Seven super urban agglomerations, governments’ debt risk, and land leasing, China

2021 ◽  
Author(s):  
Juanfeng Zhang ◽  
Lele Li ◽  
Danxia Zhang ◽  
Jie Gu
Keyword(s):  
Urban Agglomerations ◽  
Land Leasing

The Impact of High-Speed Rail on the Spatial Structure of Chinese Urban Agglomerations

2020 ◽  
Vol 46 (3) ◽  
pp. 379-397
Author(s):  
Chunyang Wang
Keyword(s):  
High Speed ◽  
River Delta ◽  
City Size ◽  
High Speed Rail ◽  
Local Conditions ◽  
Urban Agglomerations ◽  
Large Cities ◽  
Spatial Systems ◽  
Economic Concentration ◽  
The Impact

This paper measures the spatial evolution of urban agglomerations to understand be er the impact of high-speed rail (HSR) construction, based on panel data from fi ve major urban agglomerations in China for the period 2004–2015. It is found that there are signi ficant regional diff erences of HSR impacts. The construction of HSR has promoted population and economic diff usion in two advanced urban agglomerations, namely the Yang e River Delta and Pearl River Delta, while promoting population and economic concentration in two relatively less advanced urban agglomerations, e.g. the middle reaches of the Yang e River and Chengdu–Chongqing. In terms of city size, HSR promotes the economic proliferation of large cities and the economic concentration of small and medium-sized cities along its routes. HSR networking has provided a new impetus for restructuring urban spatial systems. Every region should optimize the industrial division with strategic functions of urban agglomeration according to local conditions and accelerate the construction of inter-city intra-regional transport network to maximize the eff ects of high-speed rail across a large regional territory.

scholarly journals Spatio-Temporal Coupling Characteristics and the Driving Mechanism of Population-Land-Industry Urbanization in the Yangtze River Economic Belt

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 400
Author(s):  
Liejia Huang ◽  
Peng Yang ◽  
Boqing Zhang ◽  
Weiyan Hu
Keyword(s):  
Economic Development ◽  
Spatial Correlation ◽  
Yangtze River ◽  
Three Dimensional ◽  
Driving Mechanism ◽  
The Yangtze River ◽  
Central Cities ◽  
Urban Agglomerations ◽  
Development Level ◽  
Coordination Degree

The purpose of this paper is to probe into the coupled coordination of urbanization in population, land, and industry to improve urbanization quality. A coupled coordination degree model, spatial analysis method and spatial metering model are employed. The study area is 110 prefecture-level cities in the Yangtze River Economic Belt. The study shows that: (1) the coupling degree of the population-land-industry urbanization grew very slowly between 2006 and 2016. On the whole, the three-dimensional urbanization is in a running-in period, and land-based urbanization dominates, while population-based urbanization and industry-based urbanization are relatively lagging behind. (2) The three major urban agglomerations, the Chengdu-Chongqing, the middle reaches of the Yangtze River and the Yangtze River Delta, are parallel to the whole area in terms of the coupling degree of the three dimensional urbanization with a well-ordered structure, especially in the central cities of the three major urban agglomerations. (3) There is significant spatial correlation in the coupling degree and coordination degree of the three-dimensional urbanization. The high value of coupling degree and coordination degree are clustered continuously in developed cities, provincial capitals, and central cities of the downstream reaches of the Yangtze River. (4) The coordinated degree has significant positive spatial autocorrelation, showing obvious spatial agglomeration characteristics: H-H agglomeration areas are concentrated in the downstream developed areas such as Jiangsu, Zhejiang, and Shanghai. L-L agglomeration areas are mainly concentrated in upstream undeveloped areas, but the number of their cities shows a decreasing trend. (5) The coordination degree of the three-dimensional urbanization is the result of the comprehensive effect of economic development level, the government’s decision-making behavior, and urban location. Among them, the economic development level, urbanization investment, traffic condition, and urban geographical location play a decisive role. This paper contributes to the existing literatures by exploring urbanization quality, spatial correlation and influencing factors from the perspectives of the three-dimensional urbanization in the Yangtze River Economic Belt. The conclusion might be helpful to promote the coupling and coordinated development of urbanization in population-land-industry, and ultimately to improve urbanization quality in the Yangtze River Economic Belt.

scholarly journals Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

2021 ◽  
Vol 18 (5) ◽  
pp. 2222
Author(s):  
Jin-Wei Yan ◽  
Fei Tao ◽  
Shuai-Qian Zhang ◽  
Shuang Lin ◽  
Tong Zhou
Keyword(s):  
Geographically Weighted Regression ◽  
Yangtze River ◽  
National Level ◽  
National Development ◽  
Urban Agglomeration ◽  
Spatiotemporal Distribution ◽  
Weighted Regression ◽  
Series Data ◽  
The Yangtze River ◽  
Urban Agglomerations

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

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