The worsening urban heat island (UHI) effect poses a great challenge to the thermal comfort of people outdoors. However, there has not been a summary of the mechanisms by which UHI affects outdoor thermal comfort (OTC). This paper reviews the commonly used OTC evaluation indexes, data collection methods, and mitigation measures and discusses the relationship between UHI and OTC. The review is limited to peer-reviewed journal publications found in five databases: Science Direct, Scopus, Google Scholar, PubMed, and Web of Science. The review results indicate that physiological equivalent temperature (PET), universal thermal climate index (UTCI), and wet bulb globe temperature (WBGT) are the most widely used indexes in outdoor thermal comfort studies. The data collection methods mainly include questionnaire surveys, measurement, simulation, and formula calculation. There are four main approaches to mitigating the UHI effect in order to improve the comfort of people outdoors: vegetation strategies, water strategies, urban planning strategies, and material strategies. Future research can focus on developing OTC research methods and indexes and combine thermal comfort with visual comfort, auditory comfort, etc. to better evaluate the overall comfort.

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%.

Global excessive CO₂ emissions have caused serious environmental and health problems, such as global warming, melting glaciers, droughts, floods, and extreme temperatures, and have become a common challenge for the world. China has set a dual carbon goal, with the peak carbon emissions before 2030. In China, the building sector accounts for 50.9% of the country’s carbon emissions. In particular, public buildings are characterized by a high carbon emission intensity, accounting for 38.6% of carbon emissions in the building sector, which affects the achievement of the dual carbon goal in China’s building sector. Establishing a reasonable baseline of carbon emissions contributes to quota management and trading of carbon emissions for public buildings in Tianjin, China, and will ultimately contribute to the reduction of carbon emissions. This study investigates the operational energy consumption and carbon emissions of 721 public buildings in Tianjin (including electricity, natural gas, and district heating). The applicability of the Quartile method and the K-means clustering algorithm was compared to determine the carbon emission baseline of different types of public buildings, such as constraint value, guiding value, and advanced value, based on which the dynamic baseline from 2022 to 2030 was determined. The results show that the advanced value, guiding value, and constraint value of the Tianjin public building carbon emission baseline obtained using the Quartile method are more reasonable than those obtained by the K-means clustering algorithm. Furthermore, the carbon emission baseline in 2030 will be reduced by 3.4~9.2% compared to 2022. This study can guide the formulation of carbon emission trading schemes, and support Tianjin’s building sector to achieve the “carbon peak”.

To enhance the energy performance of a central air-conditioning system, an effective control method for the chilled water system is always essential. However, it is a real challenge to distribute exact cooling energy to multiple terminal units in different floors via a complex chilled water network. To mitigate hydraulic imbalance in a complex chilled water system, many throttle valves and variable-speed pumps are installed, which are usually regulated by PID-based controllers. Due to the severe hydraulic coupling among the valves and pumps, the hydraulic oscillation phenomena often occur while using those feedback-based controllers. Based on a data-calibrated water distribution model which can accurately predict the hydraulic behaviors of a chilled water system, a new Model Predictive Control (MPC) method is proposed in this study. The proposed method is validated by a real-life chilled water system in a 22-floor hotel. By the proposed method, the valves and pumps can be regulated safely without any hydraulic oscillations. Simultaneously, the hydraulic imbalance among different floors is also eliminated, which can save 23.3% electricity consumption of the pumps.

Rational planning and the use of renewable energy are effective means to reduce urban carbon emissions. In view of the few urban renewable energy planning cases and unclear methods, the paper takes the Sino-Singapore Tianjin Eco-City renewable energy planning project as a case to provide a renewable energy planning method under the guidance of carbon constraints. Based on scenario analysis, the energy demand of urban buildings, industry and transportation is analyzed and predicted. On the basis of meeting the needs of terminal energy use, with the goal of reducing carbon emissions, the renewable energy planning scheme from 2021 to 2050 under the low-carbon scenario has been formulated, including the promotion of energy-efficient buildings, the utilization of renewable energy in buildings, the electrification of terminal energy use, and the application of large-scale municipal renewable energy. It is planned that, by 2050, the overall renewable energy utilization rate of the Sino-Singapore Tianjin Eco-City will reach 76.76%. It will use renewable energy to heat about 60 million square meters, generate about 766 million kWh of electricity, save about 0.723 million tons of standard coal and reduce 1.287 million tons of carbon dioxide every year, which will have a good effect of energy conservation and emission reduction. In this paper, the renewable energy planning method under the guidance of carbon constraint is established, which can achieve the purposes of saving resources, protecting the environment and driving sustainable development. The Sino-Singapore Tianjin Eco-City is an international co-creation city, which will receive extensive attention and provide theoretical guidance and demonstration cases for urban renewable energy planning in the context of carbon peak and carbon neutrality in the new era.

In light of carbon peak and carbon neutrality goals, China has attached great importance to energy savings and carbon reduction. Carbon reduction in the transport sector is critical to achieving the two-carbon target, as it accounts for 9.41% of total carbon emissions. As the sharing economy grows, car sharing is considered to present excellent carbon reduction potential in the transportation sector. However, the current research is focused on car sharing usage, with a lack of research on the carbon reduction capability of car sharing in China. Hence, this study aims to investigate the carbon reduction capacity of car sharing, including usage rates of car-share services and changes in travel behavior, through an online questionnaire combined with carbon emission data from the transportation sector. The study aims to analyze the contribution of car-share services to carbon reduction in the transportation sector under the current model. The well-to-wheel (WTW) approach is employed, including the energy consumption of vehicles and carbon emissions in the production process. The research results indicate that the introduction of car-sharing services increases driving energy consumption; however, this increase is offset by the decrease in carbon emissions as a result of the production process. Therefore, the overall effect is a reduction in carbon emissions of 1.058971 million tons in 2021, accounting for 1.95 percent of total transport carbon emissions. In addition, the impact on different modes on carbon emission reduction is also explored in this study. The results demonstrate that the private car disposal rate shows the most significant influence on traffic carbon emissions; a 10% reduction in the number of private cars can lead to a 2.48% carbon reduction. The relevant conclusions of this study can provide support for the future development of car sharing in China and the reduction of carbon emissions in the transportation sector.