1. Introduction
Urban settlements contribute roughly 70% of CO2 emissions from all settled areas worldwide [1]. As urban populations continue to grow, cities are becoming more and more crucial for climate change mitigation efforts [2,3,4,5]. The transport sector is one of the main contributors to the emissions of both CO2 and other air pollutants in urban areas [6]. Densification of the urban form has been a strategy to reduce transport emissions from reduced levels of car use, shorter everyday travel distances, as well as smaller living spaces that require less energy and, broadly, less infrastructure required per person [7,8,9]. Lohrey and Creutzig (2016) estimate that the population density should be between 50 and 150 people/ha, and public transport and active transport modes combined should make up more than half of the urban modal share for cities to fit within the sustainability window [10]. However, within a dense urban form, people have been shown to extend their living spaces with external services and goods consumption, thus increasing their emissions [11,12,13,14]. In addition, the dense urban form, while reducing the need for personal car ownership and use [15], has also been linked to higher engagement in long-distance leisure travel, which counteracts the emission reductions [16,17]. Prior studies have suggested that part of the reason for engaging in more leisure travel is due to the increased density and lack of greenness in the urban environment [18]. Considering the above, this study examines the connections between exposure to the urban environment during local travel and its impact on both long-distance leisure travel and perceived wellbeing. Understanding these dynamics is crucial for developing policies that not only mitigate emissions but also enhance the overall life satisfaction of urban residents, particularly in the face of increasing urbanization.
1.1. Travel, Wellbeing, and the Urban Environment
Travel both within and away from one’s urban environment is connected to the perception of one’s satisfaction with life, or to one’s perceived wellbeing. While prior studies have covered varying measures and used a variety of different terms, in the below overview, the general term, “wellbeing”, is used. On a broad level, the perspectives to wellbeing can be divided into two categories: studies on hedonic wellbeing and studies on eudaimonic wellbeing. Hedonic wellbeing refers to the enjoyment of life, happiness, and comfort, whereas eudaimonic wellbeing is related to the meaning and value we place on different life aspects and our contentment with life based on those expectations [19,20]. When examining the relationship between travel and wellbeing, more studies focus on the hedonic wellbeing associated with local travel behavior than eudaimonic wellbeing. In local or daily travel, travel satisfaction is mostly linked to travel mode and travel distances and is related to one’s emotional wellbeing and overall life satisfaction. For example, reduced car use could support satisfaction with life, even if the person’s goal is not to reduce their environmental impact [21]. However, the influence of long-distance leisure travel, driven by urban form and residents’ quest for wellbeing, also plays a significant role in shaping life satisfaction.
Local travel in our context refers to the regular, day-to-day mobility patterns of people. Local travel can impact wellbeing in varying directions, depending on people’s mood, personality, and many other factors. Local level travel can influence wellbeing through travel mode [22,23,24], travel time [24,25], travel distance [23,24,25], quality of transport mode and travel experience [22,25], trip purpose [24,26], companionship [27], and accessibility [24].
Leisure travel, on the other hand, can have a mainly positive effect on wellbeing, as leisure trips offer a break from daily life and worries. In our context, leisure travel refers to long-distance travel away from people’s usual area, or in this case, away from the city they live in. Leisure travel could influence wellbeing through a multitude of factors, like trip duration or trip type. Even short weekend getaways have shown positive results on wellbeing and stress reduction [28,29]. Trips with the purpose of relaxation increase hedonic wellbeing more, whereas trips with activity purpose stimulate eudaimonic wellbeing through the sense of achievement (e.g., doing extreme sports) [30]. Furthermore, events that occur on the trip relate to many life satisfaction domains and therefore can trickle upwards into one’s overall wellbeing [31]. Also, anticipation of the leisure trip has been associated with increased wellbeing of individuals [30,32].
It has been suggested that long-distance leisure travel of urban residents could be driven by the urban form and characteristics within both their immediate and broader urban environments [33] as well as traveling for the sake of wellbeing [18]. The compensation or escape hypothesis addresses this by saying that people leave the urban area for leisure because it does not meet their needs, is too dense, or creates general distress in their lives [13,14,18,34]. Other studies indicate that people adjust their travel-related attitudes and behaviors to their urban environment [35]. This hints at a complex linkage between local travel/urban mobility, leisure travel, and wellbeing.
Beyond travel behaviors, the physical attributes of urban environments, particularly the availability of green spaces, are closely linked to the wellbeing of residents [36]. Urban form and land use can influence the wellbeing of people living in urban areas [37,38]. Compact cities have the potential to improve wellbeing if they consider people’s needs [23,39]. More open and green spaces have been found to be positively associated with satisfaction with the local living environment [40] and with life overall [41,42,43,44]. Furthermore, urban green spaces can have a positive impact on physical and psychological health [43,45].
Conversely, the loss of urban green spaces, or higher levels of green space fragmentation, may have a negative relationship with wellbeing [46,47]. Higher levels of gray areas in the home neighborhood or, alternatively, a lack of green space, have been associated with increased leisure travel [48]. Walking in green areas has shown more benefits to psycho-physiological health than walking in gray areas, with the added benefit of temperature and pollution mitigation [49]. A study of 60 developed countries indicates that urban green spaces are conducive to happiness in countries with higher GDPs, suggesting that urban green spaces could be important factors in “understanding happiness beyond economic success” [50]. Moreover, lower levels of wellbeing have been associated with higher carbon footprints [51], while people who engage in environmentally friendly behaviors may have a higher satisfaction with life overall [52].
It is worth noting that in this study, we use life satisfaction as a measure of wellbeing. Life satisfaction is a cognitive component of subjective wellbeing [53] and can be used to measure both the hedonic and eudaimonic components of wellbeing [54], although leaning more toward the eudaimonic side due to its dependence on people’s perception of and expectations for life [19,20,55].
1.2. Research Aims
Considering the rising populations in urban areas, the significant environmental impact of cities and travel [1,2,3,4,5,6], and the connection of both to wellbeing [18], it is imperative to understand the interplay between urban environments, travel behavior, and wellbeing. To reduce the environmental impact of cities, urban planning and policies should aim to meet the needs of the residents on multiple levels [23,39]. Thus, the aim of this study is to examine the connections between environmental exposure during local travel and its impact on both long-distance leisure travel habits, with a focus on the amount of travel emissions, and perceived wellbeing, with a focus on life satisfaction. The study is based on a softGIS survey conducted in 2017 in Reykjavík, Iceland, wherein people were tasked with mapping their regularly visited locations in the city and answering questions about them alongside questions about their travel habits, perceived life satisfaction, and background. These data were used to analyze regular urban mobility through activity space modeling, with the exposure to green and gray spaces within each activity space calculated and studied in relation to both life satisfaction and greenhouse gas (GHG) emissions from leisure travel. Canonical correlation analysis was applied to explore these associations. More specifically, the study seeks to answer the following research questions:
RQ1: How is exposure during local travel associated with life satisfaction?
RQ2: How is exposure during local travel connected to GHG emissions from long-distance leisure travel?
The results of the study show that exposure to green and gray spaces during daily mobility contributes to overall life satisfaction; however, socio-economic background is likely more relevant. The study finds indications of high levels of urban mobility and higher exposure to gray spaces leading to more leisure travel away from the city.
3. Results
The results show a limited relationship between exposure and life satisfaction when socio-economic factors are accounted for. Exposure during daily mobility is important in explaining some of the leisure travel emissions when accounting for socio-economic background, but adding attitudinal factors limits the impact of exposure. In this section, we will walk through the statistical analysis and key findings following the models as described in Section 2.3. This is followed by a discussion in Section 4, which interprets the results in the context of the previous literature.
First, the most relevant exposure variables were identified. Canonical correlation analysis showed one significant variate (set of variables) (p < 0.05), in which total green exposure and total gray exposure appeared as the most relevant variables due to their high canonical loadings (Table 5).
Similarly to Model 1a, when examining the canonical correlations between exposure and travel emissions in Model 2a, one set of variables was revealed as significant (p < 0.01). The canonical loadings revealed the same as Model 1a—total green exposure and total gray exposure were the most relevant variables (Table 6). Average green and gray exposure also had high values, but due to the overlap in function—the variables describe the same pattern but in a different form—they were excluded in the next steps.
Next, the significant exposure variables from Models 1a and 2a were taken and the models were repeated as 1b and 2b. For Model 1b, there were two significant canonical correlations between exposure and life satisfaction variables. Canonical loadings for Model 1b show that both exposure variables are relevant for both canonical variates (Table 7). Variate 1b-1 showed a relationship with satisfaction with job/studies, sense of achievement, and life overall (loading over 0.3), whereas variate 1b-2 showed a stronger relationship with satisfaction with leisure time and the local environment (Table 7).
In analyzing the effect direction between the two variable groups in Model 1b, the focus is put on the variables highlighted by the canonical loadings in Table 8. For Variate 1b-1, it can be seen that as total green exposure increases and gray exposure decreases, satisfaction with job/studies, sense of achievement, and life overall increases (Figure 4).
For Variate 1b-2, it can be seen that as total green exposure decreases and gray exposure increases, satisfaction with leisure time availability increases and satisfaction with the local environment decreases (Figure 5).
A similar procedure was repeated for Model 2b, wherein exposure is the dependent (X) and participation in leisure travel is the independent (Y) set. The analysis revealed one significant variate. Canonical loadings showed the importance of all variables in both sets (Table 8).
For Model 2b, it can be seen that more green exposure and less gray exposure are connected to less domestic and international leisure travel emissions. The influence of exposure is higher on domestic emissions than on international emissions, and gray exposure had an overall higher influence on both leisure travel emissions than green exposure (Figure 6).
Average exposure to green or gray spaces did not significantly differ between the socio-demographic groups (Table A1 and Table A3). Some significant differences between the groups remained for both life satisfaction (tested with a composite wellbeing variable summarizing all life satisfaction variables used here) and leisure travel emissions (varying results for both domestic and international travel emissions) (Table A1 and Table A2). After adding socio-economic background to the independent set of variables, the canonical correlation analysis revealed five significant variates for Model 3 for the relationship between exposure and socio-economic background (independent) and life satisfaction (dependent). Total green and gray space exposures are not the main influencing factors in most life satisfaction categories when socio-economic background is accounted for as well (Table 9). Exposure had a strong role in Variate 3-3 together with overall life satisfaction.
Lower gray exposure and higher green exposure are associated with higher overall life satisfaction (Variates 3-3 and 3-5) (Table 10). For other life satisfaction categories, the impact of socio-economic background was stronger than the impact of exposure (Table 9). For example, in Variate 3-1, it can be seen that weekly working hours and income level have a moderate positive correlation with satisfaction with the material standard of living (Table 10).
Model 4 examined the relationship between exposure and socio-economic background (independent set) and long-distance leisure travel emissions (dependent set). The canonical correlation analysis revealed one significant variate. In the composition of the variate, exposure to both green and gray spaces was more important than some socio-economic variables like working hours and income level. The strongest contributors to the variate were total green exposure, total gray exposure, and household size. Age and education level were also included but had a weaker relationship (Table 11).
When looking at the standardized correlation coefficients (Figure 7), higher education level was associated with higher leisure travel emissions both domestically and abroad. Younger age and smaller household size were correlated with higher emissions in both categories. Exposure to green and gray spaces had a moderate influence on both domestic and international emissions, but in varying directions. The coefficients showed that a decrease in total green exposure was linked to an increase in domestic and international leisure travel emissions, whereas gray exposure was positively associated with both emissions (Figure 7).
Previous studies have also indicated the importance of people’s attitudes to leisure travel behavior [17]. The canonical correlation analysis showed two statistically significant canonical correlation variates when attitudinal factors were controlled for (Table 12).
Firstly, when accounting for attitudinal variables, exposure is not very important in explaining travel emissions. In the first variate, education level, working hours, household size, cosmopolitan attitude, and preference for urban leisure travel seem more important in relation to travel emissions (Table 12). Secondly, however, it appears that exposure is a more important variable in relation to domestic travel, alongside climate awareness, pro-environmental attitude, and a preference for urban leisure travel (Table 12). Variate 5-1 did not show a strong relationship between exposure and travel emissions, but Variate 5-2 did. Therefore, here we present the standardized coefficients for Variate 5-2 only (Figure 8).
Model 5 shows that when socio-economic and attitudinal variables are accounted for, exposures to both green and gray spaces have a positive correlation with domestic leisure travel emissions and a negative relationship with international leisure travel emissions (Figure 8). With less exposure to both green and gray spaces, domestic leisure travel emissions are smaller. However, less green exposure, and less gray exposure during daily mobility, were connected to higher international leisure travel emissions. Higher climate awareness, pro-environmental attitude, and a preference for urban leisure travel were associated with lower domestic leisure travel emissions and higher international leisure travel emissions.
4. Discussion and Conclusions
The aim of the study was to examine how exposure to green and gray spaces in an urban area during regular commutes is related to people’s life satisfaction and leisure travel GHG emissions. The study analyzed softGIS survey results of about 670 respondents from Reykjavík, Iceland. In this section, we will discuss the findings of the study in the context of the prior literature. First, the relationship between exposure and life satisfaction will be discussed. Second, the relationship between exposure and travel emissions will be explored. Lastly, the limitations, suggestions for future research, and policy implications of the study will be presented.
4.1. Exposure and Wellbeing
The canonical correlation analysis showed that exposure to both green and gray spaces is important for overall life satisfaction, but other socio-economic factors are more dominant in relation to the examined life satisfaction sub-categories. A positive correlation was observed between exposure to green spaces and satisfaction with one’s job/studies, sense of achievement, the local environment, and life overall and a negative relationship with satisfaction with leisure time availability. The latter might be a sign of an inverse causality, since people who are dissatisfied with how much free time they have might visit green spaces less frequently. Exposure to gray spaces had the opposite relationships with the abovementioned life satisfaction categories. The findings are similar to those of previous studies, wherein a positive relationship has been found between exposure to green spaces and satisfaction with the local environment [80,81] and life overall [41,42,43]. Similarly, negative relationships have been found between gray spaces (or lack of greenness) and life satisfaction as well [46,47]. There could be an underlying reason related to residential sorting of people with similar socio-economic statuses [82]. For example, people with a lower socio-economic status might live in a less central and less green area, thus having lower exposure to green spaces. In addition, due to their lower socio-economic status, they might also have lower life satisfaction levels and travel less for leisure. Our data set captures a snippet of people’s socio-economic status, but this could be expanded in future studies to include wealth, migrant status, job type, etc.
It has been suggested that investigations of urban green spaces could help researchers go beyond GDP in understanding happiness unrelated to economic growth [50]. Within our study, which is conducted in a country with a higher GDP, we see that socio-economic factors still play an important role in people’s life satisfaction, to a somewhat greater extent than exposure. A possible explanation is the overall low greenery in Iceland due to the sub-arctic tundra-like conditions. The ratio of green space among the total activity spaces of people was very low. Another possible reason is the predominant car-oriented lifestyle and built environment [15], which means that people could be less exposed to the greenery during daily commutes [83] or feel less of a positive feeling from it due to the short exposure duration [78,79]. In addition, there could be other underlying reasons connecting life satisfaction and daily mobility, such as accessibility [24,84], commuting-related stress [33], or companionship [27], that overshadow exposure impacts but are not captured in our data.
4.2. Exposure and Travel
Even when socio-economic background was included, the canonical correlation analysis showed that having less exposure to green spaces during daily mobility was correlated with higher domestic and international travel emissions. In addition, more exposure to gray spaces was positively correlated with domestic and international leisure travel emissions. However, exposure becomes less significant when attitudinal variables are added, and the results indicate something different. As exposure overall goes up, so do emissions, which could indicate that people who are highly mobile and busy in their day-to-day also travel more for leisure, or conversely, exposure does not matter much in decision making. High mobility levels could stem from mobility due to residential location or socio-economic status or from a general disposition toward being mobile. In addition, exposure to gray and green spaces during daily mobility could affect attitudinal variables, such as feeling it is necessary to take a break from urban life or the preference for spending weekends in the city rather than in wilderness areas. Prior studies have also indicated the significance of a cosmopolitan attitude, i.e., a desire to experience new things and different cultures, in long-distance leisure travel behavior, especially in the case of international and air travel [11,12,33].
Although not captured within our models, it is possible that there is a connection between exposure and wellbeing via engagement in leisure travel, as studies have shown the positive impact of leisure travel on life satisfaction from the trip experience itself [28,29,30]. A lack of green spaces has been linked to increased leisure travel [48] and is a theme also covered in the travel behavior literature in the form of the compensation or escape hypothesis, which hints that people travel to escape negative or limited aspects of the urban environment [18].
It is important to note that both wellbeing and travel are complex topics, as they depend heavily on human behavior, which is difficult to model accurately. Humans are emotional and social beings, which leads to uncertainties and unpredictability in such studies. Therefore, studies like this can only capture a portion of the patterns related to wellbeing and travel.
4.3. Policy Implications
Considering the significant climate impact of urban settlements and transportation [85], cities are vital to climate change mitigation efforts [2,3]. As the study was based on a single case study of Reykjavík, Iceland, strong and generalizable policy suggestions cannot be made. However, there are broader implications for urban planning that could be considered for the benefit of wellbeing and climate. Urban areas in their form and land use can impact the wellbeing of the residents living there [37,38]. At the same time, GHG emission reduction targets in high-income countries such as Iceland could be more ambitious, as they could also support wellbeing [86]. There are many ways in which urban planning can meet the needs of residents and help with climate mitigation targets, especially considering that people tend to adjust their behaviors to the environment they live in [35].
In this study, we found that being more exposed to gray spaces during daily mobility could lead to more emissions from long-distance leisure travel. Since we spend a lot of our time commuting, efforts should be made to make the commuting experience more pleasant by transport mode [22,23,24,87] and by increasing accessibility through public and active travel modes and reducing distances [23,24,88]. This could be achieved by transforming the already-built infrastructure [89,90,91,92] but also by enabling more remote or co-working opportunities (while considering the environmental impacts of co-working versus on-site work) [93]. Reykjavík, specifically, has been criticized for being car oriented and having a sprawled urban form with long daily commuting distances for many [15,94].
Local policy should take a people-oriented approach by creating mixed-use neighborhoods that support walkability [95,96], which could benefit both people’s wellbeing and the climate. The creation of walkable neighborhoods should be combined with improved public transport. The City of Reykjavík is currently working on a bus rapid transit system, although the plan has received criticism due to the slow speed of implementation. Usage of public transport could be encouraged by both governmental and employer incentives as well as by disincentivizing car use through, e.g., reduced parking spaces and increased costs, and by improving the time-competitiveness of public transport with lane space allocation. However, planning needs to consider seasonal and diurnal changes that may affect the usability of the urban environment [97], particularly in rapidly changing Arctic weather conditions like those in Reykjavík. For public transport, schedules should be frequent, and pavilions should withstand heavy wind and rain or snow, to reduce discomfort due to weather during the daily commute.
Increases in densification and less exposure to greenness can have negative implications on human health [98,99]. Urban planning should promote the establishment of parks and other public spaces with free-to-use amenities, which could provide an attractive alternative to frequent leisure travel away from the city [89,100,101,102]. In general, having more free time, even if it is spent at home on weekends with activities to do locally, has shown benefits for wellbeing [29]. Furthermore, several studies have noted the importance of the accessibility and usability of parks and green spaces for the residents [103,104,105]. Urban planning should make sure that parks are accessible to all residents, regardless of socio-demographic background or physical ability, and that a car is not a necessity to reach urban green spaces. Green spaces and infrastructure have also shown positive impacts in reducing air and noise pollution, limiting the urban heat island effect, and increasing urban biodiversity [90,92]. Therefore, policies supporting urban green space establishment, maintenance, and usability could yield many benefits for climate change mitigation as well as for people’s wellbeing. Aside from greenery, the city could support creating colorful buildings and street art, which could improve the satisfaction of commuters [106] and encourage the use of active transport modes [83,107].
In addition to local-level policies and planning, reductions in long-distance leisure travel emissions need to occur to meet climate mitigation targets [108]. People are more likely to change their travel destination choice than the mode of transport [109], which could be an incentive to provide more diverse leisure opportunities both within the city and domestically to reduce travel distances. Furthermore, national policies should aim to reduce work-related air travel, as it is less likely to impact wellbeing [110].
4.4. Limitations and Future Research
This study has several limitations that invite future research. Firstly, the low greenery in Iceland, due to its sub-arctic nature, is reflected in Reykjavík. Our dataset captures land use typology but does not account for street-level greenery or private yards, potentially affecting the findings related to green space exposure and wellbeing or travel behavior. Furthermore, the study area is relatively low in density. Future studies could benefit from more detailed datasets, and similar studies could be conducted in urban areas that are greener or denser.
Secondly, the cross-sectional design limits the ability to infer causality, making the observed relationships correlational. Longitudinal studies are needed to understand causal pathways and dynamics over time. While canonical correlation analysis is suitable for exploring the relationships between multidimensional variables, it assumes linear relationships, which may not capture the complexity of human behavior and environmental interactions.
The use of softGIS data, although generally a strength, also has limitations. The self-reported nature of the survey data may introduce biases such as social desirability bias. While previous research indicates generally satisfactory quality in softGIS data [111], the spatial accuracy of markings can be variable, introducing biases to the modeling. The lack of a temporal dimension is another limitation [112]. The availability of temporal information could enhance exposure assessment quality. Although we tried to mitigate this by incorporating the frequency of visits and travel speeds into exposure estimations, future studies would benefit from including temporal data to further improve exposure estimation.
The current data capture a snapshot of environmental exposure at a specific time, without accounting for seasonal or daily variations. Future studies should incorporate temporal data for a more dynamic understanding. Additionally, expanding the age range of participants to include older adults could provide insights into how different age groups perceive and are influenced by urban green and gray spaces, enhancing the generalizability of the findings. What is more, using diverse and complementary measures for urban greenness could enhance the understanding of the connections between urban greenness and wellbeing [113].
Investigating exposure to environmental pollution such as air and noise pollution in relation to life satisfaction and travel behavior could be valuable. Given Reykjavík’s car-oriented urban form, understanding how pollution exposure affects residents’ wellbeing and travel choices would be beneficial. Comparative studies across different urban contexts, both within and outside Iceland, could help assess the generalizability of the findings, considering variations in urban form, climate, and socio-economic conditions.
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Johanna Raudsepp www.mdpi.com
