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.

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 data derived from Local Climate Zone (LCZ) field measurements can contribute to the construction of regional climate datasets with urban heat island (UHI) effects and accurately present urban heat island intensity (UHII) characteristics in different areas, thereby improving the accuracy of building energy consumption simulations. This study focuses on Shenyang, a severe cold-region city, as the research area. By mapping the LCZs in the central city of Shenyang and selecting eight different types of LCZ plots for field temperature measurement, the UHI effect of various LCZs in Shenyang was analyzed. Air temperature and UHII were used to evaluate the UHII characteristics of LCZs under typical meteorological conditions. Additionally, this study investigated the temperature dynamics and heating/cooling rates of each LCZ under typical meteorological days. The results reveal significant differences in UHII characteristics among LCZ types, closely related to their surface structure and land cover characteristics. These findings further validate the effectiveness of the LCZ classification method in severe cold regions. The data obtained in this study can be used as high-precision climate model parameters for urban energy consumption models and building energy efficiency models, thus making simulation results more consistent with local characteristics and enabling more accurate energy consumption predictions.