Analysis on Decoupling between Urbanization Level and Urbanization Quality in China

The dynamic evolution and coupling coordination development mechanism of new urbanization and green economy: an empirical study based on various provinces in China

Under the background that the country is vigorously promoting the construction of new urbanization and seeking green development, this paper takes 31 provinces of China as the research object. It constructs a comprehensive evaluation system that integrates new urbanization and green economy into the same research framework, analyzes the evolution characteristics of new urbanization and green economy from the scale analysis of time and space, and, through the relative development model, coupling coordination model, and the random panel Tobit model, further explores the relative development and coupling coordination relationship between the two, deconstructing the internal driving factors. Research shows that the regional differences in the development level of new urbanization are decreasing year by year, while the efficiency of the green economy is increasing yearly. The relative development types of the two have obvious regional distribution characteristics. The level of coupling coordination gradually increases over time. The impact of economic development and growth, urban construction, regional opening up, urban expansion, government investment, industrialization level, and industrial structure on the coupling coordination degree of new urbanization and green economy is inconsistent.

The local coupling and telecoupling of urbanization and ecological environment quality based on multisource remote sensing data

Coordinating the relationship between urbanization and ecological environment quality (EEQ) is crucial to achieving sustainable development. With the development of globalization, the pattern of remote interaction between urbanization and EEQ has gradually increased. However, the current study on the coupling of urbanization and EEQ lacks a remote perspective, and the remote sensing ecological index (RSEI) model has not yet considered the environmental pollution caused by population agglomeration. For these reasons, this study proposes the remote sensing ecological environment index (RSEEI) model and measures the local coupling and telecoupling coordination degree (LTCCD) of urbanization and EEQ in China from 2000 to 2020. According to the results, the rate of change of EEQ in China was -0.00011a^-1. RSEEI widens the gap between the east and west of EEQ, differentiated by the Heihe-Tengchong Line. China's urbanization is growing at a 0.0008a^-1 rate, with a spatially driven radiation potential with Beijing, Shanghai, Hong Kong, and Macao as the core. LTCCD follows an increasing trend from inland to coastal and west to east. Over 70% of provinces experienced a shift in adjacent LTCCD levels, and 14 provinces moved from disorder to coordination after 2010. The telecoupling strengthens the correlation between urbanization and EEQ among regions compared with traditional coupling. In addition, the eastern coastal areas, the northern and central-south inland areas, and the northwest face different coordination problems.

Investigating into the Coupling and Coordination Relationship between Urban Resilience and Urbanization: A Case Study of Hunan Province, China

In the context of accelerated urbanization, constructing resilient cities is an effective approach to tackling risks, such as extreme weather, and various urban challenges. The coupling and coordinated development of urbanization and urban resilience is a prominent embodiment of urban sustainable development and high-quality development capacity. In this study, Hunan Province, China, which is frequently affected by various disasters, is selected as a representative for examining the coupling and coordination relationship between urban resilience and urbanization level. The panel data are adopted to construct a dual-system evaluation framework integrating urban resilience and urbanization level based on the entropy weight-coefficient of variation (CV)-CRITIC method. The coupling coordination degree of this dual-system evaluation framework is calculated with the coupling model in physics and GM (1, 1) grey prediction model. Additionally, the spatial–temporal evolution characteristics of the coupling coordination degree are investigated and analyzed by ArcGIS and Geoda software. The following are indicated from the results: (1) The resilience of all cities is related to their geographical location and is characterized by a decrease from east to west; in addition, the resilience level of most cities presents a downward trend with time. (2) The urbanization level of most cities develops stably with time, but there is a growing gap in the urbanization level between regions. (3) There is a strong correlation between urban resilience and urbanization level in all cities; the unbalanced coupling and coordinated development emerge, specifically manifested by the polarization phenomenon. Eventually, a circle-difference spatial distribution pattern that starts from the central urban agglomeration and gradually decreases to the periphery is formed. (4) The prediction results of the coupling coordination degree suggest that there is an increasingly distinct polarization trend for the coupling and coordinated development between cities, and it is necessary to pay attention to those cities with a declined predicted value. (5) There is a significant positive spatial autocorrelation and agglomeration effects in the distribution of the coupling coordination degree of all cities, and the correlation is getting stronger with each passing year; the correlation mode is mainly characterized by homogeneity and supplemented by heterogeneity. Finally, several suggestions are proposed in this paper, in an attempt to lead the coordinated development of regions by novel urbanization and thus promote the sustainable development of cities. The methods and insights adopted in this study contribute to investigating the relationship between urban resilience and urbanization in China and other regions worldwide.

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

Urbanization has an abundant connotation in dimensions such as population, economy, land, and society and is an important sign to measure regional economic development and social progress. The use of Night Light Data from remote sensing satellites as a proxy variable can significantly improve the accuracy and comprehensiveness of the measurement of urbanization development dynamics. Based on the Night Light Data and statistical data from 2015 to 2019, this paper quantitatively analyzes the spatio-temporal evolution pattern of urbanization in Guangxi and its driving mechanism using exploratory time-space data analysis, GeoDetector and Matrix: Boston Consulting Group, providing an important basis for sustainable urban development planning and scientific decision-making by the government. The findings show that (1) there is a high level of spatial heterogeneity and spatial autocorrelation of urbanization in Guangxi, with the Gini index of urban night light index and urban night light expansion vitality index always greater than 0.5, the global Moran’s I greater than 0.17, the spatial differentiation converging but the spatial correlation increasing. (2) The spatial pattern of urbanization in Guangxi has long been solidified, but there is a differentiation in urban development trend, with the coexistence of urban expansion and shrinkage, requiring differentiated policy design for urban governance. (3) The development and evolution of urbanization in Guangxi present a complex intertwined dynamic mechanism of action, with interaction effects of bifactor enhancement and non-linear enhancement among factors. It should be noted that the influence of factors varies greatly, with the added value of the tertiary industry, gross domestic product, total retail sales of social consumer goods having the strongest direct effect on the urban night light index, while the added value of secondary industry, per capita GDP, gross domestic product having the strongest direct effect on the urban night light expansion vitality index. All of them are key factors, followed by some significant influence factors such as government revenue, population urbanization rate, per government revenue, population urbanization rate, per capita disposable income of urban and rural residents that should not be ignored, and the rest that play indirect roles mainly by interaction.

Is Urbanization Good for the Health of Middle-Aged and Elderly People in China?—Based on CHARLS Data

The purpose of this paper is to test whether improved healthcare services can mitigate health hazards resulting from environmental pollution in the urbanization process. Specifically, using China Health and Retirement Longitudinal Study (CHARLS) data and official statistics, this paper constructs comprehensive urbanization indicators and healthcare service indicators by applying the fully arrayed polygonal graphical indication method. Then, we introduce healthcare service indicators, urbanization indicators, environmental pollution indicators, and the interaction term between environmental pollution and healthcare into an ordered-logistics regression model. Our results indicate that improvement in health conditions can decrease the health risks from multiplied emissions of industrial sulfur dioxide, industrial soot and dust, and industrial effluents, but it cannot counteract the adverse health effects of PM2.5. Furthermore, heterogeneity tests show that, when considering the multidimensionality of urbanization, the positive influence of healthcare is the greatest in residential surroundings urbanization and economic urbanization, which reduces the prevalence of chronic diseases by 18.4% and 14.9%, respectively. Among the diverse city types, mixed-economy cities have the most obvious positive effects, where healthcare has the greatest mitigating effect on the health damage caused by industrial sulfur dioxide and industrial soot and dust, decreasing the prevalence of chronic diseases among the middle-aged and elderly by 27.3% and 16.4%, respectively. When considering the regional impacts of urbanization, there is a large difference in the positive effects brought about by medical care, which is reflected mainly in eastern and western China. In eastern China, although healthcare does not offset the health damage of PM2.5, the increase in chronic diseases among the middle-aged and elderly is only 0.5%, while in western China, the increase rises to 22.4%.