Urban outdoor space is an important activity place for residents, and its thermal environment directly affects residents’ quality of life and physical and mental health. Due to global climate change and the acceleration of urbanization, the outdoor thermal comfort of urban residents has seriously declined, causing more and more scholars to pay attention to this problem and to carry out research. This paper summarizes the development history and evaluation principles of outdoor thermal comfort evaluation indices and sorts out the methods for achieving outdoor thermal comfort. This paper reviews the effects of urban climate, local microclimate, physiological, psychological, social, and cultural factors on outdoor thermal comfort. In addition, strategies for improving thermal comfort in urban outdoor spaces are discussed from the aspects of urban geometry, vegetation, surface materials, and water bodies. Finally, the existing problems and development directions of current urban outdoor space thermal comfort studies are pointed out. This review paper can provide a reference for the scientific planning and construction of urban outdoor spaces to improve people’s thermal comfort.

Thermal comfort is a fundamental goal of architecture aiming at protecting individuals from harsh weather conditions. In Nigeria’s savanna climate zone, such as Kaduna, poor indoor thermal comfort leads to over-reliance on air-conditioning systems. There is limited research on the application of passive design strategies in the Nigerian savanna climate, which creates a barrier to their widespread implementation in residential buildings. In response to the increased awareness of climate change and the need for sustainable design, this study explores the potential of passive design strategies, focusing on the combination of rooftop insulation and reflective materials with mechanical ventilation as a means of improving indoor thermal comfort solutions. This study conducted a 3-day field experiment of typical dwellings in Kaduna, a major city in the Nigerian savanna climate zone. The data collected from this experiment served as the basis for a simulation study using EnergyPlus software, which tested and evaluated 3 different strategies: passive design (roof insulation + reflective materials), mechanical ventilation, and a combination of passive design and mechanical ventilation. This study highlights the potential for passive design strategies to provide a more sustainable, cost-effective solution, reducing dependence on air conditioning while supporting indoor comfort. Additionally, the research methodology and insights gained offer a basis for developing future building codes in Nigeria that emphasize sustainable practices. Such codes would guide architects, builders, and policymakers in designing homes that respond to local climate needs and align with broader sustainability goals. Further research could explore additional passive measures, including advanced window technologies, shading, and natural ventilation, to maximize sustainable residential design potential in tropical savanna climates.

The thermal comfort evaluation of the urban environment arouses widespread concern among scholars, and research in this field is mostly based on thermal comfort evaluation indexes such as PMV, PET, SET, UTCI, etc. These thermal comfort index evaluation models are complex in the calculation process and poor in operability, which makes it difficult for people who lack a relevant knowledge background to understand, calculate, and apply them. The purpose of this study is to provide a simple, efficient, and easy-to-operate outdoor thermal comfort evaluation model for severe cold areas in China using a machine learning method. In this study, the physical environment parameters are obtained by field measurement, and individual information is obtained by a field questionnaire survey. The applicability of four machine learning models in outdoor thermal comfort evaluation is studied. A total of 320 questionnaires are collected. The results show that the correlation coefficients between predicted values and voting values of the extreme gradient lifting model, gradient lifting model, random forest model, and neural network model are 0.9313, 0.7148, 0.9115, and 0.5325, respectively. Further analysis of the extreme gradient model with the highest correlation coefficient shows that individual factors (such as residence time, distance between hometown and residence, clothing, age, height, and weight) and environmental factors (such as air humidity (RH), wind speed (v), air temperature (Ta), and black bulb temperature (Tg)) have different influences on thermal comfort evaluation. In summary, using a machine learning method to evaluate outdoor thermal comfort is simpler, more direct, and more efficient, and it can make up for the lack of consideration of complex individual factors in the evaluation method of thermal comfort index. The results have reference value and application value for the research of outdoor thermal comfort evaluation in severe cold areas of China.

The unique climate and the landscape of severely cold regions in winter attract many tourists. The outdoor thermal environment affects the space use and the tourist experience, becoming one of the key factors in the design of tourist attractions. The outdoor thermal comfort of tourists from different regions should be considered, but it has been poorly studied in winter in severely cold regions. This paper explores the differences in outdoor thermal comfort in winter between local and non-local tourists through the field measurement of the thermal environment and a questionnaire survey of thermal comfort at tourist attractions in Harbin, China. The results show that the proportion of local tourists who expect the air temperature and solar radiation to rise in winter is higher than that of non-local tourists. The thermal sensation vote of local tourists is generally higher than that of non-local tourists. When the Physiologically Equivalent Temperature (PET) < −6 °C, the thermal satisfaction of non-local tourists is higher than that of local tourists. When the PET value is −10 °C, the thermal comfort of non-local tourists is the highest. The thermal comfort decreases with the rise or fall of the PET value. When −28 °C < PET < −7 °C, the thermal comfort of non-local tourists is generally higher than that of local tourists. This paper provides a reference and evaluation basis for urban tourist attractions’ outdoor thermal environment design in severely cold regions.

It is important for engineering applications that we evaluate the thermal environment based on long-term tracking and investment. Methods merging environmental, physiological, and psychological domains to implement a human-centered approach were applied in this study to assess the outdoor thermal environment in a park. The constant influence of humans in the outdoor environment can change people’s physiological, psychological, and thermal responses. Additionally, the relationship between human physiological, psychological, and thermal factors was explored in this study. The results of this study provide the following findings: (1) In summer, subjects’ skin temperature increased by 0.35 to 2.83 °C during a one-hour outdoor test without shelter, while when tree shade was provided, subjects’ skin temperature dropped by 0.50 to 1.87 °C (except for motion segments). (2) In winter, if subjects stayed outdoors for 1 h, their body segments’ skin temperature dropped by a maximum of 7.93 °C. (3) When subjects went outside, in the early stage, their thermal responses fluctuated for a long time. Therefore, TSV, TCV, and TAV should be measured after they stay outdoors for 45 to 55 min in future studies. (4) Different body segments show different sensitivities to hot or cold. Considering this, a new group of formulas for mean skin temperature calculation are proposed with high accuracy (winter: 0.95; summer: 0.89). (5) Data for the one-hour change in different assessment indicators provide a good viewpoint for park design considering multiple aims such as comfort (TCV), pleasure (EVI), and increasing energy (PFI). Overall, this study took Stalin Waterfront Park as a case study, and some suggestions involving landscaping nodes, space types, and facilities are offered. Moreover, this study provides a novel theory and reasonable method that can be referred to in urban planning and landscape design.