Overwhelming evidence shows that the harsh climate conditions are affecting urban residents who are living in severe cold areas of China in winter, particularly affecting the frequency and length of outdoor space usage of the elderly. This study aims (1) to establish the modified model which is suitable for the harsh climate region, (2) to verify whether the physiological equivalent temperature (PET) index can be evaluated for the outdoor thermal comfort of older adults in severe cold areas of China in winter, (3) to draw the thermal comfort map that is based on the former conclusions. In this study, the outdoor environments in typical residential areas for the elderly of Changchun, China, has been investigated by using field measurement, questionnaire survey, and Computational Fluid Dynamics (CFD) simulation. The results show that the wind direction is the important aspects of model modification and quite possibly one of the most neglected. In addition, it is convenient to evaluate outdoor thermal comfort of the elderly on the basis of the PET index and the neutral PET temperature of elderly people who live in severe cold areas of China in winter is −0.5 degrees Celsius. According to the thermal comfort map, the park green land of urban residential is the best area for the elderly.

With the development of urbanization and the ageing population, the improvement of the urban environment and the quality of life (QOL) of the elderly in cities with a cold climate have become critical issues to be addressed. However, only a few studies have focused on this aspect. According to a review of the literature, the contents of the built environment (BE) in severe cold regions are defined as thirteen key factors of four categories (density, environmental aesthetics, outdoor environment, and accessibility) and the QOL of old people consists of three aspects (residential, health, and social satisfaction). This study explores how BE variables are associated with the QOL of older adults by using ordered logit and gologit2 models. The data consist of the results of 1945 questionnaires from field surveys in 11 different residential areas, across two cities in northeastern China. The results show: (1) Walkability in winter, distance to a public park of 400–1000 m, outdoor shelters and seating, less than five buses available, and a mixture of evergreen and deciduous trees are five of the most important variables of BE that have a significant positive impact on QOL compared with other climatic regions; (2) “Pocket parks” and pedestrian walkway safety are appropriate approaches to improve wellbeing under local economic conditions. For public transportation, metro and rail transit systems are encouraged, and some rules are needed to reduce the number of buses in harsh weather conditions; (3) Compared with spatial distribution in other climates, the scope of a 15 min city should be less than 1km in severe cold areas. From the findings, we conclude that there are six possible pattern languages to improve the urban environment, and they can provide information for further study on environmental planning in severe cold regions.

The harsh climate and the aging of urban populations have negatively impacted the quality of life of the elderly in severely cold regions. As a result, there is an urgent need to improve environment quality and accurately evaluate outdoor thermal comfort. However, existing studies have paid little attention to older adults living in severely cold climates. This paper aims to fill this gap by studying the elderly population in severely cold cities with high aging rates in China. By combining on-site testing, questionnaire surveys, CFD modeling, parametric computing, and statistical analysis, the study presents an adapted UTCI for elderly people, as well as multi-seasonal prediction models. The results (1) show that the neutral ranges of the UTCI are significantly affected by both climate zones and age groups. Older people are more tolerant to heat but more sensitive to cold. (2) The results also reveal the importance of factors such as air temperature, wind speed, solar radiation temperature, wind direction, relative humidity, and cloud cover in evaluating outdoor thermal comfort. (3) Multi-seasonal thermal comfort models based on neural networks were developed, and empirical studies verified that the model had the highest accuracy in the transitional season and the lowest accuracy in the winter season.