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.

Hospital buildings provide healthcare services at the costs of significant amounts of energy consumption and carbon emissions, further exacerbating the environmental load. Because of the limited research on the life cycle carbon emissions of Chinese hospitals, this study conducted a detailed carbon-accounting and comparative study. Firstly, BIM and LCA were used to quantify the carbon emissions of the inpatient building in each stage of the life cycle. Secondly, the differences in carbon emissions by stage were compared on the basis of 20 cases of public buildings. The results show that the whole-life carbon emissions of the inpatient building was 10,459.94 kgCO₂/m². The proportion of operational carbon emissions was 94.68%, with HVAC (52.57%), equipment (27.85%), and lighting (10.11%) being the main sources. Embodied carbon emissions accounted for 4.54%, and HRB400 steel and C30 concrete were the main sources of carbon emissions. Hospitals are second only to emporiums in terms of operational carbon intensity, being 1.71 and 1.41 times that of schools and office buildings, with inpatient buildings being 3 and 1.7 times that of medical complexes and outpatient buildings, respectively. The future sustainable development of hospital buildings should promote efficient building performance and good environmental quality, both in terms of energy efficiency and carbon reduction.

Urban landscape parks play a crucial role in providing recreational opportunities for citizens. Different types of landscapes offer varying levels of comfort experiences. However, the assessment of landscape comfort primarily relies on subjective evaluations and basic physiological measurements, which lack sufficient quantification of relevant data. This study employed electroencephalography (EEG) technology and subjective questionnaire evaluation methods. Participants observed two sets of landscape demonstration videos using virtual reality (VR) devices, and EEG alpha values and subjective evaluation scores were collected to assess the comfort levels of free-form landscape and regular-form landscape. Additionally, this study explored the correlation between landscape characteristics and physiological comfort. The analysis of the results showed that: 1. The average amplitude of EEG alpha waves recorded from 11 electrodes in the left temporal lobe and right parietal lobe of the participants was higher after they watched the free-form landscape demonstration. The increased alpha values suggest that free-form landscapes are more likely to induce physiological comfort in these specific brain regions. In contrast, regular-form landscape was found to induce higher alpha values at seven specific electrodes located in the occipital cortex, right temporal lobe, and central regions of the participants. In general, free-form landscape provided physiological comfort to a greater number of brain regions. 2. The two groups of landscapes exhibit distinct subjective cognitive differences in terms of their landscape characteristics. These differences, ranked in order of magnitude, include rhythmicity, sense of order, sense of security, and sense of dependence. 3. This study examined the α-waves of specific brain regions, including the right and left temporal lobe and occipital lobe, as well as subjective scoring. It discovered that the rhythmicity, degree of variation, degree of color, and sense of nature of a landscape impact the α-wave value of electrodes in different brain regions. Moreover, there exists a certain linear relationship between the four landscape features and the α-wave values in different regions of the brain. The results of this study provide some reference for the creation of a comfortable landscape design.

Amid the current global trend toward urbanization, there is a growing need for an improved quality of life. While rural public spaces are essential components of community life, their design and planning have traditionally focused on material aspects, neglecting the spiritual well-being of rural residents. This study uses the public spaces of Huixingtou Village in Handan City, Hebei Province, China, as an example to investigate the impact of five key elements within rural public spaces (architecture, streets, greenery, nodes, and landmarks) on individual emotions and perceptions, exploring how these elements contribute to the satisfaction of individuals’ spiritual lives. Initially, we compared the disparities among these elements concerning perceptual dimensions (functionality, technology, and aesthetics) and emotional dimensions (sense of achievement, safety, and well-being). This analysis revealed significant variations in emotional and perceptual dimensions influenced by different rural public space elements. Subsequently, we examined the associations between these elements and objective physiological response data from participants, using eye-tracking technology to delve into the interplay between emotions and perceptions. The results indicate that distinct rural public space elements elicit specific emotional responses, with visual elements and features exerting a pronounced influence on emotional reactions. This in-depth analysis provides comprehensive recommendations for emotional design strategies. Ultimately, this study furnishes emotionally driven design strategies for rural public spaces based on objective data, offering practical guidance for sustainable development and the enhancement of rural public space quality. These findings have significant implications for creating more attractive, inclusive, and sustainable rural spatial environments, addressing the needs of individuals seeking a high and enhanced quality of life.

Released green building evaluation standards for operation stage include a huge number of indicators, which are very comprehensive and systematic. However, the indicators of these standards are very complicated and a large amount of time and manpower are consumed for their evaluation. To evaluate the operational performance of green buildings more practically and efficiently, some studies collect the operational data for part of the indicators (mainly focusing on building energy performance, indoor environmental quality or occupant satisfaction), which are too rough to evaluate the performance of green building. This paper proposed a total of 27 key performance indicators (KPIs) for green building operations monitoring. The number of proposed indicators is much fewer than the evaluation standards, as well as suitable for long-term monitoring, which can dramatically reduce evaluation time and cost. On the other hand, the indicators involving Outdoor environmental quality, Indoor environmental quality, HVAC system, P&D system, Renewable energy system, Total resource consumption and User behavior, which are more comprehensive and systematic than the conventional monitoring studies for operational performance of green building. Firstly, an indicators library for operations monitoring of green building was established based on relevant standards and literature review in this field. Secondly, “SMART” principle and Delphi method were adopted to select the key performance indicators for green building operations monitoring. Different background experts regarding green building industry were chosen to screen the most relevant, accessible and measurable indicators. Subsequently, two projects in China were selected for case study of key performance indicators proposed in this paper for green building operations monitoring to validate the feasibility and advancement.

With the rapid increase in demand for the construction and development of island cities in the South China Sea, the urban heat island phenomenon in such cities should become a key factor to be considered in future urban planning. This paper took Sanya, China as a typical case, and long-term field experiments were conducted in the Mangrove Bay Area in summer and winter. An innovative urban green space cooling model was proposed, using the “green space cooling index” to quantitatively characterize the green space cooling effect, and aiming to minimize the intensity of urban heat islands. This paper studied the optimization method of green space planning and layout under the constraint of a centralized green space total area. Moreover, a genetic algorithm was adopted to optimize the calculation and the layout of the urban green space. The experimental results showed that the urban heat island intensity was more significant at night and was less effective in the daytime during summer. In winter, the urban heat island intensity had a greater effect in the daytime and was less influential at night. Finally, optimization results indicated that the average urban heat island intensity in summer was reduced by 8.8% under the optimal layout urban green space of 0.025 km². The maximum reduction in heat island intensity occurred at 7:00 am (0.48 °C). When 0.0625 km² urban green space was planned, the average urban heat island intensity index in summer was reduced to 0.27 °C, with a decrease of 20.5%.