Urban morphology influences the distribution and variation of land surface temperature (LST) by altering surface cover type. However, the coupling effects of the daily LST cycle and the multidimensional morphological driving mechanisms remain insufficiently explored in existing studies. This study, based on ECOSTRESS diurnal LST data, focuses on Harbin, a representative city in China’s cold climate regions. By integrating land cover data, urban morphology vector data, and interpretable machine learning models, it investigates the intricate relationship between urban morphology indicators and LST over a 24 h cycle under cold climate conditions. LST prediction was carried out using Gradient Boosting Decision Trees (XGBoost), Random Forests (RF), Support Vector Machines (SVM), and Multiple Linear Regression (MLR), with an evaluation of the prediction accuracy of each model. The findings indicated the following: (1) The influence of 2D and 3D urban morphological indicators on LST exhibits significant seasonal variation, with Building Otherness (BO), Mean Building Height (BH), and the Normalized Difference Vegetation Index (NDVI) exerting notable impacts on LST in both winter and summer. (2) Significant interactions exist between certain urban morphological indicators that can effectively reduce LST, when the Patch Land Area Proportion (PLAND) exceeds 40%, increasing the Largest Patch Index (LPI) contributes to lowering LST in summer. (3) Among the evaluated machine learning algorithms, XGBoost demonstrates the highest prediction accuracy. This study provides scientific insights for urban planning and policy development, aiding in the optimization of urban morphological designs to effectively regulate LST.

Membrane materials have been widely applied in large-span roofs and atrium enclosures of commercial spaces due to their light weight, high strength, excellent light transmittance, and geometric adaptability. However, their high solar transmittance and low thermal inertia often lead to conflicts between daylight performance and thermal comfort. This study proposes an interpretable surrogate-model-based optimization framework for improving the photothermal performance of membrane-structured atrium spaces. A parametric model was developed with five design variables, including visible transmittance, solar heat gain coefficient, membrane area ratio, roof lift height, and spatial arrangement. A total of 800 simulation cases were generated using Latin hypercube sampling, followed by performance prediction using an XGBoost-based surrogate model, SHAP-based interpretation, and NSGA-II multi-objective optimization. The results show that the surrogate model accurately captures the nonlinear relationship between membrane design parameters and photothermal performance, achieving coefficients of determination of 0.90 and 0.86 for daylight compliance rate and thermal comfort performance, respectively. SHAP analysis identifies visible transmittance and membrane area ratio as dominant factors for daylight performance, while visible transmittance, area ratio, and roof lift height are critical for thermal comfort. A membrane area ratio of 0.47–0.56 provides a balanced range between daylight improvement and thermal load control, and roof lift heights above 6.55 m contribute to thermal comfort improvement. The proposed framework provides an efficient and interpretable approach for photothermal co-optimization of membrane atrium design.

As a distinctive type of historic urban area shaped by complex topography, historic urban areas in mountainous cities have undergone multiple rounds of urban regeneration and social–spatial restructuring amid the continuous influx of capital and the development of the industrial economy. Under these multifaceted influences, the spatial form of historic urban areas in mountainous cities has undergone a series of transformations. To reveal the characteristics and underlying mechanisms of spatial evolution in historic urban areas in mountainous cities, this study has established a research framework tailored to such areas. Selecting the Yuzhong Peninsula Historic District in Chongqing—a typical example of historic urban areas in mountainous cities in China—as the study area, the research employs land transfer matrices, spatial design network analysis (sDNA), spatial overlay and statistical methods to analyse its spatial evolution characteristics across three dimensions: land-use evolution, road network evolution and building renewal. Seven key indicators were selected for historic urban areas in mountainous cities, and the GeoDetector was utilised to investigate the driving forces and interactions among these factors. The results indicate that in the Yuzhong Peninsula Historic District, residential and special-purpose land use has decreased whilst commercial land use has increased; the road network has been continuously improved, leading to increased choice and accessibility; and the proportion of small-scale buildings has decreased whilst that of large-scale buildings has increased. The primary factors influencing spatial evolution are elevation, social and location factors, with the combined interaction of these factors exerting a greater influence than any single factor alone, indicating that topography acts as a significant persistent spatial constraint guiding the spatial evolution of historic urban areas in mountainous cities. Finally, based on the above findings, recommendations are put forward for the sustainable conservation and utilisation of historic urban areas in mountainous cities, covering land-use management, traffic guidance and the typological control of urban architectural fabric.