In recent years, the study of thermal comfort has gained significant attention for its profound impact on human well-being and productivity. Addressing thermal comfort in healthcare environments has become increasingly important as public demand for improved healthcare standards increases and patients prioritize their overall experience during medical visits. This study explores advances in thermal comfort research in healthcare facilities, tailored to the specific characteristics of Chinese healthcare facilities. It covers various aspects, including China's five major climatic regions, different categories of healthcare facilities, distinct functional zones within healthcare facilities, and different demographic groups. Additionally, this study summarized thermal sensation data across diverse demographic groups from reviewed literature, analyzing median indicators of different groups. When compared with current standards, it appears that while these standards generally support the broad needs of healthcare facility occupants, they fall short of accommodating the detailed design requirements specific to environments of healthcare facilities. The study identifies shortcomings in domestic research and suggests future research directions. It provides valuable insights for optimizing healthcare facilities' designs and environmental parameters in accordance with evolving global standards in healthcare facility management in China.

Purpose: This study aims to assess carbon emissions in urban aged residential buildings in Qingdao, Shandong Province, constructed prior to 2000, and to evaluate retrofitting and rebuilding strategies for potential carbon reduction. Design/methodology/approach: Field investigations and literature reviews were conducted to identify key factors influencing carbon emissions, such as shape coefficient, window-to-wall ratio and envelope structure. A combination of generalization and mathematical statistical methods was used to classify buildings based on construction year, form, structural type and energy-saving goals. Cluster analysis was employed to extract six typical building models. Findings: Results demonstrate that building form complexity positively correlates with carbon emissions per unit area, while longer lifespans reduce emission intensity. Retrofitting exhibits shorter carbon payback periods (1.62–3.92 years) than rebuilding (18.7–49.94 years), indicating superior environmental performance. Pre-1986 buildings are advised for demolition/rebuilding due to limited retrofit benefits. For 1986–1995 buildings, retrofitting is recommended if structurally viable. Post-1996 buildings favor retrofitting over new construction for its shorter payback and lower emissions, enhancing long-term carbon reduction. Originality/value: This study contributes to the understanding of carbon emissions in urban aged residential buildings by considering various factors and providing specific recommendations for retrofitting and rebuilding strategies tailored to different construction periods. Additionally, it highlights the importance of building form complexity and remaining lifespan in determining carbon emissions, offering insights for sustainable urban development and carbon reduction initiatives.

Occupant behavior in buildings is becoming a critical factor in determining indoor comfort and energy consumption performance. The diversity and uncertainty of occupant behavior affect the implementation of occupant-centric building operations. However, existing shading behavior models do not account for various shading states (fully open, fully closed, or partially open, significantly deviating from real-world scenarios), accessibility of shading behavior (ASB), room configurations (single-person rooms and multi-person rooms), occupancy, and other factors, resulting in lower model accuracy. This study aims to address these limitations by proposing a high-resolution shading occupant behavior (HRSOB) model that integrates occupancy considerations, accessibility of shading behavior, and data mining techniques. This HRSOB model can classify various shading occupant behaviors and be integrated into the optimization controls of occupant-centric buildings. The utilization of Markov chain occupancy models significantly improves the accuracy of predicting stochastic shading behavior. The ASB classification method analyzes the features of the occupant shading adjustment behavior (hereafter referred to as shading behavior) and objectively reflects shading behavior patterns using spatial features, thereby enhancing the resolution of the shading occupant behavior model. Data-driven techniques, such as feature selection and soft voting, enable the analysis, training, and application of shading behavior data, and rigorous evaluation and validation of the HRSOB model are conducted. The results demonstrated a 10.92% improvement in the predictive performance of the HRSOB model compared with the typical shading occupant behavior (SOB) model.

The utilization of primary school buildings is multifaceted, primarily due to the high occupancy density, varying thermal preferences among occupants, diverse indoor activities (such as walking, sports, and conversation), and a constant flow of individuals entering and exiting the building. This results in the frequent opening and closing of external windows and doors and fluctuations in internal heat gain. Consequently, frequent interactions between the indoor and outdoor microenvironments lead to energy losses. This study conducts a comprehensive literature review on building energy loss stemming from occupant behavior and the interactions between indoor and outdoor microenvironments. Furthermore, it proposes a dynamic real-time monitoring system based on a foundation of computer data capture and a visualization platform for building energy loss. The research methods include data crawling, data association rule mining, and data association analysis. The research findings yield a universally applicable and informative building energy-saving design system based on extensive data analysis. Additionally, the system presents information on occupants’ behavior and the microclimate data of indoor and outdoor environments on a computer screen, facilitating human–machine communication and enabling timely adjustments to be made, thus facilitating the construction of design strategies for new buildings and operation and maintenance strategies for existing buildings.

The utilization of primary school buildings is multifaceted, primarily due to the high occupancy density, varying thermal preferences among occupants, diverse indoor activities (such as walking, sports, and conversation), and a constant flow of individuals entering and exiting the building. This results in frequent opening and closing of external windows and doors and fluctuations in internal heat gain. Consequently, frequent interactions between the indoor and outdoor microenvironments lead to energy losses. This study conducts a comprehensive literature review on building energy loss stemming from occupant behavior and the interactions between indoor and outdoor microenvironments. Furthermore, it proposes a dynamic real-time monitoring system based on a computer data capture and visualization platform foundation for building energy loss. The research methods include data crawling, data association rule mining, and data association analysis. The research findings yield a universally applicable and informative building energy-saving design theory based on extensive data analysis. Additionally, the system presents information on occupants' behavior and the microclimate data of indoor and outdoor environments on a computer screen, facilitating human-machine communication and enabling timely adjustments to design strategies for new buildings and operation and maintenance strategies for existing buildings.

Window opening is a key adaptive strategy for balancing thermal comfort and indoor air quality (IAQ) in school classrooms in cold regions, yet its behavioral mechanisms remain insufficiently quantified under institutionalized teaching schedules. This study conducted a long-term field monitoring campaign in a middle school classroom in Qingdao, China, collecting 120,960 high-resolution environmental and behavioral records across 84 school days in four seasons. A Random Forest (RF) model combined with SHapley Additive exPlanations (SHAP) was used to identify key drivers and nonlinear behavioral thresholds of window-opening decisions. Results show that the RF model significantly outperformed logistic regression, achieving an AUC–ROC of 0.897 and an F1-score of 0.748. Driving factors varied across teaching phases. During the arrival period, outdoor temperature was the dominant trigger with a contribution of 38–47%. During break periods, indoor CO₂ became the primary driver, with a maximum contribution of 83.14%, indicating ventilation behavior primarily aimed at restoring air quality. SHAP analysis revealed a typical three-stage response pattern (inhibition–sensitivity–saturation). CO₂ thresholds were approximately 1000–1150 ppm during class periods but decreased to 400–600 ppm during breaks, while outdoor temperature thresholds ranged from 15–19 °C in spring and autumn. These findings demonstrate that classroom window-opening behavior follows a threshold-driven and institutionally embedded decision process, providing quantitative insights for behavior-based ventilation control and healthy school building design.