Psychological, sociodemographic, and infrastructural factors as determinants of ecological impact caused by mobility behavior1

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Journal of Environmental Psychology 27 (2007) 277–292 www.elsevier.com/locate/jep

Psychological, sociodemographic, and infrastructural factors as determinants of ecological impact caused by mobility behavior1 Marcel Huneckea,, Sonja Hausteina, Sylvie Grischkatb, Susanne Bo¨hlerc a

Ruhr-Universita¨t Bochum, Faculty of Psychology, Workgroup Environmental and Cognitive Psychology, 44780 Bochum, Germany b Leuphana University of Lu¨neburg, International Research Centre for Environmental and Sustainability Management, Germany c Wuppertal Institute for Climate, Environment and Energy, Germany Available online 12 August 2007

Abstract In this study, the relevance of psychological variables as predictors of the ecological impact of mobility behavior was investigated in relation to infrastructural and sociodemographic variables. The database consisted of a survey of 1991 inhabitants of three large German cities. In standardized interviews attitudinal factors based on the theory of planned behavior, further mobility-related attitude dimensions, sociodemographic and infrastructural characteristics as well as mobility behavior were measured. Based on the behavior measurement the ecological impact of mobility behavior was individually assessed for all participants of the study. In a regression analysis with ecological impact as dependent variable, sociodemographic and psychological variables were the strongest predictors, whereas infrastructural variables were of minor relevance. This result puts findings of other environmental studies into question which indicate that psychological variables only influence intent-oriented behavior, whereas impact-oriented behavior is mainly determined by sociodemographic and household variables. The design of effective intervention programs to reduce the ecological impact of mobility behavior requires knowledge about the determinants of mobility-related ecological impact, which are primarily the use of private motorized modes and the traveled distances. Separate regression analyses for these two variables provided detailed information about starting points to reduce the ecological impact of mobility behavior. r 2007 Elsevier Ltd. All rights reserved. Keywords: Environmental behavior; Environmental impact; Attitudes; Mobility behavior; Transportation

1. Introduction One of the biggest global ecological challenges consists in the reduction of the ecological impact of individual mobility behavior. According to the Kyoto Protocol industrialized countries have to reduce their total greenhouse gas emissions by an average of 5.4% below 1990 levels in the first commitment period of 2008–2012 (Lenzen, Dey, & Hamilton, 2003). In Germany, within the last decades emissions of most pollutants caused by transportation could be reduced, whereas emissions of greenhouse gases, respectively, CO2, from transport Corresponding author. Tel.: +49 234 32 23030; fax: +49 234 32 14308.

E-mail address: [email protected] (M. Hunecke). The results are based on research conducted by the junior research group MOBILANZ, which was supported by the German Federal Ministry of Education und Research (BMBF) in the framework of the program ‘‘Social-Ecological Research’’. 1

0272-4944/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvp.2007.08.001

increased by about 6.3% between 1990 and 2003 (SRU (German Advisory Council on the Environment), 2005). These tendencies can be found in all western countries (IEA, 2000). Several strategies have been proposed to implement environmentally sustainable passenger transportation, e.g. an increase of the efficiency of transportation technologies (Lovins & Cramer, 2004), the densification of housing, employment, shopping, and cultural activities (Stead & Marshall, 2001), and regulatory and fiscal measures (ECMT, 2004). In addition, the attractiveness of sustainable mobility has to be increased by soft policy measures such as public awareness campaigns for sustainable mobility and social marketing for public transportation (Bro¨g, Erl, & Mense, 2004). For the design of soft policy interventions it is necessary to know the motivations of the users of different transport modes. Stern (2000) introduced the differentiation between an intent perspective and an

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impact perspective into environmental psychology. The intent perspective analyzes the motivational basis of conservation behavior; the impact perspective determines the ecological consequences of environmental behavior. In the present study, we take into account both perspectives. From an impact perspective we analyze the relation of psychological variables to greenhouse gas emissions resulting from mobility behavior. In order to avoid an overestimation of psychological variables, sociodemographic and infrastructural variables are included in the analysis. From an intent perspective we analyze the motivational basis of mobility behavior. 1.1. Psychological variables and mobility behavior In transport science, it is agreed that infrastructural factors have a great impact on mobility behavior because they determine behavioral options. For example, if no public transportation services exist, people have to use the car, in spite of a high motivation to use a bus or train. Mobility behavior, however, is not solely determined by infrastructural constraints. There are two types of personal factors relevant for individual mobility, sociodemographic characteristics and attitudinal factors. Sociodemographic aspects include factors such as age or employment status, which determine individual options and necessities for mobility activities (e.g. Hanson & Schwab, 1995). Attitudinal factors include values, norms, and attitudes, which affect preferences for specific activities, destinations, routes, and means of transport (e.g. Anable, 2005; Anable & Gatersleben, 2005; Bamberg & Schmidt, 2001, 2003; Heath & Gifford, 2002; Hunecke, Blo¨baum, Matthies, & Ho¨ger, 2001; Steg, 2005; Steg, Vlek, & Slotegraaf, 2001). Consequently, the most important task for mobility research is an integrated analysis of the infrastructural and personal determinants of mobility behavior. So far, only one interdisciplinary study has tested multivariate regression models for travel mode choice and distances traveled by including psychological, sociodemographic as well as infrastructural variables (Van Wee, Holwerda, & Van Baren, 2002). In this study the psychological influences are operationalized as a preference for a certain transport mode. The results indicate an increase of explanatory power for a model including the preference variable compared to a model that only comprises sociodemographic and infrastructural variables. Furthermore, the analysis shows that the predictive power of preferences is higher for travel mode choice than for traveled distances. One crucial restriction of the Van Wee study is the low reliability of the preference measurement by one item only; here people have to categorize themselves as preferring a certain mode of transportation. In social and behavioral research, more sophisticated theoretical approaches like the Theory of Planned Behavior (TPB; Ajzen, 1991) have been applied to explain mobility behavior by personal factors rather than by simple preferences for different transport modes. The TPB regards

the constructs attitude, subjective norm (SN), perceived behavioral control (PBC), and intention as predictors of behavior. Intention is seen as a summary of all the pros and cons a person takes into account when deliberately reasoning whether a behavior should be performed or not. Intention itself is viewed as causally determined by attitude, SN, and PBC. Attitude toward a behavior is the degree to which the performance of the behavior is positively or negatively valued. SN is defined as the perceived social pressure to engage or not to engage in a behavior. PBC refers to people’s perceptions of their ability to perform a behavior. It is assumed to be a direct predictor of both, intention and behavior. The TPB also postulates that sociodemographic and contextual factors, values, and general beliefs affect behavior only indirectly via the four predictors of the TPB. There are two reasons why the TPB offers an adequate theoretical framework to explain goal-directed mobility behavior: On the one hand, applications of the TPB in the domain of mobility behavior provide strong empirical support for this model (e.g. Bamberg, Hunecke, & Blo¨baum, in press; Bamberg & Schmidt, 2001, 2003; Heath & Gifford, 2002). On the other hand, comprising four predictors only, the TPB is a comprehensive and economical model to explain mobility behavior with the limited resources of survey studies. In mobility research further mobility-related attitudinal factors could be identified that affect mobility behavior and are not measured explicitly by the constructs of the TPB. Several studies have demonstrated a positive effect of personal norm (PN) on the use of environmentally friendly travel modes (e.g. Harland, Staats, & Wilke, 1999; Hunecke et al., 2001; Nordlund & Garvill, 2003). The TPB only measures the SN, which is defined as the perceived social pressure to engage or not to engage in a behavior and is determined by normative expectations of important referents. In contrast to SN, PN measures the intrinsic moral obligation to behave morally right (Schwartz, 1977). The relevance of moral norms in travel mode choice is relatively well analyzed. A direct effect of PN on travel mode choice could not be shown when controlling for TPB constructs systematically (Bamberg & Schmidt, 2003; Heath & Gifford, 2002). Instead, the relation between PN and behavior is an indirect one, mediated by intention (Bamberg et al., in press). In addition, the psychological construct perceived mobility necessities (PMN) extends the TPB providing a more differentiated understanding of the use of environmentally friendly transport modes. Haustein and Hunecke (2007) could demonstrate that PMN, defined as people’s perceptions of mobility-related consequences of their personal living circumstances, have an independent effect on travel mode choice in the context of TPB. The factor PMN differentiates the measurement of control beliefs, which were previously only measured implicitly by PBC. Regarding travel mode choice, PBC is defined as people’s perceptions of their ability to use a certain mode of

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transportation. In this sense PBC is determined by the traffic infrastructure as well as by personal living circumstances. The introduction of PMN separates these two kinds of control beliefs. PBC measures the perception of the physical accessibility of transport modes, whereas PMN operationalize the subjective perceptions of mobility-related necessities resulting from social constraints like having a job or children, for instance. Another mobility-related attitude dimension results from symbolic-affective evaluations of transport modes. Steg et al. (2001) have demonstrated that symbolic-affective functions, like excitement and prestige, as well as instrumental-reasoned functions, like financial costs and driving conditions, are important dimensions underlying the attractiveness of car use. In a follow-up study, Steg (2005) has shown that commuter car use is most strongly related to symbolic and affective motives, and not to instrumental ones. Examining the relative importance of different instrumental and affective journey attributes, Anable and Gatersleben (2005) found that flexibility and convenience are the most important instrumental attributes for car users, whereas freedom is the most important affective one. With regard to these aspects the private car is evaluated by far more positively than public transportation or the bike. Hunecke (2000) has differentiated four basic symbolic dimensions of mobility: autonomy, excitement, status, and privacy. All symbolic-affective evaluations of transport modes can finally be reduced to these four dimensions, which are characterized by a functional core of physical or socioeconomic aspects, but depend strongly on processes of social interpretation. For example, the evaluation of autonomy of various transport modes is influenced by the infrastructure and the accessibility of transport systems. But the extent to which autonomy is evaluated as necessary or sufficient varies strongly between different people. Theoretically, each transport mode could be evaluated separately with respect to the four basic dimensions. But factor analytical results of two German large-scale surveys with representative samples have shown that only public transport is evaluated independently on the four symbolic dimensions (Hunecke, Schubert, & Zinn, 2005; Hunecke & Schweer, 2006). In contrast the car is evaluated homogenously positive or negative in relation to all four dimensions. Against the theoretical background of an extended norm activation model, which also considers the constructs of SN and PBC, the autonomy dimension could explain additional variance of travel mode choice (Hunecke, 2000). In the perspective of the TPB the symbolic-affective evaluations are behavioral beliefs about how good and bad or rather pleasant and unpleasant the transport modes are for real and potential users. For this reason the symbolicaffective evaluations of excitement, status, and privacy can be used—in accordance with the TPB—as one possibility to operationalize the attitude variable in the domain of travel mode choice (e.g. Haustein & Hunecke, 2007).

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In relation to the other three symbolic-affective evaluations, the autonomy dimension shows a higher conceptual similarity to PBC than to attitude. In spite of this conceptual similarity, there is still a difference between these constructs. The autonomy dimension measures the general belief that all activities could be carried out by the respective mode of transport, whereas PBC is operationalized as the evaluation of the difficulty to use a certain mode of transportation. 1.2. Personal factors as determinants of ecological impact So far only few studies have been published in which interrelations between personal variables and the ecological impact of environmental behavior are analyzed. Gatersleben, Steg, and Vlek (2002) have investigated the influence of attitudinal variables and sociodemographic characteristics on pro-environmental behavior, which was operationalized by an index of 13 kinds of behavior, and on the ecological impact of household energy use. They found out that pro-environmental behavior is more strongly related to attitudinal variables, such as environmental awareness and beliefs, whereas the ecological impact of household energy use is primarily related to socio-economic variables, such as income and household size. Their results confirm the assumption of Stern (2000) that it is worthwhile to distinguish two different measures of environmentally significant behavior: an intent-oriented measure and an impact-oriented measure. An additional study by Poortinga, Steg, and Vlek (2004) has analyzed the role of value dimensions concerning different aspects of quality of life, and general and specific environmental concern in the field of home and transport energy use. So far this study has been the only one focusing on the influence of psychological variables on the ecological impact of mobility behavior. Their results confirm the findings of Gatersleben et al. (2002): Home and transport energy use are especially related to sociodemographic variables, whereas values and environmental concern are less important. Only the value dimension ‘‘openness to change’’ was found as a relevant predictor of the ecological impact of transport energy use. 1.3. Ecological Impact Assessment (EcIA) The EcIA has its origin in the Material Flow Analysis in business companies. EcIA was developed to identify ecological impact associated with the production and use of goods and services (Baccini & Brunner, 1991).2 The objective of EcIA is to make material flows transparent, to improve environmental-related decisions, and to serve as an instrument for political consulting. 2 Other methods for analyzing material and energy flows are Life Cycle Analysis, Product Line Analysis, Process Chain Analysis, Ecological Footprint, Material Intensity Analysis (Colborn, Meyers, & Dumanoski, 1996; Hofmeister, 1998).

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EcIA has been applied for several consumption areas, also for mobility as an area of need with ecological relevance (INFRAS, 1995). In their application of EcIA to mobility, these studies differ with regard to their choice of the examined environmental impact, transportation modes, and life cycle sections of vehicles (production, modes of usage, infrastructure, disposal). Most surveys investigate traffic systems as a whole, but do not take into account the ecological impact on an individual level, where the used transportation mode, the distances traveled, and the capacity rate of the used transportation mode are the crucial parameters (Ifeu-Institute (Ifeu-Institute for Energy and Environmental Research, Heidelberg), 2005). 1.4. The present study The overall objective of the present study was to test an explanation model for ecological impact of mobility behavior that allows specifying the predictive power of psychological, sociodemographic, and infrastructural variables. This explanation model should be as exhaustive as possible with respect to psychological, sociodemographic, and infrastructural factors, so that policy makers can define priorities when determining the most important starting points to promote environmental sustainable transport. There is only one study that examines the relationship between attitudinal variables and the ecological impact of individual mobility behavior (Poortinga et al., 2004). A central result of this study is that the impact of environmental behavior is more strongly related to sociodemographic and household variables than to values and environmental beliefs. However, this result has to be interpreted with the restriction that behavior specific attitudes have not been included in their analyses. Results from environmental behavior research indicate that behavior specific attitudes and beliefs are better predictors of behavior than values or general environmental concern (e.g. Dietz, Stern, & Guagnano, 1998; Oreg & Katz-Gerro, 2006). For this reason the current research on psychological determinants of mobility behavior is based on theoretically and empirically well-founded models of an attitude–behavior relationship, such as the TPB. In the present study we aimed at maximizing the explanatory power of the psychological variables and included only mobility-related variables that empirically proved to be relevant predictors for travel mode choice. Most of these variables were theoretically derived from the TPB like SN, PBC, and attitude, while the latter was operationalized via symbolic-affective evaluations on the four mobility dimensions autonomy, status, excitement, and privacy. Moreover, the psychological part of the model was extended by the constructs PN and PMN as well as by attitude dimensions related to the symbolic dimensions of mobility, independent of the constructs of the TPB. The TPB construct ‘‘intention’’ was not integrated into the model because it does not offer enough information on how mobility behavior could be changed despite of its high

correlation with behavior. A short value inventory was included in all analyses to test the relevance of values when mobility-related attitudes were also considered. Another restriction of previous research is the assessment of ecological impact, which has been carried out only in an approximate way in psychological studies. In our interdisciplinary study we shall overcome this restriction and realize an ecological assessment of individual mobility behavior that satisfies the methodological standards of current environmental science. 1.5. Hypotheses Assigning political priorities in planning processes for climate protection requires information on the relative importance of psychological, sociodemographic, and infrastructural factors for the ecological impact of mobility behavior. For this reason in a first step we analyzed the relationship between psychological, sociodemographic and infrastructural variables and greenhouse gases resulting from mobility behavior (cf. impact hypothesis). Impact hypothesis: Psychological variables are significant predictors of mobility-related greenhouse gas emissions even if infrastructural and sociodemographic factors are controlled for. The design of effective intervention programs to reduce the ecological impact of mobility behavior demands various strategies focusing on different aspects of mobility behavior. The two most relevant aspects that have to be changed are the rate of using private motorized modes and the traveled distances in general. We expect that these two aspects of mobility behavior are related to different psychological, sociodemographic, and infrastructural factors, which require specific interventions in each case. For this reason in a second step we analyzed separately the relationship between psychological, sociodemographic, and infrastructural variables and travel mode choice as well as traveled distances. Regarding psychological variables, we expect a stronger relation of mobility-related attitudes to travel mode choice than to distances traveled as these attitudes were operationalized referring directly to the decision process of travel mode choice. In contrast, for values a stronger relation with distances traveled is expected because values as general orientations influence how often people are mobile, what kind of destinations they choose, and which distances they cover to reach their destinations. Travel mode hypothesis: Psychological variables are significant predictors of the use of private motorized modes, even if infrastructural and sociodemographic factors are controlled for. Additionally, it is expected that mobility-related attitudes are better predictors for travel mode choice than values. Traveled distances hypothesis: Psychological variables are significant predictors of traveled distances, even if infrastructural and sociodemographic factors are controlled for.

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We further hypothesized that the relevance of psychological variables in this analysis is weaker than when predicting the use of private motorized modes. Instead, sociodemographic and infrastructural factors are expected to be the most important predictors. With respect to psychological variables, values are expected to be better predictors than mobility-related attitudes.

tive for the core areas regarding age and gender, whereas education level was above average (43.5% with higher education). This can be traced back to a higher willingness to participate of well-educated people and to a high share of students living in the selected inner-city districts.

2. Method

2.2.1. Psychological variables The TPB constructs PBC, SN, and intention were measured with two items each. The statements refer to the use of environment-friendly means of transport instead of private car use. Attitude was operationalized with statements on the symbolic dimensions autonomy, excitement, status, and privacy, concerning the travel modes private car and public transport. For the bicycle only the two dimensions autonomy and excitement were measured, because previous research indicates that the status and privacy dimensions are not relevant for the use of bicycles in everyday life. Instead the weather is an important contextual factor of bicycle use. We took the weather into account by measuring the willingness to use the bicycle in bad weather conditions. This attitude dimension is called ‘‘weather resistance’’. The PN to use environment-friendly means of transport was measured with two items, just as perceived mobility necessities were. All responses were provided on a 5-pointagreement-scale (1 ¼ do not agree at all, 2 ¼ agree slightly, 3 ¼ agree moderately, 4 ¼ agree very much, 5 ¼ agree totally).4 The items measuring the psychological constructs were presented in random order. Values were assessed by a short version of the Schwartz Value Inventory (Schwartz & Bilsky, 1990) developed by Bamberg (2001). Schwartz distinguishes between 10 motivational types of values ordered in a two-dimensional structure constituted by four higher order value types, openness to change vs. conservation and self-enhancement vs. self-transcendence (Schwartz, 1992). The pole openness to change includes stimulation and self-direction, whereas the opposite pole stresses the preference of tradition, security, and conformity. The pole self-enhancement subsumes power and achievement, whereas self-transcendence includes universalism and benevolence. Each of the four higher order value types was measured by three items on a 9-point scale (1 ¼ opposed to my values, 0 ¼ not important, 1–2 [unlabeled], 3 ¼ important, 4–5 [unlabeled], 6 ¼ very important, 7 ¼ of supreme importance). All psychological items are listed in the Appendix.

2.1. Sample and procedure Data for this study was collected from June to December 2003, in the German cities of Augsburg, Bielefeld, and Magdeburg. The selection of these three cities was based on several indicators. The main indicator was the size of the cities and their function in the regional context, which was measured by the German BIK classification.3 According to the BIK classification the three cities are indicated as core areas, which is comparable to the classification Standard Metropolitan Area (SMA) in the USA. The BIK category core area reflects urban areas that comprise 443 German communities and cover 43.3% of the German population. A further indicator for the selection of the survey areas was the transportation infrastructure, which comprised a local tram system as well as a car-sharing service. Additionally, these cities were not characterized by any topographic specifics that could drastically influence the choice of transportation modes in any way and they provided for a regionally balanced selection. Once Augsburg, Bielefeld, and Magdeburg were chosen, three types of city districts were selected within each city: an inner-city district, a city border district, and a suburban district (cf. Table 1). For each of the city districts the survey population was randomly produced by the cities’ registration offices. 11,028 German citizens aged 18–80 received a letter announcing the survey. The people were personally contacted by trained interviewers and were asked if they wished to participate. The survey was conducted via standardized face-to-face interviews that lasted about 60 min. Altogether, 1991 interviews were carried out, with approximately 660 interviews per city and about 220 interviews per city district. The response rate was 25% after correcting for address errors and for people not contacted because the number of intended interviews had already been achieved. In consideration of the time investment required for a 1-h-interview without incentives, we regard the response rate as satisfactory. The sample consisted of 1056 women (53%) and 935 men (47%) with a mean age of 46.7. The sample was representa3 The BIK region types are the most accepted analytical model to structure space in Germany (Hoffmeyer-Zlotnik, 2000). The model’s main indicator is the density of population and workplaces (inhabitants+employees/square kilometer). This indicator reflects the intensity of interaction within regions, which is essential for transportation issues (Aschpurwis+Behrens GmbH, 2001).

2.2. Measures

2.2.2. Infrastructural variables: spatial characteristics and accessibility to transport systems Spatial aspects were operationalized independently from subjective judgments through the district types and the cities the people belonged to. Each type of district is characterized by a certain spatial and infrastructural situation, which is 4 The equidistance of the German version of this answer scale was validated by Rohrmann (1978).

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Table 1 General criteria for three types of city districts Criteria

Characteristics District 1 Inner-city district

District 2 City border district

District 3 Suburban district

Distance

Settlement close to the city center

Settlement at the city border

Settlement in the suburban area in interaction with the core city (labor, leisure)

Density

High density of population and housing

Medium density of population and housing

Low density of population and housing

Housing

Mostly apartment buildings, partly historical buildings

Mostly apartment houses but also single and semi-detached houses

Mostly single and semi-detached houses

Infrastructure

High variety of commercial and public facilities (for shopping, cultural, social, leisure purposes)

Medium variety of commercial and public facilities (mainly for shopping and social purposes)

Basic needs available (mainly for shopping and social purposes)

General accessibility of the city center by foot and bike

High accessibility by both modes

Accessibility of the city center by bike

Limited accessibility

Local Public Transport

Easy access to bus and tram

Connected by tram or light rail, bus

Connected by regional train, bus

Long-distance traveling

Easy access to main train station

Change of bus or tram necessary

Change of bus or train necessary

described in Table 1. By means of the selection of these types of city districts different levels of accessibility and transportation infrastructure could be analyzed systematically. In addition to city districts the cities themselves served as control variables measuring regional specifics that might not be regarded in the other infrastructural variables. However, there are also differences in the accessibility to different transport modes for residents within the separate districts and cities. Thus, the accessibility to transport systems was also measured for each participant by individual ratings of the access to public transport and private car. Additionally, the participants were asked about the number of cars per household, possession of a driving license, a season ticket for public transport and of a ‘‘Bahncard’’—a discount card for frequent users of German Rail. 2.2.3. Demographics Sociodemographic data stating sex, age, education, occupation (full-time/part-time-employment), income, household size, number of children per household, and family form (single, living apart together relationship, cohabitant, married couple) were recorded.

activities. For each activity, data about the place, the distance covered and the transportation mode used was collected. To obtain realistic information about the choice of transportation mode and the individual modal split, the participants could specify the use of up to three different means of transportation per activity. Two mobility behavior variables were used as dependent variables. Firstly, the percentage of trips conducted by private motorized modes6 and secondly the traveled distance per year and person. To calculate these variables, mobility data were projected for 1 year, taking factors such as average working and holiday days per year into account. As a result, traveled distance per year and person could be determined for all purposes and transportation modes. Summarizing the distances of all purposes, the total traveled distance per year and person was determined. Moreover, for each person the frequencies of trips by each mode of transportation per year were assessed. On this basis the percentage of trips conducted by private motorized modes could be determined. 2.3. Ecological impact analysis

2.2.4. Mobility behavior The mobility behavior was measured by the participants’ specifications about their daily purposes and transportation modes. For 14 purposes,5 the participants were asked how often per week or month they performed these

Assessing the ecological impact only the relevant matters of the phase of vehicle utilization were considered: pollution, greenhouse gases, and primary energy consumption. In this study we concentrate on the emissions of greenhouse gases

5 The 14 purposes belong to four categories: (1) working (working/ training, trips to second home because of work); (2) shopping (daily shopping, bulk buying); (3) private errands (trips to administration, bringing and picking up children, supply of relatives/dependants); (4) leisure time activities (shopping expedition, meeting the partner, meeting

(footnote continued) friends and relatives, visit of cultural events, sport/association, allotment garden, day trips). 6 Private motorized modes include powered two-wheelers, private cars, car sharing, rental cars and taxis.

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in relation to mobility behavior. The greenhouse gases were calculated as CO2 equivalents based on the Global Warming Potential (IPCC (Intergovernmental Panel on Climate Change), 1996).7 As Fig. 1 illustrates, there are three factors relevant to the calculation of the ecological impact of individual mobility behavior: distance traveled, the technical data of the transportation mode used, and the number of passengers per vehicles (capacity rate). These data were linked with the specific emission factors included in the software-tool and data base TREMOD to calculate the emissions of greenhouse gases for each person caused by his or her mobility behavior (Ifeu-Institute, 2005). Apart from the data about the distances traveled and the transportation modes used (cf. measure of mobility behavior), the participants gave detailed information about the technical data of private passenger cars and powered two-wheelers.8 Information about brand and model as well as the following data were collected: mode of drive (Diesel, Otto, hybrid (Otto/electric), other), cubic capacity (o1.4 l; 1.4–2.0 l;42.0 l), year of construction and the existence of an air conditioning system. Intra- and extra-urban trips and air conditioning systems are relevant for fuel consumption. For short trips, which are mostly intra-urban, a cold start factor was taken into consideration for the specific emission factor. Public transportation data were made available by the transportation companies and was included in TREMOD (Ifeu-Institute, 2005). Furthermore, the number of passengers per motorized trip is relevant for individual emission calculation. The capacity rate of a vehicle makes a big difference for the individual emission rate. For motorized vehicles like the personal car, car-sharing, rental car, and motorbike, people indicated the respective number of passengers. For collective vehicles, the following capacity rates (percentage of occupied seats) were applied: buses, trams, and subways 25.0%; regional trains 23.9%; long distance trains 41.3%; aircraft 60.0%. These values refer to the mean capacity rates of the vehicle types in Germany, 2003. In the last step, the mobility data were linked with the specific emission factors. Results of these calculations represent the annual emissions of each participant of the survey. 7 The Global Warming Potential is defined as the ratio of the timeintegrated radiative forcing from the instantaneous release of 1 kg of a trace substance relative to that of 1 kg of a reference gas (IPCC, 2001, p. 385). The considered time horizon is 100 years. CO2 is the reference gas. The relations of the considered gases are for Methane (CH4): 1 kg CO2/ 21 kg CH4; Nitrous oxide (N2O): 1 kg CO2/310 kg N2O (IPCC, 1996). 8 For alternative transit modes like car-sharing cars, rental cars and taxis, the following assumptions were made: For the car-sharing cars a small car, for the rental car a middle class car and for the taxi an upper class car were assumed. Public transport data were made available by the transportation companies and are included in the database for motorized traffic in Germany TREMOD, which was created by the Ifeu-Institute in Heidelberg, Germany. TREMOD was also used as a basis for specific emission factors (see Fig. 1).

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3. Results 3.1. Descriptive results 3.1.1. Psychological variables In order to confirm the independence of the four constructs SN, PN, PBC7, and PMN, to explore the structure of the symbolic dimensions of mobility and to reduce the number of psychological variables to their underlying dimensions, a principal component analysis (PCA) with varimax rotation was carried out (see Table 2). Retaining only factors with eigenvalues greater than one, we received an eight factors solution, which was well interpretable and explained 58.2% of the variance. Against theoretical expectations SN and PN became one factor, which was called ‘‘ecological norm’’. PBC and the symbolic dimension with respect to public transport autonomy also formed one common factor. One reversed car autonomy item similar in content also loaded on this factor (cf. Appendix). Because of their conceptual similarity it is not surprising that PBC and autonomy load on the same factor. The common core of both constructs is the subjective evaluation of objective behavioral options. Thus, they were combined in one construct called ‘‘public transport control’’. Following the factor solution, perceived mobility necessities became the expected independent factor. In contrast to previous findings, public transport excitement and status loaded on the same factor. Public transport privacy, however, could be confirmed as a separate dimension. In case of the private car and bicycle, several symbolic dimensions were reduced to one factor, a general car attitude and a general bike attitude. Weather resistance became a separate dimension. The items of the eight resulting factors are listed in the Appendix. Based on the eight factor solution mean scales were constructed. The four value scales were constructed following the theoretically specified structure. Table 3 displays mean, standard deviation, and internal consistency (Cronbach’s alpha) for the calculated multi-item-scales. 3.1.2. Mobility behavior With regard to basic mobility behavior data (e.g. number and availability of the private car), the results of our survey were quite similar to both of the two representative mobility surveys in Germany MiD (Infas & DIW, 2004) and Mobility Panel (BMVBW (Bundesministerium fu¨r Verkehr, Bau- und Wohnungswesen), 2002). With 2.9 trips per day and person, the number of trips in this study was lower than the result of MiD with 3.3 and of the Mobility Panel with 3.5 trips per day. Working and leisure trips were dominant in people’s mobility (cf. Table 4). The preferred modes of transportation were private motorized modes, which were used in 48% of all daily trips. Walking was used in 22%, cycling in 16% and public transport in 14% of all daily trips.

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Motorbike1) Type Year of constr. Cylinder capacity Intra-/extra-urban

Private car1) Year of constr. Cylinder capacity Fuel Air condition Intra-/extra-urban

Specific emission factor2) Motorbike [g/vehicle-km]

Taxi, CarSharing, Rental car Assumption of year of constr., cylinder cap., fuel, In-/ex-urb

Specific emission factor2) Private car [g/vehicle-km]

Buses2) Public service Vehicle Coach Intra-/extra-urban

Trains2) Fuel Public Electricity Electricity of DB AG

Specific emission factor2) Public transport [g/Pkm]

Aircraft2) Fuel

Specific emission factor2) aircraft [g/Pkm]

Degree of capacity use [g/vehicle-km / number of persons in the car per activity]1)

Annual distances travelled per person for each vehicle and activity1)

Direct emissions Pollutant and fuel consumption [g] Electric current consumption [kilowatt hour]

Indirect emissions Fuel consumption [g emission per kg fuel consumption] Electric current consumption [g emission per kilowatt hour electric current consumption]

Annual emissions, differentiated by individual, vehicle and activity [g/Pkm]

linkage

1) Data Source MOBILANZ

acting on

2) Data Source IFEU Fig. 1. Calculation of the individual ecological assessment.

3.1.3. Ecological impact For the ecological assessment, only motorized modes have been taken into consideration. The importance of the private car for daily mobility and for ecological impact is indicated by its share of greenhouse gas emissions. Private motorized modes had a share of 87% in all transportationrelated emissions of the participants, whereas public transport only had a share of 13%. With regard to activity type, most greenhouse gases were produced by trips to work followed by leisure trips (cf. Table 4). 3.2. Multivariate analysis 3.2.1. Predicting ecological impact of mobility behavior The hypothesis that psychological variables are significant predictors of mobility-related greenhouse gas emissions even if infrastructural and sociodemographic factors are controlled for (impact hypothesis) was tested with a hierarchical regression analysis predicting greenhouse gas

emissions. In this analysis psychological, sociodemographic, and infrastructural variables were entered as independent variables in two steps.9 In the first step, sociodemographic and infrastructural characteristics were entered and already explained 40% of the variance of greenhouse gas emissions. The strongest predictors were full-time employment, which was positively related to the ecological impact, and age, which was negatively related to the ecological impact. Car availability was the strongest predictor of the group of infrastructural variables. In the second step, when psychological variables were entered, explained variance of the resulting regression model increased from 40% to 48%. Four psychological variables showed significant effects, of which PMN and PBC with beta-weights of .18 and .11, respectively, 9

Interaction terms were not tested in the regression models because no founded hypothesis about interactions between the independent variables in their effect on dependent variables existed.

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Table 2 Results of principal component analysis (N ¼ 1991)

PBC 1a,b PBC 2 PT autonomy 1a,b PT autonomy 2 Car autonomy 1b Car autonomy 2 Car privacy 1 Car privacy 2 Car excitement 1 Car excitement 2 Car excitement 3 Car excitement 4 Bicycle excitement 1 Bicycle excitement 2 Bicycle autonomy 1 Bicycle autonomy 2 PT excitement 1 PT excitement 2 PT status 1 PT status 2 Car status 1b SN 1 SN 2 PN 1 PN 2 PT privacy 1a,b PT privacy 2a,b PMN 1 PMN 2 Weather resistance 1a,b Weather resistance 2

Factor 1

Factor 2

Factor 3

Factor 4

Factor 5

Factor 6

Factor 7

Factor 8

.67 .66 .54 .71 .69 .29 .07 .11 .08 .14 .06 .02 .02 .10 .44 .40 .39 .30 .08 .22 .09 .22 .09 .06 .22 .12 .02 .32 .34 .08 .12

.05 .01 .25 .00 .10 .60 .43 .57 .83 .59 .73 .73 .10 .02 .11 .00 .11 .01 .00 .03 .03 .08 .00 .17 .10 .11 .08 .06 .06 .09 .04

.04 .09 .11 .10 .18 .10 .03 .11 .01 .20 .07 .01 .79 .78 .61 .65 .03 .04 .05 .09 .08 .10 .02 .21 .11 .02 .01 .00 .01 .25 .43

.00 .02 .14 .13 .06 .05 .05 .10 .02 .10 .06 .06 .07 .13 .00 .06 .56 .54 .73 .47 .68 .08 .07 .16 .30 .02 .02 .02 .06 .06 .03

.09 .19 .09 .13 .17 .01 .06 .04 .07 .16 .05 .02 .08 .02 .01 .04 .04 .05 .22 .16 .29 .56 .72 .64 .61 .01 .00 .12 .07 .08 .14

.05 .03 .20 .06 .01 .04 .39 .02 .04 .14 .13 .02 .04 .08 .10 .05 .12 .33 .04 .01 .14 .12 .06 .04 .02 .83 .83 .06 .08 .10 .04

.37 .19 .03 .09 .21 .07 .05 .05 .06 .05 .07 .05 .04 .00 .06 .03 .19 .16 .06 .09 .14 .01 .10 .00 .04 .07 .07 .81 .82 .05 .01

.09 .06 .08 .09 .09 .28 .28 .31 .02 .24 .04 .03 .22 .03 .10 .16 .02 .10 .09 .16 .05 .00 .04 .07 .06 .02 .05 .03 .00 .77 .68

Note: See appendix for an explanation of items. a Recoded. b Reversed statement used (high agreement means low parameter value). Mean substitution was used for missing data. Pairwise and listwise deletions led to comparable results. Table 3 Description of psychological variables Factors

Constructs (number of items)

n

M

SD

Cronbach’s a

Ecological norm

1962

2.60

1.00

.67

1989

3.13

1.06

.80

1987

2.84

.83

.66

1870 1871

3.55 3.00

1.08 .91

.72 .80

1771

3.54

.99

.77

Weather resistance PMN

SN (2) PN (2) PBC (2) PT autonomy (2) Car autonomy (1) PT status (2) Car status (1) PT excitement (2) PT privacy (2) Car autonomy (1) Car privacy (2) Car excitement (4) Bicycle autonomy (2) Bicycle excitement (2) Weather resistance (2) PMN (2)

1729 1971

2.54 3.25

1.23 1.37

.70 .84

Openness to change Conservation Self-enhancement Self-transcendence

Openness to change (3) Conservation (3) Self-enhancement (3) Self-transcendence (3)

1986 1989 1988 1988

3.11 4.16 4.44 5.24

1.62 .88 1.46 1.32

.76 .60 .76 .80

PT control

PT status & excitement

PT privacy Car attitude

Bicycle attitude

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Table 4 Mobility pattern for various mobility purposes

Working Shopping Private errands Leisure time

(N ¼ 1991)

Average km traveled per trip and person (N ¼ 1991)

Greenhouse gas emissions in CO2 equivalent per person and year (kg) (N ¼ 1935)

Greenhouse gas emissions in CO2 equivalent per person and year (%) (N ¼ 1935)

4482.0 486.6 576.6 4052.6

15.6 2.0 4.2 10.4

744.3 71.6 86.1 446.9

55.2 5.3 6.4 33.1

Trips/year and person

Km traveled per person and year

(N ¼ 1991) 286.4 241.6 137.2 390.2

proved to be the strongest predictors. Relations of similar quantity were only found between the ecological impact and the sociodemographic variables full-time (b ¼ .25) and part-time employment (b ¼ .12), age (b ¼ .15) and the living apart together relationship (b ¼ .11). Thus, sociodemographic and psychological variables were of similar importance for greenhouse gas emissions, whereas infrastructural variables were of minor relevance. Within the group of infrastructural variables only car availability and the number of cars per household remained significant predictors of ecological impact after psychological variables had been entered. The results of the regression analysis are summarized in Table 5. 3.2.2. Predicting travel mode choice and traveled distances The use of private motorized modes and traveled distances are the most important determinants of the ecological impact of individual mobility behavior. To analyze the relevance of psychological variables as predictors of these two variables we conducted hierarchical regressions, in which psychological, sociodemographic, and infrastructural variables were entered as independent variables in two steps. Table 6 shows the results of both regression analyses. In the first step, we entered sociodemographic and infrastructural characteristics. By far the highest positive relation to the use of motorized private transport was shown by car availability and the number of cars per household, the highest negative relation by possession of a season ticket, which allows permanent access to public transportation. Moreover, people in the city center less often used private motorized modes than in the suburbs. This can be explained by a highly accessible social and commercial infrastructure of the city center for non-motorized modes and public transport. Regarding sociodemographic structure, full-time employment was the most important predictor for the use of private motorized modes. When psychological variables were entered in the second step, explained variance increased by 14% and reached 60%. Now, the strongest predictor of the use of private motorized modes was public transport control: People with a high perceived ability to use public transportation used the car less often. Another significant psychological attitude was weather resistance: The higher the sensitivity to bad weather conditions the more often

motorized private transport was used. Moreover, the attitude towards the private car positively and the attitude towards the bike negatively predicted the use of private motorized modes. Apart from psychological factors, variables that pertain to the accessibility of different transport modes remained strong predictors of the use of private motorized modes. Here the accessibility of the private car, which comprises car availability, possession of a driving license, and the number of cars in the household as well as the possession of a season ticket were most important. Compared to these variables spatial characteristics showed only little relevance. Mainly the city center as a district type still had a significant effect on the use of private motorized modes. The fact that the city of Augsburg remained a significant predictor in the regression after psychological variables had been entered can be explained by regional specifics: A post hoc analysis of the traffic situation in Augsburg showed a high use of bicycles that decreased the use of private motorized modes. Finally, sociodemographic variables turned out to be of least relevance for the prediction of the use of motorized modes. The only significant variable was full-time employment. In the regression analysis explaining traveled distances, full-time employment and age were the most important predictors in the first step, whereas spatial and infrastructural variables were of minor importance. When psychological variables were entered in the second step, explained variance increased by 4% from 33% to 37%. Thus, the increase of explained variance on account of psychological variables is smaller than in case of travel mode choice. The only significant psychological variable in this regression is PMN with a beta-weight of .18. Regarding sociodemographic variables, full-time employment with a beta-weight of .24 and age with a beta-weight of .18 were the strongest predictors. Within the infrastructural variables there are not any other relations of similar quantity. However, the results show regional specifics concerning the city of Bielefeld.10 The inhabitants of this city traveled less distances in their everyday life—a regional effect for which we could not find a valid reason in post hoc analyses. 10

The significant positive beta-weights of the both cities Augsburg and Magdeburg, which were entered in the regression analysis, indicate that the reference city of Bielefeld is negatively related to the ecological impact.

ARTICLE IN PRESS M. Hunecke et al. / Journal of Environmental Psychology 27 (2007) 277–292 Table 5 Summary of hierarchical regression analysis for variables predicting greenhouse gas emissions (N ¼ 1433) Variable

B

SE

b

Step 1 City center [1 ¼ yes; 0 ¼ no] Suburban area [1 ¼ yes; 0 ¼ no] Augsburg [1 ¼ yes; 0 ¼ no] Magdeburg [1 ¼ yes; 0 ¼ no] Car availability [1 ¼ yes; 0 ¼ no] Driving license [1 ¼ yes; 0 ¼ no] Number of cars Access to PT Distance next bus stop Distance next rail station Season ticket [1 ¼ yes; 0 ¼ no] Bahncard [1 ¼ yes; 0 ¼ no] Age Gender Higher Education [1 ¼ yes; 0 ¼ no] Number of persons per household Number of childreno18 years Living apart together relationship [1 ¼ yes; 0 ¼ no] Income Full-time employment [1 ¼ yes; 0 ¼ no] Part-time employment [1 ¼ yes; 0 ¼ no]

.49 .32 .04 .37 1.29 .58 .53 .08 .02 .08 .13 .13 .04 .03 .33 .24 .12 .98 .07 1.81 1.10

.15 .17 .15 .15 .20 .21 .10 .07 .06 .04 .14 .19 .00 .12 .13 .07 .10 .19 .04 .15 .19

.08** .05 .01 .06 .19*** .07** .15*** .03 .01 .04 .02 .01 .21*** .01 .06 .11*** .03 .11*** .05 .31*** .14***

Step 2 City center [1 ¼ yes; 0 ¼ no] Suburban area [1 ¼ yes; 0 ¼ no] Augsburg [1 ¼ yes; 0 ¼ no] Magdeburg [1 ¼ yes; 0 ¼ no] Car availability [1 ¼ yes; 0 ¼ no] Driving license [1 ¼ yes; 0 ¼ no] Number of cars Access to PT Distance next bus stop Distance next rail station Season ticket [1 ¼ yes; 0 ¼ no] Bahncard [1 ¼ yes; 0 ¼ no] Age Gender Higher Education [1 ¼ yes; 0 ¼ no] Number of persons per household Number of childreno18 years Living apart together relationship [1 ¼ yes; 0 ¼ no] Income Full-time employment [1 ¼ yes; 0 ¼ no] Part-time employment [1 ¼ yes; 0 ¼ no] Ecological norm Public transport control Public transport status & excitement Public transport privacy Car attitude Bicycle attitude Weather resistance Perceived mobility necessities Self-enhancement Openness to change Self-transcendence Conservation

.38 .26 .27 .27 1.14 .41 .29 .04 .01 .07 .20 .33 .03 .11 .31 .14 .22 .98 .07 1.45 .94 .13 .31 .21 .00 .09 .15 .19 .38 .09 .02 .05 .03

.15 .16 .14 .14 .19 .20 .10 .06 .05 .04 .14 .18 .00 .12 .13 .07 .10 .18 .04 .15 .18 .07 .08 .08 .05 .07 .07 .05 .05 .05 .04 .05 .07

.06** .04 .04 .04 .16*** .05 .08** .01 .00 .04 .03 .04 .15*** .02 .05 .07 .06 .11*** .05 .25*** .12*** .05 .11*** .06** .00 .03 .05 .08*** .18*** .04 .01 .02 .01

Note: The dependent variable ‘‘greenhouse gas emissions’’ was split in 10 categories of the same size so that the residues follow a normal distribution. R2 ¼ .41 for step 1; DR2 ¼ .08 for step 2. Adjusted R2 ¼ .40 for step 1; D adjusted R2 ¼ .08 for step 2. Predictors were checked for multicollinearity: variance inflation factors (VIF) of all variables wereo3. **po.01, ***po.001. Pairwise deletion of missing data was used.

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4. Discussion The present study examined the relationship between psychological, sociodemographic, and infrastructural variables and the ecological impact of mobility behavior. With respect to the impact hypothesis the results confirm our expectation that psychological variables show significant relationships to the ecological impact of mobility behavior if sociodemographic and infrastructural factors are controlled for. Our results challenge the findings of Poortinga et al. (2004), who come to the conclusion that the ecological impact of transport behavior rather depends on sociodemographic and household variables than on attitudinal variables. Our results indicate that psychological variables are not only related to intent-oriented mobility behavior, but also to the ecological impact of mobility behavior. Two reasons can account for these contradictory results: On the one hand, in the present study we used mobility-related attitudinal variables and not values or general beliefs as predictors for the ecological impact. On the other hand, we conducted a reliable measurement of all relevant aspects of mobility behavior and measured the ecological impact with a methodological approach that reflects the state of the art in the assessment of mobility-related ecological impact. Our results entail important consequences for programs to support sustainable mobility behavior. Now policy makers can legitimize the application of soft policy measures, because the ecological impact of mobility behavior is not only affected by infrastructural factors or unchangeable sociodemographic characteristics, but also by mobility-related attitudinal variables. At this point one could argue that there is an explanatory gap between psychological variables and the ecological impact of mobility behavior. The question here is: In what way do attitudinal variables affect the emission of greenhouse gases? We answer that question by an analysis of the determinants of travel mode choice and traveled distances as these two aspects of mobility behavior are the most important behavioral determinants of individual caused greenhouse gas emissions. Concerning the travel mode hypothesis the results clearly confirmed our expectations. Six psychological variables are significant predictors for the use of private motorized modes if sociodemographic and infrastructural factors are controlled for. Psychological variables were responsible for additional 14% of explained variance compared to a model based on sociodemographic and infrastructural predictors only. Data also verified our assumption that mobilityrelated attitudes are better predictors of travel mode choice than values are. A closer look at the relevance of significant predictors reveals that public transport control is the strongest predictor of the use of motorized private transport. Thus, the use of private motorized modes highly depends on people’s perception of their ability to use public transportation. Similarly, a relation between the perception of

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Table 6 Summary of hierarchical regression analysis for variables predicting the percentage of trips by private motorized modes and distance traveled (N ¼ 1433) Variable

Percentage of trips by private motorized modes

Distance traveled

B

SE B

b

B

SE B

b

Step 1 City center [1 ¼ yes; 0 ¼ no] Suburban area [1 ¼ yes; 0 ¼ no] Augsburg [1 ¼ yes; 0 ¼ no] Magdeburg [1 ¼ yes; 0 ¼ no] Car availability [1 ¼ yes; 0 ¼ no] Driving license [1 ¼ yes; 0 ¼ no] Number of cars Access to PT Distance next bus stop Distance next rail station Season ticket [1 ¼ yes; 0 ¼ no] Bahncard [1 ¼ yes; 0 ¼ no] Age Gender Higher Education [1 ¼ yes; 0 ¼ no] Number of persons per household Number of childreno18 years Living apart together relationship [1 ¼ yes; 0 ¼ no] Income Full-time employment [1 ¼ yes; 0 ¼ no] Part-time employment [1 ¼ yes; 0 ¼ no]

11.06 4.32 8.15 .71 18.60 11.19 9.27 2.05 .64 1.30 14.45 10.98 .14 3.40 .47 2.43 1.96 .88 .11 8.95 4.68

1.82 2.01 1.74 1.76 2.38 2.46 1.21 .79 .66 .51 1.69 2.23 .05 1.48 1.57 .82 1.20 2.29 .47 1.79 2.26

.15*** .06 .11*** .01 .21*** .11*** .21*** .06** .02 .05 .19*** .10*** .07** .05 .01 .09** .05 .01 .01 .12*** .05

.15 .38 .39 .58 .70 .55 .30 .02 .03 .07 .45 .36 .04 .07 .39 .18 .01 1.03 .07 1.79 1.19

.16 .18 .15 .16 .21 .22 .11 .07 .06 .05 .15 .20 .00 .13 .14 .07 .11 .20 .04 .16 .20

.02 .06 .06 .09*** .10** .07 .08** .01 .01 .04 .07** .04 .24*** .01 .07** .08 .00 .12*** .05 .30*** .15***

Step 2 City center [1 ¼ yes; 0 ¼ no] Suburban area [1 ¼ yes; 0 ¼ no] Augsburg [1 ¼ yes; 0 ¼ no] Magdeburg [1 ¼ yes; 0 ¼ no] Car availability [1 ¼ yes; 0 ¼ no] Driving license [1 ¼ yes; 0 ¼ no] Number of cars Access to PT Distance next bus stop Distance next rail station Season ticket [1 ¼ yes; 0 ¼ no] Bahncard [1 ¼ yes; 0 ¼ no] Age Gender Higher Education [1 ¼ yes; 0 ¼ no] Number of persons per household Number of childreno18 years Living apart together relationship [1 ¼ yes; 0 ¼ no] Income Full-time employment [1 ¼ yes; 0 ¼ no] Part-time employment [1 ¼ yes; 0 ¼ no] Ecological norm Public transport control Public transport status & excitement Public transport privacy Car attitude Bicycle attitude Weather resistance Perceived mobility necessities Self-enhancement Openness to change Self-transcendence Conservation

7.93 4.13 4.90 2.69 14.49 8.05 4.94 .78 .38 1.02 11.48 5.34 .05 1.98 .91 .74 .39 1.00 .43 4.83 4.06 2.31 8.71 1.06 .52 3.11 2.72 4.38 2.66 .23 .45 .47 1.12

1.58 1.73 1.52 1.54 2.07 2.14 1.07 .70 .57 .44 1.51 1.94 .05 1.36 1.37 .71 1.04 1.96 .41 1.59 1.97 .73 .84 .85 .59 .76 .74 .58 .55 .50 .48 .54 .74

.10*** .05** .06** .04 .17*** .08*** .11*** .02 .01 .04 .15*** .05** .02 .03 .01 .03 .01 .01 .02 .07** .04 .06** .26*** .02 .02 .08*** .08*** .15*** .10*** .01 .02 .02 .03

.13 .32 .55 .51 .62 .45 .13 .05 .04 .07 .54 .38 .03 .01 .34 .12 .08 1.05 .08 1.43 .98 .07 .18 .16 .02 .05 .09 .05 .39 .09 .05 .05 .01

.16 .17 .15 .16 .21 .22 .11 .07 .06 .04 .15 .20 .01 .14 .14 .07 .11 .20 .04 .16 .20 .07 .08 .09 .06 .08 .07 .06 .05 .05 .05 .05 .07

.02 .05 .09*** .08*** .09** .06 .04 .02 .01 .04 .09*** .04 .18*** .00 .06 .05 .02 .12*** .05 .24*** .12*** .02 .07 .05 .01 .02 .03 .02 .18*** .04 .03 .02 .00

Note: Private motorized modes: R2 ¼ .47 for step 1; DR2 ¼ .15 for step 2. Adjusted R2 ¼ .46 for step 1; D adjusted R2 ¼ .14 for step 2; Distance traveled: R2 ¼ .34 for step 1; DR2 ¼ .04 for step 2. Adjusted R2 ¼ .33 for step 1; D adjusted R2 ¼ .04 for step 2. The dependent variable ‘‘distance traveled’’ was split in 10 categories of the same size so that the residues follow a normal distribution. Predictors were checked for multicollinearity: variance inflation factors (VIF) of all variables wereo3. **po.01, ***po.001. Pairwise deletion of missing data was used.

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mobility necessities and the use of private motorized modes could be demonstrated. From a perspective of behavior change, the six significant psychological predictors can be divided into two classes of variables: On the one hand variables that refer to the subjective evaluation of the behavioral scope regarding travel mode choice and on the other hand attitudes that express preferences for the different transport modes. PBC and PMN belong to the first category. Car and bicycle attitude, which relate to the symbolic-affective evaluation of the corresponding transport modes, pertain to the second category. Weather resistance is another newly conceptualized significant attitude dimension predicting the use of private motorized modes. It belongs to the second category as it expresses a preference for cycling. Ecological norm forms a preference for environmentally friendly transport modes and can also be attributed to the second category. Concerning the traveled distances hypotheses our basic expectations were also met. Sociodemographic variables showed strong relationships to the traveled distances, especially age and full-time employment. Psychological variables turned out to be of minor importance in the prediction of traveled distances. They only explained 4% of additional variance that can rather be attributed completely to the only significant psychological predictor PMN. Contrary to our expectations neither values nor infrastructural variables were better predictors than mobilityrelated attitudes. The predictive power of the model for traveled distances is weaker than the predictive power of the model for travel mode choice. Consequently, additional research is needed that adapts the TPB to traveled distances more specifically and identifies further attitudinal variables related to the choice of destinations. Generally, our results confirm the empirical findings of other studies that mobility behavior is influenced by situational and personal factors (Collins & Chambers, 2005; Hunecke et al., 2001; Van Wee et al., 2002). Moreover, the conceptualization of personal factors derived from the theoretical background of an extended mobility specific TPB offers a good explanatory framework for travel mode choice. However, one limitation of the attitude-based approach used in the present study is that it only describes a current state of mental representations without explaining the dynamics, when mental representations or attitudes are changing. An alternative approach that has to be applied in explaining travel mode choice is the behavioral decision theory, which focuses on information processing (Svenson, 1998). But the models based on information processing are far from integrating the whole set of infrastructural, sociodemographic, and psychological variables that were analyzed in the present study. For this reason it seems to be adequate to start with a static attitudinal model that can be operationalized reliably in the context of survey research. Before we finally draw conclusions from our results for programs to support sustainable mobility, we have to

289

mention some methodological aspects that restrict the interpretation of our results. Firstly, we are using correlational data, which should be interpreted very cautiously in a causal way. Secondly, the results of our regression analyses highly depend on the number and operationalization of the included predictors. Therefore, the beta-weights of single predictors in our study are not directly comparable to those of other studies. Thirdly, it is still possible to improve the measurement of mobility behavior. In the current study mobility behavior was measured by a retrospective questioning, which has the disadvantage that it is susceptible for memory effects. A methodological improvement could be the measurement of mobility behavior with a target date method, in which participants record their behavior in a short time distance to its performance—usually in the evening of the same day. But for an ecological assessment that focuses on the time slot of a whole year, this method is not practicable because the target data method is limited to time slots of a few days as a result of its high effort. Nevertheless, the results of our survey were quite similar to both representative mobility surveys in Germany, MiD and the Mobility Panel, which are based on the target date method. Fourthly, the measurement of the two symbolic-affective dimensions of excitement and status could be improved. These two dimensions should be measured as two separate factors because each of them has a high relevance for the design and promotion of public transport services. Finally, infrastructural aspects could have been controlled for in more detail on an objective level. Within the three selected district types, no differentiation was made regarding accessibility and infrastructural situation on an objective level. Individual differences were only regarded with a subjective measurement of distances to bus stops and rail stations. In future studies the individual accessibility and infrastructural setting as well as timetable data could also be measured objectively. In spite of these restrictions the results provide detailed information for programs to support sustainable mobility behavior. The different patterns of predictors between the psychological, sociodemographic, and infrastructural factors and the use of private motorized modes and traveled distances clearly show the necessity for a differentiated planning of information- and communication-oriented soft policy measures. On the basis of the existing results we expect that an attitude-based strategy is more promising in achieving a change in travel mode choice than in achieving a reduction of traveled distances. In case of travel mode choice, providing information can help users to realize existing mobility services in public transport that offer better or comparable opportunities to travel than by private motorized modes. Additionally, a more positive symbolic-affective evaluation of public transport can be advanced by the application of persuasive communication strategies in the context of social marketing. However, a precondition of successful social marketing programs is a public

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transport system that offers user-friendly services and works reliably. Measures to reduce traveled distances are more difficult to design on the basis of psychological variables. Here the choice of destinations highly depends on the perception of personal necessities and constraints of spatial structure and infrastructure, which cannot be changed by soft policy interventions. Just the appeal for traveling less will not be a very successful intervention strategy to support sustainable mobility behavior. One important aspect concerning traveled distances pertains to holiday trips. The focus of our study was the ecological assessment of daily mobility behavior. The ecological relevance of daily mobility is due to the frequency of trips conducted, whereas the crucial point of holiday mobility is the long distances that are covered, resulting in high emission rates. A comparison of the results of the present study with those of a study analyzing holiday mobility (Bo¨hler, Grischkat, Haustein, & Hunecke, 2006) indicates a higher relevance of socio-economic variables— especially income and household size—for the ecological impact of holiday mobility. Consequently, the strategies for changing holiday behavior also differ from those for changing daily mobility behavior and it is advisable for transport policy to consider both aspects separately.

of movement (PT autonomy 1, recoded) Using public transport I can do everything I want to do (PT autonomy 2) I can deal with my everyday life without a private car. (Car autonomy 1) Public transport status & excitement

I’m impressed by people who cover a lot of distances by public transport (PT status 1) I think that using public transport is trendy (PT status 2) I look up to people who arrange their every day life in a way that they do not posses a private car (Car status 1) I like public transportation because there are a lot of interesting things to see (PT excitement 1) For me using public transportation is relaxing (PT excitement 2)

Public transport privacy

When using public transport, my privacy is limited in an unpleasant way (PT privacy 1, recoded) When I use public transport, other persons come close in an unpleasant way (PT privacy 2, recoded)

Car attitude

Driving a car means freedom to me (Car autonomy 2) I like driving a car because I can decide whom to drive with (Car privacy 1) In my private car I feel safe and secure (Car privacy 2) Driving a car means fun and passion to me (Car excitement 1) Sometimes I enjoy driving without a special destination (Car excitement 2) Driving a car is sometimes a pleasant challenge to me (Car excitement 3) I enjoy applying my driving competence (Car excitement 4)

Bicycle attitude

I love riding my bike (Bicycle excitement 1) Riding my bike is relaxing (Bicycle excitement 2) By bike I can get anywhere (Bicycle autonomy 1) I can reach many of my important destinations by bike (Bicycle autonomy 2)

Appendix Scale

Item

Ecological norm

People who are important to me think that I should use public transportation instead of my private car (SN 1) People who are important to me would support me in using public transportation instead of the private car (SN 2) For environmental reasons I feel obliged to leave the car unused in everyday life as often as possible (PN 1) Due to my personal values I feel personally obliged to use environmental-friendly modes like bus or tram for my regular trips (PN 2)

Public transport control

For me, using public transportation instead of the private car would be difficult in everyday life (PBC 1, recoded) Using public transportation instead of the private car is easy for me if I want to (PBC 2) If I used public transport only, I would feel restricted in my freedom

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Weather resistance

I don’t like riding my bike when the weather is chilly (Weather resistance 1, recoded) I ride my bike even in bad weather conditions (Weather resistance 2)

PMN

The organization of my everyday life requires a high level of mobility (PMN 1) I have to be mobile all the time to meet my obligations (PMN 2)

Values

How important is y to you as a guiding principle of life? y having an exciting life y y having a diversified life y y being daring y y social order y y national security y y family safety y y being ambitious y y being competent y y being successful y y unity with nature y y saving the environment y y respect for nature y

Openness to change

Conservation

Self-enhancement

Self-transcendence

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