Carbon emissions from buildings account for approximately half of China's total social carbon emissions. Focusing only on the carbon emissions of building operation tends to neglect the carbon emissions of other related parts of the building sector, thus slowing down the progress of carbon peaking in the building sector. By applying life-cycle analysis to calculate carbon emissions throughout the building's life cycle, the performance of carbon emissions at each stage of building materials, construction, operation and end-of-life demolition can be identified, so that carbon reduction strategies in building design can be selected.. This paper constructed a method for calculating the carbon emissions of green buildings in whole-building life cycle, and conducted a summary analysis of the carbon emissions of 33 projects that were awarded green building certification. The study found that the Chinese Assessment Standard for Green Buildings has a significant effect on reducing the carbon emissions of buildings in whole-building life cycle. Compared with the current average operational carbon emissions of buildings in China, the carbon intensity of green public buildings is 41.43% lower under this standard and the carbon intensity of green residential buildings is 13.99% lower. A carbon correlation analysis of the provisions of the current Chinese Assessment Standard for Green Buildings was conducted, comparing the changes in the carbon intensity of buildings before and after the revision of the standards. The study concluded that the new version of the standards has a greater impact on public buildings than residential buildings, the requirement of carbon emission reduction in the production stage of building materials is strengthened in terms of carbon emission during the whole-building life cycle. This study addresses the current problem of unclear carbon emission reduction effect of green buildings.

This study integrates four complementary datasets—a global compilation of 100 extreme-climate hotel sites, 631 embodied carbon intensity comparisons buildings, 56 construction robotics firms, and 517 Martian habitat studies—to identify how climate-environment, artificial intelligence geometry design, and robotic automation construction technology jointly determine carbon efficiency across terrestrial and extraterrestrial contexts. Clustering analysis revealed distinct climate–design relationships, from compact insulated geometries in Arctic regions to expansive, ventilated forms in hot deserts. Random Forest regression showed that perimeter, surface area, and temperature range are the strongest predictors of embodied carbon reduction, confirming the geometric dependence of carbon performance. Global refurbishment-phase data further demonstrated that prefabrication consistently lowers absolute emissions, even where relative efficiency diminishes under extreme environmental conditions. Industrial mapping indicated a rapid global rise of robotic and 3D printing construction firms, concentrated in Europe, China, and North America. Bibliometric analysis of Martian habitat research identified additive manufacturing and in-situ resource utilization (ISRU) as dominant strategies, but also revealed limited integration with life support and human-centered design domains. Together, these findings establish a unified computational, AI-assisted, and systems-level approach framework connecting climate-environment-responsive geometry, prefabricated and additive manufacturing, and robotic 3D printing automation, offering a scalable pathway toward architecture on Earth, Moon, Mars and other off-Earth habitats/ extraterrestrial architecture.

Chain hotels constitute 40.09% of all hotels, with indoor room decorations typically undergoing replacement cycles every 2 to 5 years. Robotic 3D printing-enabled prefabrication offers transformative potential for sustainable construction, combining precision engineering with environmental consciousness. This study explores its application in the hospitality sector, addressing both sustainability and ergonomic data. Using AI-driven multidimensional ergonomic latent modeling, key environmental factors such as sound insulation, lighting adaptability, and air quality are evaluated for their impact on service flexibility, safety, and user experience. By aligning technological innovation, this research provides a latent model for advancing prefabricated building adaptive decoration and low-carbon hotel design. The study underscores the importance of interdisciplinary efforts to achieve global sustainability goals and redefines prefabrication technologies, especially robotic 3D printing prefabricated decoration, as catalysts for operational excellence and transformative industry standards in sustainable hospitality. Moreover, this work extends the exploration of robotic 3D printing prefabricated solutions to the context of extreme environments and even extraterrestrial buildings, such as building habitats on Mars or the Moon, contributing valuable human factors engineering insights for future adaptive built environment research in these challenging settings.

Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.

Public building retrofit is an important pathway for reducing operational carbon emissions, but evidence-based technology prioritisation remains limited under real-world multi-technology retrofit conditions. This study develops an interpretable data-driven framework to identify priority technologies and technology co-adoption patterns for public buildings in China’s Hot Summer and Cold Winter (HSCW) region. Based on 218 completed retrofit projects, the carbon reduction rate (CRR) was used as the performance indicator, and 15 retrofit technologies were analysed using FDR-adjusted Mann–Whitney U tests, repeated-validation XGBoost–SHAP analysis, and Apriori association-rule mining. The projects showed substantial variation in CRR, with a mean of 24.98% and a median of 21.70%. Across five repeated 5-fold cross-validations, the predictive XGBoost model achieved a mean R² of 0.417 and a mean RMSE of 0.127. Roof insulation, external wall insulation, and ventilation system retrofit showed the strongest combined evidence and were classified as core technologies. Apriori analysis further revealed three empirical co-adoption patterns: integrated passive-envelope retrofit, solar-control and renewable-energy integration, and operational-management improvement. The findings suggest that retrofit planning in the HSCW region should prioritise envelope insulation and ventilation performance, while selecting shading, system, renewable-energy, and operational-control measures according to project-specific conditions.

Operational carbon emissions of buildings account for more than 25% of global carbon emissions and, generally, over 50% of the total carbon emissions across the whole life cycle of a building. The evaluation and management of carbon emission levels during the operational phase of buildings are at present important tasks for China’s construction industry administration departments. This paper analyzes the indicators for evaluating the operational carbon emission levels of buildings and the content of carbon emission calculations, and introduces a specific operation mode for characterizing building energy consumption by the equivalent electricity method from the perspective of building energy consumption statistics. A calculation method for the carbon emission intensity per unit energy consumption of buildings based on the equivalent electricity method is constructed, and its validity is verified through calculations on 15 actual projects. The results show that the variances in carbon emission intensity per unit energy consumption index based on the equivalent electricity method are 0.03, 0.04 and 0.03 for residential, office and venue buildings, respectively, which are far lower than those of the carbon emission intensity per unit building area index (72.79, 123.33 and 153.35) and close to those of the building energy saving rate index (0.02, 0.01 and 0.02). Compared with the building energy saving rate and carbon emission intensity per unit building area, this index can better characterize the degree of building decarbonization and exhibits good evaluation stability across buildings of different functional types. Combined with the building energy consumption quota index, it is conducive to promoting the transformation of the construction sector from dual control of energy consumption to dual control of carbon emissions.