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Transport sufficiency: Introduction & case study
Travel Behaviour and Society 15 (2019) 54–62
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Travel Behaviour and Society journal homepage: www.elsevier.com/locate/tbs
Transport sufficiency: Introduction & case study a,⁎
b
E. Owen D. Waygood , Yilin Sun , Jan-Dirk Schmöcker
T
c
a École supérieure d’aménagement du territoire et de développement régional, Université Laval, Pavillon Félix-Antoine-Savard, 2325, rue des Bibliothèques, Local 1622, Québec, QC G1V 0A6, Canada b Polytechnic Institute, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China c Department of Urban Management, Kyoto University, Japan
A R T I C LE I N FO
A B S T R A C T
Keywords: Transport sufficiency Energy efficiency Quality of life Leisure Built environment Travel behaviour
A new concept, termed transport sufficiency is introduced based on the concept of energy sufficiency, which argues that in addition to efficiency, limits to usage must be applied to achieve environmental sustainability. How that concept translates to transport and its implications are discussed. One key assumption of increased mobility (the usage) is an increase in quality of life through less time spent travelling. Supported by time-use data surveys one might propose that a decrease in travel time and greater mobility would increase activities that better contribute to quality of life such as leisure activities. Based on data from Osaka, Japan, this paper examines how a range of factors influences the number of trips, the average duration of those trips, and whether rates of leisure activities vary. As mobility options and distances vary by built form, the built environment is proposed as a key variable. It is found that the number and type of activities varies by household lifecycles (having children, etc.), thus that is the second key variable analysed here. The paper then connects activities with energy emissions and argues that those being “transport sufficient” can remain below accepted CO2 consumption while maintaining activities such as leisure travel that likely support quality of life.
1. Introduction
However, the improvements in energy efficiency have often been offset by increases in travel distances (Millard-Ball and Schipper, 2011). This is likely a result of behavioural responses such as rebound effect (Herring, 2006) and changes in land development in response to reduced direct costs of car travel (Millard-Ball and Schipper, 2011; Kitamura et al., 2008). The increase in mobility may have offered greater opportunities of access to dispersed locations, but the increase in travel distances has often offset the energy efficiency improvements and one might question whether quality of life has improved. This paper introduces the concept of sufficiency and how it might relate to transport. As will be argued, new measures such as subjective well-being or life satisfaction will be valuable tools in future studies of this concept. In this first study, the traditional measure of travel time is examined, as it is fundamental to much theory on transport planning (e.g. travel time savings (Mackie and Nellthorp, 2001)).
Sustainable development encompasses three main considerations of economy, environment, and society. Improving energy efficiency has been one proposed solution that addresses those three areas. Basically, the idea is that improved energy efficiency is a means of achieving the same with less energy. With the same amount of energy, more can be accomplished. Thus, it might be called, “doing more with the same.” In economic terms, this would be growth of the output over a stable input. The assumption for society is that this economic growth brings a better quality of life. From an environmental viewpoint, if growth remains equal to improvements in efficiency, then such growth would be environmentally stagnant (i.e. neither better nor worse). However, if growth outpaces energy efficiencies, then the system becomes less sustainable. In transport, the concept of energy efficiency has been applied by requiring vehicles to travel further for each unit of fuel (e.g. better fuel economy). Ceteris paribus, with improved energy efficiency, the levels of mobility that society had come to expect could be maintained and the impact on energy use and the environment would be reduced. Considering estimations of global sustainability, most countries need to reduce transport’s environmental impacts (e.g. Sager et al., 2011).
⁎
2. Concept of sufficiency The idea of sufficiency is that at some point the gains from an activity is sufficient, it is enough, and that to go further is wasteful at best and potentially harmful. Numerous examples can be given that relate to consumption. Eating more than enough will likely have negative health
Corresponding author. E-mail addresses: [email protected] (E.O.D. Waygood), [email protected] (Y. Sun), [email protected] (J.-D. Schmöcker).
https://doi.org/10.1016/j.tbs.2018.12.002 Received 6 August 2018; Received in revised form 2 November 2018; Accepted 11 December 2018 2214-367X/ © 2018 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved.
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for California 86.2% of work trips were by car, and that for all trips it was about 70% (Ferrell and Deakin, 2001). In contrast to this, for the Netherlands in 1998, the modal share for cars was 52.7% in urban settings, and 64.9% in other areas (Schwanen et al., 2004). Taking greater car use to signify greater mobility, Californian’s had greater mobility than the Dutch (people from the Netherlands). Traditional transport conceptions would thus suggest that Californians should do more, or at least the same with less travel time. However, the findings showed that the Dutch spent more time on entertainment and sports/hobbies and less time on travel than people in California. For working men, Dutch men worked slightly longer, but spent nearly twice the amount of time on entertainment, the same on sports/hobbies, but also over 30 min less in travel per day. This might suggest that despite using “slower modes” more frequently (over 45% of all trips were likely non-motorised (Pucher and Dijkstra, 2000)) the Dutch might be enjoying more activities that contribute to a good quality of life and less time travelling to them. Thus, one might question the assumption that quality of life is improved by greater mobility (at least on an aggregate). In the above example, it might be that transport infrastructure to support car travel and the resulting built form may create such a vast dispersion of destinations that even the mobility of cars loses to the economies of agglomeration. Economies of agglomeration refer generally to the benefits that firms gain from locating near one another (e.g. Melo et al., 2009). However, in this case, we are using it to refer to the benefits to individuals of locating in an area with a “cluster” of landuses that facilitate daily needs and wants. One review of the built environment’s impact on travel behaviour in the USA found that it explained distance and trip frequency, but that socio-economic factors were equally important in explaining mode use (Ewing and Cervero, 2001). However, those results may not match exactly to experiences in other cities that do not use the same strict segregation of land uses or where social segregation is not as distinct. For example, a study in the Osaka Metropolitan area, which is denser than most American cities and has lower social segregation (Fielding, 2004), found that the built environment explained household car use and the household life cycle stage explained trip frequency (Sun et al., 2009). Further, questions have been asked related to self-selection where people who desire to limit their use or need for a car choose neighbourhoods that facilitate such choices. Despite those differences, the built environment has been found to have an impact on travel behaviour in many different contexts (Cao et al., 2009; Sun et al., 2009; Susilo and Maat, 2007). Taking that we described the aim of sufficiency as, to achieve the best quality of life given global constraints, there are several factors that must be considered in the evaluation of a transport system. This preliminary work will use existing transport data to ask questions about household travel related to global environmental constraints and what activities are being performed.
impacts, drinking a few beers may be enough whereas more could reduce pleasure and could cause pain the next day. To address the problem of increased energy use despite improvements in energy efficiency, the concept of energy sufficiency (Herring, 2006) has been introduced in policy debates on energy consumption. This concept examines how best to distribute and use resources so that sufficient quality of life is obtained through a minimum of resource consumption (Darby, 2007). We suggest that another way of writing it would be: to achieve the best quality of life given global constraints. As opposed to energy efficiency’s “doing more with the same,” the concept of sufficiency may be described as “living well on less” (Herring, 2006). The definition of living well can vary from easily quantifiable measures such as the number of consumer goods and the size of homes, to concepts such as subjective well-being. In his book, “The Logic of Sufficiency”, Princen (2005) argues that efficiency needs to be subordinated by the concept of sufficiency. That is, sufficiency as a principle recognizes that environmental limits exist and so when developing solutions, efficiency must work together with practice/use. This is in contrast to the prevailing application of improved efficiency with no thought as to limiting practice/use which leads to such problems as mentioned above of increased use of cars negating improvements in energy efficiencies per kilometre driven. 2.1. Application to transport: transport sufficiency A high level goal of transport is to improve quality of life through better access; which is often interpreted as a reduction in time to travel a certain distance (i.e. mobility). Sufficiency applies here in at least two ways: the use of energy and its related negative impacts; the use of time and participation in activities as contributing to quality of life or wellbeing. There is of course tension between the two. First, previous research on the emissions from daily passenger transport find that they decrease with increased urbanization (Barla et al., 2011; Zahabi et al., 2012; Song et al., 2016) as well as increased population density (Kennedy et al., 2009; Frank et al., 2000). When an environmental limit such as two tonnes per person is considered, one study found that the more urbanized areas of a region stayed within the limits as those living in less developed areas travelled longer distances and used motorised modes more often (Waygood et al., 2014). As well as more motorized trips that are each of greater distances, households in less developed areas are more likely to have more vehicles and larger vehicles (Lindsey et al., 2011; Liu and Shen, 2011). However, those studies did not take into account measures related to quality of life. Restraining or reducing mobility (e.g. travel distances) to respect environmental limits (1st application of sufficiency) may be perceived as negatively affecting quality of life or well-being (2nd application of sufficiency). The latter was found in a study by Steg and Gifford (2005), where residents in the Netherlands reported a perception that a reduction in mobility would reduce their quality of life. Thus, one question for the concept of transport sufficiency would be whether quality of life is necessarily improved by greater mobility. The amount and type of mobility and its relation to access certainly depends on the built environment. If potential destinations are highly dispersed, then certainly high mobility is of benefit. However, if a local area provides for most needs, and the region is sufficiently dense to support public transport for distant destinations, then a reduced mobility might be sufficient to achieve daily needs and wants (e.g. food, services, work, socialization, etc.). How people use their time throughout the day may be one indicator of quality of life. Desirable activities might include social ones such as entertainment or sports/hobbies (Bergstad et al., 2012). Through the lens of travel time reduction, an undesirable consumption of time would be greater travel time during the day. In his study of time use in the Netherlands and California, Kitamura (1992) examined how much time people (men and women, working and non-working) spent doing various activities over one day. The USA’s 1990 Census reported that
2.2. Related work Other concepts exist that relate transportation with measures of quality of life such as liveability. The key difference between the concept of transportation sufficiency and those is that transportation sufficiency asks directly what kind of quality of life might be expected if transportation was kept within environmental limits and not just financial ones. As the idea is to have a high quality of life, within the limits that exist, it is of benefit to summarize recent work in this area. A general equation related to environmental impact (I) states that the three key factors are population (P), affluence (A), and technology (T), referred to as the I = PAT equation (e.g. Alcott, 2008). The technology component is perhaps the easiest approach from a political perspective. In this context of emissions, affluence is the amount of emissions per person. The idea with sufficiency is to maintain or lower this (consumption causing emissions) so that, along with technological 55
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suggests that non-motorised modes may be linked to seeing known people and people in general more frequently that car travel (Waygood and Friman, 2015; Waygood et al., 2017). Thus, if social connections and social capital are key components of overall well-being, then it is not clear whether high levels of mobility ultimately contribute more. Likely, there is a minimum amount of mobility required, but the benefits do not necessarily accrue with ever increasing mobility. Social interaction and social support are key explanatory variables of greater subjective well-being and life satisfaction. Subjective wellbeing (SWB) is an “umbrella term for the different valuations people make regarding their lives” (Diener, 2006). Once a basic income threshold (e.g. able to feed and shelter) has been achieved, explanatory variables that contribute more to an increase in subjective well-being include social integration and social support (Park, 2004; Graham, 2009) such as connections with family and friends. Sufficiency is linked to SWB by emphasizing a measure of well-being that is not a proxy of that based on assumptions that greater consumption equals better wellbeing.
improvements and potentially population growth, the impacts would stop increasing. The principle of sufficiency would also likely suggest that behaviour is missing from this equation. As previously discussed, such an equation as I = PAT assumes that the behaviour of individuals does not change or is explained by affluence. Taking the example of improved vehicle fuel efficiency, assume that affluence and population remain the same but that fuel efficiency improves. Individuals may interpret such gains economically (fuel costs) and choose to drive more, thus negating the implied benefit of improved technology. The assumption is however, that such increased mobility is improving the individual’s quality of life as it would increase their sphere of choice. Another related concept from sustainability is the triple bottom line. The triple bottom line for transport can be seen in the Federal Highway (USA) Administration’s tool “Invest” where they write (FHA, 2012): “the goal of sustainability is the satisfaction of basic social and economic needs, both present and future, and the responsible use of resources, all while maintaining or improving the well-being of the environment on which life depends”. The concept presented here, transport sufficiency, differs from such definitions in that it could be written: the goal of transport sufficiency is to create a transport system that facilitates the highest quality of life possible while staying within the capacity of the environment to handle emissions. Thus, rather than simply aiming to reduce environmental impacts and meet basic needs, it has a clear environmental limit and aims to create the best conditions within that limit. Such distinctions are sometimes referred to as “soft sustainable development” as compared to “hard sustainable development” (e.g. Gudmundsson et al., 2015). Soft sustainable development assumes that different forms of capital can be substituted for each other so that as long as the overall measure of capital is maintained or increased, then this could be considered sustainable development. In such an approach, a massive degradation in the environment is acceptable as long as financial or social capital is “equally” increased. Hard sustainable development proposes that environmental capital is not equivalent to other forms of capital as one cannot substitute for many environmental functions. One cannot live without clean water, no matter how much money is substituted for example. This approach stipulates that there are limits to the environment’s capacity to absorb pollutants, and these limits must be respected. Thus, the idea that one can surpass the environment’s capacity to absorb GHG emissions as long as mobility is increasing, would be acceptable in soft sustainable development, but not hard sustainable development. The latter can be seen in the most common definitions of sustainable transportation such as that proposed by the Centre for Sustainable Transportation (CST, 1997). In contrast to the focus on environmental impact, concepts such as liveability discuss the social, or human, side of urban design and transportation. Recently, Appleyard et al. (2014) have given the planning community a set of ethic and process principles. For our purposes here, the ethic principles are of primary interest. The first ethic principle suggests that a focus should be made on thriving, as opposed to simply surviving, with attention paid to aspects such as happiness and community pride. The second principle relates to accessibility and ease of exchange that can contribute to building social capital. Those two principles relate to transportation sufficiency in that happiness, community pride, and social capital are not necessarily built through greater mobility. Incidental social interactions can help build social connections and social capital. Grannis (2011) explains four steps starting from two individuals that do not know each other but have geographic proximity. The possibility of walking to local destinations creates the chance that those individuals would see each other. If this is frequent enough, familiarity would increase, and some social interaction could develop (e.g. nodding hello, smiling, small talk, etc.). Eventually, this may lead to intentional social interaction (e.g. visiting the neighbour’s home). Through this process, Grannis argues how a walkable, mixed-use environment might support social capital in a community. Recent work
3. Previous work on study area This paper builds on previous research on travel patterns and impacts from the Osaka Metropolitan Area (including the cities of Osaka, Kyoto, and Kobe). Previous research has examined the role of the built environment and life-cycle stages on car use and carbon dioxide (CO2) production. In separate work, the built environment was found to play a strong role in influencing mode use, while household life-cycle stages determined the number of trips (Sun et al., 2009). Car use was examined over a 40-year period from 1970 to 2000. The greatest growth in car use was in the Autonomous and Rural areas, with little to no growth in the two most urban areas of Highly-Commercial and MixedCommercial. The Mixed-Residential areas fell in-between the two trends. With respect to transport CO2 emissions, the average energy use per km for motorized vehicles was examined in (Waygood et al., 2014). Based on energy use, per capita transport CO2 emissions were calculated and then considered with respect to a global constraint of 2 tonnes (Sager et al., 2011) where transport would represent 17% of the total (based on the current share in Japan). With such a limit, per capita totals could be considered sustainable for nearly all household life-cycle stages in the two most urban areas (Highly-Commercial, Mixed-Commercial). Mixed-Residential areas contributed roughly 29%, but the areas termed Autonomous and Rural contributed 39% and 40% respectively. Thus, reductions in emissions are still required. Those improvements could be through mode choice (to less energy-intensive modes), reduced distances travelled, improved fuel efficiencies, or any combination of the three. However, there has been a general spreading out of the built environment over the past decades (Kitamura and Susilo, 2005) that is likely linked to more car-oriented development (Kitamura et al., 2008; Sun et al., 2014) resulting in larger distances and greater energy being consumed. The question remaining for this research is whether households across the built environments are performing the same number of trips (e.g. are they as actively engaged in society), how much time are they spending travelling, and what is associated with more leisure trips. 4. Data 4.1. Data source For this analysis, the Osaka metropolitan area (OMA) person trip data for the year 2000 was used because it is supported by supplementary work on land use (Fukui, 2003), network data, and household lifecycle stages (Sun et al., 2009). This was a conventional large-scale household travel survey with a sampling rate of 3.0%. This dataset contains the socio-demographic characteristics of the observed samples 56
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Table 1 Descriptions and Definitions of lifecycle stages.
A B C D E F G H I J
Younger single Younger childless couple Pre-school nuclear Young school nuclear Older school nuclear All adults Older childless couple Older single Single parent Others
Descriptions
Definitions
Younger single household Younger childless-couple household Nuclear families with pre-school children Nuclear families with young school children Nuclear families with older school children Families of all adults Older childless-couple household Older single household Single-parent household Other households
Single adult younger than 60 Oldest person younger than 60 Youngest child younger than 6 Youngest child 6 or older but younger than 12 Youngest child 12 or older but younger than 18 Nuclear families and single-parent families with all members of working age Oldest person 60 or older Age 60 or older Youngest child younger than 18 Families with three generation, other related persons, and unrelated persons
as well as their household characteristics. An adult entered information on children under the age of 15 years. The survey also records the duration, purpose and number of activities, and trip engagements of the observed samples on the observed day. Further, the chosen travel modes, as well as home and work locations (zone) of the observed individuals are recorded. Distances are estimated by taking the x-y centroid of the zones.
Table 3 ANOVA analysis of total household trips by car ownership, lifecycle stage (LCS), and built environment (BE).
4.2. Household lifecycle stages The lifecycle stages were developed primarily through analysis of household characteristics such as children’s age(s) and the age of the “head-of-household”. Ten distinct stages of lifecycle were formulated, as shown in Table 1. 4.3. Built environment categories Five built environment categories were used in this research were developed by Fukui (Fukui, 2003) and representative characteristics can be found in Table 2. The areas were estimated through cluster analysis of various factors such as information about the residences’ characteristics, the densities of both people (daytime and night) and shops, along with the employment situation.
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model Intercept car ownership LCS BE car ownership * LCS car ownership * BE LCS * BE car ownership * LCS * BE Error Total Corrected Total
977,700 12,809 1422 5807 225 5632 1522 664 3861 2,007,718 9,349,978 2,985,418
384 1 14 10 4 87 39 40 188 146,470 146,855 146,854
2546.1 12808.5 101.5 580.7 56.1 64.7 39.0 16.6 20.5 13.7
185.7 934.4 7.4 42.4 4.1 4.7 2.8 1.2 1.5
0 0 0 0 0.003 0 0 0.17 0
– the less developed the built environment, the more household trips. The average trips per weekday by built environment and car ownership for the life cycle stage “Young childless couple” can be seen in Fig. 1. This life cycle stage was chosen as an example as the influence of the number of individuals and the number of adults per car is clear. Here, it can be seen that outside of the most commercial areas, increased car ownership is associated with more trips. The difference between not owning a car and owning more cars increases with decreasing urbanization. Analysis of variance, ANOVA, was then carried out on the average time per trip by car ownership, lifecycle stage (LCS), and built environment (BE) (Table 4). The results suggest that there are statistical differences between the numbers of household trips with respect to those three main effects with interactions between the three measures having the largest effects (judging by the mean square value). Using Tukey’s post-hoc analysis, general trends are:
5. Analysis Analysis of variance, ANOVA, was carried out on the number of household trips by car ownership, lifecycle stage (LCS), and built environment (BE) (Table 3). The results suggest that there are statistical differences between the numbers of household trips with respect to those three main effects with the lifecycle stage having the largest effect (judging by the mean square value). Using Tukey’s post-hoc analysis, general trends are: – household trip numbers increase with car ownership; – households with children make more trips; Table 2 Household and area characteristics of the five built environment areas.
Household size HH cars HH motorcycles HH bicycles Population Density (people/km2) Service Density (businesses/km2)
Range Average Range Average Range Average Range Average Range Average Range Average
Highly Commer-cial
Mixed Commer-cial
Mixed Residential
Auto-nomous
Un-developed
1 to 10 2.2 0 to 12 0.49 0 to 10 0.15 0 to 7 1.13 4224 to 12,594 8985 (36/acre) 222 to 1137 558 (2.3/acre)
1 to 13 2.5 0 to 11 0.623 0 to 8 0.17 0 to 9 1.44 5493 to 18,757 12,620 (51/acre) 97 to 776 189 (0.76/acre)
1 to 13 2.9 0 to 15 1.11 0 to 11 0.3 0 to 9 1.34 48 to 16,114 3770 (15/acre) 0 to 208 38.1 (0.15/acre)
1 to 10 3.1 0 to 12 1.63 0 to 7 0.45 0 to 9 1.36 74 to 2457 1138 (4.6/acre) 0.6 to 25 15 (0.06/acre)
1 to 9 3.3 0 to 15 1.82 0 to 6 0.5 0 to 9 0.92 35 to 1976 493 (2.0/acre) 0.5 to 15 4 (0.02/acre)
57
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Fig. 1. Average trips per weekday by built environment and vehicle ownership (0, 1, and 2; “3 or more” was not included because it was highly uncommon for a household of two people to have so many vehicles) for the life cycle stage “Young childless couple”.
both the more urbanized areas had lower average duration than the Autonomous area (Fig. 3). Also note that the average duration between these two stages is different as for the latter more trips are likely local. However, again the trend of increasing duration with increasing car ownership was found in the Autonomous areas. Analysis of the categories of trip destinations found that there is no clear pattern for the influence of the built environment on the percentage of household trips to leisure destinations (Fig. 4). However, in nearly all lifecycle stages, the percentage is lowest for the autonomous category, which has one of the highest car use rates (Sun et al., 2009) suggesting that higher mobility through car use does not necessarily lead to higher rates of leisure. For the rates of leisure trips by household lifecycle stage, there is a general pattern that is in line with expectations. Households without children have higher rates of leisure trips and the rate tends to increase as children age.
Table 4 ANOVA analysis of the average time per trip by car ownership, lifecycle stage (LCS), and built environment (BE). Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model Intercept car ownership BE LCS BE * LCS BE * car ownership LCS * car ownership BE * LCS * car ownership Error Total Corrected Total
17,827,482 835,240 49,918 67,775 89,829 219,856 211,931 313,164
385 1 14 4 10 40 39 88
46,305 835,240 3566 16,944 8983 5496 5434 3559
59.14 1066.79 4.55 21.64 11.47 7.02 6.94 4.55
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
455,181
188
2421
3.09
0.000
756,637,074 1,429,836,027 774,464,556
966,396 966,782 966,781
783
6. Discussion and future directions – average trip duration is shortest for households with no cars, but trip duration decreases with an increase in car ownership for households with cars; – households with older children have the longest average trip duration, and households with pre-school aged children have the shortest average trip duration (likely related to local daycares, local parks, etc.); – the less developed the built environment, the longer the trip duration.
6.1. Connecting the case study with the transport sufficiency concept Transport sufficiency in essence is living well with mobility that is constrained by the environment’s limits. Lower mobility is often thought to lead to a lower quality of life, even in countries where lower mobility options such as cycling and public transport are well supported (Steg and Gifford, 2005). This assumption that greater mobility facilitates greater quality of life can be seen in the assumptions of transport planning. In countries that use the benefit-cost approach for transport planning, the majority of the benefits are from time savings based primarily on higher speeds (Mackie and Nellthorp, 2001). The assumption is that more can be accomplished if travel time is reduced, and through that, more desirable activities will be conducted that contribute to improving people’s quality of life. However, a dynamic reaction is that people also spread out further and travel times do not necessarily diminish, and may in fact increase. In this preliminary research, traditional transport data in the form of origin–destination data was used to examine questions related to these assumptions. First, activity participation through the number of trips conducted was analyzed. Then, as a measure of accessibility, travel time was examined across different built environments: from high density and very urban, to low density and rural. The car, as a high mobility tool, was considered as an explanatory variable for household the number of trips
The combination of the three variables (built environment, life cycle stage, and car ownership) had the greatest explanatory power to explain differences in average trip duration. To demonstrate the differences found by trip duration by built environment, an example of the life cycle stage “All adults” is given in Fig. 2. In the most car-oriented development type (Autonomous), an increase in car ownership is actually associated with an increase in trip duration. However, average trip duration is the second lowest after highly commercial. This may be related to trips to work, as Autonomous built environments were typically villages and towns outside of the main urban centers that had a better work-resident balance than was found in the Mixed Residential area. This is in contrast with the results for the life cycle stage “Preschool nuclear” (two parents and children all five and under) where 58
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Fig. 2. Average trip duration in minutes by household vehicle ownership for the “All adults” life cycle stage.
Fig. 3. Average trip duration in minutes by household vehicle ownership (0, 1, 2, or 3 or more) for the “Pre-school Nuclear” life cycle stage (results with less than 100 responses are not included).
trips per day, it could be argued that it is associated with greater engagement and participation in society. However, studies have often found that walking trips are underreported (e.g. Clifton and Muhs, 2012), which would likely negatively affect the results found here on the number of trips made for households with fewer cars than those with more. The number of leisure trips does not appear to increase with more car-oriented development. Thus, it is not necessarily true that increased mobility leads to an increase in trips that would likely contribute more to quality of life. These results are reflected in the work by Farber and Paez (2011) who examined the change in trip duration times and activity frequency between 1992 and 2005 in Canada. They found that the assumption of improved accessibility by automobile does not hold if increases in mobility are accompanied by dispersal of activities, a common result of car-oriented development. Those results suggest that although an increase in car ownership leads to more trips being made, a more urban environment can reduce the time spent travelling and more trips may be to leisure destinations.
and travel times. Finally, leisure trips frequency was used as a proxy for desirable activities. This preliminary research on transportation sufficiency found that differences in the number of household trips exist by car ownership and built environment when lifecycle stages are accounted for. Higher car ownership was associated with more trips being made on average suggesting that households may be more active. Explaining trip duration is more complicated and the largest portion of the variation was explained by interactions between the various explanatory variables (built environment, household lifecycle stage, and car ownership). The direct influence of car ownership was minimal (less than 0.3%). Overall, trips were shortest for households without cars, but for household with cars, there was a small decrease with increasing car ownership. Again overall, the more urban the built environment, the less time was spent on average per trip, and households with young children had shorter average trip durations likely relating to conducting more local trips. As car ownership was associated with an increase in the number of 59
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Fig. 4. Percentage of household trips to leisure destinations by lifecycle stage across five built environment categories.
was typically the lowest in the autonomous area. As such, these results taken together, suggest that transport sufficiency can be achieved as the types of built environment that support travel within environmental limits do not necessarily mean a diminished quality of life as measured by the transport outcomes measured here.
However, it is not clear from such a study whether people are actually conducting more leisure, but it does suggest that they are conducting more of their leisure in locations outside of their residence. Obvious directions for future research would include more analysis of time-use studies, but also incorporating direct quality of life measures into such large-scale travel surveys. Previous work examined the amount of CO2 emissions produced by each household lifecycle stage by the different built environments (Fig. 5; Waygood et al., 2014). If two tonnes per person is considered a sustainable amount for all activities, and transport (in Japan) represents 17% of emissions (ibid), than only the two most urban areas would meet the first limitation of transport sufficiency (staying within the environment’s capacity to absorb emissions). That study also showed that trip distances by mode generally increased as the built environment became less developed, and travel by lower mobility modes such as active travel and public transport also decreased. That suggests that people in those less developed areas are more “mobile” and practice greater mobility. However, this study found that the average number of trips remained fairly constant across all areas, and only minor increases were observed for increased car ownership. Average trip duration was also not dissimilar between the most urban areas and the most caroriented area (autonomous). Finally, the percentage of trips for leisure
6.2. Implications In this work we examined whether greater automobile access necessarily lead to an increase in leisure activities, as a proxy for improved quality of life through participation in activities that should improve subjective well-being and life satisfaction. Other possible applications would include the balance between travel by car to conduct social activities, and local travel by foot that likely improves connections to neighbours and improve social capital. In studies from Japan, Suzuki and Fujii (2009) found that walking to local shops was associated with an increased sense of place, while (Hanibuchi et al., 2012), who studied local built environments as they relate to numerous measures of sociability, found that a measure of walkability was not associated for the most part with increased sociability, but that years of residence and increased urbanization were positively associated. That link with increased urbanization is also associated with our findings for
Fig. 5. Tonnes of CO2 emissions per person per year from daily travel for the Osaka Metropolitan area by household lifecycle stage and built environment (from Waygood et al., 2014). 60
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direct measures of quality of life. The use of measures such as subjective well-being and life satisfaction in transport research will help examine this question better. However, the majority of that research has focused on mode use, and not how it might vary by built environment. Although travel by bicycle is often found to be associated with more positive outcomes, this may depend on the context. Cycling on a separated path may be more enjoyable than cycling in the dense traffic of a congested urban setting. Further, it may be that advantages gained through using active travel modes that are more associated with better health outcomes may not result in overall better health outcomes if there are negative impacts from built environments that better support such travel (typically more dense in population and services). Another perspective to consider in this research would also be the inverse of the question posed here. Thus, would a life that practices low mobility necessarily improve quality of life? This may depend on whether this is a forced condition or a choice. An individual who has made the choice to live using modes that have lower mobility, such as active or public transport modes, might be happier than someone who is forced (through whatever reason) to use those same modes. Again, this may also be dependent on the built environment context: is there a wide variety of destinations nearby, Is the public transport of high service levels and quality, is there proper infrastructure for active modes, etc. Lastly, the data used here is from the year 2000. Recently, a number of new mobility options have become available or more common such as improved trip-planning applications for public transport, ride hailing applications, car share, bike share, scooter share, etc. How these new mobility options might influence individual travel, their quality of life, and the amount of emissions produced is not yet clear. With such options available, are more people forgoing owning cars and are using more sustainable options? Is this increasing or decreasing quality of life? Are there agglomeration effects (e.g. fewer cars leading to improved air quality, reduced traffic danger)?
increased leisure trips. Recently, (Waygood and Friman, 2015) found that children who walked were the most likely to see other people and people that they knew (and that travel by car to or from their home was strongly negatively correlated), and that increased automobility was negatively associated with the frequency of seeing known people. This finding has been replicated in Canada and Sweden (Waygood et al., 2017). Also related to children, (Waygood, 2009) found that the majority of physical activity was conducted when children reached their destination by independent means (typically active travel). Thus, there is some suggestion that other benefits to well-being apart from reduced energy consumption and emissions when trips are made locally by foot. 6.3. Limitations It is difficult to interpret the results for the number of trips, as the number of individuals in the household was not included. The demands at each life cycle stage will be similar, but the next step would be to account for the number of individuals in the households by life cycle stage, built environment, and car ownership. As well, other factors could influence the number of trips such as employment and income. A household with two employed adults will likely have a different travel pattern to a household with only one employed adult. Higher income households may be more likely to make more trips for non-essential reasons, but a household with an adult who is not in full-time employment may make more discretionary trips. Lastly, short distance trips and trips by non-motorised modes may be under reported, thus biasing the results towards car travel. For example, Stopher et al. (2007) found that 15% of trips under 2 km were missed. For trip duration, data from GPS systems would be more accurate, but it is unlikely that one could achieve sufficient information for a representative population that would allow for the analysis done here (five built environments × 11 life cycle stages × multiple car ownership levels). However, studies with GPS systems that show the biases of such data can help with interpretation. For example, (Stopher et al., 2007) found that nearly about 60% of trips are accurately reported with ± 2 min by in a household travel survey as compared to GPS results. About 20% were off by more than ± 5 min. The majority (52%) of errors estimated longer durations versus 27% under-estimation. That study did suggest that the duration of car trips was over-estimated as compared with walking trips, though the average duration of car trips was longer. Finally, different frameworks, policies, and planning approaches exist to help change travel patterns. The Avoid/Reduce-Shift-Improve framework is one such example (e.g. Bongardt et al., 2010). It promotes the avoidance or reduction of car trips through approaches integrated land-use and transport planning. Shifting refers to the use of more energy-efficient modes such as active travel and mass transport which can be achieved through approaches such as mobility management (or Travel Demand Management). Part of that shift can likely be achieved through a more neutral planning system that more accurately reflects the costs associated with development for cars (e.g. Litman, 2014). Solutions to achieve that include land-use reform (related to Avoid/ Reduce), comprehensive evaluation, and least-cost planning amongst others (ibid). Finally, improve relates to improving the energy efficiency of vehicles (as discussed in the background of this study). The key question of transport sufficiency is that those approaches must first and foremost respect the limits of the environment, and then aim to achieve the best quality of life within those limits.
7. Conclusion This paper introduced the concept of sufficiency and described different applications to individual transportation. Sufficiency takes into consideration that there are limits, that improving efficiency without addressing behavioral changes (i.e. increased use) is ineffective. However, the notion of reducing or restricting transport due to environmental limits can raise questions related to quality of life. Thus, the question was raised whether greater mobility (using car ownership as a proxy) was necessarily associated with greater engagement (number of trips), shorter travel time (average trip duration), and an increase in desirable activities (percentage of trips for leisure as a proxy). Car ownership was found to be associated with an increase in trips, however it was not found to clearly decrease trip duration or to increase the number of leisure trips. The example of young couples without children was used for trips, and it could be seen that very little difference existed in the most urbanized area, but that for the most car-oriented areas there was about a one-trip per day difference between households without a car those with two. No clear pattern could be found for the influence of car ownership on trip duration, though overall results suggest that households with no cars had the shortest average trip duration. Lastly, the percentage of trips that were for leisure was generally the lowest in the Autonomous areas, which have the highest car usage. As leisure trips were used a simple, available measure of quality of life, future studies that directly address quality of life would help contribute to this research question.
6.4. Future directions This paper was limited by type of information available in a traditional origin–destination survey. As with traditional transport planning, an assumption is made here that increased activity engagement and decreased duration of travel would improve quality of life. However, such measures are proxies and a better approach would be to use more
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.tbs.2018.12.002. 61
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Travel Behaviour and Society journal homepage: www.elsevier.com/locate/tbs
Transport sufficiency: Introduction & case study a,⁎
b
E. Owen D. Waygood , Yilin Sun , Jan-Dirk Schmöcker
T
c
a École supérieure d’aménagement du territoire et de développement régional, Université Laval, Pavillon Félix-Antoine-Savard, 2325, rue des Bibliothèques, Local 1622, Québec, QC G1V 0A6, Canada b Polytechnic Institute, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China c Department of Urban Management, Kyoto University, Japan
A R T I C LE I N FO
A B S T R A C T
Keywords: Transport sufficiency Energy efficiency Quality of life Leisure Built environment Travel behaviour
A new concept, termed transport sufficiency is introduced based on the concept of energy sufficiency, which argues that in addition to efficiency, limits to usage must be applied to achieve environmental sustainability. How that concept translates to transport and its implications are discussed. One key assumption of increased mobility (the usage) is an increase in quality of life through less time spent travelling. Supported by time-use data surveys one might propose that a decrease in travel time and greater mobility would increase activities that better contribute to quality of life such as leisure activities. Based on data from Osaka, Japan, this paper examines how a range of factors influences the number of trips, the average duration of those trips, and whether rates of leisure activities vary. As mobility options and distances vary by built form, the built environment is proposed as a key variable. It is found that the number and type of activities varies by household lifecycles (having children, etc.), thus that is the second key variable analysed here. The paper then connects activities with energy emissions and argues that those being “transport sufficient” can remain below accepted CO2 consumption while maintaining activities such as leisure travel that likely support quality of life.
1. Introduction
However, the improvements in energy efficiency have often been offset by increases in travel distances (Millard-Ball and Schipper, 2011). This is likely a result of behavioural responses such as rebound effect (Herring, 2006) and changes in land development in response to reduced direct costs of car travel (Millard-Ball and Schipper, 2011; Kitamura et al., 2008). The increase in mobility may have offered greater opportunities of access to dispersed locations, but the increase in travel distances has often offset the energy efficiency improvements and one might question whether quality of life has improved. This paper introduces the concept of sufficiency and how it might relate to transport. As will be argued, new measures such as subjective well-being or life satisfaction will be valuable tools in future studies of this concept. In this first study, the traditional measure of travel time is examined, as it is fundamental to much theory on transport planning (e.g. travel time savings (Mackie and Nellthorp, 2001)).
Sustainable development encompasses three main considerations of economy, environment, and society. Improving energy efficiency has been one proposed solution that addresses those three areas. Basically, the idea is that improved energy efficiency is a means of achieving the same with less energy. With the same amount of energy, more can be accomplished. Thus, it might be called, “doing more with the same.” In economic terms, this would be growth of the output over a stable input. The assumption for society is that this economic growth brings a better quality of life. From an environmental viewpoint, if growth remains equal to improvements in efficiency, then such growth would be environmentally stagnant (i.e. neither better nor worse). However, if growth outpaces energy efficiencies, then the system becomes less sustainable. In transport, the concept of energy efficiency has been applied by requiring vehicles to travel further for each unit of fuel (e.g. better fuel economy). Ceteris paribus, with improved energy efficiency, the levels of mobility that society had come to expect could be maintained and the impact on energy use and the environment would be reduced. Considering estimations of global sustainability, most countries need to reduce transport’s environmental impacts (e.g. Sager et al., 2011).
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2. Concept of sufficiency The idea of sufficiency is that at some point the gains from an activity is sufficient, it is enough, and that to go further is wasteful at best and potentially harmful. Numerous examples can be given that relate to consumption. Eating more than enough will likely have negative health
Corresponding author. E-mail addresses: [email protected] (E.O.D. Waygood), [email protected] (Y. Sun), [email protected] (J.-D. Schmöcker).
https://doi.org/10.1016/j.tbs.2018.12.002 Received 6 August 2018; Received in revised form 2 November 2018; Accepted 11 December 2018 2214-367X/ © 2018 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved.
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for California 86.2% of work trips were by car, and that for all trips it was about 70% (Ferrell and Deakin, 2001). In contrast to this, for the Netherlands in 1998, the modal share for cars was 52.7% in urban settings, and 64.9% in other areas (Schwanen et al., 2004). Taking greater car use to signify greater mobility, Californian’s had greater mobility than the Dutch (people from the Netherlands). Traditional transport conceptions would thus suggest that Californians should do more, or at least the same with less travel time. However, the findings showed that the Dutch spent more time on entertainment and sports/hobbies and less time on travel than people in California. For working men, Dutch men worked slightly longer, but spent nearly twice the amount of time on entertainment, the same on sports/hobbies, but also over 30 min less in travel per day. This might suggest that despite using “slower modes” more frequently (over 45% of all trips were likely non-motorised (Pucher and Dijkstra, 2000)) the Dutch might be enjoying more activities that contribute to a good quality of life and less time travelling to them. Thus, one might question the assumption that quality of life is improved by greater mobility (at least on an aggregate). In the above example, it might be that transport infrastructure to support car travel and the resulting built form may create such a vast dispersion of destinations that even the mobility of cars loses to the economies of agglomeration. Economies of agglomeration refer generally to the benefits that firms gain from locating near one another (e.g. Melo et al., 2009). However, in this case, we are using it to refer to the benefits to individuals of locating in an area with a “cluster” of landuses that facilitate daily needs and wants. One review of the built environment’s impact on travel behaviour in the USA found that it explained distance and trip frequency, but that socio-economic factors were equally important in explaining mode use (Ewing and Cervero, 2001). However, those results may not match exactly to experiences in other cities that do not use the same strict segregation of land uses or where social segregation is not as distinct. For example, a study in the Osaka Metropolitan area, which is denser than most American cities and has lower social segregation (Fielding, 2004), found that the built environment explained household car use and the household life cycle stage explained trip frequency (Sun et al., 2009). Further, questions have been asked related to self-selection where people who desire to limit their use or need for a car choose neighbourhoods that facilitate such choices. Despite those differences, the built environment has been found to have an impact on travel behaviour in many different contexts (Cao et al., 2009; Sun et al., 2009; Susilo and Maat, 2007). Taking that we described the aim of sufficiency as, to achieve the best quality of life given global constraints, there are several factors that must be considered in the evaluation of a transport system. This preliminary work will use existing transport data to ask questions about household travel related to global environmental constraints and what activities are being performed.
impacts, drinking a few beers may be enough whereas more could reduce pleasure and could cause pain the next day. To address the problem of increased energy use despite improvements in energy efficiency, the concept of energy sufficiency (Herring, 2006) has been introduced in policy debates on energy consumption. This concept examines how best to distribute and use resources so that sufficient quality of life is obtained through a minimum of resource consumption (Darby, 2007). We suggest that another way of writing it would be: to achieve the best quality of life given global constraints. As opposed to energy efficiency’s “doing more with the same,” the concept of sufficiency may be described as “living well on less” (Herring, 2006). The definition of living well can vary from easily quantifiable measures such as the number of consumer goods and the size of homes, to concepts such as subjective well-being. In his book, “The Logic of Sufficiency”, Princen (2005) argues that efficiency needs to be subordinated by the concept of sufficiency. That is, sufficiency as a principle recognizes that environmental limits exist and so when developing solutions, efficiency must work together with practice/use. This is in contrast to the prevailing application of improved efficiency with no thought as to limiting practice/use which leads to such problems as mentioned above of increased use of cars negating improvements in energy efficiencies per kilometre driven. 2.1. Application to transport: transport sufficiency A high level goal of transport is to improve quality of life through better access; which is often interpreted as a reduction in time to travel a certain distance (i.e. mobility). Sufficiency applies here in at least two ways: the use of energy and its related negative impacts; the use of time and participation in activities as contributing to quality of life or wellbeing. There is of course tension between the two. First, previous research on the emissions from daily passenger transport find that they decrease with increased urbanization (Barla et al., 2011; Zahabi et al., 2012; Song et al., 2016) as well as increased population density (Kennedy et al., 2009; Frank et al., 2000). When an environmental limit such as two tonnes per person is considered, one study found that the more urbanized areas of a region stayed within the limits as those living in less developed areas travelled longer distances and used motorised modes more often (Waygood et al., 2014). As well as more motorized trips that are each of greater distances, households in less developed areas are more likely to have more vehicles and larger vehicles (Lindsey et al., 2011; Liu and Shen, 2011). However, those studies did not take into account measures related to quality of life. Restraining or reducing mobility (e.g. travel distances) to respect environmental limits (1st application of sufficiency) may be perceived as negatively affecting quality of life or well-being (2nd application of sufficiency). The latter was found in a study by Steg and Gifford (2005), where residents in the Netherlands reported a perception that a reduction in mobility would reduce their quality of life. Thus, one question for the concept of transport sufficiency would be whether quality of life is necessarily improved by greater mobility. The amount and type of mobility and its relation to access certainly depends on the built environment. If potential destinations are highly dispersed, then certainly high mobility is of benefit. However, if a local area provides for most needs, and the region is sufficiently dense to support public transport for distant destinations, then a reduced mobility might be sufficient to achieve daily needs and wants (e.g. food, services, work, socialization, etc.). How people use their time throughout the day may be one indicator of quality of life. Desirable activities might include social ones such as entertainment or sports/hobbies (Bergstad et al., 2012). Through the lens of travel time reduction, an undesirable consumption of time would be greater travel time during the day. In his study of time use in the Netherlands and California, Kitamura (1992) examined how much time people (men and women, working and non-working) spent doing various activities over one day. The USA’s 1990 Census reported that
2.2. Related work Other concepts exist that relate transportation with measures of quality of life such as liveability. The key difference between the concept of transportation sufficiency and those is that transportation sufficiency asks directly what kind of quality of life might be expected if transportation was kept within environmental limits and not just financial ones. As the idea is to have a high quality of life, within the limits that exist, it is of benefit to summarize recent work in this area. A general equation related to environmental impact (I) states that the three key factors are population (P), affluence (A), and technology (T), referred to as the I = PAT equation (e.g. Alcott, 2008). The technology component is perhaps the easiest approach from a political perspective. In this context of emissions, affluence is the amount of emissions per person. The idea with sufficiency is to maintain or lower this (consumption causing emissions) so that, along with technological 55
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suggests that non-motorised modes may be linked to seeing known people and people in general more frequently that car travel (Waygood and Friman, 2015; Waygood et al., 2017). Thus, if social connections and social capital are key components of overall well-being, then it is not clear whether high levels of mobility ultimately contribute more. Likely, there is a minimum amount of mobility required, but the benefits do not necessarily accrue with ever increasing mobility. Social interaction and social support are key explanatory variables of greater subjective well-being and life satisfaction. Subjective wellbeing (SWB) is an “umbrella term for the different valuations people make regarding their lives” (Diener, 2006). Once a basic income threshold (e.g. able to feed and shelter) has been achieved, explanatory variables that contribute more to an increase in subjective well-being include social integration and social support (Park, 2004; Graham, 2009) such as connections with family and friends. Sufficiency is linked to SWB by emphasizing a measure of well-being that is not a proxy of that based on assumptions that greater consumption equals better wellbeing.
improvements and potentially population growth, the impacts would stop increasing. The principle of sufficiency would also likely suggest that behaviour is missing from this equation. As previously discussed, such an equation as I = PAT assumes that the behaviour of individuals does not change or is explained by affluence. Taking the example of improved vehicle fuel efficiency, assume that affluence and population remain the same but that fuel efficiency improves. Individuals may interpret such gains economically (fuel costs) and choose to drive more, thus negating the implied benefit of improved technology. The assumption is however, that such increased mobility is improving the individual’s quality of life as it would increase their sphere of choice. Another related concept from sustainability is the triple bottom line. The triple bottom line for transport can be seen in the Federal Highway (USA) Administration’s tool “Invest” where they write (FHA, 2012): “the goal of sustainability is the satisfaction of basic social and economic needs, both present and future, and the responsible use of resources, all while maintaining or improving the well-being of the environment on which life depends”. The concept presented here, transport sufficiency, differs from such definitions in that it could be written: the goal of transport sufficiency is to create a transport system that facilitates the highest quality of life possible while staying within the capacity of the environment to handle emissions. Thus, rather than simply aiming to reduce environmental impacts and meet basic needs, it has a clear environmental limit and aims to create the best conditions within that limit. Such distinctions are sometimes referred to as “soft sustainable development” as compared to “hard sustainable development” (e.g. Gudmundsson et al., 2015). Soft sustainable development assumes that different forms of capital can be substituted for each other so that as long as the overall measure of capital is maintained or increased, then this could be considered sustainable development. In such an approach, a massive degradation in the environment is acceptable as long as financial or social capital is “equally” increased. Hard sustainable development proposes that environmental capital is not equivalent to other forms of capital as one cannot substitute for many environmental functions. One cannot live without clean water, no matter how much money is substituted for example. This approach stipulates that there are limits to the environment’s capacity to absorb pollutants, and these limits must be respected. Thus, the idea that one can surpass the environment’s capacity to absorb GHG emissions as long as mobility is increasing, would be acceptable in soft sustainable development, but not hard sustainable development. The latter can be seen in the most common definitions of sustainable transportation such as that proposed by the Centre for Sustainable Transportation (CST, 1997). In contrast to the focus on environmental impact, concepts such as liveability discuss the social, or human, side of urban design and transportation. Recently, Appleyard et al. (2014) have given the planning community a set of ethic and process principles. For our purposes here, the ethic principles are of primary interest. The first ethic principle suggests that a focus should be made on thriving, as opposed to simply surviving, with attention paid to aspects such as happiness and community pride. The second principle relates to accessibility and ease of exchange that can contribute to building social capital. Those two principles relate to transportation sufficiency in that happiness, community pride, and social capital are not necessarily built through greater mobility. Incidental social interactions can help build social connections and social capital. Grannis (2011) explains four steps starting from two individuals that do not know each other but have geographic proximity. The possibility of walking to local destinations creates the chance that those individuals would see each other. If this is frequent enough, familiarity would increase, and some social interaction could develop (e.g. nodding hello, smiling, small talk, etc.). Eventually, this may lead to intentional social interaction (e.g. visiting the neighbour’s home). Through this process, Grannis argues how a walkable, mixed-use environment might support social capital in a community. Recent work
3. Previous work on study area This paper builds on previous research on travel patterns and impacts from the Osaka Metropolitan Area (including the cities of Osaka, Kyoto, and Kobe). Previous research has examined the role of the built environment and life-cycle stages on car use and carbon dioxide (CO2) production. In separate work, the built environment was found to play a strong role in influencing mode use, while household life-cycle stages determined the number of trips (Sun et al., 2009). Car use was examined over a 40-year period from 1970 to 2000. The greatest growth in car use was in the Autonomous and Rural areas, with little to no growth in the two most urban areas of Highly-Commercial and MixedCommercial. The Mixed-Residential areas fell in-between the two trends. With respect to transport CO2 emissions, the average energy use per km for motorized vehicles was examined in (Waygood et al., 2014). Based on energy use, per capita transport CO2 emissions were calculated and then considered with respect to a global constraint of 2 tonnes (Sager et al., 2011) where transport would represent 17% of the total (based on the current share in Japan). With such a limit, per capita totals could be considered sustainable for nearly all household life-cycle stages in the two most urban areas (Highly-Commercial, Mixed-Commercial). Mixed-Residential areas contributed roughly 29%, but the areas termed Autonomous and Rural contributed 39% and 40% respectively. Thus, reductions in emissions are still required. Those improvements could be through mode choice (to less energy-intensive modes), reduced distances travelled, improved fuel efficiencies, or any combination of the three. However, there has been a general spreading out of the built environment over the past decades (Kitamura and Susilo, 2005) that is likely linked to more car-oriented development (Kitamura et al., 2008; Sun et al., 2014) resulting in larger distances and greater energy being consumed. The question remaining for this research is whether households across the built environments are performing the same number of trips (e.g. are they as actively engaged in society), how much time are they spending travelling, and what is associated with more leisure trips. 4. Data 4.1. Data source For this analysis, the Osaka metropolitan area (OMA) person trip data for the year 2000 was used because it is supported by supplementary work on land use (Fukui, 2003), network data, and household lifecycle stages (Sun et al., 2009). This was a conventional large-scale household travel survey with a sampling rate of 3.0%. This dataset contains the socio-demographic characteristics of the observed samples 56
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Table 1 Descriptions and Definitions of lifecycle stages.
A B C D E F G H I J
Younger single Younger childless couple Pre-school nuclear Young school nuclear Older school nuclear All adults Older childless couple Older single Single parent Others
Descriptions
Definitions
Younger single household Younger childless-couple household Nuclear families with pre-school children Nuclear families with young school children Nuclear families with older school children Families of all adults Older childless-couple household Older single household Single-parent household Other households
Single adult younger than 60 Oldest person younger than 60 Youngest child younger than 6 Youngest child 6 or older but younger than 12 Youngest child 12 or older but younger than 18 Nuclear families and single-parent families with all members of working age Oldest person 60 or older Age 60 or older Youngest child younger than 18 Families with three generation, other related persons, and unrelated persons
as well as their household characteristics. An adult entered information on children under the age of 15 years. The survey also records the duration, purpose and number of activities, and trip engagements of the observed samples on the observed day. Further, the chosen travel modes, as well as home and work locations (zone) of the observed individuals are recorded. Distances are estimated by taking the x-y centroid of the zones.
Table 3 ANOVA analysis of total household trips by car ownership, lifecycle stage (LCS), and built environment (BE).
4.2. Household lifecycle stages The lifecycle stages were developed primarily through analysis of household characteristics such as children’s age(s) and the age of the “head-of-household”. Ten distinct stages of lifecycle were formulated, as shown in Table 1. 4.3. Built environment categories Five built environment categories were used in this research were developed by Fukui (Fukui, 2003) and representative characteristics can be found in Table 2. The areas were estimated through cluster analysis of various factors such as information about the residences’ characteristics, the densities of both people (daytime and night) and shops, along with the employment situation.
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model Intercept car ownership LCS BE car ownership * LCS car ownership * BE LCS * BE car ownership * LCS * BE Error Total Corrected Total
977,700 12,809 1422 5807 225 5632 1522 664 3861 2,007,718 9,349,978 2,985,418
384 1 14 10 4 87 39 40 188 146,470 146,855 146,854
2546.1 12808.5 101.5 580.7 56.1 64.7 39.0 16.6 20.5 13.7
185.7 934.4 7.4 42.4 4.1 4.7 2.8 1.2 1.5
0 0 0 0 0.003 0 0 0.17 0
– the less developed the built environment, the more household trips. The average trips per weekday by built environment and car ownership for the life cycle stage “Young childless couple” can be seen in Fig. 1. This life cycle stage was chosen as an example as the influence of the number of individuals and the number of adults per car is clear. Here, it can be seen that outside of the most commercial areas, increased car ownership is associated with more trips. The difference between not owning a car and owning more cars increases with decreasing urbanization. Analysis of variance, ANOVA, was then carried out on the average time per trip by car ownership, lifecycle stage (LCS), and built environment (BE) (Table 4). The results suggest that there are statistical differences between the numbers of household trips with respect to those three main effects with interactions between the three measures having the largest effects (judging by the mean square value). Using Tukey’s post-hoc analysis, general trends are:
5. Analysis Analysis of variance, ANOVA, was carried out on the number of household trips by car ownership, lifecycle stage (LCS), and built environment (BE) (Table 3). The results suggest that there are statistical differences between the numbers of household trips with respect to those three main effects with the lifecycle stage having the largest effect (judging by the mean square value). Using Tukey’s post-hoc analysis, general trends are: – household trip numbers increase with car ownership; – households with children make more trips; Table 2 Household and area characteristics of the five built environment areas.
Household size HH cars HH motorcycles HH bicycles Population Density (people/km2) Service Density (businesses/km2)
Range Average Range Average Range Average Range Average Range Average Range Average
Highly Commer-cial
Mixed Commer-cial
Mixed Residential
Auto-nomous
Un-developed
1 to 10 2.2 0 to 12 0.49 0 to 10 0.15 0 to 7 1.13 4224 to 12,594 8985 (36/acre) 222 to 1137 558 (2.3/acre)
1 to 13 2.5 0 to 11 0.623 0 to 8 0.17 0 to 9 1.44 5493 to 18,757 12,620 (51/acre) 97 to 776 189 (0.76/acre)
1 to 13 2.9 0 to 15 1.11 0 to 11 0.3 0 to 9 1.34 48 to 16,114 3770 (15/acre) 0 to 208 38.1 (0.15/acre)
1 to 10 3.1 0 to 12 1.63 0 to 7 0.45 0 to 9 1.36 74 to 2457 1138 (4.6/acre) 0.6 to 25 15 (0.06/acre)
1 to 9 3.3 0 to 15 1.82 0 to 6 0.5 0 to 9 0.92 35 to 1976 493 (2.0/acre) 0.5 to 15 4 (0.02/acre)
57
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Fig. 1. Average trips per weekday by built environment and vehicle ownership (0, 1, and 2; “3 or more” was not included because it was highly uncommon for a household of two people to have so many vehicles) for the life cycle stage “Young childless couple”.
both the more urbanized areas had lower average duration than the Autonomous area (Fig. 3). Also note that the average duration between these two stages is different as for the latter more trips are likely local. However, again the trend of increasing duration with increasing car ownership was found in the Autonomous areas. Analysis of the categories of trip destinations found that there is no clear pattern for the influence of the built environment on the percentage of household trips to leisure destinations (Fig. 4). However, in nearly all lifecycle stages, the percentage is lowest for the autonomous category, which has one of the highest car use rates (Sun et al., 2009) suggesting that higher mobility through car use does not necessarily lead to higher rates of leisure. For the rates of leisure trips by household lifecycle stage, there is a general pattern that is in line with expectations. Households without children have higher rates of leisure trips and the rate tends to increase as children age.
Table 4 ANOVA analysis of the average time per trip by car ownership, lifecycle stage (LCS), and built environment (BE). Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model Intercept car ownership BE LCS BE * LCS BE * car ownership LCS * car ownership BE * LCS * car ownership Error Total Corrected Total
17,827,482 835,240 49,918 67,775 89,829 219,856 211,931 313,164
385 1 14 4 10 40 39 88
46,305 835,240 3566 16,944 8983 5496 5434 3559
59.14 1066.79 4.55 21.64 11.47 7.02 6.94 4.55
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
455,181
188
2421
3.09
0.000
756,637,074 1,429,836,027 774,464,556
966,396 966,782 966,781
783
6. Discussion and future directions – average trip duration is shortest for households with no cars, but trip duration decreases with an increase in car ownership for households with cars; – households with older children have the longest average trip duration, and households with pre-school aged children have the shortest average trip duration (likely related to local daycares, local parks, etc.); – the less developed the built environment, the longer the trip duration.
6.1. Connecting the case study with the transport sufficiency concept Transport sufficiency in essence is living well with mobility that is constrained by the environment’s limits. Lower mobility is often thought to lead to a lower quality of life, even in countries where lower mobility options such as cycling and public transport are well supported (Steg and Gifford, 2005). This assumption that greater mobility facilitates greater quality of life can be seen in the assumptions of transport planning. In countries that use the benefit-cost approach for transport planning, the majority of the benefits are from time savings based primarily on higher speeds (Mackie and Nellthorp, 2001). The assumption is that more can be accomplished if travel time is reduced, and through that, more desirable activities will be conducted that contribute to improving people’s quality of life. However, a dynamic reaction is that people also spread out further and travel times do not necessarily diminish, and may in fact increase. In this preliminary research, traditional transport data in the form of origin–destination data was used to examine questions related to these assumptions. First, activity participation through the number of trips conducted was analyzed. Then, as a measure of accessibility, travel time was examined across different built environments: from high density and very urban, to low density and rural. The car, as a high mobility tool, was considered as an explanatory variable for household the number of trips
The combination of the three variables (built environment, life cycle stage, and car ownership) had the greatest explanatory power to explain differences in average trip duration. To demonstrate the differences found by trip duration by built environment, an example of the life cycle stage “All adults” is given in Fig. 2. In the most car-oriented development type (Autonomous), an increase in car ownership is actually associated with an increase in trip duration. However, average trip duration is the second lowest after highly commercial. This may be related to trips to work, as Autonomous built environments were typically villages and towns outside of the main urban centers that had a better work-resident balance than was found in the Mixed Residential area. This is in contrast with the results for the life cycle stage “Preschool nuclear” (two parents and children all five and under) where 58
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Fig. 2. Average trip duration in minutes by household vehicle ownership for the “All adults” life cycle stage.
Fig. 3. Average trip duration in minutes by household vehicle ownership (0, 1, 2, or 3 or more) for the “Pre-school Nuclear” life cycle stage (results with less than 100 responses are not included).
trips per day, it could be argued that it is associated with greater engagement and participation in society. However, studies have often found that walking trips are underreported (e.g. Clifton and Muhs, 2012), which would likely negatively affect the results found here on the number of trips made for households with fewer cars than those with more. The number of leisure trips does not appear to increase with more car-oriented development. Thus, it is not necessarily true that increased mobility leads to an increase in trips that would likely contribute more to quality of life. These results are reflected in the work by Farber and Paez (2011) who examined the change in trip duration times and activity frequency between 1992 and 2005 in Canada. They found that the assumption of improved accessibility by automobile does not hold if increases in mobility are accompanied by dispersal of activities, a common result of car-oriented development. Those results suggest that although an increase in car ownership leads to more trips being made, a more urban environment can reduce the time spent travelling and more trips may be to leisure destinations.
and travel times. Finally, leisure trips frequency was used as a proxy for desirable activities. This preliminary research on transportation sufficiency found that differences in the number of household trips exist by car ownership and built environment when lifecycle stages are accounted for. Higher car ownership was associated with more trips being made on average suggesting that households may be more active. Explaining trip duration is more complicated and the largest portion of the variation was explained by interactions between the various explanatory variables (built environment, household lifecycle stage, and car ownership). The direct influence of car ownership was minimal (less than 0.3%). Overall, trips were shortest for households without cars, but for household with cars, there was a small decrease with increasing car ownership. Again overall, the more urban the built environment, the less time was spent on average per trip, and households with young children had shorter average trip durations likely relating to conducting more local trips. As car ownership was associated with an increase in the number of 59
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Fig. 4. Percentage of household trips to leisure destinations by lifecycle stage across five built environment categories.
was typically the lowest in the autonomous area. As such, these results taken together, suggest that transport sufficiency can be achieved as the types of built environment that support travel within environmental limits do not necessarily mean a diminished quality of life as measured by the transport outcomes measured here.
However, it is not clear from such a study whether people are actually conducting more leisure, but it does suggest that they are conducting more of their leisure in locations outside of their residence. Obvious directions for future research would include more analysis of time-use studies, but also incorporating direct quality of life measures into such large-scale travel surveys. Previous work examined the amount of CO2 emissions produced by each household lifecycle stage by the different built environments (Fig. 5; Waygood et al., 2014). If two tonnes per person is considered a sustainable amount for all activities, and transport (in Japan) represents 17% of emissions (ibid), than only the two most urban areas would meet the first limitation of transport sufficiency (staying within the environment’s capacity to absorb emissions). That study also showed that trip distances by mode generally increased as the built environment became less developed, and travel by lower mobility modes such as active travel and public transport also decreased. That suggests that people in those less developed areas are more “mobile” and practice greater mobility. However, this study found that the average number of trips remained fairly constant across all areas, and only minor increases were observed for increased car ownership. Average trip duration was also not dissimilar between the most urban areas and the most caroriented area (autonomous). Finally, the percentage of trips for leisure
6.2. Implications In this work we examined whether greater automobile access necessarily lead to an increase in leisure activities, as a proxy for improved quality of life through participation in activities that should improve subjective well-being and life satisfaction. Other possible applications would include the balance between travel by car to conduct social activities, and local travel by foot that likely improves connections to neighbours and improve social capital. In studies from Japan, Suzuki and Fujii (2009) found that walking to local shops was associated with an increased sense of place, while (Hanibuchi et al., 2012), who studied local built environments as they relate to numerous measures of sociability, found that a measure of walkability was not associated for the most part with increased sociability, but that years of residence and increased urbanization were positively associated. That link with increased urbanization is also associated with our findings for
Fig. 5. Tonnes of CO2 emissions per person per year from daily travel for the Osaka Metropolitan area by household lifecycle stage and built environment (from Waygood et al., 2014). 60
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direct measures of quality of life. The use of measures such as subjective well-being and life satisfaction in transport research will help examine this question better. However, the majority of that research has focused on mode use, and not how it might vary by built environment. Although travel by bicycle is often found to be associated with more positive outcomes, this may depend on the context. Cycling on a separated path may be more enjoyable than cycling in the dense traffic of a congested urban setting. Further, it may be that advantages gained through using active travel modes that are more associated with better health outcomes may not result in overall better health outcomes if there are negative impacts from built environments that better support such travel (typically more dense in population and services). Another perspective to consider in this research would also be the inverse of the question posed here. Thus, would a life that practices low mobility necessarily improve quality of life? This may depend on whether this is a forced condition or a choice. An individual who has made the choice to live using modes that have lower mobility, such as active or public transport modes, might be happier than someone who is forced (through whatever reason) to use those same modes. Again, this may also be dependent on the built environment context: is there a wide variety of destinations nearby, Is the public transport of high service levels and quality, is there proper infrastructure for active modes, etc. Lastly, the data used here is from the year 2000. Recently, a number of new mobility options have become available or more common such as improved trip-planning applications for public transport, ride hailing applications, car share, bike share, scooter share, etc. How these new mobility options might influence individual travel, their quality of life, and the amount of emissions produced is not yet clear. With such options available, are more people forgoing owning cars and are using more sustainable options? Is this increasing or decreasing quality of life? Are there agglomeration effects (e.g. fewer cars leading to improved air quality, reduced traffic danger)?
increased leisure trips. Recently, (Waygood and Friman, 2015) found that children who walked were the most likely to see other people and people that they knew (and that travel by car to or from their home was strongly negatively correlated), and that increased automobility was negatively associated with the frequency of seeing known people. This finding has been replicated in Canada and Sweden (Waygood et al., 2017). Also related to children, (Waygood, 2009) found that the majority of physical activity was conducted when children reached their destination by independent means (typically active travel). Thus, there is some suggestion that other benefits to well-being apart from reduced energy consumption and emissions when trips are made locally by foot. 6.3. Limitations It is difficult to interpret the results for the number of trips, as the number of individuals in the household was not included. The demands at each life cycle stage will be similar, but the next step would be to account for the number of individuals in the households by life cycle stage, built environment, and car ownership. As well, other factors could influence the number of trips such as employment and income. A household with two employed adults will likely have a different travel pattern to a household with only one employed adult. Higher income households may be more likely to make more trips for non-essential reasons, but a household with an adult who is not in full-time employment may make more discretionary trips. Lastly, short distance trips and trips by non-motorised modes may be under reported, thus biasing the results towards car travel. For example, Stopher et al. (2007) found that 15% of trips under 2 km were missed. For trip duration, data from GPS systems would be more accurate, but it is unlikely that one could achieve sufficient information for a representative population that would allow for the analysis done here (five built environments × 11 life cycle stages × multiple car ownership levels). However, studies with GPS systems that show the biases of such data can help with interpretation. For example, (Stopher et al., 2007) found that nearly about 60% of trips are accurately reported with ± 2 min by in a household travel survey as compared to GPS results. About 20% were off by more than ± 5 min. The majority (52%) of errors estimated longer durations versus 27% under-estimation. That study did suggest that the duration of car trips was over-estimated as compared with walking trips, though the average duration of car trips was longer. Finally, different frameworks, policies, and planning approaches exist to help change travel patterns. The Avoid/Reduce-Shift-Improve framework is one such example (e.g. Bongardt et al., 2010). It promotes the avoidance or reduction of car trips through approaches integrated land-use and transport planning. Shifting refers to the use of more energy-efficient modes such as active travel and mass transport which can be achieved through approaches such as mobility management (or Travel Demand Management). Part of that shift can likely be achieved through a more neutral planning system that more accurately reflects the costs associated with development for cars (e.g. Litman, 2014). Solutions to achieve that include land-use reform (related to Avoid/ Reduce), comprehensive evaluation, and least-cost planning amongst others (ibid). Finally, improve relates to improving the energy efficiency of vehicles (as discussed in the background of this study). The key question of transport sufficiency is that those approaches must first and foremost respect the limits of the environment, and then aim to achieve the best quality of life within those limits.
7. Conclusion This paper introduced the concept of sufficiency and described different applications to individual transportation. Sufficiency takes into consideration that there are limits, that improving efficiency without addressing behavioral changes (i.e. increased use) is ineffective. However, the notion of reducing or restricting transport due to environmental limits can raise questions related to quality of life. Thus, the question was raised whether greater mobility (using car ownership as a proxy) was necessarily associated with greater engagement (number of trips), shorter travel time (average trip duration), and an increase in desirable activities (percentage of trips for leisure as a proxy). Car ownership was found to be associated with an increase in trips, however it was not found to clearly decrease trip duration or to increase the number of leisure trips. The example of young couples without children was used for trips, and it could be seen that very little difference existed in the most urbanized area, but that for the most car-oriented areas there was about a one-trip per day difference between households without a car those with two. No clear pattern could be found for the influence of car ownership on trip duration, though overall results suggest that households with no cars had the shortest average trip duration. Lastly, the percentage of trips that were for leisure was generally the lowest in the Autonomous areas, which have the highest car usage. As leisure trips were used a simple, available measure of quality of life, future studies that directly address quality of life would help contribute to this research question.
6.4. Future directions This paper was limited by type of information available in a traditional origin–destination survey. As with traditional transport planning, an assumption is made here that increased activity engagement and decreased duration of travel would improve quality of life. However, such measures are proxies and a better approach would be to use more
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.tbs.2018.12.002. 61
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