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Article

Disparities in the Health Benefits of Urban Green/Blue Space: A Case Study from Shandong Province, China

by
Xinrui Wang
1,
Jian Lin
1,
Xuemeng Sun
1,
Yutong Zhang
1,
Hiutung Wong
1,
Libin Ouyang
1,
Lin Liu
2 and
Longfeng Wu
1,*
1
College of Urban and Environmental Sciences, Peking University, No. 100, Zhongguancun North Street, Haidian District, Beijing 100871, China
2
Institute of Governance, Shandong University, 72 Binhai Ave., Qingdao 266237, China
*
Author to whom correspondence should be addressed.
Land 2023, 12(4), 900; https://doi.org/10.3390/land12040900
Submission received: 21 February 2023 / Revised: 30 March 2023 / Accepted: 4 April 2023 / Published: 18 April 2023
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Abstract

:
This study examined the relationships between different types of urban green and blue space (UGS/UBS) and self-reported health (SRH), and the disparities in the health benefits associated with them. Using data from a social survey in Shandong Province and multi-source data including remote sensing land use and vector polygons of parks from map service providers, we measured the proximity and coverage ratio of various UGS/UBS types. The Euclidean distance measures the proximity of homes to parks and rivers. The coverage ratio measures the total green space, forests, grassland, and freshwater. The health benefits were gauged by SRH. We found that the proximity to parks and rivers had a positive influence on the SRH of all the respondents. For the elderly, the proximity to parks and the coverage ratio of total green space and grassland within a 0.5 km circular buffer were significantly associated with SRH. The coverage ratio of the total green space and grassland both had positive relationships with the SRH of the high-income groups. The closer they were to rivers, the healthier the youth and females were. Our results suggest that urban planners should take the types of UGS/UBS into account to create a better living environment that optimally benefits residents’ SRH.

1. Introduction

Urban green space and blue space provide important ecosystem services and benefit human well-being [1,2]. Urban green space (UGS) is natural or artificial outdoor areas consisting of vegetation, such as community gardens, forests, street trees, and parks [3]. Urban blue space (UBS) is considered a hydrographic feature including waterbodies (e.g., rivers and lakes) or water drainage networks (e.g., canals and ditches) [4]. Urban green space improves the living environment by regulating the urban climate, mitigating air pollutants, and reducing noise levels [5]. Urban green space also provides places for residents to relax, exercise, and promote social interaction, which are beneficial for the physical health and mental health of residents [6]. Meanwhile, studies have suggested that urban blue space could enhance health by increasing physical activity levels [7], improving psychological restoration, and reducing distress [8,9].
Various studies about “green/blue-health” relations identified the discrepancies of health benefits on different demographical and socio-economic groups of age, sex, education degree, and socio-economic status. For instance, the connection between green spaces in residential areas in the Netherlands and public health conditions was more pronounced among specific groups such as housewives, the elderly, and lower-educated populations compared to other groups. [10]. Another study from the Netherlands showed that the elderly, youth, and lower socioeconomic and secondary educated groups benefit more from green areas than other groups in large cities [11]. Green space could lower men’s mortality rates according to a UK study; however, no such effects prevailed for women [12]. In Utah, USA, people of lower socio-economic status and Hispanic respondents lived closer to the urban blue space, but higher socio-economic status and White respondents visited the blue space more frequently and potentially gained more health benefits from the waterbodies [13].
To further explore the discrepancies of health-related benefits of UGS/UBS, emerging studies investigated how public accessibilities, various types, and different functions of UGS/UBS were associated with public health. Research from the UK indicated that parks with young-people-targeted facilities and private gardens are both important to children’s physical activity [14]. Exposure to forests, farmland, gardens, grassland, and the coast in Great Britain was found to have positive associations with people’s health, with the forests and grassland showing the highest effects [15]. Another study conducted in Scotland found that while adults’ overall health condition was related to the visitation of green spaces, frequent visits to various types of water bodies such as rivers and canals were associated with better mental health [16]. On the whole, studies that examined how different types of UGS/UBS matter in health outcomes noted that more comprehensive consideration of UGS/UBS types is necessary, which could avoid treating UGS/UBS as a homogenous entity [14].
While precedent studies have suggested that the “green/blue space-health” relationships vary based on demographic and socioeconomic characteristics [17], and have further examined different types and characteristics of UGS/UBS, these studies were mostly conducted in developed countries. Therefore, it remains unclear whether similar observations hold in China due to the limited number of relevant studies. Furthermore, research on the health effects of freshwater within urban areas is scarce [18,19], especially in the Chinese context. Additionally, investigations on the relationships between people’s general health and UGS/UBS in China often relied on measurements by using the centroid or boundary of residential neighborhoods, which could not fully reflect UGS/UBS conditions around the home and thus may lead to biased estimations [20].
Against this background, our research expanded the current studies in the following aspects. First, we explored how different types of UGS/UBS (total green space; forests; grassland; parks; freshwater; rivers) are associated with residents’ health. Second, we further analyzed whether these relationships differed by various demographic and socioeconomic attributes of age, gender, and income levels, to find potential disparities in the health benefits. The questionnaire survey data we used involved a large sample of urban residents in Shandong Province. What is more, we measured UGS/UBS indices based on finer spatial scales of personal residential addresses to reflect actual nearby green/blue space. Our results could provide planning recommendations aiming to encourage the effective utilization of green and blue space for promoting public health.
The structure of this paper is as follows: Section 1 is the introduction to the whole article. Section 2 describes the study area. Section 3 includes three parts which are data processing, the measures of variables, and the analytical methods. Section 4 is the results of the paper including the main findings and the sensitivity analyses. Section 5 discusses the divergent effects of different types of UGS/UBS on the SRH of all residents and subgroups. Section 6 concludes the findings of this study.

2. Study Area

The study area is China’s Shandong Province and Figure 1 shows the location and sample distribution in our study. Shandong has a seasonal temperate semi-humid monsoon climate, with moderate rainfall and good photothermal conditions [21]. At the end of 2017, Shandong Province had a total population of 101.7 million [22], making it the second most populous province. Its GDP was 7263.4 billion CNY, making it the third-largest province in China. Meanwhile, the urbanization level of Shandong Province is higher than the average, with an urbanization rate of 60.58%, although economic development is unbalanced within the province [23]. The urban population density was 634 people per square kilometer, ranking first in 2017. As to urban greening, Shandong also has an above-average level. The area of parks is 63,042 hectares, second only to Guangdong Province. What is more, the overall green space in urban area is 235,690 hectares, accounting for 1.5 percent of the total land area of the province [24].
Shandong Province has been attaching great importance to the construction of urban green space. The provincial government emphasized that Shandong should safeguard green and blue spaces to improve the living environments of the residents [25]. In 2017, Shandong Province stepped up efforts to build green space in cities, issuing several related plans and policies to prioritize the comprehensive benefits and the equilibrium of the layout of UGS, highlighting the improvement of urban green space planning and relevant benefits to residents [26].

3. Measures and Methods

3.1. Data Processing

The Shandong General Social Survey (SGSS) is a comprehensive and continuous large-scale social research project led by Shandong University. Since its inception in 2017, it collected comprehensive socio-economic data in Shandong Province for three consecutive years. The respondents were individuals over 18 years of age who had lived in Shandong Province for more than 6 months at the time the survey was taken. All 17 prefecture-level cities of Shandong Province were included in the sampling frame, and 52 districts/counties and 199 communities were selected by a multi-stage sampling method. The survey recorded the detailed address locations and socio-economic attributes of the residents. In this study, we used the 2017 wave SGSS with a total sample number of 4312.
Based on the detailed address location, we were able to identify the longitude and latitude by searching a mapping service provider Gaode Map. To ensure the accuracy of the data, we further manually compared the names of the addresses in the survey data with the actual names of the addresses on the Gaode Map. Given that our focus was urban areas, the samples in rural areas and those with missing information were excluded, and a total of 1208 urban samples were finally used in the analyses.

3.2. Measures

3.2.1. Dependent Variable

We used a widely implemented indicator—self-reported health (SRH)—for assessing the current health status of individuals. The respondents were asked to evaluate their current health condition, and they responded with one of the following five categories: very bad; bad; fair; good; or very good, which were coded into a five-point Likert scale. All respondents answered this question. Despite SRH being measured by a single item, it has been proven to be a robust predictor of people’s general health condition, and it is found to be highly correlated with other physical and psychological dimensions of health and is a reliable predictor of all-cause mortality, morbidity, and mortality for a range of diseases [27,28,29,30]. A one-item question is easy to implement in a survey and is favorable among urban planners to assess the health benefits of the built environment [31,32,33].

3.2.2. Key Variables

Our study not only considered all types of urban green space and blue space but analyzed different types separately. Urban green space is gauged by total green space that includes all possible green space in the urban area and different types including forests, grassland, and park. Urban blue space was reflected by total freshwater and rivers. The data for measuring total green space, total freshwater, and green space types (forests and grassland) was extracted from the finer resolution observation and monitoring global land cover dataset (2017) with 10 m resolution [34]. The land cover of this dataset was classified into ten types, including cropland, forests, grassland, shrubland, wetland, water, tundra, impervious, barren, and snow/ice. We considered forests, grassland, and shrubland as urban total green space and defined water land use as total freshwater. We also measured forests and grassland separately to differentiate the types of green space. Due to the very small proportion of shrubland (0.13%) within 0.5 km, we excluded the shrubland from the measurement. The parks’ locations and boundaries were extracted from Google Maps in 2017, which included publicly accessible ones only and excluded all gardens in gated private or institutional premises or green spaces on university campuses that restrain the entry of all residents. The rivers of Shandong Province came from the Chinese watershed and the river networks dataset [35].
We utilized the following indicators based on previous studies to measure UGS/UBS: (1) the proximity to parks and rivers, measured by the Euclidean distance from the respondent’s home location to the boundary of the nearest parks or rivers; (2) the coverage ratio was used to measure the quantity of total green space and freshwater, forests, grassland, and parks within a 0.5 km circular buffer around each respondent’s address; and (3) the number of parks were also counted within a 0.5 km circular buffer around each respondent’s home location. We also calculated the coverage ratio of total green space, forests, grassland, parks, freshwater, and the number of parks within 1 km, 1.5 km, and 3 km circular buffers for the sensitivity test. All indicators were treated as continuous variables except for the coverage ratio of parks and total freshwater within the 0.5 km circular buffer because of their large number of 0 values. We treated the coverage ratios of the parks and freshwater within the 0.5 km circular buffer as dummy variables according to their existence or absence.

3.2.3. Covariates

The following variables were included as covariates in our models: (1) demographical and socio-economic indicators: age; gender; marital status; children; annual total personal income level; household registration; education level; housing property; housing area level; family car; and exercise frequency; (2) neighborhood indicators: community type; and townships and urban sub-districts; and (3) a time indicator: the month in which the participants were surveyed to control for seasonal variance. There were 277 missing values for the income variable, so we treated the missing values as other classes in our analysis to include more observations, and the omissions of other covariates were low. Table 1 shows the statistical description of the dependent variable, key variables, and covariates.

3.3. Analytical Methods

We used ordinary least squares (OLS) regression models for analysis based on the following equation.
SRHi = β0 + β1Ki + β2Di +β3Ni + β4Ti + εi
More specifically, SRHi is the dependent variable, representing the level of self-rated health of respondent i. The vector Ki represents key variables including green and blue space indicators. The vector Di represents the demographic and socio-economic indicators. The vector Ni represents the neighborhood indicators, and the vector Ti represents the time indicator. We run the regression models in all samples and different subgroups defined by age, gender, and income level and confirmed that there was no obvious multicollinearity among those independent variables and the mean VIF (variance inflation factor) was lower than 10, and the distribution of the error term (εi) was accorded with normal distribution.

4. Results

4.1. Main Findings

Figure 2 and Table 2 show the regression results of all respondents and Figure 3 shows the regression results of different subgroups. The full regression results of the stratification analyses are available in the Supplementary Material Tables S1–S5.
Base Model 1 in Table 2 showed the effects of demographic and socio-economic indicators as well as other covariates on SRH from all samples. We found that having children, earning a high income, living in a large house, and frequently exercising had positive effects on the respondents’ SRH, while age had a negative effect on the respondents’ SRH. The results of Model 2 and Model 3 showed that the distances to parks and rivers were negatively associated with the SRH of all respondents when the covariates were controlled. The results of Model 4, Model 5, and Model 6 show that the coverage ratio of total green space, freshwater, forests, grassland, and parks, as well as the number of parks, had nonsignificant effects on the SRH of all respondents. The estimated coefficients were also plotted in Figure 2.
Figure 3 presents the results of regression analyses on various subgroups regarding different types of green and blue spaces. The distance to parks was found to be negatively related to the SRH of the elderly, as shown in Figure 3A. In Figure 3B, a statistically significant relationship was observed between the distance to rivers and the SRH of the youth and females. The results in Figure 3C indicate a positive association between the coverage ratio of total green space and the SRH of the elderly population and individuals with a high income. Figure 3D shows that the coverage ratio of grassland was positively associated with the SRH of the elderly and residents with a high-income level.
Conversely, there were no associations between the distance to parks and the SRH of middle-aged, youth, male, female, high-income, or low-income individuals, as shown in Figure 3A. Similarly, Figure 3B shows no significant relationships between the distance to rivers and the SRH of the old, adults, and males nor were there any associations between the distance to rivers and the SRH of residents with different income levels. In Figure 3C, the coverage ratio of total green space tended not to be related to the SRH of middle-aged, youth, male, female, or low-income individuals. The coverage ratio of freshwater had no relationship with the SRH of any subgroups. Finally, in Figure 3D, the coverage ratio of the forests had no relationship with the SRH of any subgroups, and the coverage ratio of forests and grassland appeared not to be related to the SRH of the middle-aged, youth, male, female, and low-income individuals. Neither the coverage ratio of parks nor the number of parks had any associations with any subgroups’ SRH, so these results were not reported.

4.2. Sensitivity Analysis

We checked whether the relationships between UGS/UBS and SRH varied across the different buffer sizes. According to Tables S6–S14 in the Supplementary Material, the 1 km, 1.5 km, and 3 km circular buffers were chosen for sensitivity analysis. The analysis results showed that the relationships between UGS/UBS and SRH within the 1 km buffer were consistent with the 0.5 km circular buffer, and no significant associations were identified within the 1.5 km or 3 km circular buffer. Different distance thresholds were tested regarding the proximity to parks and rivers. This is because there were samples with very large distances to parks and rivers that might lead to biased results. In the models with total population and subgroups, we included only the samples within 5 km of parks and 5 km of rivers [32,36]. Following previous studies [37,38], we also tested 3 km, 4 km, and 10 km thresholds for parks and 10 km for rivers, and the results were largely consistent.

5. Discussion

This study has examined associations between UGS/UBS and self-reported health and how the relationships varied among different subgroups of urban residents by using a social survey in Shandong Province, China. In general, we found that different types of green/blue space had varying associations with the respondents’ SRH. Of particular interest was whether specific green/blue spaces would have different effects on the subgroups. The results indicated that the proximities to parks and rivers had positive relations with the SRH of all residents in the sample. In the stratification analyses, the SRH of the elderly was significantly associated with their proximity to parks. Living closer to rivers had significant and positive impacts on the SRH of the youth and females. Meanwhile, the SRH of the elderly and residents with a high-income level was significantly associated with the coverage ratio of total green space and grassland within a 0.5 km buffer.

5.1. Effects of Different Types of UGS/UBS on the SRH of All Residents

Closer proximity to parks was positively related to SRH in our research, and this finding is consistent with previous research [39,40]. Living close to parks could encourage more physical activity, which significantly contributes to residents’ health by reducing the risk for all-cause mortality and numerous chronic diseases [39], as well as decreasing the probability of adult obesity [41]. Visiting a park could help to relieve stress and enhance contemplation, contributing to residents’ psychological well-being [42]. Additionally, parks often serve as places for people to socialize, and frequent social contact could effectively improve social well-being [43,44]. Surprisingly, the coverage of parks within the 0.5 km buffer had no significant effects on SRH, which is not consistent with studies from Beijing, China [45,46]. One possible explanation was that both urban areas and peri-urban areas were included in our study, and the coverage ratio of parks in peri-urban areas was small, leading to a merely 23.7% coverage ratio of parks within a 0.5 km circular buffer.
There are a few nuances compared with studies conducted in other countries. The results of our study indicated that the total green space coverage ratio and SRH did not show any significant relationship. One potential explanation might be that the total green space coverage ratio could not fully reflect specific functionalities, characteristics, and quality, such as sports facilities and species diversity [47,48]. Another explanation might be due to the different definitions of green space in urban land use in different countries [33,49]. For instance, we did not include agricultural land use in our analyses as it is not considered green space in China. However, other countries such as the Netherlands might consider agricultural green space to be a major component of UGS, which could improve health outcomes [50]. Furthermore, green space mainly provides aesthetic functions in many Chinese cities, while residents in Western countries could have private gardens allowing gardening activities that might play an important role in promoting physical activities for better health [51,52].
Specific vegetation types (i.e., forests and grassland) were not related to the SRH of all residents according to our findings, which contrasts with studies that indicated exposure to grassland or forests were associated with better health condition [15,48]. The minimal effects in our study might be related to the proportion of forests in our study being small, 14.31%, within the 0.5 km buffer and 20.9% within the 1 km circular buffer. Moreover, different spatial patterns of forests or grassland might be related to people’s active usage or other health-related benefits such as air pollution reduction, which could also have different effects on health [53,54]. This warrants a future study but is beyond the scope of our research.
Our study found that living at a closer distance to rivers was associated with better SRH, and this is similar to previous studies [16,55]. Rivers are known to improve residents’ health in multiple ways. First, rivers could help people relieve stress, and actively visiting rivers could make them feel refreshed and happy [56]. Second, the open space along rivers supports low-level physical activities, such as walking [57]. Moreover, rivers can serve as large open spaces to help air circulation and bring fresh air from peripheral areas to nearby residents [58]. The results of our study show that the freshwater coverage ratio and self-reported health were not related. This may be caused by the non-accessibility of water bodies in this study area, such as the fountains or ponds within inaccessible gated premises, which could inhibit residents’ actual use [59]. A lack of consideration for specific water types and water quality might also lead to this outcome.

5.2. Effects of Different Types of UGS/UBS on the Different Subgroups

Our findings indicated that the health and UGS/UBS associations differed across the subgroups. For the elderly, the proximity to parks, total green space, and grassland were positively correlated with their SRH. Having more access to parks can provide the elderly with space to conduct physical exercise, relax, and have sufficient social contact, which could promote their physical and physiological health [60]. In addition, it has been confirmed that exposure to green space could enhance health even if people are not actively using green space [61,62]. Due to physical limitations, older people may have a lower capability to frequently use green spaces. However, by living in a greener environment, they have a higher chance of viewing green spaces from their windows, which can benefit their health [60].
Our results show that grassland (but not forests) could promote the SRH of the elderly, which is consistent with studies indicating grassland can bring about more health benefits to older people than forests [63,64]. Grassland is better maintained in terms of quality compared with other types of vegetation in northern parts of China. The higher quality can encourage older people’s usage of green space and thus bring about more health benefits [63]. Apart from the limited area of forests within 0.5 km of older people’s homes, the dense tree within forests hinders the use availability, especially among older people who are less active or willing to walk or gather within forests [65], thus marginalizing the health benefit.
The study indicated that the health benefits of the coverage ratio of total green space were significant among the high-income group. One possible explanation was that the high-income group often lives in high-end residential communities that have high-quality internal green space proxied by total green space coverage and grassland in our study. The high-quality green space can provide greater aesthetic value and diverse functionalities for the residents which significantly promote their SRH [66]. For lower-income people, the overall quality of green space around their community is usually low, which discourages their active usage and subsequently brings about marginal health benefits [67].
The proximity to rivers was positively associated with the SRH of the youth and females in our study, which is per the previous evidence that youth and female residents could benefit more from blue space [48,68]. Research has found that females could gain more health benefits from living closer to natural elements such as rivers [48]. Such significant benefits could be explained by the gender characteristics that moderate the health effects of rivers. Female residents tend to value blue space more than males especially when the environmental quality along rivers is high, thus gaining more restorative and stress reduction functions and resulting in higher SRH [18,69]. Such characteristic differences could also explain rivers’ significant health effects on younger people. Studies have found that younger people are more willing to socialize or perform physical activities along riversides than other age groups, which explains the significant health outcomes of rivers [18].

6. Conclusions

We studied the relationships between urban residents’ SRH and different types of UGS/UBS in Shandong Province. We further explored the different relationships in seven subgroups defined by age, gender, and income level. We found different types of green space could generate heterogeneous effects on residents’ health. For the total samples, the proximity to parks and rivers had positive effects on residents’ SRH. Further stratification analyses identified potential disparities in the health benefits of green/blue space. A closer distance to parks and a higher coverage ratio of total green space and grassland were significantly associated with the higher SRH of the elderly. The coverage ratios of total green space and grassland were positively related to high-income residents’ SRH. Closer proximity to rivers was related to the higher SRH of the youth and females. Other green and blue spaces showed non-significant associations with SRH. The results suggest that urban planners should take into account the various types of urban green and blue spaces, as well as the diverse health benefits they provide to different individuals when optimizing residents’ self-rated health through the implementation or renewal of such spaces.

Supplementary Materials

Supplementary data associated with this article can be found in the online version at: https://www.mdpi.com/article/10.3390/land12040900/s1. Table S1: Distance to parks for subgroups; Table S2: Distance to rivers for subgroups; Table S3: Results of regressions of subgroups for SRH (coverage ratio of green space and freshwater); Table S4: Results of regressions of sub-groups for SRH (coverage ratio of forest and grassland); Table S5 Results of regressions of sub-groups for SRH (Coverage ratio and number of parks); Table S6: Sensitivity test of coverage ratio of greenspace and water within 1 km; Table S7: Sensitivity test of coverage ratio of greenspace and water within 1.5 km; Table S8: Sensitivity test of coverage ratio of greenspace and water within 3 km; Table S9: Sensitivity test of coverage ratio of grassland and forest within 1 km; Table S10: Sensitivity test of coverage ratio of grassland and forest within 1.5 km; Table S11: Sensitivity test of coverage ratio of grassland and forest within 3 km; Table S12: Sensitivity test of the coverage ratio of park within 3 km; Table S13: Sensitivity test of the coverage ratio of parks and the number of parks within 1 km; Table S14: Sensitivity test of the coverage ratio of parks and the number of parks within 1.5 km.

Author Contributions

Conceptualization, L.W., X.W. and J.L.; methodology, L.W., X.W. and J.L.; software, X.W. and X.S.; validation, L.W., X.W. and J.L.; formal analysis, L.W., X.W., J.L., X.S. and L.O.; resources, L.L., J.L. and L.W.; data curation, X.W., X.S. and L.W.; writing—original draft preparation, X.W., J.L., X.S. and Y.Z.; writing—review and editing, L.W. and J.L.; visualization, H.W., Y.Z. and L.O.; supervision, L.W. and J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation project (42171247).

Data Availability Statement

The Shandong General Social Survey (SGSS) data were obtained from the Institute of Public Governance, Shandong University, and are available from Lin Liu with the permission of the Institute of Public Governance, Shandong University.

Conflicts of Interest

The authors declare no conflict of interest.

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Land 12 00900 g001 550
Figure 1. The number of samples used in our study (n = 1208) aggregated at the prefecture city level within Shandong Province.
Figure 1. The number of samples used in our study (n = 1208) aggregated at the prefecture city level within Shandong Province.
Land 12 00900 g001
Land 12 00900 g002 550
Figure 2. The regression results of all respondents. Note: Model 2: Distance to Parks; Model 3: Distance to Rivers; Model 4a: Coverage Ratio of Green Space; Model 4b: Coverage Ratio of Freshwater; Model 5a: Coverage Ratio of Forests; Model 5b: Coverage Ratio of Grass; Model 6a: Coverage Ratio of Parks; and Model 6b: Number of Parks.
Figure 2. The regression results of all respondents. Note: Model 2: Distance to Parks; Model 3: Distance to Rivers; Model 4a: Coverage Ratio of Green Space; Model 4b: Coverage Ratio of Freshwater; Model 5a: Coverage Ratio of Forests; Model 5b: Coverage Ratio of Grass; Model 6a: Coverage Ratio of Parks; and Model 6b: Number of Parks.
Land 12 00900 g002
Land 12 00900 g003 550
Figure 3. The regression results of the different subgroups. Note: (A) Coefficients between SRH and park distances by subgroup. (B) Coefficients between SRH and river distances by subgroup. (C) Coefficients between SRH and the coverage ratio of green space and freshwater by subgroup. (D) Coefficients between SRH and the coverage ratio of forests and grassland by subgroup.
Figure 3. The regression results of the different subgroups. Note: (A) Coefficients between SRH and park distances by subgroup. (B) Coefficients between SRH and river distances by subgroup. (C) Coefficients between SRH and the coverage ratio of green space and freshwater by subgroup. (D) Coefficients between SRH and the coverage ratio of forests and grassland by subgroup.
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Table
Table 1. Descriptive statistics of the variables.
Table 1. Descriptive statistics of the variables.
No. of Observations: 1208
VariablesDescriptionsMeanS.D.MinMax
Self-reported health (SRHi)Measured on a scale from 1“Very bad” to 5 “Very good”4.0300.9701.0005.000
Demographic and socio-economic indicators (Di)
AgeMeasured in years48.06217.26918.00096.000
Genderdummy: 1 = male, 0 = female0.4590.4980.0001.000
Marital status
Unmarrieddummy: 1 = unmarried, 0 else0.1300.3360.0001.000
Marrieddummy: 1 = married, 0 else0.7860.4110.0001.000
Divorceddummy: 1 = divorced, 0 else0.0150.1210.0001.000
Widoweddummy: 1 = widowed, 0 else0.0700.2540.0001.000
Children
No childrendummy: 1 = no, 0 else0.1500.3570.0001.000
Have childrendummy: 1 = have, 0 else0.8440.3630.0001.000
No answer about having childrendummy: 1 = no answers, 0 else0.0060.0760.0001.000
Income level (unit: CNY)
0 ≤ Income ≤ 5000dummy: 1 = yes, 0 else0.1930.3950.0001.000
5000 < Income ≤ 30,000dummy: 1 = yes, 0 else0.2520.4340.0001.000
30,000 < Income ≤ 50,000dummy: 1 = yes, 0 else0.1600.3670.0001.000
50,000 < Income ≤ 100,000dummy: 1 = yes, 0 else0.1660.3730.0001.000
No answer about incomedummy: 1 = no answers, 0 else0.2290.4210.0001.000
Household registration
Agricultural residencedummy: 1 = agricultural, 0 else0.4850.5000.0001.000
Non-agricultural residencedummy: 1 = non-agricultural, 0 else0.5120.5000.0001.000
Residence is uncleardummy: 1 = no answers, 0 else0.0020.0500.0001.000
Education level
Uneducateddummy: 1 = yes, 0 else0.0770.2670.0001.000
Primary school or junior high schooldummy: 1 = yes, 0 else0.4170.4930.0001.000
Senior high schooldummy: 1 = yes, 0 else0.2200.4150.0001.000
Junior college or abovedummy: 1 = yes, 0 else0.2820.4500.0001.000
No answer about education leveldummy: 1 = no answers, 0 else0.0030.0570.0001.000
Housing property
Do not own a housedummy: 1 = own, 0 else0.3610.4800.0001.000
Own a housedummy: 1 = not own, 0 else0.6270.4840.0001.000
No answer about housing propertydummy: 1 = no answers, 0 else0.0120.1110.0001.000
Housing area level (unit: m2)
Housing area ≤ 71dummy: 1 = yes, 0 else0.2330.4230.0001.000
71 < Housing area ≤ 90dummy: 1 = yes, 0 else0.2810.4490.0001.000
90 < Housing area ≤ 110dummy: 1 = yes, 0 else0.1970.3980.0001.000
Housing area > 110dummy: 1 = yes, 0 else0.2220.4160.0001.000
No answer about housing area leveldummy: 1 = no answers, 0 else0.0670.2500.0001.000
Family car
Do not own a cardummy: 1 = not own, 0 else0.5030.5000.0001.000
Own a cardummy: 1 = own, 0 else0.4880.5000.0001.000
No answer about car ownershipdummy: 1 = no answers, 0 else0.0090.0950.0001.000
Exercise frequency
Never exercisedummy: 1 = yes, 0 else0.3290.4700.0001.000
Exercise several times a yeardummy: 1 = yes, 0 else0.1800.3850.0001.000
Exercise several times a monthdummy: 1 = yes, 0 else0.1170.3210.0001.000
Exercise several times a weekdummy: 1 = yes, 0 else0.1870.3900.0001.000
Exercise every daydummy: 1 = yes, 0 else0.1850.3880.0001.000
No answer about exercisedummy: 1 = no answers, 0 else0.0020.0410.0001.000
Neighborhood indicators (Ni)
Community type
Inner-city communitydummy: 1 = yes, 0 else0.1750.3800.0001.000
Welfare housing communitydummy: 1 = yes, 0 else0.0990.2980.0001.000
Affordable housing communitydummy: 1 = yes, 0 else0.0220.1450.0001.000
Commercial communitydummy: 1 = yes, 0 else0.4060.4910.0001.000
Senior residential communitydummy: 1 = yes, 0 else0.0080.0910.0001.000
Community transformed from villagesdummy: 1 = yes, 0 else0.2880.4530.0001.000
Other community typedummy: 1 = other, 0 else0.0020.0500.0001.000
Green and blue space indicators (Ki)
Total green spaceCoverage ratio of total green space within the 0.5 km circular buffer0.0900.0990.0000.450
ForestsCoverage ratio of forests within the 0.5 km circular buffer0.0130.0420.0000.371
GrassCoverage ratio of grassland within the 0.5 km circular buffer0.0770.0930.0000.450
Freshwater existencedummy: 1 = exist and 0 = else (existence within the 0.5 km circular buffer)0.8050.3970.0001.000
Distance to parksDistance to the nearest parks (km)2.8736.5290.01941.025
Distance to riversDistance to the nearest rivers (km)8.2047.6660.34831.150
Parks existencedummy: 1 = exist and 0 = else (existence within the 0.5 km circular buffer)0.3400.4740.0001.000
Number of parksThe number of parks within the 0.5 km circular buffer0.1130.3880.0002.000
Note: 1. The variables controlling the townships and the urban sub-districts were entered into the analyses but are not shown in this table. Monthly dummy variables were used to control for seasonal variation but are not shown in this table.
Table
Table 2. Results of the regressions of all respondents.
Table 2. Results of the regressions of all respondents.
SRHiModel 1Model 2Model 3Model 4Model 5Model 6
Demographic and socio-economic indicators (Di)
Age−0.023 ***−0.024 ***−0.026 ***−0.023 ***−0.023 ***−0.023 ***
(0.002)(0.002)(0.004)(0.002)(0.002)(0.002)
Gender0.009−0.0050.0120.0090.0090.009
(0.055)(0.058)(0.091)(0.055)(0.055)(0.055)
Marital status
UnmarriedReferenceReferenceReferenceReferenceReferenceReference
Married−0.175−0.1600.011−0.172−0.172−0.177
(0.110)(0.114)(0.193)(0.110)(0.110)(0.110)
Divorced−0.266−0.281−0.358−0.268−0.269−0.269
(0.194)(0.197)(0.273)(0.195)(0.195)(0.193)
Widowed−0.041−0.0160.284−0.039−0.039−0.044
(0.171)(0.179)(0.309)(0.171)(0.172)(0.171)
Children
No childrenReferenceReferenceReferenceReferenceReferenceReference
Have children0.259 **0.277 **0.1510.258 **0.258 **0.263 **
(0.111)(0.114)(0.198)(0.111)(0.111)(0.112)
No answer about children0.466 *0.496 *0.895 ***0.472 *0.473 *0.478 *
(0.261)(0.257)(0.330)(0.259)(0.259)(0.263)
Income level
0 ≤ Income ≤ 5000ReferenceReferenceReferenceReferenceReferenceReference
5000 < Income ≤ 30,0000.176 **0.193 **0.224 *0.176 **0.176 **0.175 **
(0.085)(0.092)(0.130)(0.085)(0.085)(0.085)
30,000 < Income ≤ 50,0000.151 *0.1330.2360.151 *0.152 *0.150
(0.091)(0.097)(0.155)(0.091)(0.091)(0.091)
50,000 < Income ≤ 100,0000.170 *0.1610.0970.168 *0.169 *0.169 *
(0.096)(0.102)(0.159)(0.096)(0.096)(0.096)
No answer about income0.162 *0.177 *0.253 *0.159 *0.159 *0.163 *
(0.092)(0.096)(0.144)(0.092)(0.092)(0.092)
Household registration
Agricultural residenceReferenceReferenceReferenceReferenceReferenceReference
Non-agricultural residence−0.029−0.055−0.031−0.029−0.029−0.030
(0.066)(0.068)(0.108)(0.066)(0.066)(0.066)
Residence is unclear−0.153−0.124−0.550−0.155−0.154−0.147
(0.546)(0.535)(0.400)(0.544)(0.543)(0.551)
Education level
UneducatedReferenceReferenceReferenceReferenceReferenceReference
Primary or junior high school0.1360.075−0.1290.1390.1390.138
(0.124)(0.135)(0.175)(0.124)(0.124)(0.124)
Senior high school0.0930.011−0.1360.0960.0960.096
(0.134)(0.144)(0.189)(0.134)(0.134)(0.134)
Junior college or above0.1080.031−0.0980.1110.1110.111
(0.142)(0.154)(0.200)(0.142)(0.142)(0.142)
No answer about education level0.6800.5660.1840.687 *0.685 *0.681 *
(0.414)(0.428)(0.459)(0.415)(0.414)(0.413)
Housing property
Do not own a houseReferenceReferenceReferenceReferenceReferenceReference
Own a house0.0680.0410.252 **0.0680.0680.067
(0.062)(0.065)(0.102)(0.062)(0.062)(0.062)
No answer about house property−0.247−0.2490.485−0.246−0.246−0.247
(0.237)(0.232)(0.554)(0.237)(0.237)(0.234)
Housing area level
Housing area ≤ 71ReferenceReferenceReferenceReferenceReferenceReference
71 < Housing area ≤ 900.148 *0.165 ***0.0760.145 *0.145 *0.150 *
(0.077)(0.080)(0.129)(0.078)(0.078)(0.078)
90 < Housing area ≤ 1100.1030.1320.0260.1030.1020.107
(0.089)(0.094)(0.140)(0.089)(0.089)(0.090)
Housing area > 1100.1360.200 **0.1990.1350.1340.143
(0.089)(0.095)(0.139)(0.089)(0.089)(0.091)
No answer about housing area0.1150.1110.0900.1160.1160.122
(0.113)(0.118)(0.171)(0.114)(0.114)(0.114)
Family car
Do not own a carReferenceReferenceReferenceReferenceReferenceReference
Own a car0.0320.011−0.0120.0320.0320.029
(0.058)(0.061)(0.090)(0.058)(0.058)(0.058)
No answer about car ownership−0.128−0.0700.185−0.133−0.133−0.138
(0.333)(0.345)(0.525)(0.335)(0.334)(0.332)
Exercise frequency
Never exerciseReferenceReferenceReferenceReferenceReferenceReference
Exercise several times a year0.207 ***0.242 ***0.1450.205 ***0.204 ***0.206 ***
(0.076)(0.080)(0.119)(0.077)(0.076)(0.076)
Exercise several times a month0.151 *0.195 **0.0910.154 *0.153 *0.151
(0.091)(0.097)(0.159)(0.092)(0.091)(0.092)
Exercise several times a week0.205 **0.221 ***0.2000.205 **0.205 **0.206 **
(0.081)(0.084)(0.135)(0.081)(0.081)(0.081)
Exercise everyday0.331 ***0.392 ***0.243 *0.331 ***0.331 ***0.331 ***
(0.078)(0.084)(0.130)(0.079)(0.078)(0.078)
No answer about exercise0.1030.0850.2930.1050.1050.106
(0.232)(0.257)(0.278)(0.234)(0.234)(0.233)
Neighborhood indicators (Ni)
Community type
Inner-city communityReferenceReferenceReferenceReferenceReferenceReference
Welfare housing community0.1420.1950.0880.1360.1340.141
(0.121)(0.125)(0.192)(0.122)(0.123)(0.121)
Affordable housing community0.1390.1670.4000.1340.1340.128
(0.219)(0.227)(0.299)(0.219)(0.219)(0.221)
Commercial community0.0960.1290.1810.0940.0930.094
(0.096)(0.105)(0.184)(0.096)(0.096)(0.096)
Senior residential community0.1400.1521.701 ***0.1320.1310.124
(0.331)(0.332)(0.374)(0.331)(0.331)(0.324)
Community transformed from villages0.1110.208 *0.0970.1080.1080.115
(0.109)(0.125)(0.183)(0.109)(0.109)(0.110)
Other community type0.3800.3900.0880.3650.3650.347
(0.310)(0.320)(0.192)(0.320)(0.321)(0.318)
Green and blue space indicators (Ki)
Distance to parks −0.128 *
(0.068)
Distance to rivers −0.263 **
(0.132)
Total green space (Model 4a) 0.300
(0.581)
Freshwater existence (Model 4b) −0.013
(0.121)
Forests (Model 5a) 0.374
(0.959)
Grassland (Model 5b) 0.292
(0.713)
Parks’ existence (Model 6a) 0.063
(0.092)
Number of parks (Model 6b) 0.011
(0.126)
Constant4.770 ***4.881 ***4.853 ***4.776 ***4.762 ***4.732 ***
(0.281)(0.302)(0.457)(0.295)(0.284)(0.286)
Observations12081084505120812081208
R-squared0.3010.2930.3360.3010.3010.301
TownshipsYESYESYESYESYESYES
MonthsYESYESYESYESYESYES
Note: The robust standard errors are shown in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1
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MDPI and ACS Style

Wang, X.; Lin, J.; Sun, X.; Zhang, Y.; Wong, H.; Ouyang, L.; Liu, L.; Wu, L. Disparities in the Health Benefits of Urban Green/Blue Space: A Case Study from Shandong Province, China. Land 2023, 12, 900. https://doi.org/10.3390/land12040900

AMA Style

Wang X, Lin J, Sun X, Zhang Y, Wong H, Ouyang L, Liu L, Wu L. Disparities in the Health Benefits of Urban Green/Blue Space: A Case Study from Shandong Province, China. Land. 2023; 12(4):900. https://doi.org/10.3390/land12040900

Chicago/Turabian Style

Wang, Xinrui, Jian Lin, Xuemeng Sun, Yutong Zhang, Hiutung Wong, Libin Ouyang, Lin Liu, and Longfeng Wu. 2023. "Disparities in the Health Benefits of Urban Green/Blue Space: A Case Study from Shandong Province, China" Land 12, no. 4: 900. https://doi.org/10.3390/land12040900

APA Style

Wang, X., Lin, J., Sun, X., Zhang, Y., Wong, H., Ouyang, L., Liu, L., & Wu, L. (2023). Disparities in the Health Benefits of Urban Green/Blue Space: A Case Study from Shandong Province, China. Land, 12(4), 900. https://doi.org/10.3390/land12040900

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