Urbanization and industrialization have led to the coexistence of winter haze and summer heat island in some cities in northern China, but the mitigation effect of ventilation corridors is lack of quantitative evaluation. This paper introduces circuit theory into urban climate research. Taking Shenyang as a case study, it comprehensively employs three-dimensional urban landscape pattern indices (including SVF, FAD, and Z0) to guide ventilation corridor construction, establishes an analytical framework for PM_2.5 and LST, and quantifies the environmental benefits of ventilation corridors. The results show that the corridor generated by circuit theory can make 65.14% of path PM lower than the average level of the city; Among the 7 exit paths of wind corridors, the surface temperature of 4 channels is lower than the average level of the city. FAD is positively correlated with Z0 (R² = 0.7) and negatively correlated with SVF (R² = 0.61). Meanwhile, the circuit theory model identifies eight pinch points along ventilation paths. CFD software is employed to simulate atmospheric environments for six typical building layouts to guide subsequent urban planning. Therefore, the reasonable layout of urban morphology indicators and the construction of reasonable ventilation corridors can effectively control the atmospheric particulate pollution and the heat island effect in summer.

In people-centered urban planning, enhancing the well-being of residents and tourists is one of the core objectives. Tourist emotion serves not only as a key indicator of the tourism experience but also indirectly reflects the quality of a city’s public spaces and built environment. In recent years, user-generated content has provided abundant data for understanding human emotional responses in urban environments, while deep learning models offer new technological pathways for extracting spatial–emotional associations from such data. However, existing research lacks a systematic evaluation of emotion analysis models from an urban spatial perspective and their application to uncover the relationship between emotional distribution and spatial characteristics in specific urban contexts. Based on a dataset of 9419 manually annotated travel reviews from Harbin, this study developed a multi-level evaluation framework and conducted a systematic comparison of seven emotion analysis models. This study then screened for the optimal model combinations based on two dimensions—spatial location and emotion polarity—to create a model matching matrix for mapping Harbin’s emotion map. Subsequently, a regression analysis was performed to examine the relationship between emotions and built environment elements. The results show that the ERNIE model demonstrated the best overall performance. Road density, green space density, and accommodation facility density were positively correlated with emotion, while POI diversity showed a negative correlation. This study demonstrates that emotion analysis technology can serve as a valuable analytical tool for identifying spatial patterns of sentiment, thereby offering empirical support for optimizing spatial design parameters and advancing a more people-centered approach to urban development.

Urban green spaces are an integral part of urban ecosystems and play a crucial role in improving the urban ecological environment and promoting sustainable urban development. This study aimed to quantify the spatiotemporal evolution of urban green spaces in central Shenyang and to assess how their relationships with selected urban development and socioeconomic drivers varied across space and time. Urban green space information for 2010, 2015, and 2020 was extracted from remote sensing imagery using ENVI and ArcGIS. Eight indicators—impervious surface intensity, population density, nighttime light intensity, economic density, industrial structure, road density, traffic accessibility, and land use intensity—were examined using spatial-pattern and contribution analyses, ordinary least squares regression (OLS), and geographically weighted regression (GWR). Urban green space areas were 330.56 km² (23.55%), 344.55 km² (24.55%), and 337.32 km² (24.03%) in 2010, 2015, and 2020, respectively, representing a net increase of 6.76 km² (2.05%) and an annual dynamic degree of 0.20% over the study period. Spatially, green spaces were concentrated in peripheral areas, fragmented in the central area, and prominently distributed along river corridors. At α = 0.05, coefficient-level OLS results showed negative associations with impervious surface intensity in 2010 (standardized β = −0.184, p < 0.001) and 2015 (β = −0.098, p = 0.005). In 2020, impervious surface intensity showed a small positive coefficient (β = 0.056, p = 0.043), whereas traffic accessibility showed a negative coefficient (β = −0.0556, p = 0.033). The OLS models had low explanatory power, with R² values of 0.034, 0.008, and 0.007 and adjusted R² values of 0.029, 0.003, and 0.002 for 2010, 2015, and 2020, respectively. The GWR local R² values ranged from 0.000 to 0.310 and showed spatially shifting high-value areas, indicating marked spatial variation in model explanatory power. These findings provide case-specific evidence for Shenyang and a transferable analytical framework for spatially differentiated green space planning in comparable old industrial and rapidly restructuring cities.