Public sentiment can effectively evaluate the public’s feelings of well-being in the urban environment and reflect the quality of the spatial environment to a certain extent. Previous studies on the relationship between public sentiment and urban built environmental factors have yielded meaningful results. However, few studies have focused on the effect of micro-built environment on public sentiment at the street level, which directly shapes people’s perceptions. In addition, the nonlinear relationship and synergistic effect among urban built environmental factors have been commonly disregarded in previous studies, resulting in an incomplete understanding of the impact of urban built environment on public emotions. Therefore, this paper takes San Francisco as a study case to explore the complex relationship between urban built environmental factors and public emotions. Specifically, this paper measures the polarity of public emotions through sentiment analysis on Twitter data, establishes a comprehensive built environment index system from both macro- and micro- perspectives, and subsequently explores the complex relationship between the urban built environment and public sentiment through the OLS model and Shapley Additive Explanation algorithm. Results show that: (1) micro-built environmental factors have a significant influence on public emotion, although they have been frequently ignored. (2) Public sentiment tends to be more positive in areas with recreation facilities, mixed land use, rich street view visual environment, suitable thermal and acoustic environment, balanced income, and a suitable degree of high population density. (3) A nonlinear relationship and threshold effect exist between the built environmental variables and the semantic orientations of public emotion. Environment improvement strategies based on the synergic effect between variables can effectively promote the generation of positive emotions. Our empirical findings can offer valuable insights to promote feelings of well-being and foster an urban development approach through strategic interventions within the urban built environment.

Due to massive urbanization and industrialization, modern constructions tend to be designed as technique-dependent, at the cost of high consumption and emissions for indoor environment control such as heating ventilation and air conditioning. Space heating accounts for about 40% of total building energy usage in northern China in winter. This calls for self-reflection and tracing of local traditional architectural wisdom. In this paper, Sibe Traditional Houses were chosen as a typical illustrative example to reveal the building mechanisms behind such local-adaptive traditional constructions. Based on the field investigation in Shifosi Village, a traditional Sibe settlement in Shenyang City, northern China, thermal modeling and indoor heating effects are studied in Sibe Traditional Houses with unique building spatial patterns. The indoor thermal environment is comparatively analyzed for both passive envelope insulation and active heating considerations. Preliminary results indicate that enhancing roof thermal insulation enhancement is the key passive strategy for improving indoor thermal comfort in winter. It also suggests that a space-heating configuration that combines the traditional “kang” with the architectural layout has a more significant effect on the enhancement of indoor thermal comfort in Sibe dwellings. This paper can provide methodological support and an application reference for the improvement of indoor thermal environment of traditional village dwellings.

Railway stations are key nodes connecting urban space with regional transport networks. In the context of compact city development and station–city integration, developing railway stations as compact, three-dimensional composite spaces is an important means of improving land-use efficiency and coordinating transport and urban functions. To quantitatively evaluate the compactness of three-dimensional composite space and the level of station–city coordination, this study develops an evaluation framework comprising 26 indicators across three dimensions: functional configuration, spatial organization, and efficiency and accessibility. Following indicator selection and data standardization, the entropy weight method is used to derive objective weights, which are incorporated into a TOPSIS model. Eighteen representative railway stations are evaluated and ranked using the model, while their development levels are classified using the rank-sum ratio (RSR) method. The results show that Kyoto Station, Osaka Station, Shanghai Hongqiao Railway Station, and Berlin Central Station exhibit relatively high spatial compactness in their three-dimensional composite spaces, whereas Milan Central Station and London King’s Cross Station show comparatively weaker levels. The five clusters identified through K-means clustering were interpreted in light of the evaluation results and spatial characteristics, and subsequently consolidated into four spatial development types: High-Compactness Urban Comprehensive Type, High-Compactness Underground Station Type, Moderate-Compactness Transport-Dominant Type, Low-Compactness Regionally Oriented Type. The study identifies key spatial factors influencing the three-dimensional integrated development of railway stations. The findings provide a quantitative basis for the intensive space use, station–city coordination strategies, and architectural design optimization.

People spend approximately 90% of their time in indoor environments, making indoor air quality (IAQ) a critical factor influencing human health and overall well-being. However, traditional dwellings in western Sichuan, China, still rely heavily on biomass combustion as the primary source of heating. This practice leads to elevated concentrations of indoor pollutants and generally poor IAQ, posing considerable health risks to residents, particularly during the cold and transitional seasons. To address this issue, the present study adopts a passive design perspective and employs an interdisciplinary research framework that integrates field measurements with computational simulation modeling. The objective is to systematically identify the key passive factors affecting IAQ and to develop an effective multi-factor coupled optimization strategy. Three representative passive variables were examined, and the results indicate that the window-to-wall ratio is the most influential factor governing indoor CO ₂ levels. Furthermore, the analysis shows that optimizing any single factor provides only limited improvement, whereas a multi-factor coupled strategy yields a substantial reduction in indoor CO ₂ concentration—up to 64%. Based on these findings, the study proposes a passive IAQ optimization approach specifically tailored to traditional Banbi-style dwellings in northwest Sichuan. This approach demonstrates significant potential for improving IAQ, and the methodology offers valuable guidance for passive environmental optimization in traditional buildings located in regions with similar climatic and socioeconomic conditions.

The concept of a “Park City” emphasizes the integration of urban development and ecological sustainability, requiring robust methods to monitor vegetation dynamics. This study employs satellite remote sensing imagery to calculate the Normalized Difference Vegetation Index (NDVI) for Chengdu, China, aiming to evaluate spatial-temporal variations in urban vegetation cover and support evidence-based ecological planning. Since the concept was introduced in 2018, radiation calibration, atmospheric correction, and cloud masking were performed on the Landsat-8/9 and Sentinel-2A/C datasets spanning from 2013 to 2023, enabling the acquisition of NDVI values. Results indicate a gradual increase in NDVI values (0.45–0.53) within established green spaces, reflecting successful afforestation policies, while urban expansion areas exhibited lower NDVI (0.23– 0.37), underscoring the pressure of rapid urbanization. Additionally, the integration of multi-source satellite data enhanced the resolution and reliability of NDVI assessments. This study demonstrates the efficacy of remote sensing in quantifying urban ecological quality, providing actionable insights for optimizing green infrastructure and advancing “Park City” initiative.