Influence of objective and perceived exposures to urban nature on people’s happiness

Influence of objective and perceived exposures to urban nature on people’s happiness | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Influence of objective and perceived exposures to urban nature on people’s happiness Maosu Li, Song Guo, Fábio Duarte, Ashutosh Kumar, Norimasa Kobori, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6235999/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2026 Read the published version in npj Urban Sustainability → Version 1 posted 9 You are reading this latest preprint version Abstract Exposure to nature influences urban dwellers’ well-being and happiness, thereby impacting urban sustainability. However, urban dwellers are exposed to nature in different ways: indirect exposure through window views, incidental exposure when walking along streets, and intentional exposure when visiting parks. Moreover, objective exposure does not necessarily align with how people perceive their exposure to nature. This study examines how three types of objective nature exposure—indirect, incidental, and intentional—provided by greenery and water bodies, along with perceived exposure, impact happiness in Tokyo, Japan. To measure the objective exposure, we use 3D photorealistic city information models, street view imagery, road network datasets, and remote sensing imagery. To measure happiness and perceived nature exposure, we use data from a national survey, focusing on the results from 10,798 residents in 801 neighborhoods in Tokyo. Results showed that perceived nature exposure has higher explanatory power for happiness than objective exposure. Views of greenery from windows (indirect exposure) and proximity to parks (intentional exposure) influenced perceived nature exposure and happiness the most. The quantitative evidence suggests that urban planning align with human behavior, e.g., by prioritizing the improvement of greenery views from windows and park accessibility in Tokyo to promote urban sustainability. Health sciences/Health care/Quality of life Social science/Psychology/Human behaviour Earth and environmental sciences/Environmental social sciences Humanities/Health humanities Social science/Development studies Social science/Environmental studies Social science/Geography Nature exposure Happiness Human perception Green-blue infrastructure Window view Street view Visual AI Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Mental disorder affects approximately 13.9% of the global population (WHO 2022) , and extreme cases lead to about 632,700 suicides per year worldwide (WHO 2021) . Urban dwellers in densely populated cities, like Tokyo, are particularly vulnerable to mental disorders and suicide (Tanaka & Okamoto 2021; Chiba et al. 2023) due to the fast-paced lifestyles and crowded living environments (WHO 2020). Exposure to nature, such as greenery and water bodies in cities, has been found to significantly mitigate urban well-being issues (Ulrich et al. 1991; Kaplan 1995), facilitating urban sustainability (Fagerholm et al. 2022; Lin et al. 2023; Lee & Han 2025) . The benefits of the exposure include improvement of physical and mental issues (e.g., fatigue, stress, and depression) (Maes et al. 2021), productivity (van Esch et al. 2019), and life satisfaction (Chen et al. 2022). As a result, green-blue infrastructures (e.g., street trees, parks, and water bodies), as the nature-based solution (Li et al. 2023; Raymond et al. 2025), are planned, designed, and maintained in cities to foster urban sustainability and address the growing well-being crisis (Ordóñez et al. 2023; van Oorschot et al. 2024). Urban environments offer different ways for people’s exposure to nature (Cox et al. 2017). Natural elements, e.g., greenery and water bodies, are “indirectly” experienced from the window when staying at home and in the workplace. Natural elements are also “incidentally” viewed by urban dwellers commuting on streets. Last, greenery and water bodies in parks, gardens, and ponds are “intentionally” visited. However, these three types of objective exposure to nature are not equal to how people perceive nature. Urban planners and designers find it challenging to offer all three types of nature exposures (Spotswood et al. 2021; van Oorschot et al. 2024) to improve human-perceived nature exposure and well-being, especially in high-density urban areas (Li et al. 2023). Most studies (Sarkar et al. 2018; Maes et al. 2021; Berdejo-Espinola et al. 2024) tend to assess the objective attributes of greenery and water bodies, e.g., areas and volumes. However, the presence and quantities of greenery and water bodies do not indicate how much objective exposure they can provide. With the increasingly available street view imagery and road network datasets, researchers measure the objective amount of “incidental” and “intentional” exposures from streets and parks (Wang et al. 2021; Yue et al. 2022; Mao et al. 2024). However, “indirect” exposure from windows was previously challenging to be quantified due to the unavailable datasets on window views (Li et al. 2023). As we spend most of our lives indoors, not quantifying the “indirect” exposure overlooks an important dimension of exposure to nature. Leveraging up-to-date photorealistic City Information Models (CIMs), window views can be generated to assess indirect exposure to nature (Li et al. 2022). The quantified indirect exposure supplements the quantified incidental (e.g., along streets) and intentional (e.g., access to parks) exposure. We use Tokyo, the world’s largest megacity, as a case study to examine how three types of objective nature exposure—indirect, incidental, and intentional—provided by greenery and water bodies, along with perceived exposure, impact urban dwellers’ declared well-being. We use happiness as a proxy of well-being (Kammann et al. 1984; Veenhoven 2011; Medvedev & Landhuis 2018) since Japan has been conducting a national survey about citizens’ happiness since 2022, covering 73,358 respondents on average annually. Perceived exposure to nature, which refers to how close respondents feel to nature, has been collected in the national survey. Figure 1a shows the average ratings of happiness for 10,798 respondents in 801 neighborhoods of urban Tokyo ranging from 0 (low) to 10 (high). The average ratings of the perceived exposure to nature range from 1 to 5 as shown in Figure 1b. A high value indicates that respondents in the neighborhood feel close to nature. In the study, a neighborhood encompasses multiple city blocks covered by the same zip code. Urban Tokyo includes 23 special wards and 26 cities. Using photorealistic CIMs, street view imagery, network datasets, and remote sensing imagery, we quantified three types of exposures to nature objectively provided by green-blue infrastructures, e.g., street trees, parks, lakes, rivers, and the sea. For the indirect exposure, we calculated the Window Greenery View Index (WV green ) and Window Water View Index (WV water ); For the incidental exposure, we calculated the Street Greenery View Index (SV green ) and Street Water View Index (SV water ); For the intentional exposure, we used two metrics for the closest park and accessible parks in the buffer zone: the Closest Park Greenery Index (CP green ), the Closest Park Water Index (CP water ), the Average Park Greenery Index (AP green ), and the Average Park Water Index (AP water ). We also assessed the amount of Unplanned Forests (UF) and Water bodies (UW), to present disparity of natural resources in local contexts. Last, analyses of Ordinary Least Square (OLS) and Structural Equation Modeling (SEM) were applied to associate three types of objective exposures to nature, perceived exposure to nature, and happiness. For systematic analyses, SV green , SV water , AP green , AP water , UF, and UW at the neighborhood level were calculated for residences within 500 m, 750 m, 1,000 m, 1,250 m, and 1,500 m. Unless stated otherwise, our results are based on fully adjusted models with six indicators using the 15-minute road network buffer zone (travel distance γ = 1,250 m). This is the first study to systematically examine the impact difference of objective and perceived exposures to nature on people’ happiness for sustainable urban planning and design. We decomposed urban nature into components by their usability (planned and unplanned), quantified the objective exposure by the types of human-nature interaction (indirect, incidental, and intentional), and collected the perceived exposure from the national survey, as shown in Figure 2. Using happiness data of 10,798 residents of Tokyo, we revealed the importance of perceived nature exposure on urban dwellers’ happiness, as well as the impact difference of three types of objective nature exposures on perceived nature exposure and happiness. Last, we presented planning and design suggestions that align with human behavior, e.g., prioritizing the improvement of greenery views from windows and park accessibility to promote urban sustainability. Results Spatial distribution of three types of objective exposures to nature in urban Tokyo. Figure 3 shows the spatial distribution of three types of exposures objectively provided by greenery and water bodies in 801 neighborhoods of urban Tokyo. High values of the three types of greenery exposures exist in more neighborhoods than those of three types of water exposures. For greenery exposure, neighborhoods with high values of WV green existed in the southwestern part of urban Tokyo, as shown in Fig. 3 a. Neighborhoods with high values of SV green were located in the central part as shown in Fig. 3 c. By contrast, Figs. 3 e and 3 g show high values of CP green and AP green scattered across individual neighborhoods and clusters of neighborhoods surrounding large parks, respectively. For water exposure, low values of WV water , SV water , CP water , and AP water existed in most neighborhoods; High values existed in neighborhoods close to the river and sea. Except for green-blue infrastructures that provide three types of exposures, Fig. 3 i and j show mostly low-accessible unplanned forests and water bodies located in the western and eastern parts of Tokyo, respectively. Associations between objective exposures to nature and urban dwellers’ happiness. Table 1 shows no observable significant associations between the amount of three types of nature exposures objectively provided by neighborhood greenery and water bodies, and happiness. The insignificant associations indicated no direct contribution of objective nature exposure to happiness. Models OLS CP 1250 and OLS AP 1250 differ by applying two types of intentional nature exposure metrics, i.e., CPs and APs, respectively. The insignificant associations were also found when using buffer zones of 500 m, 750 m, 1,000 m, and 1,500 m (see Supplementary Table 1). By contrast, covariates, i.e., average mental status ( β = 0.418, 0.417, p < 0.001), average income satisfaction ( β = 0.168, 0.171, p < 0.001), and average age ( β = 0.080, 0.078, p < 0.05) at the neighborhood level positively influenced respondents’ happiness. One-unit improvement in mental status and income satisfaction increased happiness by 41.8% (± 8.2%, 95% Confidence Interval (CI)) and 16.8% (± 6.9%, 95% CI), respectively. Table 1 Associations between three types of objective exposures to nature and happiness. OLS CP 1250 OLS AP 1250 Descriptor Standardized coefficients ( β ) (SE) Significance ( p ) Standardized coefficients ( β ) (SE) Significance ( p ) Intercept 0.000 (0.029) 1.000 0.000 (0.029) 1.000 ZMaleProp 0.023 (0.030) 0.438 0.022 (0.030) 0.460 ZAvgAge 0.080 (0.031) 0.010* 0.078 (0.031) 0.012* ZPhysicalStatus 0.074 (0.042) 0.080 0.073 (0.042) 0.084 ZMentalStatus 0.418 (0.042) < 0.001*** 0.417 (0.042) < 0.001*** ZIncomeSatisfaction 0.168 (0.035) < 0.001*** 0.171 (0.035) < 0.001*** Zln(WV green ) 0.072 (0.047) 0.125 0.061 (0.046) 0.185 Zln(WV water ) 0.003 (0.032) 0.937 0.006 (0.032) 0.838 Zln(SV green ) -0.071 (0.045) 0.116 -0.071 (0.046) 0.122 Zln(SV water ) 0.054 (0.035) 0.120 0.066 (0.034) 0.057 Zln(CP green ) -0.018 (0.035) 0.614 Zln(CP water ) 0.039 (0.035) 0.265 Zln(AP green ) -0.027 (0.032) 0.401 Zln(AP water ) -0.018 (0.040) 0.651 Zln(UF) 0.057 (0.037) 0.121 0.052 (0.037) 0.154 Zln(UW) -0.029 (0.033) 0.387 -0.009 (0.036) 0.811 R 2 / adjusted R 2 0.380/0.317 0.328/0.316 AIC 1984.260 1984.338 Note: Significance codes: ***: < 0.001; **: < 0.01; *: < 0.05. Associations among objective and perceived nature exposures and urban dwellers’ happiness. Table 2 shows respondents’ perceived exposure to nature was positively associated with happiness ( β = 0.062, p = 0.036). Controlling other covariates, the increase of every unit of perceived exposure improved 6.2% (± 5.9%, 95% CI) of happiness. Compared to the observed insignificant associations between objective exposures and happiness, the significant positive association with perceived exposure further indicated that respondents tended to feel happy if they subjectively perceived nature. Respondents’ perceived exposure mediated the impacts of objective exposures on happiness. WV green , SV green , CP water , and AP green were significantly and positively associated with perceived exposure to nature. Mediated by perceived exposure, indirect greenery exposure represented by WV green ( β = 0.015, 0.017, p < 0.001) impacted most on happiness, whereas incidental greenery exposure represented by SV green ( β = 0.007, 0.005, p ≤ 0.029) showed the least significant impact. Additionally, intentional exposures represented by CPs and APs showed varied impacts. AP green (0.010, p < 0.001) positively influenced happiness, whereas CP green showed no significant impacts. Conversely, CP water ( β = 0.006, p = 0.001) was significant, whereas AP water showed no significant impacts on perceived exposure and happiness. The inverse relationship between CP and AP for greenery and water bodies suggested that nearby water bodies, rather than greenery, had a greater influence on urban dwellers’ perceptions. Last, unplanned natural resources like forests, rivers, and the sea (UF: ( β = 0.016, 0.015, p < 0.001; UW: ( β = 0.005, p ≤ 0.007) also positively influenced urban dwellers’ happiness, even being less viewed and visited from windows, streets, and parks. Table 2 Associations between three types of objective exposures to nature and happiness with the mediation of perceived exposure. M CP 1250 M AP 1250 Standardized coefficients ( β ) (SE) Significance ( p ) Standardized coefficients ( β ) (SE) Significance ( p ) Direct effects on happiness ZMaleProp 0.028 (0.030) 0.352 0.028 (0.030) 0.352 ZAvgAge 0.077 (0.030) 0.012** 0.077 (0.030) 0.012** ZPhysicalStatus 0.074 (0.042) 0.074 0.074 (0.042) 0.074 ZMentalStatus 0.411 (0.042) < 0.001*** 0.411 (0.042) < 0.001*** ZIncomeSatisfaction 0.157 (0.033) < 0.001*** 0.157 (0.033) < 0.001*** ZPerceivedNatureExposure 0.062 (0.030) 0.037* 0.062 (0.030) 0.037* Indirect effects mediated by perceived exposure to nature Zln(WV green ) 0.015 (0.007) < 0.001*** 0.017 (0.009) < 0.001*** Zln(WV water ) -0.002 (0.002) 0.170 -0.002 (0.002) 0.361 Zln(SV green ) 0.007 (0.004) 0.003** 0.005 (0.004) 0.029* Zln(SV water ) -0.002 (0.002) 0.180 -0.003 (0.002) 0.063 Zln(CP green ) 0.003 (0.002) 0.066 Zln(CP water ) 0.006 (0.003) 0.001** Zln(AP green ) 0.010 (0.005) < 0.001*** Zln(AP water ) 0.004 (0.003) 0.093 Zln(UF) 0.016 (0.008) < 0.001*** 0.015 (0.008) < 0.001*** Zln(UW) 0.005 (0.003) 0.007** 0.005 (0.003) 0.005** ZMaleProp -0.004 (0.003) 0.005** -0.004 (0.003) 0.005** ZAvgAge -0.003 (0.002) 0.116 -0.002 (0.002) 0.135 ZPhysicalStatus 0.004 (0.003) 0.077 0.003(0.003) 0.116 ZMentalStatus 0.003 (0.003) 0.190 0.004 (0.003) 0.099 ZIncomeSatisfaction 0.007 (0.004) < 0.001*** 0.006 (0.003) 0.002** CHISQ 8.215 0.513 9.297 0.410 CFI 1.000 0.999 GFI 0.999 0.999 SRMR 0.006 0.007 RMSEA 0.000 0.006 Note: Significance codes: ***: < 0.001; **: < 0.01; *: < 0.05. Sensitivity of associations between objective and perceived exposures. Figure 4 shows the impact variations of indirect greenery exposure (WV green ), incidental greenery exposure (SV green ), intentional greenery and water exposures (AP green , CP water , and AP water ), and unplanned natural resources (UF and UW) across buffer zones of 500 m, 750 m, 1,000 m, 1,250 m, and 1,500 m. Indirect and incidental water exposures (WV water and SV water ) and CP green owned insignificant associations across nearly all the buffer zones, so they were not presented in Fig. 4 . First, three greenery exposure indicators, (WV green , SV green , and AP green ) consistently influenced perceived exposure, with window view having the strongest effect ( β ≥ 0.226, p < 0.001), average park proximity the moderate ( β ≥ 0.121, p < 0.001), and the street view the weakest ( β ≥ 0.078, p < 0.05). By contrast, water exposure indicators showed mixed results. CP water became significant ( β ≥ 0.096, p < 0.01) beyond the 1,000-m buffer, while AP water was significant ( β ≥ 0.069, p < 0.05) at 500 m, 1,000 m, and 1,500 m. Second, the impacts of greenery and water exposure indicators changed with the increasing sizes of buffer zones. The influences of WV green and SV green showed a decreasing trend beyond 750 m, reflecting their limited influence over longer distances. AP green reached a peak ( β = 0.168, p < 0.001) at the 1000-m buffer zone, indicating an optimal range for its effect. Both the generally increasing impact of CP water and the decreasing impact of AP water reflected the high significance of nearby water bodies. Last, UF and UW were consistently significant but differently changed with the increasing sizes of buffer zones. UF’s impact ( β ≥ 0.113, p < 0.001) increased as buffer zones expanded, while UW peaked ( β ≤ 0.123, p < 0.001) at the 750 m buffer zone. Overall, indirect greenery exposure (WV green ) influenced perceived exposure to nature the most, whereas intentional and incidental greenery exposure (AP green and SV green ) showed consistently moderate and low impacts, as shown in Fig. 4 . Water exposure (CP water and AP water ) had positive impacts only at specific buffer sizes. The varying changes of significance and impacts between greenery and water exposures suggested that urban dwellers’ perception of water is more distance-sensitive than greenery. Discussion Different impacts of objective and perceived exposures to nature on urban dwellers’ happiness. Our findings revealed different impacts of objective and perceived exposures to nature on happiness in urban Tokyo. We found urban dwellers’ perceived exposure to nature had a direct effect on happiness, whereas the indirect effects of objective exposures on happiness were mediated by the perceived exposure. In urban Tokyo, fast-paced lifestyles may limit the ability of urban dwellers, especially employees, to directly benefit from nature exposure (Otsuka et al. 2023 ), reducing the impact of objective measures. Conversely, urban dwellers with higher perceived exposure to nature reported greater happiness, possibly due to their high motivation and efforts to access nearby green-blue infrastructures, such as street trees, parks, and ponds, from windows, streets, and parks. Unequal impacts of three types of objective exposures on perceived exposure to nature. Regarding the greenery exposures, there existed varied impacts of indirect (Window view), incidental (Street view), and intentional (Proximity to parks) exposures on urban dwellers’ perceived exposure. Window view (WV green ) had the strongest impact, likely due to the long-term indoor occupation of urban dwellers in Tokyo (Althoff et al. 2017 ; Abe et al. 2023 ). Intentional exposure showed a moderate impact. Average amount of greenery from all accessible parks (AP green ) was more impactful than that from the closest park (CP green ), reflecting the importance of accessibility of multiple “pocket” parks and larger parks in the neighborhood. The 1000-m buffer zone with the highest impact of AP green suggested an optimal range for park planning to maximize perceived exposure to nature and happiness. By contrast, incidental greenery exposure (SV green ) showed the weakest impact. Observed reasons include limited street greenery in Tokyo’s dense urban area and urban dwellers’ limited experience of street greenery due to daily commuting via public transportation, e.g., railways (Liang et al. 2023 ). Regarding the water exposures, only intentional (Proximity to parks) water exposure showed an observed significant association with perceived exposure at specific buffer sizes. The result indicated the importance of physical visits of water bodies in parks on improvement urban dwellers’ perceived exposure instead of indirect and incidental viewing of water bodies from windows and streets. The nearby water bodies in parks had a high impact on urban dwellers’ perceived exposure possibly due to their high accessibility. For instance, AP water showed the strongest impact at the 500-m buffer zone. Another evidence is the increasing impact of CP water with the expansion of the buffer zone. Human behavior-guided planning strategy for urban sustainability. The different impacts of objective and perceived exposures to nature on happiness, as well as the unequal effects of three types of objective exposures on perceived exposure, call for considerations in sustainable urban planning and management policies. How can we strategically optimize different types of nature exposure to maximize perceived exposure and happiness in cities? First, as the most impactful indicator, greenery exposure provided by windows needs to be enhanced. At the building level, policymakers could require proposed construction projects to conduct visual impact analyses to safeguard and enhance greenery visibility for surrounding buildings. Specific design strategies like green facades and rooftops can also increase indirect exposure to greenery in high-rise, high-density urban areas. At the neighborhood level, strategically coordinating building attributes, such as height and window orientation, with the spatial configuration of greenery, is promising to ensure equitable access to greenery views across all buildings. The statistically significant impact of intentional exposure from parks suggests that urban planners should prioritize the availability of greenery and water bodies within multiple accessible parks and the nearby parks, respectively. For incidental exposure from streets, strategically targeting ‘hotspots’ in road networks for tree planting and maintenance, have the potential to enhance exposure more efficiently. Example hotspots include roads with high pedestrian flows. The findings from the sensitivity analysis provide quantitative evidence on radii of planning and design. The “sweet spot” of 1,000 m can be used as a benchmark to assess whether buildings’ accessible greenery needs improvement, as the impact of AP green declined beyond this distance. A 500-m buffer is recommended for evaluating water bodies in accessible parks from the building, given the high impact of nearby water bodies. Additionally, buffer distances of 1,500 m and 750 m are recommended for assessing the impacts of unplanned forests and water bodies surrounding buildings, respectively, when planning green-blue infrastructures for improving three types of objective exposures. The role of systematic assessment of nature exposure for sustainable urban planning. The findings point out that an even spatial distribution of green-blue infrastructures does not necessarily ensure equal exposure for all urban dwellers. Assessing both objective and perceived nature exposures improves understanding of the relationship between the availability of green-blue infrastructures and human perceptions. If a strong bias exists, public policies can guide and encourage urban dwellers’ active daily nature exposure, alongside planning and design interventions. Furthermore, completely understanding impact difference of three types of objective nature exposures on perceived exposure and happiness can better inform urban planning and design practices. For example, prioritizing improvement of window views of greenery and park proximity over street-level exposure could be more beneficial. Multi-scale analysis validates the robustness of findings and offers quantitative planning suggestions, e.g., values of radii, for specific spatial contexts. Contribution. First, this is the first study to systematically explore how objective exposure to nature in cities influences urban dwellers’ happiness through their subjective perceptions. We classified urban nature elements, i.e., greenery and water bodies by their usability (planned/unplanned), fully quantified three types of objective exposures (indirect, incidental, and intentional), and collected the ratings of perceived exposure from the national survey. The fine-grained decomposition of urban nature enabled a systematic path model between urban nature and urban dwellers’ happiness. Second, using the happiness data of 10,798 urban dwellers in urban Tokyo, we achieved robust quantitative evidence on the mechanism of human-nature interaction for happiness. We found that perceived nature exposure had a higher explanatory power for urban dwellers’ happiness than objective nature exposure; ii) views of greenery from windows (indirect exposure) and proximity to parks (intentional exposure) influenced perceived nature exposure and happiness the most. Last, our findings argued the importance of understanding how people perceive nature to plan and design green-blue infrastructures for urban sustainability. Our findings demonstrated that greenery exposure provided by windows and accessible parks in the activity range (travel distance γ = 1,000 m) needs to be first enhanced to improve urban dwellers’ perceived nature exposure and happiness in urban Tokyo. Overall, the findings in the study identified the linkages among objective exposure to nature, human-perceived exposure, and people’s happiness in cities, contributing to research in environmental studies, psychology, and planning for urban sustainability. Limitation and future work. First, due to privacy regulation, the analytical results at the neighborhood level may not capture individual-level perception differences on three types of objective exposures. Additionally, the generalizability of analytical results needs to be examined in other cities with different urban morphologies and lifestyles. Last, the study is cross-sectional, where the analytical results may not capture changes over time. Future work could consider individual-level longitudinal analysis in multiple cities to compare the impact difference of objective and perceived exposures on urban dwellers’ happiness. Methods Study area. Supplementary Fig. 1 shows urban Tokyo, Japan, i.e., 23 special wards and 26 cities as the study area. As a typical high-density city, Tokyo owned both undulating skyscrapers and low-rise buildings. There existed 20,405 buildings with heights above 30 m in 23 special wards, whereas most low-rise buildings existed in 26 cities with an average height of 7.60 m (MLIT 2023 ). The maximum building density of neighborhoods exceeds 0.90 (MLIT 2023 ), and the population density is 6,158 persons per square kilometer. Supplementary Fig. 1 shows unevenly distributed greenery and water bodies. Unplanned forests and large parks existed in the mountainous areas of central western and western Tokyo. On the contrary, large areas of greenery resources were scarce in central eastern and eastern Tokyo due to intense urban development. As compensation, considerable small parks were constructed in central eastern and eastern Tokyo to enhance urban dwellers’ greenery exposure. Additionally, there existed low quantities of water bodies in most areas. The sea bordering eastern Tokyo and rivers were major water bodies that provided water exposure. The diverse morphology of buildings and green-blue infrastructure results in varying levels of three types of objective nature exposures for urban dwellers. Conceptual modeling. The study aims to examine the impacts of three types of nature exposures objectively provided by neighborhood greenery and water bodies and perceived nature exposure on urban dwellers’ happiness in Tokyo. To carefully examine the objective and perceived nature exposures, we first classify the greenery and water bodies into planned and unplanned natural resources as shown in Fig. 2 . Compared to lowly accessible unplanned natural resources, e.g., prime forest and water bodies, green-blue infrastructures, e.g., street trees and parks are planned for the provision of nature exposure in cities. Then, we assess the amount of three types of nature exposures (indirect, incidental, and intentional) objectively provided by the neighborhood green-blue infrastructures. Last, we collected human-perceived nature exposure from the national survey. By controlling the impact of local unplanned natural resources and the demographic and socioeconomic profiles of respondents, we try to examine: i. The significance of perceived and three types of objective nature exposures on happiness, and ii. The impact difference of three types of objective nature exposures on perceived nature exposure and happiness. Happiness and perceived nature exposure. The happiness dataset for 10,798 respondents in urban Tokyo, i.e., 23 special wards and 26 cities, was shared by the Japan Digital Agency. The online questionnaire survey was conducted in May 2024. Respondents needed to be Japanese permanent residents aged between 18 and 89. Basic demographic, socioeconomic, and health profiles, e.g., age, gender, physical status, mental status, and income satisfaction were recorded. Respondents grouped by the neighborhood rated their happiness together with the perceived nature exposure. According to the population of urban dwellers aged between 18 and 89 in Tokyo at 11,462,100, the survey with a sample size of 10,798 (≥ 9,595) could achieve representative statistical results with confidence at 95% and a margin error of 1%. The age composition between the samples and the population of urban Tokyo achieved a good agreement. The average age of 10,798 respondents and the urban dwellers aged between 18 and 89 in Tokyo were 50.90 and 49.87, respectively. The survey collected happiness ratings of relatively more male respondents than female respondents. The ratio between male and female respondents was 1.35 : 1. Due to the privacy regulation, the data were shared by the Japan Digital Agency following two rules. First, the dataset was shared at the cohort level rather than the individual level. Besides, data for cohorts with less than five respondents were hidden and excluded. Figure 1 shows 801 available cohorts in neighborhoods covering all special wards and cities of Tokyo. Objective nature exposure metrics. We represent the indirect, incidental, and intentional nature exposures of 801 cohorts at the neighborhood level. At each neighborhood, buildings are randomly sampled with confidence at 90% and margin error at 10%. The sampling size of buildings ranges from 6 to 68 for neighborhoods with varied areas and building densities. The total number of sampled buildings is 52,987. Window views were quantified from the sampled buildings to represent the average level of indirect nature exposure provided by the neighborhood greenery and water bodies, whereas greenery-water amount from street views and parks were quantified within multiple road network buffer zones of sampled buildings to represent the average levels of incidental and intentional nature exposures. We estimated three types of exposures to nature within the 15-minute buffer zone with the travel distance γ at 1,250 m (Kolcsár et al. 2021 ). To understand the sensitivity of buffer zone sizes, we further generated buffer zones for each sampled building with γ at 500 m, 750 m, 1,000 m, and 1,500 m for 6, 9, 12, and 18 minutes, respectively. i) Indirect exposure through window view Window Greenery View Index (WV green ) and Window Water View Index (WV water ) refer to the proportions of greenery and water bodies occurring in the photorealistic window view image (Li et al. 2022 ), respectively, as shown in Supplementary Fig. 2a. We computed the WV green and WV water at the neighborhood level via Equations 1 and 2. WV nbhd = ( WV 1 bldg + ... + WV m bldg ) ∕ m , (1) WV bldg = ( WV 1 f × d 1 + ... + WV n f × d n ) ∕ ( d 1 + ... + d n ), (2) where WV m bldg is WV green or WV water of the sampled building m , and WV nbhd is the average values of WVs of m sampled buildings in the neighborhood. WV n f represents the average values of WV green or WV water quantified from floors of the building facade n , as shown in Supplementary Fig. 2b. n is the total number of building facades, while d is the facade width. Supplementary Fig. 2 shows WV bldg quantified from Google photorealistic CIM-generated window view images. Since neighborhood windows often share similar views, we sampled window sites at varying floors of a building for a cost-effective quantification (Li et al. 2022 ), as shown in Supplementary Fig. 2a. Referring to Li’s method (2022), we controlled the height gap between sampled floors ≤ 5 m. The 3D building geometries, e.g., shape and heights, were collected from the project Plateau (MLIT 2023 ) launched by the Ministry of Land, Infrastructure, Transport and Tourism, Japan. Then, a virtual camera in the Geo-visualization platform, Cesium (ver. 1.111), was placed at each window site to capture the outside photorealistic view, as shown in Supplementary Fig. 2a. Thereafter, photorealistic window view images with the size of 900 × 900 pixels were segmented into the sky, greenery, water body, and construction using the up-to-date deep learning model, Segment of Anything (SAM) (Kirillov et al. 2023 ). Finetuned on 3,000 annotated photorealistic window views (Li et al. 2024 ), the SAM achieved a satisfactory performance with mIoU at 91.61%. The per-class IoUs for greenery and water bodies were 90.59% and 94.88%, respectively. Then, WV green and WV water were quantified by counting pixels of the segmentation mask generated from SAM. The width of the building facade determined the number of neighborhood windows represented by the sampled window view on each floor (Li et al. 2023 ). Thus, we aggregated the values of WV f green and WV f water at all building facades using the facade width d as the weight via Eq. 2. Last, WV nbhd was computed by averaging the values of WV bldg of sampled buildings via Eq. 1. We generated a total of 302,470 window view images for sampled buildings in 801 neighborhoods. ii) Incidental exposure through street view Street Greenery View Index (SV green ) and Street Water View Index (SV water ) refer to the proportions of greenery and water bodies occurring in the street view image, respectively, as shown in Supplementary Fig. 3. We computed SV green and SV water at the neighborhood level via Equations 3 and 4. SV nbhd = ( SV 1 bldg + … + SV m bldg ) ∕ m , (3) SV bldg = ( SV 1 bf + … + SV p bf ) ∕ p , (4) where SV m bldg is SV green or SV water of the sampled building m , and SV nbhd is the average value of SVs of m buildings in the neighborhood. SV p bf represents the SV green or SV water of the sampled street view p within the buffer zone. The irregular buffer zones were convex hulls covering the road network segments sprawling from the sampled building with γ at 500 m, 750 m, 1,000 m, 1,250 m, and 1,500 m. Supplementary Fig. 3 shows SV green and SV water quantified from street view images. We first collected the 360-degree panoramas from Google Street View at a 50-m interval for the whole study area, as shown in Supplementary Fig. 3a. We manually removed street views captured inside commercial buildings, train stations, and developed green-blue spaces, e.g., temples, gardens, and parks, for pure quantification of nature exposures on the streets. Additionally, low-quality street views, e.g., the overexposed and blurred were excluded regarding the information loss. Then, the front and rear views of the street with the size of 2,048 × 1,024 pixels were converted from the panorama, as shown in Supplementary Fig. 3b. We extracted the greenery and water bodies from the front and rear street views using the Dense Prediction Transformer (DPT) (Ranftl et al. 2021 ) trained on the ADE20K dataset (Zhou et al. 2017 ). The erroneous segmentation of large-area greenery and waterbody within street views was manually improved to enhance DPT’s performance, with per-class IoUs at 83.56% and 73.89%, respectively. Thereafter, SV green and SV water were aggregated in the buffer zone of each sampled building via Eq. 4. Last, the SV nbhd was computed by averaging the values of SV bldg of all sampled buildings via Eq. 3. Overall, 699,934 Google Street Views were quantified for sampled buildings of 801 neighborhoods. iii) Intentional exposure through available greenery and water bodies in parks Urban dwellers’ intentional nature exposure was represented using two types of metrics, i.e., the average area of greenery and water resources of the i) closest park and ii) accessible parks within the buffer zones of the sampled building. The two types of metrics highlight individual neighborhoods and clusters of neighborhoods that benefit from high levels of nature exposure from surrounding large parks, respectively. The corresponding indexes are the Closest Park Greenery Index (CP green ), Closest Park Water Index (CP water ), Average Park Greenery Index (AP green ), and Average Park Water Index (AP water ). In this study, the park refers to multiple types of natural spaces that provide visitable greenery and water bodies for recreation. The quantified natural spaces include the garden, playground, and temple. Particularly, we computed the CP green and CP water at the neighborhood level via Equations 5 and 6. CP nbhd = ( CP 1 bldg + … + CP m bldg ) ∕ m , (5) CP bldg = area ( closest ({ P 1 , P 2 , …, P q })), (6) where CP m bldg is the CP green or CP water of the closest park from the sampled building m . CP nbhd refers to the average values of CP bldg of m sampled buildings within the neighborhood. Closest is a spatial function to identify the closest park P of the sampled building, whereas area is a function to calculate the areas of the greenery or water bodies of the closest park. P q refers to the park q . Additionally, we computed the AP green and AP water at the neighborhood level via Equations 7 and 8. AP nbhd = ( AP 1 bldg + … + AP m bldg ) ∕ m , (7) AP bldg . = ( area ( P 1 bf ) + … + area ( P k bf )) ∕ k , (8) where AP m bldg is the AP green or AP water of the sampled building m . AP nbhd refers to the average values of AP bldg of m sampled buildings within the neighborhood. P k bf refers to the park k within the buffer zone. Supplementary Fig. 4 shows CP green , CP water , AP green , and AP water computed from the network analysis. The network analysis was conducted based on park sets and road networks from Open Street Map datasets, building footprints from Plateau, and remote sensing imagery of greenery and water bodies from the European Space Agency World Cover 10 m via Google Earth Engine. First, the closest park was extracted by comparing the road network distance between the sampled building and parks, as shown in Supplementary Fig. 4a. Accessible parks of the sampled building were identified by spatially joining the affiliated buffer zones and the polygon vectors of parks, as shown in Supplementary Fig. 4b. Then, the areas of greenery and water bodies of the closest and accessible parks were quantified from the land cover map for CP green , CP water , AP green , and AP water via Equations 6 and 8. Last, we aggregated the four building-level indexes for CP green , CP water , AP green , and AP water via Equations 5 and 7. Overall, 11,931 parks were collected for the analyses of 52,987 sampled buildings in 801 neighborhoods. Covariates. We controlled the typical demographic and socioeconomic profile attributes related to happiness within the cohorts. The five covariates include the average age, gender proportion, average physical status, average mental status, and average income satisfaction. On a five-point Likert scale, the average physical and mental status of the cohorts were 3.23 and 3.31, respectively. Both values above 3 indicated a relatively good health status. By contrast, there existed a slightly low income satisfaction, with the mean at 2.91. We also controlled the area of unplanned natural resources in the local context. Examples are forests on mountains and hills in western Tokyo. Different from developed green-blue infrastructures, e.g., street trees and parks, there existed a considerable amount of unplanned natural resources with low accessibility for urban dwellers’ greenery and water exposures. Nevertheless, urban dwellers may also feel close to nature due to long-term local experiences, even with limited viewable and visitable greenery and water bodies from windows, streets, and accessible parks. Thus, we defined the Unplanned Forest Index (UF) and Unplanned Water Index (UW) as the area of unplanned natural resources within the sampled building’s buffer zone for control purposes. We quantified UF and UW at the neighborhood level via Equations 9 and 10. U nbhd = ( U 1 bldg + … + U m bldg ) ∕ m , (9) U bldg = ( area ( U 1 bf ) + … + area ( U s bf )) ∕ s , (10) where U m bldg is UF or UW of the building m , and U s bf refers to the unplanned natural resources within the buffer zone. Supplementary Fig. 5 shows the assessment of unplanned natural resources. In this study, we regarded greenery (e.g., forests) and water bodies (e.g., sea and rivers) that were not from streets and parks as unplanned natural resources. The UF and UW of each sampled building were computed from the European Space Agency World Cover 10 m via Google Earth Engine. The polygon vectors of unplanned natural resources were collected from Open Street Map datasets. We aggregated the area of greenery and water bodies of each vector polygon within a buffer zone of the sampled buildings. Particularly, due to urban dwellers’ low accessibility to the unplanned natural resources through road networks, we used the round buffer zones (γ = 500 m, 750 m, 1,000 m, 1,250 m, and 1,500 m) to compute UF bldg and UW bldg as shown in Supplementary Figs. 5a and b. Last, the UF nbhd and UW nbhd were computed by averaging the values of UF bldg and UW bldg via Eq. 9. Statistical analyses. We first conducted an OLS regression to test the associations between objective exposures and happiness. Then, we added the mediator variable, perceived nature exposure into the relationships through Path Analysis, a subset of SEM. Carefully following the basic assumptions, we conducted regression analyses using R programming language (ver. 4.4.1). For OLS regression, we first confirmed all independent variables, including three types of objective exposure indicators and covariates without high correlations. Pearson and Spearman correlation analyses were conducted for independent variables according to their normality. The scatter plots between independent variables and dependent variables, between independent variables and the mediator, were examined to identify and transform the non-linear relationships. For example, all three greenery exposure indicators were transformed through a natural logarithmic function to maintain a linear relationship with human-perceived nature exposure. Then, by controlling covariates, we examined the associations between three types of objective nature exposure indicators and average ratings of happiness (see Eq. 11). All variables were normalized and R 2 was applied to assess the model performance. A high value of R 2 indicates a high explainability of the observation variables and covariates. Last, we conducted a series of analyses for the regression model to confirm the validity of the statistical result. The analyses included collinearity analysis, Durbin–Watson analysis for the independence of the observation, casewise diagnostics for outlier detection, and analysis of variance (ANOVA) for the regression models as shown in Supplementary Tables 2 and 3. Scatter plots for predicted value and residuals, as well as scatter plots for independent variables and error terms, were examined to confirm an even distribution. ZHappiness = β 0 + β 1 × ZMaleProp + β 2 × ZAvgAge + β 3 × ZPhysicalStatus + β 4 × ZMentalStatus + β 5 × ZIncomeSatisfaction + β 6 × Zln ( WV green ) + β 7 × Zln ( WV water ) + β 8 × Zln ( SV green ) + β 9 × Zln ( SV water ) + β 10 × Zln ( CP green or AP green ) + β 11 × Zln ( CP water or AP water ) + β 12 × Zln ( UF ) + β 13 × Zln ( UW ) + ε. (11) For SEM analysis, we constructed models based on the conceptual modeling as shown in Fig. 2 . Due to the insignificant associations between three types of objective exposures and happiness, we turned to examine the direct effect of perceived exposure on happiness, and the indirect effects of three types of objective exposures on happiness mediated by perceived exposure (see Equations 12 and 13). The constructed models among three types of objective nature exposures, covariates, human-perceived nature exposure, and happiness were adjusted step-by-step to achieve a satisfactory fit performance. We added spatial error terms ( u ) to avoid spatial autocorrelation of residuals in the analyses (see Eq. 13). The estimator, Generalized Least Squares, was used to estimate model parameters. The evaluation metrics of the model fit include Chi-Square statistic (CHISQ), CHISQ/degree of freedom (df), Goodness of Fit (GFI), Comparative Fit Index (CFI), Standardized Root Mean Square Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA) as shown in Supplementary Table 5. ZHappiness = γ 0 + γ 1 × ZMaleProp + γ 2 × ZAvgAge + γ 3 × ZPhysicalStatus + γ 4 × ZMentalStatus + γ 5 × ZIncomeSatisfaction + γ 6 × ZMentalStatus + γ 7 × ZPerceivedNatureExposure + δ , (12) ZPerceivedNatureExposure = λ 0 + λ 1 × Zln ( WV green ) + λ 2 × Zln ( WV water ) + λ 3 × Zln ( SV green ) + λ 4 × Zln ( SV water ) + λ 5 × Zln ( CP green or AP green ) + λ 6 × Zln ( CP water or AP water ) + λ 7 × Zln ( UF ) + λ 8 × Zln ( UW ) + λ 9 × u + σ . (13) Declarations Author Contribution M.L. and S.G. equally contributed to the study by conceiving and designing the research, collecting and analyzing the data, and writing the paper. F.D. conceived and designed the research and reviewed and edited the paper. A.K. and N.K., as local experts, supported the project and discussed the findings. F.X., W.Z., A.G.O.Y., and C.R. reviewed the paper with critical comments and suggestions. F.D. and C.R. supervised the project. All authors approved the manuscript and this submission. Acknowledgement The authors thank specially Woven by Toyota for their valuable support and HPC2021 computing services offered by Information Technology Services, the University of Hong Kong, for this research. Data Availability Full analytical results are provided within the manuscript or supplementary information files. Original processed data will be made available on request. References Abe, R., Ikarashi, T., Takada, S. & Fukuda, D. (2023). Impact of working from home on travel behavior of rail and car commuters: A case study in the Tokyo metropolitan area. Case Studies on Transport Policy, 11 , 100938. doi: 10.1016/j.cstp.2022.100938 Althoff, T., Sosič, R., Hicks, J. L., King, A. C., Delp, S. L. & Leskovec, J. (2017). Large-scale physical activity data reveal worldwide activity inequality. Nature, 547 , 336–339. doi: 10.1038/nature23018 Berdejo-Espinola, V., Fuller, R. A. & Zahnow, R. (2024). Well-being from nature exposure depends on socio-environmental contexts in Paraguay. Nature Cities, 1 (5), 335–345. doi: 10.1038/s44284-024-00058-0 Chen, B., Tu, Y., Wu, S., Song, Y., Jin, Y., Webster, C., Xu, B. & Gong, P. (2022). Beyond green environments: multi-scale difference in human exposure to greenspace in China. Environment International, 166 , 107348. doi: 10.1016/j.envint.2022.107348 Chiba, T., Ide, K., Murakami, M., Kobayashi, N., Oka, T., Nakai, F., Yorizawa, R., Miyake, Y., Hamamura, T., Honjo, M. & Toda, H. (2023). Event-related PTSD symptoms as a high-risk factor for suicide: longitudinal observational study. Nature Mental Health, 1 , 1013–1022. doi: 10.1038/s44220-023-00157-2 Cox, D. T., Hudson, H. L., Shanahan, D. F., Fuller, R. A. & Gaston, K. J. (2017). The rarity of direct experiences of nature in an urban population. Landscape and Urban Planning, 160 , 79–84. doi: 10.1016/j.landurbplan.2016.12.006 Fagerholm, N., Samuelsson, K., Eilola, S., Giusti, M., Hasanzadeh, K., Kajosaari, A., Koch, D., Korpilo, S., Kyttä, M., Legeby, A. & Liu, Y. (2022). Analysis of pandemic outdoor recreation and green infrastructure in Nordic cities to enhance urban resilience. npj Urban Sustainability, 2 (1), 25. doi: 10.1038/s42949-022-00068-8 Kammann, R., Farry, M. & Herbison, P. (1984). The analysis and measurement of happiness as a sense of well-being. Social Indicators Research, 15 , 91–115. doi: 10.1007/BF00426282 Kaplan, S. (1995). The restorative benefits of nature: Toward an integrative framework. Journal of Environmental Psychology, 15 (3), 169–182. doi: 10.1016/0272-4944(95)90001-2 Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A., Lo, W. & Dollár, P. (2023). Segment anything. Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4015–4026). Piscataway, New Jersey: IEEE. doi: 10.1109/ICCV51070.2023.00371 Kolcsár, R. A., Csikós, N. & Szilassi, P. (2021). Testing the limitations of buffer zones and Urban atlas population data in urban green space provision analyses through the case study of Szeged, Hungary. Urban Forestry & Urban Greening, 57 , 126942. doi: 10.1016/j.ufug.2020.126942 Lee, Y. & Han, S. (2025). From greenery to community: exploring the mediating role of loneliness in social cohesion. npj Urban Sustainability, 5 (1), 6. doi: 10.1038/s42949-025-00194-z Li, M., Xue, F. & Yeh, A. G. (2023). Bi-objective analytics of 3D visual-physical nature exposures in high-rise high-density cities for landscape and urban planning. Landscape and Urban Planning, 233 , 104714. doi: 10.1016/j.landurbplan.2023.104714 Li, M., Xue, F., Wu, Y. & Yeh, A. G. (2022). A room with a view: Automated assessment of window views for high-rise high-density areas using City Information Models and transfer deep learning. Landscape and Urban Planning, 226 , 104505. doi: 10.1016/j.landurbplan.2022.104505 Li, M., Yeh, A. G. & Xue, F. (2024). CIM-WV: A 2D semantic segmentation dataset of rich window view contents in high-rise, high-density areas based on photorealistic City Information Models. Urban Informatics, 3 , 1–24. doi: 10.1007/s44212-024-00039-7 Liang, J., Miwa, T., Wang, J. & Morikawa, T. (2023). Impact of telecommuting on Japanese Citizen's travel, activities, and residential locations: Experiences and future expectations under COVID-19 pandemic. Asian Transport Studies, 9 , 100105. doi: 10.1016/j.eastsj.2023.100105 Lin, B. B., Chang, C. C., Astell-Burt, T., Feng, X., Gardner, J. & Andersson, E. (2023). Nature experience from yards provide an important space for mental health during Covid-19. npj Urban Sustainability, 3 (1), 14. doi: 10.1038/s42949-023-00094-0 Maes, M. J., Pirani, M., Booth, E. R., Shen, C., Milligan, B., Jones, K. E. & Toledano, M. B. (2021). Benefit of woodland and other natural environments for adolescents’ cognition and mental health. Nature Sustainability, 4 (10), 851–858. doi: 10.1038/s41893-021-00751-1 Mao, Y., Xia, T., Hu, F., Chen, D., He, Y., Bi, X., Zhang, Y., Cao, L., Yan, J., Hu, J. & Ren, Y. (2024). The greener the living environment, the better the health? Examining the effects of multiple green exposure metrics on physical activity and health among young students. Environmental Research, 250 , 118520. doi: 10.1016/j.envres.2024.118520 Medvedev, O. N. & Landhuis, C. E. (2018). Exploring constructs of well-being, happiness and quality of life. PeerJ, 6 , e4903. doi: 10.7717/peerj.4903 MLIT. (2023). Project PLATEAU | The Initiative of Digital Twin in Japan. Ministry of Land, Infrastructure, Transport and Tourism. Retrieved from https://www.mlit.go.jp/en/toshi/daisei/plateau_en_2.html Ordóñez, C., Labib, S. M., Chung, L. & Conway, T. M. (2023). Satisfaction with urban trees associates with tree canopy cover and tree visibility around the home. npj Urban Sustainability, 3(1), 37., 3 (1), 37. doi: 10.1038/s42949-023-00119-8 Otsuka, Y., Imanishi, J., Nasu, M. & Iwasaki, Y. (2023). Probabilistic inference on the factors promoting park use by machine learning using panel data from 2014 to 2020 in Tokyo, Japan. Cities, 141 , 104509. doi: 10.1016/j.cities.2023.104509 Ranftl, R., Bochkovskiy, A. & Koltun, V. (2021). Vision transformers for dense prediction. Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 12179–12188). Piscataway, New Jersey: IEEE. doi: 10.1109/ICCV48922.2021.01196 Raymond, C., Rautio, P., Fagerholm, N., Aaltonen, V., Andersson, E., Celermajer, D., Christie, M., Hällfors, M., Saari, M., Mishra, H. & Lechner, A. (2025). Raymond, C.M., Rautio, P., Fagerholm, N., Aaltonen, V.A., Andersson, E., Celermajer, D., Christie, M., Hällfors, M., Saari, M.H., Mishra, H.S. and Lechner, A.M., 2025. Applying multispecies justice in nature-based solutions and urban sustainability planni. npj Urban Sustainability, 5 (1), 2. doi: 10.1038/s42949-025-00191-2 Sarkar, C., Webster, C. & Gallacher, J. (2018). Residential greenness and prevalence of major depressive disorders: a cross-sectional, observational, associational study of 94 879 adult UK Biobank participants. The Lancet Planetary Health, 2 (4), e162-e173. doi: 10.1016/S2542-5196(18)30051-2 Spotswood, E. N., Benjamin, M., Stoneburner, L., Wheeler, M. M., Beller, E. E., Balk, D., McPhearson, T., Kuo, M. & McDonald, R. I. (2021). Nature inequity and higher COVID-19 case rates in less-green neighbourhoods in the United States. Nature Sustainability, 4 (12), 1092–1098. doi: 10.1038/s41893-021-00781-9 Tanaka, T. & Okamoto, S. (2021). Increase in suicide following an initial decline during the COVID-19 pandemic in Japan. Nature Human Behaviour, 5 (2), 229–238. doi: 10.1038/s41562-020-01042-z Ulrich, R. S., Simons, R. F., Losito, B. D., Fiorito, E., Miles, M. A. & Zelson, M. (1991). Stress recovery during exposure to natural and urban environments. Journal of Environmental Psychology, 11 (3), 201–230. doi: 10.1016/S0272-4944(05)80184-7 van Esch, E., Minjock, R., Colarelli, S. M. & Hirsch, S. (2019). Office window views: View features trump nature in predicting employee well-being. Journal of Environmental Psychology, 64 , 56–64. doi: 10.1016/j.jenvp.2019.05.006 van Oorschot, J., Slootweg, M., Remme, R. P., Sprecher, B. & van der Voet, E. (2024). Optimizing green and gray infrastructure planning for sustainable urban development. npj Urban Sustainability, 4 (1), 41. doi: 10.1038/s42949-024-00178-5 Veenhoven, R. (2011). Happiness, Also Known as "Life Satisfaction" and "Subjective Well-Being". In K. C. Land, A. C. Michalos & M. J. Sirgy, Handbook of Social Indicators and Quality of Life Research (pp. 79–98). Berlin: Springer Science & Business Media. Wang, R., Feng, Z., Pearce, J., Zhou, S., Zhang, L. & Liu, Y. (2021). Dynamic greenspace exposure and residents’ mental health in Guangzhou, China: From over-head to eye-level perspective, from quantity to quality. Landscape and Urban Planning, 215 , 1042. doi: 10.1016/j.landurbplan.2021.104230 WHO. (2020). Integrating Health in Urban and Territorial Planning: A Sourcebook . Geneva: World Health Organization. WHO. (2021). Suicide Worldwide in 2019: Global Health Estimates . Geneva: World Health Organization. WHO. (2022). World Mental Health Report: Transforming Mental Health for All . Geneva: World Health Organization. Yue, Y., Yang, D. & Van Dyck, D. (2022). Urban greenspace and mental health in Chinese older adults: Associations across different greenspace measures and mediating effects of environmental perceptions. Health & Place, 76 , 102856. doi: 10.1016/j.healthplace.2022.102856 Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A. & Torralba, A. (2017). Scene parsing through ade20k dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 633–641). Piscataway, New Jersey: IEEE. doi: 10.1109/CVPR.2017.544 Additional Declarations No competing interests reported. Supplementary Files 3SupplementaryInfluenceofobjectiveandperceivedexposurestourbannatureonpeopleshappiness.docx Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2026 Read the published version in npj Urban Sustainability → Version 1 posted Editorial decision: Revision requested 05 Aug, 2025 Reviews received at journal 27 Jul, 2025 Reviews received at journal 26 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers invited by journal 08 May, 2025 Editor assigned by journal 30 Apr, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 16 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6235999","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":453627654,"identity":"d17e4aef-f6c1-44eb-a1ed-254bd12754ce","order_by":0,"name":"Maosu Li","email":"","orcid":"","institution":"Massachusetts Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Maosu","middleName":"","lastName":"Li","suffix":""},{"id":453627655,"identity":"f91e72b8-bee8-49d3-aada-0d6a94b76c17","order_by":1,"name":"Song Guo","email":"","orcid":"","institution":"Massachusetts Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Guo","suffix":""},{"id":453627656,"identity":"df72e780-08b1-4605-9ad7-32d78f181779","order_by":2,"name":"Fábio Duarte","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBAC9oYEMC3HwAyi2IjQwnMApCWBwZiBmZlELYkNDERrYc8x/Fz5wyZ9bTv/AYYPZYeJ0MLzxljyTEJa7rbDzAyMM84RocVeIsdAsiHhMFgLM28bMbZI5Bj/bEj4n24G0vKXSC1mQFsOJIC1MBKlhedZmWVDWrIh0GEGB3vOpROhhT15880GGzt5s/MHHz74UWZNWAsKOECi+lEwCkbBKBgFuAAAG4U26ASU1koAAAAASUVORK5CYII=","orcid":"","institution":"Massachusetts Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Fábio","middleName":"","lastName":"Duarte","suffix":""},{"id":453627657,"identity":"77d4d48f-d939-4d74-b466-33758d544889","order_by":3,"name":"Ashutosh Kumar","email":"","orcid":"","institution":"Woven by Toyota","correspondingAuthor":false,"prefix":"","firstName":"Ashutosh","middleName":"","lastName":"Kumar","suffix":""},{"id":453627659,"identity":"5a8160a4-6afa-465d-84f6-7589db014a75","order_by":4,"name":"Norimasa Kobori","email":"","orcid":"","institution":"Woven by Toyota","correspondingAuthor":false,"prefix":"","firstName":"Norimasa","middleName":"","lastName":"Kobori","suffix":""},{"id":453627662,"identity":"a5140b97-a8da-418c-9267-bfd9d76206f1","order_by":5,"name":"Fan Xue","email":"","orcid":"","institution":"University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Xue","suffix":""},{"id":453627663,"identity":"32d68c96-2b1b-4dc1-b371-e206e8c7ddf7","order_by":6,"name":"Weimin Zhuang","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Zhuang","suffix":""},{"id":453627664,"identity":"6dfdaf01-018c-4cd7-a959-f923eacf4d1c","order_by":7,"name":"Anthony G. O. Yeh","email":"","orcid":"","institution":"University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"G. O.","lastName":"Yeh","suffix":""},{"id":453627665,"identity":"d88f013a-bfba-4d3d-88ce-b10197eed9a6","order_by":8,"name":"Carlo Ratti","email":"","orcid":"","institution":"Massachusetts Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Carlo","middleName":"","lastName":"Ratti","suffix":""}],"badges":[],"createdAt":"2025-03-16 06:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6235999/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6235999/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42949-025-00306-9","type":"published","date":"2026-01-09T15:58:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82655898,"identity":"2a9654b0-7bc4-4ebe-b899-436f70c6f0c2","added_by":"auto","created_at":"2025-05-13 18:44:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":976016,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of average happiness ratings and perceived exposure to nature of 10,798 respondents in 801 neighborhoods of urban Tokyo.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6235999/v1/70cebf3a26d224f6d2c31e2b.png"},{"id":82655048,"identity":"77dd2e1c-c043-4710-be6b-77c3fbbfca8c","added_by":"auto","created_at":"2025-05-13 18:36:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":436719,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual modeling.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6235999/v1/b10c06251e08df477af72a04.png"},{"id":82655045,"identity":"02117147-713f-4dc9-ba27-712ba89a14e2","added_by":"auto","created_at":"2025-05-13 18:36:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1905720,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of three types of objective exposures and unplanned natural resources in 801 neighborhoods of urban Tokyo. Indirect exposure represented by (a) WV\u003csub\u003egreen\u003c/sub\u003e and (b) WV\u003csub\u003ewater\u003c/sub\u003e, incidental exposure represented by (c) SV\u003csub\u003egreen\u003c/sub\u003e and (d) SV\u003csub\u003ewater\u003c/sub\u003e, intentional exposure represented by (e)-(h) CP\u003csub\u003egreen\u003c/sub\u003e, CP\u003csub\u003ewater\u003c/sub\u003e, AP\u003csub\u003egreen\u003c/sub\u003e, and AP\u003csub\u003ewater\u003c/sub\u003e, and unplanned natural resources represented by (i) UF and (j) UW.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6235999/v1/65f15fb104907e200a2e15f5.png"},{"id":82655046,"identity":"b5669e96-53da-44ef-a006-3968b324ccea","added_by":"auto","created_at":"2025-05-13 18:36:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":274203,"visible":true,"origin":"","legend":"\u003cp\u003eImpact variation of indirect and incidental greenery exposures, intentional greenery and water exposures, and unplanned natural resources on perceived nature exposure across five sizes of buffer zones. Coefficients from (a) M\u003csub\u003eCP\u003c/sub\u003e\u003csup\u003e1250\u003c/sup\u003e and (b) M\u003csub\u003eAP\u003c/sub\u003e\u003csup\u003e1250\u003c/sup\u003e. Note: Significance codes: ***: \u0026lt; 0.001; **: \u0026lt; 0.01; *: \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6235999/v1/1c823b3b0f278b2292b6dedd.png"},{"id":100070051,"identity":"31199fe7-2102-4f5b-884b-cb04dada4b74","added_by":"auto","created_at":"2026-01-12 16:16:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5149549,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6235999/v1/2eb2f5cd-981a-4655-b8ae-71b883b9a71d.pdf"},{"id":82655899,"identity":"7f19417f-ec05-4c2f-b919-1263ddd2f69a","added_by":"auto","created_at":"2025-05-13 18:44:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4780754,"visible":true,"origin":"","legend":"","description":"","filename":"3SupplementaryInfluenceofobjectiveandperceivedexposurestourbannatureonpeopleshappiness.docx","url":"https://assets-eu.researchsquare.com/files/rs-6235999/v1/863d23bef2c230528605b6a5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of objective and perceived exposures to urban nature on people’s happiness","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMental disorder affects approximately 13.9% of the global population \u0026nbsp;(WHO 2022) , and extreme cases lead to about 632,700 suicides per year worldwide \u0026nbsp;(WHO 2021) . Urban dwellers in densely populated cities, like Tokyo, are particularly vulnerable to mental disorders and suicide (Tanaka \u0026amp; Okamoto 2021; Chiba et al. 2023) due to the fast-paced lifestyles and crowded living environments (WHO 2020). Exposure to nature, such as greenery and water bodies in cities, has been found to significantly mitigate urban well-being issues (Ulrich et al. 1991; Kaplan 1995), facilitating urban sustainability \u0026nbsp;(Fagerholm et al. 2022; Lin et al. 2023; Lee \u0026amp; Han 2025) . The benefits of the exposure include improvement of physical and mental issues (e.g., fatigue, stress, and depression) (Maes et al. 2021), productivity (van Esch et al. 2019), and life satisfaction (Chen et al. 2022). As a result, green-blue infrastructures (e.g., street trees, parks, and water bodies), as the nature-based solution (Li et al. 2023; Raymond et al. 2025), are planned, designed, and maintained in cities to foster urban sustainability and address the growing well-being crisis (Ord\u0026oacute;\u0026ntilde;ez et al. 2023; van Oorschot et al. 2024).\u003c/p\u003e\n\u003cp\u003eUrban\u0026nbsp;environments offer different ways for people\u0026rsquo;s exposure to nature (Cox et al. 2017). Natural elements, e.g., greenery and water bodies, are \u0026ldquo;indirectly\u0026rdquo; experienced from the window when staying at home and in the workplace. Natural elements are also \u0026ldquo;incidentally\u0026rdquo; viewed by urban dwellers commuting on streets. Last, greenery and water bodies in parks, gardens, and ponds are \u0026ldquo;intentionally\u0026rdquo; visited. However, these three types of objective exposure to nature are not equal to how people perceive nature. Urban planners and designers find it challenging to offer all three types of nature exposures (Spotswood et al. 2021; van Oorschot et al. 2024) to improve human-perceived nature exposure and well-being, especially in high-density urban areas (Li et al. 2023).\u003c/p\u003e\n\u003cp\u003eMost studies \u0026nbsp;(Sarkar et al. 2018; Maes et al. 2021; Berdejo-Espinola et al. 2024) tend to assess the objective attributes of greenery and water bodies, e.g., areas and volumes. However, the presence and quantities of greenery and water bodies do not indicate how much objective exposure they can provide. With the increasingly available street view imagery and road network datasets, researchers measure the objective amount of \u0026ldquo;incidental\u0026rdquo; and \u0026ldquo;intentional\u0026rdquo; exposures from streets and parks (Wang et al. 2021; Yue et al. 2022; Mao et al. 2024). However, \u0026ldquo;indirect\u0026rdquo; exposure from windows was previously challenging to be quantified due to the unavailable datasets on window views (Li et al. 2023). As we spend most of our lives indoors, not quantifying the \u0026ldquo;indirect\u0026rdquo; exposure overlooks an important dimension of exposure to nature. Leveraging up-to-date photorealistic City Information Models (CIMs), window views can be generated to assess indirect exposure to nature (Li et al. 2022). The quantified indirect exposure supplements the quantified incidental (e.g., along streets) and intentional (e.g., access to parks) exposure.\u003c/p\u003e\n\u003cp\u003eWe use Tokyo, the world\u0026rsquo;s largest megacity, as a case study to examine\u0026nbsp;how three types of objective nature exposure\u0026mdash;indirect, incidental, and intentional\u0026mdash;provided by greenery and water bodies, along with perceived exposure, impact\u0026nbsp;urban dwellers\u0026rsquo; declared well-being.\u0026nbsp;We use happiness as a proxy of well-being (Kammann et al. 1984; Veenhoven 2011; Medvedev \u0026amp; Landhuis 2018) since Japan has been conducting a national survey about citizens\u0026rsquo; happiness since 2022, covering 73,358 respondents on average annually. Perceived exposure to nature, which refers to how close respondents feel to nature, has been collected in the national survey. Figure 1a shows the average ratings of happiness for 10,798 respondents in 801 neighborhoods of urban Tokyo ranging from 0 (low) to 10 (high). The average ratings of the perceived exposure to nature range from 1 to 5 as shown in Figure 1b. A high value indicates that respondents in the neighborhood feel close to nature. In the study, a neighborhood encompasses multiple city blocks covered by the same zip code. Urban Tokyo includes 23 special wards and 26 cities.\u003c/p\u003e\n\u003cp\u003eUsing photorealistic CIMs, street view imagery, network datasets, and remote sensing imagery, we quantified three types of exposures to nature objectively provided by green-blue infrastructures, e.g., street trees, parks, lakes, rivers, and the sea.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eFor the indirect exposure, we calculated the Window Greenery View Index (WV\u003csub\u003egreen\u003c/sub\u003e) and Window Water View Index (WV\u003csub\u003ewater\u003c/sub\u003e);\u003c/li\u003e\n \u003cli\u003eFor the incidental exposure, we calculated the Street Greenery View Index (SV\u003csub\u003egreen\u003c/sub\u003e) and Street Water View Index (SV\u003csub\u003ewater\u003c/sub\u003e);\u003c/li\u003e\n \u003cli\u003eFor the intentional exposure, we used two metrics for the closest park and accessible parks in the buffer zone: the Closest Park Greenery Index (CP\u003csub\u003egreen\u003c/sub\u003e), the Closest Park Water Index (CP\u003csub\u003ewater\u003c/sub\u003e), the Average Park Greenery Index (AP\u003csub\u003egreen\u003c/sub\u003e), and the Average Park Water Index (AP\u003csub\u003ewater\u003c/sub\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe also assessed the amount of Unplanned Forests (UF) and Water bodies (UW), to present disparity of natural resources in local contexts. Last, analyses of Ordinary Least Square (OLS) and Structural Equation Modeling (SEM) were applied to associate three types of objective exposures to nature, perceived exposure to nature, and happiness. For systematic analyses, SV\u003csub\u003egreen\u003c/sub\u003e, SV\u003csub\u003ewater\u003c/sub\u003e, AP\u003csub\u003egreen\u003c/sub\u003e, AP\u003csub\u003ewater\u003c/sub\u003e, UF, and UW at the neighborhood level were calculated for residences within 500 m, 750 m, 1,000 m, 1,250 m, and 1,500 m. Unless stated otherwise, our results are based on fully adjusted models with six indicators using the 15-minute road network buffer zone (travel distance \u0026gamma; = 1,250 m).\u003c/p\u003e\n\u003cp\u003eThis is the first study to systematically examine the impact difference of objective and perceived exposures to nature on people\u0026rsquo; happiness for sustainable urban planning and design. We decomposed urban nature into components by their usability (planned and unplanned), quantified the objective exposure by the types of human-nature interaction (indirect, incidental, and intentional), and collected the perceived exposure from the national survey, as shown in Figure 2. Using happiness data of 10,798 residents of Tokyo, we revealed the importance of perceived nature exposure on urban dwellers\u0026rsquo; happiness, as well as the impact difference of three types of objective nature exposures on perceived nature exposure and happiness. Last, we presented planning and design suggestions that align with human behavior, e.g., prioritizing the improvement of greenery views from windows and park accessibility to promote urban sustainability.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eSpatial distribution of three types of objective exposures to nature in urban Tokyo.\u003c/b\u003e Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the spatial distribution of three types of exposures objectively provided by greenery and water bodies in 801 neighborhoods of urban Tokyo. High values of the three types of greenery exposures exist in more neighborhoods than those of three types of water exposures. For greenery exposure, neighborhoods with high values of WV\u003csub\u003egreen\u003c/sub\u003e existed in the southwestern part of urban Tokyo, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea. Neighborhoods with high values of SV\u003csub\u003egreen\u003c/sub\u003e were located in the central part as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec. By contrast, Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg show high values of CP\u003csub\u003egreen\u003c/sub\u003e and AP\u003csub\u003egreen\u003c/sub\u003e scattered across individual neighborhoods and clusters of neighborhoods surrounding large parks, respectively. For water exposure, low values of WV\u003csub\u003ewater\u003c/sub\u003e, SV\u003csub\u003ewater\u003c/sub\u003e, CP\u003csub\u003ewater\u003c/sub\u003e, and AP\u003csub\u003ewater\u003c/sub\u003e existed in most neighborhoods; High values existed in neighborhoods close to the river and sea. Except for green-blue infrastructures that provide three types of exposures, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei and j show mostly low-accessible unplanned forests and water bodies located in the western and eastern parts of Tokyo, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociations between objective exposures to nature and urban dwellers\u0026rsquo; happiness.\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows no observable significant associations between the amount of three types of nature exposures objectively provided by neighborhood greenery and water bodies, and happiness. The insignificant associations indicated no direct contribution of objective nature exposure to happiness. Models OLS\u003csub\u003eCP\u003c/sub\u003e\u003csup\u003e1250\u003c/sup\u003e and OLS\u003csub\u003eAP\u003c/sub\u003e\u003csup\u003e1250\u003c/sup\u003e differ by applying two types of intentional nature exposure metrics, i.e., CPs and APs, respectively. The insignificant associations were also found when using buffer zones of 500 m, 750 m, 1,000 m, and 1,500 m (see Supplementary Table\u0026nbsp;1). By contrast, covariates, i.e., average mental status (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.418, 0.417, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), average income satisfaction (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.168, 0.171, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and average age (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.080, 0.078, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) at the neighborhood level positively influenced respondents\u0026rsquo; happiness. One-unit improvement in mental status and income satisfaction increased happiness by 41.8% (\u0026plusmn;\u0026thinsp;8.2%, 95% Confidence Interval (CI)) and 16.8% (\u0026plusmn;\u0026thinsp;6.9%, 95% CI), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between three types of objective exposures to nature and happiness.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOLS\u003csub\u003eCP\u003c/sub\u003e\u003csup\u003e1250\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOLS\u003csub\u003eAP\u003c/sub\u003e\u003csup\u003e1250\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescriptor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized coefficients (\u003cem\u003eβ\u003c/em\u003e) (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSignificance (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized coefficients (\u003cem\u003eβ\u003c/em\u003e) (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificance (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000 (0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000 (0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZMaleProp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.023 (0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022 (0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZAvgAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.080 (0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078 (0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZPhysicalStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.074 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.073 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZMentalStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.418 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.417 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZIncomeSatisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.168 (0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.171 (0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(WV\u003csub\u003egreen\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.072 (0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061 (0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(WV\u003csub\u003ewater\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003 (0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006 (0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(SV\u003csub\u003egreen\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.071 (0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.071 (0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(SV\u003csub\u003ewater\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.054 (0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066 (0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(CP\u003csub\u003egreen\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.018 (0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(CP\u003csub\u003ewater\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.039 (0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(AP\u003csub\u003egreen\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.027 (0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(AP\u003csub\u003ewater\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.018 (0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(UF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.057 (0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052 (0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(UW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.029 (0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.009 (0.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e/ adjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.380/0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.328/0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1984.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1984.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Significance codes: ***: \u0026lt; 0.001; **: \u0026lt; 0.01; *: \u0026lt; 0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociations among objective and perceived nature exposures and urban dwellers\u0026rsquo; happiness.\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows respondents\u0026rsquo; perceived exposure to nature was positively associated with happiness (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036). Controlling other covariates, the increase of every unit of perceived exposure improved 6.2% (\u0026plusmn;\u0026thinsp;5.9%, 95% CI) of happiness. Compared to the observed insignificant associations between objective exposures and happiness, the significant positive association with perceived exposure further indicated that respondents tended to feel happy if they subjectively perceived nature.\u003c/p\u003e \u003cp\u003eRespondents\u0026rsquo; perceived exposure mediated the impacts of objective exposures on happiness. WV\u003csub\u003egreen\u003c/sub\u003e, SV\u003csub\u003egreen\u003c/sub\u003e, CP\u003csub\u003ewater\u003c/sub\u003e, and AP\u003csub\u003egreen\u003c/sub\u003e were significantly and positively associated with perceived exposure to nature. Mediated by perceived exposure, indirect greenery exposure represented by WV\u003csub\u003egreen\u003c/sub\u003e (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, 0.017, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) impacted most on happiness, whereas incidental greenery exposure represented by SV\u003csub\u003egreen\u003c/sub\u003e (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, 0.005, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.029) showed the least significant impact. Additionally, intentional exposures represented by CPs and APs showed varied impacts. AP\u003csub\u003egreen\u003c/sub\u003e (0.010, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) positively influenced happiness, whereas CP\u003csub\u003egreen\u003c/sub\u003e showed no significant impacts. Conversely, CP\u003csub\u003ewater\u003c/sub\u003e (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) was significant, whereas AP\u003csub\u003ewater\u003c/sub\u003e showed no significant impacts on perceived exposure and happiness. The inverse relationship between CP and AP for greenery and water bodies suggested that nearby water bodies, rather than greenery, had a greater influence on urban dwellers\u0026rsquo; perceptions. Last, unplanned natural resources like forests, rivers, and the sea (UF: (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016, 0.015, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; UW: (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.007) also positively influenced urban dwellers\u0026rsquo; happiness, even being less viewed and visited from windows, streets, and parks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between three types of objective exposures to nature and happiness with the mediation of perceived exposure.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eM\u003csub\u003eCP\u003c/sub\u003e\u003csup\u003e1250\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eM\u003csub\u003eAP\u003c/sub\u003e\u003csup\u003e1250\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized coefficients (\u003cem\u003eβ\u003c/em\u003e) (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSignificance (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized coefficients (\u003cem\u003eβ\u003c/em\u003e) (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificance (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effects on happiness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZMaleProp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.028 (0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028 (0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZAvgAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.077 (0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.077 (0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZPhysicalStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.074 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZMentalStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.411 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.411 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZIncomeSatisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.157 (0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.157 (0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZPerceivedNatureExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.062 (0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062 (0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndirect effects mediated by perceived exposure to nature\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(WV\u003csub\u003egreen\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.015 (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017 (0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(WV\u003csub\u003ewater\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.002 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(SV\u003csub\u003egreen\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007 (0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005 (0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(SV\u003csub\u003ewater\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.003 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(CP\u003csub\u003egreen\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(CP\u003csub\u003ewater\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(AP\u003csub\u003egreen\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010 (0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(AP\u003csub\u003ewater\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(UF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016 (0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015 (0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZln(UW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZMaleProp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.004 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.004 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZAvgAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.003 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.002 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZPhysicalStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZMentalStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZIncomeSatisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007 (0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHISQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Significance codes: ***: \u0026lt; 0.001; **: \u0026lt; 0.01; *: \u0026lt; 0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSensitivity of associations between objective and perceived exposures.\u003c/b\u003e Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the impact variations of indirect greenery exposure (WV\u003csub\u003egreen\u003c/sub\u003e), incidental greenery exposure (SV\u003csub\u003egreen\u003c/sub\u003e), intentional greenery and water exposures (AP\u003csub\u003egreen\u003c/sub\u003e, CP\u003csub\u003ewater\u003c/sub\u003e, and AP\u003csub\u003ewater\u003c/sub\u003e), and unplanned natural resources (UF and UW) across buffer zones of 500 m, 750 m, 1,000 m, 1,250 m, and 1,500 m. Indirect and incidental water exposures (WV\u003csub\u003ewater\u003c/sub\u003e and SV\u003csub\u003ewater\u003c/sub\u003e) and CP\u003csub\u003egreen\u003c/sub\u003e owned insignificant associations across nearly all the buffer zones, so they were not presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. First, three greenery exposure indicators, (WV\u003csub\u003egreen\u003c/sub\u003e, SV\u003csub\u003egreen\u003c/sub\u003e, and AP\u003csub\u003egreen\u003c/sub\u003e) consistently influenced perceived exposure, with window view having the strongest effect (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.226, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), average park proximity the moderate (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.121, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the street view the weakest (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.078, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). By contrast, water exposure indicators showed mixed results. CP\u003csub\u003ewater\u003c/sub\u003e became significant (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.096, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) beyond the 1,000-m buffer, while AP\u003csub\u003ewater\u003c/sub\u003e was significant (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.069, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) at 500 m, 1,000 m, and 1,500 m.\u003c/p\u003e \u003cp\u003eSecond, the impacts of greenery and water exposure indicators changed with the increasing sizes of buffer zones. The influences of WV\u003csub\u003egreen\u003c/sub\u003e and SV\u003csub\u003egreen\u003c/sub\u003e showed a decreasing trend beyond 750 m, reflecting their limited influence over longer distances. AP\u003csub\u003egreen\u003c/sub\u003e reached a peak (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.168, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) at the 1000-m buffer zone, indicating an optimal range for its effect. Both the generally increasing impact of CP\u003csub\u003ewater\u003c/sub\u003e and the decreasing impact of AP\u003csub\u003ewater\u003c/sub\u003e reflected the high significance of nearby water bodies.\u003c/p\u003e \u003cp\u003eLast, UF and UW were consistently significant but differently changed with the increasing sizes of buffer zones. UF\u0026rsquo;s impact (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.113, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) increased as buffer zones expanded, while UW peaked (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.123, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) at the 750 m buffer zone.\u003c/p\u003e \u003cp\u003eOverall, indirect greenery exposure (WV\u003csub\u003egreen\u003c/sub\u003e) influenced perceived exposure to nature the most, whereas intentional and incidental greenery exposure (AP\u003csub\u003egreen\u003c/sub\u003e and SV\u003csub\u003egreen\u003c/sub\u003e) showed consistently moderate and low impacts, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Water exposure (CP\u003csub\u003ewater\u003c/sub\u003e and AP\u003csub\u003ewater\u003c/sub\u003e) had positive impacts only at specific buffer sizes. The varying changes of significance and impacts between greenery and water exposures suggested that urban dwellers\u0026rsquo; perception of water is more distance-sensitive than greenery.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003eDifferent impacts of objective and perceived exposures to nature on urban dwellers\u0026rsquo; happiness.\u003c/b\u003e Our findings revealed different impacts of objective and perceived exposures to nature on happiness in urban Tokyo. We found urban dwellers\u0026rsquo; perceived exposure to nature had a direct effect on happiness, whereas the indirect effects of objective exposures on happiness were mediated by the perceived exposure. In urban Tokyo, fast-paced lifestyles may limit the ability of urban dwellers, especially employees, to directly benefit from nature exposure (Otsuka et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), reducing the impact of objective measures. Conversely, urban dwellers with higher perceived exposure to nature reported greater happiness, possibly due to their high motivation and efforts to access nearby green-blue infrastructures, such as street trees, parks, and ponds, from windows, streets, and parks.\u003c/p\u003e \u003cp\u003e \u003cb\u003eUnequal impacts of three types of objective exposures on perceived exposure to nature.\u003c/b\u003e Regarding the greenery exposures, there existed varied impacts of indirect (Window view), incidental (Street view), and intentional (Proximity to parks) exposures on urban dwellers\u0026rsquo; perceived exposure. Window view (WV\u003csub\u003egreen\u003c/sub\u003e) had the strongest impact, likely due to the long-term indoor occupation of urban dwellers in Tokyo (Althoff et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Abe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Intentional exposure showed a moderate impact. Average amount of greenery from all accessible parks (AP\u003csub\u003egreen\u003c/sub\u003e) was more impactful than that from the closest park (CP\u003csub\u003egreen\u003c/sub\u003e), reflecting the importance of accessibility of multiple \u0026ldquo;pocket\u0026rdquo; parks and larger parks in the neighborhood. The 1000-m buffer zone with the highest impact of AP\u003csub\u003egreen\u003c/sub\u003e suggested an optimal range for park planning to maximize perceived exposure to nature and happiness. By contrast, incidental greenery exposure (SV\u003csub\u003egreen\u003c/sub\u003e) showed the weakest impact. Observed reasons include limited street greenery in Tokyo\u0026rsquo;s dense urban area and urban dwellers\u0026rsquo; limited experience of street greenery due to daily commuting via public transportation, e.g., railways (Liang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the water exposures, only intentional (Proximity to parks) water exposure showed an observed significant association with perceived exposure at specific buffer sizes. The result indicated the importance of physical visits of water bodies in parks on improvement urban dwellers\u0026rsquo; perceived exposure instead of indirect and incidental viewing of water bodies from windows and streets. The nearby water bodies in parks had a high impact on urban dwellers\u0026rsquo; perceived exposure possibly due to their high accessibility. For instance, AP\u003csub\u003ewater\u003c/sub\u003e showed the strongest impact at the 500-m buffer zone. Another evidence is the increasing impact of CP\u003csub\u003ewater\u003c/sub\u003e with the expansion of the buffer zone.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHuman behavior-guided planning strategy for urban sustainability.\u003c/b\u003e The different impacts of objective and perceived exposures to nature on happiness, as well as the unequal effects of three types of objective exposures on perceived exposure, call for considerations in sustainable urban planning and management policies. How can we strategically optimize different types of nature exposure to maximize perceived exposure and happiness in cities? First, as the most impactful indicator, greenery exposure provided by windows needs to be enhanced. At the building level, policymakers could require proposed construction projects to conduct visual impact analyses to safeguard and enhance greenery visibility for surrounding buildings. Specific design strategies like green facades and rooftops can also increase indirect exposure to greenery in high-rise, high-density urban areas. At the neighborhood level, strategically coordinating building attributes, such as height and window orientation, with the spatial configuration of greenery, is promising to ensure equitable access to greenery views across all buildings.\u003c/p\u003e \u003cp\u003eThe statistically significant impact of intentional exposure from parks suggests that urban planners should prioritize the availability of greenery and water bodies within multiple accessible parks and the nearby parks, respectively. For incidental exposure from streets, strategically targeting \u0026lsquo;hotspots\u0026rsquo; in road networks for tree planting and maintenance, have the potential to enhance exposure more efficiently. Example hotspots include roads with high pedestrian flows.\u003c/p\u003e \u003cp\u003eThe findings from the sensitivity analysis provide quantitative evidence on radii of planning and design. The \u0026ldquo;sweet spot\u0026rdquo; of 1,000 m can be used as a benchmark to assess whether buildings\u0026rsquo; accessible greenery needs improvement, as the impact of AP\u003csub\u003egreen\u003c/sub\u003e declined beyond this distance. A 500-m buffer is recommended for evaluating water bodies in accessible parks from the building, given the high impact of nearby water bodies. Additionally, buffer distances of 1,500 m and 750 m are recommended for assessing the impacts of unplanned forests and water bodies surrounding buildings, respectively, when planning green-blue infrastructures for improving three types of objective exposures.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe role of systematic assessment of nature exposure for sustainable urban planning.\u003c/b\u003e The findings point out that an even spatial distribution of green-blue infrastructures does not necessarily ensure equal exposure for all urban dwellers. Assessing both objective and perceived nature exposures improves understanding of the relationship between the availability of green-blue infrastructures and human perceptions. If a strong bias exists, public policies can guide and encourage urban dwellers\u0026rsquo; active daily nature exposure, alongside planning and design interventions. Furthermore, completely understanding impact difference of three types of objective nature exposures on perceived exposure and happiness can better inform urban planning and design practices. For example, prioritizing improvement of window views of greenery and park proximity over street-level exposure could be more beneficial. Multi-scale analysis validates the robustness of findings and offers quantitative planning suggestions, e.g., values of radii, for specific spatial contexts.\u003c/p\u003e \u003cp\u003e \u003cb\u003eContribution.\u003c/b\u003e First, this is the first study to systematically explore how objective exposure to nature in cities influences urban dwellers\u0026rsquo; happiness through their subjective perceptions. We classified urban nature elements, i.e., greenery and water bodies by their usability (planned/unplanned), fully quantified three types of objective exposures (indirect, incidental, and intentional), and collected the ratings of perceived exposure from the national survey. The fine-grained decomposition of urban nature enabled a systematic path model between urban nature and urban dwellers\u0026rsquo; happiness. Second, using the happiness data of 10,798 urban dwellers in urban Tokyo, we achieved robust quantitative evidence on the mechanism of human-nature interaction for happiness. We found that perceived nature exposure had a higher explanatory power for urban dwellers\u0026rsquo; happiness than objective nature exposure; ii) views of greenery from windows (indirect exposure) and proximity to parks (intentional exposure) influenced perceived nature exposure and happiness the most. Last, our findings argued the importance of understanding how people perceive nature to plan and design green-blue infrastructures for urban sustainability. Our findings demonstrated that greenery exposure provided by windows and accessible parks in the activity range (travel distance γ\u0026thinsp;=\u0026thinsp;1,000 m) needs to be first enhanced to improve urban dwellers\u0026rsquo; perceived nature exposure and happiness in urban Tokyo. Overall, the findings in the study identified the linkages among objective exposure to nature, human-perceived exposure, and people\u0026rsquo;s happiness in cities, contributing to research in environmental studies, psychology, and planning for urban sustainability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitation and future work.\u003c/b\u003e First, due to privacy regulation, the analytical results at the neighborhood level may not capture individual-level perception differences on three types of objective exposures. Additionally, the generalizability of analytical results needs to be examined in other cities with different urban morphologies and lifestyles. Last, the study is cross-sectional, where the analytical results may not capture changes over time. Future work could consider individual-level longitudinal analysis in multiple cities to compare the impact difference of objective and perceived exposures on urban dwellers\u0026rsquo; happiness.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cp\u003e \u003cb\u003eStudy area.\u003c/b\u003e Supplementary Fig.\u0026nbsp;1 shows urban Tokyo, Japan, i.e., 23 special wards and 26 cities as the study area. As a typical high-density city, Tokyo owned both undulating skyscrapers and low-rise buildings. There existed 20,405 buildings with heights above 30 m in 23 special wards, whereas most low-rise buildings existed in 26 cities with an average height of 7.60 m (MLIT \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The maximum building density of neighborhoods exceeds 0.90 (MLIT \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the population density is 6,158 persons per square kilometer. Supplementary Fig.\u0026nbsp;1 shows unevenly distributed greenery and water bodies. Unplanned forests and large parks existed in the mountainous areas of central western and western Tokyo. On the contrary, large areas of greenery resources were scarce in central eastern and eastern Tokyo due to intense urban development. As compensation, considerable small parks were constructed in central eastern and eastern Tokyo to enhance urban dwellers\u0026rsquo; greenery exposure. Additionally, there existed low quantities of water bodies in most areas. The sea bordering eastern Tokyo and rivers were major water bodies that provided water exposure. The diverse morphology of buildings and green-blue infrastructure results in varying levels of three types of objective nature exposures for urban dwellers.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConceptual modeling.\u003c/b\u003e The study aims to examine the impacts of three types of nature exposures objectively provided by neighborhood greenery and water bodies and perceived nature exposure on urban dwellers\u0026rsquo; happiness in Tokyo. To carefully examine the objective and perceived nature exposures, we first classify the greenery and water bodies into planned and unplanned natural resources as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Compared to lowly accessible unplanned natural resources, e.g., prime forest and water bodies, green-blue infrastructures, e.g., street trees and parks are planned for the provision of nature exposure in cities. Then, we assess the amount of three types of nature exposures (indirect, incidental, and intentional) objectively provided by the neighborhood green-blue infrastructures. Last, we collected human-perceived nature exposure from the national survey. By controlling the impact of local unplanned natural resources and the demographic and socioeconomic profiles of respondents, we try to examine:\u003c/p\u003e \u003cp\u003ei. The significance of perceived and three types of objective nature exposures on happiness, and\u003c/p\u003e \u003cp\u003eii. The impact difference of three types of objective nature exposures on perceived nature exposure and happiness.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHappiness and perceived nature exposure.\u003c/b\u003e The happiness dataset for 10,798 respondents in urban Tokyo, i.e., 23 special wards and 26 cities, was shared by the Japan Digital Agency. The online questionnaire survey was conducted in May 2024. Respondents needed to be Japanese permanent residents aged between 18 and 89. Basic demographic, socioeconomic, and health profiles, e.g., age, gender, physical status, mental status, and income satisfaction were recorded. Respondents grouped by the neighborhood rated their happiness together with the perceived nature exposure. According to the population of urban dwellers aged between 18 and 89 in Tokyo at 11,462,100, the survey with a sample size of 10,798 (\u0026ge;\u0026thinsp;9,595) could achieve representative statistical results with confidence at 95% and a margin error of 1%. The age composition between the samples and the population of urban Tokyo achieved a good agreement. The average age of 10,798 respondents and the urban dwellers aged between 18 and 89 in Tokyo were 50.90 and 49.87, respectively. The survey collected happiness ratings of relatively more male respondents than female respondents. The ratio between male and female respondents was 1.35 : 1. Due to the privacy regulation, the data were shared by the Japan Digital Agency following two rules. First, the dataset was shared at the cohort level rather than the individual level. Besides, data for cohorts with less than five respondents were hidden and excluded. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows 801 available cohorts in neighborhoods covering all special wards and cities of Tokyo.\u003c/p\u003e \u003cp\u003e \u003cb\u003eObjective nature exposure metrics.\u003c/b\u003e We represent the indirect, incidental, and intentional nature exposures of 801 cohorts at the neighborhood level. At each neighborhood, buildings are randomly sampled with confidence at 90% and margin error at 10%. The sampling size of buildings ranges from 6 to 68 for neighborhoods with varied areas and building densities. The total number of sampled buildings is 52,987. Window views were quantified from the sampled buildings to represent the average level of indirect nature exposure provided by the neighborhood greenery and water bodies, whereas greenery-water amount from street views and parks were quantified within multiple road network buffer zones of sampled buildings to represent the average levels of incidental and intentional nature exposures. We estimated three types of exposures to nature within the 15-minute buffer zone with the travel distance γ at 1,250 m (Kolcs\u0026aacute;r et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To understand the sensitivity of buffer zone sizes, we further generated buffer zones for each sampled building with γ at 500 m, 750 m, 1,000 m, and 1,500 m for 6, 9, 12, and 18 minutes, respectively.\u003c/p\u003e \u003cp\u003ei) Indirect exposure through window view\u003c/p\u003e \u003cp\u003eWindow Greenery View Index (WV\u003csub\u003egreen\u003c/sub\u003e) and Window Water View Index (WV\u003csub\u003ewater\u003c/sub\u003e) refer to the proportions of greenery and water bodies occurring in the photorealistic window view image (Li et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), respectively, as shown in Supplementary Fig.\u0026nbsp;2a. We computed the WV\u003csub\u003egreen\u003c/sub\u003e and WV\u003csub\u003ewater\u003c/sub\u003e at the neighborhood level via Equations 1 and 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWV\u003c/em\u003e\u003csup\u003enbhd\u003c/sup\u003e = (\u003cem\u003eWV\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e + ... + \u003cem\u003eWV\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e) ∕ \u003cem\u003em\u003c/em\u003e,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWV\u003c/em\u003e\u003csup\u003ebldg\u003c/sup\u003e = (\u003cem\u003eWV\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003ef\u003c/sup\u003e \u0026times; \u003cem\u003ed\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e + ... + \u003cem\u003eWV\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ef\u003c/sup\u003e \u0026times; \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e) ∕ (\u003cem\u003ed\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e + ... + \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e),\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eWV\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e is \u003cem\u003eWV\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e or \u003cem\u003eWV\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e of the sampled building \u003cem\u003em\u003c/em\u003e, and \u003cem\u003eWV\u003c/em\u003e\u003csup\u003enbhd\u003c/sup\u003e is the average values of WVs of \u003cem\u003em\u003c/em\u003e sampled buildings in the neighborhood. \u003cem\u003eWV\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ef\u003c/sup\u003e represents the average values of \u003cem\u003eWV\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e or \u003cem\u003eWV\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e quantified from floors of the building facade \u003cem\u003en\u003c/em\u003e, as shown in Supplementary Fig.\u0026nbsp;2b. \u003cem\u003en\u003c/em\u003e is the total number of building facades, while \u003cem\u003ed\u003c/em\u003e is the facade width.\u003c/p\u003e \u003cp\u003eSupplementary Fig.\u0026nbsp;2 shows WV\u003csup\u003ebldg\u003c/sup\u003e quantified from Google photorealistic CIM-generated window view images. Since neighborhood windows often share similar views, we sampled window sites at varying floors of a building for a cost-effective quantification (Li et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as shown in Supplementary Fig.\u0026nbsp;2a. Referring to Li\u0026rsquo;s method (2022), we controlled the height gap between sampled floors\u0026thinsp;\u0026le;\u0026thinsp;5 m. The 3D building geometries, e.g., shape and heights, were collected from the project Plateau (MLIT \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) launched by the Ministry of Land, Infrastructure, Transport and Tourism, Japan. Then, a virtual camera in the Geo-visualization platform, Cesium (ver. 1.111), was placed at each window site to capture the outside photorealistic view, as shown in Supplementary Fig.\u0026nbsp;2a. Thereafter, photorealistic window view images with the size of 900 \u0026times; 900 pixels were segmented into the sky, greenery, water body, and construction using the up-to-date deep learning model, Segment of Anything (SAM) (Kirillov et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Finetuned on 3,000 annotated photorealistic window views (Li et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the SAM achieved a satisfactory performance with mIoU at 91.61%. The per-class IoUs for greenery and water bodies were 90.59% and 94.88%, respectively. Then, WV\u003csub\u003egreen\u003c/sub\u003e and WV\u003csub\u003ewater\u003c/sub\u003e were quantified by counting pixels of the segmentation mask generated from SAM. The width of the building facade determined the number of neighborhood windows represented by the sampled window view on each floor (Li et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, we aggregated the values of WV\u003csup\u003ef\u003c/sup\u003e\u003csub\u003egreen\u003c/sub\u003e and WV\u003csup\u003ef\u003c/sup\u003e\u003csub\u003ewater\u003c/sub\u003e at all building facades using the facade width d as the weight via Eq.\u0026nbsp;2. Last, WV\u003csup\u003enbhd\u003c/sup\u003e was computed by averaging the values of WV\u003csup\u003ebldg\u003c/sup\u003e of sampled buildings via Eq.\u0026nbsp;1. We generated a total of 302,470 window view images for sampled buildings in 801 neighborhoods.\u003c/p\u003e \u003cp\u003eii) Incidental exposure through street view\u003c/p\u003e \u003cp\u003eStreet Greenery View Index (SV\u003csub\u003egreen\u003c/sub\u003e) and Street Water View Index (SV\u003csub\u003ewater\u003c/sub\u003e) refer to the proportions of greenery and water bodies occurring in the street view image, respectively, as shown in Supplementary Fig.\u0026nbsp;3. We computed SV\u003csub\u003egreen\u003c/sub\u003e and SV\u003csub\u003ewater\u003c/sub\u003e at the neighborhood level via Equations 3 and 4.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSV\u003c/em\u003e\u003csup\u003enbhd\u003c/sup\u003e = (\u003cem\u003eSV\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e + \u0026hellip; + \u003cem\u003eSV\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e) ∕ \u003cem\u003em\u003c/em\u003e,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSV\u003c/em\u003e\u003csup\u003ebldg\u003c/sup\u003e = (\u003cem\u003eSV\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003ebf\u003c/sup\u003e + \u0026hellip; + \u003cem\u003eSV\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebf\u003c/sup\u003e) ∕ \u003cem\u003ep\u003c/em\u003e,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eSV\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e is \u003cem\u003eSV\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e or \u003cem\u003eSV\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e of the sampled building \u003cem\u003em\u003c/em\u003e, and \u003cem\u003eSV\u003c/em\u003e\u003csup\u003enbhd\u003c/sup\u003e is the average value of SVs of \u003cem\u003em\u003c/em\u003e buildings in the neighborhood. \u003cem\u003eSV\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebf\u003c/sup\u003e represents the \u003cem\u003eSV\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e or \u003cem\u003eSV\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e of the sampled street view \u003cem\u003ep\u003c/em\u003e within the buffer zone. The irregular buffer zones were convex hulls covering the road network segments sprawling from the sampled building with γ at 500 m, 750 m, 1,000 m, 1,250 m, and 1,500 m.\u003c/p\u003e \u003cp\u003eSupplementary Fig.\u0026nbsp;3 shows SV\u003csub\u003egreen\u003c/sub\u003e and SV\u003csub\u003ewater\u003c/sub\u003e quantified from street view images. We first collected the 360-degree panoramas from Google Street View at a 50-m interval for the whole study area, as shown in Supplementary Fig.\u0026nbsp;3a. We manually removed street views captured inside commercial buildings, train stations, and developed green-blue spaces, e.g., temples, gardens, and parks, for pure quantification of nature exposures on the streets. Additionally, low-quality street views, e.g., the overexposed and blurred were excluded regarding the information loss. Then, the front and rear views of the street with the size of 2,048 \u0026times; 1,024 pixels were converted from the panorama, as shown in Supplementary Fig.\u0026nbsp;3b. We extracted the greenery and water bodies from the front and rear street views using the Dense Prediction Transformer (DPT) (Ranftl et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) trained on the ADE20K dataset (Zhou et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The erroneous segmentation of large-area greenery and waterbody within street views was manually improved to enhance DPT\u0026rsquo;s performance, with per-class IoUs at 83.56% and 73.89%, respectively. Thereafter, SV\u003csub\u003egreen\u003c/sub\u003e and SV\u003csub\u003ewater\u003c/sub\u003e were aggregated in the buffer zone of each sampled building via Eq.\u0026nbsp;4. Last, the SV\u003csup\u003enbhd\u003c/sup\u003e was computed by averaging the values of SV\u003csup\u003ebldg\u003c/sup\u003e of all sampled buildings via Eq.\u0026nbsp;3. Overall, 699,934 Google Street Views were quantified for sampled buildings of 801 neighborhoods.\u003c/p\u003e \u003cp\u003eiii) Intentional exposure through available greenery and water bodies in parks\u003c/p\u003e \u003cp\u003eUrban dwellers\u0026rsquo; intentional nature exposure was represented using two types of metrics, i.e., the average area of greenery and water resources of the i) closest park and ii) accessible parks within the buffer zones of the sampled building. The two types of metrics highlight individual neighborhoods and clusters of neighborhoods that benefit from high levels of nature exposure from surrounding large parks, respectively. The corresponding indexes are the Closest Park Greenery Index (CP\u003csub\u003egreen\u003c/sub\u003e), Closest Park Water Index (CP\u003csub\u003ewater\u003c/sub\u003e), Average Park Greenery Index (AP\u003csub\u003egreen\u003c/sub\u003e), and Average Park Water Index (AP\u003csub\u003ewater\u003c/sub\u003e). In this study, the park refers to multiple types of natural spaces that provide visitable greenery and water bodies for recreation. The quantified natural spaces include the garden, playground, and temple. Particularly, we computed the CP\u003csub\u003egreen\u003c/sub\u003e and CP\u003csub\u003ewater\u003c/sub\u003e at the neighborhood level via Equations 5 and 6.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCP\u003c/em\u003e\u003csup\u003enbhd\u003c/sup\u003e = (\u003cem\u003eCP\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e + \u0026hellip; + \u003cem\u003eCP\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e) ∕ \u003cem\u003em\u003c/em\u003e,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCP\u003c/em\u003e\u003csup\u003ebldg\u003c/sup\u003e = \u003cem\u003earea\u003c/em\u003e (\u003cem\u003eclosest\u003c/em\u003e ({\u003cem\u003eP\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e, \u0026hellip;, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eq\u003c/em\u003e\u003c/sub\u003e})),\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eCP\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e is the \u003cem\u003eCP\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e or \u003cem\u003eCP\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e of the closest park from the sampled building \u003cem\u003em\u003c/em\u003e. CP\u003csup\u003enbhd\u003c/sup\u003e refers to the average values of CP\u003csup\u003ebldg\u003c/sup\u003e of \u003cem\u003em\u003c/em\u003e sampled buildings within the neighborhood. Closest is a spatial function to identify the closest park \u003cem\u003eP\u003c/em\u003e of the sampled building, whereas \u003cem\u003earea\u003c/em\u003e is a function to calculate the areas of the greenery or water bodies of the closest park. \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eq\u003c/em\u003e\u003c/sub\u003e refers to the park \u003cem\u003eq\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, we computed the AP\u003csub\u003egreen\u003c/sub\u003e and AP\u003csub\u003ewater\u003c/sub\u003e at the neighborhood level via Equations 7 and 8.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAP\u003c/em\u003e\u003csup\u003enbhd\u003c/sup\u003e = (\u003cem\u003eAP\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e + \u0026hellip; + \u003cem\u003eAP\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e) ∕ \u003cem\u003em\u003c/em\u003e,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAP\u003c/em\u003e\u003csup\u003ebldg\u003c/sup\u003e. = (\u003cem\u003earea\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003ebf\u003c/sup\u003e) + \u0026hellip; + \u003cem\u003earea\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebf\u003c/sup\u003e)) ∕ \u003cem\u003ek\u003c/em\u003e,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eAP\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e is the \u003cem\u003eAP\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e or \u003cem\u003eAP\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e of the sampled building \u003cem\u003em\u003c/em\u003e. AP\u003csup\u003enbhd\u003c/sup\u003e refers to the average values of AP\u003csup\u003ebldg\u003c/sup\u003e of \u003cem\u003em\u003c/em\u003e sampled buildings within the neighborhood. P\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebf\u003c/sup\u003e refers to the park \u003cem\u003ek\u003c/em\u003e within the buffer zone.\u003c/p\u003e \u003cp\u003eSupplementary Fig.\u0026nbsp;4 shows CP\u003csub\u003egreen\u003c/sub\u003e, CP\u003csub\u003ewater\u003c/sub\u003e, AP\u003csub\u003egreen\u003c/sub\u003e, and AP\u003csub\u003ewater\u003c/sub\u003e computed from the network analysis. The network analysis was conducted based on park sets and road networks from Open Street Map datasets, building footprints from Plateau, and remote sensing imagery of greenery and water bodies from the European Space Agency World Cover 10 m via Google Earth Engine. First, the closest park was extracted by comparing the road network distance between the sampled building and parks, as shown in Supplementary Fig.\u0026nbsp;4a. Accessible parks of the sampled building were identified by spatially joining the affiliated buffer zones and the polygon vectors of parks, as shown in Supplementary Fig.\u0026nbsp;4b. Then, the areas of greenery and water bodies of the closest and accessible parks were quantified from the land cover map for CP\u003csub\u003egreen\u003c/sub\u003e, CP\u003csub\u003ewater\u003c/sub\u003e, AP\u003csub\u003egreen\u003c/sub\u003e, and AP\u003csub\u003ewater\u003c/sub\u003e via Equations 6 and 8. Last, we aggregated the four building-level indexes for CP\u003csub\u003egreen\u003c/sub\u003e, CP\u003csub\u003ewater\u003c/sub\u003e, AP\u003csub\u003egreen\u003c/sub\u003e, and AP\u003csub\u003ewater\u003c/sub\u003e via Equations 5 and 7. Overall, 11,931 parks were collected for the analyses of 52,987 sampled buildings in 801 neighborhoods.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCovariates.\u003c/b\u003e We controlled the typical demographic and socioeconomic profile attributes related to happiness within the cohorts. The five covariates include the average age, gender proportion, average physical status, average mental status, and average income satisfaction. On a five-point Likert scale, the average physical and mental status of the cohorts were 3.23 and 3.31, respectively. Both values above 3 indicated a relatively good health status. By contrast, there existed a slightly low income satisfaction, with the mean at 2.91.\u003c/p\u003e \u003cp\u003eWe also controlled the area of unplanned natural resources in the local context. Examples are forests on mountains and hills in western Tokyo. Different from developed green-blue infrastructures, e.g., street trees and parks, there existed a considerable amount of unplanned natural resources with low accessibility for urban dwellers\u0026rsquo; greenery and water exposures. Nevertheless, urban dwellers may also feel close to nature due to long-term local experiences, even with limited viewable and visitable greenery and water bodies from windows, streets, and accessible parks. Thus, we defined the Unplanned Forest Index (UF) and Unplanned Water Index (UW) as the area of unplanned natural resources within the sampled building\u0026rsquo;s buffer zone for control purposes. We quantified UF and UW at the neighborhood level via Equations 9 and 10.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eU\u003c/em\u003e\u003csup\u003enbhd\u003c/sup\u003e = (\u003cem\u003eU\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e + \u0026hellip; + \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e) ∕ \u003cem\u003em\u003c/em\u003e,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eU\u003c/em\u003e\u003csup\u003ebldg\u003c/sup\u003e = (\u003cem\u003earea\u003c/em\u003e (\u003cem\u003eU\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003ebf\u003c/sup\u003e) + \u0026hellip; + \u003cem\u003earea\u003c/em\u003e (\u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebf\u003c/sup\u003e)) ∕ \u003cem\u003es\u003c/em\u003e,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebldg\u003c/sup\u003e is \u003cem\u003eUF\u003c/em\u003e or \u003cem\u003eUW\u003c/em\u003e of the building \u003cem\u003em\u003c/em\u003e, and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e\u003csup\u003ebf\u003c/sup\u003e refers to the unplanned natural resources within the buffer zone.\u003c/p\u003e \u003cp\u003eSupplementary Fig.\u0026nbsp;5 shows the assessment of unplanned natural resources. In this study, we regarded greenery (e.g., forests) and water bodies (e.g., sea and rivers) that were not from streets and parks as unplanned natural resources. The UF and UW of each sampled building were computed from the European Space Agency World Cover 10 m via Google Earth Engine. The polygon vectors of unplanned natural resources were collected from Open Street Map datasets. We aggregated the area of greenery and water bodies of each vector polygon within a buffer zone of the sampled buildings. Particularly, due to urban dwellers\u0026rsquo; low accessibility to the unplanned natural resources through road networks, we used the round buffer zones (γ\u0026thinsp;=\u0026thinsp;500 m, 750 m, 1,000 m, 1,250 m, and 1,500 m) to compute UF\u003csup\u003ebldg\u003c/sup\u003e and UW\u003csup\u003ebldg\u003c/sup\u003e as shown in Supplementary Figs.\u0026nbsp;5a and b. Last, the UF\u003csup\u003enbhd\u003c/sup\u003e and UW\u003csup\u003enbhd\u003c/sup\u003e were computed by averaging the values of UF\u003csup\u003ebldg\u003c/sup\u003e and UW\u003csup\u003ebldg\u003c/sup\u003e via Eq.\u0026nbsp;9.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analyses.\u003c/b\u003e We first conducted an OLS regression to test the associations between objective exposures and happiness. Then, we added the mediator variable, perceived nature exposure into the relationships through Path Analysis, a subset of SEM. Carefully following the basic assumptions, we conducted regression analyses using R programming language (ver. 4.4.1).\u003c/p\u003e \u003cp\u003eFor OLS regression, we first confirmed all independent variables, including three types of objective exposure indicators and covariates without high correlations. Pearson and Spearman correlation analyses were conducted for independent variables according to their normality. The scatter plots between independent variables and dependent variables, between independent variables and the mediator, were examined to identify and transform the non-linear relationships. For example, all three greenery exposure indicators were transformed through a natural logarithmic function to maintain a linear relationship with human-perceived nature exposure. Then, by controlling covariates, we examined the associations between three types of objective nature exposure indicators and average ratings of happiness (see Eq.\u0026nbsp;11). All variables were normalized and R\u003csup\u003e2\u003c/sup\u003e was applied to assess the model performance. A high value of R\u003csup\u003e2\u003c/sup\u003e indicates a high explainability of the observation variables and covariates. Last, we conducted a series of analyses for the regression model to confirm the validity of the statistical result. The analyses included collinearity analysis, Durbin\u0026ndash;Watson analysis for the independence of the observation, casewise diagnostics for outlier detection, and analysis of variance (ANOVA) for the regression models as shown in Supplementary Tables\u0026nbsp;2 and 3. Scatter plots for predicted value and residuals, as well as scatter plots for independent variables and error terms, were examined to confirm an even distribution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eZHappiness\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZMaleProp\u0026thinsp;+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZAvgAge\u0026thinsp;+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZPhysicalStatus\u0026thinsp;+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZMentalStatus\u0026thinsp;+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZIncomeSatisfaction\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eWV\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e) + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eWV\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e) + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eSV\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e) + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e9\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eSV\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e) + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e10\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eCP\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e or \u003cem\u003eAP\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e) + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e11\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eCP\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e or \u003cem\u003eAP\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e) + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e12\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eUF\u003c/em\u003e) + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e13\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eUW\u003c/em\u003e) + \u003cem\u003eε.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor SEM analysis, we constructed models based on the conceptual modeling as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Due to the insignificant associations between three types of objective exposures and happiness, we turned to examine the direct effect of perceived exposure on happiness, and the indirect effects of three types of objective exposures on happiness mediated by perceived exposure (see Equations 12 and 13). The constructed models among three types of objective nature exposures, covariates, human-perceived nature exposure, and happiness were adjusted step-by-step to achieve a satisfactory fit performance. We added spatial error terms (\u003cem\u003eu\u003c/em\u003e) to avoid spatial autocorrelation of residuals in the analyses (see Eq.\u0026nbsp;13). The estimator, Generalized Least Squares, was used to estimate model parameters. The evaluation metrics of the model fit include Chi-Square statistic (CHISQ), CHISQ/degree of freedom (df), Goodness of Fit (GFI), Comparative Fit Index (CFI), Standardized Root Mean Square Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA) as shown in Supplementary Table\u0026nbsp;5.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eZHappiness\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZMaleProp\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZAvgAge\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZPhysicalStatus\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZMentalStatus\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZIncomeSatisfaction\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZMentalStatus\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZPerceivedNatureExposure\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eδ\u003c/em\u003e,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(12)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eZPerceivedNatureExposure\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eWV\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e) + \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eWV\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e) + \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eSV\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e) + \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eSV\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e) + \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eCP\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e or \u003cem\u003eAP\u003c/em\u003e\u003csub\u003egreen\u003c/sub\u003e) + \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eCP\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e or \u003cem\u003eAP\u003c/em\u003e\u003csub\u003ewater\u003c/sub\u003e) + \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eUF\u003c/em\u003e) + \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eZln\u003c/em\u003e(\u003cem\u003eUW\u003c/em\u003e) + \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e9\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eu\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eσ\u003c/em\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.L. and S.G. equally contributed to the study by conceiving and designing the research, collecting and analyzing the data, and writing the paper. F.D. conceived and designed the research and reviewed and edited the paper. A.K. and N.K., as local experts, supported the project and discussed the findings. F.X., W.Z., A.G.O.Y., and C.R. reviewed the paper with critical comments and suggestions. F.D. and C.R. supervised the project. All authors approved the manuscript and this submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank specially Woven by Toyota for their valuable support and HPC2021 computing services offered by Information Technology Services, the University of Hong Kong, for this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eFull analytical results are provided within the manuscript or supplementary information files. Original processed data will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbe, R., Ikarashi, T., Takada, S. \u0026amp; Fukuda, D. (2023). Impact of working from home on travel behavior of rail and car commuters: A case study in the Tokyo metropolitan area. Case Studies on Transport Policy, \u003cem\u003e11\u003c/em\u003e, 100938. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cstp.2022.100938\u003c/span\u003e\u003cspan address=\"10.1016/j.cstp.2022.100938\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlthoff, T., Sosič, R., Hicks, J. L., King, A. C., Delp, S. L. \u0026amp; Leskovec, J. (2017). Large-scale physical activity data reveal worldwide activity inequality. Nature, \u003cem\u003e547\u003c/em\u003e, 336\u0026ndash;339. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature23018\u003c/span\u003e\u003cspan address=\"10.1038/nature23018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerdejo-Espinola, V., Fuller, R. A. \u0026amp; Zahnow, R. (2024). Well-being from nature exposure depends on socio-environmental contexts in Paraguay. Nature Cities, \u003cem\u003e1\u003c/em\u003e(5), 335\u0026ndash;345. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s44284-024-00058-0\u003c/span\u003e\u003cspan address=\"10.1038/s44284-024-00058-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, B., Tu, Y., Wu, S., Song, Y., Jin, Y., Webster, C., Xu, B. \u0026amp; Gong, P. (2022). Beyond green environments: multi-scale difference in human exposure to greenspace in China. Environment International, \u003cem\u003e166\u003c/em\u003e, 107348. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.envint.2022.107348\u003c/span\u003e\u003cspan address=\"10.1016/j.envint.2022.107348\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiba, T., Ide, K., Murakami, M., Kobayashi, N., Oka, T., Nakai, F., Yorizawa, R., Miyake, Y., Hamamura, T., Honjo, M. \u0026amp; Toda, H. (2023). Event-related PTSD symptoms as a high-risk factor for suicide: longitudinal observational study. Nature Mental Health, \u003cem\u003e1\u003c/em\u003e, 1013\u0026ndash;1022. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s44220-023-00157-2\u003c/span\u003e\u003cspan address=\"10.1038/s44220-023-00157-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCox, D. T., Hudson, H. L., Shanahan, D. F., Fuller, R. A. \u0026amp; Gaston, K. J. (2017). The rarity of direct experiences of nature in an urban population. Landscape and Urban Planning, \u003cem\u003e160\u003c/em\u003e, 79\u0026ndash;84. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.landurbplan.2016.12.006\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2016.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFagerholm, N., Samuelsson, K., Eilola, S., Giusti, M., Hasanzadeh, K., Kajosaari, A., Koch, D., Korpilo, S., Kytt\u0026auml;, M., Legeby, A. \u0026amp; Liu, Y. (2022). Analysis of pandemic outdoor recreation and green infrastructure in Nordic cities to enhance urban resilience. npj Urban Sustainability, \u003cem\u003e2\u003c/em\u003e(1), 25. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42949-022-00068-8\u003c/span\u003e\u003cspan address=\"10.1038/s42949-022-00068-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKammann, R., Farry, M. \u0026amp; Herbison, P. (1984). The analysis and measurement of happiness as a sense of well-being. Social Indicators Research, \u003cem\u003e15\u003c/em\u003e, 91\u0026ndash;115. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/BF00426282\u003c/span\u003e\u003cspan address=\"10.1007/BF00426282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaplan, S. (1995). The restorative benefits of nature: Toward an integrative framework. Journal of Environmental Psychology, \u003cem\u003e15\u003c/em\u003e(3), 169\u0026ndash;182. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0272-4944(95)90001-2\u003c/span\u003e\u003cspan address=\"10.1016/0272-4944(95)90001-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A., Lo, W. \u0026amp; Doll\u0026aacute;r, P. (2023). Segment anything. \u003cem\u003eProceedings of the IEEE/CVF International Conference on Computer Vision\u003c/em\u003e (pp. 4015\u0026ndash;4026). Piscataway, New Jersey: IEEE. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/ICCV51070.2023.00371\u003c/span\u003e\u003cspan address=\"10.1109/ICCV51070.2023.00371\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolcs\u0026aacute;r, R. A., Csik\u0026oacute;s, N. \u0026amp; Szilassi, P. (2021). Testing the limitations of buffer zones and Urban atlas population data in urban green space provision analyses through the case study of Szeged, Hungary. Urban Forestry \u0026amp; Urban Greening, \u003cem\u003e57\u003c/em\u003e, 126942. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ufug.2020.126942\u003c/span\u003e\u003cspan address=\"10.1016/j.ufug.2020.126942\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, Y. \u0026amp; Han, S. (2025). From greenery to community: exploring the mediating role of loneliness in social cohesion. npj Urban Sustainability, \u003cem\u003e5\u003c/em\u003e(1), 6. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42949-025-00194-z\u003c/span\u003e\u003cspan address=\"10.1038/s42949-025-00194-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, M., Xue, F. \u0026amp; Yeh, A. G. (2023). Bi-objective analytics of 3D visual-physical nature exposures in high-rise high-density cities for landscape and urban planning. Landscape and Urban Planning, \u003cem\u003e233\u003c/em\u003e, 104714. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.landurbplan.2023.104714\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2023.104714\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, M., Xue, F., Wu, Y. \u0026amp; Yeh, A. G. (2022). A room with a view: Automated assessment of window views for high-rise high-density areas using City Information Models and transfer deep learning. Landscape and Urban Planning, \u003cem\u003e226\u003c/em\u003e, 104505. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.landurbplan.2022.104505\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2022.104505\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, M., Yeh, A. G. \u0026amp; Xue, F. (2024). CIM-WV: A 2D semantic segmentation dataset of rich window view contents in high-rise, high-density areas based on photorealistic City Information Models. Urban Informatics, \u003cem\u003e3\u003c/em\u003e, 1\u0026ndash;24. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s44212-024-00039-7\u003c/span\u003e\u003cspan address=\"10.1007/s44212-024-00039-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang, J., Miwa, T., Wang, J. \u0026amp; Morikawa, T. (2023). Impact of telecommuting on Japanese Citizen's travel, activities, and residential locations: Experiences and future expectations under COVID-19 pandemic. Asian Transport Studies, \u003cem\u003e9\u003c/em\u003e, 100105. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eastsj.2023.100105\u003c/span\u003e\u003cspan address=\"10.1016/j.eastsj.2023.100105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, B. B., Chang, C. C., Astell-Burt, T., Feng, X., Gardner, J. \u0026amp; Andersson, E. (2023). Nature experience from yards provide an important space for mental health during Covid-19. npj Urban Sustainability, \u003cem\u003e3\u003c/em\u003e(1), 14. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42949-023-00094-0\u003c/span\u003e\u003cspan address=\"10.1038/s42949-023-00094-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaes, M. J., Pirani, M., Booth, E. R., Shen, C., Milligan, B., Jones, K. E. \u0026amp; Toledano, M. B. (2021). Benefit of woodland and other natural environments for adolescents\u0026rsquo; cognition and mental health. Nature Sustainability, \u003cem\u003e4\u003c/em\u003e(10), 851\u0026ndash;858. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41893-021-00751-1\u003c/span\u003e\u003cspan address=\"10.1038/s41893-021-00751-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao, Y., Xia, T., Hu, F., Chen, D., He, Y., Bi, X., Zhang, Y., Cao, L., Yan, J., Hu, J. \u0026amp; Ren, Y. (2024). The greener the living environment, the better the health? Examining the effects of multiple green exposure metrics on physical activity and health among young students. Environmental Research, \u003cem\u003e250\u003c/em\u003e, 118520. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.envres.2024.118520\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2024.118520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedvedev, O. N. \u0026amp; Landhuis, C. E. (2018). Exploring constructs of well-being, happiness and quality of life. PeerJ, \u003cem\u003e6\u003c/em\u003e, e4903. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7717/peerj.4903\u003c/span\u003e\u003cspan address=\"10.7717/peerj.4903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMLIT. (2023). \u003cem\u003eProject PLATEAU | The Initiative of Digital Twin in Japan.\u003c/em\u003e Ministry of Land, Infrastructure, Transport and Tourism. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mlit.go.jp/en/toshi/daisei/plateau_en_2.html\u003c/span\u003e\u003cspan address=\"https://www.mlit.go.jp/en/toshi/daisei/plateau_en_2.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrd\u0026oacute;\u0026ntilde;ez, C., Labib, S. M., Chung, L. \u0026amp; Conway, T. M. (2023). Satisfaction with urban trees associates with tree canopy cover and tree visibility around the home. \u003cem\u003enpj Urban Sustainability, 3(1), 37., 3\u003c/em\u003e(1), 37. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42949-023-00119-8\u003c/span\u003e\u003cspan address=\"10.1038/s42949-023-00119-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtsuka, Y., Imanishi, J., Nasu, M. \u0026amp; Iwasaki, Y. (2023). Probabilistic inference on the factors promoting park use by machine learning using panel data from 2014 to 2020 in Tokyo, Japan. Cities, \u003cem\u003e141\u003c/em\u003e, 104509. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cities.2023.104509\u003c/span\u003e\u003cspan address=\"10.1016/j.cities.2023.104509\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRanftl, R., Bochkovskiy, A. \u0026amp; Koltun, V. (2021). Vision transformers for dense prediction. \u003cem\u003eProceedings of the IEEE/CVF International Conference on Computer Vision\u003c/em\u003e (pp. 12179\u0026ndash;12188). Piscataway, New Jersey: IEEE. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/ICCV48922.2021.01196\u003c/span\u003e\u003cspan address=\"10.1109/ICCV48922.2021.01196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaymond, C., Rautio, P., Fagerholm, N., Aaltonen, V., Andersson, E., Celermajer, D., Christie, M., H\u0026auml;llfors, M., Saari, M., Mishra, H. \u0026amp; Lechner, A. (2025). Raymond, C.M., Rautio, P., Fagerholm, N., Aaltonen, V.A., Andersson, E., Celermajer, D., Christie, M., H\u0026auml;llfors, M., Saari, M.H., Mishra, H.S. and Lechner, A.M., 2025. Applying multispecies justice in nature-based solutions and urban sustainability planni. \u003cem\u003enpj Urban Sustainability, 5\u003c/em\u003e(1), 2. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42949-025-00191-2\u003c/span\u003e\u003cspan address=\"10.1038/s42949-025-00191-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarkar, C., Webster, C. \u0026amp; Gallacher, J. (2018). Residential greenness and prevalence of major depressive disorders: a cross-sectional, observational, associational study of 94 879 adult UK Biobank participants. The Lancet Planetary Health, \u003cem\u003e2\u003c/em\u003e(4), e162-e173. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2542-5196(18)30051-2\u003c/span\u003e\u003cspan address=\"10.1016/S2542-5196(18)30051-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpotswood, E. N., Benjamin, M., Stoneburner, L., Wheeler, M. M., Beller, E. E., Balk, D., McPhearson, T., Kuo, M. \u0026amp; McDonald, R. I. (2021). Nature inequity and higher COVID-19 case rates in less-green neighbourhoods in the United States. Nature Sustainability, \u003cem\u003e4\u003c/em\u003e(12), 1092\u0026ndash;1098. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41893-021-00781-9\u003c/span\u003e\u003cspan address=\"10.1038/s41893-021-00781-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka, T. \u0026amp; Okamoto, S. (2021). Increase in suicide following an initial decline during the COVID-19 pandemic in Japan. Nature Human Behaviour, \u003cem\u003e5\u003c/em\u003e(2), 229\u0026ndash;238. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41562-020-01042-z\u003c/span\u003e\u003cspan address=\"10.1038/s41562-020-01042-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlrich, R. S., Simons, R. F., Losito, B. D., Fiorito, E., Miles, M. A. \u0026amp; Zelson, M. (1991). Stress recovery during exposure to natural and urban environments. Journal of Environmental Psychology, \u003cem\u003e11\u003c/em\u003e(3), 201\u0026ndash;230. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0272-4944(05)80184-7\u003c/span\u003e\u003cspan address=\"10.1016/S0272-4944(05)80184-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Esch, E., Minjock, R., Colarelli, S. M. \u0026amp; Hirsch, S. (2019). Office window views: View features trump nature in predicting employee well-being. Journal of Environmental Psychology, \u003cem\u003e64\u003c/em\u003e, 56\u0026ndash;64. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jenvp.2019.05.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvp.2019.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Oorschot, J., Slootweg, M., Remme, R. P., Sprecher, B. \u0026amp; van der Voet, E. (2024). Optimizing green and gray infrastructure planning for sustainable urban development. npj Urban Sustainability, \u003cem\u003e4\u003c/em\u003e(1), 41. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42949-024-00178-5\u003c/span\u003e\u003cspan address=\"10.1038/s42949-024-00178-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeenhoven, R. (2011). Happiness, Also Known as \"Life Satisfaction\" and \"Subjective Well-Being\". In K. C. Land, A. C. Michalos \u0026amp; M. J. Sirgy, \u003cem\u003eHandbook of Social Indicators and Quality of Life Research\u003c/em\u003e (pp. 79\u0026ndash;98). Berlin: Springer Science \u0026amp; Business Media.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, R., Feng, Z., Pearce, J., Zhou, S., Zhang, L. \u0026amp; Liu, Y. (2021). Dynamic greenspace exposure and residents\u0026rsquo; mental health in Guangzhou, China: From over-head to eye-level perspective, from quantity to quality. Landscape and Urban Planning, \u003cem\u003e215\u003c/em\u003e, 1042. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.landurbplan.2021.104230\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2021.104230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. (2020). \u003cem\u003eIntegrating Health in Urban and Territorial Planning: A Sourcebook\u003c/em\u003e. Geneva: World Health Organization.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. (2021). \u003cem\u003eSuicide Worldwide in 2019: Global Health Estimates\u003c/em\u003e. Geneva: World Health Organization.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. (2022). \u003cem\u003eWorld Mental Health Report: Transforming Mental Health for All\u003c/em\u003e. Geneva: World Health Organization.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue, Y., Yang, D. \u0026amp; Van Dyck, D. (2022). Urban greenspace and mental health in Chinese older adults: Associations across different greenspace measures and mediating effects of environmental perceptions. Health \u0026amp; Place, \u003cem\u003e76\u003c/em\u003e, 102856. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.healthplace.2022.102856\u003c/span\u003e\u003cspan address=\"10.1016/j.healthplace.2022.102856\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A. \u0026amp; Torralba, A. (2017). Scene parsing through ade20k dataset. \u003cem\u003eProceedings of the IEEE Conference on Computer Vision and Pattern Recognition\u003c/em\u003e (pp. 633\u0026ndash;641). Piscataway, New Jersey: IEEE. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/CVPR.2017.544\u003c/span\u003e\u003cspan address=\"10.1109/CVPR.2017.544\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-urban-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjurbansustain","sideBox":"Learn more about [npj Urban Sustainability](https://www.nature.com/npjurbansustain/)","snPcode":"42949","submissionUrl":"https://submission.springernature.com/new-submission/42949/3","title":"npj Urban Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nature exposure, Happiness, Human perception, Green-blue infrastructure, Window view, Street view, Visual AI","lastPublishedDoi":"10.21203/rs.3.rs-6235999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6235999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExposure to nature influences urban dwellers\u0026rsquo; well-being and happiness, thereby impacting urban sustainability. However, urban dwellers are exposed to nature in different ways: indirect exposure through window views, incidental exposure when walking along streets, and intentional exposure when visiting parks. Moreover, objective exposure does not necessarily align with how people perceive their exposure to nature. This study examines how three types of objective nature exposure\u0026mdash;indirect, incidental, and intentional\u0026mdash;provided by greenery and water bodies, along with perceived exposure, impact happiness in Tokyo, Japan. To measure the objective exposure, we use 3D photorealistic city information models, street view imagery, road network datasets, and remote sensing imagery. To measure happiness and perceived nature exposure, we use data from a national survey, focusing on the results from 10,798 residents in 801 neighborhoods in Tokyo. Results showed that perceived nature exposure has higher explanatory power for happiness than objective exposure. Views of greenery from windows (indirect exposure) and proximity to parks (intentional exposure) influenced perceived nature exposure and happiness the most. The quantitative evidence suggests that urban planning align with human behavior, e.g., by prioritizing the improvement of greenery views from windows and park accessibility in Tokyo to promote urban sustainability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Influence of objective and perceived exposures to urban nature on people’s happiness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 18:36:39","doi":"10.21203/rs.3.rs-6235999/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-05T05:26:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T13:24:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-26T09:10:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56486213747137606943916512324157280518","date":"2025-06-09T08:27:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78866727161285698147038804736512106674","date":"2025-05-13T08:46:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-08T07:04:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-30T06:39:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-27T06:02:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Urban Sustainability","date":"2025-03-16T06:16:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-urban-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjurbansustain","sideBox":"Learn more about [npj Urban Sustainability](https://www.nature.com/npjurbansustain/)","snPcode":"42949","submissionUrl":"https://submission.springernature.com/new-submission/42949/3","title":"npj Urban Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db37854f-7d0a-43e0-9fd2-c19d8e85d527","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48239036,"name":"Health sciences/Health care/Quality of life"},{"id":48239037,"name":"Social science/Psychology/Human behaviour"},{"id":48239038,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":48239039,"name":"Humanities/Health humanities"},{"id":48239040,"name":"Social science/Development studies"},{"id":48239041,"name":"Social science/Environmental studies"},{"id":48239042,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2026-01-12T16:09:51+00:00","versionOfRecord":{"articleIdentity":"rs-6235999","link":"https://doi.org/10.1038/s42949-025-00306-9","journal":{"identity":"npj-urban-sustainability","isVorOnly":false,"title":"npj Urban Sustainability"},"publishedOn":"2026-01-09 15:58:59","publishedOnDateReadable":"January 9th, 2026"},"versionCreatedAt":"2025-05-13 18:36:39","video":"","vorDoi":"10.1038/s42949-025-00306-9","vorDoiUrl":"https://doi.org/10.1038/s42949-025-00306-9","workflowStages":[]},"version":"v1","identity":"rs-6235999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6235999","identity":"rs-6235999","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}