Promoting new-type urbanization with the concept of green development has become an inevitable requirement for high-quality development in the Yellow River Basin. Grasping the development trend and influencing factors of green urbanization level in the Yellow River Basin is of great significance for implementing the international conventions on environmental protection and participating in global environmental governance. This paper selects the green urbanization level panel data of nine provinces in the Yellow River Basin from 2006 to 2018. Then, principal component analysis and factor analysis are applied to measure and evaluate the green urbanization level of each province. Furthermore, this paper constructs a dynamic panel estimation model and uses differential generalized method of moments (DIF-GMM) model and system generalized method of moments (SYS-GMM) model to explore the influencing factors. The results show that the overall level of green urbanization in the Yellow River Basin has steadily and rapidly increased, and there are significant spatial differences. The green urbanization level of eastern provinces is significantly higher than that of central and western provinces. In addition, the overall level of green urbanization shows a convergence trend. From the perspective of influencing factors, the factors that have significant positive effects on the level of green urbanization include economic development level, technological innovation level, and urban size. Industrial structure, foreign direct investment (FDI), and education level counteract the level of green urbanization. However, environmental regulation strength and opening degree fail to pass the significance test. Therefore, it is necessary to promote and upgrade industrial transformation, improve the quality of opening up, and strengthen cooperation in technological innovation and environmental governance. There are requirements that the government control the urban size and population scientifically and implement the environmental access system strictly in order to improve the level of green urbanization in the Yellow River Basin. It is more possible to achieve harmonious economic and ecological environment development.
Access to public health services is a cause that benefits the people and concerns the vital interests of the people. Everyone has access to basic health care services. The continuous improvement in people’s health is an important indicator of the improvement in people’s quality of life. This paper selects data from the European Union (EU) on aspects of public health expenditure, medical care resources, and government emergency coordination capacity from the period 2008 to 2017. Principal component analysis and factor analysis are used to measure their public health service capacity scores and conduct a comparative analysis. On this basis, the TOBIT model is adopted to explore the driving factors that lead to the spatial differentiation of public health service capabilities, and to combine it with the data of the COVID-19 epidemic as of 8 August 2020 from the official announcements of the World Health Organization and governments for further thinking. The results indicate that the public health service capacity of countries in the EU is showing a gradual increase. The capacity in Western Europe is, in turn, higher than that of Northern Europe, Southern Europe and Eastern Europe. In addition, the overall capacity in Western Europe is relatively high, but it is not balanced and stable, while Northern Europe has remained stable and balanced at a high level. Population density, degree of opening up, education level, economic development level, technological innovation level, and degree of aging have a positive effect on public health service capabilities. The level of urbanization has a negative effect on it. However, in countries with strong public health service capabilities, the epidemic of COVID-19 is more severe. The emergence of this paradox may be related to the detection capabilities of countries, the high probability of spreading thCOVID-19 epidemic, the inefficient implementation of government policy, the integrated system of the EU and the adverse selection of youth. This paper aims to improve the ability of the EU to respond to public health emergencies, improve the utilization of medical and health resources, and better protect people’s health from the perspective of public health service capacity.
It is greatly important to promote low-carbon green transformations in China, for implementing the emission reduction commitments and global climate governance. However, understanding the spatial spillover effects of carbon emissions will help the government achieve this goal. This paper selects the carbon-emission intensity panel data of 11 provinces in the Yangtze River Economic Belt from 2004 to 2016. Then, this paper uses the Global Moran’s I to explore the spatial distribution characteristics and spatial correlation of carbon emission intensity. Furthermore, this paper constructs a spatial econometric model to empirically test the driving path and spillover effects of relevant factors. The results show that there is a significant positive correlation with the provincial carbon intensity in the Yangtze River Economic Belt, but this trend is weakening. The provinces of Jiangsu, Zhejiang, and Shanghai are High–High agglomerations, while the provinces of Yunnan and Guizhou are Low–Low agglomerations. Economic development, technological innovation, and foreign direct investionFDI) have positive effects on the reduction of carbon emissions, while industrialization has a negative effect on it. There is also a significant positive spatial spillover effect of the industrialization level and technological innovation level. The spatial spillover effects of FDI and economic development on carbon emission intensity fail to pass a significance test. Therefore, it is necessary to promote cross-regional low-carbon development, accelerate the R&D of energy-saving and emission-reduction technologies, actively enhance the transformation and upgrade industrial structures, and optimize the opening up of the region and the patterns of industrial transfer.
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