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Article

Subjective Well-Being, Active Travel, and Socioeconomic Segregation

by
Mohammad Paydar
1,*,† and
Asal Kamani Fard
2,*,†
1
Escuela de Arquitectura Santiago, Facultad de Ciencias Sociales y Artes, Universidad Mayor, Av. Portugal 351, Santiago 8330231, Chile
2
Departamento de Planificación y Ordenamiento Territorial, Facultad de Ciencias de la Construcción y Ordenamiento Territorial, Universidad Tecnológica Metropolitana, Dieciocho 390, Santiago 8330526, Chile
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2025, 17(23), 10571; https://doi.org/10.3390/su172310571
Submission received: 31 August 2025 / Revised: 15 November 2025 / Accepted: 18 November 2025 / Published: 25 November 2025
Browse Figure
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Abstract

The relationships among subjective well-being (SWB), active travel, and the built and social environment have been rarely studied, especially in southern cities of Chile. The goal of this research is to investigate the connections between SWB and active travel, along with the associated social, built environment, and individual aspects in Temuco. Furthermore, due to the high levels of socioeconomic segregation (SES) in the city’s various urban neighborhoods, these relationships were studied independently based on two categories of neighborhoods, namely low-SES (NLSES) and high-SES (NHSES), which represent the majority of the city’s areas and population. To ascertain the number of responders in each SES category, a power analysis and simple random sampling were used. Consequently, 481 and 301 respondents were identified for NLSES and NHSES, respectively. A quantitative method and hierarchical multiple regression analysis were used to investigate the goals. The findings indicate that SWB is generally higher in NHSES than in NLSES. It was also found that there was a correlation between subjective well-being and several factors, such as age, some job-related categories, social cohesion, role models, and accessibility to shops, parks, and bus stops. Less SWB is a result of a higher unemployment rate in NLSES as opposed to NHSES. Additionally, a certain lifestyle type in NHSES demonstrated a positive correlation with SWB. Furthermore, there was a positive association found between the NHSES’s SWB and access to the bus network. This study provides evidence from a highly segregated Latin American city that shows how SWB is shaped differently across low- and high-SES neighborhoods. Temuco’s urban policymakers could use these data to improve SWB according to the different types of neighborhoods within this city.

1. Introduction

Subjective well-being (SWB) refers to a person’s overall assessment of their quality of life as perceived by them [1]. It is closely related to sustainability, since living sustainably—particularly by adopting environmentally friendly habits and spending time in nature—often increases subjective well-being, life satisfaction, and personal happiness [2]. A growing body of empirical research shows that, in various situations and locations, a well-designed physical environment is positively correlated with subjective well-being [3,4,5]. According to these studies, the enhancement of SWB can be attributed to several social and built environmental factors, such as social cohesion, residential density, and urban greenery [6]. A built environment that is pedestrian-friendly, well-designed, accessible, diverse, and dense can affect people’s subjective well-being [7]. One of the most significant environmental elements influencing SWB is urban greenery [8]. The links between green spaces and SWB are theoretically underpinned by two theories: the “Attention Restoration Theory” and the “Psycho-Physiological Stress Reduction Theory” [9,10,11,12].
Positive associations between SWB and active transportation were also demonstrated by earlier research [13,14]. Several studies have shown that active travel is advantageous in promoting behaviors that enhance SWB and health. The relationship between active travel and SWB has been studied concerning several related factors to both concepts as well [6]. However, prior research has rarely examined the impact of lifestyle and attitudes on different travel modes on SWB.
Furthermore, studies have demonstrated that people’s subjective well-being is influenced by their neighborhood’s socioeconomic status (SES) [15] According to Kraus [16] and Link and Phelan [17], SES resources provide individuals with access to assets that enable them to meet their basic needs, which may explain the positive correlation between SES and SWB. People are often healthy and may also be happier when their basic needs are met. Thus, increased socioeconomic status is known to be associated with increased subjective well-being [18,19], mostly due to the likelihood that their basic needs will be met [20,21].
Despite being one of Latin America’s wealthiest nations, Chile remains among the most unequal countries in the continent and in the majority of its cities, socioeconomic segregation is apparent [22,23]. This inequality is more highlighted while considering the related environmental factors to SWB, such as the availability of facilities that promote active transportation and the standard of urban greenery. For example, high socioeconomic status (NHSES) neighborhoods in several Chilean cities have numerous well-maintained parks and plazas, while neighborhoods with low socioeconomic status (NLSES) lack enough green space, and most of the existing green spaces in the former type of neighborhoods are not well-maintained, which affects the esthetic experiences and the residents’ SWB in these neighborhoods [22,23,24,25,26].
Temuco, a medium-sized city in southern Chile, exhibits a similar segregation between NHSES and NLSES. This study uses data from two distinct neighborhood types—NHSES and NLSES—to examine the relationship between SWB and its individual, social, and built environmental factors. The results of the research would enable Temuco’s urban and transportation policymakers to determine the best measures to enhance SWB depending on these two major neighborhood categories. The study’s conceptual framework is shown in Figure 1. The following are the research questions.
-
Is there an association between SWB and active travel according to Temuco’s NLSES and NHSES?
-
What sociodemographic characteristics, attitudes, lifestyles, social variables, and built environment features are linked to SWB in Temuco based on NLSES and NHSES?
Sustainability 17 10571 g001
Figure 1. The conceptual framework.
Figure 1. The conceptual framework.
Sustainability 17 10571 g001

2. Literature Review on SWB and Its Contributing Factors

The effects of the urban environment on mental health have not received as much attention in the pertinent literature as the aspects related to physical health [27]. Public interest in SWB research is growing since it is one of the promising frameworks that offer critical information for a more accurate assessment of human well-being [28]. Consequently, SWB research is currently a rapidly expanding field of study [29]. SWB has been linked to several built environment factors, including land use, density, greenness, and noise [7,8]. Autumnal walks in urban parks have been shown to have relaxing benefits for both the body and mind [30]. The esthetics of the neighborhood’s buildings and social environment characteristics influence the degree of SWB more than other aspects of the neighborhood [31]. According to Barton et al. [32] and Houlden et al. [33], one of the primary environmental elements influencing SWB is greenery. Among the social features of neighborhoods, the relationship between neighbors has a substantial link with SWB, according to Dong and Qin’s [34] research. However, among the other objective aspects of the built environment, only urban parks exhibited a strong correlation with SWB [34]. People who lived in areas with more desirable features, like better roads or more green spaces, had better mental health, according to a study by Araya et al. [35] that studied the prevalence of common mental disorders attributable to both individuals and the built environment in Santiago, Chile.
In addition, neighborhood safety and environmental quality have a positive effect on SWB [36,37]. A shorter distance to the city center in Bandung, London, and Beijing may have a positive effect on SWB [38,39]. This positive effect may result from higher access to facilities and easier travel, both of which are positively correlated with SWB in urban areas [40,41,42]. The difference between compact and low-density urban forms also affects subjective well-being [43]. According to Mouratidis [40], low-density development can provide tranquility, access to nature, a stronger sense of safety and cleanliness, and better ties with neighbors, whereas compact development can provide convenient access to people, amenities, and workplaces. Population density and SWB are negatively correlated, according to an analysis conducted in Oslo [44]. However, according to Feng et al. [45], density among senior populations in Nanjing, China has little or no effect on SWB. According to one study, life satisfaction is higher in the high-rise urban areas of Chicago than it is in the low-density suburban neighborhoods [46]. Additionally, several studies have demonstrated the effects of social characteristics, such as social cohesion, on SWB [47,48]. Araya et al. [49] found that social cohesion and trust are two essential elements of social capital that are associated with mental health. Finally, SWB is also enhanced by active travel [13,14].

3. Methodology

3.1. Study Area, Neighborhood Selection, and Participants

According to the 2017 Census, Temuco, the capital of the Araucanía, is a medium-sized city in the South with roughly 300,000 residents. In this city, bicycles are used for just 2% of all daily trips [50,51]. As a result, it is crucial to choose neighborhoods that will increase the percentage of respondents who commute every day on a bicycle. In this way, the city’s transit zones with the lowest cycling rates were determined using Plan De Transporte Temuco [50]. The neighborhoods of the city’s seven urban sectors were identified concurrently, and the neighborhoods that fall under these transit zones were not given any additional processing. We carried out this elimination process so that all seven of the city’s macro sectors remained and were part of the subsequent neighborhood selection step. This was crucial for the neighborhoods that were ultimately selected to be a representation of the entire city. Additionally, the final sampling should include all three types of Temuco neighborhoods—low, medium, and high-SES—due to the city’s socioeconomic segregation and the apparent differences in walking and bicycling facilities depending on the neighborhoods with different levels of SES [50]. In this way, the remaining transport zones were modified to match the socioeconomic map of the city. The three primary socioeconomic status categories—low, middle, and high—were used to identify and categorize each neighborhood on this map. Three factors that reflect the aspects of income and social standing were used to calculate the remaining neighborhoods’ SES summary score: average household income, unemployment (the percentage of unemployed people), and lack of education (the percentage of those over 18 who have only completed elementary school). These parameters were mostly used in previous studies to define neighborhood SES [52,53]. The National Census [54] provided these three socioeconomic factors.
By determining the neighborhoods with high and low-SES, a simple random sampling and a power analysis were used in each type of SES to determine the number of respondents in each category. Consequently, 481 and 301 respondents were identified for NLSES and NHSES, respectively. Six neighborhoods in all, three from each SES level, were selected for the final stage. These three neighborhoods were chosen based on the coverage levels of the bicycle network in each socioeconomic class: high, medium, and poor. Thus, the neighborhoods that were ultimately selected for each SES group comprise the most diverse neighborhoods in terms of the coverage of the bike network since active travel is impacted by this factor. The three neighborhoods selected for each SES level received an equal share of all respondents. Following neighborhood selection, random city blocks were selected within each neighborhood, and random residences were selected inside the city blocks. Participants in the study had to be willing, capable of walking independently for twenty minutes or more each day, and at least eighteen years old. Until each neighborhood had the required number of responders, the data collection process continued. With 569 and 471 replies in the NLSES and NHSES, respectively, the required number of respondents for each SES category was met. Consequently, 84% and 63% of NLSES and NHSES, respectively, responded. This shows that the response rate for NLSES was higher than that of NHSES.

3.2. Questionnaire Design and Measurments

The survey questionnaire includes questions about social characteristics, subjective built environment, walking and cycling patterns, walking attitudes and lifestyles, and SWB level. Most of these items are scored on a five-point Likert scale (three being strongly disagree and five being strongly agree). The built environment features were assessed using both types of measurement, whether real/objective or perceptual/subjective, in accordance with the past studies’ emphasis on these components. Features of the perceived built environment were examined using the “Neighborhood Environment Walkability Scale (NEWS)” [55]. However, we made changes to the tool according to what we observed in the selected neighborhoods. Walking attitudes were measured using a five-item scale that was developed from previous studies [56]. To assess attitudes on private vehicles and cycling, a six-item scale that was developed from previous studies was used as well [57]. Additionally, the previous studies offered a variety of items that may be used to establish the lifestyle [58,59]. With the help of experts, a wide spectrum of residents—especially those from the selected areas—were consulted in order to create the eight-item lifestyle measure utilized in this investigation. The scale was modified from previous studies. For instance, in order to reduce the final list after these discussions, some of the initial entries were removed and others were consolidated. The reduction was essential because it made room for a condensed survey form, which was necessary because when it first emerged, numerous inhabitants of the selected neighborhoods refused to respond to the long questions. Following earlier research, SWB was assessed using two items with a five-point Likert scale [2]. The final phase involved completing the “International Physical Activity Questionnaire (IPAQ)” to accumulate physical activity, as stated by oneself over the preceding seven days [60]. Walking and cycling were measured in terms of frequency (number of days) and duration (hours and minutes per day for trips lasting at least 10 min).

3.3. Analysis

The exploratory factor analysis (EFA) was performed to minimize the amount of data. Additionally, the data were analyzed using version 23.0 of the SPSS software. The residuals from the regression analysis were verified to be normally distributed using a normal probability plot. The dependent variables, SWB, were predicted from the independent factors using hierarchical multiple regression analysis. First, factors pertaining to the respondents’ sociodemographic traits were incorporated into a model. The following step included the personal components, social factors, and built environment, and in this stage, highly multicollinear variables (VIF > 5) were eliminated [61]. Thus, the final model incorporates interpretable and minimally correlated representations of the built environment elements with sociodemographic, individual, and social variables.

3.4. Ethical Approval

The Universidad Mayor’s Ethics Committee (authorization number 0239) in Chile has granted ethical approval for this investigation. All subjects agreed to participate in the study.

4. Results

4.1. Descriptive Statistics

Gender has a large range of missing values among the sociodemographic variables. It was then decided not to include gender in the analysis. Table 1 shows the descriptive statistics for each type of neighborhood, including SWB, social elements, sociodemographic indicators, and the quantity of active travel. On average, people have higher SWB in NHSES as compared to NLSES. The average weekly walking time for responders in NHSES is 60 min, compared to 81 min in NLSES. The majority of respondents in the NHSES (76%) and NLSES (77%) do not ride bicycles on their daily travels.
The monthly income levels of NHSES and NLSES differed considerably, as expected. Despite accounting for 90.3% of NLSES households, just 15.8% of NHSES households are low- and lower-middle-income households. Furthermore, in contrast to NHSES, the proportions of housewives, retirees, and jobless individuals are substantially greater in NLSES. Furthermore, NHSES has a far larger percentage of respondents with driver’s licenses (26% in NLSES and 76% in NHSES) and private vehicles (38% in NLSES and 78% in NHSES). In a similar vein, a significantly greater proportion of NHSES residents—61 percent, compared to 31 percent of NLSES residents—have bicycles in their homes. Last but not least, “helping the neighbors in the neighborhood” has the highest level in comparison to other social-related variables in NLSES; however, in NHSES, “being motivated by seeing other active people” has the highest level of social-related elements.

4.2. The Results of Exploratory Factor Analysis with Respect to Perceived Built Environment and Lifestyle in NLSES and NHSES

To reduce the quantity of data, exploratory factor analysis (EFA) was employed. Two primary determinants were identified based on lifestyle in NLSES (62.72% variance explained, KMO = 0.659) (Table 2). “Physically active people” and “going to restaurants and malls with friends” were the names given to these aspects. However, in this first EFA, three factors that were highly loaded in both factors were removed from additional examination. These factors included “reading books, magazines, and newspapers”, “going to local parks and plazas,” and “watching online content, including TV programs and videos.”
Based on lifestyle in NHSES, two primary determinants were identified (Table 3; KMO = 0.645, explaining 59.81% of the variation). “Visiting restaurants and socializing with friends” and “visiting parks for exercise and reading” were the names given to these aspects. However, three variables that were strongly loaded in both factors were eliminated from additional analysis in this initial EFA. These factors were “Going to commercial centers,” “Going to the gym or indoor sports,” and “Watching online programs, including TV programs and videos.”
Six factors—namely, (1) “Safety,” (2) “Accessibility,” (3) “Basic walking infrastructure,” (4) “Best cycling infrastructure,” (5) “Esthetic vistas during day and night,” and (6) “Slope and traffic lights”—were retrieved with reference to the subjective built environment in NLSES (Table 4). Safety stands for the enhancement of both personal security and traffic safety. Distances to and from land uses, such as parks, stores, and bus stops, are referred to as accessibility. “Basic walking infrastructure” refers to some fundamental walking infrastructures, such as sidewalk width and maintenance. Bicycle parking spaces and bike lanes are two examples of “basic cycling infrastructure.” “Esthetic vistas during day and night” relates to a pair of factors: having attractive neighborhood views and well-lit surroundings at night. Lastly, the phrase “Slope and Traffic lights” describes slopes and the presence of enough traffic signals to make pedestrian crossings easier. The former factor shows the relationship between NLSES’s slope and the condition of its pedestrian crossing lights.
Furthermore, five factors were identified in relation to the subjective built environment in NHSES: (1) “Accessibility,” (2) “Basic walking infrastructure both during the day and at night,” (3) “Safety from traffic,” (4) “Basic cycling infrastructure and sufficient traffic lights for pedestrian crossing safety,” and (5) “Access to bus stations” (Table 5). Distances to and from land uses, such as parks and stores, are referred to as accessibility. “Basic walking infrastructure in both day and night” refers to some fundamental walking infrastructure, such as the dimensions and quality of the walkways. The factors that contribute to increased traffic safety are represented by traffic safety. “Basic cycling infrastructure, including sufficient traffic lights for crossing” referred to a few essential bicycle-related features, such as bike lanes and ample bike parking. Lastly, whether a bus stop is easily accessible by foot from one’s residence is referred to as access to bus stations.

4.3. The SWB-Related Factors in NLSES

Several factors were significantly linked with SWB in the NLSES, as indicated in Table 6. About 25.4% of the variance in SWB can be explained by the factors under investigation (R2 = 0.254). Older adults in the NLSES report higher SWB (β = 0.147, p = 0.020). Subjective well-being is higher among retirees than among jobless individuals (β = 0.180, p = 0.014). Similarly, compared to jobless individuals, the students (β = 0.159, p = 0.004), the people who work (β = 0.316, p = 0.000), and housewives (β = 0.251, p = 0.000) all significantly report higher SWB.
Among the social factors, a category of social cohesion described as “People are willing to help their neighbors” (β = 0.141, p = 0.004) and the role model category’s “My family/friends participate in physical activities regularly” (β = 0.112, p = 0.005) showed a significant positive association with SWB. Finally, accessibility revealed a substantial positive relationship (β = 0.110, p = 0.015) with SWB, while “Slope and traffic lights” revealed a significant negative association (β = −0.140, p = 0.004) with the same.

4.4. The SWB-Related Factors in NHSES

Table 7 shows that SWB in NHSES was substantially correlated with a number of parameters. Thirty percent of the variation in SWB can be attributed to the variables under examination. (R2 = 0.302). Age is positively correlated with SWB in the NLSES (β = 0.227, p = 0.006). A noteworthy positive correlation was observed between the social cohesion category’s “People are willing to help their neighbors” and SWB (β = 0.137, p = 0.018) among the socially related categories. There was also a significant positive correlation between the second lifestyle type, which was “Going to parks for engagement in physical activity and reading books” and SWB (β = 0.177, p = 0.005).
Subjective well-being is higher among retired individuals than among jobless individuals (β = 0.180, p = 0.014). Likewise, those with jobs (β = 0.316, p = 0.000), housewives (β = 0.251, p = 0.000), and students (β = 0.159, p = 0.004) report significantly greater SWB than those without jobs. Among the social factors, the social cohesion category’s “People are willing to help their neighbors” (β = 0.141, p = 0.004) and the indicator of a role model as described in “My family/friends participate in physical activities regularly” (β = 0.112, p = 0.005) showed a significant positive association with SWB. Furthermore, among the built environmental factors, accessibility demonstrated a noteworthy positive correlation (β = 0.110, p = 0.015), whereas “Slope and traffic lights” demonstrated a significant negative correlation (β = −0.140, p = 0.004) with subjective well-being. Finally, among the built environmental factors, access to bus stations demonstrated a significant positive correlation with the NHSES participants’ subjective well-being (β = 0.108, p = 0.05).

5. Discussion

On average, the level of subjective well-being is higher in NHSES compared to NLSES. According to theory, this is reinforced by the fact that SES resources provide people with access to resources that help them meet their basic needs, which could account for the positive relationship between SES and SWB [16,17]. Because their basic requirements are more likely to be met, people with higher socioeconomic status are known to have higher subjective well-being [18,19,20,21]. Therefore, to improve SWB in this city, urban policymakers need to give NLSES more attention. Several correlations between SWB and sociodemographic characteristics, attitudes, lifestyles, socially linked elements, and built environmental factors were found based on these two types of urban neighborhoods. Older people have higher SWB scores in the NHSES and NLSES. Given the multidimensionality of the mechanisms via which age may impact SWB (SWB), a variety of theoretical interpretations are possible [62]. Kassenboehmer and Haisken–DeNew [63] found that age has no effect on subjective well-being. Wunder et al. [64] suggest that life satisfaction (SWB) has a wave-like pattern throughout the life cycle. Life satisfaction specifically declines until middle adulthood, when it starts to increase, and then it seems to decline again with the oldest seniors. Thus, this study’s improvement in SWB may be more related to middle-aged adults and the early careers of older adults (those between the ages of 40 and 70) compared to other ranges, such as young adults (18–39) or the majority of older adults (over 70). In comparison, almost every category of job situation has a significantly greater SWB than the unemployment rate in NLSES.
Additionally, it was found that the unemployment rate in NLSES is significantly greater than that in NHSES. Notably, the unemployment rate in the Araucania province, which has Temuco as its capital, is among the highest in Chile (14.6 percent in 2023) [65]. Previous studies have demonstrated the negative association between SWB and the unemployment rate. Rokicka et al. [66] discovered, using a fixed effect model, that young people who lose their jobs feel worse off than others who keep their jobs. Gedikli et al. [67] reviewed 29 studies from the EU-15, the US, Australia, and the UK that were published between 1990 and 2020 and found an adverse association between life satisfaction and the unemployment rate. After losing SWB due to unemployment, people may have poor mental or physical health or even consider suicide [68,69]. Additionally, compared to NHSES, a significantly larger proportion of housewives exist in NLSES. This is a promising opportunity, and policymakers may consider including more housewives in their SWB enhancement program in NLSES.
Furthermore, a lifestyle category named “Going to parks for engagement in physical activity and reading books” was found to have a positive association with the SWB in NHSES. One of the key environmental factors influencing SWB is vegetation, including green spaces [32,33]. As with the findings in Ambrey and Fleming’s [70] study, public green spaces positively affect residents’ neighborhood satisfaction. Through theories such as the Attention Restoration Theory (ART), which posits that nature restores directed attention, and the Stress Reduction Theory (SRT), which associates nature with reduced levels of stress and cortisol, access to green spaces has a favorable impact on subjective well-being [71,72,73]. Promoting physical exercise, cultivating social relationships, and meeting fundamental psychological requirements for relatedness, competence, and autonomy are other approaches [72,74]. In this city, NHSES has more well-maintained urban parks than NLSES. This has resulted in the creation of a lifestyle type in NHSES where individuals use these urban green areas to do physical activity in addition to other complementary hobbies, such as reading books and newspapers. Also, there is a favorable association between this lifestyle type and their SWB. Thus, the primary factor in improving SWB in NHSES is the maintenance of urban green spaces by urban policymakers in these types of neighborhoods. This lifestyle type also includes activities like reading books and newspapers, going to local parks, and engaging in outdoor activities like walking and cycling, according to EFA. To enhance SWB in these neighborhoods, it is necessary to upgrade the facilities for bicyclists, pedestrians, and people to sit, particularly in local parks. In this sense, enhancing urban green spaces along bike and walking routes and around the seating areas in local parks could be one way to boost SWB.
From the social-related factors, greater social cohesion is positively correlated with SWB in both NLSES and NHSES. This is supported by other studies, which found that social cohesion—including neighborhood social bonding—strongly increases the likelihood of higher SWB [34,49,75]. The policymakers of this city should pay special attention to fostering social cohesion because of its involvement in SWB in both the NHSES and NLSES. Furthermore, for NLSES, SWB is positively correlated with the role model category item “My family/friends participate in physical activities regularly.” Few studies have examined the connection between SWB and role models. This shows the need for some policies, such as those that encourage physical activity via the media or other methods, in order to enhance role models and reinforce the SWB in NLSES.
Moreover, in NLSES, accessibility showed a positive relationship with SWB. Accessibility to cultural amenities and subjective well-being were found to be positively correlated [42]. Denser neighborhoods also have better access to different land uses, and Ma et al. [76] found that a neighborhood’s density increases the subjective well-being of its residents. This study’s factor analysis shows that accessibility in NLSES encompasses several everyday places like stores, parks, and plazas, as well as bus stations. Enhancing SWB in this type of urban neighborhood may involve evaluating the distribution of common daily destinations, particularly stores in NLSES, and their expansion in the areas that do not have this service. In addition, there are not enough urban green spaces in NLSES, and the types of urban neighborhoods in this city need to significantly enhance the quantity and quality of urban plazas and parks.
Furthermore, the SWB in NHSES is strongly correlated with accessibility to bus stations. NHSES’s bus network is poor, and increased SWB in these types of urban neighborhoods can be attributed to transportation policymakers’ improved bus station coverage in NHSES. In this sense, the bus system needs to be more widely available in these kinds of neighborhoods, particularly in the vicinity of important land uses like commercial and healthcare centers, as well as in areas where a greater proportion of the population, including women and older adults, depend on public transportation for daily mobility. Finally, in NLSES, a greater slope corresponds to a lower SWB. This is because one of the neighborhoods selected from the NLSES is situated in a hilly area, and in light of this discovery, urban development in this type of terrain is inappropriate for improving the SWB in this city.

6. Limitations

Since exposure and outcome are measured at a single moment in time in a cross-sectional design, it is typically hard to establish the temporal sequence required to show cause and effect, hence preventing direct causal inference. Additionally, SWB was measured using just two items. This was performed in order to minimize the length of the questionnaire because, prior to conducting the main survey, we conducted a pilot test and found that the majority of respondents in this city disliked answering lengthy surveys. It is also possible to consider the limitations of this research, which included the exclusion of gender due to missing numbers. Finally, generalizability of findings may not apply to other cities around the world and is restricted to Temuco or other medium-sized cities in Chile or Latin America, based on specific social and structural similarities.

7. Conclusions

SWB is a significant urban concern, and little research has been performed on the elements that influence it, especially in southern Chilean cities. This study aims to investigate the relationships between SWB and relevant individual, social, and built environmental factors in the city of Temuco. Furthermore, the high levels of socioeconomic segregation found in Chile’s various urban sectors suggest a separate study of these relationships based on Temuco’s two main urban neighborhood types, NHSES and NLSES, which together account for majority of the city’s land area and population. This would lead to more comprehensive outcomes that later could be utilized by urban officials in this city to improve SWB in each neighborhood type. This study adds to the body of knowledge on well-being in segregated cities and emphasizes the significance of adjusting urban and transportation strategies to local socioeconomic conditions.
The results show the associations of several factors with SWB in each type of urban neighborhood including age, several categories based on job situation, lifestyle, social-related factors, and some built environmental factors. Nevertheless, no association was found for the first research question of this study, which examined the relationship between SWB and active travel. One of the most important findings is that the level of SWB, on average, is higher in NHSES than in NLSES. To improve SWB, NLSES thus needs significantly more attention from this city’s policymakers.
Moreover, NLSES has a far greater unemployment rate than NHSES, and nearly every type of job situation has a higher SWB than the jobless in NLSES. This demonstrates the necessity for urban policymakers to give more attention to decreasing unemployment in NLSES. From the social-related factors, two factors as indicators of social cohesion and role models showed a significant relationship with SWB in NLSES. In NHSES, there is an association between social cohesion and SWB. It suggests that to increase SWB in both types of neighborhoods in this city, individuals need to strengthen these socially linked elements. This shows that these social-related factors are to be reinforced among the people to improve the SWB in both types of neighborhoods in this city.
Furthermore, a lifestyle category named “Visiting parks for exercise and reading” was found to have a positive association with SWB in NHSES. In this city, NHSES has more urban parks than NLSES, both in terms of quantity and quality, and greenery is the significant environmental factor that influences SWB. Therefore, to increase SWB in this neighborhood type, urban green spaces in NHSES need to be preserved and enhanced in terms of both quantity and quality.
Finally, to improve SWB in NLSES, it is recommended that built environmental variables such as accessibility to stores, parks, and bus stops be improved and that the development of urban areas in hilly terrain be discouraged. The access to the bus network is also to be reinforced in NHSES to improve SWB in this type of urban neighborhood.
In summary, the research presented a number of practical implications and policies to improve SWB in both types of neighborhoods, such as lowering unemployment in NLSES, improving the upkeep of urban green spaces, making stores and bus stations more accessible, and deterring urban development in hilly areas. Urban policymakers in Temuco might use these data to enhance the SWB according to the pertinent elements in each of the city’s neighborhood types. Given that socioeconomic segregation frequently hinders sustainability objectives, such policies will support environmental justice, which ultimately improves social equality and sustainability in this city.

Author Contributions

Conceptualization, M.P.; methodology, M.P.; software, M.P.; validation, M.P.; formal analysis, M.P. and A.K.F.; investigation, M.P.; resources, M.P.; data curation, M.P.; writing—original draft preparation, M.P.; writing—review and editing, M.P. and A.K.F.; visualization, M.P.; supervision, M.P.; project administration, A.K.F.; funding acquisition, M.P. and A.K.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The Ethics Committee of Universidad Mayor in Chile has granted ethical permission for this project (approval number 0239, date 26 January 2022).

Informed Consent Statement

Every participant gave their permission to participate in the study.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Table 1. Descriptive statistics for the sociodemographic factors and additional variables included in this study’s conceptual framework (n = 301; high socioeconomic status; n = 481, low socioeconomic status).
Table 1. Descriptive statistics for the sociodemographic factors and additional variables included in this study’s conceptual framework (n = 301; high socioeconomic status; n = 481, low socioeconomic status).
VariablesVariable DescriptionNLSES NHSES
FrequencyPercentageMeanSDFrequencyPercentageMeanSD
Level of walking (minutes per week) 81.0192.25 60.1982.55
Cycling level0 = Did not ride a bicycle last week40477.3 23176.7
1 = Using bicycle during last week7722.6 7023.3
Subjective well-being 3.160.56 3.410.53
Sociodemographic variables
Age18–299018.7 8026.7
30–396212.9 4916.3
40–496914.3 6922.9
50–5912826.6 6521.5
60–698517.7 237.6
70–79469.6 113.7
More than 8010.2 41.3
Monthly incomeLower than 324,000 CLP18338.2 165.4
324,000–562,00014830.2 62
562,000–899,00010521.9 258.4
899,000–1,360,000316.1 5718.9
1,360,000–1,986,000102.2 8829.1
More than 1,986,00040.9 10936.2
Private car0 = Do not Have29561.30.390.486421.30.790.40
1 = Have18638.7 23778.7
Driver’s license0 = Do not Have35273.20.280.467023.30.770.42
1 = Have12926.8 23176.7
Bicycle0 = Do not Have332690.310.4610936.20.640.47
1 = Have14931 19263.8
Job situationRetired8317.2 268.6
Student377.7 5417.9
With work22246.2 18862.5
Unemployed5110.6 62
Housewife8818.3 279
Social-related variables
Take a walk or go biking with others 2.381.17 2.741.10
Being motivated to engage in physical activity 2.721.08 3.030.96
Helping the neighbors in the neighborhood 3.020.98 2.960.81
Participating regularly in physical activities 2.511.05 2.910.93
Seeing other active people inspires me 2.671.07 3.080.93
Table 2. Results from exploratory factor analysis for lifestyle in NLSES.
Table 2. Results from exploratory factor analysis for lifestyle in NLSES.
ComponentHow Often (in the Last Month) Do You Engage in Various Activities During Your Free Time?Loadings
Going to restaurants and malls with friendsA visit to commercial centers.0.633
Joining friends at the cafeteria or restaurant.0.845
A gathering with friends.0.706
Physically active peopleParticipate in indoor sports or the gym.0.804
Participating in outdoor activities, cycling, running, or walking.0.858
Table 3. Results from exploratory factor analysis for lifestyle in NHSES.
Table 3. Results from exploratory factor analysis for lifestyle in NHSES.
ComponentHow Often (in the Last Month) Do You Engage in Various Activities During Your Free Time?Loadings
Visiting restaurants and socializing with friendsJoining friends at the cafeteria or restaurant.0.870
A gathering with friends.0.820
Visiting parks for exercise and readingVisit nearby parks and plazas.0.661
Participating in outdoor activities, cycling, running, or walking.0.785
Read magazines, newspapers, and books.0.605
Table 4. Results from exploratory factor analysis for subjective built environment in NLSES.
Table 4. Results from exploratory factor analysis for subjective built environment in NLSES.
Component Loadings
SafetyThe sidewalks are covered with several obstacles.0.657
The amount of traffic on the surrounding streets makes it difficult or unpleasant to walk or bike.0.706
Most drivers in my neighborhood travel faster than the posted speed limits.0.731
My neighborhood has a high crime rate.0.671
AccessibilityThe place I live in is easily accessible on foot from a number of locations.0.656
I can walk a short distance from my house to the bus station.0.692
My house is within walking distance of parks and plazas.0.782
To travel from one location to another, there are numerous alternate paths.0.559
Basic walking infrastructureThe walkways in my neighborhood are generally sufficiently wide.0.812
The sidewalks in my neighborhood are well-maintained (paved, even, and not a lot of cracks).0.851
Basic cycling infrastructureBicycle trails are easily accessible in or close to my neighborhood.0.783
In the locations I typically go to on my daily journey, there is plenty of space for bicycle parking.0.829
Esthetic vistas during the day and at nightIt is pleasant for me to walk through my neighborhood and take in the scenery.0.495
At night, my neighborhood’s streets are well-lit.0.829
Traffic lights and slopeWalking on the streets in my neighborhood is challenging due to their steepness.0.868
In my neighborhood, pedestrian lights and crosswalks make it easier to cross major streets.0.553
Table 5. Results from exploratory factor analysis for subjective built environment in NHSES.
Table 5. Results from exploratory factor analysis for subjective built environment in NHSES.
Component Loadings
AccessibilityI can easily walk from my house to stores.0.742
The place I live is easily accessible on foot from a number of locations.0.733
My house is within walking distance of parks and plazas.0.721
To travel from one location to another, there are numerous alternate paths.0.719
Basic walking infrastructure both during the day and at nightThe walkways in my neighborhood are generally sufficiently wide.0.777
The sidewalks in my neighborhood are well-maintained (paved, even, and not a lot of cracks).0.829
At night, my neighborhood’s streets are well-lit.0.666
Traffic safetyThe sidewalks are covered with several obstacles.0.721
The amount of traffic on the surrounding streets makes it difficult or unpleasant to walk or bike.0.782
Most drivers in my neighborhood travel faster than the posted speed limits.0.714
Basic bike infrastructure, such as enough traffic signals for crossingsBicycle trails are easily accessible in or close to my neighborhood.0.822
In the locations I typically go to on my daily journey, there is plenty of space for bicycle parking.0.530
In my neighborhood, pedestrian lights and crosswalks make it easier to cross major streets.0.484
Bus station accessI can walk a short distance from my house to the bus station.0.801
Table 6. The outcomes of using adjusted hierarchical multiple regression analysis for predicting SWB in NLSES (n = 481).
Table 6. The outcomes of using adjusted hierarchical multiple regression analysis for predicting SWB in NLSES (n = 481).
VariablesStandardized
Coefficients		tp-ValueVIF
Age0.1472.3270.020 *2.38
Retired0.1802.4790.014 *3.15
Students0.1592.8600.004 **1.85
People with work0.3164.2310.000 **3.32
Housewife0.2513.7640.000 **2.62
People are eager to assist their neighbors0.1412.8940.004 **1.41
My family/friends participate in physical activities regularly0.1121.9240.05 *2.01
Built environment (accessibility)0.1102.4530.015 *1.19
Built environment (alope and traffic lights)−0.140−2.8990.004 **1.39
* p < 0.05; ** p < 0.01. Dependent variable: walking behavior (average weekly walking minutes); R Square: 0.254.
Table 7. The outcomes of using adjusted hierarchical multiple regression analysis for predicting SWB in NHSES (n = 301).
Table 7. The outcomes of using adjusted hierarchical multiple regression analysis for predicting SWB in NHSES (n = 301).
VariablesStandardized
Coefficients		tp-ValueVIF
Age0.2272.7780.006 **2.56
People are eager to assist their neighbors0.1372.3730.018 **1.26
Lifestyle (reading books and engaging in physical activity in parks)0.1772.8180.005 **1.50
Built environment (access to bus stations)0.1081.8800.05 *1.26
* p < 0.05; ** p < 0.01. Dependent variable: walking behavior (average weekly walking minutes); R Square: 0.302.
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Paydar, M.; Kamani Fard, A. Subjective Well-Being, Active Travel, and Socioeconomic Segregation. Sustainability 2025, 17, 10571. https://doi.org/10.3390/su172310571

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Paydar M, Kamani Fard A. Subjective Well-Being, Active Travel, and Socioeconomic Segregation. Sustainability. 2025; 17(23):10571. https://doi.org/10.3390/su172310571

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Paydar, Mohammad, and Asal Kamani Fard. 2025. "Subjective Well-Being, Active Travel, and Socioeconomic Segregation" Sustainability 17, no. 23: 10571. https://doi.org/10.3390/su172310571

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Paydar, M., & Kamani Fard, A. (2025). Subjective Well-Being, Active Travel, and Socioeconomic Segregation. Sustainability, 17(23), 10571. https://doi.org/10.3390/su172310571

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