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Article

The Influence of Vegetation Environment on Thermal Experience in Hot Summer: A Case Study from Perspectives of Fitting Scale and Gender Disparity

by
Chenming Zhang
1,*,
Wei Li
2,
Qindong Fan
1,*,
Jian Hu
1,
Dongmeng Wang
1,
Xiaoying Ping
3 and
Wenjie Li
1
1
College of Architecture, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
2
School of Art and Design, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
3
School of Public Administration, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
*
Authors to whom correspondence should be addressed.
Buildings 2024, 14(10), 3036; https://doi.org/10.3390/buildings14103036
Submission received: 10 August 2024 / Revised: 21 September 2024 / Accepted: 22 September 2024 / Published: 24 September 2024
(This article belongs to the Topic Climate Change and Environmental Sustainability, 3rd Volume)
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Abstract

:
Vegetation exerts a significant cooling effect, particularly during the hot summer; however, the spatial scale effects and gender difference among occupants’ subjective thermal comfort remain elusive. Developing a comprehensive model to elucidate the multidimensional relationship between green spaces and thermal experience holds paramount importance. Taking Longzi River Park in Zhengzhou city as a case study, this research examined the influence of vegetation on thermal experience by using structural equation modeling (SEM) from perspectives of fitting scale and gender disparities. It was found that (1) The vegetation environment not only influences thermal sensation, comfort and demand independently, but also influences the pathway between them. These influence paths constitute a complex causal network, functioning as a framework of “sensation → comfort → demand” and its influencing factors. (2) There exists a scaling effect in the pathway framework, which conforms to a threshold of 10 m for the inner radius and 30 m for the outer radius. The goodness of SEM model fit declines with the increase in either the inner radius or the outer radius, or both. (3) Differences in genders are exhibited for the pathway framework, with the vegetation exerting a stronger influence on female sensation and comfort, as well as male demand. The pathway from sensation to comfort to demand is more pronounced in male populations. The research findings contribute to the development of improved and sustainable vegetation distribution in urban parks.

1. Introduction

With the rapid development of economic and social progress, urban spaces are experiencing a series of environmental problems, such as climate change and heat island effects [1]. Since the frequent extreme heat has been seriously threatening residents’ daily life and their physical and mental health [2], many cities have implemented strategies aimed at enhancing ecological infrastructure and urban green space systems in order to mitigate the urban heat island effect [3,4,5]. The urban park serves as an important component of the green space system and provides a wide range of ecosystem services such as microclimate regulation and recreational amenities [6,7]. The investigation of thermal experience patterns and their influencing factors in urban parks has emerged as a focal point of research.
Thermal sensation, thermal comfort and thermal demand are subjective indicators commonly used in related research. Based on the previous studies, thermal sensation refers to the subjective perception of the thermal environment, thermal comfort refers to the state of satisfaction in a thermal environment and thermal demand refers to the level of desire for thermal environment improvement [8,9,10]. Complex correlations between these indicators exist. For example, thermal sensation has the potential to influence an individual’s emotional state, consequently impacting their assessment of thermal comfort [11]. When individuals experience cooling during the summer, they are inclined to provide more favorable assessments [12,13]. Thermal comfort is influenced by a multitude of objective and subjective external factors, making it a focal point for interdisciplinary research [14,15,16,17]. Existing studies found that environmental factors, such as temperature, humidity, wind speed, solar radiation and sky view factors, have an impact on human thermal sensation and comfort [18,19]. Moreover, Lai et al. (2020) suggest that outdoor thermal comfort is directly influenced by physical, physiological and psychological factors, and indirectly by behavioral, individual, social, and cultural factors [20]. Considering the above, the influencing factors associated with outdoor thermal comfort are multifaceted and intricate; nevertheless, the combined and interactive impacts of diverse environmental factors on thermal comfort remain elucidated.
The layout of the vegetation environment exerts cooling effects on the thermal experience and exhibits variability across different spatial scales. Previous studies suggest that the cooling effects are facilitated by two primary mechanisms: shading and evapotranspiration, which operate effectively only within a specific spatial range [21,22]. Therefore, a geo-spatial perspective is offered to elucidate the regulation of the thermal environment through various spatial arrangements (clustered, dispersed or equal intervals) of green space [23]. Such studies have found that the more dispersed and uniform the plant layout, the more conducive it is to reducing the temperature of the summer courtyard space and enhancing ventilation [24]. When considering the scale of the vegetation layout, differences emerge. For instance, Erlwein demonstrated the distinct roles of green space in regulating microclimates at both district and block scales [25]. The influencing radius constitutes a critical parameter for multi-scale studies. In a case study, the radius of the influencing buffer zone is set to 20 m, which effectively reveals the effects of park landscape on air temperature and relative humidity [26]. Additionally, another study investigated the relationship between the spatial configurations of vegetation and thermal comfort within circular buffer zones of 5 m, 10 m and 20 m [27]. Nevertheless, the scaling law governing the influencing radius of vegetation on thermal comfort remains inadequately understood.
Gender differences in thermal comfort have received a lot of attention from scholars. The findings from previous research on gender disparities in evaluating outdoor comfort have yielded inconsistent results. While certain studies have reported minimal or statistically insignificant impact of gender [28,29,30,31], others have suggested that the influence of gender varies depending on specific circumstances [32,33]. Specifically, females demonstrate a higher thermal physiological tolerance and prefer higher temperatures compared to males [34,35]. Moreover, Yang et al. elucidated the reasons behind the higher sweat production and lower thermal comfort experienced by males compared to females in warm conditions, attributing it to their elevated metabolic rates and increased skin wetness [36]. However, women may exhibit an aversion to heat and sun exposure, prompting behavioral adaptation through the use of umbrellas, hats or other shielding objects [37]. For the abovementioned studies, it is imperative to conduct further investigation into thermal comfort with a specific focus on gender-related disparities.
Based on a comprehensive review of the aforementioned studies, this research endeavors to address the following questions:
  • What is the structure of the pathway framework that encompasses thermal sensation, thermal comfort, thermal demand and their influencing factors?
  • What variations exist in the relationship between thermal experience and its vegetative surroundings across different spatial scales?
  • What are the gender-specific differences in the influence of the vegetation environment on the pathway of “sensation → comfort → demand”?
Accordingly, the flowchart of this study is presented in Figure 1. (1) Subjective thermal data in conjunction with location and digital vegetation information are gathered through a geo-spatial positioning survey and subsequent GIS analysis. (2) A theoretical hypothesis is proposed to delineate the complex interplay between the questionnaire and the spatial vegetation data set. (3) The intricate interconnections among variables are encapsulated within a structural equation modeling (SEM) framework, and the model fit of SEM across multiple spatial scales is compared to ascertain the fitting scale. (4) The model is tested to validate the theoretical hypothesis through reliability and validation testing. (5) The findings are interpreted in terms of the scale effect, pathways of influence and gender disparities.

2. Method

2.1. Study Area

The Longzi River Park is situated in the downtown area of Zhengzhou, which falls under the North Temperate Continental Monsoon Climate, characterized by hot and rainy summers, cold and dry winters, and distinct seasonal variations. The average annual temperature is 15.6 °C; during the summer months, the average daytime temperature reaches 30.0 °C, accompanied by an average wind speed of 3.0 m/s and relative humidity exceeding 70%.
This park is positioned between the third and fourth ring roads of Zhengzhou city (Figure 2a). It is proximate to a metro station and surrounded by numerous universities, residential areas, and commercial centers, resulting in a dense population concentration. The park is designed to blend ecological conservation with recreational, fitness, and entertainment amenities, providing a serene sanctuary for local residents seeking respite from the scorching summer heat.
The study identified a roughly ribbon-shaped green area located on the western bank of Longzi River (Figure 2b), ranging from 113°47′17″E to 113°47′37″E and from 34°47′9″N to 34°47′37″N, covering an area of 19.3 hectares. Based on high-resolution satellite imagery and a GIS platform, the lawn, tree canopy, buildings and pavement within the study area were digitized and mapped through a combination of visual interpretation and on-site survey verification. Subsequently, this facilitated the generation of a thematic map (Figure 2c) to establish the foundational framework for subsequent research.

2.2. Questionnaire with Positioning and Multi-Scaled Environment

A geo-spatial questionnaire survey was conducted from July to August 2023, the specific days are labeled in Figure 3 with the dynamics of outdoor thermal environment for each research day. From Figure 3, the parameters of the outdoor thermal environment remained relatively constant during the survey days. Clear evening hours (17:00–19:00) were chosen for distributing questionnaires, during which period the park experiences peak visitor numbers. Consequently, research conducted during this period can benefit a larger number of individuals, thereby enhancing the practical implications of this study.
The researchers distributed questionnaires at various locations throughout the park in a randomized manner and simultaneously recorded the latitude and longitude coordinates of respondents’ positions as they completed the surveys. The questionnaire comprises three categories of items pertaining to thermal experience: “What are your sensations for the current thermal feeling?”, “How comfortable are you feeling at the present moment?” and “To what extent does your demand for current thermal regulation?”. Each item comprises 2 to 3 sub-questions and employs a five-point Likert scale format to evaluate the extent of participants’ thermal experience (the questionnaire can be found in Appendix A).
A total of 525 valid questionnaires were collected, with 284 from male participants and 241 from female participants. The statistical results of the survey are shown in Figure A1. As illustrated in Figure A1, over 60% of participants reported feeling muggy and lacking a breeze, experienced discomfort with sweating, and expressed a strong demand for thermal regulation. All questionnaire points have been georeferenced in the GIS platform based on recorded coordinates. Subsequently, a distribution map of the questionnaires was generated and overlaid onto the digital map of the physical environment (see Figure 2c).
Moreover, a foundational research premise was postulated that the real-time thermal comfort of visitors varies with their location and is influenced by multi-scaled vegetation environments. At each of the 525 geo-questionnaire points, statistical buffer zones of varying radii are established (Figure 4). The areas of lawn and tree canopy within each radius are calculated and attributed to the central points where the geo-questionnaires are completed, thereby correlating vegetation density with thermal comfort across different radius ranges.

2.3. Construction of SEM Model and Its Applicability

2.3.1. Variables

Structural equation modeling (SEM) represents a method that integrates a structural model and measurement model for the purpose of measuring the intricate relationships and effects among multiple independent variables and multiple dependent variables [38]. In this research, the multi-scaled vegetation areas are regarded as independent variables and the subjective thermal experience is regarded as dependent variables.
Independent variables encompass two types of elements, specifically lawns and tree canopies. Given that the scale effects of the influence of the environment on the thermal experience remain unidentified, accordingly, different radii (ranging from 10 m to 60 m) were established with the questionnaire filling point as the center, and the areas of lawns and tree canopies within various radii were chosen as the observed variables. The areas of lawns and tree canopies within different radii can be calculated in the GIS platform through the following steps: first, the digital map recording lawn and tree canopy is generated from the satellite map; second, the 525 questionnaire location points are positioned onto the digital map; then, circular buffers (from 10 m to 60 m) are drawn from each of the 525 questionnaire location points; finally, the areas of lawns and tree canopies within each circular buffer can be calculated through spatial overlay and subsequently assigned to the corresponding questionnaire points. The observed variables can be explained by the latent variables within this multi-scale and variable-radius framework. In other words, if a questionnaire filling point is situated in a position surrounded by abundant vegetation, all the areas of lawns and tree canopies within a certain radius should be considerable. This resolves the issues of ambiguous spatial boundaries and undefined scales in the measurement of the vegetation environment around the questionnaire points.
Dependent variables encompass thermal sensation, thermal comfort and thermal demand. Thermal sensation refers to the subjective feeling of the thermal environment, incorporating three observed variables, namely temperature sensation, humid sensation and breeze sensation. Temperature sensation portrays the participants’ responses to air temperatures ranging from cool to hot, while humid sensation conveys the subjective experience of humidity, and breeze sensation captures the participants’ perception of a cool breeze. Each of them are ordinal variables and categorized into five distinct levels from low to high. These three variables provide distinct perspectives on thermal sensation. Collectively high values of these variables correspond to a muggy and windless thermal experience. Thermal comfort comprises two observed variables, namely comfort and sweatlessness. The former is a positive indicator, while the latter is a negative one; that is, a high level of thermal comfort corresponds to a state of being comfortable without sweating. The current thermal demand for regulation includes three observed variables, namely, cooling demand, dehumidification demand and breeze demand, all of which are positive indicators.

2.3.2. Hypothetical Path Concept of SEM

The construction of SEM began with the development of a conceptual model of hypothesis paths, which included 10 hypothesis paths (θ, γ and β), as shown in Table 1 and Figure 5.
θ, γ, β are the coefficients to be estimated for the model.
θ represents the interaction relationship among different vegetation compositions. During the design process of park greening, lawn and tree are spatially matched to achieve a better landscape effect. In other words, trees in the park are often not isolated but accompanied by the spread of the surrounding lawn. Therefore, Hypothesis 1 (H1) is formulated: the lawn coverage exerts a positive influence on the layout of the tree canopies.
γ represents the influence of environmental factors on the subjective thermal index. Considering the regulating effect of vegetation on the thermal environment, Hypothesis 2 (H2) is constructed: the lawn and tree canopy exert a reducing impact on thermal sensation, an enhancing influence on thermal comfort and a diminishing effect on thermal demand (their current thermal demands are more prone to be satisfied in regions with a higher degree of vegetation). Correspondingly, γ11, γ21, γ13 and γ23 are negative, while γ12 and γ22 are positive.
β is the influence among different thermal indicators. Hypothesis 3 (H3) is constructed: a hot–humid and windless sensation will result in poor thermal comfort, thus entailing to a greater demand for thermal improvement. Correspondingly, β12, β23 are negative, while β13 is positive.
Based on the aforementioned hypothesis, the pathway concept diagram of SEM can be depicted as Figure 5.
In Figure 5, ξ1 and ξ2 denote the latent variables estimating vegetation density, X represents the observation vector constituted by the multi-scaled vegetation measurements corresponding to ξ; η1, η2 and η3 are the latent variables related to thermal perception, and Y represents the corresponding observation vector. The residuals of the model range from e1 to e19. The measurement model delineates the relationship between latent variables ξ, η and observation variables X, Y; the structural model reveals the causal relationship among latent variables, and their expressions are respectively
X = Λ X ξ + e x
Y = Λ Y η + e y
η = B η + Γ ξ + ε
In the equation, ΛX constitutes the factor loading matrix concerning the latent variable ξ, and ΛY represents the factor loading matrix related to the latent variable η. B represents the effect coefficient matrix among different η; Γ denotes the effect coefficient matrix of multi-scale vegetation on thermal experience; ε constitutes the residual of the structural model.

2.3.3. The Applicability of SEM Model

Both the reliability test and EFA (Exploratory Factor Analysis) should be conducted before the SEM model is established to guarantee the model applicability.
(1) Firstly, a reliability test is carried out, the essence of which is to examine the consistency, stability and reliability of the initial data. Generally, the level of reliability is quantified by Cronbach’ α (≤1). The higher the value, the greater the reliability of the data. This research established a table encompassing 14 items. The Cronbach’ α values of the five latent variables among the male and female subjects were calculated respectively and listed in Table 2. It can be observed from Table 2 that the Cronbach’ α of thermal sensation, thermal comfort, thermal demand, lawn coverage and tree canopy coverage are all greater than 0.7, indicating a good reliability.
(2) Secondly, an EFA (Exploratory Factor Analysis) test was carried out on the initial data, with the aim of guaranteeing both the consistency within the same latent variables and the distinctiveness among different latent variables simultaneously. The criteria for the EFA test encompass three aspects: first, the KMO (Kaiser–Meyer–Olkin) value should exceed 0.8; second, the p-value of the χ2 statistic in Bartlett’s sphere test should be below 0.005; and third, variables sharing identical principal components should be classified into the same categories as delineated by the hypothetical model. The results of the EFA indicate that the KMO value of males is 0.814, with the p-value of the χ2 statistic being 0.000; the KMO value of females is 0.801, with the p-value of the χ2 statistic being 0.000. Furthermore, the 14 observation variables can be categorized into 5 groups based on principal component analysis, which is consistent with the classification derived from the latent variables (Table A1 in the Appendix A). This indicates that the initial data possess robust factor clustering features, and the clustering outcomes are highly congruent with the configuration of the observed variables. Both male and female subjects passed the EFA test.
Based on the above, the fundamental data are appropriate for conducting structural equation modeling.

3. Results

3.1. The Empirical Model and Fitting Scale Selection

The model was calculated through the Amos (version 4.0) software, which is a powerful tool supporting research and theories by extending standard multivariate analysis methods (“https://www.ibm.com/products/structural-equation-modeling-sem” accessed on 3 July 2024). Eventually, the empirical pathway diagram was obtained as depicted in Figure 6. Comparisons can be made between Figure 5 and Figure 6, along with the corresponding results. (1) The number of observation variables corresponding to lawn coverage and tree canopy coverage has been reduced from 3 to 2 to avoid over fitting. X2 data can be linearly represented by X1 and X3, analogous to X5 by X4 and X6. In other words, the multi-scale vegetation characteristic can be optimally depicted by employing two layers of concentric circles. (2) The pathways γ11, γ12 and γ13 presented in Figure 5 are removed in Figure 6, suggesting that the direct impact of lawn coverage on the thermal experience is not significant. Instead, lawn coverage effects thermal experience indirectly via the tree canopy. (3) The pathway β13 depicted in Figure 5 is insignificant in Figure 6, indicating that thermal sensation exerts an indirect influence on thermal demand via thermal comfort rather than a direct one, which will be elaborated on in Section 3.2.2.
A scaling study has been carried out within the framework of the two-layer concentric circles. The two-layer concentric circles are characterized by an inner radius (R1) and an outer radius (R2). The areas of the lawn and tree canopy within these radii are calculated, respectively, and integrated into the geo-questionnaire to establish a connection between the questionnaire and its surrounding vegetation in R1 and R2. The range for R1 is set at 10 m, 20 m, ..., and 50 m; the range for R2 is set at 20 m, 30 m, ..., and 60 m. By pairing the values of R1 and R2 (where R1 < R2), various scale dual-layer change scenarios are generated, with the χ2/df, GFI and RMSEA values of the SEM model for each scenario presented in Table 3. Based on the statistical principles underlying the SEM model and the extensive empirical practices established by other research [39,40], goodness of fit in the SEM model is assessed using the following criteria: χ2/df ≤ 3, GFI ≥ 0.9 and RMSEA ≤ 0.08. From Table 3, the model’s criteria can be simultaneously fulfilled for both male and female only when R1 ≤ 10 m and R2 ≤ 30 m. This means that the maximum fitting scales for SEM modeling can be ascertained such that the inner radii extend to 10 m and the outer radii reach 30 m. Moreover, there exists differences in the model construction across various radius ranges. It is observed that an increase in radius results in a rise in the χ2/df value, a decline in the GFI value and an increase in the RMSEA value, all of which indicate a decreasing trend in model fit quality. Specifically, these differences suggest a gradient descent response of thermal sensation, thermal comfort and thermal demand to the outward external green environment.
Moreover, the fitting results of the SEM models in Table 3 reveal both similarity and difference between the male and female subjects. The similarity lies in a spatial attenuation phenomenon from center to periphery regarding the influence of the vegetation environment on thermal experience, with 30 m serving as a threshold. Within the threshold radius, the influence of the vegetation environment on thermal experience is remarkable; beyond the threshold, such influence becomes negligible. The difference lies in the fact that SEM demonstrates greater suitability among the female subjects. The female subjects exhibited higher χ2/df and RMSEA values and a lower GFI value compared to the male subjects, with the identical radii parameter. In other words, females are more susceptible to the impact of vegetation on thermal experience.

3.2. The Results of the SEM Model with the Fitting Scale

As mentioned above, when the scale is set to R1 = 10 m and R2 = 30 m, the data fit the model well from the perspective of χ2/df, GFI and RMSEA in both male and female subjects. Based on this fitting scale, the following research has been conducted and the results follow below.

3.2.1. Evaluation of the Convergent Validity and Discriminant Validity

The convergent validity and discriminant validity for the fitting-scaled model are reported in the Appendix A.
(1) As for the convergent validity, Standard Load (Std.), Composite Reliability (CR) and Average Variance Extracted (AVE) are considered to determine whether the observed variables under the same latent variable converge (Table A2 and Table A3 in Appendix A). On the one hand, the absolute values of the Std. for each observation variable are all greater than 0.7 and exhibit significance, thereby suggesting that the model possesses a robust measurement. On the other hand, the CR values are all above 0.8 and the AVE values are all greater than 0.6, which implies that the model exhibits excellent convergent validity.
(2) As for the discriminant validity, the implied correlations are considered to determine whether different latent variables are distinguished from each other (Table A4 in Appendix A). From Table A4, the values along the two diagonals (marked green) are respectively greater than all the absolute values of the numbers in their lower-left triangular matrix, suggesting that the correlations among the observation variables within the same latent variable are all smaller than those between distinct latent variables, and the model demonstrates excellent discriminant validity.

3.2.2. Interpretation of the Influencing Path

The standardized estimate values of the influencing path are drawn in Figure 7 and the compound effects are list in Table 4. In the proposed theoretical hypotheses, most of the direct effecting pathways can be validated. Despite the fact that a small number of direct pathways are insignificant, they present indirect influencing paths, sharing the same positive and negative characteristics as hypothesized. The details are as follows:
Hypothesis H1 holds valid: the layout of the lawn has a direct and positive influence on that of the tree canopy.
Hypothesis H2: The paths of γ21,γ22 and γ23 hold valid, with γ21 being nearly twice as potent as γ22 or γ23, suggesting that the direct impact of the tree canopy on thermal sensation is notably stronger than that on thermal comfort and demand. Although the direct paths of γ11,γ12 and γ13 are not statistically significant, the positive and negative characteristics of the indirect effects corresponding to them are in accordance with the hypotheses. Therefore, tree canopy coverage constitutes the primary influencing factor of the thermal experiencing process, exerting both direct and indirect impacts. Lawn coverage merely exerts an indirect influence on thermal experience, and its positive or negative characteristics are in accordance with those of the tree canopy but are more feeble.
Hypothesis H3: Both β12 and β23 remain valid and significantly negative, with the path of β12 being marginally stronger than that of β23. This suggests that the thermal experiencing sequence of “sensation → comfort → demand” exhibits a phenomenon of decreasing intensity. Although the direct path β13 is insignificant, its corresponding indirect path is positive (Table 4), which is consistent with the hypothesis. More importantly, the indirect and positive effect of “sensation → demand” is conveyed through two negative effects, with thermal comfort serving as the perceptual medium, which constitutes a crucial process in the thermal experience.

3.2.3. Gender Disparities

The influence of the vegetation environment on thermal experience varies between male and female subjects. Regarding the indirect effects of γ11 and γ12, as well as the direct effects of γ21 and γ22, the absolute values of these effects are greater in the female subjects than in the male subjects (Table 4), indicating a stronger influence of vegetation on females’ thermal sensation and thermal comfort. Concerning the indirect effect of γ13 and the direct effect of γ23, the absolute values of these effects are greater in the male compared to that of the female, suggesting a more significant influence of vegetation on males’ thermal demand.
There exist disparities between men and women regarding the thermal experiencing process under the influence of vegetation environments. Whether it is the direct effect of β12 and β23, or the indirect effect of β13, the absolute values of these path coefficients exhibit higher values in men than in women, indicating a stronger sequence of “sensation → comfort → demand” in males compared with females. Consequently, if given the identical vegetation environment and the same level of thermal sensation, men subjects exhibit lower thermal comfort and higher thermal requirements.

4. Discussion

4.1. Discussion to the Scaling Effect

This research investigated the multiple influencing pathways of vegetation environments on thermal experience across various spatial scales. Although previous studies have also examined the correlation and fitting law between thermal experience and environments under diverse scales, they have neglected to explore the spatial scaled variation laws for the complex causal relationship network composed of multiple influencing pathways. The appropriate scale for the construction of the SEM model that discloses multiple influencing pathways among thermal comfort and its surroundings remains undefined.
One significant finding is that variations in the scale exert an influence on the fitness of the model. The fitness of the SEM model declined as the radius increased, and the fair fit effects extended up to 10 m for the inner radius and 30 m for the outer radius. This suggests the presence of two distinct thresholds for environmental observation variables within the SEM framework. Although the applicability of the inner and outer thresholds to other parks still requires further validation, it can be affirmed that a threshold phenomenon regarding the fitting scale for thermal experience influence exists. Within this threshold, the influence of vegetation on thermal experience is substantial, while beyond the threshold, the influence is weak, which is consistent with previous studies [27,41]. Nevertheless, findings regarding two thresholds represent a novel contribution compared to previous studies that reported only a single threshold. Furthermore, it can be inferred that vegetation with an inner radius and the outer radius play different roles. The inner radius influences the thermal environment at specific points where the participants complete the questionnaire, primarily impacting their immediate feedback regarding thermal experience, while the outer radius influences the thermal environment of the movement ranges occupied by the participants just before completing the questionnaire, primarily affecting their accumulation of thermal experiences from the previous moment. In summary, the effects of the inner radius serve as both the spatial focus and the temporal response to that of the outer radius.

4.2. Discussion to the Influence of Vegetation on Thermal Experience

It is widely recognized that the existence of vegetation exerts a significant cooling effect, as has been revealed in previous studies [42,43]. However, the research on the causal relationship network connected by the influencing pathways among multiple environment factors and the subjective thermal experiencing process has been overlooked. Particularly within urban parks, the spatial combination of vegetation influences individuals’ thermal experience and exhibits spatial heterogeneity at diverse locations. How this combined vegetation structurally impacts the visitors’ internal feelings, such as thermal sensation, thermal comfort and thermal demand, remains undisclosed.
In this study, the indirect negative effects of “lawn → thermal sensation” and “lawn → thermal demand”, as well as the indirect positive effect of “lawn → thermal comfort”, were observed; while the direct negative effects of “tree canopy → thermal sensation” and “tree canopy → thermal demand” were noted alongside the direct positive effect of “tree canopy → thermal comfort”. These findings are consistent with previous research results, respectively [44,45,46]. Moreover, these influencing pathways can be assembled into a network in which an influencing chain of “sensation → comfort → demand” is observed and regulated by lawn and tree canopy coverage, which has not been reported in the previous studies. In combination with the expectation theory [47], the above phenomenon can be expounded as follows: When individuals are in an environment featuring high vegetation coverage, their sensation of hot–humid and windless conditions lessens and they experience enhanced comfort with less thermal demand.
Moreover, the vegetation environment not only affects thermal indices such as sensation and comfort individually, but also influences the chain of “sensation → comfort → demand”. To facilitate a more comprehensive discussion of this issue, an additional SEM model excluding vegetation was constructed (see Figure 8). The absolute values of the coefficients in the pathways of “sensation → comfort” and “comfort → demand” are greater in scenarios devoid of vegetation compared to those with it, for both males and females. It is deduced that the vegetation environment compensates the decrease in comfort resulting from an increase in hot sensation, and facilitates the fulfillment of cooling demand.

4.3. Discussion to the Difference between Male and Female

Gender constitutes one of the crucial factors influencing thermal experience. A plethora of studies have remarked the disparities in thermal comfort between men and women [48]. However, the disparities manifested between men and women with respect to the influencing network consist of pathways of “environmental → thermal experience ” and the pathways of “sensation → comfort → demand” have received scant attention.
In this study, the impact of the vegetation environment on the thermal sensation and thermal comfort is more pronounced in females than in males. This is to say, with a slight increase in vegetation coverage, the increment of comfort for females is greater than that for males. This indicates that females are more sensitive to outdoor thermal environments than males, which is consistent with the research findings of the majority [49,50]. However, the impact of the vegetation environment on the thermal demand is more pronounced in males than in females. This can be attributed to when in the same vegetation environments, males are more prone to experiencing a hot–humid and windless sensation with lower comfort than females. Consequently, males have a greater demand for thermal regulation compared to females.
Furthermore, under the identical vegetation environmental influence, the absolute values of the coefficients in the pathway “sensation → comfort → demand” in males are higher than those in females. When thermal sensation exhibits a consistent increase during the initial stage, the decline in thermal comfort among the male population is greater than that observed in females. Similarly, when thermal comfort undergoes a uniform decrease in the subsequent stage, the rise in thermal demand among males exceeds that noted in females. This suggests that the causal chain of “sensation → comfort → demand” for thermal experience in men is more pronounced than that in women. The above findings cannot be referenced from other research as this psychological processing has not been proposed by others. However, previous studies revealed that the thermal tolerance is stronger in females than in males [51,52], as females possess lower metabolic rates [36], which substantiates our “influencing chain” theory from an analogical perspective. The increase in hot–humid and windless sensations leads to less comfort reduction and lower demand for thermal improvement in females compared to males.

4.4. Limitations and Future Works

(1) In the aspect of the model’s generalizability, the specific thermal conditions and characteristics of the study area may constrain the direct transferability of the findings to other geographical regions and seasons. Nevertheless, the study makes a significant contribution to a comprehensive framework of “sensation → comfort → demand” along with its influencing factors, which may exhibit similarities across different regions while featuring distinct sub-frameworks. For instance, in cold climates, the influence of tree canopies on wind speed is likely to emerge as a more significant factor than their cooling effect, and the positive or negative relationships within the “sensation → comfort → demand” framework may reverse. Consequently, it may be necessary to appropriately adjust this influencing pathway framework when employing such an SEM approach. Therefore, enhancing the model from both mechanistic and structural perspectives is essential for improving its generalizability across diverse geographical areas or seasons in future research.
(2) In the aspect of influencing the environment and its physical essence, thermal perceptions, thermal environment and vegetation surroundings exhibit spatial heterogeneity. Within local spaces in the park, the vegetation surroundings exert physical regulatory influences on the parameters of the thermal environment, and the outcome of this regulation has an impact on the individuals’ thermal experience. This study, with a focus on vegetation, statistically discloses a significant association between green space and thermal perceptions. Nevertheless, the lack of a quantitative exploration of the physical essence of the thermal environment constitutes a bottleneck in current research. Comprehending the binding function of the thermal environment between vegetation surroundings and human perception will be the direction of the subsequent research.

5. Conclusions

By means of an outdoor geo-spatial survey and statistical data analysis, a thermal experience study in hot summer was carried out in Longzi river park, which is located in Zhengzhou city. The following conclusions can be drawn:
  • The vegetation environment not only influences thermal experiencing indices independently, but also reshapes the chain of “sensation → comfort → demand”. Multiple pathways of influencing effect exist between vegetation and thermal experience indexes, giving rise to a complex network of causal linkages. The presence of lawns and tree canopies has been found to have a negative impact on thermal sensation and the demand for thermal regulation, while also demonstrating a beneficial effect on thermal comfort. Moreover, a thermal experiencing process characterized by the chain of “sensation → comfort → demand” is observed to emerge under the combined influence of lawn and tree canopy.
  • The influence of the vegetation on thermal experience exhibits a scale effect through the SEM performance. As the scale of the influence radius setting increases, the model’s performance declines. The fitting scale consists of an inner radius with 10 m radii and an outer radius with 30 m radii, which are identified as the thresholds. The conditions of χ2/df ≤ 3, GFI ≥ 0.9 and RMSEA ≤ 0.08 can be simultaneously satisfied only when the inner and outer radii remain below their respective thresholds, which are regarded as the requirements of the SEM framework.
  • The thermal experiencing framework, consisting of multiple influencing pathways, exhibits similar attributes in terms of positive and negative characteristics but with varying degrees of intensity in genders. Women demonstrate heightened sensitivity to environmental factors and have a greater likelihood of experiencing thermal comfort and reduced thermal demand in comparison to men. Additionally, the males’ coefficients for the pathways “sensation → comfort” and “comfort → demand” are −0.578 and −0.422, respectively; their absolute values exceed those observed in females. Therefore, male individuals demonstrate a more pronounced influence pathway of “sensation → comfort → demand” in the presence of vegetation compared to female individuals.
The aforementioned findings provide a new framework for enhancing the outdoor thermal environment, facilitating the following applications. Firstly, a deeper understanding of design scale for effective thermal comfort regulation can be achieved; based on this spatial scale, park design can optimize microclimate improvements and enhance visitors’ thermal comfort experience. Secondly, the spatial organization of different types of vegetation can be refined, allowing for a spatial optimal arrangement of lawns and trees in key scenic nodes to maximize their cooling effects. Furthermore, a vegetation design parameter can be tailored to account for behavioral patterns and spatial distribution differences among genders, thereby enriching the thermal experiences for diverse user groups. These applications guide planners and designers in creating improved and sustainable built environments within urban parks.

Author Contributions

Conceptualization, C.Z. and Q.F.; methodology, W.L. (Wei Li); software, X.P.; validation, C.Z. and W.L. (Wenjie Li); formal analysis, Q.F.; investigation, J.H.; resources, D.W.; data curation, D.W.; writing—original draft preparation, X.P.; writing—review and editing, C.Z.; visualization, D.W.; supervision, J.H.; project administration, Q.F.; funding acquisition, C.Z.; Q.F. and W.L. (Wei Li). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by (1) the Natural Science Foundation of Henan Province, grant number 232300421402; (2) the Henan Province Philosophy and Social Science Planning Project, grant number: 2022CSH040; (3) the Henan Provincial Key R&D and Promotion Special Project (Science and Technology Tackling), grant number 222102320064.

Informed Consent Statement

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

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Questionnaire
1.
What are your sensations for the current thermal feeling?
  • Q1: Temperature sensation: Cool ☐−2 ☐−1 ☐0 ☐1 ☐2 Hot
  • Q2: Humidity sensation: Dry ☐−2 ☐−1 ☐0 ☐1 ☐2 Humid
  • Q3: Breeze sensation: Breezy ☐−2 ☐−1 ☐0 ☐1 ☐2 Breezeless
2.
How comfortable are you feeling at the present moment?
  • Q4: Comfort: Uncomfortable ☐−2 ☐−1 ☐0 ☐1 ☐2 Comfortable
  • Q5: Sweatless: Sweating ☐−2 ☐−1 ☐0 ☐1 ☐2 Without Sweat
3.
To what extent does your demand for current thermal regulation?
  • Q6: Demand for cooling: Week ☐−2 ☐−1 ☐0 ☐1 ☐2 Strong
  • Q7: Demand for dehumidify: Week ☐−2 ☐−1 ☐0 ☐1 ☐2 Strong
  • Q8: Demand for breeze: Week ☐−2 ☐−1 ☐0 ☐1 ☐2 Strong
  • Gender:
  • ☐Male ☐Female
  • Questionnaire Location:
  • longitude______latitude______
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Figure A1. Statistics on the questionnaire scores.
Figure A1. Statistics on the questionnaire scores.
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Table A1. Matrix of the principal components.
Table A1. Matrix of the principal components.
Latent
Variables		Observation
Variables		MaleFemale
PC1PC2PC3PC4PC5PC1PC2PC3PC4PC5
Thermal
sensation		Temperature sensation 0.841 0.780
Humid sensation 0.778 0.820
Breeze sensation 0.728 0.734
Thermal
comfort		Comfort 0.752 0.701
Sweatless 0.806 0.862
Demand for current regulationCooling demand 0.827 0.847
Dehumidify demand 0.810 0.767
Breeze demand 0.742 0.804
LawnLawn within 20 m0.843 0.826
Lawn within 40 m0.913 0.900
Lawn within 60 m0.914 0.874
Tree canopyTree within 20 m 0.672 0.643
Tree within 40 m 0.854 0.862
Tree within 60 m 0.838 0.864
Note: PC is short for principal component.
Table A2. Convergent validity of males.
Table A2. Convergent validity of males.
Latent
Variables		Observation
Variables		Std.Unstd.S.E.t-ValuePSMCCRAVE
Thermal
sensation		Temperature sensation0.8261 0.6820.8450.645
Humid sensation0.8110.9930.06714.848***0.658
Breeze sensation0.7710.9430.06913.721***0.594
Thermal
comfort		Comfort0.8371 0.7010.8020.669
Sweatless0.7990.9940.07113.915***0.638
Demand for current regulationCooling demand0.8851 0.7830.850.654
Dehumidify demand0.7680.8540.05714.929***0.590
Breeze demand0.7680.8980.06214.387***0.590
LawnLawn within 10 m0.8981 0.8060.9320.873
Lawn within 30 m0.9698.7950.44919.601***0.939
Tree canopyTree within 10 m0.7711 0.5940.8360.72
Tree within 30 m0.9199.2100.63314.550***0.845
Note: *** refers to statistical significance.
Table A3. Convergent validity of females.
Table A3. Convergent validity of females.
Latent
Variables		Observation
Variables		Std.Unstd.S.E.t-ValuePSMCCRAVE
Thermal
sensation		Temperature sensation0.8221 0.6760.8480.650
Humid sensation0.7900.9880.07513.115***0.624
Breeze sensation0.8071.0340.07813.301***0.651
Thermal
comfort		Comfort0.9621 0.9250.8660.766
Sweatless0.7790.7860.05314.731***0.607
Demand for current regulationCooling demand0.8731 0.7620.8470.650
Dehumidify demand0.8231.0610.07613.938***0.677
Breeze demand0.7140.8770.07212.159***0.510
LawnLawn within 10 m0.9091 0.8260.9390.885
Lawn within 30 m0.9718.5290.40121.267***0.943
Tree canopyTree within 10 m0.7511 0.5640.8230.702
Tree within 30 m0.9169.2300.68413.499***0.839
Note: *** refers to statistical significance.
Table A4. The implied correlation (discriminant validity).
Table A4. The implied correlation (discriminant validity).
MaleFemale
LawnTree CanopyThermal SenseThermal ComfortThermal
Demand		LawnTree CanopyThermal SenseThermal ComfortThermal
Demand
Lawn0.934 ----0.941 ----
Tree canopy0.709 0.849 ---0.777 0.838 ---
Thermal sense−0.478 −0.674 0.803 --−0.530 −0.682 0.806 --
Thermal comfort0.501 0.707 −0.792 0.818 -0.561 0.721 −0.766 0.875 -
Thermal
demand		−0.491 −0.693 0.600 −0.701 0.809 −0.485 −0.624 0.534 −0.641 0.806

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Buildings 14 03036 g001
Figure 1. Flow chart of the whole research.
Figure 1. Flow chart of the whole research.
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Figure 2. The location and the green environment of the study area.
Figure 2. The location and the green environment of the study area.
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Figure 3. The dates on which the surveys fall and the dynamics of thermal environment.
Figure 3. The dates on which the surveys fall and the dynamics of thermal environment.
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Figure 4. The illustration of the multi-scaled radius from the questionnaire location.
Figure 4. The illustration of the multi-scaled radius from the questionnaire location.
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Figure 5. Hypothetical pathway concept diagram of SEM.
Figure 5. Hypothetical pathway concept diagram of SEM.
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Figure 6. Empirical path diagram of SEM.
Figure 6. Empirical path diagram of SEM.
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Figure 7. The standardized estimate values of the directly influencing path.
Figure 7. The standardized estimate values of the directly influencing path.
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Figure 8. The comparison of the SEM model under different scenarios.
Figure 8. The comparison of the SEM model under different scenarios.
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Table 1. The ten hypothesized paths.
Table 1. The ten hypothesized paths.
HypothesisPaths Positive or NegativeDefinition
Hypothesis (H1)θ+Lawn → tree canopy
Hypothesis (H2)γ11−Lawn → sensation
γ12+Lawn → comfort
γ13−Lawn → improvement
γ21−Lawn → sensation
γ22+Lawn → comfort
γ23−Lawn → improvement
Hypothesis (H3)β12−Sensation → comfort
β13−Sensation → improvement
β23+Comfort → improvement
Table 2. The reliability test of the initial data.
Table 2. The reliability test of the initial data.
Latent VariablesCronbach’s α (Male)Cronbach’s α (Female)
Thermal sensation0.8430.847
Thermal comfort0.8060.858
Thermal demand0.8440.840
Lawn coverage0.8900.892
Tree coverage0.8830.879
Table 3. The construct validity of models fit with small radius and large radius combinations.
Table 3. The construct validity of models fit with small radius and large radius combinations.
χ2/dfR1χ2/dfR1
(Male)10 m20 m30 m40 m50 m(Female)10 m20 m30 m40 m50 m
R220 m2.426- R220 m2.513 -
30 m2.6082.840- 30 m2.520 2.970 -
40 m3.0323.0733.898- 40 m2.778 3.114 3.403 -
50 m3.3683.3074.0333.649-50 m2.990 3.390 3.913 4.571 -
60 m3.6413.5824.3844.2914.38960 m3.184 3.792 4.514 5.237 5.244
GFIR1GFIR1
(male)10 m20 m30 m40 m50 m(female)10 m20 m30 m40 m50 m
R220 m0.939 - R220 m0.924 -
30 m0.936 0.933 - 30 m0.924 0.916 -
40 m0.927 0.930 0.914 - 40 m0.918 0.914 0.906 -
50 m0.921 0.926 0.912 0.918 -50 m0.914 0.909 0.896 0.881 -
60 m0.917 0.922 0.907 0.907 0.904 60 m0.908 0.901 0.885 0.868 0.867
RMSEAR1RMSEAR1
(male)10 m20 m30 m40 m50 m(female)10 m20 m30 m40 m50 m
R220 m0.071 - R220 m0.079 -
30 m0.075 0.081 - 30 m0.080 0.091 -
40 m0.085 0.086 0.101 - 40 m0.086 0.094 0.100 -
50 m0.091 0.090 0.104 0.097 -50 m0.091 0.100 0.110 0.122 -
60 m0.097 0.096 0.109 0.108 0.109 60 m0.095 0.108 0.121 0.133 0.133
NoteExcellent fit critical fit unacceptable fit
Table 4. The compound effects.
Table 4. The compound effects.
MaleFemale
Total
Effects 		Direct
Effects 		Indirect
Effects 		Total
Effects 		Direct
Effects 		Indirect
Effects
Lawn → thermal sensation (γ11)−0.478 Not significant−0.478 −0.530 Not significant−0.530
Lawn → thermal comfort (γ12)0.501 Not significant0.501 0.561 Not significant0.561
Lawn → regulation demand (γ13)−0.491 Not significant−0.491 −0.485 Not significant−0.485
Trees → thermal sensation−0.674 −0.674 (γ21)0.000 −0.682 −0.682 (γ21)0.000
Trees → thermal comfort0.707 0.317 (γ22)0.390 0.721 0.372 (γ22)0.349
Trees → regulation demand−0.693 −0.394 (γ23)−0.299 −0.624 −0.337 (γ23)−0.287
thermal sensation → regulation demand (β13)0.244Not significant0.2440.204Not significant0.204
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Zhang, C.; Li, W.; Fan, Q.; Hu, J.; Wang, D.; Ping, X.; Li, W. The Influence of Vegetation Environment on Thermal Experience in Hot Summer: A Case Study from Perspectives of Fitting Scale and Gender Disparity. Buildings 2024, 14, 3036. https://doi.org/10.3390/buildings14103036

AMA Style

Zhang C, Li W, Fan Q, Hu J, Wang D, Ping X, Li W. The Influence of Vegetation Environment on Thermal Experience in Hot Summer: A Case Study from Perspectives of Fitting Scale and Gender Disparity. Buildings. 2024; 14(10):3036. https://doi.org/10.3390/buildings14103036

Chicago/Turabian Style

Zhang, Chenming, Wei Li, Qindong Fan, Jian Hu, Dongmeng Wang, Xiaoying Ping, and Wenjie Li. 2024. "The Influence of Vegetation Environment on Thermal Experience in Hot Summer: A Case Study from Perspectives of Fitting Scale and Gender Disparity" Buildings 14, no. 10: 3036. https://doi.org/10.3390/buildings14103036

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