Multi-scale urban innovation networks are important channels for intra- and inter-city knowledge spillovers and play an important role in urban industrial innovation and growth. However, there is a lack of direct evidence on the impact of multi-scale urban innovation networks on industrial development. Drawing upon the “buzz-and-pipeline” model, this paper analyzes the impact of multi-scale urban innovation networks on industrial development by taking the automobile manufacturing industry in China’s five urban agglomerations as an example. Firstly, based on the Form of Correlation between International Patent Classification and Industrial Classification for National Economic Activities (2018) and co-patents, we construct urban innovation networks on three different geographical scales, including intra-city innovation networks, inter-city innovation networks within urban agglomerations, and innovation networks between cities within and beyond urban agglomerations. Then, we employ the ordinary least squares model with fixed effects at the urban agglomeration level to explore the impact of urban multi-scale knowledge linkages on the development of the automobile manufacturing industry and the results showed that urban innovation networks at three different geographical scales have different impacts on industrial development. Specifically, intra-city innovation networks have a facilitating effect on industrial development, while both inter-city innovation networks within urban agglomerations and innovation networks between cities within and beyond urban agglomerations have an inverted U-shaped impact on industrial development. The interactions between urban innovation networks on three different geographical scales have a negative effect on industrial development. Simultaneously, the agglomeration level of urban industry plays a positive moderating role in the impacts of multi-scale urban innovation networks on industrial development.

Despite the efforts to examine the influence of urban forms on CO₂ emissions, most studies have mainly measured urban forms from a two-dimensional perspective, with relatively little attention given to three-dimensional urban forms and their causal relationships. Utilizing the built-up area dataset from the Global Human Settlement Layer (GHSL) project and the carbon emission dataset from the China City Greenhouse Gas Working Group (CCG), we examine a causal and heterogeneous effect of three-dimensional urban forms on CO₂ emissions—specifically urban height, density, and intensity—in 285 Chinese cities. The empirical results reveal a robust and positive causal effect of 3D urban forms on carbon emissions. Even when incorporating the spatial spillover effect, the positive effect of 3D urban forms remains. Moreover, GDP per capita and total population have a greater impact on urban CO₂ emissions. Additionally, we find that the influence of 3D urban forms on CO₂ emissions is U-shaped, with total population serving as a moderating factor in this effect. Importantly, there is significant geographic and sectoral heterogeneity in the influence of 3D urban forms on CO₂ emissions. Specifically, the influence of 3D urban forms is greater in eastern cities than in non-eastern cities. Furthermore, 3D urban forms primarily influence household carbon emissions rather than industrial and transportation carbon emissions. Therefore, in response to the growing challenges of global climate change and environmental issues, urban governments should adopt various strategies to develop more rational three-dimensional urban forms to reduce CO₂ emissions.

The metropolitan fringe area is of great significance to a city’s future growth. However, relatively little attention has been paid to delineating and characterizing the metropolitan fringe area from a spatial morphology perspective, which contributes to the planning and design of metropolitan fringe areas. Therefore, the aim of this study is to develop a morphology–based method to delineate the metropolitan fringe area and investigate the characteristics of its spatial morphology. Drawing upon a large–scale dataset on the spatial morphology of Shanghai, this study finds that the metropolitan fringe area is generally circular in shape and is dominated by residential, industrial, agricultural and forestry land. The metropolitan fringe area accounts for 24.65% of the total area of Shanghai and is mainly located between its outer ring and suburban ring areas. The distributions of spatial characteristics of the metropolitan fringe area suggest that the area has a relatively lower level in terms of building height, building density, and development intensity. Furthermore, the metropolitan fringe area of Shanghai contains five key spatial elements, including residential and industrial clusters, shadow spaces accompanying clusters, corridor lines, green wedges, and surfaces. The interaction of the five spatial elements lays the foundation for the prototype of the spatial structure of the metropolitan fringe area of Shanghai, which is of great significance to understanding the heterogeneity within the metropolitan fringe area in terms of the distribution of spatial morphological characteristics. Such heterogeneity also needs to be considered in the planning and design of the metropolitan fringe area.

While the benefits of land-use mix have been widely analyzed with regard to transportation, public health, and economic development, relatively little attention has been paid to empirically investigating the impact of land-use mix on technological innovation at the intra-urban level. Drawing upon a database of geo-coded patents that are used to reflect the capacity of technological innovation, this paper takes Shanghai as a case study and analyzes how the intra-urban distribution of technological innovation has been associated with land-use mix at the 1 km × 1 km grid cell level. Empirical results, which are robust when the grids are divided at the 2 km × 2 km level, show that the degree of land-use mix is positively associated with the number of patents for a given grid, suggesting that grids with a higher level of land-use mix are likely to have more patents, ceteris paribus. Moreover, the results demonstrate an inverted U-curve relationship between land-use mix and technological innovation, indicating that a too much higher level of land-use mix could lead to a smaller number of patents for a certain grid. In addition, the empirical results suggest the existence of spatial dependence in the effect of land-use mix on technological innovation.

Numerous studies have suggested a positive correlation between spatial and population densities. However, few have systematically conducted quantitative analysis and deciphered the detailed correlation in block scale. Here, we construct a population–space correlation algorithm to quantify and compare the correlation between mobile phone signalling data and vector spatial data and identify blocks with uneven population density. We analyse the influences of various urban spatial characteristics on population density and the distribution characteristics of the identified city blocks. Changzhou City, China, was selected as the study case. The results indicate that (1) population density distribution is unbalanced only when spatial density exceeds a critical value, reflecting the level and sphere of influence of blocks with varying spatial densities; (2) low population density distribution is concentrated in the zonal space, along the boundary between primary and secondary urban centres; (3) spatial characteristics affecting population density distribution vary with the type of block, and the green landscape’s attractiveness is reduced. Our study provides a novel perspective on quantifying the link between urban form and population distribution. It can help decision-makers and planners in accurately recommending urban intervention in population density distribution by adjusting the spatial morphology and promoting rational use of urban public resources.

Urban shadow areas, formed by long-term unbalanced and inadequate development during the rapid process of urbanization, are of great significance to a city’s overall development. However, relatively little attention has been paid to identifying and characterizing urban shadow areas. Drawing upon a dataset on urban morphology and cellular signaling, and taking Nanjing as a case study, this paper proposes a method to identify urban shadow areas from the perspective of people flows. The empirical results show that there are 19 urban shadow areas within the downtown areas of Nanjing, 11 of which are distributed in the old downtown areas and the rest are relatively scattered in the periphery. As for morphological characteristics, these urban shadow areas differ from each other in terms of indicators such as building density and development intensity. Moreover, the empirical results show that these urban shadow areas are not isolated but closely connected with other parts of Nanjing. Based on the different spatio-temporal distribution patterns of their connections, the 19 urban shadow areas are divided into four types, and the characteristics of each type have been investigated by analyzing a representative shadow area. It is suggested that policies aiming to eliminate the negative effects of urban shadow areas should consider heterogeneity in their spatial distributions within a city, the temporal distribution of their external connections, and their dominant functions.