Innovation is an important motivating force for regional sustainable development. This study measures the innovation efficiency of 280 cities in China from 2014–2018 using the super-efficiency slack-based measure and it also analyzes its impact on the ecological footprint using the generalized spatial two-stage least squares (GS2SLS) method and uses the threshold regression model to explore the threshold effect of innovation efficiency on the ecological footprint at different economic development levels. We find the corresponding transmission mechanism by using a mediating effect model. The major findings are as follows. First, we find an inverse U-shaped relationship between innovation efficiency and the ecological footprint for cities across China as well as in the eastern and central regions. That is, innovation efficiency promotes then suppresses the ecological footprint. Conversely, in western and northeastern China, improvements in innovation efficiency still raise the ecological footprint. Second, for the entire country, as economic development increases from below one threshold value (4.4928) to above another (4.8245), the elasticity coefficient of innovation efficiency to the ecological footprint changes from −0.0067 to −0.0313. This indicates that the ability of innovation efficiency improvements to reduce the ecological footprint is gradually enhanced with increased economic development. Finally, the industrial structure, the energy structure, and energy efficiency mediate the impacts of innovation efficiency on the ecological footprint.

In recent decades, the availability of diverse location-based service (LBS) data has largely stimulated the research in individual human mobility. However, less attention has been paid on the intra-city movement of cyclists coupled with their spatiotemporal dynamics. To fill the knowledge gap, drawing on bicycle-sharing data over one week in Shanghai, China, this study investigates the dynamics of bicycle-sharing users at two spatial scales (i.e., city level and subdistrict level) and explores the intra-city spatial interactions by those cyclists. At the city level, by applying the analysis of variance (ANOVA) test and the Wilcoxon signed-rank test, this study examines the temporal variation of cyclists across a seven-day period. At the subdistrict level, we develop a new index to capture the urban vitality using bicycle-sharing data with the consideration of trip flow allied with spatial weights. In terms of the computed urban vitality over the course of a day, 98 subdistricts are partitioned into 7 groups by using K-means clustering. In addition, spatial autocorrelation and hot spot analysis are also applied to examine the spatial features of urban vitality at different periods. Our results reveal that urban vitality has an obvious character of the spatial cluster and this cluster feature varies markedly over the course of a day. By shedding new lights on intra-city movement, we argue our results are important in informing urban planners on how to better allocate public facilities and increase bicycle usage as a way to progress towards more sustainable urban areas.

In recent decades, the ecological security pattern (ESP) has drawn increasing scientific attention against the backdrop of rapid urbanization and worsening ecological environment. Despite numerous achievements in identifying and constructing the ecological security pattern, limited attention has been paid on applying ESP to predict urban growth. To bridge the research gap, this paper took Quanzhou, China as a study case and incorporated the identified ESP into an urban growth simulation with three distinct scenarios. Following the “ecological source–ecological corridor–ecological security pattern” paradigm, the ESP identification was carried out from four single aspects (i.e., water, geology, biodiversity, and recreation) into three levels (i.e., basic ESP, intermediate ESP, and optimal ESP). Grounded in an equally weighted superposition algorithm, the four single ESPs were combined as an integrated ESP (IESP) with three levels. Taking IESP as an exclusion element, urban growth simulation in 2030 was completed with thee SLEUTH model. Drawing on the three levels of IESP, our urban growth simulation contained three scenarios. In terms of urban sprawl distribution coupled with urban growth rate, an optimal urban growth scenario is recommended in this paper to balance both urban development and eco-environment protection. We argue that our ESP-based urban growth simulation results shed new light on predicting urban sprawl and have the potential to inform planners and policymakers to contribute to more environmentally-friendly urban development.