A Study on the Influence of an Outdoor Built Environment on the Activity Behavior of the Elderly in Small Cities in Cold Regions—A Case Study of Bei’an City

Abstract

Under the dual constraints of limited resources and cold climate, the built environment of small cities in cold areas has a particularly prominent impact on the outdoor activities of the elderly and the need for aging optimization. Based on a questionnaire survey and field measurement data, combined with multiple linear regression, Spearman correlation analysis, and difference analysis, this paper systematically discusses the effects of different built environment characteristics on outdoor activity behaviors (frequency, intensity, and stay time) of the elderly in Bei’an City, Heilongjiang Province, China. The difference in environmental satisfaction of the elderly with different genders, ages, and education levels was analyzed. The results show that green coverage, air quality, leisure facilities, and barrier-free facilities are the core environmental factors that significantly improve the activity behavior of the elderly. In contrast, noise level, road damage, and inadequate facility maintenance significantly inhibit the activity willingness of the elderly. It was found that older adults are more dependent on barrier-free facilities and site safety, while younger people pay more attention to sports facilities and social space. Older women pay more attention to environmental details and cultural elements, while men tend to evaluate environmental functionality and so on. Further analysis shows that green environments and leisure facilities in cold climates provide visual beauty and play an important role in improving air quality and enhancing mental health. These elements are particularly critical in winter activities for older people, demonstrating the potential of the built environment to promote health and social participation. Starting from the unique background of small cities in cold regions, this study verified the applicability of the WHO age-friendly city framework in small cities in cold climates through empirical data, and revealed the necessity of climate-adaptive design (such as winter anti-slip facilities and cold-resistant greening) to improve the activity behavior of the elderly, providing a regional supplement to the existing theories.

1. Introduction

With the acceleration of global population aging and the rapid advancement of urbanization in China, how to improve the quality of life and the physical and mental health of the elderly through environmental design and infrastructure optimization has become a common concern in society and academia. According to the World Health Organization forecast, by 2050, the global elderly population aged 65 and above will account for about 16% of the total population. China’s aging process has also entered a rapid development stage, and the proportion of people aged 60 and above is expected to reach about 21% by 2030 [1]. With the elderly as the core members of the family and society, their dependence on the daily living environment is becoming increasingly obvious. Optimizing the built environment to promote the daily activity ability and social communication of the elderly has become an urgent problem to solve.

In this context, the World Health Organization (WHO) launched the Global Age-Friendly Cities Initiative in 2006, which aims to support the daily life and social participation of older people by improving urban environments. The initiative emphasizes that age-friendly cities should not only provide a safe and convenient living environment, but also promote the elderly to better participate in social activities and improve their quality of life [2]. The core ideas of the WHO initiative provide a framework for governments and local communities to create age-friendly urban planning, emphasizing the sustainable development of older persons’ health and social participation through the improvement of public facilities, transport systems, architectural design, and socio-cultural facilities in the built environment [3].

The built environment has become an important part of urban and community physical space and has become an important research object in urban and rural planning, public health, and other fields since the middle and late 20th century [4]. Foreign scholars have recognized the built environment’s potential impact on residents’ health behavior earlier and gradually applied it to the research of elderly groups. For example, since the 1990s, European and American countries have extensively studied how pedestrian-friendly community design can promote older adults’ initiative in activity behavior, forming a theoretical framework for healthy cities and sustainable development [5]. In contrast, China started a little later in the field of the built environment and outdoor behavior of the elderly, but has made rapid development in recent years. In the 21st century, with the intensification of the aging problem, Chinese scholars began to pay attention to the impact of the built environment on the quality of life of the elderly. Earlier studies focused on large and medium-sized cities, examining the relationship between the community environment (such as green spaces, parks, and public facilities) and the daily activities of the elderly [6]. Chen Tianyi and Zhu Longbin analyzed the influence of barrier-free facilities on the walking behavior of older people in Nanjing [7]. These studies generally agree that a friendly outdoor environment for older people is important for ensuring their participation in daily activities and improving their physical and mental health. The influence of the built environment on crowd activity behavior is an important topic in research, including sidewalk width [8] and safety [9], green coverage rate [10], traffic control facilities [11], and surrounding service facilities [12]. For example, studies based on the aggregation effect of greenery and light environments on population behavior have shown that greening rates and actual light conditions significantly affect the frequency of daily walking and community activities in older adults [13]. At the same time, transportation accessibility [14] and safety play a key role in promoting activity frequency among older adults. Some scholars further pointed out that community walkability is highly correlated with the physical health and psychological well-being of the elderly [15,16], and the improvement of barrier-free facilities to enhance their mobility cannot be ignored [17].

However, the above studies mainly focus on humid subtropical monsoons and humid continental and temperate marine climate zones. In particular, the impact of climatic factors on the residential, social, and health aspects of quality of life cannot be ignored, especially as harsh climates in cold regions can cause significant differences in urban environments [18,19,20]. So far, only a few papers have focused on cold regions, such as those by Stout, M., Collins, D., Stadler, S. L., Soans, R., Sanborn, E., and Summers, R. It has been explored that in cold regions, climatic conditions (such as long-term low temperatures in winter and snow freezing) have a particularly significant impact on outdoor activities [21]. For example, snow can make roads slippery and make walking less safe, and cold weather may limit the time older people spend outdoors [22], as shown in studies by Jin Anruo, Long Yu-ping, and Li Ying. Using a data-driven artificial intelligence algorithm, it is concluded that the built environment of small cities usually faces the problems of limited resources and imperfect facilities, such as the lack of activity space specifically suitable for the elderly, the unreasonable layout of public facilities, and insufficient barrier-free design [23]. In small cities in cold regions, the elderly’s activity behavior and demand for the built environment show obvious regional and seasonal characteristics.

Therefore, combined with the framework of the Global Age-Friendly Cities Initiative, this study will not only provide theoretical support for the optimization of the living environment of the elderly in specific climate zones, but also contribute to existing research. Especially in small cities in cold regions, the understanding of the needs of the elderly and the exploration of countermeasures should be further strengthened according to the guiding ideology of the initiative of WHO. The results of this study will provide a reference for age-friendly urban planning worldwide, especially for the design and improvement of urban environments in cold areas, and provide new perspectives and practical solutions.

1.1. Characteristics of Outdoor Environment in Small Cities in Cold Regions

Because of its unique geographical location and climatic conditions, the outdoor built environment of small cities in cold regions is significantly different from that in other regions, especially in supporting the daily activities of the elderly. Small cities in cold regions are usually distributed in high latitudes, such as northeast China, and typical characteristics include long winters, extremely low temperatures, frequent snowfall, and significant temperature differences between day and night [24,25]. These climatic conditions not only significantly impact the willingness and frequency of older people to participate in outdoor activities but also place unique requirements on the planning, design, and management of the outdoor built environment.

In small cities in cold regions, where winter temperatures are often below −20 °C, older people are more susceptible to cold-related health problems (e.g., hypothermia, arthritis, respiratory diseases), significantly reducing their willingness to participate in outdoor activities [26]. At the same time, the long-term low-temperature environment also considerably shortens the use time of outdoor space. Winter outdoor activities are more concentrated in the limited day period, and night activities are almost completely suppressed [27]. In addition, the snowfall is significant and lasts for a long time. Snow and icy surfaces not only significantly reduce the accessibility of pedestrian roads but also increase the risk of older adults slipping and falling, which is particularly problematic. Studies have shown that older people in the north are more likely to reduce their travel in winter or only move to relatively familiar and non-slip-treated areas [28,29]. In addition, some small cities have insufficient capacity for snow clearance and road maintenance, which often leads to long-term snow accumulation and further limits the use of space for older people [30].

At the same time, small cities in cold regions have significantly shorter daylight hours in winter, especially in the late winter season (such as December to February), when daylight hours may be less than 6 h [31]. This lack of light not only directly affects time spent outdoors but also negatively affects the mental health of older adults, potentially leading to an increase in seasonal depressive symptoms [32]. In addition, visual degradation in some older adults also affects the safety and comfort of activities in low-light environments [33].

Therefore, small cities in cold regions are widely representative of the whole country, and the characteristics of their outdoor environment reflect the climate influence in the high latitude region and the characteristics of the built environment under the economic and resource constraints of small cities. The in-depth study of the outdoor built environment of small cities in cold regions can fill the gap in the current study of cold regions and elderly groups and provide practical guidance for improving the quality of life of the elderly in small cities in cold regions.

1.2. Outdoor Activity Behaviors of the Elderly

Older adults’ participation in outdoor activities is an important way to promote physical and mental health, not only by maintaining physical functions (e.g., improved cardiovascular health, prevention of chronic diseases) but also by enhancing mental health (e.g., reduced depression, improved cognitive function), while maintaining positive social connections through social activities [34]. Studies have shown that the outdoor behavior of older adults can be divided into a variety of types and intensities, including low-intensity activities (such as walking and lounging) and medium-intensity activities (such as fitness exercises and physical labor) [35]. However, older adults tend to have significantly lower frequency, duration, and scope of outdoor activities than other age groups due to age-related physical degradation (e.g., reduced balance, reduced exercise capacity) and increased sensitivity to environmental safety [36,37].

The literature shows that the factors affecting the outdoor activity behavior of the elderly can be divided into individual and environmental factors. Individual factors include health status, cognitive ability, gender, and age. In recent years, environmental factors have become the focus of research, especially the impact of the built environment on the outdoor behavior of the elderly. For example, studies by Kim Jaehyun, Kim Junhyoung, and Han Areum show that pedestrian-friendly environments (such as convenient walking paths and safe street crossings) and green-space coverage in communities can significantly increase the frequency of walking among older adults [38]. Costamagna, Lind-Waldock, and Stjernstrom also found that the appropriate community activities and social environment can help enhance the elderly moderate intensity activity participation [39]. Therefore, this paper divides the behavioral activity intensity of the elderly into three levels; see

Table 1. Outdoor activity intensity classification table for the elderly.

	Activity Intensity Level	Behavior That Represents the Intensity of Activity
1	Quiet behavior activity intensity	Basking in the sun, enjoying the scenery, chatting, chess and cards;
2	Light behavioral activity intensity	Walk, play with children, play Musical Instruments, walk the dog, etc.
3	Moderate to severe behavioral activity intensity	Square dancing, fitness equipment, running, ball games, Tai Chi, sword dancing, shuttlecock kicking, etc.

.

1.3. Research Purpose

The literature review shows that many studies focus on large cities with relatively large population bases and relatively perfect facilities. In contrast, studies on outdoor environmental elements in small cities and cities are minimal, and no corresponding suggestions are given for planning and improving public open-space environments. Secondly, although many studies exist on the elderly’s preference factors for public-space activities, there is no distinction between medium and high-intensity physical activities and low-intensity social and recreational activities, and the elderly’s preference for environmental factors with different behavioral characteristics is distinguished. In addition, cold areas have low average temperatures in winter, insufficient total sunshine, and other factors, which may lead to a large difference in public open-space use preference in cold areas and other different areas. This may lead to more significant differences in the use preferences of public open spaces in cold regions and spaces in other regions. Limited papers have determined that the built environment in cold regions significantly impacts the outdoor activities and physical health of urban residents. However, there is a lack of a systematic summary of the environmental elements of specific spaces (public open spaces) and the behavioral characteristics of elderly groups. In addition, the exploration of environmental elements with winter characteristics, such as factors related to the safety of the activity venue (pavement anti-slip degree, warm rest facilities, etc.), also has limitations.

Therefore, given the limitations of existing studies, this study took Bei’an City as an example, centered on the relationship between the outdoor activity behavior of the elderly and the built environment in small cities in cold regions, explored the key factors affecting outdoor behavior of the elderly and their mechanisms, and further proposed targeted optimization suggestions. Specific research objectives include the following four aspects:

(1) To explore the characteristics and influencing factors of outdoor activity behavior of the elderly in Bei’an City.

Based on a questionnaire survey and interview data, this paper analyzes the behavioral characteristics (such as frequency, intensity, time, and space distribution) of outdoor activities of the elderly in Bei’an City and its influencing factors, especially the key elements in the built environment (such as road safety, green-space coverage, accessibility of public facilities, etc.).

(2) The mechanism of building the built environment of small cities in cold areas and the activity behavior of the elderly.

Based on quantitative and qualitative analysis, a model of the relationship between the built environment and the activity behavior of the elderly in Bei’an City is established to reveal the interaction mechanism between the environment and behavior in small cities in cold regions under specific climate conditions.

(3) To explore the differences in satisfaction with different environmental factors among the elderly with different personal characteristics.

(4) Put forward policies and practical suggestions for optimizing the built environment of small cities in cold regions.

Based on the research conclusions, specific suggestions for optimizing outdoor environments suitable for aging are put forward for Bei’an City and other small cities in cold areas, including planning and design, facility maintenance, policy support, etc., to promote the development of age-friendly cities.

2. Materials and Methods

Based on a questionnaire survey and field outdoor environment exploration in Bei’an City, China, the influence of various outdoor environment factors on the outdoor behavior of the elderly was explored by integrating subjective and objective data, and an outdoor-built-environment evaluation system was constructed. Firstly, through multiple linear regression between the elderly’s satisfaction with different outdoor environment characteristics and their outdoor activity behavior, the influence of the construction degree of different street environment characteristics on the elderly’s activity behavior was explored. Secondly, Spearman correlation analysis was carried out on subjective and objective data to test whether subjective satisfaction evaluation was consistent with objective field data and whether objective indicators significantly affected subjective feelings. The third step is to analyze the perception measure of the outdoor built environment of the elderly with different personal characteristics and show the difference in the satisfaction of the elderly with different personal characteristics with different environmental characteristics. The specific method framework is shown in Figure 1.

2.1. Overview of the Study Area

Bei’an City is located northwest of Heilongjiang Province in northeast China. Its geographical coordinates are 126°3′~127°4′ east longitude and 47°5′~48°0′ north latitude. It belongs to the high-latitude cold climate region (See Figure 2). As an important county-level city in Heilongjiang Province, Bei’an City is typical of small cities in cold regions. Its natural environment, socio-economic conditions, and population structure represent this study’s research scene. Bei’an City is located in the north of Songnen Plain, with Wudalianchi City in the east, Nenjiang City in the west, Qinggang County in the south, and Xunke County in the north. The terrain is mainly plain and flat. Bei’an City has a cold temperate continental monsoon climate; the average annual temperature is about 1 °C, the annual precipitation is about 500 mm, the winter is long and cold, up to 6 months at the longest, and the extreme minimum temperature can drop to −35 °C below. Bei’an City receives a lot of snow in winter, and the snow accumulation period lasts about 120 days. These climatic characteristics require higher requirements for the use and maintenance of the outdoor built environment. The cold-weather conditions have significantly affected the daily lives and outdoor activities of the residents of Bei’an City. In particular, the elderly, due to physical degradation and a weak ability to adapt to the environment, are more susceptible to low temperatures, snow, insufficient light, and other factors when carrying out their outdoor activities.

This study mainly uses the outdoor built environment as the research site, so three parks in Bei’an City are selected; see

Table 2. Regional information table of the study area.

Location	Serial Number	Name of Park/Square	Area hm2
Bei’an City	1	People’s Park	28.47
	2	Qinghua Martyrs Park	4.52
	3	Xianghe Square	7.28

.

2.2. Questionnaire Design and Setting of Environmental Factors

2.2.1. Questionnaire Design

According to the main outdoor activities of the elderly in winter (quiet, light, moderate, and heavy), three outdoor parks in Bei’an City were divided into seven blocks (see Figure 3) through a literature search and field investigation, and questionnaires were randomly distributed to the elderly over 60 years old in these blocks. Based on the feasibility of field research and the particularity of climate in small cities in the northeast cold region, two time groups were selected—from October to the end of December 2023 (winter) and from June to August 2024 (summer). Among them, to avoid the influence of control variables such as temperature and weather on the survey results, as far as possible, the time when the temperature is similar and there is no wind is chosen. In winter, the average temperature was −5 °C, and the average sensory temperature was −10 °C. The average temperature during the summer survey period was 17 °C, and the average felt temperature was 20 °C.

A total of 200 questionnaires were issued, and 186 were effectively recovered (See Appendix A). The questionnaire consists of two parts. The first part investigates the personal characteristics of the elderly, including their gender, age, education level, and activity characteristics (including the length of stay, frequency of visit, and intensity of activity). The second part investigates the satisfaction degree of the elderly to different environmental characteristics. This study uses a five-point Likert scale to measure the satisfaction degree of the elderly to different environmental characteristics. The scale is designed to capture different levels of satisfaction, with ratings ranging from 1 (very satisfied) to 5 (very dissatisfied). The Likert scale has been widely used in similar studies to quantify respondents’ subjective evaluation of environmental factors. In this study, the use of the scale helped us to understand the elderly’s satisfaction with the environment more clearly, and the relationship between this satisfaction and its behavioral performance (such as the frequency and intensity of outdoor activities, etc.) was subsequently analyzed. Moreover, the questionnaire has passed the reliability test, and the reliability is 0.706.

2.2.2. Setting of Environmental Factors

Through the method of literature collection, the essential factors affecting the choice of outdoor activity space for the urban elderly can initially be obtained. Through the field observation method and cognitive map method, the distribution location of the population and the types of activities frequently carried out can be obtained, and the environmental factors affecting the activities of the elderly in the outdoor space can be optimized and summarized. Through on-site interviews and exploratory questionnaires, people’s demands for outdoor space activities can be obtained, and the purpose, reasons, and needs of people visiting outdoor spaces can be summarized. The above-influencing factors are combined with the actual demands of the elderly in cold areas, analyzed and sorted, and finally, the types and quantities of the influencing factors of the elderly’s environmental choice required in the subjective questionnaire and the objective environmental measurement are determined, respectively (

Table 3. The chosen outdoor environment evaluation factors for the satisfaction survey.

Classification	Environmental Assessment Factors	Definition and Interpretation of Environmental Assessment Factors
Spatial level	Barrier free facilities	Facilities designed and built for people with reduced mobility, the elderly, people with disabilities, and other groups requiring special support.
	Surrounding commercial prosperity	To assess the level of development and prosperity of commercial activities in the surrounding commercial areas.
	The level of noise inside the site	To assess the level of noise within the site: the main concern is the level of noise in the environment, and whether there is excessive noise disturbance, affecting the experience and comfort of the elderly. This can include crowd sounds, equipment noise, or interference from the external environment.
Activity level	Number and type of leisure facilities	Assess whether the number and type of rest facilities can meet the different needs of older persons.
	Children’s facilities and places are satisfied	Assess whether children’s facilities and places meet their needs.
	Fitness facilities and places satisfied	Assess the quantity and quality of fitness facilities and whether they meet the needs of older people.
	Indicates facility clarity	Assess the clarity of signage and facilities to help seniors find their destination and facilities within the site.
	The type and number of group activities	Evaluate the type and quantity of collective activities to reflect the diversity of activity spaces.
Scenic level	Plant cover difference	Assess the degree of change in plant cover in the site, focusing on the density or variety of plants planted.
	Plant species difference	Assess plant species differences to reflect plant diversity.
	Temperature change	Assess changes in temperature within the site that may be related to climate change or site temperature control measures.
	Variation in wind speed	Assess changes in wind speed, too much or too little wind speed may affect the comfort of the site.
	Weather change	Assess the impact of changes in weather conditions, such as climate change, on activities.
	Air quality change	Assess changes in the air quality of the site, differences in air pollution or freshness.
	Number of art pieces	Assess the number of small works of art or decorations in the venue.
	Visual beauty degree	Evaluate the visual beauty of the site and the beauty of the scenery.
	Historical element attraction degree	Assess the attractiveness of the historical elements of the site and whether they have cultural or historical value.
Security level	Night illumination	Evaluate the brightness and effect of night site lighting.
	The level of security within the site	Assess the safety of the site, focusing on whether there are hazards or safety hazards on the site.
	Cleanliness of the site	Assess the cleanliness of the site, whether it is clean and tidy.
	Level of maintenance within the site	Evaluate the maintenance status of the site and pay attention to the repair and maintenance status of the facility.

and

Table 4. Objective survey indicators for the outdoor built environment.

Classification	Evaluation Indicator	Calculation Formula	Measurement Mode	Variable Type	Quantitative Interpretation
Event bedding plane	Seat width	—	Field measurement	sort	3: <30 cm; 2: 30–60 cm; 1: >60 cm
	Density of leisure facilities	Srest/SThe total	Baidu map + site survey	continuous	The ratio of the area occupied by the recreational facilities to the total area of the zoning space
	Facility shade coverage	—	site survey	sort	3: 100–70%; 2: 70–40%; 1: <40%
	Proportion of distribution space	SCollect and distribute/SThe total	Baidu map + site survey	continuous	The ratio of the total area of the open space larger than 20 m × 20 m to the total area of the partition space
	Density of sanitation facilities	Ndustbin/SThe total	Baidu map + site survey	continuous	The ratio of the area of sanitation facilities such as toilets and garbage cans to the total area of the partition space
	Guideline marking density	Nidentification/Sroad	Baidu map + site survey	continuous	Indicates the ratio of the marked area to the total area of the partition space
	Barrier-free facilities	—	Field evaluation	sort	4: reasonable; 3: damaged; 2: misplaced or obstructed; 1: none
	Proportion of facilities for children	SChildren/SThe total	Baidu map + site survey	continuous	The ratio of the children’s facilities to the total area of the subdivision
	Proportion of fitness facilities	Smovement/SThe total	Baidu map + site survey	continuous	The ratio of the fitness facility area to the total area of the zoning space
	Degree of facility maintenance	—	Field evaluation	sort	3: a large number of facilities are intact; 2: a small number of facilities in good condition; 1: can hardly be used
	Snow clearance in winter	—	Field evaluation	sort	4: no snow cover; 3: there is a small amount of snow; 2: there is a lot of snow; 1: unmanageable
Traffic bedding plane	Walkable road width	Droad	Field measurement	continuous	Walkable road width within the subdivision
	Walkable road slope	—	Field measurement	sort	4 (more than 3%); 3 (2–3%); 2 (1–2%); 1 (less than 1%)
	Easy access	—	Baidu map	sort	4: directions close to urban road exits; 3: direction exits; 2: exits
	Road network density	Lroad/SThe total	Baidu map	continuous	The ratio of the sum of walkable road lengths to the total area space
	Pavement damage degree	—	Field evaluation	sort	3: no damage; 2: slight damage; 1: serious damage
	Skid resistance of pavement	Santi-slip/Sroad	Baidu map + site survey	continuous	The ratio of walkable non-slip length to the total length of the space road in the zone
	Elevation step	—	Field evaluation	sort	4: a large number of steps with a height difference; 3: some steps with more height difference; 2: few steps with height difference; 1: none
Secure bedding plane	Monitoring facility density	Number of monitors /SThe total	Baidu map + site survey	sort	Ratio of the number of monitors to the total area space
	Dog walkers	—	Field evaluation	sort	1: no dog walk; 2: 1–3; 3: 4–6; 4: >6
	Interface circumference	(Sunit + Swall + Stree)/SThe total × 100%	Live shooting +FCN semantic segmentation	continuous	The sum of the number of buildings, walls and trees in the scene and the percentage of the total number of scene pixels in the area
	Interference degree outside the garden	—	Field evaluation	sort	3: almost no interference; 2: mild interference; 1: very intrusive
	Lighting density	Nlamp/SThe total	Baidu map + site survey	continuous	The ratio of the number of street lights to the total area of the partition
	Average level illumination at night	—	Mobile illuminance measurement APP	continuous	unit: lx
Beautiful view Bedding plane	Green visual indicator	SGreen/SThe total × 100%	Live shooting +FCN semantic segmentation	continuous	The percentage of the area occupied by the pixel number of green plants and the total pixel number of the scene
	Plant variety	—	Field evaluation	sort	3: more than 6 kinds; 2: 3–6 kinds; 1: 1–3 kinds
	Plant color diversity	—	Field evaluation	sort	There is 1/0
	Warm color richness	SWarm/SThe total × 100%	Live shooting +FCN semantic segmentation	continuous	The percentage of the area occupied by the number of warm color pixels in the scene and the total pixel number of the scene
	Sky openness	SSky/SThe total × 100%	Live shooting +FCN semantic segmentation	continuous	The percentage of the area occupied by the sky pixels and the total pixels of the scene
	Beauty richness	SBeautiful view/SThe total	Baidu map + site survey	continuous	The ratio of the amount of beautiful scenery such as flowers, fountains and sculptures to the total area of the partition
	Spatial cleanliness		Field evaluation	sort	3: no litter, a few natural leaves; 2: there is a small amount of garbage; 1: there is a lot of garbage

).

2.3. Construction of Multiple Linear Regression Models of Older Adults’s Subjective Satisfaction with Different Built Environments and Their Outdoor Activity Behaviors

Multiple linear regression (MLR) is a statistical modeling method that aims to explain or predict a dependent variable (response variable) through a linear combination of multiple independent variables (independent variables) [40]. This model is based on the “linear relationship” and is one of the most widely used tools in classical statistical analysis methods. It can help researchers and decision makers quantify the relationship between variables and provide a basis for prediction and decision making.

Multiple linear regression assumes a linear relationship between the dependent and independent variables. The regression coefficient directly reflects the influence of each independent variable on the dependent variable and its direction (positive or negative). This direct linear interpretation makes it a standard analytical tool in academic research and practical work. Multiple linear regression can be extended to include interaction terms, quadratic terms, etc., to capture more complex relationships between variables, such as polynomial regression [41]. In addition, standardizing variables can also be used to analyze the relative importance of variables. Therefore, this paper analyzes the satisfaction data collected by the Likert scale and uses the multiple linear regression model to explore the influence of different environmental factors on the outdoor activity behaviors (such as activity frequency, stay time, and activity intensity) of the elderly. The mathematical expression of multiple linear regression is as follows:

$$y={\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+\cdots +{\beta }_k{x}_k+ϵ$$

where y is the dependent variable, $x_1$, $x_2$, … $x_k$ is the independent variable, beta 0, beta 1, etc.; ${\beta }_k$ is the regression coefficient, and ϵ is the error term. In this paper, the subjective satisfaction of the elderly with different built environments was set as the independent variable, and the outdoor activity characteristics (activity frequency, stay time, activity intensity) of the elderly were set as the dependent variable.

The degree to which the model explains the dependent variable is $R^2$:

$$R^2=1-{\displaystyle \frac{SSE}{SST}}$$

$SSE$ is the sum of squares of error, and $SST$ is the sum of squares (variation in response variables).

In order to scientifically evaluate the quality of the model, the impact of independent variables on dependent variables, the existence of multicollinearity problems, and ensure the credibility and explanatory power of regression results, several indicators are introduced into the multiple linear regression analysis. The reasons and significance of each indicator are as follows:

(1) The unstandardized coefficient ($B$) is used to visually show the specific impact of each independent variable on the dependent variable, keeping the original unit. The calculation formula is:

$$B={(X^{T}X)}^{-1}{X}^{T}Y$$

where X represents the independent variable matrix (containing the constant series), Y represents the dependent variable vector, and ${(X^{T}X)}^{-1}{X}^T$ represents the inverse matrix part of the least square method.

(2) The standardization coefficient (Beta) is used to measure the influence of a variable after eliminating its dimension. The larger the value (positive or negative), the more significant the influence. The calculation formula is:

$$\beta =B\times {\displaystyle \frac{{\sigma }_X}{{\sigma }_Y}}$$

where $B$ represents the unstandardized coefficient, ${\sigma }_X$ represents the standard deviation of the independent variable X, and ${\sigma }_Y$ represents the standard deviation of the dependent variable Y.

(3) T-value statistics are used to test whether the regression coefficient is significant (that is, whether the variable has a significant impact on the dependent variable). The larger the T-value is, the more significant the regression coefficient is. The formula is:

$$t={\displaystyle \frac{B}{SE(B)}}$$

where $B$ represents the unstandardized coefficient, $SE(B)$ representing the standard error of the coefficient, the formula is:

$$SE(B)=\sqrt{{\displaystyle \frac{MSE}{\sum {(X_i-\overline{X})}^2}}}$$

where $MSE$ is the mean square error, $X_i$ is the value of the i th independent variable, and $\overline{X}$ is the mean of the independent variable.

(4) Significance level (Sig.) is used to measure the probability of significance of the independent variable coefficient (calculated based on the T-value), and a value below the significance level (such as 0.05 or 0.01) indicates that the variable significantly affects the dependent variable and is not random noise. The calculation formula is:

$$p=2\times (1-\Phi (\left|t\right|))$$

where $\Phi$ represents the cumulative distribution function of the standard normal distribution, and $\left|t\right|$ represents the absolute value of the t statistic.

(5) The multicollinearity indicator is further divided into $Tolerance$ and variance inflation factor (VIF). Tolerance is used to measure the linear correlation between an independent variable and other independent variables and to detect multicollinearity problems. When the tolerance value is low, it indicates that the independent variable is highly correlated with other variables, and the information in the model may be redundant, leading to the instability of the regression coefficient. The calculation formula is:

$$Tolerance=1-R^2$$

where $R^2$ represents the goodness of regression fit of the current independent variable to other independent variables. The tolerance value is close to 1, indicating low collinearity. A value close to 0 indicates severe collinearity.

VIF is used to measure the influence of multicollinearity on the stability of regression coefficient estimation, and VIF is the reciprocal of tolerance, which more directly reflects the collinearity problem. The calculation formula is:

$$VIF={\displaystyle \frac{1}{Tolerance}}$$

Multicollinearity results in the extreme instability of coefficient estimates, which affects the model’s explanatory power and predictive performance. Suppose the VIF is too high (generally more than 10). In that case, it may be necessary to reduce the dimension of the independent variables in the model (such as principal component analysis) or eliminate the variables with high correlations.

2.4. Construction of Correlation Model Between Objective Data of Outdoor Built Environment and Subjective Satisfaction

Correlation analysis is a statistical method used to study the correlation between variables. Quantifying the degree of correlation between variables reveals whether they are trending together [42]. The core of correlation analysis is the calculation of the correlation coefficient, which is used to quantify the strength of the relationship between variables, usually with values between −1 and +1 [43]. Standard correlation coefficients include the Pearson correlation coefficient, Spearman correlation coefficient, and Kendall correlation coefficient.

Because the data of the correlation analysis in this paper have a nonlinear relationship, the Spearman correlation analysis is selected in this paper. Spearman correlation is a rank-based, non-parametric method used to measure a monotonic relationship between two variables [44]. It applies to nonlinear but monotonic relationships between variables, where the data contain outliers or rank data (such as “satisfaction” scores) and where the data distribution deviates from the normal distribution. The correlation coefficient calculated by Spearman correlation analysis is denoted by $\rho (rho)$, which ranges from −1 to +1. Its calculation formula is as follows:

$$\rho =1-{\displaystyle \frac{6\sum d_i^2}{n(n^2-1)}}$$

where d_i represents the ranking difference in each pair of data, and n represents the number of samples.

3. Results

3.1. The Multiple Linear Regression Results of the Elderly’s Subjective Satisfaction with Different Built Environments and Their Outdoor Activities

3.1.1. Multiple Regression Analysis of the Elderly’s Subjective Satisfaction with Different Built Environments and Their Stay Time in Outdoor Activities

The model’s goodness of fit was tested. The results showed that the coefficient of determination (adjusted R² value) was 0.607, indicating that the model’s goodness of fit was good (see

Table 5. Model abstract A.

Model	R	R2	Adjusted R2	Errors in Standard Estimates	Durbin–Watson
1	0.807 a	0.652	0.607	0.49638	2.074

^a is the predictive variable and A is the dependent variable: Stay time for outdoor activities.

). The results show that the VIF of all variables is less than 10, so there is no collinearity problem in the variables (see

Table 6. Multiple linear regression result A.

	Unnormalized Coefficient	Standardization Coefficient			Collinearity Statistics
Model	B	Beta	t	significance	allowance	VIF
Constant	−0.977		−2.737	0.007
Temperature change	−0.025	−0.045	−0.934	0.352	0.916	1.092
Variation in wind speed	0.025	0.044	0.909	0.365	0.888	1.127
Weather change	−0.026	−0.049	−0.997	0.320	0.876	1.141
Air-quality change	0.170	0.321	6.548	0.000	0.885	1.130
The level of noise at the site	−0.002	−0.004	−0.073	0.942	0.819	1.220
Plant cover difference	0.093	0.167	3.497	0.001	0.928	1.077
Plant species difference	−0.006	−0.010	−0.196	0.845	0.813	1.230
Number and type of leisure facilities	0.225	0.418	8.490	0.000	0.878	1.139
Surrounding commercial prosperity	−0.011	−0.019	−0.389	0.698	0.923	1.083
Night illumination	0.090	0.165	3.172	0.002	0.781	1.281
Barrier-free facilities	−0.016	−0.029	−0.601	0.549	0.915	1.093
Children’s facilities and places are satisfactory	−0.007	−0.012	−0.252	0.802	0.917	1.090
Fitness facilities and places are satisfactory	−0.029	−0.054	−1.113	0.268	0.916	1.092
Indicates facility clarity	−0.036	−0.065	−1.306	0.193	0.859	1.164
Number of art pieces	0.003	0.005	0.096	0.923	0.924	1.082
Visual beauty	0.199	0.378	7.892	0.000	0.928	1.077
Historical element attraction	0.030	0.052	1.065	0.289	0.895	1.118
The type and number of group activities	0.029	0.054	1.082	0.281	0.865	1.156
The level of security at the site	0.228	0.413	8.766	0.000	0.957	1.045
Cleanliness of the site	0.139	0.249	4.931	0.000	0.833	1.201
Level of maintenance at the site	0.137	0.240	4.985	0.000	0.915	1.093

). The factors that had a positive impact on the stay time of outdoor activities of the elderly were, in order of importance, the satisfaction of leisure facilities (Beta = 0.418), the satisfaction of the internal safety of the site (Beta = 0.413), the satisfaction of visual beauty (Beta = 0.378), the satisfaction of air quality (Beta = 0.321), and the integrity of the internal environment of the site, as well as the satisfaction degree of cleanliness (Beta = 0.249), satisfaction degree of internal maintenance (Beta = 0.240), satisfaction degree of plant coverage (Beta = 0.167), and satisfaction degree of cultural and recreational facilities (Beta = 0.165). The satisfaction of other factors did not significantly affect the model.

3.1.2. Multivariate Regression Analysis of the Elderly’s Subjective Satisfaction with Different Built Environments and the Elderly’s Outdoor Activity Frequency

Observing the model’s goodness of fit, the results show that the determination coefficient (adjusted R² value) is 0.610, indicating that the model’s goodness of fit is good (see

Table 7. Model abstract B.

Model	R	R2	Adjusted R2	Errors in Standard Estimates	Durbin Watson
2	0.809 a	0.655	0.610	0.48736	2.024

^a is the predictive variable and B is the dependent variable: activity frequency.

). The results show that the VIF of all variables is less than 10, so there is no collinearity problem in the variables. The results show that the VIF of all variables is less than 10, so there is no collinearity problem in the variables. Factors that had a positive impact on the frequency of outdoor activities of the elderly and had a more significant impact were the satisfaction degree of leisure facilities (Beta = 0.413), the satisfaction degree of internal safety of the site (Beta = 0.325), and the satisfaction degree of internal cleanliness of the site (Beta = 0.310). The following most influential positive factors were satisfaction with sports facilities (0.262), satisfaction with air quality (Beta = 0.250), satisfaction with plant cover (Beta = 0.250), and satisfaction with noise level (Beta = 0.222). The negative factor was noise satisfaction (−0.242). The satisfaction of other factors did not significantly affect the model. It can be seen initially that the greater the noise of the outdoor built environment, the lower the activity frequency of the elderly (

Table 8. Multiple linear regression result B.

	Unnormalized Coefficient	Standardization Coefficient			Collinearity Statistics
Model	B	Beta	t	significance	allowance	VIF
Constant	−0.562		−1.603	0.111
Temperature change	0.123	0.222	4.634	0.000	0.916	1.092
Variation in wind speed	−0.003	−0.005	−0.099	0.921	0.888	1.127
Weather change	0.010	0.019	0.396	0.693	0.876	1.141
Air-quality change	0.131	0.250	5.126	0.000	0.885	1.130
The level of noise at the site	−0.134	−0.242	−4.770	0.000	0.819	1.220
Plant cover difference	0.137	0.250	5.244	0.000	0.928	1.077
Plant species difference	0.002	0.004	0.074	0.941	0.813	1.230
Number and type of leisure facilities	0.219	0.413	8.425	0.000	0.878	1.139
Surrounding commercial prosperity	−0.024	−0.043	−0.904	0.367	0.923	1.083
Night illumination	0.033	0.063	1.205	0.230	0.781	1.281
Barrier-free facilities	−0.011	−0.020	−0.415	0.679	0.915	1.093
Children’s facilities and places are satisfactory	0.004	0.007	0.146	0.884	0.917	1.090
Fitness facilities and places are satisfactory	0.142	0.262	5.453	0.000	0.916	1.092
Indicates facility clarity	−0.004	−0.008	−0.156	0.876	0.859	1.164
Number of art pieces	−0.003	−0.005	−0.114	0.910	0.924	1.082
Visual beauty	0.035	0.068	1.425	0.156	0.928	1.077
Historical element attraction	−0.048	−0.084	−1.736	0.085	0.895	1.118
The type and number of group activities	0.004	0.007	0.147	0.884	0.865	1.156
The level of security at the site	0.177	0.325	6.923	0.000	0.957	1.045
Cleanliness of the site	0.170	0.310	6.156	0.000	0.833	1.201
Level of maintenance at the site	0.024	0.043	0.895	0.372	0.915	1.093

).

3.1.3. Multivariate Regression Analysis of Subjective Satisfaction of the Elderly with Different Built Environments and the Intensity of Outdoor Activities of the Elderly

The goodness of fit of the model was observed. The results showed that the coefficient of determination (adjusted R² value) was 0.537, indicating that the model’s goodness of fit was good (see

Table 9. Model abstract C.

Model	R	R2	Adjusted R2	Errors in Standard Estimates	Durbin Watson
3	0.768 a	0.590	0.537	0.34131	1.809

^a is the predictive variable and C is the dependent variable: Outdoor activity intensity.

). The results show that the VIF of all variables is less than 10, so there is no collinearity problem in the variables. First, air quality (Beta = 0.257, p < 0.001) and noise level (Beta = −0.198, p < 0.001) were shown to be important environmental factors that significantly affected activity intensity. The improvement in air quality significantly increased participants’ activity intensity, which may be related to the fact that a comfortable breathing environment can enhance physical performance and willingness to exercise. In contrast, higher noise levels significantly reduced activity intensity, related to the possibility that noise could cause participants to become distracted and mentally fatigued. Secondly, plant cover (Beta = 0.326, p < 0.001) and exercise facilities (Beta = 0.353, p < 0.001) were the most important factors affecting activity intensity. High plant coverage provides shade and visual beauty and may create a better thermal environment and psychological comfort for the participants. The perfection of sports facilities significantly improves the activity intensity, which indicates that the facility conditions are the key material basis for promoting activity behavior. In addition, leisure facilities (Beta = 0.258, p < 0.001) and the degree of safety inside the site (Beta = 0.244, p < 0.001) also had a significant positive impact on activity intensity. Recreation facilities can provide participants with a space to relax and recover, and the level of internal safety of the site further encourages more sports behavior by reducing the psychological burden and sense of risk for the participants. However, some variables, such as wind speed change, weather change, plant species, indicator facilities, and historical elements, did not significantly affect activity intensity (p > 0.05). This may be because these variables have a minor direct effect on short-term activity or because the uniformity of the sample environment weakens their effect (

Table 10. Multiple linear regression result C.

	Unnormalized Coefficient		Standardization Coefficient			Collinearity Statistics
Model	B	Standard error	Beta	t	significance	allowance	VIF
Constant	0.020	0.245		0.083	0.934
Temperature change	−0.003	0.019	−0.009	−0.181	0.857	0.916	1.092
Variation in wind speed	−0.014	0.019	−0.039	−0.727	0.469	0.888	1.127
Weather change	−0.012	0.018	−0.035	−0.652	0.515	0.876	1.141
Air-quality change	0.087	0.018	0.257	4.840	0.000	0.885	1.130
The level of noise inside the site	−0.071	0.020	−0.198	−3.592	0.000	0.819	1.220
Plant cover difference	0.115	0.018	0.326	6.273	0.000	0.928	1.077
Plant species difference	0.029	0.020	0.081	1.457	0.147	0.813	1.230
Number and type of leisure facilities	0.088	0.018	0.258	4.829	0.000	0.878	1.139
Surrounding commercial prosperity	−0.047	0.019	−0.129	−2.480	0.014	0.923	1.083
Night illumination	0.019	0.019	0.055	0.966	0.336	0.781	1.281
Barrier-free facilities	0.030	0.019	0.085	1.634	0.104	0.915	1.093
Children’s facilities and places are satisfactory	0.017	0.018	0.049	0.933	0.352	0.917	1.090
Fitness facilities and places are satisfactory	0.117	0.018	0.335	6.418	0.000	0.916	1.092
Indicates facility clarity	−0.027	0.019	−0.078	−1.445	0.150	0.859	1.164
Number of art pieces	−0.014	0.020	−0.038	−0.728	0.468	0.924	1.082
Visual beauty	−0.004	0.017	−0.011	−0.221	0.825	0.928	1.077
Historical element attraction	−0.011	0.019	−0.031	−0.589	0.557	0.895	1.118
The type and number of group activities	−0.014	0.019	−0.041	−0.769	0.443	0.865	1.156
The level of security at the site	0.100	0.018	0.285	5.575	0.000	0.957	1.045
Cleanliness of the site	0.086	0.019	0.244	4.458	0.000	0.833	1.201
Level of maintenance at the site	−0.014	0.019	−0.039	−0.747	0.456	0.915	1.093

).

3.2. The Correlation Results Between Objective Data of Outdoor Built Environment and Subjective Satisfaction are Possible

In this study, we explored the relationship between different built-environment elements and activity behavior of the elderly through Spearman correlation analysis. The results show (Figure 4) that there are significant correlations among multiple environmental features, and these relationships are of great significance for optimizing the active environment of the elderly. Firstly, there was a significant positive correlation between the clarity of indication facilities and the number of cultural and recreational facilities (p < 0.001), indicating that in clearly marked environments, the setting of cultural and recreational facilities is also better, which may help the elderly better participate in various activities. At the same time, there was also a positive relationship between the barrier-free facilities and the number of art pieces (p < 0.01), indicating that barrier-free design areas were often accompanied by more art decoration, which enhanced the beauty and attractiveness of the environment.

There was also a significant positive correlation between the difference in plant cover and plant species (p < 0.05), which indicated that the increase in plant cover was usually accompanied by the abundance of plant species, reflecting the important role of green environment in small cities in cold regions. In addition, the positive correlation between seat width and children’s facilities (p < 0.05) shows that more spacious seating areas tend to be matched with more children’s activity facilities, taking into account the dual needs of the elderly and children groups.

In terms of children’s facilities, the study also found a negative correlation between the proportion of children’s facilities and the level of noise (p < 0.05), which means that in places with children’s activity areas, noise is relatively low, thereby improving the comfort of the environment. Correspondingly, the proportion of children’s facilities was positively correlated with plant coverage and fitness facility satisfaction, respectively (p < 0.05), indicating that places with more children’s facilities usually have more abundant greenery and fitness facilities that meet the needs of the elderly. In addition, the negative correlation between the proportion of children’s facilities and the cleanliness of the site (p < 0.05) also indicates that while increasing the number of children’s facilities helps to meet the needs of children, these areas may require more cleaning and maintenance, resulting in a decrease in overall cleanliness. Similarly, the positive correlation between fitness facilities and plant coverage (p < 0.05) suggests that sites with more fitness facilities generally also have better greenery, thereby enhancing older adults’ willingness to be outdoors.

Further analysis showed that there was a significant negative correlation between the level of facility maintenance and the level of noise (p < 0.05), which suggests that in an environment where the facilities are well maintained, the noise is lower, thereby improving the comfort of activities of the elderly. At the same time, there was a positive correlation between the degree of facility maintenance and the number of art pieces and the clarity of indicating facilities (p < 0.05), suggesting that well-maintained environments generally have higher artistry and clearer indicating systems, further enhancing the experience of the elderly.

There was also a positive correlation (p < 0.05) between the degree of snow-clearing work in winter and the definition of art pieces and indicating facilities, which means that places where snow is thoroughly cleared in winter usually have more art decoration and clearer indicating signs, which enhances the willingness of the elderly to participate in activities. In terms of the external environment of the site, the interference level outside the park was negatively correlated with air quality (p < 0.05), indicating that external interference (such as traffic noise and air pollution) may have a negative impact on the air quality inside the site, thus affecting the activity frequency of the elderly.

Finally, the positive correlation between night lighting level, plant species, and fitness facility satisfaction (p < 0.05) indicates that in areas with better night lighting, the elderly have higher satisfaction with plants and fitness facilities, which provides an important reference for improving the elderly’s night-activity environment. Taken together, these significant correlations suggest that there are tight interactions between different built environment elements, and optimizing these elements can help increase older adults’ satisfaction with and engagement with their outdoor environments.

In summary, the results of correlation analysis reveal the multi-dimensional factors affecting the site environment and their interactions and emphasize the synergistic value of environment and facilities in optimizing user experience. These findings provide a theoretical basis for site design and management and a reference direction for subsequent research.

3.3. Older Adults with Different Personal Characteristics Have Different Satisfaction with Different Environmental Characteristics

3.3.1. Older Adults of Different Genders Have Different Satisfaction with Different Environmental Characteristics

The results from the analysis of different genders (see Figure 5) show that older men and women are more satisfied overall, and the scores are concentrated in the range of 2.50 to 3.50, indicating that the characteristics of the research environment are generally recognized. However, men and women show some differences in some specific characteristics. Women scored significantly higher than men on features such as “accessibility”, “recreational facilities”, and “historical elements”. In contrast, men showed higher satisfaction with features such as “physical activity”, “plant cover”, and “temperature change”. In addition, women were significantly less satisfied than men with the “in-site vegetation maintenance management” score, which may be related to women’s higher expectations of environmental details. Overall, there was little difference in satisfaction between older men and women for features such as “ease of transportation” and “air quality”. These results suggest that although gender has a relatively limited impact on older adults’ environmental satisfaction, differences in some areas still warrant attention. Future environmental design and policymaking should consider these nuances and optimize services for gender-specific needs to further enhance the life experience and well-being of older people.

3.3.2. Older Adults of Different Ages Have Different Satisfaction with Different Environmental Characteristics

From the analysis of different age groups (see Figure 6), in general, the satisfaction scores of the elderly of all ages for most environmental characteristics are between 2.50 and 3.50, indicating a high level of overall recognition. However, there are significant differences in some characteristics between different age groups. Regarding satisfaction with plant cover, the satisfaction score of the 75-and-older group was significantly lower than that of other age groups (the score gap was close to 0.60), indicating that the needs of the elderly in accessibility facilities were not fully met. Similarly, regarding satisfaction with indicated facilities), the score for the 75-and-above group was 2.70. In comparison, the score for the 65–69 group was close to 3.50, with a gap of 0.80, indicating that the design of the indicated facilities may be more suitable for the younger age group and may not adequately prompt the elderly to exercise. In addition, for some environmental features, such as satisfaction with wind-speed changes and satisfaction with collective activity venues, the scores of all age groups are highly consistent, with a difference of less than 0.10, indicating that these features meet the everyday needs of older adults of different ages.

3.3.3. The Satisfaction of the Elderly with Different Educational Levels and Different Environmental Characteristics Is Different

From the analysis of different educational levels (see Figure 7), 1 represents primary school and below, 2 represents junior high school, 3 represents high school or secondary school, and 4 represents junior college or university and above. In general, the satisfaction scores of all education groups on environmental characteristics were concentrated in the range of 2.50 to 3.50, indicating that the research environmental characteristics have a high overall recognition. However, the satisfaction of some characteristics showed significant differences between educational levels. In terms of actual functional characteristics (such as “convenient transportation” and “convenient ticketing”), the primary school and below group (Group 1) had the highest satisfaction score of nearly 3.60. In contrast, the high school or technical secondary school group (group 3) and the college and above group (group 4) had relatively lower scores (about 3.20–3.30), with a gap of 0.30. This indicates that the elderly with low education levels are more sensitive to environmental functional needs. In the cultural category (such as “historical elements” and “cultural activities”), the college and above group (4 groups) scored significantly higher than the other groups, with a score above 3.50. In contrast, the primary school and below group scored lower, only about 2.80, with a gap of 0.70. This shows that the elderly with high education levels have higher expectations and demands for cultural and environmental characteristics. For some basic environmental features (such as “barrier-free facilities” and “vegetation cover”), the scores of different education groups had slight differences, ranging from 3.00 to 3.20, indicating that these features met the general needs of the elderly in each group. In addition, for basic natural features (such as “air quality” and “temperature change”), the scores were highly consistent across literacy groups, with a difference of less than 0.10, indicating that these features were generally accepted across literacy levels. To sum up, the satisfaction of the elderly with environmental characteristics is greatly affected by their educational level, especially in terms of their actual functional and cultural characteristics.

4. Discussion

Through a questionnaire survey, field measurement, and statistical analysis, this study systematically discussed the influence of outdoor built environment on the activity behavior of the elderly in small cities in cold areas. The results showed that green coverage, air quality, leisure facilities, and barrier-free facilities were the core factors that significantly promoted the activity behavior of the elderly, while noise level, road damage, and inadequate maintenance of facilities significantly inhibited the activity intention of the elderly. In addition, the elderly of different genders, ages, and education levels show significant differences in the use and demand of the built environment. The elderly depend more on barrier-free facilities and site safety, while the younger prefer social and sports facilities. Older women pay more attention to environmental details and cultural elements, while men are more likely to evaluate environmental functionality. The study further reveals the unique effects of cold climate conditions on using the built environment, such as the climate-regulating role of green-space environments and the criticality of road anti-skid facilities in winter.

These findings have important theoretical and practical significance for designing age-appropriate cities and the social management of aging in cold regions. First, this study confirmed the positive effects of green coverage and air quality on the activity behavior of the elderly, emphasizing that a good natural environment ought not only provide visual beauty for the elderly but also play an important role in mental health, social participation, and microclimate regulation. Second, the significant impact of accessibility and leisure facilities suggests that age-appropriate design needs to prioritize mobility, convenience, and safety for the elderly, which is particularly important in high latitudes and cold regions. The results also show that noise and facility maintenance can significantly inhibit the willingness of older people to engage in outdoor activities. This reminds policymakers to further optimize infrastructure construction and environmental governance in small cities, especially in the context of limited resources. In addition, the significant gender and age differences indicate that future age-appropriate design needs to focus on diversity and refinement to meet the unique needs of different groups.

This study systematically discusses the influence of the built environment on the activity behavior of the elderly in the context of small cities in cold regions. It is consistent with previous studies in many aspects but also reflects important innovations and differences.

First of all, the environmental psychological perspective [45] of this study further reveals the impact of cold climate on the psychological security of the elderly. For example, seasonal affective disorder may be exacerbated by low light in winter, while improved greenery and air quality can alleviate such problems. In addition, combined with the Urban Health study [46], this study validated the dual role of ‘outdoor accessibility’ in extreme climates: although the cold inhibits outdoor activity frequency, optimized outdoor safety and social facilities can significantly promote the willingness of older adults to be active. And, consistent with previous studies on large and medium-sized cities, this study also found that green coverage is an important factor in improving outdoor activities of the elderly (such as Chen Yunfeng and Li Lingling’s study in Harbin City [47]). However, in the cold region, the role of greenery is more complex and critical. In particular, this study pointed out that in the cold season, greenery can improve air quality, provide psychological comfort, and directly improve the frequency and intensity of outdoor activities for the elderly by shielding them from strong winds and alleviating low temperatures. This finding enriches the theoretical understanding of the role of greenery. It highlights the important value of greenery in regulating microclimate in cold climates, which has received less attention in research in temperate and subtropical regions. Secondly, this study has once again verified the importance of barrier-free facilities, which is consistent with the findings of Li Ruoyu, Yang Minan, and Qian Yongsheng et al. [17]. However, this study further found that in cold areas, older adults are more dependent on barrier-free facilities, especially in the case of snow cover and icy roads. The completeness of barrier-free facilities significantly affects their willingness to move and their sense of security. This shows that barrier-free facilities not only assume auxiliary functions in the conventional sense but are also important facilities for coping with extreme weather and protecting the basic activities of the elderly. Moreover, this study has some similarities with the concept of a pedestrian-friendly community in the healthy city theory in Europe and the United States [48]. However, the research results show the particularity of small cities in cold regions. For example, in environmental psychology research, noise is mainly regarded as disruptive to health behavior [49]. However, this study shows that in small cities in cold areas, noise may not only affect the duration of activity but also may have a more significant adverse effect on the frequency of activity. Due to the lack of sound insulation design and zoning layout in small cities with considerable traffic noise, the elderly generally reduce their outdoor stay time, which is especially prominent in small cities with limited resources. In addition, existing studies have generally paid attention to the promoting effect of urban green space and facility density on the behavior of the elderly. For example, Handy S L, Boarnet M G, and Ewing R et al.’s study shows that the diversity of facilities directly impacts activity intensity [50]. This study verified this point and found that leisure facilities’ influence on activity behavior was significantly increased in cold environments, especially in winter conditions; the warmth preservation and comfort of leisure facilities became an important factor in promoting behavior. This differs from the perspective of the “quantity” of facilities, which is the main focus of previous studies, and expands the dimension of “functionality” of facilities. Regarding individual differences, the differences in gender, age, and education revealed in this study are similar to those in previous studies. For example, consistent with the Echavarren J M study [51], older men are more inclined to pay attention to function. At the same time, women are more inclined to pay attention to environmental details and sociability. However, this study found that women pay significantly more attention to culture and accessibility facilities than men, which may be related to women’s safety needs for outdoor activities and emotional needs for social activities in cold regions. In addition, the elderly group has a more significant demand for barrier-free facilities and safety, while the younger group pays more attention to sports and social functions. The existing literature has not fully revealed this age difference in specific cold conditions.

Finally, this study complements the study of the unique environmental characteristics of small cities, which are relatively scarce in existing studies. Wang Tao and Ma Liang point out that the built environment of small cities often has problems of insufficient resources and an unreasonable layout of facilities [52]. This study further verifies these problems from the perspective of cold regions and points out that seasonal climate factors can exacerbate these constraints. For example, inadequate snow clearing and a lack of anti-skid measures on roads are specific problems in small cities in cold regions, and these environmental factors significantly impact the elderly more than in warm regions or well-equipped large and medium-sized cities. In summary, based on verifying the existing theories and combining them with the unique situation of small cities in cold regions, this study expands the theoretical boundary of the influence of the built environment on the activities and behaviors of the elderly, providing a new perspective for related research.

5. Conclusions

The outdoor built environment is of great practical significance in the planning and constructing small cities in cold regions. It is an important part of urban functions and a key link to improving the quality of life of the elderly and social well-being. This study profoundly discusses the influence of the outdoor built environment on the outdoor activity behavior of the elderly in the cold region. It provides a new perspective for optimizing the aging environment of small cities in the cold region. The results show that a good outdoor environment can not only significantly increase the willingness and frequency of walking in the elderly but also promote their mental health, social integration, and independent living ability by providing a safe and comfortable space.

The study found that core environmental features such as green coverage, barrier-free facilities, road safety, and noise levels are the key factors affecting the activity behavior of the elderly. Optimizing these characteristics can effectively promote the walking activity of the elderly and further improve their physical and mental health. In addition, a good street environment also provides more opportunities for older people to socialize and interact, enhancing a sense of community and social support networks. These positive effects are significant in cold climates, where the harsh conditions of winter significantly increase the challenge of outdoor activities for older people.

According to the research results, this study puts forward several suggestions for optimizing the street environment of small cities in cold areas. First, the construction and maintenance of barrier-free facilities should be comprehensively strengthened, especially in winter conditions, to ensure that roads are smooth, non-slip facilities are complete, and slopes and low steps suitable for the elderly are set up. Secondly, we should increase street greenery, optimize plant types to adapt to the cold climate, and enhance the street’s comfort by setting sunshades, windproof facilities, and rest seats in a reasonable layout. Third, we should increase the investment in traffic safety, reduce the speed of motor vehicles, set priority areas for pedestrians, and optimize traffic lights and signs to maximize the safety of the elderly. In addition, by increasing community services and activity places, such as community activity centers and seasonal group activities, older persons are provided with more opportunities to participate in the community, promoting their social inclusion. Finally, smart city technology is used to optimize the travel conditions of the elderly further, such as introducing real-time monitoring and rescue systems to provide security.

Despite several innovative findings, this study still has some limitations. First, the sample scope is limited to Bei’an City, and the sample size is small, which may limit the universality of the research conclusions. Future studies can expand the sample scope to other small cities in cold regions to verify the extrapolation of the conclusions. Second, the period study period focused on winter and summer and failed to fully cover the potential effects of the four seasons on older adults’ behavior. Through multi-season data collection, further research can fully reveal the dynamic effects of cold environments on older adults’ behavior. In addition, although this study preliminarily explored the interaction between environmental variables, it failed to fully explore the complex mechanism relationship. For example, the synergistic effect of greening rate and shade degree under different climatic conditions may be significant. In the future, introducing a multi-variable interaction model can further analyze this. Finally, the lack of depth in integrating subjective and objective data also limits the study’s comprehensiveness. In the future, the dynamic relationship between the elderly’s behavior and the environment can be further revealed through behavior-tracking technology or smart devices combined with subjective questionnaire data.

In summary, this study reveals the profound influence of the outdoor built environment in small cities in cold regions on the outdoor activity behavior of the elderly. It proposes an optimization design strategy with the elderly as the core. By improving the outdoor built environment, we are able to not only improve the quality of life and well-being of older people but also promote inclusive and sustainable urban development. Future urban planning and policy formulation need to achieve a more age-friendly and inclusive outdoor built environment through scientific methods and innovative practices, with the cooperation of many parties. This would provide a friendly and safe living space for the elderly in a rapidly aging society and promote social harmony and common well-being.