in China. Spatial Durbin model was adopted to explore the spatial spillover effect of digital economy on PM2.5 emissions. In addition, considering the impact of smart city pilot (SCP) policy, a spatial difference-in-differences (SDID) model was used to analyze policy effects. The estimation results indicated that (1) the development of the digital economy significantly reduces PM2.5 emissions. (2) The spatial spillover effect of the digital economy significantly reduces PM2.5 emissions in neighboring cities. (3) Smart city construction increases PM2.5 emissions in neighboring cities. (4) The reduction effect of the digital economy on PM2.5 is more pronounced in the sample of eastern cities and urban agglomerations.

Local Government Debt, Financial Circle, and Sustainable Economic Development

Local government debt is the biggest “gray rhino” of China’s economy and one of the most significant factors affecting the sustainability of economic growth. We use the macroeconomic data of China’s real economy development level and local government debt from 2000 to 2020 to investigate the impact of local government debt on the real economy using the spatial Durbin model, focusing on the impact of the local government debt scale on the development of the real economy in jurisdictions and non-jurisdictions and the intermediation effect of finance under the geospatial correlation characteristics of economic development. The results show that the spatial correlation of the real economy between jurisdictions prevails and the correlation deepens over time. The scale of local government debt in China has exceeded a reasonable threshold, and the crowding-out effect of debt expansion on the real economy is obvious and not limited by jurisdictions, with significant spatial spillover effects. Financial marketization can effectively mitigate the crowding-out effect of local government debt on the real economy. These findings provide useful references for mapping the correlated development characteristics of local government debt and the real economy in China, effectively preventing local government debt risks and high leverage of the real economy and financial systemic risks, and providing effective insights for other countries to resolve government debt problems, prevent crises, and promote local economic development.

Evolution and analysis of urban resilience and its influencing factors: a case study of Jiangsu Province, China.

This research uses panel data of cities in Jiangsu from 2009 to 2018 to construct a resilience framework that measures the level of urban resilience. A combination of the entropy method, Theil index, Moran ' sI , and the Spatial Durbin Model (SDM) is used to explore regional resilience development differences, the spatial correlation characteristics of urban resilience, and its influencing factors. The study finds that: (1) The spatial heterogeneity of regional resilience development is significant, as the overall level of resilience presents a spatial distribution pattern of descending from southern Jiangsu to central Jiangsu and to northern Jiangsu. (2) The total Theil index shows a wave-like downward trend during the study period. The differences between southern Jiangsu, central Jiangsu, and northern Jiangsu make up the main reason for the overall difference of urban resilience in Jiangsu Province. Among the three regions, the gap in resilience development level within southern Jiangsu is the largest. (3) There is a clear positive spatial correlation between urban resilience in the province and an obvious agglomeration trend of urban resilience levels. Among all subsystems, urban ecological resilience is the weakest and needs to be further improved. (4) Lastly, among the five factors affecting urban resilience, general public fiscal expenditure/GDP, which characterizes government factors, has the largest positive impact on urban resilience, while foreign trade has a negative impact. In the following studies, the theme of urban resilience should be constantly deepened, and more extensive data monitoring should be carried out for the urban system to improve the diversity of data sources, so as to assess urban resilience more accurately. Supplementary Information: The online version contains supplementary material available at 10.1007/s11069-022-05368-x.

Evaluating the Effects of Renewable Energy Consumption on Carbon Emissions of China’s Provinces: Based on Spatial Durbin Model

Renewable energy consumption is considered as the main form of energy consumption in the future. The carbon emissions produced by renewable energy can be approximately ignored, and renewable energy is essential for regional sustainable development. In this study, we used the Durbin model with panel data to explore the spatial dependence between renewable energy consumption the and carbon emissions of China’s 30 provinces from 1997 to 2017. The results show that: (1) there is a negative spatial correlation between renewable energy consumption and carbon emissions, and “High-Low” areas are mainly concentrated in southern provinces in 1997–2011;(2) the center of gravity of renewable energy consumption moves southwest, which is consistent with the center of gravity of carbon emissions;(3) renewable energy consumption has a significant inhibitory effect on carbon emissions of a local region, but the spatial spillover effect is not significant. Specifically, a 1% increase in renewable energy consumption in a region will reduce carbon emissions by 0.05%. Finally, on the basis of this study, it was proposed to give full play to the advantages of renewable energy in the western region, and further accelerate the development of the renewable energy industry.

Does government intervention affect CO2 emission reduction effect of producer service agglomeration? Empirical analysis based on spatial Durbin model and dynamic threshold model.

Achieving carbon peak and carbon neutrality is an inherent requirement for countries to promote green recovery and transformation of the global economy after the COVID-19 pandemic. As "a smoke-free industry," producer services agglomeration (PSA) may have significant impacts on CO2 emission reduction. Therefore, based on the nightlight data to calculate the CO2 emissions of 268 cities in China from 2005 to 2017, this study deeply explores the impact and transmission mechanism of PSA on CO2 emissions by constructing dynamic spatial Durbin model and intermediary effect model. Furthermore, the dynamic threshold model is used to analyze the nonlinear characteristics between PSA and CO2 emissions under different degrees of government intervention. The results reveal that: (1) Generally, China's CO2 emissions are path-dependent in the time dimension, showing a "snowball effect." PSA significantly inhibits CO2 emissions, but heterogeneous influences exist in different regions, time nodes, and sub-industries; (2) PSA can indirectly curb CO2 emissions through economies of scale, technological innovation, and industrial structure upgrading. (3) The impact of PSA on China's CO2 emissions has an obvious double threshold effect under different degree of government intervention. Accordingly, the Chinese government should increase the support for producer services, dynamically adjust industrial policies, take a moderate intervention, and strengthen market-oriented reform to reduce CO2 emissions. This study opens up a new path for the low-carbon economic development and environmental sustainability, and also fills in the theoretical gaps on these issues. The findings and implications will offer instructive guideline for early achieving carbon peak and carbon neutrality.

COVID-19 and the aviation industry: The interrelationship between the spread of the COVID-19 pandemic and the frequency of flights on the EU market.

This study aims to investigate the contribution of aviation related travel restrictions to control the spread of COVID-19 in Europe by using quasi-experiment approaches including the regression discontinuity design and a two-stage spatial Durbin model with an instrumental variable. The study provides concrete evidence that the severe curtailing of flights had a spontaneous impact in controlling the spread of COVID-19. The counterfactual analysis encapsulated the spillover effects deduced that a 1% decrease in flight frequency can decrease the number of confirmed cases by 0.908%. The study also reveals that during the lockdown, the aviation industry cancelled over 795,000 flights, which resulted in averting an additional six million people being from being infected and saving 101,309 lives.