Next Article in Journal
Early Childhood STEM Education for Sustainable Development
Previous Article in Journal
Smart City 4.0: Sustainable Urban Development in the Metropolis GZM
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 6
10.3390/su14063522
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effect of Environmental Planning on Elderly Individual Quality of Life in Severe Cold Regions: A Case Study in Northeastern China

by
Hong Leng
1,2 and
Bingbing Han
1,2,*
1
School of Architecture, Harbin Institute of Technology, Harbin 150006, China
2
Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150006, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(6), 3522; https://doi.org/10.3390/su14063522
Submission received: 5 February 2022 / Revised: 14 March 2022 / Accepted: 15 March 2022 / Published: 17 March 2022
(This article belongs to the Topic Architectures, Materials and Urban Design)
Browse Figures
Versions Notes

Abstract

:
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.

1. Introduction

Over the past few years, as the economy has increased, urban space and the life expectancy of the elderly have been growing. According to the UN data, the proportion of old people over 65 reached 1 in 7 of the global population in 2019, and will reach 1 in 6 by 2050 [1]. For most developing countries, there is a general lack of sustainable strategies for regional sustainability [2], which causes the unrestrained use of resources; the main consequence of a crowded city [3]. The living environment demanded by the urban elderly is facing increasingly serious challenges. Moreover, the availability of outdoor environment is becoming more limited in cities due to the impact of different national prevention and control measures, which significantly reduced well-being and quality of life (QOL) during the coronavirus (COVID-19) pandemic [4,5]. A 15 min city is frequently presented as an optimal strategy in this situation. Additionally, as a method that can be easily used, pattern language is utilized to improve towns and neighbourhoods. Like the network of language, the sequence of patterns is not only a summary of language, but also an index to the pattern. Urban authorities and planners can make a new language by choosing the patterns which are the most useful. In this context, the urban built environment (BE), urban design pattern [6] and 15 min city [7] have become the major focus of research for improving the quality of life of the elderly.
Currently, there is still no consensus in academia on the definition of QOL. The World Health Organization (WHO) considers QOL to be a complex variable, which can be interpreted as including physical and mental health, social relations and the physical environment [8]. It has been recognized by many scholars. Owen and Puts both note that there is a strong connection between QOL and health status, and the attributes of a person, such as a healthy body, psychological state, independence, social relations, and indoor environmental quality [9,10]. Bilotta et al. suggest that QOL should be measured with health level, neighbourhood, leisure, well-being, physical activity, and religion [11]. However, the meaning of elderly QOL is more complicated because of their biological, cognitive and psychological changes. Some scholars have worked on this, and certain findings have been reached. Bowling and Gabriel indicated that the QOL of the aged has close relationships with social relationships, outdoor activity, physical and mental health, family, neighbourhood, financial condition, security and service facilities [12,13]. Furthermore, Borglin et al. presented somewhat different views. From in-depth interviews, they found that the QOL of people over 80 years is associated with the meaning of home, death and dying and telling stories [14]. Although the contents of QOL are controversial, the evaluation methods are relatively consistent. Following the definition of Campbell, the indicator of satisfaction are a popular measurement of QOL [15]. According to the results of WHO and previous studies, the QOL of the elderly in this study is divided into three aspects, which are residential satisfaction (RS), health satisfaction (HS) and social satisfaction (SS).
For the definition of BE, the 3D model of Cervero and 5D model of Ewing are frequently mentioned [16,17]. They include density, diversity, design, destination, and distance. Since the definition of 3D/5D is too broad, many academics have explored the study of BE factors that are related to the QOL of the elderly. Liu believed that living space per capita is closely related to QOL [18]; in Jia’s study, they found that vegetation and walkability have a significant effect on QOL [19]. In addition, Rantakokko and Halaweh stated that the flatness of road and accessibility to financial services facilities are strongly associated with the QOL of older adults [20,21]. By literature review, Padeiro summarized the results of 39 existing studies and indicated that urban furniture and accessibility to healthcare are the most important factors of BE [22]. Based on interview data collected from 4183 older adults over the age of 60, Tiraphat et al. proposed that a walkable neighbourhood, neighbourhood aesthetics, service accessibility, criminal safety, social trust, social support, and social cohesion are closely related to QOL [23], similar to the findings of Rojo. They collected data on 1106 seniors over the age of 60 and revealed that the elements of housing, neighbourhood and neighbours have a positive impact on QOL [24]. On the basis of a systematic literature review, Zhang et al. suggested that four categories (16 variables) of BE are mentioned more frequently, including physical and natural; social; facilities services; and physical safety and psychological security [25]. However, it is important to note that whether some environmental elements affect QOL significantly is controversial. For instance, Curl found that street intervention is not significantly related to QOL among older adults through a longitudinal study [26], and Vogt et al. believe that a senior center has no association with the health satisfaction of elderly people [27]. Based on the above findings, this paper attempts to present four significant categories (13 variables) as contents of BE in severe cold regions. The first category is density, including square footage and building typology. The second is socio-physical environment, which consists of safety, cleanliness, types of outdoor fitness and green space. The third is outdoor facilities, which involve outdoor benches, path surface materials and sidewalk color. The last category is accessibility, which comprises the number of accessible bus stops, community hospitals, distance to public parks and commercial facilities.
Additionally, many references indicate that the BE has great influence on the QOL of the elderly. As for the RS of QOL, Cristina et al. found that the physical properties of the house and the quality of the environment within the residence are decisive for the QOL and satisfaction levels of the elderly [28]. After analysing data from surveys of older adults in Naples, Italy, Gargiulo et al. stated that the accessibility of services and the adequacy of settlement facilities are positively correlated with the QOL of the aged [29]. For the HS of QOL, Parra et al. showed that the physical environmental attributes and subjective perceptions of BE correlate with the elderly’s self-rated health [30]. Moreover, high density, low density, dense service, commercial decline and health satisfaction have a positive impact; this result is derived from the study of Spring [31], and Yu et al. also reach similar conclusions. They indicate that safety, greenery, seat, recreational facilities, and the width and height of the street have a statistically significant effect on health levels [32]. In a statistical analysis of the outdoor activity behaviour and neighbourhood built environment of 1010 males older than 66 years in Wales, UK, Gong et al. concluded that when there is more green space in a residential area, the more frequently the elderly will participate in outdoor activities, which will improve their physical and mental health, reduce psychological stress, and even extend their life span [33]. Liu et al. suggested that housing quality and community safety are helpful in increasing physical and mental health satisfaction, and improving subjective well-being among older adults [18]. In terms of the SS of QOL, few studies focus on groups of aging people. In existing studies, there are some more significant findings. For example, Mouratidis et al. proposed that more frequent use of community amenities and reducing travel time are beneficial in improving the social communication and leisure satisfaction of residents [34]. Moreover, Boessen et al. believed the density, diversity of land use, and design are essential aspects of the improvement of social satisfaction [35]. Similar findings are found in the study of Tao, where it is revealed that proximity to parks, road connectivity, population density, and road connectivity have a significant effect on neighbourhood interaction [36]. Jun and Orban believed that physical walkability [37], residential surrounding greenness and safety [38] have a greater influence on social satisfaction.
It is notable that the aforementioned studies mainly focus on humid subtropical [18,31,39], humid continental [36,40,41], warm Mediterranean [28,29], temperate continental [34,42], temperate Mediterranean [43] and temperate oceanic [30,33] climate zones. In particular, the impact of climatic factors on the residential, social and health aspects of quality of life cannot be ignored, especially since the harsh climate in severe cold regions can cause major differences in urban environments. At present, only a few papers have focused on severe cold regions. Through a survey of 891 residents in Harbin, China, Ban et al. found that environmental issues change the health of older adults [44]. Zeng et al. found that cold weather significantly increases the risk of disability and even mortality among older adults during daily activities after analysing the data of demographic characteristics, and the socioeconomic and environmental information of the elderly in 22 provinces of China [45]. In addition, Leng et al. explored the relationship between green spaces, facilities allocation and cardiovascular health in winter in the cold city of Harbin, China, and showed that environmental factors have a significant influence on health levels in harsh climates [46]. They also obtained similar conclusions by analysing the relationship between the ground covered with snow, outdoor activities and cardiovascular exercise prescription of elderly residents [47].
In general, the main limitations of existing research are as follows: Firstly, although the density, environment, outdoor facilities and accessibility are closely related to the QOL of elderly people, it is not clear which categories or variables of BE have great influence on the QOL of the aged in severe cold regions. Due to different climate features and climate change, the characteristics of variables in each category are obviously different. For example, in severe cold areas, the ground being covered with snow causes the length of outdoor activities for the elderly to be shortened because of long winters, which poses a threat to travel safety. Additionally, the total amount of sunlight in cold areas is not sufficient, which may lead to large differences in the usage rate of different areas of the urban outdoor environment [48]. Secondly, previous studies have paid insufficient attention to the elderly population in severe cold regions. The limited papers identified that the built environment in severe cold areas has obvious effects on the outdoor activities and physical health of urban residents; however, there is a lack of a systematic summary of the urban built environment and quality of life characteristics of elderly groups, and there are also no proposed measures for urban environment planning and improvement, as well as the pattern languages of cities in severe cold regions. For these reasons, the research questions (RQ) in this paper are as follows:
RQ 1: Which elements of BE in severe cold regions can effectively improve the QOL of older adults? How much does each element of BE affect the QOL of older adults?
RQ 2: How can we improve the urban environments and develop new design patterns in severe cold regions, with the goal of improving the QOL of older people and meeting their requirements?
This paper focuses on the effect of various BE characteristics on the QOL of the elderly in severe cold areas of China (as shown in Figure 1). Thirteen variables of four categories of BE are presented by using field observations, a questionnaire survey and map capture from two typical severe cold cities in China. Meanwhile, data on the QOL of the elderly, defined as RS, HS, and SS, are drawn from a survey questionnaire completed by 1945 residents. Based on the generalized ordered logistic regression, it is possible to illustrate the most important variables of BE. Once these variables are developed, it is critical to provide an estimation value for built environment variables and subjective satisfaction through the marginal effect calculation, and differences in the impact of various environmental elements on the QOL of older adults are demonstrated. These results are concluded as possible pattern languages to improve the urban environment in severe cold regions.

2. Materials and Methods

2.1. Site Study

In this study, Changchun and Harbin, located in northeast of China, were selected as the research cities, as shown in Figure 2. On the one hand, according to the statistics data of local authorities, the population of permanent residents in these cities amounted to 19.077 million in 2021. Additionally, the number of people aged 65 or above was 3.453 million, accounting for 18.1% [49]. On the other hand, they are both capital cities in severe cold areas of China, of Jilin and Heilongjiang provinces, respectively. The average air temperature in summer and winter was 25 °C and −15 °C, respectively, and winter season can last for five months [47,48].
Based on the results of field investigation, 11 typical residential areas (more than 20% of people were aged 65 and over) were selected from 56 areas in 15 districts, as shown in Table 1. The features of these residential areas were as follows: (1) almost all of the residential buildings were built before 2010; (2) built environment characteristics were different; and (3) the average ages of residents were approximately similar.

2.2. Measurement

In this study, the data were obtained from September until January in 2018 to 2019. The decade-long inhabitants aged 60 or older were chosen to be participants in 11 typical residential areas. Almost all of the questionnaires were completed in recreational areas, residential parks, sidewalks, or during community gardening. The elderly people who had cognitive disorders and disability or that were living in severe cold areas for less than 10 years were eliminated before fulfilment of the survey. Finally, a total of 1945 valid questionnaires were collected.
The questionnaire contained three parts and twenty questions. The first part was about features of the participants, including the age, gender, educational stage and health status. Education was categorized into two groups: tertiary education or better, and others. Health was divided into two parts: severe disease, and health or mild disease. The second part concerned the three aspects of QOL, RS, HS, and SS. For example, RS was measured by a series of questions, such as “are you satisfied with the public space?” Furthermore, a 7-point Likert scale was selected to evaluate the different range of satisfaction, with 1 meaning completely dissatisfied and 7 meaning completely satisfied. The third part was the four categories of BE, including density, environment, outdoor facilities and accessibility. The variables and questions were both from field investigations and a previous study [48]. It should be noted that the walking distance was a contentious factor. Some studies indicated that it was comforting when people walked 1600 m at 6.4 km/h [50]. In terms of senior citizens, some papers presented that walking within 400 m was the most appropriate distance [51,52]. However, some other works in the literature stated that a suitable block scale (within 1000 m) for cities was appropriate for the outdoor activities of the aged [53]. Therefore, based on the existing research and field survey in two cities, the variables of distance to public park, and distance to commercial facilities both fell into three categories: less than 400 m, from 400 to 1000 m and more than 1000 m.

2.3. Data Description

As illustrated in Table 2, the square footage of the houses of the aged were for the majority less than 60 m2. This result shows that the elderly people live in high-density areas in severe cold regions. In addition, the majority of the environment had semi-open space, outdoor fitness areas and a mixture of various types of trees. As for facilities, most residential areas had normal outdoor seating and grey concrete sidewalks. For accessibility, there were more than five buses available that surrounded residential areas and the distance to the community hospital was more than 1000 m. Furthermore, a few of the public parks were located within 400 m and the distribution of commercial facilities was almost equal. Moreover, the mean values were slightly different when comparing RS, HS, and SS. The highest was RS (4.704), and the next was HS (4.653). SS (3.872) was the lowest. In addition, the majority of participants was under 65 years of age and the number of males was higher than that of females. The mean value of chronic diseases was 0.737, which meant that most participants had no disease. As for education, the elderly people of this study were generally of a low level.

2.4. Research Methods

The data analysis revealed that most of the explanatory variables were binary and the response variables (RS, HS, and SS) were ordinal variables, which could be used with the ordered logistic regression model. However, the most important assumption of this method was that the relationship between each outcome group should be same. It also meant all categories of the outcome variable followed the proportional odds assumption or the parallel regression assumption. If the data violated the proportional odds assumption, the generalized ordered logistic regression model was a better choice. As for the research on the ordinal scale of the data, many studies have used this approach to analyse subjective well-being [54], traffic accident injury [55,56] and perceived health [57].

3. Results and Discussion

3.1. Ordered Logit Model

The Stata version 19.0 software for windows (StataCorp LLC, College Station, TX, USA) was used for statistical analysis in this study. The estimation of the ordered logit regression model with a 95% confidence level in severe cold regions is illustrated in Table 3. It was noted that the positive numbers of the coefficient indicated a positive relationship between the explanatory variable and the outcome variable, while the negative numbers indicated an inverse relationship [58]. The results show that 9, 10 and 10 independent variables had a significant relationship with RS, HS, and SS, respectively.
Although all of the independent variables were quite remarkable, as shown in Table 3, it was important to know whether the chi-square test showed a significant relationship in order to check whether to reject the null hypothesis. The results of the proportion test were 310.366, 250.143 and 210.03 for 95 degrees of freedom (p < 0.05), which meant at least one independent variable had a different impact on the final result. Therefore, the generalized ordered logit model was selected in this study, which could broaden the equal-interval assumption and display the rank of various dependent variables to obtain more accurate results.

3.2. Generalized Ordered Logit Model

Based on the gologit2 program proposed in the research of Williams [59,60], the expression of RS was as follows, and the other two expressions (HS and SS) were similar.
P(RSi > j) = exp (αj + X1iβ1 + X2iβ2 + X3iβ3j)/[1 + exp (αj + X1iβ1 + X2iβ2 + X3iβ3j)] + εj, j = 1, 2, 3, 4, 5, 6
P(RS > j) means the cumulative probability of RS; j means the different scales of satisfaction, from j = 1 (strongly dissatisfied) to j = 7 (strongly satisfied); i means the subscript of independent variables; and αj means the intercept or threshold. In addition, X1i, X2i were the descriptive variables of BE that followed the proportional odds assumption; β1 and β2 are the regression coefficients of X1i and X2i, respectively. Because all of these variables can fit the proportional odds assumption, there was no need to calculate coefficient values for each j. This also meant that the β values of X1 and X2 were same. On the contrary, X3i was the descriptive variable of BE that violated the proportional odds assumption; β3j was the regression coefficient of X3i. Due to the fact that the variable did not fit the proportional odds assumption, the value of β3j was different according to various j values. εj was the regression error term. The results of the gologit2 model estimation are shown in Table 4.

3.3. Model Validation

Furthermore, the Akaike Information Criterion (AIC) and Pseudo R2 could be used to assess the goodness-of-fit of the models. The value of Pseudo R2 was closer to 1 and the model with a smaller AIC was considered the most fitting model [59]. The results of the model fitting goodness test are given in Table 5. The Pseudo R2 value for the gologit2 model (0.044, 0.0448, 0.0631) was closer to 1 than that for the ordered logit model (0.0161, 0.0272, 0.0462), and the AIC value for the gologit2 model (6587.195, 6698.022, 6955) was smaller than the value for the ordered logit model (6606.88, 6719.526, 6977.892). It can be concluded that the gologit2 model was much better than the ordered logit model.

3.4. Marginal Effects

Because the coefficient of the generalized ordered logit model only showed how the independent variable had an influence on the dependent variable, it was essential to compute the marginal effects, which presented the impact that the change in one variable had on the outcome variable while all other variables were held constant. The results of the marginal effects are shown in Table 6.
For RS, HS, and SS, four variables had a positive impact on being completely satisfied, including “outdoor shelters and seating”, “health or mild diseases”, “the number of buses available ≤5”, and “snow clearing ≤48 h”. Additionally, two variables had a negative impact on this, including “deciduous tree”, and “distance to public parks >1000 m”. For RS and HS, three key variables, “distance to public parks from 400 to 1000 m”, “age ≤ 65” and “the distance to community hospital >1000 m”, were closely connected. Specifically, the former two variables had a forward influence, while the last one had the opposite. In terms of HS and SS, “the sidewalk with grey concrete” had negative effectiveness.
More specifically, three categories of BE, “environment”, “outdoor facilities”, and “accessibility”, were found to significantly affect RS, HS, and SS. For “environment”, the results show that the elderly who lived in severe cold regions had increased demand for evergreen plants, which was consistent with previous studies [48]. However, the residential area with deciduous trees dropped 4.59%, 5.31%, and 6.44% in the QOL, which was different from existing studies. Vegetation is often used to regulate urban microclimate and outdoor thermal comfort to enhance well-being [61]; however, the capacity for regulation was almost negligible in severe cold regions [62,63]. In addition, this study found that a variety of colours had a sustained positive effect on the outdoor activity of older adults. Specifically, the blue or green concrete of the sidewalks were more than 9.14% and 2.12% better in terms of SS and HS than grey concrete. Similar findings were not found in other papers.
In terms of “outdoor facilities”, cleaning up the snow for pedestrians in under 48 h had a discernible positive influence on HS (8.61%). A snowy or icy sidewalk would cause serious injuries resulting from slips and falls in severe cold areas. Hence, it was crucial to clear pedestrian walking areas of snow and ice in time. Furthermore, long-term snow cover had serious negative effects on the durability of pavement materials. Nonetheless, more than half of the residential areas in this study could not guarantee snow removal within 48 h of the end of the snowfall because of the different economic conditions and management procedures in various districts. Meanwhile, the results present the positive effects of the availability of pedestrian walkways on RS and SS (4.99% and 3.65%). Another important variable was outdoor shelters, which had a huge beneficial impact on the RS and HS (7.83% and 7.75%), as well as a greater change with SS (from completely dissatisfied −24.99% to completely satisfied 4.21%). This structure could effectively protect the aged from the harsh weather in severe cold regions, mitigating the impact of strong winds and snow on outdoor thermal comfort. However, the majority of outdoor benches in research sites lacked consideration because of the lower economic level in severe cold areas of China. The outdoor shelters and seating were still not taken seriously enough. The weather-protected seating has appeared in studies of nursing homes, but there is a lack of literature suggesting a stronger demand in severe cold climates [64].
As for “accessibility”, the number of buses available in surrounding areas being less than five had a positive (2.45%, 1.86%, 2.73%) effect on QOL. It should be noted, though, that there had been a significant drop for SS, from neutral (4.37%) to completely satisfied (2.73%). This result might be closely correlated with the climatic characteristics of severe cold areas, the travel habits of the aged, and regional economic conditions. A greater number of buses represented better modes of transportation available in transition season, which was consistent with the results of studies in other climatic zones [65]. However, long-distance bus travel in the city would be increasingly dangerous during the five months of winter. Meanwhile, a substantial number of elderly residents in interviews mentioned that although long-distance travel relied mainly on public transportation, excessive public transportation may lack easy access to the required information about the routes and schedules due to individual cognitive deterioration. Additionally, if the number of buses is excessive, it may increase the possibility of traffic congestion in the area. The relevant traffic noise can influence the emotions of the elderly, and numerous papers have been conducted to support this [66,67]. In addition, participants preferred to take underground public transportation that was less affected by weather, such as the metro if the option is available. These results are inconsistent with other studies in areas with similar economic conditions but different climates. A study on age-friendly cities in a cold semi-arid climate zone concluded that the most significant factor in public transportation was seats for the elderly and special facilities for the disabled, and the amount of public transportation was not discussed [68]. Another study on the travel behaviour of the elderly in a tropical rainforest climate zone showed that the majority of the elderly (62.46%) travelled by private transportation, followed by public transportation (20.47%) [69].
The distance to a public park was another significant variable, which could remarkably decrease the RS and HS (12.87%, 11.69%) when the distance was over 1000 m. According to the field survey, due to the physiological characteristics of older adults and the harsh climate, they would take more than 15 min to walk about 1000 m (usually 17 to 19 min) in winter. Despite this fact, it was also found that public parks with long distances could reflect positively on HS and SS (5.79%, 1.62%). This illustrated that the extent of the effect that the distance to a public park had on different levels of HS and SS was controversial. Moreover, it had a positive impact on RS (1.35%) and HS (2.18%) when the distance to a public park was 400–1000 m. This finding had some similarity to existing studies. Both a study based in Sweden [70], which was also in a severe cold climate zone, and in Nanjing [71], which was in a humid subtropical climate, suggested to keep urban parks within close vicinity of the areas where people lived. However, a different point of view was provided by a study that took place during the COVID-19 pandemic. Distance was not decisive factor; instead, increasing the area of green space per capita as much as possible and reducing congestion in the park were the most effective measures [72].
In addition, the most important variable in terms of personal attributes was chronic disease, which greatly affect the QOL of the elderly. However, even the healthy people still had lower values of RS and HS in severe cold regions. The next most important variable was age; RS and HS fell significantly when people were over the age of 65.

4. Conclusions

In general, this study presents the relationship between built environment variables and the subjective satisfaction of the aged in a severe cold climate. Based on the results of a questionnaire survey and statistical model, it will definitely provide more effective guidance on how to optimize the key variables of BE, which improve the QOL of the elderly. According to the pattern language from Alexander and the new pattern language for growing regions from Mehaffy, it is a well-designed tool to summarize the findings of this study into three parts: places, networks and processes.
Firstly, pedestrian walkways, the number of available buses, distance to parks, outdoor seating facilities, and vegetation types are the five most important variables of BE. More specifically, the durability and availability of walkways in winter, less than five buses available (it would be better if the platforms have heating), distance from public parks to residential areas being between 400 to 1000 m, outdoor shelters and seating, and a mixture of evergreen plants and deciduous trees have a significant positive impact and could be used as pattern languages to guide the environmental design in harsh regions. For places, this includes urban patterns (number of available buses), neighbourhood patterns (pedestrian walkways, distance to parks), and streetscape patterns (outdoor seating facilities, vegetation types).
The second part is networks, which consists of walkable space patterns (pocket parks, pathway safety) and transportation patterns (transit systems, public transportation modes). The development strategies should be closely related to socioeconomic status and regional economic development plan. As the “old industrial” cities in northeast China, the GDP of Changchun and Harbin only account for 8.2% of the total of the Chinese mainland in 2020 [73]. In this situation, the high quality of pocket parks and pathway safety are an appropriate approach to improve the well-being of elderly people. Whether newly built or improved, they both contain street furnishings (outdoor shelters, handrails) and landscape design (evergreen and deciduous trees). As for funding sources, community managers, public–private ventures, individual contributions, and philanthropic support should be encouraged. Furthermore, walkability is the most important factor for environmental planning in severe cold areas. For snowy or icy sidewalks, local snow removal laws should be legislated in the future. In terms of transit systems, the effect of the number of buses available on the QOL of the elderly is markedly different depending on the transition or winter season. The weather and climate conditions have an apparent effect on outdoor activity attitudes, intentions, and behaviour. In order to increase fuel efficiency, emission reduction, and cost saving, some other public transportation modes, such as metro and rail transit systems, are encouraged [74], and government administration needs to develop rules for reducing the number of buses during harsh weather conditions.
Thirdly, for processes, community life circle patterns are included. In further detail, the retail, medical, catering and other industries that are closely correlated with the elderly should be arranged at about 800–850 m (15 min walk) from the residential area, while other industries with a lesser correlation can be arranged at about 1000 m according to the 15 min community life circle in cold-climate areas. The pattern language framework is shown in Figure 3.

Limitation

The COVID-19 pandemic has taken a significant toll on the health and wellbeing of the elderly. However, the results in this paper came from survey data before coronavirus. The latest data should be collected to be used for future research. Furthermore, the control variable of this paper is the lack of personal finances, because substantial numbers of the elderly are unwilling to discuss this. Whether income affects the relationship between BE and the QOL of the elderly in severe cold areas is a significant research direction. Because the intervening variable data are lacking in this study, the next stage of research is to discuss the mechanism of influence of BE and the QOL of the aged. In addition, another study revealed that the pathways of the influence of BE on QOL are distinct depending on the different age groups of the elderly [75]. The next important direction for research in severe cold regions is to follow different age groups of elderly people.

Author Contributions

Conceptualization and methodology, B.H. and H.L.; resources and data curation, B.H.; writing—original draft preparation, B.H.; writing—review and editing, H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 51978192. The APC was funded by the National Natural Science Foundation of China.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Acknowledgments

Special thanks are given to Yingling Fan at the University of Minnesota for the assistance with data processing methods. The authors also grateful to Chunhong Wang and Yujie Yuan for their help during the field data collection.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. United Nations. World Population Prospects 2019: Highlights. 2019. Available online: https://www.un.org/development/desa/publications/world-population-prospects-2019-highlights.html (accessed on 17 June 2021).
  2. Zeng, X.; Li, T.; Chen, C.; Si, Z.; Huang, G.; Guo, P.; Zhuang, X. A hybrid land-water-environment model for identification of ecological effect and risk under uncertain meteorological precipitation in an agroforestry ecosystem. Sci. Total Environ. 2018, 633, 1613–1628. [Google Scholar] [CrossRef]
  3. Zeng, X.; Xiang, H.; Liu, J.; Xue, Y.; Zhu, J.; Xu, Y. Identification of Policies Based on Assessment-Optimization Model to Confront Vulnerable Resources System with Large Population Scale in a Big City. Int. J. Environ. Res. Public Health. 2021, 18, 13097. [Google Scholar] [CrossRef] [PubMed]
  4. Vindegaard, N.; Benros, M.E. COVID-19 pandemic and mental health consequences: Systematic review of the current evidence. Brain Behav. Immun. 2020, 89, 531–542. [Google Scholar] [CrossRef]
  5. Pappa, S.; Ntella, V.; Giannakas, T.; Giannakoulis, V.G.; Papoutsi, E.; Katsaounou, P. Prevalence of depression, anxiety, and insomnia among healthcare workers during the COVID-19 pandemic: A systematic review and meta-analysis. Brain Behav. Immun. 2020, 88, 901–907. [Google Scholar] [CrossRef] [PubMed]
  6. Salingaros, N.A. Rules for Urban Space: Design Patterns Create the Human Scale. J. Urban Res. Dev. 2021, 2, 4–16. Available online: https://ojs.emu.edu.tr/index.php/jurd/article/view/240 (accessed on 25 December 2021).
  7. Moreno, C.; Allam, Z.; Chabaud, D.; Gall, C.; Pratlong, F. Introducing the 15-Minute City: Sustainability, Resilience and Place Identity in Future Post-Pandemic Cities. Smart Cities 2021, 4, 93–111. [Google Scholar] [CrossRef]
  8. WHOQOL-SRPB Field-Test Instrument: WHOQOL Spirituality, Religiousness and Personal Beliefs (SRPB) Field-Test Instrument: The WHOQOL-100 Questions Plus 32 SRPB Questions. 2012. Available online: https://www.who.int/publications/i/item/WHO-MSD-MER-Rev-2012-04 (accessed on 18 September 2021).
  9. Owen, D.; Russell, P.; Scott, M. Positive perceptions of green and open space as predictors of neighbourhood quality of life: Implications for urban planning across the city region. J. Environ. Plan. Manag. 2018, 62, 626–646. [Google Scholar] [CrossRef]
  10. Puts, M.T.; Shekary, N.; Widdershoven, G.; Heldens, J.; Lips, P.; Deeg, D.J. What does quality of life mean to older frail and non-frail community-dwelling adults in The Netherlands? Qual. Life Res. 2007, 16, 263–277. [Google Scholar] [CrossRef] [PubMed]
  11. Bilotta, C.; Bowling, A.; Casè, A.; Nicolini, P.; Mauri, S.; Castelli, M.; Vergani, C. Dimensions and correlates of quality of life according to frailty status: A cross-sectional study on community-dwelling older adults referred to an outpatient geriatric service in Italy. Health Qual. Life Outcomes 2010, 8, 56. [Google Scholar] [CrossRef] [Green Version]
  12. Bowling, A.; Gabriel, Z.; Dykes, J.; Dowding, L.M.; Evans, O.; Fleissig, A.; Banister, D.; Sutton, S. Let′s ask them: A national survey of definitions of quality of life and its enhancement among people aged 65 and over. Int. J. Aging Hum. Dev. 2003, 56, 269–306. [Google Scholar] [CrossRef] [PubMed]
  13. Gabriel, Z.; Bowling, A. Quality of life from the perspectives of older people. Ageing Soc. 2004, 24, 675–691. [Google Scholar] [CrossRef] [Green Version]
  14. Borglin, G.; Edberg, A.K.; Hallberg, I.R. The experience of quality of life among older people. J. Aging Stud. 2005, 19, 201–220. [Google Scholar] [CrossRef]
  15. Campbell, A.; Converse, P.E.; Rodgers, W.L. The Quality of American Life. Acad. Manag. Rev. 1977, 2, 694–696. [Google Scholar] [CrossRef]
  16. Cervero, R.; Kockelman, K. Travel demand and the 3Ds: Density, diversity, and design. Transp. Res. Part D Transp. Environ. 1997, 2, 199–219. [Google Scholar] [CrossRef]
  17. Ewing, R.; Robert, C. Travel and the Built Environment: A Synthesis. Transp. Res. Rec. J. Transp. Res. Board 2001, 1780, 87–114. [Google Scholar] [CrossRef] [Green Version]
  18. Liu, Y.; Dijst, M.; Faber, J.; Geertman, S.; Cui, C. Healthy urban living: Residential environment and health of older adults in Shanghai. Health Place 2017, 47, 80–89. [Google Scholar] [CrossRef] [PubMed]
  19. Jia, X.; Yu, Y.; Xia, W.; Masri, S.; Sami, M.; Hu, Z.; Yu, Z.; Wu, J. Cardiovascular diseases in middle aged and older adults in China: The joint effects and mediation of different types of physical exercise and neighborhood greenness and walkability. Environ. Res. 2018, 167, 175–183. [Google Scholar] [CrossRef]
  20. Rantakokko, M.; Iwarsson, S.; Portegijs, E.; Viljanen, A.; Rantanen, T. Associations Between Environmental Characteristics and Life-Space Mobility in Community-Dwelling Older People. J. Aging Health 2015, 27, 606–621. [Google Scholar] [CrossRef]
  21. Halaweh, H.; Dahlin-Ivanoff, S.; Svantesson, U.; Willen, C. Perspectives of Older Adults on Aging Well: A Focus Group Study. J. Aging Res. 2018, 2018, 9858252. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Padeiro, M.; Jose, J.; Amado, C.; Sousa, L.; Oliveira, C.R.; Esteves, A.; McGarrigle, J. Neighborhood Attributes and Well-Being Among Older Adults in Urban Areas: A Mixed-Methods Systematic Review. Res. Aging 2021. [Google Scholar] [CrossRef]
  23. Tiraphat, S.; Pelltzer, k.; Thamma-Aphiphol, K.; Suthisukon, K. The Role of Age-Friendly Environments on Quality of Life among Thai Older Adults. Int. J. Environ. Res. Public Health 2017, 14, 282. [Google Scholar] [CrossRef] [PubMed]
  24. Rojo-Pérez, F.; Fernández-Mayoralas, G.; Forjaz, M.J.; Prieto-Flores, M.E.; Martínez-Martí, P. Residential environment and health conditions among older-adults in community-dwelling in Spain: What influences quality of life? Environ. Gerontol. Eur. Lat. Am. 2016, 13, 149–174. [Google Scholar] [CrossRef] [Green Version]
  25. Zhang, F.; Li, D.; Ahrentzen, S.; Feng, H. Exploring the inner relationship among neighborhood environmental factors affecting quality of life of older adults based on SLR-ISM method. J. Hous. Built Environ. 2020, 35, 215–242. [Google Scholar] [CrossRef]
  26. Curl, A.; Thompson, C.W.; Aspinall, P. The effectiveness of ‘shared space’ residential street interventions on self-reported activity levels and quality of life for older people. Landsc. Urban Plan. 2015, 139, 117–125. [Google Scholar] [CrossRef] [Green Version]
  27. Vogt, S.; Mielck, A.; Berger, U.; Grill, E.; Peters, A.; Döring, A.; Holle, R.; Strobl, R.; Zimmermann, A.; Linkohr, B.; et al. Neighborhood and healthy aging in a German city: Distances to green space and senior service centers and their associations with physical constitution, disability, and health-related quality of life. Eur. J. Ageing 2015, 12, 273–283. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Cristina, F.; David, A.; Ángela, B.P. Dwelling conditions and life satisfaction of older people through residential satisfaction. J. Environ. Psychol. 2017, 49, 1–7. [Google Scholar] [CrossRef]
  29. Gargiulo, C.; Zucaro, F.; Gaglione, F. A Set of Variables for the Elderly Accessibility in Urban Areas. 2018. Available online: http://www.serena.unina.it/index.php/tema/article/view/5738 (accessed on 5 December 2021).
  30. Parra, D.C.; Gomez, L.F.; Sarmiento, O.L.; Buchner, D.; Brownson, R.; Schimd, T.; Gomez, V.; Lobelo, F. Perceived and objective neighborhood environment attributes and health related quality of life among the elderly in Bogota, Colombia. Soc. Sci. Med. 2010, 70, 1070–1076. [Google Scholar] [CrossRef] [PubMed]
  31. Spring, A. Short- and Long-Term Impacts of Neighborhood Built Environment on Self-Rated Health of Older Adults. Gerontologist 2018, 58, 36–46. [Google Scholar] [CrossRef] [PubMed]
  32. Yu, S.; Liu, Y.; Cui, C.; Xia, B. Influence of Outdoor Living Environment on Elders’ Quality of Life in Old Residential Communities. Sustainability 2019, 23, 6638. [Google Scholar] [CrossRef] [Green Version]
  33. Gong, Y.; Gallacher, J.; Palmer, S.; Fone, D. Neighbourhood green space, physical function and participation in physical activities among elderly men: The Caerphilly Prospective study. Int. J. Behav. Nutr. Phys. Act. 2014, 11, 40. [Google Scholar] [CrossRef] [Green Version]
  34. Mouratidis, K. Built environment and leisure satisfaction: The role of commute time, social interaction, and active travel. J. Transp. Geogr. 2019, 80, 102491. [Google Scholar] [CrossRef]
  35. Boessen, A.; Hipp, J.R.; Butts, C.T.; Nagle, N.N.; Smith, E.J. The built environment, spatial scale, and social networks: Do land uses matter for personal network structure? Environ. Plan. B Urban Anal. City Sci. 2017, 45, 400–416. [Google Scholar] [CrossRef] [Green Version]
  36. Tao, Y.; Yang, J.; Chai, Y. The Anatomy of Health-Supportive Neighborhoods: A Multilevel Analysis of Built Environment, Perceived Disorder, Social Interaction and Mental Health in Beijing. Int. J. Environ. Res. Public Health 2019, 17, 13. [Google Scholar] [CrossRef] [Green Version]
  37. Jun, H.; Hur, M. The relationship between walkability and neighborhood social environment: The importance of physical and perceived walkability. Appl. Geogr. 2015, 62, 115–124. [Google Scholar] [CrossRef]
  38. Orban, E.; Sutcliffe, R.; Dragano, N.; Jockel, K.H.; Moebus, S. Residential Surrounding Greenness, Self-Rated Health and Interrelations with Aspects of Neighborhood Environment and Social Relations. J. Urban Health 2017, 94, 158–169. [Google Scholar] [CrossRef] [Green Version]
  39. Buys, L.; Evonne, M. Residential satisfaction in inner urban higher-density Brisbane, Australia: Role of dwelling design, neighbourhood and neighbours. J. Environ. Plan. Manag. 2012, 55, 319–338. [Google Scholar] [CrossRef] [Green Version]
  40. Cao, X. How does neighborhood design affect life satisfaction? Evidence from Twin Cities. Travel Behav. Soc. 2016, 5, 68–576. [Google Scholar] [CrossRef]
  41. Dang, Y.; Dong, G.; Chen, Y.; Jones, K.; Zhang, W. Residential environment and subjective well-being in Beijing: A fine-grained spatial scale analysis using a bivariate response binomial multilevel model. Environ. Plan. B Urban Anal. City Sci. 2017, 46, 648–667. [Google Scholar] [CrossRef] [Green Version]
  42. Kyttä, M.; Anna, B.; Mohammed, H.; Kaisa, S. Urban happiness: Context-sensitive study of the social sustainability of urban settings. Environ. Plan. B Plan. Des. 2015, 43, 34–57. [Google Scholar] [CrossRef]
  43. Sohn, D. Residential crimes and neighbourhood built environment: Assessing the effectiveness of crime prevention through environmental design (CPTED). Cities 2016, 52, 86–93. [Google Scholar] [CrossRef]
  44. Ban, J.; Lan, L.; Yang, C.; Wang, J.; Chen, C.; Huang, G.; Li, T. Public Perception of Extreme Cold Weather-Related Health Risk in a Cold Area of Northeast China. Disaster Med. Public Health Prep. 2017, 11, 417–421. [Google Scholar] [CrossRef] [PubMed]
  45. Zeng, Y.; Gu, D.; Purser, J.; Hoenig, H.; Christakis, N. Associations of environmental factors with elderly health and mortality in China. Am. J. Public Health 2010, 100, 298–305. [Google Scholar] [CrossRef]
  46. Leng, H.; Li, S.; Zhao, H.; Song, Y.; Yuan, Q. Planning for Supportive Green Spaces in the Winter City of China: Linking Exercise of Elderly Residents and Exercise Prescription for Cardiovascular Health. Int. J. Environ. Res. Public Health 2020, 17, 5762. [Google Scholar] [CrossRef] [PubMed]
  47. Liang, S.; Leng, H.; Yuan, Q.; Wang, B.; Yuan, C. How does weather and climate affect pedestrian walking speed during cool and cold seasons in severely cold areas? Build. Environ. 2020, 175, 106811. [Google Scholar] [CrossRef]
  48. Jin, H.; Wang, B.; Han, B. Study on Environment Regulation of Residential in Severe Cold Area of China in Winter: Base on Outdoor Thermal Comfort of the Elderly. Sustainability 2019, 11, 6509. [Google Scholar] [CrossRef] [Green Version]
  49. The Seventh National Population Census of China. 2021. Available online: http://www.stats.gov.cn/tjsj/tjgb/rkpcgb/qgrkpcgb/202106/t20210628_1818824.html (accessed on 11 May 2021).
  50. Morris, J.N.; Hardman, A.E. Walking to health. Sports Med. 1997, 23, 306–332. [Google Scholar] [CrossRef] [PubMed]
  51. Nathan, A.; Wood, L.; Giles-Corti, B. Exploring socioecological correlates of active living in retirement village residents. J. Aging Phys. Act. 2014, 22, 1–15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Cerin, E.; Zhang, C.J.; Barnett, A.; Sit, C.H.; Cheung, M.M.; Johnston, J.M.; Lai, P.C.; Lee, R.S. Associations of objectively-assessed neighborhood characteristics with older adults′ total physical activity and sedentary time in an ultra-dense urban environment: Findings from the ALECS study. Health Place 2016, 42, 1–10. [Google Scholar] [CrossRef]
  53. Adams, M.A.; Frank, L.D.; Schipperijn, J.; Smith, G.; Chapman, J.; Christiansen, L.B.; Coffee, N. International variation in neighborhood walkability, transit, and recreation environments using geographic information systems: The IPEN adult study. Int. J. Health Geogr. 2014, 13, 43. [Google Scholar] [CrossRef] [Green Version]
  54. Zhu, J.; Fan, Y. Daily travel behavior and emotional well-being: Effects of trip mode, duration, purpose, and companionship. Transp. Res. Part A Policy Pract. 2018, 118, 360–373. [Google Scholar] [CrossRef]
  55. Haqverdi, M.Q.; Seyedabrishami, S.; Groeger, J. Identifying psychological and socio-economic factors affecting motorcycle helmet use. Accid Anal. Prev. 2015, 85, 102–110. [Google Scholar] [CrossRef] [PubMed]
  56. Stipancic, J.; Zangenehpour, S.; Miranda-Moreno, L.; Saunier, N.; Granie, M. Investigating the gender differences on bicycle-vehicle conflicts at urban intersections using an ordered logit methodology. Accid. Anal. Prev. 2016, 97, 19–27. [Google Scholar] [CrossRef]
  57. Yoojin, Y.; Kim, E. The Effects of Accessibility to Medical Facilities and Public Transportation on Perceived Health of Urban and Rural Elderly: Using Generalized Ordered Logit Model. J. Korean Reg. Dev. Assoc. 2015, 27, 65–88. [Google Scholar]
  58. Mack, G.; Andreas, K.; Katja, H.; Nadja, E. Determinants of the perceived administrative transaction costs caused by the uptake of an agri-environmental program. J. Environ. Plan. Manag. 2019, 62, 1802–1819. [Google Scholar] [CrossRef] [Green Version]
  59. Williams, R. Generalized Ordered Logit/Partial Proportional Odds Models for Ordinal Dependent Variables. Stata J. Promot. Commun. Stat. Stata 2006, 6, 58–82. [Google Scholar] [CrossRef] [Green Version]
  60. Williams, R. Understanding and interpreting generalized ordered logit models. J. Math. Sociol. 2016, 40, 7–20. [Google Scholar] [CrossRef]
  61. Lai, D.; Liu, W.; Gan, T.; Liu, K.; Chen, Q. A review of mitigating strategies to improve the thermal environment and thermal comfort in urban outdoor spaces. Sci. Total Environ. 2019, 661, 337–353. [Google Scholar] [CrossRef] [PubMed]
  62. Lai, D.; Guo, D.; Hou, Y.; Lin, C.; Chen, Q. Studies of outdoor thermal comfort in northern China. Build. Environ. 2014, 77, 110–118. [Google Scholar] [CrossRef]
  63. Wang, B.; Yi, Y.K. Developing an adapted UTCI (Universal Thermal Climate Index) for the elderly population in China’s severe cold climate region. Sustain. Cities Soc. 2021, 69, 102813. [Google Scholar] [CrossRef]
  64. Berg, M.; Winsall, M.; Dyer, S.M.; Breen, F.; Gresham, M.; Crotty, M. Understanding the Barriers and Enablers to Using Outdoor Spaces in Nursing Homes: A Systematic Review. Gerontol. 2019, 60, e254–e269. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Kim, J.; Schmocker, J.; Nakamura, T.; Uno, N.; Iwamoto, T. Integrated impacts of public transport travel and travel satisfaction on quality of life of older people. Transp. Res. Part A Policy Pract. 2020, 138, 15–27. [Google Scholar] [CrossRef]
  66. Tong, H.; Kang, J. Characteristics of noise complaints and the associations with urban morphology: A comparison across densities. Environ. Res. 2021, 197, 111045. [Google Scholar] [CrossRef]
  67. Tong, H.; Aletta, F.; Mitchell, A.; Oberman, T.; Kang, J. Increases in noise complaints during the COVID-19 lockdown in Spring 2020: A case study in Greater London, UK. Sci. Total Environ. 2021, 785, 147213. [Google Scholar] [CrossRef]
  68. Khoddam, H.; Dehghan, M.; Sohrabi, A.; Modanloo, M. The age–friendly cities characteristics from the viewpoint of elderly. J. Fam. Med. Prim. Care 2020, 9, 5745. [Google Scholar] [CrossRef]
  69. Mohd, S.; Latiff, A.; Senadjki, A. Travel Behavior of Elderly in George Town and Malacca, Malaysia. Sustainability 2019, 11, 5251. [Google Scholar] [CrossRef] [Green Version]
  70. Sang, A.O.; Knez, I.; Gunnarsson, B.; Hedblom, M. The effects of naturalness, gender, and age on how urban green space is perceived and used. Urban For. Urban Green. 2016, 18, 268–276. [Google Scholar] [CrossRef]
  71. Cheng, L.; Caset, F.; Vos, J.D.; Derudder, B.; Witlox, F. Investigating walking accessibility to recreational amenities for elderly people in Nanjing, China. Transp. Res. Part D Transp. Environ. 2019, 76, 85–99. [Google Scholar] [CrossRef]
  72. Shoari, N.; Ezzati, M.; Baumgartner, J.; Malacarne, D. Accessibility and allocation of public parks and gardens in England and Wales: A COVID-19 social distancing perspective. PLoS ONE 2020, 15, e0241102. [Google Scholar] [CrossRef] [PubMed]
  73. Gross Domestic Product of Provincial Capital Cities and Municipalities in China in 2019. 2020. Available online: https://www.statista.com/statistics/1099570/china-gross-domestic-product-gdp-of-provincial-capital-cities (accessed on 20 November 2021).
  74. Puchalsky, C.M. Comparison of Emissions from Light Rail Transit and Bus Rapid Transit. Transp. Res. Rec. 2005, 1927, 31–37. [Google Scholar] [CrossRef]
  75. Zheng, Z.; Chen, H.; Gao, J. Age Differences in the Influence of Residential Environment and Behavior on the Life Quality of Older Adults: The Transfer from Physical-Environment to Social-Behavior. Int. J. Environ. Res. Public Health 2021, 18, 895. [Google Scholar] [CrossRef]
Sustainability 14 03522 g001 550
Figure 1. Theoretical model in this study.
Figure 1. Theoretical model in this study.
Sustainability 14 03522 g001
Sustainability 14 03522 g002 550
Figure 2. Location of 11 residential areas in two cold-climate cities.
Figure 2. Location of 11 residential areas in two cold-climate cities.
Sustainability 14 03522 g002
Sustainability 14 03522 g003 550
Figure 3. The pattern language framework in severe cold areas.
Figure 3. The pattern language framework in severe cold areas.
Sustainability 14 03522 g003
Table
Table 1. Description of the characteristics of the eleven residential areas.
Table 1. Description of the characteristics of the eleven residential areas.
Inside Residential AreaScale of 15-Minute Walk
NameBuilt YearSite Area
(m2)		Building HeightPlot
Ratio		Site
Coverage
(%)		Green
Coverage
Ratio (%)		Number of HouseholdsRatio of
Elderly (%)		Quantity of Buses Per StationGreen
Areas (m2)		Commercial FacilitiesMedical FacilitiesDistance to Downtown (km)Region
Name
Nan Hu No.1200868,054multi-story1.50293529121.505.5368,02844153.9Chao yang
Zhong Hai2006123,287multi-story1.23353579024.802.27613,90054317.7Jing ji
Wan Sheng201090,000multi-story1.403530148119.902.875393,73858317.0Lv yuan
Mei Jing2004150,280small
high-rise
(11 floor)		1.873037207425.903.4136,56592415.7Kuan
cheng
Tian Jia2011185,500small
high-rise
(11 floor)		1.733040201422.903.75595,16732424.5Lv yuan
Yu Jing2010120,000high-rise3.802535315528.402.7877,25953334.8Er dao
Dong Kang199253,280multi-story2.003525225030.007.5240,14286361.2Nan guan
Zhu Jiang200119,000high-rise4.50273656324.504.9593,21896555.4Xiang Fang
Tai Gu200098,500multi-story2.303530404826.205.7634,79771532.5Dao Wai
Da Zhong2007135,000high-rise4.002538310223.306.25272,90285327.3Nan Gang
Li Qun199521,205high-rise4.30302574525.807.665,56665461.1Nan Gang
Table
Table 2. Variable description of BE, QOL (RS, HS, and SS) and individual characteristics.
Table 2. Variable description of BE, QOL (RS, HS, and SS) and individual characteristics.
VariablesDescriptionMeanStd Dev
Independent Variable
DensitySquare footage (SF)Dummy variable: 1 if square footage per person <60 m2, 0 if >60 m20.6140.487
Building typology (BT)Dummy variable: 1 if other building typology, 0 if linear building0.1780.383
EnvironmentSafety (SA)Dummy variable: 1 if guarded community, 0 if community with semi-open space0.3020.459
Cleanliness (CL)Dummy variable: 1 if recycling bin of centralized build, 0 if recycling bin of decentralized build0.5020.500
Outdoor fitness (OF)Dummy variable: 1 if fitnesssimilar conclusions by outdoor workout0.0470.211
Green space (GS)Dummy variable: 1 if deciduous tree, 0 if mixture of evergreen plants and deciduous tree0.4070.491
Outdoor facilitiesOutdoor benches (OB)Dummy variable: 1 if outdoor shelters and seating, 0 if normal outdoor bench0.1170.322
Path surface (PS)Dummy variable: 1 if snow removal within 48 h, 0 if snow clearing after 48 h0.5060.501
Sidewalk colour (SC)Dummy variable: 1 if standard gray, 0 if blue or green concrete0.8280.378
AccessibilityAccessible buses (AB)Dummy variable: 1 if the number of buses available ≤5, 0 if the number of buses available >50.0570.231
Community hospital (CH)Dummy variable: 1 if the distance >1000 m, 0 if not0.5110.500
distance to public parks (PP1-PP3)
Public parks 1Dummy variable: 1 if the distance from 400 to 1000 m0.7960.403
Public parks 2Dummy variable: 1 if the distance >1000 m0.1740.379
Public parks 3Dummy variable: 1 if the distance <400 m0.0290.168
Distance to commercial facilities (CF1-CF3)
Commercial facilities 1Dummy variable: 1 if the distance from 400 to 1000 m0.4290.495
Commercial facilities 2Dummy variable: 1 if the distance >1000 m0.2300.421
Commercial facilities 3Dummy variable: 1 if the distance <400 m0.3410.474
Personal Attributes
Age (AG)Dummy variable: 1 if age ≤65, 0 if age >650.5940.491
Gender (GE)Dummy variable: 1 if respondent is male0.5070.500
Education level (EL)Dummy variable: 1 if respondent’s education level >tertiary education0.0870.282
Chronic diseases (CD)Dummy variable: 1 if health or mild diseases, 0 if respondent suffers from severe chronic diseases0.7370.441
Dependent Variable
Residential satisfaction (RS)Strongly dissatisfied = 1, strongly satisfied = 74.7041.492
Health satisfaction (HS)Strongly dissatisfied = 1, strongly satisfied = 74.6531.551
Social satisfaction (SS)Strongly dissatisfied = 1, strongly satisfied = 73.8721.874
Table
Table 3. The estimation of the ordered logit model (RS, HS, and SS).
Table 3. The estimation of the ordered logit model (RS, HS, and SS).
RS
VariableCoef.S.E.P
CD0.6380.1930.001
OB0.6290.1590.000
GS−0.4550.1540.003
AB0.3700.0960.000
AG0.4250.1460.004
CH−0.6750.2660.011
PS0.8520.2510.001
PP11.3010.2480.000
PP20.5690.2590.028
HS
VariableCoef.S.E.P
CD0.5980.1930.002
OB0.6480.1580.000
GS−0.4760.1530.002
AB0.3370.0960.000
AG0.3990.1470.007
CH−0.7290.2670.006
PS0.9410.2560.000
SC−0.3470.1690.041
PP11.2020.2480.000
PP20.5120.2580.047
SS
VariableCoef.S.E.P
SA−0.536−0.1380.000
CD0.8520.1990.000
OB0.5360.1590.001
GS−0.7320.1560.000
AB0.2900.0970.003
PS0.9920.2620.000
SC−1.1700.1700.000
PP2−0.5340.2720.049
CF1−0.4170.1230.001
CF2−0.3950.1290.002
Table
Table 4. The estimation of the gologit2 model for RS, HS, and SS.
Table 4. The estimation of the gologit2 model for RS, HS, and SS.
RS
VariableRS = 1RS = 2RS = 3
Coef.S.E.PCoef.S.E.PCoef.S.E.P
CD0.3090.6010.6070.6180.4940.2100.6770.3540.056
OB1.4900.7410.0440.7850.3900.0440.3660.2420.130
GS−0.4640.2490.063−0.5950.1840.001−0.7450.1340.000
AB−0.1380.2770.6190.0150.1940.9400.2010.1370.144
AG0.7790.5220.1360.2190.3050.4720.3240.2230.147
CH−0.5410.6380.396−0.9290.4630.045−1.0690.3290.001
PS0.0840.8030.9161.0580.5240.0440.9530.3700.010
PP11.0570.4590.0210.7020.3930.0741.2200.2940.000
PP20.8950.5060.0770.4900.4190.2420.9430.3140.003
RS
VariableRS = 4RS = 5RS = 6
Coef.S.E.PCoef.S.E.PCoef.S.E.P
CD0.7140.2490.0040.8380.2230.0000.3670.3080.234
OB0.4670.1720.0070.6580.1580.0000.9160.2040.000
GS−0.6910.1060.000−0.3140.1150.006−0.5360.1940.006
AB0.3240.1090.0030.5140.1190.0000.2870.1800.111
AG0.3120.1640.0570.1710.1620.2900.7240.2230.001
CH−0.9740.2530.000−0.8810.2510.000−0.9230.3510.009
PS0.8330.2690.0020.9000.2590.0010.5840.3510.096
PP11.5420.3120.0001.1030.3670.0030.1580.4520.726
PP20.9760.3250.003−0.4330.3970.276−1.5050.5600.007
HS
VariableHS = 1HS = 2HS = 3
Coef.S.E.PCoef.S.E.PCoef.S.E.P
CD0.4050.5870.4900.6160.4940.2120.6680.3370.047
OB1.6710.7320.0230.9160.3850.0170.4650.2350.048
GS−0.4580.2280.044−0.6960.1740.000−0.6430.1310.000
AB−0.1150.2470.6430.1210.1740.4900.2540.1310.054
AG0.4560.4330.292−0.0820.2680.7610.1130.2130.597
CH−0.8650.6820.204−1.0340.4620.025−0.9950.3650.006
PS0.6690.8080.4071.3940.5290.0081.0980.4200.009
SC−0.4840.3750.197−0.2210.2620.399−0.2540.1970.197
PP10.8860.4540.0510.5460.3950.1671.2080.2920.000
PP20.8450.5020.0920.4240.4200.3120.9640.3120.002
VariableHS = 4HS = 5HS = 6
Coef.S.E.PCoef.S.E.PCoef.S.E.P
CD0.5800.2420.0160.8510.2220.0000.3090.3030.308
OB0.5300.1740.0020.7210.1580.0000.8900.2010.000
GS−0.6370.1070.000−0.2980.1160.010−0.6090.1940.002
AB0.3150.1090.0040.5240.1190.0000.2140.1750.221
AG0.2810.1670.0920.2180.1630.1810.7040.2210.001
CH−1.1020.2750.000−0.9070.2710.001−1.1840.3700.001
PS1.0260.2980.0010.8250.2700.0020.9880.3400.004
SC−0.2340.1480.1130.0560.1450.699−0.2430.2070.241
PP11.4910.3110.0001.0530.3680.0040.2500.4430.573
PP20.9230.3240.004−0.4240.3980.287−1.3430.5450.014
SS
VariableSS = 1SS = 2SS = 3
Coef.S.E.PCoef.S.E.PCoef.S.E.P
SA−0.5280.2110.012−0.4700.1710.006−0.4800.1530.002
CD1.1450.5440.0351.7690.4720.0000.8630.2860.003
OB1.8120.4760.0001.5280.3220.0000.9330.2210.000
GS−0.6430.1980.001−0.6730.1620.000−0.6760.1480.000
AB0.2990.1390.0320.3620.1210.0030.3990.1140.000
PS0.4660.1520.0000.2940.1350.0300.3080.1270.015
SC−0.9380.2350.000−0.7950.1880.000−0.8100.1630.000
PP2−0.2900.1560.063−0.6740.1330.000−0.8950.1300.000
CF1−0.7900.1820.000−0.3830.1580.016−0.2930.1450.044
CF2−0.4640.2020.021−0.3110.1710.068−0.3850.1550.013
VariableSS = 4SS = 5SS = 6
Coef.S.E.PCoef.S.E.PCoef.S.E.P
SA−0.6360.1380.000−0.1850.1560.235−0.5120.2290.025
CD0.6530.2300.0051.0290.2260.0000.8580.2810.002
OB0.4780.1710.0050.4910.1760.0050.6680.2220.003
GS−0.6580.1380.000−0.4770.1670.004−1.0210.2780.000
AB0.1850.1160.1100.3030.1430.0340.4330.2180.047
PS0.4020.1260.0010.4530.1530.0030.5790.2220.009
SC−0.9200.1420.000−0.9110.1500.000−1.4490.2080.000
PP2−0.9420.1430.000−0.7460.1810.000−0.6840.2600.008
CF1−0.4970.1410.000−0.4190.1650.011−0.5520.2440.024
CF2−0.4760.1490.001−0.6630.1830.000−0.5390.2820.056
Table
Table 5. The model-fitting goodness test.
Table 5. The model-fitting goodness test.
Dependent VariablePseudo R²AIC
Ordered
Logit Model		Generalized Ordered Logit ModelOrdered
Logit Model		Generalized Ordered Logit Model
RS0.01610.0446606.886587.195
HS0.02720.04486719.5266698.022
SS0.04620.06316977.8926955
Table
Table 6. Marginal effects for gologit2 model (RS, HS, and SS) (%).
Table 6. Marginal effects for gologit2 model (RS, HS, and SS) (%).
VariableRS
1234567
CD−1.34−3.37−4.94−6.25−0.7813.563.14
OB−6.470.480.77−5.19−2.695.267.83
GS2.012.536.094.77−9.15−1.66−4.59
AB0.60−0.71−2.75−4.35−3.027.782.45
AG−3.381.71−2.94−2.343.55−2.786.19
CH2.354.738.176.46−4.16−9.65−7.90
PS−0.37−7.70−5.53−4.990.6512.934.99
PP1−4.59−0.77−12.05−16.9712.3920.631.35
PP2−3.880.15−9.72−8.3030.44.25−12.87
VariableHS
1234567
CD−2.11−3.39−4.79−2.69−3.8214.102.30
OB−8.710.541.00−4.70−2.376.487.75
GS2.393.823.714.33−8.36−0.58−5.31
AB0.60−1.67−2.84−3.14−3.308.491.86
AG−2.383.10−2.47−4.591.99−1.836.14
CH4.514.716.129.31−6.73−7.60−10.32
PS−3.49−8.95−4.49−6.036.677.688.61
SC2.52−0.551.941.32−6.343.22−2.12
PP1−4.62−2.45−13.75−14.7312.5618.612.18
PP2−4.410.62−11.085.7929.033.32−11.69
VariableSS
1234567
SA7.281.201.583.71−11.020.48−3.23
CD−15.79−16.0913.823.96−1.149.855.41
OB−24.99−2.568.019.203.043.084.21
GS8.873.272.020.07−7.14−0.64−6.44
AB−4.12−2.40−1.844.37−0.501.772.73
PS−6.431.14−1.16−2.241.973.073.65
SC12.931.402.622.93−6.37−4.38−9.14
PP24.008.156.591.62−9.31-6.74−4.32
CF110.90−4.00−0.784.62−4.53−2.74−3.48
CF26.40−0.792.452.22−0.45−6.44−3.40
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Leng, H.; Han, B. Effect of Environmental Planning on Elderly Individual Quality of Life in Severe Cold Regions: A Case Study in Northeastern China. Sustainability 2022, 14, 3522. https://doi.org/10.3390/su14063522

AMA Style

Leng H, Han B. Effect of Environmental Planning on Elderly Individual Quality of Life in Severe Cold Regions: A Case Study in Northeastern China. Sustainability. 2022; 14(6):3522. https://doi.org/10.3390/su14063522

Chicago/Turabian Style

Leng, Hong, and Bingbing Han. 2022. "Effect of Environmental Planning on Elderly Individual Quality of Life in Severe Cold Regions: A Case Study in Northeastern China" Sustainability 14, no. 6: 3522. https://doi.org/10.3390/su14063522

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop