Dimensions and determinants of expert and public attitudes to sustainable transport policies and technologies

Transportation Research Part A 48 (2013) 75–85

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Dimensions and determinants of expert and public attitudes to sustainable transport policies and technologies Dimitrios Xenias ⇑, Lorraine Whitmarsh School of Psychology, Cardiff University, Tower Building, Park Place, Cardiff CF10 3AT, UK ESRC Centre for Business, Responsibility, Accountability, Sustainability and Society (BRASS), BRASS Centre, Cardiff University, 55 Park Place, Cardiff CF10 3AT, UK Tyndall Centre for Climate Change Research, Zuckerman Institute for Connective Environmental Research, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK

a r t i c l e

i n f o

Keywords: Sustainable transport Transport policy Attitudes Expert-public differences

a b s t r a c t This paper investigates (a) attitudes to sustainable transport and how these differ between experts and non-experts, and (b) factors that influence these attitudes and their relevant importance in explaining why such differences occur. Attitudes of experts (N = 53) and British public (N = 40) were compared using open-ended questionnaires, attitude scales, analytic hierarchy process and preference ranking. Both samples prioritised reduction in transport demand in qualitative measures. In quantitative measures, however, experts preferred techno-economic measures while the public prioritised behaviour change and public transport improvement. Some options for sustainable transport also varied with individuals’ values, suggesting that expertise alone does not fully account for variation in attitudes. Different perspectives and values imply a need for a broader definition of expertise in transport policy-making, and that the public may not accept transport policies/technologies designed by experts – underlining the importance of early public engagement. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction 1.1. Sustainable transport and the public Transport systems currently suffer from a number of intractable problems, such as congestion, emissions of greenhouse gases and local air pollutants, noise, accidents, depletion of resources, and inaccessibility of amenities and services (e.g., European Commission, 2011). For example, transport is the sector with the highest increase of greenhouse gas emissions in recent decades, rising within Europe by 24% between 1990 and 2008 (EEA, 2010). Given these problems, and their associated economic, social and environmental impacts, a priority for transport policy is to achieve more sustainable transport systems. Although the notion of ‘sustainability’ is contested and different criteria are emphasised by different groups (e.g., Whitmarsh and Wietschel, 2008) broadly speaking sustainable mobility is understood to contribute to social and economic welfare, without damaging the environment or depleting environmental resources (e.g. WBCSD, 2004). Studies that have considered possible sustainable mobility transitions highlight the need for both technological and institutional changes (e.g., electric and fuel cell vehicles, customised mobility, teleworking, zoning policies) to achieve a radical reconfiguration of transport systems for sustainability (e.g., Kemp and Rotmans, 2004; Whitmarsh and Wietschel, 2008). Three main approaches to fostering sustainable transport can be identified (see also Nykvist and Whitmarsh, 2008): (a) improving efficiency and reducing the impact of vehicles (via improvements to existing vehicle technologies or development of new vehicle or fuel technologies) (Hamelinck and Faaij, 2006; Romm, 2006; Solomon and Banerjee, 2006); (b) using more ⇑ Corresponding author at: School of Psychology, Cardiff University, Tower Building, Park Place, Cardiff CF10 3AT, UK. Tel.: +44 2920870714. E-mail addresses: [email protected], [email protected] (D. Xenias). 0965-8564/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tra.2012.10.007

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sustainable modes of travel (via increased use of public transport, walking, cycling and car sharing) (Enoch and Taylor, 2006; Mont, 2004; Seidel et al., 2005); and (c) reducing the need to travel (via urban planning, mobility management, lifestyle change and greater use of Information and Communication Technologies (Cairns, 2004; Nieuwenhuis and Wells, 1997; Schwanen et al., 2011). Both the introduction of new technologies and policies to change travel behaviour requires public support to be successful. First, in respect of new technologies, public demand for new products is required for market success. Public acceptance of any associated infrastructure (e.g., charging points, supply networks) is also necessary. For example, European policy (European Commission, 2011) foresees conventional vehicles being phased out by 2050, and replaced with new (e.g., electric) vehicles and charging infrastructure. Second, in respect of demand reduction and modal shift, significant behavioural shifts are implied; achievement of such behaviour shifts requires public acceptance of economic, land use and associated policies. For example, across Europe a 50% modal shift from road to rail is proposed for medium distance travel by 2050 (European Commission, 2011), with significant implications on a social and individual level. In such conditions, the public can have an active role in informing and improving policy, leading to better and more acceptable decisions (Dietz and Stern, 2008; Siebenhuner, 2004). Given that citizens are a key stakeholder group for transport decision-making, we argue they warrant greater attention in transport policy and technology research. In this paper, we consider public attitudes towards different transport options, and what factors drive these attitudes. In contrast to most research on public perceptions to policies and technologies (see, e.g., Schwanen et al., 2011, for a review), we provide a system-wide perspective on transport that examines stakeholders’ qualitative and quantitative evaluation of a range of technological and behavioural alternatives. 1.2. Public and expert attitudes Research highlights that there are often differences in the perceptions of risk and preferred policy between ‘experts’ and ‘non-experts’ (e.g., Slovic, 2000). For instance, climate change is consistently rated as less risky by non-experts than by experts, whereas the converse is true for nuclear power (e.g., Breakwell, 2010). The concept of expertise is not unproblematic, and there is a considerable literature which highlights that there is no clear distinction between experts and non-experts; rather, there seem to be different forms of expertise (e.g., Collins and Evans, 2007). For the purposes of this paper, we distinguish between two broad levels of expertise, from a functional perspective, leading us to identify two respective groups. The first group comprises ‘transport experts’: individuals who work in the field of transport research, policy and implementation. This includes transport researchers, professionals in public transport, infrastructure, energy, automotive, and other transport-related industries. This group also includes transport related non-governmental organisations. The second group comprises ordinary citizens or ‘public’: individuals who do not have a professional interest in transport – but who have lay expertise as transport users. This distinction is useful because it delineates those voices which tend to inform policy (i.e., ‘experts’) from those who tend to be impacted by policy (‘public’). The distinction is also meaningful since differences between these groups have been observed previously in their attitudes to transport policies and technologies (Whitmarsh et al., 2009). Specifically, Whitmarsh et al. found that both groups agreed on the need to address problems of unsustainability in the transport sector, and identified broadly similar environmental, social and economic criteria for sustainable transport. On the other hand, amenity of transport was found to be more important for public participants, while expert participants focussed more on pragmatic and technological issues. Similarly, while both groups favoured modal shift and novel technologies, public participants also supported demand reduction measures and choices. Gerike et al. (2008) similarly found that the public and policy-makers favoured modal shift (e.g., promotion of cycling), but also that both groups considered demand management measures (e.g., parking management) unacceptable. While they found few differences between the groups, they did not examine other ‘expert’ groups, such as industry, academics and third sector. Other research highlights that the public prefers transport policies which are perceived to be fair, effective, and which do not limit freedom (e.g., Jaensirisak et al., 2005; King et al., 2009; Schuitema et al., 2010). Thus, ‘pull’ measures – to encourage behaviour change by providing and improving alternatives (e.g., investment in public transport) – are preferred to ‘push’ measures – which prohibit or constrain behaviours (e.g., taxes, parking restrictions; Eriksson et al., 2008). The reasons for the disparity between experts and public are multiple and include psychological, social and institutional factors (for a review, see Whitmarsh et al., 2011). Some studies focus on the different types of information processing which experts and non-experts apply. According to this view, in everyday life, people’s judgments are often affective, automatic and rapid (‘experiential reasoning’; Weber, 2010); whereas scientific and technical assessment is more deliberative, conscious and cognitively based (‘analytic reasoning’; Weber, 2010). However, this interpretation has been regarded as problematic since it may favour scientific judgements and imply that the public is ‘irrational’ or ‘wrong’. Indeed, notable examples exist of scientists’ assessments being flawed due to lack of understanding of the local context, which was however well-known to local communities (Wynne, 1996). Consequently, a better understanding of the criteria and factors influencing choice and attitudes to options is necessary and warrants further research. 1.3. Knowledge deficit or value-based reasoning? Sustainability and technology policy is often based on the assumption that information provision can foster public support for policy – the so-called ‘knowledge deficit’ model (e.g., Burgess et al., 1998). Yet, knowledge is not the only factor

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influencing attitudes, behaviour or support for particular policies. While there is a weak positive correlation between knowledge about science in general and attitudes to science, the relationship between knowledge and attitudes is more complex for specific technologies and issues (Allum et al., 2008; Evans and Durant, 1995). For example, a growing body of research suggests knowledge often has little or nothing to do with attitudes to climate change; views on climate change are primarily based on ideology and values (e.g., Whitmarsh, 2011; Kahan et al., 2011). Indeed, public attitudes may change or polarise as a result of increased knowledge (e.g., Bernard et al., 2003; Corner et al., 2012; Lord et al., 1979), but do not necessarily match those of experts (Kahan et al., 2011; Weber, 2010). Similarly, attitudes to transport policies – particularly less popular ‘push’ measures (e.g., taxes) – are often guided by values and personal norms (Eriksson et al., 2008). In sum, knowledge, values, and demographic characteristics, are among the many factors that influence attitudes to risk and policy (e.g., Moscovici, 1988). Our research aims to establish the relative importance of these factors in shaping attitudes to transport technologies and policies, and in particular to identify the extent to which publics and experts are influenced by values, as well as knowledge, in their preferred transport policies and technologies. Our objectives in the present study are (1) to provide a system-wide evaluation of technological and behavioural options for reducing carbon emissions from transport (i.e. via improved efficiency, modal shift, and reduced demand), focussing on stakeholders’ assessment of these options; (2) to investigate attitudes to sustainable transport (including specific technologies and behaviour change policies) and how these differ between experts and non-experts; and (3) to identify factors – including knowledge, values and demographics – that influence these attitudes and their relative importance in explaining why such differences occur. As such we aim to provide a more holistic assessment of sustainable transport, giving attention both to a range of potential measures and technical changes and to the views of diverse stakeholder groups – both those who are traditionally influential in decision-making (experts) and those who are less often heard (publics). We also aim to shed light on what shapes these views, including how they may vary with knowledge and values.

2. Method 2.1. Overview We adopted a mixed-methods approach comprising both qualitative and quantitative elements. The qualitative methods are used to examine the construction and expression of attitudes, while the quantitative measures are used to rate and rank preferred options and assess the relevant contribution of background factors to such preferences. Using both approaches enables us to address different aspects of the research questions, but also to shed light on the topic from different angles (i.e., triangulation; Bryman, 1988). 2.2. Participants We employed two independent groups: (1) Transport Experts (working on European-funded transport projects; n = 53, 58.5% male); this group comprised transport professionals (working for industry, government, civil/interest groups, and academia) who were contacted personally by the researchers during transport relevant events and meetings in the summer and autumn of 2010. We purposefully overrecruited in this group, in anticipation of a somewhat reduced response rate and partially completed materials. (2) Citizens (UK residents, registered on a Cardiff University participant database; n = 40, 47.5% male); this group comprised volunteer members of the public who were contacted by the researchers personally or by telephone in the summer of 2010. 2.3. Procedure We developed ten alternatives for reducing carbon emissions from transport, based on the three aspects identified in Nykvist and Whitmarsh (2008) and further explored in Whitmarsh et al., 2009: (1) improving efficiency and reducing the impact of vehicles (options: fuel cell/hydrogen vehicles; smaller, lighter vehicles; electric (battery-powered) vehicles); (2) using more sustainable modes of travel (options: walking/cycling to work and shops; car sharing and car clubs; improved public transport); (3) reducing the need to travel (options: less commuting (e.g. by increasing work from home); less leisure or holiday travel (e.g., taking more local vacations); incentives/pricing policies (e.g. congestion charging); establishment of car free areas/‘‘home zones’’). Expert participants were given the following materials (in this order): (a) an open-ended question (‘‘In your view, what changes need to be made in order to significantly reduce carbon emissions from transport by 2030?’’); (b) an analytic hierarchy process (AHP; Saaty and Peniwati, 2008) matrix of our ten options for sustainable transport. In this matrix, participants were asked to choose between pairs of options, and provide a numerical weighting for their choice (i.e. how much more they prefer this option over its competitor); (c) a table for overall ranking of the options; (d) two tables for the ranking of the effectiveness and achievability of the options; (e) a table for ranking the responsibility for reducing carbon emissions from transport; (f) the 15-item New Ecological Paradigm (NEP; Dunlap et al., 2000) (Cronbach´s alpha = .80); (g) the Environmental Identity scale (Whitmarsh and O’Neill, 2010) comprising four items (e.g. ‘I think of myself as an environmentally-friendly

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consumer’), measured on a 5-point Likert agreement scale anchored at ‘‘Strongly agree’’ and ‘‘Strongly disagree’’; these items formed a reliable scale (Cronbach´s alpha = .89); and (h) questions about travel habits and socio-demographic information. Citizens were recruited to focus groups. They were given the above materials upon arrival at the School of Psychology, and completed them individually while waiting for their respective focus groups to commence. The focus groups comprised three phases: (i) an initial, open discussion on the participants’ understanding and perceptions of sustainable transport; (ii) a set of expert presentations on each of the ten options and discussions on the advantages and disadvantages of each option; and (iii) a broader discussion on sustainable transport and participants’ perceptions and preferred options. Upon completing this phase, participants were given a shorter version of the initial materials, a short debrief about the study, and the session concluded.

3. Results 3.1. Qualitative data In preparation for analysis, all open-ended answers were screened for clearly irrelevant or incomprehensible material which was subsequently removed. Open-ended answers from item (a) in the experts and citizens materials were coded via a three stage thematic analysis process independently performed by the researchers, based on McLeod’s (1994) four phase model. This comprises the phases of immersion (in depth understanding of material), categorisation (search for meaningful units in the text), phenomenological reduction (combining units in larger emerging thematic categories), triangulation and interpretation (overall understanding of the data). Specifically, all open ended responses were assigned thematic codes as long as they appeared at least twice in our material; in the next step, the codes were aggregated to meaningful thematic categories. New thematic categories were created where necessary, to allow for direct comparison between samples. Finally, these were organised by frequency for our two samples as reported in Table 1. As shown in Table 1, the five most frequent spontaneous suggestions for the expert sample were: (1) improvement of public transport, (2) economic measures, (3) low-emission vehicles/R&D in new technology, (4) improvement of cycling facilities, and (5) policy change/government responsibility. The five most frequent spontaneous suggestions for the citizen sample were: (1) improvement of public transport, (2) low-emission vehicles/R&D in new technology, (3) improvement of cycling facilities, (4) economic measures and (5) business/industrial measures.

Table 1 Frequencies of expressed themes between experts and citizens’ open-ended responses for carbon reduction options. Data show the total occurrence of a theme in experts, in citizens, and the difference between the two. Themes

Expert

Public

Improve public transport (more reliable, modern, integrated, attractive, promoted) Economic measures (e.g., congestion charging, cut fuel prices, stop subsidies to polluting industry) Improve cycling facilities (tracks, storage, hire, road safety) Low emission vehicles/R&D in transport tech (e.g., solar-powered, efficient engines) Policy/government responsibility (e.g. politicians must set example; legislation needed) Promote walking (incl. walking buses) Land-use planning/infrastructure changea Electric vehicles Personal travel quotas/reduced car ownership or mileage Public education/culture shift/behaviour change Business/industrial measures (limit road haulage, employee schemes, etc.) Energy supply change (decarbonise) Working from home/videoconferencing Local economic development and local shops Global dimensiona Barriers to change (e.g., economic, cultural, spatial) Fuel cell/hydrogen vehicles Integrated policy (e.g., multi-goal policies; joined-up government)a Urban measures (trams in all cities, cycle paths) Biofuel vehiclesa Small, light-weight vehiclesa Reduce air travel Lower material consumerisma Eco-drivinga Reduce speed limitsa Car sharing/pooling Ban high-polluting vehicles Car free zones/pedestrian areas Local and seasonal products (e.g., cut food miles)

23 20 16 16 16 11 11 10 9 9 8 7 4 4 4 4 3 3 3 2 2 2 2 2 2 1 1 1 1

33 16 17 21 1 2 0 8 4 5 10 1 1 2 0 2 2 0 6 0 0 6 0 0 0 5 2 4 2

Expert – public difference 10 4 1 5 15 9 11 2 5 4 2 6 3 2 4 2 1 3 3 2 2 4 2 2 2 4 1 3 1

Note: Single responses are omitted. Top five preferences for each group and five widest discrepancies between groups are italicised. a Theme unique to one group.

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Table 2 Experts’ and citizens’ ordered preferences for sustainable transport options. Data reflect mean ranks extracted from analytic hierarchy process (AHP) analysis. Bold text denotes significantly different rankings between experts and public. Rank

1 2 3 4 5 6 7 8 9 10 * **

Overall preference Experts

Public

Walking/cycling Improve public transport** Less commuting/telework Incentives/pricing policies*** Smaller lighter vehicles Electric vehicles Car sharing Car free zones Fuel cell vehicles* Less holiday travel

Improve public transport** Walking/cycling Electric vehicles Less commuting/telework Smaller lighter vehicles Car sharing Fuel cell vehicles* Car free zones Incentives/pricing policies*** Less holiday travel

p = .07. p < .05. p < .01.

***

Eight topics were unique to experts, which is commensurate with their higher level of specialist knowledge on the topic, as well as their overall ability to consider issues from a broader perspective. Although the most frequently suggested solutions were similar for experts and citizens, qualitative content analysis also revealed that expert responses were more detailed and more abstract, generic and theoretical, than public responses. Focus groups discussions1 findings were qualitatively analysed and corroborate the survey findings. Specifically, key themes emerging from these discussions included: (a) the urgent need for improvement in quality, infrastructure and integration of public transport; (b) that financial measures are not generally preferred; more importantly, where they are implemented they should be fair; (c) there is urgent need for improvements in cycling infrastructure and support; (d) that behaviour change is necessary, but slow; (e) that new technology vehicles, notably electric and hydrogen vehicles, are too expensive and not ready for widespread use; (f) that fairness and proportionality of any implemented measures should be visible across all stakeholders and levels of governance. These findings warrant in-depth analysis and are reported in greater detail elsewhere (see Xenias and Whitmarsh, submitted for publication).

3.2. Quantitative data Analysis of descriptive statistics from our quantitative measures (see method) discarded five outlying participants who yielded results more than two standard deviations away from the mean for most measures. Levene’s test for equality of variances indicated significant differences in variance between experts and citizens for five of the 10 available transport options.2 Data were therefore log transformed to satisfy the equality of variance assumption for subsequent statistical analyses using parametric methods. Multivariate analysis of variance revealed differences in the AHP ranking of several transport options between experts and citizens (before discussion), confirming previous findings of Whitmarsh and Wietschel (2008) and Whitmarsh et al. (2009). Specifically, the options improved public transport, incentives/pricing policies and fuel cell/hydrogen vehicles were ranked significantly differently by experts and citizens: experts ranked incentives/pricing policies significantly higher than citizens, and improved public transport and fuel cell/hydrogen vehicles significantly lower than citizens, as seen in Tables 2 and 3. It is noteworthy that these differences remained after discussions, with the exception of the fuel cell/hydrogen vehicles difference, which became non-significant. Qualitative analysis of our citizens groups suggest that this change occurred as the majority of citizens became aware, during our expert presentations, that fuel cell/hydrogen vehicles and relevant infrastructure were not market ready and widely available as of summer 2010 when the workshops took place. The above findings suggest that deliberations (and information provision) did not significantly alter citizens’ views on issues they understood well; on the contrary, and crucially, citizens’ views changed on a topic they had erroneous representations about. An additional point of interest for our expert sample was their perception of effectiveness and achievability of each option. This was addressed by an additional ranking of each option in terms of these two dimensions. Wilcoxon signed ranks test showed that eight of the 10 options differed in terms of overall ranking and effectiveness ranking; whereas only two options differed in terms of overall ranking and achievability ranking. This implies that experts’ preferences were more 1 Focus groups were only conducted for our citizens’ sample, as it was not possible to gather enough expert participants to conduct focus groups at any one time. 2 This was an interesting finding in itself as it indicates very different spread of opinions between experts and citizens, with citizens’ opinions being typically more concentrated than those of experts.

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Table 3 Differences between expert and citizen ranking of carbon reduction options. Mean expert ranking (std. error)a Improved public transport Incentive/pricing policies Fuel cell/hydrogen vehicles * **

Mean citizen ranking (std. error)a

2.059 (.087) 2.448 (.120) 3.071 (.127)

F

g2p **

1.773 (.095) 2.980 (.132) 2.734 (.139)

4.922 8.904*** 3.222*

.060 .104 .040

p = .07. p < .05. p < .01. a Data were log transformed; values closer to zero indicate higher ranking.

***

Table 4 Expert ratings of effectiveness, achievability and overall ranking of carbon reduction options. Rank

Effectiveness

Overall preference (AHP)

Achievability

1 2 3 4 5 6 7 8 9 10

Incentives/pricing policies*** Walking/cycling*** Improve public transport* Electric vehicles* Less commuting Smaller lighter vehicles* Fuel cell vehicles*** Car sharing Less leisure/holiday travel** Car free zones***

***

*

Walking/cycling** * Improve public transport Less commuting *** Incentives/pricing policies * Smaller lighter vehicles* * Electric vehicles *** Car free zones Car sharing *** Fuel cell vehicles ** Less leisure/holiday travel

Smaller lighter vehicles Improve public transport ** Walking/cycling Incentives/pricing policies Electric vehicles Less commuting Car sharing Fuel cell vehicles Car free zones Less holiday travel

*

.05 < p < .06. p < .05. *** p < .01. **

accordant with their perception of achievability for each option, rather than with their corresponding perception of effectiveness see Table 4. This analysis was not performed with our citizens’ sample as procedural pilots showed that public understanding of the subtleties between achievability and effectiveness of measures was too basic – which our participants attributed to their lack of formal training. Thus, ratings of achievability and effectiveness were omitted from our citizens’ sample, and replaced by one straightforward ranking of preference, presented in Table 2. By asking citizens about ‘‘preferred’’ measures we also addressed acceptability of measures (it is reasonable to expect that preferred is also acceptable). Environmental values, as measured by the New Ecological Paradigm (NEP) scale (Dunlap et al., 2000) did not differ overall between expert and citizen samples (F(1, 80) < 1). Equally, environmental identity, as measured by the Environmental Identity scale (Whitmarsh and O’Neill, 2010) did not differ overall between expert and citizen samples (F(1, 85) < 1). However, multivariate analysis of covariance revealed that NEP score was an overall significant predictor of attitudes towards the options walking/cycling to work and shops; electric (battery-powered) vehicles; establishment of car free areas/‘‘home zones’’ and fuel cell/hydrogen vehicles while Environmental Identity was a significant predictor of attitudes towards the option walking/cycling to work and shops as seen in Table 5. Environmental values and identity, then, can and do exert significant influence on attitudes towards some of the low-carbon transport options, over and above the influence of expertise. Taken together with the between-group differences for the AHP measure (Table 2), the emerging finding is that attitudes for some options vary with level of expertise (i.e. between experts and public), whereas for other options, attitudes vary with environmental values or identity (i.e. across groups).

Table 5 Influence of environmental values and environmental identity on ranking of carbon reduction options. NEP

Environmental identity

F Walking/cycling to work, shops Electric (battery-powered) vehicles Establishment of car free areas/‘‘home zones’’ Fuel cell/hydrogen vehicles * **

.05 < p < .07. p < .05. p < .01.

***

B (standardised) ***

9.381 4.462** 3.782* 8.089***

***

.431 .415** .412* .617***

g2p .123 .062 .053 .108

F

B (standardised) **

6.422 .251 .165 2.481

**

.308 .085 .074 .187

g2p .087 .004 .002 .036

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Next, we examined the relevant influence of our measured variables on each option preference for experts and citizens. Preliminary descriptive statistics revealed very limited variance in our expert sample for variables age and qualifications in contrast to our citizens’ sample. The variable political party also did not make conceptual sense for this analysis, as experts comprised an international sample. We therefore decided to analyse our two samples separately, excluding variables age, qualifications, and political party from experts, but including them in a citizen regression models. The citizens’ sample, on the other hand, was rather representative of the Cardiff population, and no variable was excluded on conceptual grounds or due to lack of variance. Citizens’ responses were analysed in a series of stepwise (backward method) multiple regressions (Tabachnick and Fidell, 2001). This method allows a better statistical fit of existing predictor variables, by eliminating those who do not contribute significantly to the regression model. Hence, this method yields different sets of significant predictors for each given option. All variables were screened for compliance with regression modelling assumptions (Field, 2005). Myers’ (1990) VIF and Menard’s (1995) tolerance thresholds were adhered to, in order to guard against multicollinearity issues. Significant regression models emerged for nine of our 10 options, as summarised in Table 6. Not all independent predictors within these models were significant, which may be due to the small size of our citizen sample. However, it becomes clear from the current pattern of results that (a) influencing factors vary for each option, and (b) individual psychological or demographic factors or characteristics cannot adequately explain attitudes towards complex issues, as with the present topic. For our expert sample, a similar series of regression analyses revealed no significant models (comprising gender, income, environmental values and environmental identity) for the prediction of attitudes towards low-carbon transport options; therefore no regression models are reported here. This lack of predictive models is an interesting point in itself, and may be due to the composition, that is the minimal variance in age and education of this sample, which in effect may correspond to the views of a particular demographic stratum. In contrast, our citizen sample was drawn from the general public, with subsequent greater variability across all measures and demographics. Although we expected that environmental values and environmental identity would be significant predictors of some options in this sample – as suggested by the results of the aforementioned analysis of covariance – this did not emerge in our

Table 6 Determinants of citizens’ preferences for transport related carbon reduction options. Option

Regression model (predictors)

Predictor parameters Beta (standardised)

Walking/cycling to work, shops

Electric (battery powered) vehicles Incentives/pricing policies

Car free zones Fuel cell/hydrogen vehicles

Less commuting (e.g. by increasing work from home)

Less leisure/holiday travel Smaller lighter vehicles Car sharing and car clubs

*

.05 < p < .07. p < .05. *** p < .01. **

Model parameters t

Sig.

Age 25–44 Science degree or above NEP score Environmental identity score

.223 .157 .456 .366

1.262 .946 2.552 2.164

n.s. n.s.

Age 25–44 NEP score

.248 .433

1.430 2.498

n.s.

Age 25–44 Age 45–64 Party: Labour Party: Liberal democrats

.422 .471 .388 .308

2.113 2.382 2.236 1.795

**

Party: Conservative NEP score

.363 .256

2.235 1.575

**

Income >£30,000 Party: Conservative NEP score

.299 .159 .304

1.879 .991 1.892

*

Men Age 45–64 Science degree or above Income >£30,000 Party: Labour

.194 .379 .243 .316 .232

1.409 2.576 1.739 2.161 1.625

Age 45–64

.360

2.213

**

Adj. R2

F 2.783

**

.173

** **

3.267*

.118

2.760**

.206

3.301**

.119

2.871*

.142

4.967***

.368

4.898**

.103

**

** **

n.s. n.s. n.s. *

n.s. **

n.s. **

n.s.

Science degree or above Party: Labour

.336 .303

2.025 1.826

**

Age 45–64 Degree or above Income >£30,000 Party: Conservative NEP score Environmental identity score

.217 .230 .537 .298 .211 .416

1.307 1.402 3.312 1.936 1.324 2.157

n.s. n.s.

3.049

*

.108

n.s.

*** *

n.s. **

3.455***

.302

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D. Xenias, L. Whitmarsh / Transportation Research Part A 48 (2013) 75–85 Table 7 ‘‘Who do you consider to have the main responsibility for reducing carbon emissions from transport?’’ Rank

1 2 3 4 5 *

Perceived responsibility Experts

Citizens

EU government* UK government Local authorities Businesses Individuals*

UK government Individuals* EU government* Local authorities Businesses

p < .05.

results. In retrospect, perhaps the subsequent inclusion of gender and income in the regression models added sufficient variance to obscure these effects. The absence of any significant predictors or models from the expert sample, may suggest an overall effect of expertise on all other variables, which is worth exploring in future research. Further points of divergence between citizens and experts revolved around perceptions of responsibility for reducing carbon emissions from transport. A Kruskal–Wallis H test revealed a significant effect of expertise on options ‘‘EU government’’ (v2(1) = 4.212, p < 0.05) and ‘‘Individuals’’ (v2(1) = 3.768, p < 0.05): experts assigned responsibility in a top-down fashion, consistent with the established hierarchy of governance; citizens, on the other hand, placed individual responsibility significantly higher, and EU government responsibility significantly lower, as can be seen in Table 7. 4. Discussion This research set out to investigate similarities and differences between experts and non-experts in respect of their views on sustainable transport options. In addition, we explored why such differences may occur and which factors play a role in influencing these views. In accordance with previous results (Whitmarsh et al., 2009) we reinforce the finding that experts and citizens converge in their spontaneous assessment of certain low-carbon transport options: improvement of public transport, low-emission vehicles/R&D in new technology, and improvement of cycling facilities and economic measures, were all among the five most frequently mentioned options for both experts and citizens. At the same time, experts and citizens diverge in their strength of support for each option. Thus, support for public transport was much stronger in the citizen sample as well as overall assessment of other low-carbon transport options, such as business/industrial measures which featured much lower in the expert suggestions. These differences may suggest a notable divergence between top-down (expert) and bottom-up (citizen) spontaneous response on ways of reducing emissions from transport. Viewed from this angle our findings suggest that experts propose ways for reducing transport related emissions, as part of a broader range of such solutions. For example, our experts supported equally the improvement of cycling facilities and infrastructure, support for low-emission vehicles and enforcement of new legislation, regardless, at this stage, of the direct cost of these options (e.g. improving cycling infrastructure cost is a fraction of that for subsidising electric vehicles development and implementation) and of their relative – or indirect – cost (e.g. mobilising citizens to cycle more has subsequent health benefits for the population, whereas supporting electric vehicles has subsequent financial costs for governments). In other words, experts overall seem to spontaneously propose theoretical solutions – what could be done – for the issue at hand, and not necessarily what may be most desirable or appealing to the user – and therefore less likely to be accepted. Although it could be argued that expert solutions may not necessarily aim at pleasing the public, it is clear that a minimum level of acceptability is required. Citizens, on the other hand, seem to spontaneously propose fewer overall solutions, but with stronger support, and wider discrepancies between options – economic measures were for instance mentioned half as often as was improvement of public transport, reflecting differences in popularity of some measures over others. This is consistent with studies showing the public tends to prefer ‘pull’ over ‘push’ transport policy measures (e.g., Eriksson et al., 2008; Gerike et al., 2008). In addition, among citizens’ most mentioned options featured practical solutions such as employee schemes, which appeared lower in the expert agenda. This indicates a practical approach – or what needs to be done – from a user perspective, indirectly signalling acceptance levels for the implementation of respective options. The aforementioned top-down versus bottom-up division is underlined by the relevant perceptions of responsibility within the two groups with experts replicating the existing system of governance within the European Union, whereas citizens placed much greater emphasis on individual responsibility. Although this may be considered as an artefact of surveying a European versus a national (UK) sample, it also reflects the reality of how a wide range of impactful and long-term policies and decisions are made in real life. This implies, at the very least, that local publics may feel more empowered than policymakers believe, and could be prepared to participate in such decision-making and play an integral role in meaningful policy development. There are signs that national governments may have started realising the advantages of this approach (e.g. see HM Treasury et al., 2004; Xenias and Whitmarsh, submitted for publication) but more space is required for citizen participation in policy-making. On a parallel level, qualitative content analysis of the most popular themes showed that expert responses are more detailed, and more abstract, generic and theoretical, than public responses, thus exposing how attitudes are expressed. That is,

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even in options where experts and citizens agreed, experts tended to use more detailed and theoretical language, compared to the more experiential and pragmatic language of the citizens. Apart from a reflection of specialist knowledge, these differences in expression may also reflect different types of reasoning and evidence used, revealing different types of information processing, namely analytic versus experiential (Weber, 2010). An immediate implication of this, is the evident discrepancy between the nature of solutions provided by those influencing policy (experts) and the qualities such solutions must have in order to be best received by those who are asked to adopt them (public). Moreover, ranking of our ten pre-selected options did not always follow the above spontaneously reported preferences; two of the five highest ranking options were spontaneously mentioned by less than 10% of participants. This was true of experts as well as citizens (albeit for different options), and replicates previously found discrepancies in attitudes between reporting methods (e.g., Whitmarsh et al., 2009). This finding also reinforces the case for support for the use of mixed methods for this type of research in order to achieve better understanding of participants’ views. In addition, the absence of significant differences in environmental values and identity between samples, suggests that we can consider experts and non-experts equally concerned about environmental issues. This is encouraging regarding the representativeness of our sample composition; it also helps us pull apart the relative influence of knowledge versus values in relation to policy preferences. Our findings in this respect further undermine assumptions that knowledge is the key determinant of pro-environmental attitudes and policy support (e.g., Burgess et al., 1998; Whitmarsh, 2011). Indeed, the concurrent significant influence of these covariates on certain policy and technology options suggests that expertise alone does not fully account for variation in attitudes towards such options. In sum, some preferred options vary between experts and citizens with levels of expertise, while others vary with environmental values and identity and not with expertise. In contrast to Eriksson et al’s findings (2008), it was not ‘pull’ measures that were influenced by values, but some ‘push’ measures (walking/cycling and electric vehicles); nevertheless the influence of values was consistent across citizens and experts. Coupled with the notable differences in perceived responsibility for reducing transport related emissions, these findings can be related to value-based models of environmental behaviour and policy support (e.g., Kahan et al., 2010; Stern, 2000). Another important difference between our samples was the absence of any significantly predictive models for experts’ preferences. Citizens’ data, on the other hand, yielded nine regression models, employing a broad but manageable set of predictors. Despite the scantiness of independent significant predictors, which could be attributed to the small number of citizen participants, the finding of these models (that certain socio-demographic factors and environmental values predict some attitudes) can be combined with previously noted predictors (e.g., perceived fairness and efficacy; King et al., 2009; Schuitema et al., 2010) to elucidate factors shaping public attitudes towards sustainable transport. This is a finding that may partly explain the existing expert-public differences in attitudes, and merits further research. The lack of any predictive models for experts’ choices could possibly be attributed to the breadth of expert areas they represented. Although this breadth may be a prima fasciae justification for entrusting experts with important transport decisions, it also offers an opportunity for reflection on the purpose of policy and the way it is delivered. Successful implementation of policy, in a modern democracy, requires that citizens’ needs and considerations are addressed. Expertise should then be introduced to explore possible solutions to existing problems. In other words, policy should aim to deliver what satisfies the needs of a target audience; and on this basis expertise should be employed to deliver possible solutions. This suggestion has already been identified in the literature (e.g. Rogers-Hayden and Pidgeon, 2007) and successfully implemented via – among others – community-based social marketing approaches (e.g. McKenzie-Mohr and Smith, 1999). Although this approach is not by any means perfect, it demonstrates the value of successfully identifying and meeting public concerns and needs, which in turn facilitates the respective policy implementation. Issues that require urgent and impactful action, including sustainable transport, would greatly benefit from such strategies, and public engagement should play a pivotal role in their development. Finally, we cannot emphasise enough the value of mixed methods in this line of research. The social sciences have been unnecessarily plagued for a long time by a division between qualitative and quantitative approaches. The complementary nature of mixed methods can help provide a more global representation of the issue at hand, which goes further than either method alone. We therefore strongly support the use of mixed methods for an in-depth understanding of similar complex topics. Further research should extend and improve on the current study in a number of ways. Firstly, the range of methods and measures could be expanded. This might include, for example, using structured decision tools and transport system simulators as is being applied in research on attitudes to energy systems, policies and technologies (e.g., de Best-Waldhober et al., 2009). Better design with regards to the materials used across groups could also be sought. In the present research, it became evident that some of our materials did not appeal to our experts due to their length, and were subsequently slimmed down. Equally, some questions were too complex for the public sample and had to be removed or simplified. Future research should address these issues and produce streamlined materials. As well as exploring other possible measures and materials, further research should also aim for larger samples; our sample sizes were relatively small. Although this was necessary in order to provide in-depth qualitative data from the focus groups, further research could aim to provide a broader perspective – both in terms of a representative (and ideally cross-national) citizen sample, and a larger and more diverse expert sample to enable comparisons between different industry, academic, policy and NGO stakeholder groups. As noted earlier, there are multiple forms of expertise, and larger samples could explore these, as well as factors not examined here (such as cultural or geographical factors, current transport policies, experience of relevant technologies and so forth) that may shape attitudes to sustainable transport. Comparing different

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groups of expertise as well as different publics will allow disaggregation of these dimensions, and afford important opportunities for the tailored and targeted implementation of transport measures. Related to this, relevant theories to explain transport policy acceptability, such as the value-belief-norm model (cf. Eriksson et al., 2008), could be tested with these different groups. The exploration of the expert-public divergence has important implications for public engagement in policy-making as well as the risk and attitude literatures (e.g. Rogers-Hayden and Pidgeon, 2007). These differences in perspectives imply a need to depart from established top-down development and implementation of policy. These differences also imply a need for a broader definition of expertise in transport policy-making. 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