The Chengdu Metropolitan Area, located on the eastern edge of the world’s highest plateau, has experienced a period of integrating urban and rural area development for decades. With rapid urbanization and population growth, the vulnerability and security of the ecological environment have become critical aspects to consider in sustainability. Moreover, the presence of different levels of vitality in the study area has a crucial impact on land-use change. Hence, we propose a growth boundary study based on the theory of urban vitality and ecological networks. We focus on identifying the inefficient urban land and urban development potential land, explore their expansion probabilities to conduct spatial simulations for the next 15 years, and combine the ecological network to form a reasonable spatial pattern. Results showed that the proposed model could simulate the urban growth state more accurately within a certain space scale and integrate different limits and influences to simulate different growth strategies under multiple planning periods. Thus, the proposed model can be an effective decision support tool for the government.

The establishment of urban ventilation corridors (UVCs) aims to mitigate the urban heat island effect. While most studies focus on the construction and assessment of the environmental benefit of UVCs, they often overlook the analysis of UVCs’ topological features. This research integrates multi-source data including 3D urban buildings, historical meteorological observations, high-resolution remote sensing, and land use planning, combined with multiple models, including geographic information system spatial analysis, circuit theory, and complex networks. Based on an assessment of urban ventilation potential, the circuit model was applied to extract UVCs aligned with the prevailing wind direction for both summer and winter seasons. Complex network modeling was employed to analyze the topological features of the ventilation network. From the analytical results, a multi-level wind corridor system for Chengdu was quantitatively developed. The results indicate that the city’s overall ventilation resistance is high, with notable spatial clustering, and the southeastern region faces substantial ventilation obstructions. A total of 143 critical ventilation nodes were identified, with the number of air inlets and outlets in summer being significantly fewer than in winter. However, the cooling effect of ventilation corridors in the prevailing summer wind direction is superior to that in winter. The ventilation network comprises 16 communities with distinct ventilation characteristics, exhibiting moderate connectivity, lacking small-world properties, and showing congestion and instability.

Urban heat exposure, which intensifies with climate change, poses serious threats to public health in rapidly growing cities. Traditional assessments rely on static land surface temperature, often overlooking the role of human mobility in exposure frequency. This study introduces a travel-related heat exposure index (THEI) that combines ride-hailing trajectories and remote sensing data to capture dynamic human–environment thermal interactions. Using Chengdu, China, as a case study, the THEI is combined with local indicators of spatial association to outline high-exposure risk zones (HERZ). XGBoost with SHAP and partial dependence plot (PDP) methods is also applied to identify the nonlinear effects of built environment factors. Results showed the following: (1) distinct spatial clustering of high travel-related heat exposure in central urban districts and transit hubs; (2) city-wide exposure is primarily driven by transportation accessibility and urban form, such as intersection density and floor area ratio; (3) in contrast, HERZ are more strongly associated with demographic and socioeconomic factors, including population density, housing price and road density; and (4) vegetation, measured by the normalized difference vegetation index, demonstrates a consistent negative effect across scales, highlighting its critical role in mitigating thermal risks. These findings emphasize the necessity of incorporating mobility-based exposure metrics and spatial heterogeneity into climate-resilient urban planning, with differentiated strategies tailored for city-wide versus high-risk zones.

Exploring the spatiotemporal dynamic evolution of local climate zones (LCZ) associated with changes in land surface temperature (LST) can help urban planners deeply understand urban climate. Firstly, we monitored the evolution of 3D urban spatial form in Chengdu City, Sichuan Province, China from 2010 to 2020, used the ordinary least squares model to fit the dynamic correlation (DR) between the changes in urban spatial patterns and changes in LST, and revealed the changes of urban spatial patterns closely related to the rise in LST. Secondly, the spatiotemporal patterns of LST were examined by the integration of the Space–Time Cube model and emerging hotspot analysis. Finally, a prediction model based on curve fitting and random forest was integrated to simulate the LST of study area in 2025. Results show the following: the evolution of the urban spatial form consists of three stages: initial incremental expansion, midterm incremental expansion and stock renewal, and late stock renewal and ecological transformation. The influence of the built environment on the rise of LST is greater than that of the natural environment, and the building density has a greater effect than the building height. The overall LST shows a warming trend, and the seven identified LST spatiotemporal patterns are dominated by oscillating and new hotspots patterns, accounting for 51.99 and 11.44% of the study area, respectively. The DR between urban spatial form and LST varies across different time periods and built environment types, whereas the natural environment is always positively correlated with LST. The thermal environment of the city will warm up in the future, and the area affected by the heat island will shift to the central of the city.

The lower reaches of the Jinsha River, serving as a vital ecological barrier in southwestern China and playing a crucial role in advancing targeted poverty alleviation efforts, remain underexplored in terms of the coupling between ecological and economic development, creating a gap in understanding the region’s sustainable development potential. This study combines the remote sensing ecological index (RSEI) derived from MODIS data and the biodiversity richness index (BRI) based on land use data to create the ecological environment index (EEI) using a weighted approach. It also develops the economic development index (EDI) from economic data using the entropy weight method. By integrating the EEI and EDI, the study calculates key metrics, including the ecological–economic coupling degree (EECD), coupling coordination degree (EECCD), and relative development degree (EERDD), and examines their spatiotemporal changes from 2000 to 2020. Additionally, the study applies a geographic detector model to identify the spatial drivers of the EEI, an obstacle factor diagnosis model to pinpoint the main barriers to EDI, and a neural network model to uncover the underlying forces shaping EECCD. The results indicate that: (I) From 2000 to 2020, the overall improvement rate of the ecological and economic subsystems was greater than that of the ecological–economic coupling system. The entire region is still in the Running-In Stage, and the coordination level has been upgraded from near imbalance to marginal coordination. About 85% of the counties’ EERDDs are still in the EDI Behind EEI Stage. (II) The structural composition of the EEI shows a pattern of low Dry Hot Valley Area and high in other areas, mainly driven by natural factors, although human activities had a notable impact on these interactions. (III) Originating from an impact model primarily driven by economic factors and supplemented by ecological factors, both EDI and EECCD exhibit a pattern of high in the south and low in the north, with improvements spreading northward from the urban area of Kunming. The development gradient differences between 24 poverty-stricken counties and 16 non-poverty-stricken counties have been reduced. (IV) For the six types of ecological–economic coupling development zones, it is essential to adopt localized approaches tailored to the differences in resource and environmental characteristics and development stages. Key efforts should focus on enhancing ecological protection and restoration, increasing financial support, implementing ecological compensation mechanisms, and promoting innovative models for sustainable development.