The building sector contributes to 50.9 percent of China’s carbon emissions. Due to the complexity of the assessment process, it is difficult to predict the entire life cycle carbon emissions of a building at the early stage of design. In this study, a whole-life carbon emission estimation model for the early stage of building design is developed based on comparison of the standard calculations and an analysis of stock cases. Firstly, the standard calculation methods in China, Japan and Europe were compared, and the boundary of the model was defined in three parts: production, construction and demolition and operation. Second, information on 68 examples of Chinese buildings was collected and divided into a training set and a test set at a ratio of 7:3. In the training set, the relationship between carbon emissions and the design parameters was searched, and a carbon emission estimation model applicable to different stages was constructed. Finally, the model was applied to the test set for validation. The results show that the calculation error of the model is within ±15%, and it can quickly estimate carbon emissions based on the design factors, which is helpful for carbon emission assessment work in the early stages of design.

In recent years, the critical role of green buildings in addressing energy consumption and environmental issues has become widely acknowledged. Research indicates that over 40% of potential energy savings can be achieved during the early design stage. Therefore, decision-making in green building design (DGBD), which is based on modeling and performance simulation, is crucial for reducing building energy costs. However, the field of green building encompasses a broad range of specialized knowledge, which involves significant learning costs and results in low decision-making efficiency. Many studies have already applied artificial intelligence (AI) methods to this field. Based on previous research, this study innovatively integrates large language models with DGBD, creating GreenQA, a question answering framework for multimodal data reasoning. Utilizing Retrieval Augmented Generation, Chain of Thought, and Function Call methods, GreenQA enables multimodal question answering, including weather data analysis and visualization, retrieval of green building cases, and knowledge query. Additionally, this study conducted a user survey using the GreenQA web platform. The results showed that 96% of users believed the platform helped improve design efficiency. This study not only effectively supports DGBD but also provides inspiration for AI-assisted design.