## 5 Conclusions  
This study applied a performance-driven workflow framework of \"morphological generation-performance evaluation-morphological optimization\" to improve outdoor environmental performance for elderly communities. Through surveying 340 typical elderly residential buildings in Beijing, basic data such as building types and block scale were used as references and constraints for a parametric urban form generation model. Sixteen morphological indicators, including average building height and standard deviation of building height, were selected to describe the generated parametric models of the blocks. The outdoor environmental performance criteria determined for this experiment include maximizing the winter pedestrian UTCI, minimizing the summer pedestrian UTCI, and maximizing sunlight hours. By using combinations of different types of buildings in various sub-blocks as decision variables for multi-objective optimization, the three environmental performance objectives were defined as target functions. The Wallacei plugin on the Grasshopper platform was employed for the automated optimization of urban spatial forms under optimal environmental performance conditions. Additionally, explainable machine learning algorithms, specifically XGBoost, were innovatively used to explore the nonlinear relationships between variables. The optimization results show that the UTCI-S, UTCI-W, and SHs of the block have been optimized to some extent, demonstrating that the block form has an impact on outdoor environmental performance, which is consistent with previous studies. An analysis of the Pareto solutions obtained from the optimization experiment reveals that, compared to traditional blocks, the Pareto solutions for senior residential areas adapted to the climate of Beijing show a northwest-high, southeast-low layout. In terms of building form, the Pareto solutions tend to favor enclosed building types. This is likely because enclosed buildings have strong enclosure characteristics better suited to cope with the cold northern climate. To better achieve the set optimization goals, the Pareto solutions maintain the high-rise building layout on the northern side while reducing the height of buildings on the western side. This layout can accommodate the prevailing northwest winds in winter and southeast winds in summer in Beijing, ensuring outdoor thermal comfort in both winter and summer while increasing sunlight duration, which improves the outdoor activity experience for seniors. Such a block layout can create a better living environment for the elderly and improve their physical and mental health. At the same time, the three sets of machine learning models established using experimental data, after hyperparameter optimization, had test set R\({}^{2}\) values of 0.712, 0.843, and 0.966, respectively. This indicates a significant nonlinear relationship between the residential form and the environment. By using the calculated SHAP values, precise identification of the morphological indicators affecting outdoor environmental performance was achieved, which will help designers make better decisions in the early stages to create a better outdoor environment. Model results show that, among the morphological indicators affecting summer outdoor thermal comfort, a higher average building height, larger building volume, higher average building footprint, and larger floor-area ratio (FAR) significantly reduce the summer outdoor UTCI value. Meanwhile, more complex building forms and greater variability in block area volume significantly increase the summer outdoor UTCI value. Among these, the average building height and building system coefficient have been widely proven by scholars to be key factors affecting outdoor thermal comfort. This may be because increasing the height of buildings reduces the shadow gaps within the same block scale, placing outdoor spaces in the building's shadow and reducing solar radiation, thereby lowering ground and surrounding air temperatures and improving summer outdoor thermal comfort for seniors.