## 2 Literature Review
### Research Progress on the Relationship Between Urban Morphology and Environmental Performance  
In recent years, studies have shown that urban morphology significantly impacts outdoor environmental performance, including solar potential [46; 47], sunlight hours [46; 47; 48; 49], outdoor thermal comfort [34; 49; 50; 51; 52], wind performance [53; 54], and urban heat island (UHI) effects [55]. These studies have extensively explored the relationship between urban morphology and environmental performance, providing substantial empirical and theoretical support. For instance, a study from Hong Kong conducted field experiments to analyze outdoor thermal comfort factors among 48 students on campus, quantifying the effects of direct sunlight, shading, and wind turbulence on thermal perception [49]. Another study incorporated residential morphology data (e.g., building orientation, height, and density) with meteorological data to simulate the impact of urban morphology on sunlight hours and solar potential, demonstrating that optimized slab-type buildings can significantly improve environmental performance [47]. Additionally, a comparative study between Barcelona, Spain, and Tianjin, China, revealed that, when the floor-area ratio (FAR) exceeds 3.1, both cities meet the daylighting standards [48]. These studies highlight the close relationship between urban morphology and environmental performance, particularly thermal comfort and sunlight hours. Notably, many studies have employed various analytical methods to explore the linear correlations between the two. For example, research in Iran used simulated Physiological Equivalent Temperature (PET) data from Mashhad City alongside urban morphology data to conduct multiple regression analyses, revealing their associations [50]. Another study developedmultiple linear regression (MLR) models based on field-measured data to examine the relationship between built environment variables and thermal comfort indicators in hot regions [34]. Furthermore, stepwise multiple linear regression and partial least squares regression (PLSR) models have been used to analyze the linear relationship between urban morphology and UHI intensity [54]. However, traditional statistical models are often limited to analyzing linear relationships, constraining their ability to capture the complex phenomena observed in real-world scenarios. With the advancement of machine learning technologies, research has demonstrated that machine learning models are more effective in exploring the nonlinear associations between urban morphology and environmental performance. Particularly when sample sizes are limited, machine learning methods can deliver more reliable predictions. Compared to traditional statistical analyses, such as correlation and regression analyses, the nonlinear relationship interpretation methods based on XGBoost and SHAP offer the following advantages:  
1. Accuracy: On nonlinear datasets, XGBoost provides more precise predictions and interpretable analyses [55]. 2. Handling high-dimensional data: XGBoost includes built-in regularization, effectively managing high-dimensional data and preventing overfitting [56]. In contrast, traditional methods require manual adjustment of regularization parameters and often face overfitting or computational complexity in high-dimensional cases. 3. Scalability: As an efficient machine learning algorithm, XGBoost can handle large-scale datasets, while SHAP's feature importance analysis supports various model types and is capable of managing large-scale data, making it highly scalable [55; 56]. These advantages make XGBoost and SHAP effective tools for studying complex nonlinear relationships, providing a more robust methodological foundation for analyzing the associations between urban morphology and environmental performance. Many scholars emphasize that machine learning methods, particularly boosting algorithms, outperform traditional regression models, especially in capturing complex (nonlinear) relationships between independent and dependent variables.