Spatial spillover effect of environmental factors on the tuberculosis occurrence among the elderly: a surveillance analysis for nearly a dozen years in eastern China

BACKGROUND

In many areas of China, over 30% of tuberculosis cases occur among the elderly. We aimed to investigate the spatial distribution and environmental factors that predicted the occurence of tuberculosis in this group.

METHODS

Data were collected on notified pulmonary tuberculosis (PTB) cases aged ≥ 65 years in Zhejiang Province from 2010 to 2021. We performed spatial autocorrelation and spatial-temporal scan statistics to determine the clusters of epidemics. Spatial Durbin Model (SDM) analysis was used to identify significant environmental factors and their spatial spillover effects.

RESULTS

77,405 cases of PTB among the elderly were notified, showing a decreasing trend in the notification rate. Spatial-temporal analysis showed clustering of epidemics in the western area of Zhejiang Province. The results of the SDM indicated that a one-unit increase in PM2.5 led to a 0.396% increase in the local notification rate. The annual mean temperature and precipitation had direct effects and spatial spillover effects on the rate, while complexity of the shape of the greenspace (SHAPE_AM) and SO2 had negative spatial spillover effects.

CONCLUSION

Targeted interventions among the elderly in Western Zhejiang may be more efficient than broad, province-wide interventions. Low annual mean temperature and high annual mean precipitation in local and neighboring areas tend to have higher PTB onset among the elderly.

Digital economy and inclusive green growth: The moderating effect of government environmental regulation

As digital economy flourishes, there have been fundamental changes in the patterns of economic development, social structure, and ecological environment, which are the key elements of inclusive green growth (IGG). However, whether digital economy can effectively contribute to IGG requires further investigation. This paper evaluates the direct and spatial spillover effect of digital economy on IGG of 280 cities in China during 2011-2019 using Spatial Durbin Model (SDM). It further explores how government environmental regulation (GER) moderates this facilitating effect. The empirical results show that digital economy significantly reinforces IGG while GER weakens the benefits of digital economy to IGG. There is a significant spatial spillover effect of digital economy on IGG in neighboring regions; specifically, IGG in peripheral cities is more significantly encouraged by digital economy than in central cities. In addition, digital development demonstrates the most significant impact on IGG in central regions of China, followed by the eastern regions, with western regions lagging behind. This paper contributes theoretically and empirically to promote digital economy selectively for IGG in different regions and provides a guideline for policymakers to achieve IGG through proper GER.

Spatial influence of engineering construction on traffic accidents, a case study of Jinan

Due to urban construction, engineering transport vehicles are gradually increased on roads, which might speed up traffic accident risks. To investigate the influence of urban construction on traffic accidents, this paper adopted 1977 traffic accidents of engineering transport vehicles and 220 engineering construction projects for correlation analysis. First, considering three degrees (Major, Ordinary and Minor) of accidents, the spatial autocorrelation test of engineering transport vehicle accidents is carried out by using spatial econometric. Then to further evaluate and analyze the spatial regression model, the optimal model is selected to analyze the spatial influence of the floor area of different types of engineering construction projects on the accidents of engineering transport vehicles. The results show that the accident of engineering transport vehicles itself is spatially dependent, that is, the higher the severity of the accident, the more concentrated it is in space, and there is a significant spatial positive correlation with engineering construction projects. And the floor areas of synthetic land, residential land, commercial land and land for roads and transportation facilities have significant spatial effects on engineering transport vehicle accidents, and the indirect effects are also concerned. The increase of floor area of roads and transportation facilities is more likely to induce accidents of engineering transport vehicles. For every 10,000 square meters of the floor area of roads and transportation facilities, there are 12.66 accidents of engineering transport vehicles in the region and its neighboring areas.

Nonlinear Influence of Chinese Real Estate Development on Environmental Pollution: New Evidence from Spatial Econometric Model

Environmental pollution is a problem of universal concern throughout the globe. The development of real estate industry not only consumes huge resources, but also has close ties with high-consumption industries such as the construction industry. However, previous studies have rarely explored the impact of real estate development on environmental pollution. Therefore, this paper employs the entropy method to construct a comprehensive index of environmental pollution based on panel data of 31 provinces in China from 2000 to 2017, and empirically examines the impact of real estate development on environmental pollution. This article uses real estate investment to measure the development of the real estate industry. In view of the high spatial autocorrelation of environmental pollution, this paper selects a spatial econometric model. The empirical study found that: (1) By using the Spatial Durbin Model, real estate development has an inverted U-shaped impact on environmental pollution. Meanwhile, most cities have not yet reached the turning point; that is, with the continuous development of the real estate industry, environmental pollution will continue to increase. (2) Further regional heterogeneity found that the inverted U-shaped relationship still exists in coastal and inland areas. (3) Finally, this article used the Spatial Mediation Model to explain the nonlinear impact of real estate development on environmental pollution, with two important mediating variables: population density and industrial structure. Through the above analysis, it can be observed that real estate development has a significant impact on environmental pollution. Thus, the country and the government can reduce environmental pollution by improving the investment structure, using environmentally friendly building materials, guiding population flow and promoting industrial upgrading.

Differential Impact of Social Distancing on COVID-19 Spread in the U.S.: By Rurality and Social Vulnerability

Background

To quantify lessons learned to better prepare for similar pandemic crisis in the future, we assess the overall impact of social distancing on the daily growth rate of COVID-19 infections in the U.S. during the initial phase of the pandemic and the impacts' heterogeneity by urbanity and social vulnerability of the counties. The initial phase is chosen to purposely identify the essential and largest impact of the first-line of defense measure for similar pandemic: social distancing.

Methods

Spatial Durbin models with county fixed effects were used to account for spatial dependencies and identify spatial spillover effects and spatial heterogeneity.

Results

Besides the substantial curve flattening effects of social distancing, our results show significant spillover effects induced by neighboring counties' social distancing levels even in the absence of significant within-county effects. Urban and areas with high social vulnerability are the ones benefit the most from social distancing and high level of compliance is needed. Moderate level is enough in reaching the peak marginal impact in rural and areas with low social vulnerability.

Influential Factors and Spatiotemporal Characteristics of Carbon Intensity on Industrial Sectors in China

Based on the extended STIRPAT model and panel data from 2005 to 2015 in 20 industrial sectors, this study investigates the influential factors of carbon intensity, including employee, industry added value, fixed-assets investment, coal consumption, and resource tax. Meanwhile, by expanding the spatial weight matrix and using the Spatial Durbin Model, we reveal the spatiotemporal characteristics of carbon intensity. The results indicate that Manufacturing of Oil Processing and Coking Processing (S7), Manufacturing of Non-metal Products (S10), Smelting and Rolling Process of Metal (S11), and Electricity, Gas, Water, Sewage Treatment, Waste and Remediation (S17) contribute most to carbon intensity in China. The carbon intensity of 20 industrial sectors presents a spatial agglomeration characteristic. Meanwhile, industry added value inhibits the carbon intensity; however, employee, coal consumption, and resource tax promote carbon intensity. Finally, coal consumption appears to have spillover effects, and the employee has an insignificant impact on the carbon intensity of industrial sectors.

Spatial effects of carbon emission intensity and regional development in China

Due to the imbalance of technological level and industrial structure in regional economic development, the same carbon source can bring differentiated carbon emission levels in different regions, thus making the carbon emission show significant regional differences. In order to explore the regional differences in China's provincial carbon emission intensity and the effect of relevant influencing factors, this paper combines EKC model and STIRPAT model to conduct research. Using carbon emission intensity and other influencing factors of China's 30 provinces ranging from 2005 to 2017 to construct a panel data, the authors use exploratory spatial data analysis and Spatial Durbin Model to study the spatial effect of carbon emission intensity in China's provincial regions and the impact of different development factors on carbon emission intensity. The results show that from 2005 to 2017, China's carbon emission intensity gradually declined from east to west and from south to north. The inter-provincial carbon emission intensity of China presents an agglomeration effect in space, and the agglomeration effect gradually weakens with time. In addition, reducing energy intensity can reduce carbon emission intensity to a large extent. By optimizing industrial structure, increasing the degree of foreign trade and promoting financial development, carbon emission intensity can also be inhibited. Therefore, reducing the energy intensity of various industries and establishing inter-regional carbon emission cooperation mechanism will be effective to control the carbon emission intensity.

Impacts of dual decentralization on green total factor productivity: evidence from China's economic transition

China's fiscal decentralization has the dual characteristics of revenue decentralization and expenditure decentralization, and their impacts on green total factor productivity are also different. By calculating the Malmquist-Luenburger index and using the Spatial Durbin Model, this study examines the effects of dual decentralization on green total factor productivity (GTFP) from the perspective of fiscal competition. It was found that revenue decentralization significantly hindered the improvement of a local area's GTFP, and that the impact of fiscal revenue competition on a local area's GTFP is also significantly negative. The expenditure decentralization and the fiscal expenditure competition among different areas are conducive to improving the local area's GTFP. Moreover, the impact of revenue decentralization on efficiency improvement is significantly negative, while the expenditure decentralization is conducive to technical progress. All these findings may provide enlightenment for optimizing China's fiscal decentralization and promoting high-quality economic development.

Factors influencing carbon dioxide emissions in Iran's provinces with emphasis on spatial linkages

Current economic policy planning places much emphasis on balancing development and environmental protection. Hence, it is important to determine the drivers of environment pollution from the theoretical, scientific, and policymaking aspects in the context of continuous economic growth. This paper investigates the factors affecting per capita CO₂ emissions in 30 provinces in Iran from 2009 to 2014 with emphasis on spatial spillover effects using the Spatial Durbin Model. The findings show that per capita CO₂ emissions are positively and significantly affected by per capita GDP, industrialization, and urbanization but negatively affected by changes in population. The results of the spatial section of the model indicate that both the more and the less-polluted provinces tend to cluster together, indicating positive spatial dependence for CO₂ emissions in the provinces. Also, the spatial spillover of per capita GDP and the growth of urbanization have a negative and significant effect on per capita CO₂ emissions in the provinces, while the spatial effect of changes in population is significant but positive. In other words, the economic development and rise in urbanization in one province are the results of changes in pollution levels in neighboring provinces.