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Article

From Landscape Configuration to Health Outcomes: A Spatial–Behavioral Framework Linking Park Landscapes to Public Perceived Health Through Thermal Comfort and Loyalty Dynamics

by
Jiang Li
1,2,
Yudan Liu
1,2,
Xiaoxi Cai
3,
Dandi Zhu
1,2,
Xingyu Liu
1,2,
Shaobo Liu
1,2,* and
Weiwei Liu
4,*
1
Department of Environmental Design, School of Architecture and Art, Central South University, Changsha 410083, China
2
The Establishment of the Key Laboratory for High-Density Habitat Ecology and Energy Conservation, Tongji University, Ministry of Education, Shanghai 200092, China
3
School of Art and Design, Hunan First Normal University, Changsha 410205, China
4
School of Energy Science and Engineering, Central South University, Changsha 410083, China
*
Authors to whom correspondence should be addressed.
Buildings 2026, 16(2), 260; https://doi.org/10.3390/buildings16020260
Submission received: 17 November 2025 / Revised: 24 December 2025 / Accepted: 25 December 2025 / Published: 7 January 2026
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
Browse Figures
Versions Notes

Abstract

Urban park landscape design has significant potential to alleviate heat stress and promote public health, particularly during extreme summer heat. This study explores how the spatial configuration of landscapes within the Yanghu Wetland Park in Changsha, China, influences pedestrian thermal comfort and destination loyalty under hot summer conditions, and how these factors affect public perceived health. It enriches current research by examining the impact of landscape spatial configuration, thermal comfort, and destination loyalty on public perceived health from a psychological perspective. We identified connections between park users’ spatial perceptions and their psychological and health perceptions. We used structural equation modeling (SEM) to examine the relationships among visitors’ spatial perception, psychological perceptions, and health perceptions within this large urban wetland park. At the same time, we explored how landscape characteristics, thermal comfort, destination loyalty, and public perceived health interact. This research constructs a Spatial–Thermal–Perception–Behavior (SPB) theoretical framework for such complex blue-green spaces, providing a multidimensional perspective on the relationship between the environment and health. Based on a survey of 321 visitors, This study pioneers the SPB theoretical framework, clarifying how this wetland park’s landscape configurations impact public perceived health through the mediating pathways of thermal comfort and destination loyalty. It provides a scientific basis for heat-adaptive landscape design in similar wetland park settings, aiming to enhance resident well-being and improve public perceived health.

1. Introduction

The process of urbanization poses significant challenges to the environment, biodiversity, and human health. One of the prominent challenges is the urban heat island (UHI) effect, a phenomenon where cities experience higher temperatures than their surrounding rural areas [1]. This effect is driven by multiple factors, including the continued expansion of impervious surfaces, the loss of green and blue spaces, high population density, and heat generated from industrial and transportation activities [2]. Urban parks, as vital components of urban green space (UGS) systems, play a crucial role in mitigating heat stress and promoting public perceived health. Consequently, UGS in public areas has become a key research focus for addressing urban challenges and achieving sustainable urban development [3]. It is therefore essential to investigate how to design these spaces to meet public needs for health and thermal comfort, thereby providing more agreeable landscape environments [4,5].
Existing literature has extensively examined the role of landscapes in climate regulation and thermal comfort [6,7]. For instance, Skelhorn et al. [8] used the urban climate model ENVI-met (version 3.1 Beta V, revision date: 4th October 2010) to investigate the cooling effects of various landscapes in urban centers, finding that grasslands and mature trees were most effective at reducing surface temperatures. Similarly, Majid et al. [9] demonstrated that landscapes significantly mitigate the urban heat island effect, with tree clusters having the most pronounced impact. A synthesis of previous research confirms that the spatial configuration of landscapes—such as green coverage, shade structures, and water distribution—significantly influences pedestrian thermal comfort and physiological and psychological health [10]. Over the past decade, the importance of spatial configuration, encompassing both landscape composition (the type and amount of land cover) and spatial arrangement, has gained increasing recognition for mitigating urban heat, particularly in urban parks [11,12,13]. The influence of spatial configuration on ecosystem services depends on its propagation and the extent of its impact on the surrounding environment [2], making these services highly sensitive to external characteristics. In terms of actual cooling effects, factors such as building height, form, and nearby blue-green spaces all influence one another, and the mechanisms governing their interactions are complex [2]. Concurrently, a growing body of research demonstrates that specific spatial features in urban parks contribute to resident well-being and that well-designed urban green spaces can improve mental health through various pathways [14].
A user’s thermal state is a critical factor in assessing how well an environment meets human needs [15]. Depending on their thermal state, individuals may adopt different thermal adaptive behaviors (TAB) [16]. Consequently, pedestrians in urban parks exhibit varying behavioral intentions and destination choices. Destination loyalty, which reflects a resident’s willingness to revisit a place and the frequency of their visits, encompasses both attitudinal and behavioral dimensions [17]. Although numerous studies demonstrate the independent impact of green spaces on public perceived health, the potential interactions among mediating variables have been less explored. Notably, the roles of destination loyalty and thermal comfort in influencing public perceived health have been largely overlooked [5,16]. Reviews indicate a growing body of experimental studies reporting that short-term exposure to green environments, such as parks, urban woodlands, and forests, can significantly improve mood and attention and facilitate recovery from psychological stress [14]. This evidence confirms the important role of short-term exposure to green spaces in public perceived health [14]. As visiting a green space is a form of pro-environmental behavior, different landscape configurations are likely to elicit varying levels of destination loyalty [18].
The Perception–Intention–Action (PIA) framework is a comprehensive model that empirically examines public transit use in daily travel by integrating objective features with individuals’ subjective evaluations of the built environment, lifestyle, and travel [19]. The PIA model accounts for contributions from both the built environment and socio-psychological factors, incorporating an extended version of the Theory of Planned Behavior (TPB) to model the relationship between attitudes and behavior [20]. This model is built on earlier travel behavior adaptation models, such as the open-system model developed by Fried, Havens, and Thall (FHT), which is based on an individual’s adaptive responses to imbalances in person-environment fit [21,22]. Like the FHT model, the PIA framework emphasizes the role of social and psychological factors in the relationship between the built environment and travel behavior. It is an empirically tested framework for understanding the linkages between perception, intention, and action [19,21]. Informed by the PIA framework, this study develops a Space–Perception–Behavior (SPB) theoretical framework. The SPB framework is designed to guide the creation of comfortable landscapes in urban parks that meet pedestrians’ health needs. It investigates the connection to public perceived health through the lenses of landscape spatial configuration, thermal comfort, and destination loyalty.
Previous research has established that the physical environment—such as the coverage of blue-green spaces and vegetation configuration—significantly influences microclimate regulation and public perceived health [23,24]. For example, vegetation cover is negatively correlated with land surface temperature; specifically, in large cities with a subtropical monsoon climate, a 10% increase in vegetation can reduce daytime surface temperatures by 1.2–2.5 °C [23,25]. Furthermore, urban spatial form, including building layout and street canyon design, affects human thermal comfort by altering wind flow and intensifying or mitigating the urban heat island effect [26]. Concurrently, a robust body of literature attests to the positive impacts of urban green space exposure on self-reported mental well-being and perceived restoration [14,27]. However, most existing studies focus on objective physical metrics, such as temperature and humidity, or on general assessments of environmental preference and health outcomes [28]. A significant gap remains in understanding the sequential psychological and behavioral process: how subjective perceptions—such as thermal comfort and the attractiveness of a landscape’s spatial configuration—influence behavioral decisions, including destination loyalty, and thereby ultimately impact public perceived health. Specifically, an integrated framework that links spatial perception, thermal-affective response (comfort), behavioral intention (loyalty), and perceived health outcomes under heat stress is lacking. To address this gap, this study examines the interrelationships between landscape spatial configuration, thermal comfort, destination loyalty, and public perceived health. We developed a dedicated scale to investigate the mediating and moderating interactions among these four constructs [14]. Methodologically, we first employed Exploratory Factor Analysis (EFA) using Principal Component Analysis (PCA) to determine the number of underlying factors. After establishing the factor structure, we formalized the SPB theoretical framework. Subsequently, Confirmatory Factor Analysis (CFA) was used to validate the item structure and test the framework’s construct validity [19,29]. Nevertheless, the complex relationships among landscape features, thermal comfort, destination loyalty, and public perceived health—particularly from a spatial perception perspective—are not yet fully understood. This study investigates these mechanisms by combining questionnaire surveys with Structural Equation Modeling (SEM) to analyze how different landscape configurations and destination loyalty impact pedestrian thermal comfort and public perceived health in urban parks. Although the cooling effects of landscape design are well documented, the pathway from individual perception to behavioral outcomes (e.g., destination loyalty) and, ultimately, to health remains unclear.
Building upon the Perception–Intention–Action (PIA) framework, which links perception, psychological intention, and action, this study develops a Space–Perception–Behavior (SPB) theoretical framework. The SPB framework contextualizes PIA within the realm of thermal environment and park use: ‘Spatial Perception’ corresponds to the perceptual input; ‘Thermal Comfort’ and ‘Destination Loyalty’ together represent the critical psychological and behavioral intentions; and ‘Perceived Health’ is the targeted behavioral and welfare outcome. This integration offers a novel, interdisciplinary perspective focused on subjective experience. The SPB framework is specifically applied to explore the mediating and moderating relationships between landscape spatial configuration, thermal comfort, destination loyalty, and public perceived health. This research provides a scientific basis for climate-resilient urban park design and opens a new avenue for promoting public perceived health through targeted landscape interventions. Future studies could integrate multi-scale environmental data with behavioral analysis to more comprehensively reveal the dynamic relationships between landscape spatial configuration and public perceived health.

2. Theoretical Framework

Cooling landscape structures, commonly categorized as blue and green spaces, are relevant to all urban environments [1]. The ecosystem services provided by these blue-green patches are influenced by two key dimensions: their composition and their spatial configuration [19]. Composition refers to the types, quantities, and proportions of vegetation and water bodies. It is a critical factor driving the heterogeneous cooling effects of blue-green spaces, as it governs processes such as transpiration, shading, and surface radiation modification [24,30,31]. Furthermore, the spatial patterns and configuration of a park’s landscape are significantly correlated with its cooling island effect [32]. Optimizing this internal landscape arrangement is therefore a crucial strategy for enhancing cooling intensity [33,34,35]. In practice, the diversity of land cover creates considerable complexity [36]. Most urban parks are composed of core elements, including water features, walkways, recreational areas, vegetation, and various ground surfaces [3,37]. For this study, we define the variable “landscape spatial configuration” as the specific combination and arrangement of these constituent elements.
Thermal comfort is a critical factor influencing public perceived health during hot summer conditions. It is defined as “a psychological state that expresses satisfaction with the thermal environment” [38] and serves as a key measure for assessing how well an environment meets human needs [15]. When pedestrians experience stimuli from both the landscape and thermal conditions, it significantly shapes their perception of the surroundings [28]. Research indicates that thermal comfort involves a complex interaction with other sensory and psychological adaptation mechanisms [39].
Behavioral intention is a key psychological driver that leads pedestrians to interact with their environment in various ways to achieve a desired state of comfort [40], a process that subsequently influences their destination loyalty. Therefore, this study collects data on pedestrian destination loyalty across different landscape configurations to systematically examine its relationship with thermal comfort and public perceived health.
The relationship between public perceived health and the environment is central to global sustainable development. As vital public spaces for daily activity, urban parks directly impact public perceived health through the quality of their landscapes and thermal environments, which shape pedestrians’ thermal comfort. Studies indicate that the thermal comfort provided by urban parks influences health by modulating outdoor activity patterns and physiological stress responses [41]. Psychologically, thermal stress can exacerbate anxiety and depression. Conversely, well-designed landscape elements—such as vegetation, water bodies, and strategic building layouts—can create a more suitable microclimate. For instance, a park with ample shade can improve public perceived health by reducing sympathetic nervous system arousal [42]. Therefore, this study constructs a framework model that traces the pathway from landscape design to health outcomes. This SPB theoretical framework, illustrated in Figure 1, aims to provide a theoretical foundation and practical insights for healthy urban planning and improved public perceived health.

3. Research Methodology

3.1. Study Area

This study was conducted in Changsha, the capital of Hunan Province, China, characterized by a hot-summer humid subtropical climate. The study focuses on the Yanghu Wetland Park, a large and representative urban wetland park located in Yuelu District, Changsha (approximately 28.126° N, 112.934° E). This park was selected for its significant ecological and social functions, high public usage, and diverse landscape configuration that integrates expansive water bodies, rehabilitated wetlands, wooded areas, open lawns, and recreational pathways. Its complexity makes it a pertinent case for studying the interplay between spatial perception, thermal experience, and behavior. An illustrative map showing the location of Changsha and the site of Yanghu Wetland Park is provided in Figure 2. Data collection focused on users’ subjective perceptions of this environment rather than specific physical measurements of the site.

3.2. Questionnaire Design

This study employed a cross-sectional survey design to investigate the subjective perceptions of park users. All data were derived from self-reported questionnaires. The questionnaire was structured into five sections. Section 1 collected demographic information, including gender, age, occupation, distance from home to the park, walking commute time, and typical duration of stay. Section 2 presented six familiar landscape spatial configurations based on typical park usage patterns. These configurations were presented to respondents as descriptive text of typical scenes accompanied. Section 3 assessed pedestrians’ subjective thermal comfort and thermal satisfaction. Section 4 measured destination loyalty and behavioral preferences corresponding to the landscape configurations. Section 5 evaluated perceived health benefits associated with park usage. Except for the demographic section, all items were measured on a 5-point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Data were collected through an online survey platform using a convenience sampling method. The questionnaire was pretested with 30 respondents to ensure clarity and relevance; minor wording adjustments were made based on the feedback.
A total of 342 questionnaires were returned during the survey period. Of these, 321 were deemed valid, yielding a valid response rate of 93.85%.

3.3. Exploratory Factor Analysis and Model Validation

Data analysis was performed using IBM SPSS Statistics 27 and Amos 24.0.0 (Bui1d 596938) to assess the scale’s validity and reliability. In line with established analytical procedures [43,44], we conducted the following tests: the KMO measure and Bartlett’s Test of Sphericity, EFA, descriptive statistics, reliability analysis using Cronbach’s alpha, and CFA [45].
The suitability of the data for factor analysis was first assessed using the KMO measure and Bartlett’s Test of Sphericity. A KMO value closer to 1 indicates stronger correlations among variables, with values above 0.9 considered excellent, above 0.8 very good, above 0.7 acceptable, and below 0.6 unsuitable [46]. Additionally, Bartlett’s Test requires a significance level (p-value) of less than 0.05 to proceed with factor analysis [45]. As shown in Table 1, the data yielded a KMO value of 0.916 (excellent) and a significant Bartlett’s test (p < 0.05). These results confirm that the variables are sufficiently correlated and that the dataset is highly appropriate for Exploratory Factor Analysis [45].
The second step involved performing an Exploratory Factor Analysis (EFA) using the Varimax rotation method to identify the relationships between the observed variables and the underlying latent factors [47]. As shown in the Total Variance Explained table (Table 2), four factors with eigenvalues greater than one were extracted, suggesting a four-factor structure. These four factors accounted for 58.97% of the total variance, exceeding the recommended threshold of 50% and thus considered adequate for analysis [48]. The factor loadings matrix (Table 3) confirmed this four-factor structure, with each factor comprising 4 to 8 items [49,50]. Based on the items with high loadings, the factors were labeled as follows [45]:
  • Landscape Spatial Configuration (items KJ1-KJ8);
  • Thermal Comfort Perception (items ZT1-ZT4);
  • Destination Loyalty (items LQ1-LQ5);
  • Public Perceived Health (items JK1-JK4).
As illustrated in the scree plot (Figure 3), a distinct break in slope is observed at the fifth factor, with all preceding factors exhibiting eigenvalues greater than 1 [51]. This visual analysis, consistent with the results in Table 2, confirms the selection of a four-factor structure for the questionnaire.
The Principal Component Analysis successfully extracted four distinct variables, aligning with the initial research design. These comprise three independent variables—Landscape Spatial Configuration, Thermal Comfort, and Destination Loyalty—and one dependent variable, Public Perceived Health. While numerous studies on green space benefits have examined links to perceived health outcomes [52], the evidence remains mixed, and consistent associations with general health metrics are often lacking [27,53,54]. Integrating these preliminary insights with the SPB theoretical framework, we developed the conceptual SPB model illustrated in Figure 4.
H1. 
Landscape Spatial Configuration positively influences Thermal Comfort [3,10,55].
H2. 
Landscape Spatial Configuration positively influences Public Perceived Health [10].
H3. 
Landscape Spatial Configuration positively influences Destination Loyalty [55].
H4. 
Thermal Comfort positively influences Destination Loyalty [56,57].
H5. 
Thermal Comfort positively influences Public Perceived Health [3].
H6. 
Destination Loyalty mediates the relationship between Landscape Spatial Configuration and Public Perceived Health.
H7. 
Destination Loyalty mediates the relationship between Thermal Comfort and Public Perceived Health.

3.4. Structural Equation Modeling (Conceptual Formulation)

SEM is a multivariate statistical technique that integrates factor analysis and path analysis to model complex relationships among multiple variables. Its core principle is to test a theory-driven hypothetical model against empirical data. This is achieved through a dual framework: the measurement model, which handles latent constructs and their measurement error, and the structural model, which quantifies the direct and indirect effects among these constructs [58].
SEM integrates confirmatory factor analysis and multiple linear regression to analyze complex relationships and validate the underlying structure between latent constructs and their observed indicators [59]. In the conceptual framework (Figure 4), the directional arrows represent the hypothesized causal paths. Variables that receive arrows (e.g., Public Perceived Health) are treated as dependent variables, while those that do not (e.g., Landscape Spatial Configuration) are considered independent variables. The corresponding structural equations are as follows:
TC = β1 × LSC + ζ1
DL = β2 × LSC + β3 × TC + ζ2
PH = β4 × LSC + β5 × TC + β6 × DL + ζ3
where LSC, TC, DL, and PH represent the latent constructs of Landscape Spatial Configuration, Thermal Comfort, Destination Loyalty, and Public perceived Health, respectively. The path coefficients (β1 to β6) denote the strength and direction of the hypothesized relationships (see Figure 4), and ζ represents the error term for each equation. The equations form a system estimated simultaneously rather than as a series of isolated regressions. This allows for the analysis of the entire network of proposed relationships at once. A direct effect is the pathway from an independent variable to a dependent variable while controlling for any mediating factors. An indirect effect is the pathway that operates through one or more mediating variables [60].

4. Results and Discussion

4.1. Descriptive Statistical Analysis

The sample demonstrated a relatively balanced gender distribution (Table 4), with 46.4% male and 53.6% female respondents. The majority of respondents were young adults, predominantly aged 18–25 (62.0%), followed by 26–35 (18.1%), under 18 (4.4%), 36–45 (9.0%), and over 45 (6.5%). Regarding occupational distribution, students constituted the largest group (60.1%), followed by employed individuals (27.4%), freelancers (6.5%), retirees (5.0%), and unemployed persons (0.9%). This demographic profile indicates a representative sample suitable for subsequent empirical analysis.
The respondents’ behavioral data are summarized in Figure 5. Regarding visit frequency, a small portion (6.2%) reported daily visits, while 22.7% visit several times a week. The majority (53.0%) visit several times a month, and 18.1% visit only a few times per year. Regarding preferred visit times, 11.5% typically visit before 8:00, 10.3% between 8:00 and 14:00, and 16.5% between 14:00 and 18:00. The most common time for park visits was after 18:00, reported by 61.7% of respondents. Regarding the duration of their stay, 31.8% stayed for less than 30 min, and 47.7% stayed between 30 min and 1 h. A smaller proportion, 16.2%, stayed for one to two hours, while only 4.4% reported stays longer than two hours.

4.2. Scale Reliability and Validity Analysis

Data analysis was conducted using IBM SPSS Statistics 27 and Amos Graphics to evaluate the scale’s validity and reliability. In line with established methodological approaches [43,44], we performed the KMO test, Bartlett’s test of sphericity, reliability analysis using Cronbach’s alpha, and CFA [45]. The results of the KMO and Bartlett’s tests confirmed the suitability of the data for factor analysis.
In this study, the primary constructs were measured using multi-item scales, making it essential to verify data quality before proceeding with further analysis. We first assessed the internal consistency of each dimension using Cronbach’s alpha, for which values above 0.7 are widely accepted as indicating sufficient reliability [61,62]. As shown in Figure 6, all calculated Cronbach’s alpha values ranged from 0.7 to 0.9, confirming that the scales used in this study demonstrated good internal consistency and reliability.
Following the confirmation of a well-fitting CFA model, the convergent validity and composite reliability of the scale’s dimensions were assessed. Standardized factor loadings from the CFA were used to calculate the Average Variance Extracted (AVE) and Composite Reliability (CR) for each dimension. As presented in Figure 6, all dimensions demonstrated CR values above the 0.7 threshold, and all AVE values were greater than 0.4. Although the conventional threshold for AVE is 0.5, research suggests that convergent validity remains adequate if the AVE is slightly below 0.5 (but above 0.4), provided that the CR is above 0.7. The construct has strong theoretical support [63,64]. Therefore, the collective evidence indicates that all dimensions possess satisfactory convergent validity and composite reliability.
As shown in Figure 6, the mean scores for all variables ranged between 3 and 4 on a 5-point Likert scale. This indicates that the study participants reported moderate to high levels of perception and behavior regarding landscape spatial configuration, thermal comfort, destination loyalty, and public perceived health [65,66]. The normality of the data for each measurement item was assessed using skewness and kurtosis statistics. Following established guidelines, absolute values of skewness less than 2 and kurtosis less than 7 were considered indicative of an approximately normal distribution [67]. The results in Figure 6 confirm that the absolute values for both skewness and kurtosis for all items fell within these thresholds, supporting the assumption of univariate normality. The table presents the descriptive statistics, factor loadings, Cronbach’s alpha, AVE, and CR for all constructs.

4.3. Confirmatory Factor Analysis

The model fit indices, presented in Table 5, indicate that the hypothesized four-factor CFA model fits the data well. The ratio of chi-square to degrees of freedom (χ2/df) was 2.197, falling within the acceptable range of 1–3. The Root Mean Square Error of Approximation (RMSEA) was 0.061, indicating a good fit (<0.08). Furthermore, the Incremental Fit Index (IFI), Tucker–Lewis Index (TLI), and Comparative Fit Index (CFI) all exceeded the recommended threshold of 0.90 [49,58,68]. Collectively, these results demonstrate that the measurement model for Landscape Spatial Configuration, Thermal Comfort, Destination Loyalty, and Public perceived Health has a good overall fit.

4.4. Structural Equation Modeling Analysis

4.4.1. Hypothesis Testing Results

The significance level for path analysis was set at p < 0.050 [69]. As shown in Table 6, all standardized factor loadings exceeded 0.5. The path analysis results (Table 7) tested the hypothesized relationships. Landscape spatial configuration was a significant positive predictor of thermal comfort (β = 0.504, p < 0.001), thus supporting H1. It also significantly predicted destination loyalty (β = 0.371, p < 0.001), supporting H3, and public perceived health (β = 0.417, p < 0.001), supporting H2. Furthermore, thermal comfort significantly predicted destination loyalty (β = 0.537, p < 0.001), supporting H4. However, the direct effect of thermal comfort on public perceived health was not significant (β = 0.067, p > 0.05), leading to the rejection of H5. Based on these findings, the theoretical model was revised to examine additional mediating pathways, resulting in the final model presented in Figure 7.

4.4.2. Mediation Effect Analysis Results

The mediation analysis results, presented in Table 8, confirm both hypothesized mediation effects. Destination loyalty partially mediated the relationship between landscape spatial configuration and public perceived health, thus supporting Hypothesis H6. Furthermore, destination loyalty demonstrated a full mediation effect in the relationship between thermal comfort and public perceived health, confirming Hypothesis H7.

4.4.3. Limitations and Future Research

This study has certain limitations. First, the assessment of psychological perceptions was based on individual differences, geographical location, and behavioral factors. However, it did not account for the influence of demographic characteristics on these perceptions or their varying impact across different landscape configurations. Future research should incorporate demographic variables to uncover heterogeneity in perceptions. For instance, subgroup analyses (e.g., by age and gender) could examine how different populations perceive landscape configurations, providing a foundation for more refined design strategies.
Furthermore, future work should conduct more in-depth investigations of specific landscape configuration combinations by integrating objective environmental data. Supplementing survey data with spatially matched environmental parameters (e.g., temperature, humidity, wind speed) would strengthen the SPB framework’s capacity to explain the link between physical conditions and human perception. Additionally, longitudinal studies tracking the impact of seasonal variations (e.g., summer vs. winter) on destination loyalty could further validate the generalizability of the SPB framework. Such efforts would ultimately contribute to the development of more effective urban park designs that promote public perceived health. Furthermore, this study relied solely on subjective self-reported data. Future research would benefit from integrating spatially explicit objective measurements (e.g., air temperature, humidity, mean radiant temperature) with the perceptual survey data to more robustly link physical environmental conditions to human perception and behavior within the SPB framework.

5. Conclusions

A key finding is the non-significant direct path from thermal comfort to perceived health (H5 rejected). This suggests that merely feeling thermally comfortable in a park does not, by itself, translate directly into stronger health perceptions. Instead, comfort operates primarily by enhancing destination loyalty—the willingness to revisit and recommend the park. This enhanced loyalty, fostered by a comfortable environment, then becomes the active mechanism through which health benefits (e.g., physical activity, stress reduction accrued through repeated visits) are realized. This underscores the importance of designing parks that are not only comfortable but also memorable and engaging enough to promote sustained visitation.
This study investigated the relationships among landscape spatial configuration, thermal comfort, destination loyalty, and public perceived health among pedestrians in urban parks during summer heat. The findings provide valuable insights for incorporating health-oriented landscape design considerations into urban park planning.
The findings demonstrate the validity of the SPB theoretical framework, which links landscape spatial configuration, thermal comfort, destination loyalty, and public perceived health. Analysis of the seven hypotheses revealed significant direct effects of landscape configuration on thermal comfort, public perceived health, and destination loyalty, with destination loyalty partially mediating the relationship between landscape configuration and public perceived health. Consequently, designers should prioritize integrating tree canopies with water features to enhance thermal comfort and destination loyalty, thereby maximizing health benefits. Additionally, creating diverse recreational nodes can extend park usage duration during high-temperature periods.
The analysis confirmed a significant direct effect of thermal comfort on destination loyalty. However, its direct effect on public perceived health was not significant (β = 0.067, p > 0.05). This lack of a direct link may stem from questionnaire-based perceptions, detached from on-site experience, and may fail to capture the full potential benefits of environmental comfort. Crucially, destination loyalty fully mediated the effect of thermal comfort on public perceived health (indirect effect β = 0.201, p < 0.05). This indicates that the primary value of improving thermal comfort lies in enhancing a location’s appeal, which, in turn, yields health benefits through repeated visits and activities such as walking and socializing. This finding underscores the need for designers to integrate “behavioral incentives” into heat-resilient landscape strategies, for instance, by creating connected shaded pathways to encourage prolonged stays.
Research on environmental thermal comfort spans multiple disciplines. However, most existing studies rely on collecting physical data and conducting environmental simulations to examine impacts on public perceived health [72,73], often overlooking pedestrians’ psychological perceptions as a precursor to such data collection. This study addresses this gap by analyzing factors from the pedestrian’s perspective. Through an examination of the psychological perception of landscape spaces, it identifies thermal comfort and destination loyalty as key factors and explores their relationship with public perceived health. This research is the first to connect these four factors, confirming both the direct and indirect influences of landscape spatial configuration, thermal comfort, and destination loyalty on public perceived health. It provides a theoretical framework grounded in pedestrian psychological perception that precedes physical data collection and simulation, offering valuable insights for subsequent research on physical factors and the design of urban parks.

Author Contributions

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

Funding

This work was supported by the National Natural Science Foundation of China (No. 52108049), the Science Fund for Distinguished Young Scholars of Hunan Province, China (No. 20251120033), the Natural Science Foundation of Hunan Province, China (No. 2023JJ30182).

Institutional Review Board Statement

Ethical review and approval were waived for this study because it involved anonymous questionnaire surveys with adult participants, posed minimal risk, and the data were analyzed in an aggregated manner without collecting personal identifiers.

Informed Consent Statement

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

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Buildings 16 00260 g001
Figure 1. SPB Theoretical Framework Diagram.
Figure 1. SPB Theoretical Framework Diagram.
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Figure 2. Study Area.
Figure 2. Study Area.
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Figure 3. Scree Plot.
Figure 3. Scree Plot.
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Figure 4. The SPB Conceptual Framework illustrating the relationships among Landscape Spatial Configuration, Thermal Comfort, Destination Loyalty, and Public Perceived Health.
Figure 4. The SPB Conceptual Framework illustrating the relationships among Landscape Spatial Configuration, Thermal Comfort, Destination Loyalty, and Public Perceived Health.
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Figure 5. Sample Characteristics.
Figure 5. Sample Characteristics.
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Figure 6. Analysis Results of Measurement Model.
Figure 6. Analysis Results of Measurement Model.
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Figure 7. Structural Analysis Model.
Figure 7. Structural Analysis Model.
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Table 1. KMO and Bartlett’s Test Results.
Table 1. KMO and Bartlett’s Test Results.
KMO Measure of Sampling Adequacy 0.916
Bartlett’s Test of SphericityApprox. Chi-Square 2979.157
Degrees of Freedom (df)210
Significance (p-value)0
Table 2. Total Variance Explained.
Table 2. Total Variance Explained.
ComponentInitial EigenvaluesExtraction Sums of Squared LoadingsRotation Sums of Squared Loadings
TotalPercentage of VarianceCumulative%TotalPercentage of VarianceCumulative%TotalPercentage of VarianceCumulative%
17.96537.92937.9297.96537.92937.9294.29520.45420.454
22.0009.52447.4532.0009.52447.4532.99114.24334.697
31.3666.50553.9581.3666.50553.9582.57112.24246.939
41.0535.01358.9711.0535.01358.9712.52712.03258.971
50.8744.16163.132
60.8353.97467.106
70.7313.47970.585
80.6833.25173.835
90.6453.07376.908
100.5902.81279.720
110.5252.50282.222
120.4712.24184.463
130.4442.11486.577
140.4382.08488.661
150.4232.01290.673
160.4021.91292.585
170.3691.75694.341
180.3491.66396.005
190.3101.47797.482
200.2891.37598.857
210.2401.143100.00
Note. Extraction Method: Principal Component Analysis.
Table 3. Rotated Component Matrix.
Table 3. Rotated Component Matrix.
Component
1234
KJ10.703
KJ20.698
KJ30.627
KJ40.772
KJ50.666
KJ60.730
KJ70.659
KJ80.584
ZT1 0.645
ZT2 0.840
ZT3 0.615
ZT4 0.578
LQ1 0.575
LQ2 0.731
LQ3 0.673
LQ4 0.711
LQ5 0.689
JK1 0.723
JK2 0.700
JK3 0.713
JK4 0.683
Note. Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a has converged after 6 iterations.
Table 4. Descriptive Statistics of Sample Characteristics.
Table 4. Descriptive Statistics of Sample Characteristics.
VariableCategoryFrequencyPercentage
GenderMale1490.464
Female1720.536
Age<18 years140.044
18–25 years1990.62
26–35 years580.181
36–45 years290.090
>45 years210.065
OccupationStudent1930.601
Employed (non-student)880.274
Freelancer210.065
Retired160.050
Unemployed30.009
Table 5. Goodness-of-Fit Indices for the Measurement Model.
Table 5. Goodness-of-Fit Indices for the Measurement Model.
Fit IndexBenchmark for Good FitModel Result
CMIN/DFExcellent (1–3), Good (3–5)2.197
RMSEAExcellent (<0.05), Good (<0.08)0.061
IFIExcellent (>0.9), Good (>0.8)0.924
TLIExcellent (>0.9), Good (>0.8)0.912
CFIExcellent (>0.9), Good (>0.8)0.923
Table 6. Measurement Model Results.
Table 6. Measurement Model Results.
Path RelationshipEstimatep
KJ8←Landscape Spatial Configuration0.626***
KJ7←Landscape Spatial Configuration0.696***
KJ6←Landscape Spatial Configuration0.753***
KJ5←Landscape Spatial Configuration0.709***
KJ4←Landscape Spatial Configuration0.777***
KJ3←Landscape Spatial Configuration0.662***
KJ2←Landscape Spatial Configuration0.654***
KJ1←Landscape Spatial Configuration0.664***
ZT4←Thermal Comfort0.533***
ZT3←Thermal Comfort0.708***
ZT2←Thermal Comfort0.708***
ZT1←Thermal Comfort0.652***
LQ1←Destination Loyalty0.594***
LQ2←Destination Loyalty0.720***
LQ3←Destination Loyalty0.764***
LQ4←Destination Loyalty0.671***
LQ5←Destination Loyalty0.694***
JK1←Public perceived Health0.728***
JK2←Public perceived Health0.824***
JK3←Public perceived Health0.780***
JK4←Public perceived Health0.529***
Note. *** p < 0.01. The same applies to Table 7.
Table 7. SEM Path Analysis Results of Influencing Factors.
Table 7. SEM Path Analysis Results of Influencing Factors.
Path RelationshipStandardized Coefficient(r)Critical Ratiop-ValueResult
Thermal Comfort←Landscape Spatial Configuration0.5045.83***Supported
Destination Loyalty←Landscape Spatial Configuration0.3715.102***Supported
Destination Loyalty←Thermal Comfort0.5375.731***Supported
Public perceived Health←Destination Loyalty0.3743.468***Supported
Public perceived Health←Landscape Spatial Configuration0.4175.321***Supported
Public perceived Health←Thermal Comfort0.0670.7350.462Rejected
Table 8. Mediation Effect Analysis Results.
Table 8. Mediation Effect Analysis Results.
Path RelationshipEffect TypePoint EstimateBias-Corrected 95% CIMediation Conclusion
LowerUpper
Thermal Comfort → Destination Loyalty → Public perceived HealthTotal Effect0.2680.1320.416Full Mediation
Direct Effect0.067−0.1640.305
Indirect Effect0.2010.0490.397
Landscape Spatial Configuration → Destination Loyalty → Public perceived HealthTotal Effect0.5560.3970.691data
Direct Effect0.4170.2220.589
Indirect Effect0.1390.0340.283
Note. Statistical significance is determined by examining the confidence interval (CI). An effect is considered statistically significant at the p < 0.05 level if the 95% bias-corrected bootstrap CI does not include zero [70,71].
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Li, J.; Liu, Y.; Cai, X.; Zhu, D.; Liu, X.; Liu, S.; Liu, W. From Landscape Configuration to Health Outcomes: A Spatial–Behavioral Framework Linking Park Landscapes to Public Perceived Health Through Thermal Comfort and Loyalty Dynamics. Buildings 2026, 16, 260. https://doi.org/10.3390/buildings16020260

AMA Style

Li J, Liu Y, Cai X, Zhu D, Liu X, Liu S, Liu W. From Landscape Configuration to Health Outcomes: A Spatial–Behavioral Framework Linking Park Landscapes to Public Perceived Health Through Thermal Comfort and Loyalty Dynamics. Buildings. 2026; 16(2):260. https://doi.org/10.3390/buildings16020260

Chicago/Turabian Style

Li, Jiang, Yudan Liu, Xiaoxi Cai, Dandi Zhu, Xingyu Liu, Shaobo Liu, and Weiwei Liu. 2026. "From Landscape Configuration to Health Outcomes: A Spatial–Behavioral Framework Linking Park Landscapes to Public Perceived Health Through Thermal Comfort and Loyalty Dynamics" Buildings 16, no. 2: 260. https://doi.org/10.3390/buildings16020260

APA Style

Li, J., Liu, Y., Cai, X., Zhu, D., Liu, X., Liu, S., & Liu, W. (2026). From Landscape Configuration to Health Outcomes: A Spatial–Behavioral Framework Linking Park Landscapes to Public Perceived Health Through Thermal Comfort and Loyalty Dynamics. Buildings, 16(2), 260. https://doi.org/10.3390/buildings16020260

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