The level of service-industry development has become an important symbol of the competitiveness and influence of cities. The study of the dynamic evolution characteristics and patterns of urban service-industry land use, the driving factors and their interactions is helpful to provide a basis for decision making in policy design and land use planning for the development of service economies. In this study we have conducted an empirical study of China, based on the methods of spatial cold- and hot-spot analysis, Tapio’s decoupling model, and GeoDetector. We found that: (1) the scales of land use, output efficiencies and development intensities of service-industries are increasing with a trend that takes the form of a “J”, “U” and “inverted U”, respectively; (2) Spatial variabilities and agglomerations are significant, with a stable spatial pattern of the scale of service-industry land use, and a gradient in the distribution of cold- and hot-spots. The dominant spatial units of output efficiency and development intensity have changed from low and lower to high and higher, and the cold- and hot-spots gather in clusters; (3) The development of service-industries is highly dependent on the input of land-resources, and only a few provinces are in a state of strong decoupling, while most are in a state of weak decoupling, with quite a few still in a state of expansive coupling, expansive negative decoupling, or even strong negative decoupling; (4) There are many driving factors for land use changes in the service-industry, with increasingly complicated and diversified relationships between each other, ranked in intensity as the scale effect > informatization > globalization > industrialization > urbanization.

The Yangtze River Delta (YRD) is the area with the densest high-speed railway (HSR) network in China, and it leads the high-quality economic development (HQED) in the country. HSR plays an important role in regional development. However, research on the impact of the HSR on HQED is notably limited. Theoretically, this study develops an analytical framework for the mechanism of the HSR’s influence on HQED. Empirically, it calculates the HQED index and then investigates the impact of the HSR on HQED and the regional discrepancies across cities in the YRD, based on data from 2011 to 2019 using the difference-in-differences model. The results show: (1) The mechanism lies in that the HSR improves urban accessibility, accelerates the flow of the production factors, and enhances the allocation efficiency of the input factors. (2) The distribution of the HQED level presents an obvious circular pattern, with Shanghai and Suzhou at the centre, showing the prominent principle of distance decay. (3) Both the regression model and the robustness tests show that the HSR significantly promotes HQED in the YRD. Additionally, the economic development, foreign capital spent, financial level, industry advancement, and living standard are conducive to HQED. (4) The results of the heterogeneity test reveal that the HSR has an obviously varied impact on HQED in cities depending on their size and location. The HSR has a significant promotional effect on HQED in cities with a large population and those far away from a provincial city.

The surging carbon emissions of urban transportation poses a critical challenge to 2050 net-zero emissions, while the swift expansion of the digital economy is increasingly considered as a potential catalyst for reducing carbon emissions. Yet, whether the digital economy follows a simple linear pattern or a more intricate, nonlinear influence on transportation emissions across city boundaries remains poorly understood. This research will investigate the nonlinear spatial effects of digital economy development (DED) on urban transportation carbon emissions (UTCE). The empirical analysis employs Spatial Durbin Model (SDM) based on a panel of 224 prefecture-level Chinese cities from 2012 to 2023. The key findings are summarized below: (1) A distinct inverted U-shaped pattern characterizes the relationship between DED and UTCE, within which public transportation route optimization (PTRO) and traffic congestion mitigation (TCM) serve as nonlinear mediators. The initial rise in UTCE can be attributed to energy-intensive, non-intelligent infrastructure that causes worse congestion and bus underutilization, but later decrease as low-carbon technological upgrades alleviate congestion and optimize public transport routes. (2) The inverted U shape nonlinear relationship also exhibits significant heterogeneity. Regionally, the inflection appears earliest in the Central region whose local effect is insignificant, followed by the Western region, and latest in the Eastern region, while spatial effect is only insignificant in the Eastern region. By urban transport development (UTD) level, the inflection occurs earlier in low UTD regions, yet spatial effect is significant only in high UTD regions. (3) The spillover effect of DED on UTCE changes sign from positive to negative across different economic circles, with the negative spillover peaking at a distance of 800 km. These findings provide important policy implications for harnessing the digital economy to foster low-carbon urban transportation while mitigating regional imbalances.