The existing building stock faces the challenge of low energy efficiency and requires renovation and upgrading to meet society′s goals of carbon reduction and sustainable development. This study presents an optimization framework utilizing genetic algorithms to develop robust retrofit plans that balance the need for improved energy efficiency, cost-effectiveness considerations for householders, and uncertainties regarding climate conditions. A case study of an aged residential building in a hot and humid region of China is used to demonstrate the proposed method. The optimization results show a potential energy demand reduction of 81.5%. However, due to the relatively long time required to realize economic benefits from high investments, short-term optimization tends to favor solutions with high energy demand and low primary costs. To effectively reduce carbon emissions, it is recommended to consider the long-term economic benefits of retrofits and prioritize solutions with high energy efficiency. However, it is important to acknowledge that the expensive nature of retrofit investments may pose barriers to residents. Society should provide adequate support and guidance to facilitate residential renovation efforts.

The downtown cores of many cities are characterized by aged communities that tend to host a relatively high population of elderly retirement residents. The availability and usage of outdoor spaces within these communities play a crucial role in promoting active aging, providing essential locations for rest, activities, and social interaction among the elderly. However, in the planning and design of these spaces, attention is often focused on the safety and mobility requirements of the elderly population, while a lack of research is apparent in the area of elderly-specific preferences for spaces designed for relaxation and communication. In this study, we selected an aging community as the research target and conducted a detailed investigation of the outdoor spaces where the elderly residents gather and build up spontaneously in summer. Our objective was to evaluate the environmental factors influencing the selection of these outdoor spaces by the elderly for relaxation and communication. We analyzed the correlations between these factors and the number of occupants in these spaces and developed predictive models accordingly. The findings indicate that the environmental factors impacting the utilization of outdoor spaces by the elderly during heat periods within the community are, in order of importance: temperature, relative humidity, human traffic flow, and noise levels. These factors include physical and social aspects; temperature is a negative correlation factor affecting the use of outdoor space by the elderly, and the rest are positive correlation factors. This shows that the elderly like to gather and chat in a cool, crowded, and lively environment. Through the data analysis, it was determined that the random forest regression model was the most effective in predicting the number of residents remaining in these spaces. With a coefficient of determination (R²) of 0.7958, the model can assist in community update planning and design, help in selecting outdoor spaces, and improve the quality of the outdoor environment. This study discusses the factors influencing the elderly’s use of community outdoor space from the physical and social levels, and the prediction model is significant for the optimization of spatial elements and spatial location.

Community is the foundation of modern cities, where urban residents spend most of their lifetime. Effective and healthy community design plays a vital role in improving residents’ living quality. Pedestrian network is an indispensable element in the community. Successful pedestrian network design can help the residents be healthy both physically and mentally, build the awareness of “Go Green” for the society, and finally contribute to low-carbon and green cities. This paper proposes a community pedestrian network design method based on Urban Network Analysis with the help of the Rhino software. A case study of a typical community in Guangzhou, China was implemented, specifying the steps of the proposed method. The findings presented include the features of the citizens and the accessibilities of the neighbors that are obtained from the community pedestrian network simulation. The limitation and scalability of this method was discussed. The proposed method can be essential to designing healthy and sustainable communities.

The rapid and precise forecasting of building energy requirements plays a crucial role in decision-making processes for architects during the early design phase. This study introduces a data-driven framework capable of projecting energy demands in the context of evolving climate conditions. We evaluated four widely-used machine learning algorithms. Our results indicated that a hybrid approach, integrating Catboost and Bayesian optimization, excelled in both accuracy and efficiency for predicting building energy demand under climate change conditions. The framework proposed herein has potential applications in fostering sustainability in early-stage architectural design.