Efficiency impacts can be assessed based on improvements in accessibility promoted by the high-speed rail (HSR) project, focusing mainly on major urban areas. Spatial equity impacts originate from changes in the distribution of accessibility levels observed across such cities. This study uses the weighted average travel time and coefficient of variation to explore the impact of HSR on efficiency and spatial equity within the Central Plains Economic Region (CPER) and to build an “efficiency-equity” model to identify optimal upgrading of conventional rail (CR) lines to improve the accessibility of the CPER and mitigate regional disparities. The results indicate that since the operation of HSR, accessibility levels across the CPER have improved by roughly 24.56%, and the spatial distribution of gains has been uneven since the most significant improvements have occurred in HSR cities and large cities. Inequality in regional accessibility has increased by 28.12%, creating a more heterogeneous pattern of accessibility. With goals of “efficiency” and “equity”, an examination of the upgrading of CR as an approach shows that “Xinxiang-Jiaozuo” and “Xinxiang-Jiaozuo-Sanmenxia-Yuncheng” are the best upgraded lines for improving the efficiency of the CPER and limiting regional disparities. The results of this study serve as supportive information for the planning and construction of HSR lines and networks and for underdeveloped regional transport system policymaking.

A large number of studies have dealt with the driven forces of land expansion, in which the remote sensing data and statistical data are most commonly used. The recent progress based on the statistical data have not been fully tested and discussed by the remote sensing data, and the remote sensing data used in the previous studies are usually interpreted within certain areas which is not convenient for global comparison. In this paper, the 30-m GlobalLand Cover Dataset (GlobeLand30) and socioeconomic data from 2000 to 2010 are adopted to investigate the factors driving impervious surface expansion in China based on a multilevel regression model. The GlobeLand30 provides a world-wide data framework which has a sound basis for regional comparison research. The variables are selected according to the existing research. Most, but not all, results are consistent with the previous studies when using impervious surface data of GlobeLand30. The main findings are: (1) the market demand caused by economic development, such as the increase in GDP from 2000 to 2010, plays a positive role in the expansion of developed land; (2) the land supply, as reflected by the ratio of the total of land transfer fees to fiscal revenue, also has a positive effect on the increase in impervious surfaces; (3) the percentage of the increase by private workers to the increase in total workers and certain other frequently-used variables are not relevant after controlling for land demand- and supply-related variables; and (4) the growth in impervious surfaces is related to the amount of the cultivated land, which implies the necessity for a more stringent farmland protection policy. Considering the need to compare across regions, we suggest that GlobeLand30 should be used for more studies to better understand the driving forces of land expansion.

Investigation of the spatial transfer laws and dynamic mechanisms of pollution-intensive industries (PIIs) is becoming a popular field in regional sustainable development. Based on the statistical data of 30 provinces (cities and districts) in China from 2000 to 2017, this paper applied the Gini coefficient and a redistribution index as well as spatial econometric approaches to explore the spatial distribution characteristics and spatial effects of China’s PIIs. PIIs in China have experienced two transition stages: ‘from north to south’ and ‘from east to central and west’, and the spatial distribution imbalance of PIIs has been gradually improved. In terms of industries, all PIIs in the northeast region were removed; PIIs in the eastern region not only transferred outward but also have experienced an agglomeration effect. The central and western regions were the main areas where transferring PIIs were settling. The distribution of PIIs in China showed a strong spatial correlation and a relatively stable path dependence. Through use of the spatial Dubin model, it is concluded that command-and-control environmental regulation and transportation costs had a negative impact on the distribution of PIIs in this region and a positive impact on the surrounding regions; thus, the pollution haven hypothesis was supported. Resource factors, technological innovation levels, and industrial structure—whether direct or indirect—had an inhibitory effect on the distribution of PII. Capital factors not only promoted the development of PIIs in this region, but also promoted it in other regions. Agglomeration economics had a positive impact on the distribution of PIIs in this region, and a negative impact on the surrounding regions.

The rapid development of high-speed rail (HSR) and station areas has shortened the spatial and temporal distances among cities, improved the accessibility of cities, and affected the spatial agglomeration and diffusion of populations and of social and economic activities. This has led to spatial reconfiguration of production factors within cities, which has the potential to drive the reconstruction of urban spatial structures. Based on POI and land-use data, this paper defines the spatial scope of the HSR station area and explores the characteristics and influencing factors of its spatial structure from the perspective of industry. The study area i is set at 2000 m. Since the opening of the HSR, the industrial distribution has exhibited a significant circular, multi-core, and axial belt spatial structure. The spatial structure of each sub-industry is different. On the whole, internal and external transport and agglomeration economies have significant impacts on the industrial spatial distribution, and land rent has gradually decreased in importance with the development of HSR station areas. The intensity of the effects of different factors varies among different industries. The mechanisms by which the spatial structure of the station area is formed are discussed and include location accessibility, micro-market factors, node station attributes, the availability of a sufficient amount of undeveloped land, the characteristics and needs of HSR passengers, and policies and systems.

The impact of high-speed rail (HSR) on urban spatial structure has attracted much attention since the 1970s. It mainly realizes the change of urban spatial structure by affecting the spatial distribution of population and economy. Based on population and industry data on 29 cities in the Central Plains Urban Agglomeration (CPUA) located in central China during 2005–2017, in this paper difference-in-difference (DID) models are utilized to explore the influence of HSR on the spatial structure of prefecture-level cities at the single/multicenter levels according to the dimensions of polycentricity and dispersion. The main conclusions are shown as: (1) HSR on the spatial structure of the CPUA has a monocentric trend, but the characteristics of different prefecture-level cities vary. Currently, agglomeration remains the dominant force of the spatial structure of the CPUA in prefecture-level cities. (2) HSR have a noticeable effect on the spatial structure of industry, whereas the influences of the HSR on the spatial structure of the population are insignificant. Its development is the result of the combined effects of many factors, including natural factors, socioeconomic factors, policies, and transportation factors. Among the controlling factors, the administrative area, economic development level, urban population, and number of research units are the critical factors having a hold on the population spatial structure of the CPUA.