Study on the Correlation Between Perception and Utilization of Green Spaces in Residential Areas and Residents’ Self-Rated Health Under Different Vegetation Coverage Rates: A Case Study from the Central City of Beijing

Abstract

Residential green space (RGS), as a frequently visited green space by residents, is the main space for daily activities and interactions, and its quality directly affects residents’ physical and mental health. Although many studies have revealed the impact of green space characteristics on health, research on the relationship between its environmental elements and health is still insufficient. This study selected five types of residential area in the central urban area of Beijing for investigation, collecting people’s green space perception, usage, and self-rated health information, and, using stepwise regression analysis, exploring the impact of RGS environmental factors on residents’ self-rated health under different vegetation cover rates. The results suggest the following: (1) Residents’ perception and usage of RGS characteristics are closely related to their self-rated health status, but the impact of environmental factors varies depending on vegetation coverage. (2) Maximizing natural features and cultural symbols is crucial for residential areas with high greenery. In residential areas with moderate vegetation, priority should be given to enhancing path elements, maintenance and shelter. For residential areas with low greenery cover, efforts should focus on strengthening fitness facilities and improving shelter to promote people’s health. (3) The impact of activity duration on usage behavior is most significant. These findings contribute to a more comprehensive understanding of the significance of RGS quality in urban residential areas. They also provide a reference for the optimization and management of the living environment and support the sustainable development of community environments.

1. Introduction

Since the United Nations established Agenda 21 in 1992, which defined the theme of conserving and promoting human health, public awareness of the prevention of illness and promotion of health has continuously increased [1]. Expanding urbanization and a fast-paced lifestyle are posing a growing threat to people’s physical and mental well-being [2,3]. Given this, green areas in the built environment of cities are crucial for reducing pollution, fostering biodiversity, and advancing ecological sustainability [4,5,6]. Additionally, they give locals access to recreational, fitness, and social areas, promote human–nature contact, lower stress levels [7], and boost mental and physical well-being [8,9]. According to research, green spaces are beneficial to the health of people of all ages [10,11,12]. Both active and passive involvement can increase inhabitants’ well-being [13], life satisfaction [14,15], and mental and physical health. More and more evidence indicates that people who live in green communities are happier and healthier [16,17].

Some research suggests that improving the quality of green spaces has a greater impact on health than simply increasing the number of green spaces [12,18,19,20,21,22]. High-quality green spaces are more appealing, and this has a substantial positive impact on people’s health, as well as their willingness and behavior to use them [23,24,25]. Offering green spaces with expansive views, tidy surroundings, well-maintained pathways, and a lot of greenery, for instance, is more likely to encourage people to partake in aerobic and walking activities [19,26,27,28,29]. These locations give individuals a more natural experience [30], promoting an active engagement with green spaces and enhancing their health benefits. Furthermore, people’s health outcomes and the frequency of their visits to green spaces are greatly impacted by the safety of these areas as well as the availability of amenities [31,32,33]. For instance, sports facilities [34] can inspire people to participate in physical activities, which will improve their physical health, while benches and shelters [35,36] can promote wider use by a variety of groups, particularly the elderly. On the other hand, a dirty, untidy, or disorganized green space may cause more psychological stress [37], in addition to decreasing the variety of outdoor activities [38] and the frequency of visits by locals [39].

Currently, research on instruments and techniques for evaluating the quality of green spaces is still in its exploratory phase [40]. The majority of the existing research quantifies the landscape characteristics within green spaces using expert rating techniques [12]. Expert evaluations provide standardized benchmarks, but they have limitations because they do not fully take into account the residents’ subjective experiences and perceptions. In contrast, inhabitants’ perceptions of green spaces created through daily contact are more real [41], and subjective evaluations can reveal critical landscape components that influence health and well-being [42,43]. Due to the lack of a unified standard to measure residents’ perception of green space quality, some studies use single-item questions to investigate residents’ overall perception of green space quality [44,45], while others assess multiple indicators [46] such as the spaciousness, cleanliness, and maintenance of green spaces to explore the relationship between these green space elements and physical activity and health status [18].

Based on the existing research findings, there is substantial evidence that the amount and quality of green areas improves citizens’ health. However, research on the relationship between specific green space qualities and health in various types of residential settings remains limited. In the context of improving vegetation coverage, it is essential to investigate the effects of enhancing the quality of internal landscape elements in green spaces on residents’ health. Since not all residents have access to high-quality green spaces, this study attempts to explore which landscape features are most closely associated with residents’ self-rated health, and whether residential areas with varying degrees of vegetation coverage influence people’s perceptions and use behaviors of green space landscape factors. This study selected different types of residential areas and explored the inherent relationship between residents’ perception and usage behavior of RGS and their physical and mental health under different levels of vegetation coverage through the identification of green space environmental perception factors, the investigation of green space use patterns, the self-evaluation of health status by residents, and a classification analysis of vegetation coverage.

We hypothesize the following:

(1)

Residents’ physical and mental health are closely linked to their perception and use of green space features;

(2)

Varying levels of vegetation coverage will influence residents’ perception and utilization of green space features;

(3)

The effect of green space characteristics and usage behavior on inhabitants’ health varies with the amount of vegetation coverage in the residential region.

2. Materials and Methods

2.1. The Study and the Residential Areas Surveyed

The central urban area of Beijing, as the core of the capital and the municipality, represents a typical high-density agglomeration, with a total area of roughly 1378 km² and a permanent population of roughly 10.988 million. With the rapid development of urbanization and market-oriented reforms, residential space has shown highly heterogeneous characteristics [47], from the danwei compound before the 1980s to market-rate housing, danwei-managed public housing, government-priced affordable housing after the 1980s, and resettlement housing as a result of urban renewal or land development. These residential areas demonstrate both the shifts in China’s housing system during the transition period and the obvious differences in environmental quality [48].

This study chose various residential area types for examination to investigate the impact of the quality of the green space environment on the health of the people. Two sub-districts, the Shuguang sub-district, and the Jingsong sub-district, which are comparable in area, population size, development orientation, and infrastructure, were chosen to ensure the diversity of residential area type selection, lessen the influence of external environmental factors, and improve the applicability of the research findings, because these residential areas are challenging to cover entirely in one area. Thus, five categories of residential places with common features were chosen: danwei public housing, market-rate housing, replacement housing, housing reform housing (after the housing system reform), and affordable housing. Simultaneously, satellite data were used to assess the vegetation cover in these areas. Ultimately, 21 residential neighborhoods with varying amounts of vegetation were chosen. As illustrated in Figure 1, ten of these residential zones are situated in the Shuguang sub-district, while eleven are situated in the Jingsong sub-district. These residential neighborhoods can more accurately represent the features of internal environmental components, offering a solid foundation for research on how the residential environment affects the health of its occupants.

2.2. Survey Instruments and Procedure

This study aims to understand residents’ perception and use of green space environmental characteristics in residential areas, as well as their impact on health. To comprehensively collect relevant data, the survey was conducted during the peak growing season (June to September), when vegetation is at its most vigorous and the landscape quality is optimal. Local sub-district offices were engaged to facilitate resident participation in completing the questionnaires. The respondents were adults who have lived or worked in Beijing for an extended period. A random sampling method was used to invite residents aged 18 and above with independent cognitive ability to participate. The survey was conducted on sunny weekdays and weekends, between 9:00 and 11:00 in the morning and 15:00 and 20:00 in the afternoon, to guarantee a balanced sample structure and minimize the risk of the under-representation of the young and middle-aged groups and an over-representation of the elderly group.

We distributed 40 questionnaires in 21 residential areas, and data were collected in August 2023 for one month. Before the formal survey, the research team conducted a pre-test on 145 samples in May and optimized the questionnaire content based on the feedback to ensure that the questions were concise, clear, and closely aligned with real-world conditions, so that respondents with different education levels could understand and answer them easily. According to the 95% confidence interval, the sample size of the formal survey should be no less than 384 questionnaires. Finally, 835 questionnaires were collected, of which 675 were valid, with an effective rate of 80.84%, which met the statistical requirements [49,50]. The results of a reliability and validity analysis revealed a Cronbach’s alpha at 0.919, a Kaiser–Meyer–Olkin measure of sampling adequacy at 0.908, and Bartlett’s test of sphericity sig. < 0.05, indicating that the questionnaire demonstrated good reliability and validity, effectively measuring the research content and providing a reliable basis for subsequent data analysis [51].

The questionnaire consisted of 4 main parts:

• Information of respondents: including gender, age, occupation, education level, monthly income, and so forth. This section supported the investigation of health status disparities resulting from individual social variables and aids in understanding the respondents’ essential circumstances;
• Perceptions of green space characteristics in residential areas by residents: This part filtered environmental component perception indicators in three steps to ensure the research’s scientific integrity. First, based on pertinent literature, the initial environmental features of residential green spaces—such as the road system, safety, lighting, recreational amenities, and natural landscape [52,53]—were screened. Second, to make sure that the selection items more accurately reflected the real circumstances of the residential area, the first environmental elements were optimized through field surveys and resident interviews. Finally, a 31-item residential green space perception evaluation scale was created after a completed pre-survey to improve the questionnaire’s content (

  Table 1. Five elements of green space perception.

  No.	Perceptual Elements	Perception Factor
  1	Natural elements	The area of green space
  		Plant landscape design aesthetics
  		Green space has a peaceful atmosphere
  		Vegetation quantity/density
  		Plant seasonal colors/distinct four seasons
  		Combination of plants, trees, shrubs, and grasses
  		The richness of plant species
  		Can hear birds chirping and insects chirping
  2	Facility elements	Collective fitness activity venue
  		Types of fitness facilities
  		Quantity of fitness equipment
  		Number of seats and other rest facilities
  		Seat comfort
  		Children play facilities
  3	Pathway elements	Green path with smooth walking
  		Rich and smooth path changes
  		Ground traffic/parking impact
  		Comfortable pavement
  		The ground of the path is flat
  4	Cultural identifiers	Interesting water features/fountains
  		Pavilions and other architectural ornaments
  		Decorative art landscape sketches
  		Health knowledge promotion logo
  5	Maintenance and shelter	Plant growth status
  		Plant health status
  		Green space is clean and tidy
  		Trimming plants neatly and beautifully
  		Green space visual transparency
  		Green space security sense
  		Trees block wind and provide shade
  		Summer deworming and mosquito avoidance

  ). This section of the measure, which used a 5-point Likert scale with “very poor (=1)” to “very good (=5)” as the extremes, exhibited a strong internal consistency (the Cronbach’s alpha was 0.964);
• Green space usage by residents: The questions of this part assess the frequency, length, and intensity of outdoor sports activities carried out within a week. Referring to the different activity patterns observed in park green spaces (static behavior pattern, dynamic behavior pattern, and through behavior pattern) [54], they are classified into three categories based on their activity intensity: static activities (such as relaxing, resting, connecting with nature, and socializing), low-intensity activities (such as passing through, free activities, and walking), and moderate to high-intensity activities (such as facility activities, site activities, and running). According to the “Physical Activity Guidelines for Chinese Adults (2021),” people should perform at least 150 min of moderate-intensity or 75 min of high-intensity aerobic activity every week [55]. As a result, the exercise frequency is set to at least 150 min per week and divided into four levels, 1–2 times, 3–4 times, 5–6 times, and 7 times or more, with scores ranging from 1 to 4. The three sorts of activities are scored as 1 point, 2 points, and 3 points, respectively. The activity time is designed to be at least 10 min each time, divided into four time segments, ≤10 min, 11–20 min, 21–30 min, and >30 min, with scores ranging from 1 to 4;
• Health status of participants: This part uses the self-rated health scale (SF-12v2) as a resident health measurement tool. Compared with instantaneous physiological measurements (such as blood pressure, pulse, ECG, etc.) [56], self-rated health scales can comprehensively reflect an individual’s long-term health status, including quality of life, the impact of chronic diseases, and psychological state, and are widely used in health research [57]. The SF12v2 scale assesses eight health areas, including physical functioning, role physical, bodily pain, general health, vitality, social functioning, role emotional, and mental health. It can be used to comprehensively evaluate an individual’s physical and mental health. Respondents’ scores are based on their health feelings over the past month. After being standardized, the scores of each health area are counted separately into physical health and mental health [58]. Currently, the SF-12v2 scale has been validated in different populations and can effectively measure the physical and mental health of residents [59,60,61].

2.3. Measurement and Classification of Vegetation Coverage in Residential Areas

Since greenery can mitigate the adverse effects of building density on residents’ self-rated health [62], this study mainly measured the vegetation coverage of residential areas. The normalized differential vegetation index (NDVI) is the most often used metric for measuring vegetation coverage. This indicator monitors the vegetation coverage and growth of the study area by calculating the ratio of the difference between the near-infrared and visible red light band values to the sum of the two [11]. In addition, NDVI is a dimensionless indicator that can be compared across regions, and it is commonly employed to investigate the link between green space and health.

This study used the Google Earth Engine (GEE) platform to collect Sentinel-2 remote sensing images (resolution: 10 × 10 m) from the central city of Beijing between June and September 2023, the optimal period for plant growth. The average NDVI value of the area was calculated using the GEE programming language. Subsequently, the data were corrected using ENVI 5.3 software, and the NDVI value was adjusted between 0 and 1 to reduce the error caused by the accumulation of negative values around a water body. After that, ArcGIS 10.6 software was used to extract masks from 21 residential areas and calculate their average NDVI values to characterize the vegetation coverage of each area.

When analyzing the NDVI distribution, this study found that the NDVI values of the 21 residential areas showed obvious gradient differences, as shown in Figure 2. Based on the visualization results of the gradient feature, residential areas with similar NDVI values were classified into the same category, and residential areas with large value differences were classified into different categories, eventually forming four groups, high, medium-high, medium-low, and low, as illustrated in Figure 3. This grouping method is based on the natural distribution trend of the NDVI and reflects the overall situation of the greening level of residential areas. It should be pointed out that the NDVI mainly reflects the overall vegetation coverage rate and fails to cover the specific environmental characteristics of green spaces. Therefore, it is still necessary to further explore the relationship between specific environmental factors and residents’ health under different greening levels.

In addition, considering that the NDVI only reflects the overall coverage and has difficulty in describing the greening situation perceived by residents daily, this study introduces the Green View Index (GVI) as a supplementary indicator to measure the proportion of green vegetation visible to the human eye [63]. For the specific measurement, we used the WeChat applet “Pinsurvey” with a positioning function to set up measurement points at intervals of 30 m along the main road of the residential area. We took images from the left, right, front, and back for each sampling point. Then, we applied a deep learning-based fully convolutional neural network (FCN) to perform semantic separation on the acquired images [64], thereby calculating the average GVI of each residential area, which more accurately reflects the visual greenness that residents can perceive in the residential area.

2.4. Data Analysis

IBM SPSS Statistics 24.0 software was utilized to process and analyze data in the study. The population’s physiological and psychological health scores, as well as perceived green space in the residential area, are shown as mean ± SD. ANOVA was used to compare the physiological and psychological health status of individuals with different characteristics, and the chi-square test to compare the use of residential green spaces by different groups of people with a different physical and mental health status. Finally, using stepwise regression analysis with the self-assessment of physiological and psychological health as dependent variables, significant factors related to residents’ physical and mental health after the variance analysis and chi-square test were introduced into the regression model to reveal the correlation between residents’ perceptions and usage behavior of green space environmental elements in residential areas and residents’ self-rated health under different types of residential areas and vegetation coverage levels.

3. Results

3.1. Quantitative Scores for the General Situation and Physical and Mental Health Status of the Survey Subjects

This study collected self-rated data on physical and mental health issues from 675 inhabitants. The residents’ physiological health score was 46.032 ± 7.071, with a minimum value of 21.47 and a maximum value of 64.56. The mental health score was 51.892 ± 6.154, with a minimum value of 30.40 and a maximum value of 69.34. The results of the independent sample t-test indicate that there are significant differences in physical health between genders, while psychological health does not show differences (

Table 2. The scores of respondents’ physical and mental health conditions.

Gender	Number	n (%)	Physical Health Score		Mental Health Score
Total	675	100%	46.03 ± 7.07		51.89 ± 6.15
Male	273	40.4%	47.14 ± 6.78	t = 3.37 **	52.43 ± 6.44	t = 1.87
Female	402	59.6%	45.28 ± 7.18		51.53 ± 5.93

** significant at p < 0.01 level.

). From the average health status, males have a slightly higher physical and mental health than females.

The statistical analysis of other personal factors on the physical and mental health ratings of respondents shows significant differences in age, employment, education level, and average monthly income (

Table 3. Respondents’ general situation and physical and mental health status scores (n = 675).

Variable	Indicator	n (%)	Physical Health Score		Mental Health Score
			Score	p	Score	p
Age (years)	18–30	19 (7.3%)	49.834 ± 4.224	F = 64.592 ***	52.835 ± 6.058	F = 3.676 *
	31–45	198 (29.3%)	49.739 ± 5.535		52.281 ± 6.082
	46–60	173 (25.6%)	46.598 ± 6.022		52.626 ± 5.654
	>60	255 (37.8%)	42.038 ± 7.16		50.91 ± 6.449
Occupation	Administrative staff	30 (4.4%)	51.289 ± 5.407	F = 41.473 ***	54.295 ± 6.428	F = 2.618 *
	Enterprise and public institution staff	174 (25.8%)	48.938 ± 5.64		52.346 ± 6.137
	Freelancer	111 (16.4%)	49.118 ± 5.815		52.101 ± 6.511
	Student	18 (2.7%)	50.414 ± 3.016		54.572 ± 3.642
	Unemployed	28 (4.1%)	48.081 ± 2.708		51.442 ± 4.417
	Retired	314 (46.5%)	42.394 ± 6.993		51.222 ± 6.168
Educational background	Junior high school and below	61 (9.0%)	41.626 ± 8.151	F = 20.246 ***	50.883 ± 7.812	F = 3.414 **
	High school or vocational school	188 (27.9%)	43.908 ± 6.298		51.221 ± 5.557
	Associate degree	123 (18.2%)	45.927 ± 7.441		51.293 ± 6.058
	Bachelor’s degree	258 (38.2%)	48.075 ± 6.377		52.522 ± 5.962
	Master’s degree and above	45 (6.7%)	49.442 ± 5.757		54.085 ± 6.685
Average monthly income	<3000 yuan	67 (9.9%)	47.339 ± 4.964	F = 7.932 ***	53.456 ± 6.505	F = 3.714 **
	3000–5000 yuan	130 (19.3%)	43.639 ± 8.175		50.498 ± 6.485
	5000–8000 yuan	295 (43.7%)	45.646 ± 6.985		51.660 ± 6.139
	8000–10000 yuan	120 (17.8%)	47.893 ± 6.385		52.826 ± 5.049
	>10000 yuan	63 (9.3%)	47.838 ± 6.642		52.407 ± 6.525

* significant at p < 0.05 level. ** significant at p < 0.01 level. *** significant at p < 0.001 level.

). Although individual social attributes may have different effects on residents’ use of green spaces and physical and mental health [65], this does not affect the residents’ perception and evaluation of the environmental elements of green spaces in residential areas.

3.2. Analysis of the Relationship Between Perception and Use of Green Spaces in Residential Areas and Residents’ Physical and Mental Health

Table 4. Correlation analysis between perception of green spaces in residential areas, use patterns, and residents’ physical and mental health (n = 675).

Variable		Physical Health Score	Mental Health Score
Dimensions	Natural elements	0.394 **	0.466 **
	Facility elements	0.396 **	0.327 **
	Pathway elements	0.321 **	0.376 **
	Cultural identifiers	0.381 **	0.401 **
	Maintenance and shelter	0.400 **	0.455 **
Use Patterns	Weekly activity frequency	0.197 **	0.144 **
	Activity intensity	0.113 **	0.160 **
	Duration of each activity	0.222 **	0.288 **

** significant at p < 0.01 level.

shows that various green space environmental factors in residential areas are significantly positively correlated with residents’ physical and mental health (p < 0.01). Among them, the link between perceptions of maintenance and shelter in residential green areas and physical health is relatively high (r = 0.400), as is the correlation between natural factors and mental health (r = 0.466). In addition, the utilization of green spaces in residential areas is significantly positively correlated (p < 0.01) with residents’ physical and mental health. In terms of residents’ usage behavior, the duration of weekly activities is most strongly correlated with physical health (r = 0.222) and mental health (r = 0.288), the frequency of weekly activities with physical health (r = 0.197), and the intensity of weekly activities with mental health (r = 0.160). These findings indicate a close relationship between residents’ perception of green space features, their usage behavior, and self-rated physical and mental health, providing preliminary support for hypothesis 1 of this study.

3.3. Differences in Perception of Green Spaces in Residential Areas

By comparing residential areas with different levels of vegetation coverage, significant differences were observed in the residents’ perception of environmental factors (

Table 5. Differences in green space perception in residential areas with varied degrees of vegetation coverage.

Variable	Statistics	Vegetation Coverage Level				Between-Group Difference F-Value
		High	Medium-High	Medium-Low	Low
Natural elements	Mean	4.249	3.925	3.342	3.411	64.872 ***
	SD	0.548	0.619	0.849	0.718
Facility elements	Mean	3.378	3.270	2.809	2.935	14.357 ***
	SD	1.009	1.013	0.957	0.724
Pathway elements	Mean	4.242	4.132	3.690	3.665	34.219 ***
	SD	0.575	0.691	0.769	0.559
Cultural identifiers	Mean	3.612	3.562	2.491	2.516	60.717 ***
	SD	0.922	0.906	1.207	1.086
Maintenance and shelter	Mean	4.368	4.039	3.571	3.928	48.932 ***
	SD	0.562	0.655	0.737	0.503

*** significant at p < 0.001 level.

, see F-value). Overall, in residential areas with a higher vegetation coverage, residents had a more positive evaluation of green space elements and had a better perception of environmental qualities such as safety, cleanliness, pathway optimization, facility configuration, and cultural landscape, which may be related to the good environmental atmosphere they created. Residential areas with lower coverage rates were relatively insufficient in the above aspects, which may have affected the residents’ perception and satisfaction to a certain extent. These findings support hypothesis 2, that is, that the level of vegetation coverage is related to residents’ perception of green space characteristics.

3.4. Comparison of the Ways Different Health Status Groups Use Residential Green Spaces

According to the scoring criteria of the SF-12v2 scale [66], the physical and mental health status of 675 respondents was evaluated. By comparing the physical and mental health scores of residents in residential areas with different vegetation coverage, we found that residents in areas with a high vegetation coverage tend to have significantly higher physiological and psychological health scores compared to those in other areas. This advantage appears to be independent of activity frequency, intensity, and duration, suggesting that vegetation coverage may provide a foundational environmental context supportive of health, as shown in Figure 4. However, as vegetation coverage decreases, a gradient decrease in health scores is observed. In terms of activity frequency, residents in high-vegetation-coverage areas show a clear improvement in psychological health as the frequency increases. In contrast, no significant change is seen in low coverage areas, possibly reflecting differences in the visual or restorative appeal of the environment, which may influence engagement levels. Notably, residents in areas with a medium to low coverage see a significant improvement in physiological health with an increasing frequency of activity, likely because these residents typically use green spaces less frequently and increased activity can more directly activate physiological functions. Regarding activity intensity, improvements in psychological health are evident in high-coverage areas as intensity increases, whereas such situations are not observed in low-coverage areas. As for activity duration, residents in high-vegetation-coverage residential areas show a trend of improving health scores as their exposure time increases. This may reflect the positive support of good environmental quality on physical and mental health. In contrast, this trend appears less pronounced in residential areas with relatively limited green space resources.

Additionally, a total of 74 person-times of outlier data were identified in the figure. Among these, 30 person-times are related to physical health, all located at the lower edge. Of these, 80% are female, with the majority being older individuals, indicating that this group faces certain limitations across all three activity types, which may be a significant factor contributing to their lower physical health status. The remaining 47 person-times are related to mental health, with no significant differences in age or gender. In terms of distribution, outliers at the upper edge are primarily concentrated in groups with a higher variety of activities, while those at the lower edge are more dispersed, especially about activity intensity and duration. This suggests that, independent of individual characteristics, the frequency, intensity, and duration of green space activities may have a positive impact on both physical and mental health.

Further analysis was conducted by dividing the surveyed population into two groups based on the average scores of physiological and psychological health: the low group (<average) and the high group (≥average). The number of respondents in each group after grouping is shown in

Table 6. Comparison of the ways residents with different levels of green coverage use green spaces (n = 675).

Greening Level	Indicator		Number (%) of People in the Physical Health Score Groups			Number (%) of People in the Mental Health Score Groups
			<Mean	≥Mean	X2 Test	<Mean	≥Mean	X2 Test
High (n = 165)	Weekly activity frequency /times	1–2 times	5 (3.03)	10 (6.06)	X2 = 5.881	9 (5.45)	6 (3.64)	X2 = 11.158 *
		3–4 times	22 (13.33)	18 (10.91)		19 (11.52)	21 (12.73)
		5–6 times	21 (12.73)	40 (24.24)		22 (13.33)	39 (23.64)
		7 times and above	16 (9.7)	33 (20)		10 (6.06)	39 (23.64)
	Activity intensity	Static activities	11 (6.67)	22 (13.33)	X2 = 2.869	13 (7.88)	20 (12.12)	X2 = 16.963 ***
		Low-intensity activities	42 (25.45)	53 (32.12)		44 (26.67)	51 (30.91)
		Moderate/high-intensity activities	11 (6.67)	26 (15.76)		3 (1.82)	34 (20.61)
	Duration of each activity/min	≤10 min	2 (1.21)	5 (3.03)	X2 = 15.442 **	4 (2.42)	3 (1.82)	X2 = 26.952 ***
		11–20 min	20 (12.12)	12 (7.27)		23 (13.94)	9 (5.45)
		21–30 min	25 (15.15)	30 (18.18)		19 (11.52)	36 (21.82)
		>30 min	17 (10.30)	54 (32.73)		14 (8.48)	57 (34.55)
Medium-high (n = 162)	Weekly activity frequency /times	1–2 times	6 (3.70)	11 (6.79)	X2 = 1.481	6 (3.7)	11 (6.79)	X2 = 0.749
		3–4 times	19 (11.73)	23 (14.2)		20 (12.35)	22 (13.58)
		5–6 times	22 (13.58)	26 (16.05)		21 (12.96)	27 (16.67)
		7 times and above	20 (12.35)	35 (21.61)		24 (14.82)	31 (19.14)
	Activity intensity	Static activities	7 (4.32)	24 (14.82)	X2 = 5.874	12 (7.41)	19 (11.73)	X2 = 7.016 *
		Low-intensity activities	48 (29.63)	54 (33.33)		52 (32.1)	50 (30.86)
		Moderate/high-intensity activities	12 (7.41)	17 (10.49)		7 (4.32)	22 (13.58)
	Duration of each activity/min	≤10 min	2 (1.23)	3 (1.85)	X2 = 7.174	1 (0.62)	4 (2.47)	X2 = 17.767 **
		11–20 min	20 (12.35)	14 (8.64)		21 (12.96)	13 (8.02)
		21–30 min	27 (16.67)	37 (22.84)		33 (20.37)	31 (19.14)
		>30 min	18 (11.11)	41 (25.31)		16 (9.88)	43 (26.54)
Medium-low (n = 191)	Weekly activity frequency /times	1–2 times	33 (17.28)	12 (6.285)	X2 = 17.586 **	26 (13.61)	19 (9.95)	X2 = 2.423
		3–4 times	34 (17.8)	22 (11.52)		33 (17.28)	23 (12.04)
		5–6 times	23 (12.04)	34 (17.8)		26 (13.61)	31 (16.23)
		7 times and above	11 (5.76)	22 (11.52)		18 (9.42)	15 (7.85)
	Activity intensity	Static activities	31 (16.23)	23 (12.04)	X2 = 5.940	32 (16.75)	22 (11.52)	X2 = 2.031
		Low-intensity activities	62 (32.46)	49 (25.65)		60 (31.41)	51 (26.7)
		Moderate/high-intensity activities	8 (4.19)	18 (9.42)		11 (5.76)	15 (7.85)
	Duration of each activity/min	≤10 min	13 (6.81)	13 (6.81)	X2 = 5.103	18 (9.42)	8 (4.19)	X2 = 6.881
		11–20 min	19 (9.95)	7 (3.66)		18 (9.42)	8 (4.19)
		21–30 min	37 (19.37)	40 (20.94)		36 (18.85)	41 (21.47)
		>30 min	32 (16.75)	30 (15.71)		31 (16.23)	31 (16.23)
Low (n = 157)	Weekly activity frequency /times	1–2 times	9 (5.73)	8 (5.1)	X2 = 0.095	12 (7.64)	5 (3.18)	X2 = 2.543
		3–4 times	22 (14.01)	22 (14.01)		28 (17.83)	16 (10.19)
		5–6 times	27 (17.2)	26 (16.56)		33 (21.02)	20 (12.74)
		7 times and above	21 (13.38)	22 (14.01)		22 (14.01)	21 (13.38)
	Activity intensity	Static activities	27 (17.2)	21 (13.38)	X2 = 0.998	33 (21.02)	15 (9.55)	X2 = 5.066
		Low-intensity activities	39 (24.84)	42 (26.75)		50 (31.85)	31 (19.75)
		Moderate/high-intensity activities	13 (8.28)	15 (9.55)		12 (7.64)	16 (10.19)
	Duration of each activity/min	≤10 min	11 (7.01)	6 (3.82)	X2 = 4.146	14 (8.92)	3 (1.91)	X2=7.073
		11–20 min	16 (10.19)	13 (8.28)		19 (12.1)	10 (6.37)
		21–30 min	30 (19.11)	42 (26.75)		44 (28.03)	28 (17.83)
		>30 min	22 (14.01)	17 (10.83)		18 (11.47)	21 (13.38)

* significant at p < 0.05 level. ** significant at p < 0.01 level. *** significant at p < 0.001 level.

. Although the sample size in each group has decreased after grouping, according to Kline [67], the number of respondents in a factor analysis should be at least twice the number of variables. At the same time, Cattell [68] points out that in behavioral and life sciences research, the ratio of respondents to variables should be between 3:1 and 6:1. Therefore, the data volume after grouping remains within a reasonable range and can meet the analytical requirements of this study. A chi-square test (X²) was employed to examine the associations between residents’ green space usage behaviors and their health status across different vegetation coverage levels, with significance levels indicated by p-values. The results show that, for physiological health, activity duration was significantly associated with health status in high-vegetation-coverage residential areas, while activity frequency was significant in medium-low-coverage areas. No statistically significant associations were observed for activity intensity across vegetation levels. For psychological health, all three dimensions of green space activity—frequency, intensity, and duration—showed significant associations in the high-coverage group. In the medium-high-coverage group, intensity and duration were significant, while no significant relationships were found in the medium-low and low greening level groups, as shown in Table 6. These findings suggest that the relationship between green space activity and health may vary depending on vegetation coverage. In particular, under high vegetation coverage and medium-high vegetation coverage, there are significant inter-group differences in the impact of residents’ activities on health, which means that the higher the vegetation coverage, the better the residents’ activities and health status; and this effect is particularly obvious in terms of mental health. These results also imply that vegetation coverage may influence how residents engage with green spaces, aligning with the expectations of hypothesis 2.

In summary, the association between high vegetation coverage and mental health appears to be more pronounced, whereas this tendency gradually weakens or becomes negligible as vegetation coverage decreases. For physical health, the duration of stay may play a more influential role, particularly in residential areas with a high vegetation coverage, where respondents staying longer than 30 min are more frequently found in the higher health score group. In residential areas with a low and medium-low vegetation coverage, the impact of activity frequency on physiological health seems to be more obvious, indicating that in environments with relatively limited green space resources, increasing the frequency of use may be a potential way to promote physiological health.

3.5. Regression Analysis of the Effect of Green Space in Residential Areas with Different Vegetation Coverage on Residents’ Self-Rated Health

Previous studies have suggested a linear relationship between residential green spaces and residents’ physical and mental health, particularly under conditions of relatively low green coverage [69,70]. Based on this, residents’ self-rated physical and mental health were taken as dependent variables, while respondents’ demographic characteristics, perceived green space environmental features, GVI, and activity content were included as independent variables in a stepwise multiple regression analysis. All four regression models yielded variance inflation factor (VIF) values below 3, indicating no multicollinearity among the selected variables. It should be noted that the model construction is based on the above variance analysis and chi-square test; factors that have a significant impact on residents’ self-rated health are selected and introduced into the regression model. For each of the comparisons, the four groups of models are unified in one table, as shown in

Table 7. Model of the effect of perception and use of green spaces in residential areas on residents’ self-rated health (n = 675).

Mode	Variable		Standardized Coefficients β	t	Sig.	VIF	Adj. R2	F
	Dependent Variable	Predictor Variable
Model 1: High (n = 165)	Physical health	(Constant)		2.348	0.020		0.465	24.748 ***
		Age group	−0.333	−4.713	0.000	1.532
		Educational background	0.174	2.581	0.011	1.396
		GVI	0.245	3.668	0.000	1.369
		Natural elements	0.210	2.754	0.007	1.777
		Cultural identifiers	0.248	3.134	0.002	1.913
		Activity duration	0.162	2.522	0.013	1.263
	Mental health	(Constant)		4.180	0.000		0.393	36.353 ***
		GVI	0.142	2.327	0.021	1.002
		Natural elements	0.486	7.386	0.000	1.169
		Activity duration	0.240	3.646	0.000	1.171
Model 2: Medium -high (n = 162)	Physical health	(Constant)		7.637	0.000		0.357	18.870 ***
		Age group	−0.264	−4.057	0.000	1.063
		Average monthly income	0.267	4.097	0.000	1.066
		Maintenance and shelter	0.169	2.017	0.045	1.751
		Cultural identifiers	0.224	2.617	0.010	1.842
		Facility elements	0.163	2.007	0.047	1.651
	Mental health	(Constant)		10.642	0.000		0.246	27.197 ***
		Natural elements	0.258	2.605	0.010	2.098
		Maintenance and shelter	0.285	2.879	0.005	2.098
Model 3: Medium -low (n = 191)	Physical health	(Constant)		12.841	0.000		0.302	21.536 ***
		Gender	−0.144	−2.369	0.019	1.012
		Age group	−0.314	−5.017	0.000	1.068
		Facility elements	0.208	2.542	0.012	1.819
		Maintenance and shelter	0.184	2.291	0.023	1.761
	Mental health	(Constant)		16.315	0.000		0.218	27.427 ***
		Natural elements	0.327	3.707	0.000	1.889
		Pathway elements	0.187	2.123	0.035	1.889
Model 4: Low (n = 157)	Physical health	(Constant)		19.043	0.000		0.396	35.068 ***
		Gender	−0.233	−3.733	0.000	1.005
		Age group	−0.505	−7.861	0.000	1.067
		Facility elements	0.209	3.245	0.010	1.071
	Mental health	(Constant)		11.812	0.000		0.383	33.294 ***
		Educational background	0.292	4.451	0.000	1.087
		Facility elements	0.211	2.851	0.005	1.384
		Maintenance and shelter	0.336	4.510	0.000	1.404

*** significant at p < 0.001 level.

. The results show that there are differences in the association between green space elements, usage behaviors, and residents’ self-rated health in residential areas with a different vegetation coverage. These findings provide support for hypothesis 3. To further understand the role of residential green space in promoting residents’ health, it is necessary to further analyze which green space environmental elements and activity modes in residential areas with a different vegetation coverage are significantly associated with residents’ self-rated health.

3.5.1. Analysis of the Impact of Green Space Elements in Residential Areas with High Vegetation Coverage

According to regression model 1 in Table 7, the physiological health model of the high greening-level group consists of six predictor variables, and other factors have been excluded, suggesting that the remaining variables do not show statistically significant associations. This model accounts for 46.5% of the variance affecting physiological health (adjusted R² = 0.465). Except for the negative correlation between age group and physiological health score, all other variables are significantly positively correlated with it (p < 0.05). After controlling for individual attribute variables, cultural identifiers show the strongest association with residents’ physiological health, followed by GVI, natural elements, and duration of green space activities. The mental health model includes three variables with positive predictive effects, accounting for 39.3% of the variance in mental health. Variables showing relatively stronger associations with mental health include natural factors, duration of exercise, and GVI.

3.5.2. Analysis of the Impact of Green Space Elements in Residential Areas with Medium-High Vegetation Coverage

According to regression model 2 in Table 7, the physiological health model of the medium-high greening-level group consists of five predictor variables, accounting for 35.7% of the physiological health variance. Besides age groups, average monthly income, maintenance and shelter, cultural identifiers, and facility elements are positively associated with physical health. The regression coefficients of the model show that cultural identifiers are the most strongly associated with residents’ physiological health, followed by the maintenance of shelter and facility elements. The mental health model includes two predictor variables and accounts for 24.6% of mental health. Among them, maintenance and shelter have the greatest impact on residents’ mental health, followed by natural factors.

3.5.3. Analysis of the Impact of Green Space Elements in Residential Areas with Medium-Low Vegetation Coverage

According to regression model 3 in Table 7, the physiological health model of the medium-low greening-level group has four predictor variables, explaining 30.2% of the physiological health variance. Excluding demographic characteristics such as gender and age groups, facility elements and maintenance and shelter are positive predictors of physical health. In the mental health model, only natural and pathway factors have a positive predictive impact, while cultural identifiers were not retained in the final model. This model explains 21.8% of the variance in mental health, with a relatively low explanatory power, and pathway elements show a significant association with residents’ mental health.

3.5.4. Analysis of the Impact of Green Space Elements in Residential Areas with Low Vegetation Coverage

According to regression model 4 in Table 7, the physiological health model of the low greening-level group has three predictor variables, explaining 39.6% of the physiological health variance. Facility elements are favorable predictors of physiological health, while age and gender are negative predictors. The mental health model also consists of three predictor variables, explaining 38.3% of the variance in mental health. After controlling for individual attribute variables, facility elements and maintenance and shelter are favorable predictive factors for mental health; maintenance and shelter show the strongest association with residents’ mental health.

4. Discussion

4.1. Environmental Factors Affecting Residents’ Self-Rated Health in Residential Areas with Different Vegetation Coverage Rates

Previous research has demonstrated that exposure to high-quality green spaces improves inhabitants’ health [21,71]. However, due to differences in construction periods and design concepts, the quality of green spaces in different residential areas of the city varies greatly, and not everyone has access to a high-quality green environment. Therefore, based on vegetation coverage, this study classified the 21 studied residences into four categories. Compared with earlier studies that examined the relationship between greenery and health under uniform levels of green exposure, our research results broaden the understanding of how green space attributes relate to residents’ health and offer a new perspective. Specifically, we investigated how residents perceive and use green spaces under varying levels of vegetation coverage and further explored which green space elements and usage patterns are associated with residents’ physical and mental health in different green environments.

The results show that residents’ views and use of green space vary between residential areas with different greening levels, and these differences are closely related to their self-rated health status, a finding consistent with earlier research results [72,73]. Therefore, although existing research mainly focuses on the impact of green coverage on health, attention should also be paid to improving green space features that can promote residents’ self-rated health. We should focus on improving the environmental quality of residential areas through differentiated means, rather than simply pursuing an increase in green space coverage, as shown in Figure 5.

The regression analysis (Table 7) indicates that in residential areas with high levels of greenery, the primary factors associated with residents’ self-rated health are natural elements and cultural identifiers. This may be related to the overall environmental characteristics of these areas, which are often high-end residential areas, such as GSY, ChunYY, YDYSQ, etc. These communities typically feature aesthetically pleasing environments, diverse vegetation, and a well-structured landscape design, which may contribute to a stronger sense of community identity among residents. In a visual environment with good plant combinations and diverse tree species, negative emotions can be improved [7,74] and the rich natural resources enjoyed, triggering a multi-sensory experience [75]. Rich pavilions and other architectural or exquisite landscape ornaments not only provide shade in summer [76] and places for playing cards or chess but also cultivate neighborhood interaction and community participation through outdoor spaces [77,78]. Therefore, once inhabitants come into contact with high-quality green spaces, the likelihood and frequency of their visits increase, and the duration of each visit may also be extended [3].

In residential areas with medium-high levels of greenery, residents’ physical and mental health appears to be influenced by a combination of factors, including maintenance and shelter, natural elements, cultural identifiers, and facility elements. This may be because although the vegetation coverage in these residential areas is somewhat low, they are second only to the high-end residential areas mentioned above, and their vegetation and natural landscapes are still the dominant features. Excluding the influence of natural elements and cultural identifiers, maintenance and shelter are particularly important, especially in terms of vegetation health, aesthetic effects, and transparency, as well as well-established facility elements. Therefore, it is particularly important to strengthen plant management; avoid or reduce the impact of plants with health risks [79], such as allergic pollen [80], plant debris [81], etc.; and improve the environmental quality of residential areas. Providing a green, clean, and safe living environment can also provide sufficient space for the formation of a cultural identity, thereby improving people’s living comfort and environmental aesthetics.

In residential areas with medium-low levels of greenery, the main factors that play a positive role in residents’ self-rated health are facility elements, maintenance and shelter, natural elements, and pathway elements. This may be due to the relatively compact space of such residential areas, which limits residents’ outdoor activities to a certain extent, making them more concerned about cleanliness, completeness of facilities, and smoothness of pathways in the residential areas. The path elements within residential areas, especially the type of path, road conditions, and safety [82,83,84], are important factors that affect residents’ walking activities, especially for elderly people in residential areas who mainly walk [85,86]. In addition, the improper parking of cars can hinder people’s walking ability, reduce accessibility and road smoothness, and exacerbate the sense of chaos in the environment, damaging people’s walking experience. Therefore, in the planning and design of pedestrian environments and green spaces, the principle of fair distribution must be incorporated to ensure that everyone has equal access [3,87].

In residential areas with low levels of greenery, the impact of facilities’ configuration and environmental maintenance on residents’ physical and mental health is more prominent. This type of residential area is mostly old residential areas and affordable housing, with relatively more elderly people. Due to the limited vegetation area and weak natural environment foundation, residents pay special attention to community cleaning and fitness facilities in order to actively participate in outdoor activities. Therefore, here it is important to maintain the healthy growth and beauty of plants, keeping the environment clean and tidy; strengthen safe fitness and leisure facilities; and moderately add some green shading and green plant fences to enhance visual comfort and reduce heat conduction effects, further improving the comfort and security of residential areas [88]. This can not only increase residents’ enthusiasm for activities and visiting time to promote mental health [38,39] but also attract people to participate in healthy sports [89,90], such as walking, running, square dancing, physical exercise, etc., thereby improving physiological health benefits [91].

Additionally, the findings of the regression analysis (Table 7) show that, in addition to environmental influences, demographic characteristics also significantly influence health status. Generally speaking, aging is linked to a decline in physiological function, whether or not living in residential areas with vegetation coverage. Residents with higher education levels in high-vegetation-coverage areas tend to have a better physical health. This may be because they are more likely to choose high-quality living environments, are generally more health-conscious, and adopt healthier lifestyles. In areas with medium-high vegetation coverage, higher monthly incomes are linked to better health, possibly due to people’s improved access to quality healthcare services and healthier living conditions. In residents with low-medium and low vegetation coverage, men generally exhibit better physical health than women, which may reflect women’s higher health sensitivity in low-greening environments. In low-vegetation residents, people with a higher education demonstrate better mental health, possibly due to stronger psychological resilience and greater access to social support networks. Therefore, in residents with different levels of vegetation coverage, it is necessary to implement targeted optimization strategies while also placing greater emphasis on individual health management.

4.2. Policy Recommendations

The purpose of this study is to examine the health effects of residential area landscapes and to highlight their importance. The beneficial impacts of green space exposure indicators, such as the amount [92], accessibility [93,94], and availability [95,96] of green spaces, on locals’ health have been the subject of numerous studies in recent years. However, not all locals can enjoy high-quality urban green areas due to their uneven geographical distribution, particularly low-income groups that have fewer opportunities to reach them [97,98,99].

The spatial quality of residential green spaces, a type of green space that inhabitants often interact with, has a direct impact on residents’ physical and mental health. This study serves as additional evidence of how urgent it is to enhance the green spaces’ environmental quality in residential areas. To improve the general health of the population, policy interventions should instruct planners to use differential optimization in residential areas with varying amounts of greening coverage. Thus, to maximize the quality of green spaces in future residential areas, we advise the following three ideas:

(1)

Differential optimization of green space elements. Optimize green space elements based on the characteristics of residential regions with varying amounts of greening coverage. In residential areas with a lot of greenery, efforts should be made to improve the quality of cultural signage and natural landscapes to add to the distinctiveness of the living space. For residential neighborhoods with a moderate level of greening, all aspects of green space elements should be thoroughly maximized, particularly pathway safety and natural landscape visibility. For residential areas with low levels of greenery, priority should be given to updating and maintaining facilities, and efforts should be made to enhance the cleanliness and safety of green spaces;

(2)

Encourage policy advice to ensure an equal green space distribution. Policy guidelines can be used to reasonably develop and construct high-quality green space resources around residential areas with an inadequate internal green space quality or limited improvement potential. This strategy not only compensates for a lack of green spaces in residential areas but also improves the accessibility and attractiveness of nearby green spaces, encouraging residents to participate in more outdoor activities and thus improving their mental and physical health. Moreover, a well-planned layout of high-quality green spaces in the surrounding areas can relieve the green space pressure in densely populated residential areas and eliminate unfairness in the distribution of green space resources among communities;

(3)

Utilize greenway connectivity to optimize green space. Due to the low level of green coverage and the challenge of large-scale green space development, community greenways can be established to efficiently connect the dispersed green space resources in residential areas with a lot of old neighborhoods or a lack of green space resources (like the Jingsong sub-district). This not only increases the amount of green plants in the sub-district region and improves its ecological environment, but it also improves the aesthetic appeal and quality of green spaces by introducing a varied range of vegetation. At the same time, well-planned greenways can dramatically improve green space accessibility by allowing inhabitants to access high-quality greening resources within a short distance, enhancing outdoor activity options and convenience. In places with limited green space, this greenway connection can create a convenient walking network, maximize walking enjoyment, and increase the effectiveness of green space utilization, all of which will improve residents’ living conditions, foster their physical and mental well-being, and raise their standard of living in general.

4.3. Limitations

Even though this study provides new insights, there are still several limitations that need to be addressed in future studies. First, in terms of greening measurement, this study divided residential areas into four categories based on the natural distribution characteristics of the NDVI, which better reflects the gradient characteristics of greening differences. Future studies can further optimize the classification system based on the existing NDVI grouping and combine greening indicators such as GVI that reflect the perspective of residents. This multi-dimensional stratification method can help improve the accuracy in the characterization of greening exposure and provide support for an in-depth analysis of the relationship between greening and health. Second, although this study explored the association between environmental characteristics and health, the relevant mechanisms have not been fully revealed. In the future, the potential causal path can be further clarified by introducing longitudinal data and richer variables. Finally, the scope of analysis of this study is mainly limited to green spaces within residential areas. Further studies can integrate external green spaces such as parks and greenways to explore the compensatory effects and spatial spillover effects of urban green infrastructure, to achieve a more comprehensive evaluation of health benefits at different spatial scales.

5. Conclusions

Based on the five types of residential types developed under China’s housing system, this study selected 21 typical residential areas in the central urban area of Beijing, classified them according to the NDVI (reflecting vegetation coverage), and systematically examined the associations between RGS environmental elements, residents’ usage behavior, and residents’ self-rated health. The findings indicate the following: (1) Residents’ perceptions and use of RGS elements may be associated with their physical and mental health. The environmental elements appear to vary across residential areas with different levels of vegetation coverage, suggesting the need for context-specific improvement strategies. (2) In areas with higher residential vegetation, the presence of a rich natural environment seems to relate more closely to mental health, while time spent in green spaces may be more relevant to physical health. (3) In residential areas with lower vegetation coverage, the frequency of activities tends to show a stronger association with physical health. In such areas with relatively limited green resources, improving maintenance and shelter functions, optimizing the configuration of facilities, and encouraging more frequent use may help support health outcomes. Unlike previous studies that primarily emphasized the quantity of greenery, this research places greater focus on the environmental quality of residential green space and considers how different environmental features are related to health under varying levels of vegetation coverage. This study broadens current theoretical perspectives on the residential environment–health relationship and offers insights for optimizing management and health-promotion strategies in high-density urban neighborhoods, thereby contributing to their sustainable development.