Assessing the impacts of social norms on low-carbon mobility options

Pre-print version 1 Assessing the impacts of social norms on low-carbon mobility options Luis Mundaca1*, Rocío Román-Collado2,3 and José M. Cansino2,3 1 International Institute for Industrial Environmental Economics, Lund University, Sweden 2 Department of Economics, Universidad de Sevilla, Spain 3 Universidad Autónoma de Chile, Chile Abstract Policymakers and scientists are paying increasing attention to how social norms can promote proenvironmental behaviour and sustainable energy use. We contribute to this field by experimenting with and assessing the impacts of social norms on low-carbon mobility options. Taking Sweden as a case study, we develop two complementary randomised controlled experiments to: 1) analyse the role of social norms in promoting the adoption of car sharing services (CSS) via descriptive and injunctive norms (N = 720); and 2) investigate potential crowd out effects when injunctive norms are used to promote a low-carbon transport hierarchy (N = 730). First-order effects show that social norms have a positive but marginal impact on the willingness to adopt CSS, and only injunctive norms have the potential to steer behaviour in the desired direction. Results also suggest that concerns about potential substitution effects between low-carbon transport options and CSS are not valid. With due limitations, our findings have various implications for policymaking, notably that for social norms to be effective, other policy instruments are critically needed. Of particular importance are the environmental effectiveness of CSS and complementarities between public transport and active mobility (i.e. walking and cycling). Keywords: Active mobility, behavioural economics, car sharing, energy and climate policy, low-carbon transport, social norms, Sweden *Corresponding author. International Institute for Industrial Environmental Economics at Lund University, PO Box 196, 22100 Lund, Sweden. E-mail address: [email protected] (L. Mundaca). ORCID 0000-0002-1090-7744 Pre-print version 2 1. Introduction Social norms are often defined as (in)formal social considerations, patterns or unwritten rules that govern, guide or influence the behaviour and intentions of individuals, groups, communities or society (Nyborg et al., 2016; Thøgersen, 2006). According to the Theory of Normative Conduct (TNC) (Cialdini et al., 1990; Cialdini & Trost, 1998), two types of social norms can be identified: descriptive norms, which reflect the degree to which behaviour is perceived as collective; and injunctive norms, which denote the degree to which behaviour is supposed to be normally accepted or rejected. Building upon social cognitive theory (Simonson et al., 2003), the literature often distinguishes descriptive (i.e. perceptions or interpretations of “normal” behaviour) from injunctive norms (i.e. prescriptive or proscriptive beliefs about one’s behaviour based on what others expect). A key differentiating aspect is the person’s subjective perception of these realities, and not necessarily the “objective” behaviours or expectations of somebody else (Thøgersen, 2008). In the domain of sustainability, there is increasing evidence that social norms impact pro-environmental behaviours and energy use. In particular, there is growing empirical evidence that shows that what other people think and/ or do is important to the way individuals behave or the choices they make (Farrow et al., 2017). In other words, individuals appear to internalise or pay close attention to normative information about other people’s intentions, behaviours and expectations (Sparkman & Walton, 2017). According to the TNC, social norms can guide the behaviour and intentions of people if they are salient. For example, it has been shown that providing information on other people’s energy use can trigger competition among households, leading to reduced consumption (Allcott, 2011). Likewise, it has been found that willingness to purchase carbon allowances from the EU Emission Trading Scheme and permanently retire them is driven not only by price mechanisms, but also salient norms (Lindman et al., 2013). Furthermore, research reveals that normative concerns and social influence can have significant predictive power with respect to the participation and interest in smart energy systems (van der Werff & Steg, 2016), the adoption of solar PV (Mundaca & Samahita, 2020) and the willingness to Pre-print version 3 purchase electric cars (Kim et al., 2014). From a policy perspective, as a whole, there is a growing a number of studies evaluating the impacts of social norms on pro-environmental behaviour (e.g. Farrow et al., 2017; Huber et al., 2018; Terrier & Marfaing, 2015; Vesely & Klöckner, 2017) and there is increasing consensus that social norm interventions (e.g. via comparative feedback) can be a (cost-) effective way to promote sustainable energy use (see e.g. Abrahamse et al., 2005; Andor & Fels, 2018; Dolan et al., 2012; Nolan et al., 2008). The literature highlights that social norms can be a driver of societal change, and expand the options that policymakers can draw on to address sustainability challenges (Farrow et al., 2017; Janssen, 2015; Kinzig et al., 2013; Nyborg et al., 2016). The observations presented above also apply to the decarbonisation of the transport sector, which is crucially important to limit global warming to 1.5ᵒC above pre-industrial levels. The transport sector accounts for 24% of direct CO2 emission from fuel combustion, and road vehicles (e.g. cars) are responsible for nearly 75% of transport emissions (IEA, 2020). From a technology perspective, lowcarbon mobility pathways are becoming abundant in the literature (see e.g. Gota et al., 2019; Haugen et al., 2021; Rogelj et al., 2018). When it comes behavioural policy measures, research in (environmental) psychology and behavioural economics stresses the role of social norms, particularly as a potential policy intervention that can address low-carbon mode choice and travel behaviour (Avineri, 2012; Metcalfe & Dolan, 2012). For example, it is argued that social norms can have an influence on an individual’s decisions related to mode choice (Mattauch et al., 2016). By promoting alternative transport choices to private car use (e.g. public transportation, active mobility), social norms can also help to break habits and thus preferences for the status quo (Hoffmann et al., 2017; Lattarulo et al., 2019; Riggs, 2017). In fact, it has been shown that norms can affect the use of public transport (Tertoolen et al., 1998) and resistance to changing car use habits (Nordfjærn & Rundmo, 2015; Liu et al., 2017). They can affect modal choices in long distance leisure travel, including the possibility that the trip is not made (Raux et al., 2015), and they have been found to be a cost-effective way to manage demand for a particular transport mode (Offiaeli & Yaman, 2020). This body of knowledge underscores that multiple cognitive and motivational factors affect travel mode choice, and Pre-print version 4 that social norms can influence and change transport behaviour, and highlights the lack of quantitative experimental evidence from behavioural studies (Garcia-Sierra et al., 2015; Metcalfe & Dolan, 2012). There is a vast amount of literature focusing on behavioural issues and the factors that determine the adoption of low-carbon mobility (e.g. beliefs, attitudes, values, socio-demographics, pricing, safety, geographical location, travel patterns, travel time, infrastructure) (see e.g. Abrahamse et al., 2009; Creutzig et al., 2019; de Coninck et al., 2018; Donald et al., 2014; Javaid et al., 2020; Shaheen & Cohen, 2012). In the context of this paper, low-carbon transport options are simply defined as those modes that are more fuel efficient and have zero or low emission levels (Zhao et al., 2020). Following the ‘Avoid, Shift, Improve’ policy framework for sustainable transport (Bakker et al., 2014), low-carbon transport options that are often identified and assessed in the literature include walking, cycling, public transport and various forms of electro-mobility (e.g. bikes, cars) (Cansino & Yñiguez, 2018; de Coninck et al., 2018; Gota et al., 2019; Gravett & Mundaca, 2021; Nakamura & Hayashi, 2013; Rogelj et al., 2018; Zhao et al., 2020). Consistent with the above, and despite increasing interest in car sharing services (CSS) and its potential role in low-carbon mobility (Bocken et al., 2020; Ferrero et al., 2018; Hartl et al., 2018; Prettenthaler & Steininger, 1999), there appears to be no experimental analysis of the potential impacts of social norms. At the risk of oversimplification, CSS are often understood as a new transport mode “whereby consumers share access to cars rather than owning a car themselves” (Hartl et al., 2018, p. 88). The literature identifies various forms of CSS, including a ‘two-way station-based’ (Namazu & Dowlatabadi, 2018) model, where available cars are parked in pick-up stations and trips must start and finish at the same place. The ‘one-way station-based’ model (Ferrero et al., 2018) is similar to two-way use, but the journey does not need to end at the same station. Finally, in the ‘free floating’ option (Firnkorn & Müller, 2011), cars can be parked anywhere within an area served by an operator, and the journey can start and end at any point within this area. 1 1 See Curtale et al. (2021) and Ferrero et al. (2018) for a detailed description of car sharing user preferences. Pre-print version 5 The introduction and development of the CSS market—and related business models—aims to transfer mobility from a product (car ownership) to a service provider. At its core, CSS promote the simple idea of increasing car use by reducing the time they are unused. In turn, the literature underlines the positive (theoretical) impacts of CSS in terms of reduced fuel consumption, air emissions, negative health impacts and urban road congestion (Ferrero et al., 2018; Hartl et al., 2018; Terama et al., 2018). Studies also reveal that CSS can substitute for public transportation, particularly among non-car owners (Ferrero et al., 2018; Martin & Shaheen, 2011) and have positive impacts on active mobility (Kent, 2014; Khan & Machemehl, 2017). Within this context, some studies suggest that social norms may influence CSS (Prettenthaler & Steininger, 1999), notably personal norms understood as a moral obligation or self-expectation to act (Ramos et al., 2020). However, there is a dearth of experimental studies that simultaneously address low-carbon mobility and the role of CSS and social norms. One example is Kormos et al. (2015), who analysed the influence of fictional descriptive social norms on the willingness to reduce private vehicle use (N=78). Similarly Bolsen (2009) exposed participants (N=196) to normative messaging about energy conservation, and asked them to state how likely they would be to take different actions, including alternatives to private car use such as car sharing. However, the study did not measure the specific impacts on CSS. In a natural experiment, Bachmann et al. (2018) studied the determinants of ride sharing (i.e. a peer-to- peer form of CSS) and found that descriptive and personal norms can increase the intention to share rides (N=342). Likewise, while Mou et al. (2020) found that social norms affected the intention to purchase a car among CSS users, there was no experimental manipulation. Nevertheless, to the best of our knowledge, there appears to be a lack of experimental studies that focus explicitly on the impacts of descriptive and injunctive social norms on CSS, and the policy implications for low-carbon mobility options (e.g. potential crowd out effects). The objective of this paper is, therefore, to understand the impacts of social norms on the willingness to adopt low-carbon mobility options. Using CSS as an entry point for the analysis, we report the results Pre-print version 6 of two, complementary randomised controlled experiments in Sweden that: 1) analyse the role of social norms in promoting CSS adoption (i.e. nudging in) via descriptive and injunctive norms; and 2) investigate whether the promotion of CSS via injunctive norms crowds out public transport and active mobility options (i.e. nudging out). Given the lack of quantitative evidence in this area, our research is both confirmatory and exploratory in nature. Based on the reviewed literature (Farrow et al., 2017; Ferrero et al., 2018), we aim to shed light on the extent that social norms can influence mode choice intentions. Our overall hypothesis is that social norms influence the willingness of CSS adoption (experiment 1) and low-carbon transport options (experiment 2). Consistent with the experimental research focusing on the impacts of social norms and the promotion of pro-environmental (transport) behaviour (Raux et al., 2015; Sparkman & Walton, 2017), our approach applies non-parametric tests for between-group comparisons, and logistic regressions for the identification and analysis of key determinants. Given the experimental and thus exploratory nature of our research, outcomes should be seen as a departure point for further analyses. The main research questions guiding this study are:  RQ experiment 1: To what extent can descriptive and injunctive social norms increase the willingness to adopt CSS?  RQ experiment 2: What are the implications of using injunctive norms that promote lowcarbon mobility via a low-carbon transport hierarchy (including and excluding CSS)? We take Sweden as a case study, for various reasons. For example, while statistics are fragmented, recent figures indicate that more than 100,000 users access nearly 2,000 cars made available by existing CSS 2 providers (Miljöbarometern, 2019a, 2019b). While CSS use is increasing, and despite the fact that it can be traced back to the mid-1970s (Bocken et al., 2020), the literature also highlights various empirical knowledge gaps. Local policymakers are unsure about how they should integrate CSS into the low-carbon transport hierarchy (Bocken et al., 2020). In particular, a recent public inquiry into CSS (SOU, 2020) raised various concerns, particularly regarding crowding out or substitution effects on 2 These figures mostly include business-to-consumer (B2C) car sharing as well as private collectives’ car sharing schemes, or car clubs (like ‘Lunds Bilpool’). Peer-to-peer car sharing and ride sharing are not considered. Pre-print version 7 public transport, but also potential complementarities with active transport modes such as walking and cycling. However, little is known about these aspects empirically. At the same time, national authorities see municipalities as the key actor to support the adoption of CSS (Plepys et al., 2015) and, in theory, the use of social norms to promote low-carbon transport is acknowledged (Nässén et al., 2015). Moreover, there are no experimental applications at national level, and there is a lack of information about potential CSS adopters (Sprei & Ginnebaugh, 2018). In addition, as the nation has become wealthier and purchasing power has increased, both the frequency and distance of travel have increased along with carbon emissions (Norden, 2016). The transport sector accounts for half of energy-related CO2 emissions, suggesting that policy interventions must go beyond traditional instruments such as taxation (IEA, 2019). Finally, the COVID-19 pandemic has affected public transport uptake (Trafikanalys, 2021), and there is a risk that private car use will bounce back, or remain high, as the country recovers from the pandemic. The objectives and specific hypotheses of our experiments are as follows. The first experiment assessed and compared the impacts of descriptive and injunctive norms on CSS. Based on the reviewed literature and existing policy knowledge gaps in Sweden (Frederiks et al., 2015; Smith et al., 2012; SOU, 2020), we defined two hypotheses:  H1a: Exposure to a descriptive social norm increases the likelihood of the adoption of car sharing compared to a control group.  H1b: Exposure to an injunctive social norm marginally increases the likelihood of the adoption of car sharing compared to those exposed to the descriptive social norm and a control group. The aim of the second experiment was to analyse the influence of injunctive norms on decisions regarding low-carbon transport options, with the inclusion or exclusion of CSS in the decision-making framework. According to the theory of planned behaviour (Ajzen, 1991) and the norm-activation theory (Schwartz, 1977), injunctive norms are relevant factors that determine behaviour. Various studies have used these theoretical frameworks and found positive causal inferences between Pre-print version 8 injunctive norms and environmental and energy issues (Biel & Thøgersen, 2007; Bonan et al., 2020; Farrow et al., 2017). In our case, injunctive norms were considered influential factors to promote modal shifts (Javaid et al., 2020), and experimental studies have shown positive relationships between injunctive norms and the choice of low-carbon transport options such as public transport and electric vehicles (Cherchi, 2017; Raux et al., 2015; Saleem et al., 2021). Taking into account these theoretical and empirical constructs, we investigated the extent to which the promotion of CSS (via an injunctive social norm) crowds out (or not) public transport and active mobility options. Based on the reviewed literature and existing policy knowledge gaps in Sweden (Bocken et al., 2020; SOU, 2020), we defined two hypotheses:  H2a: Support for low-carbon transport options (i.e., walking, cycling and public transportation) is triggered by an injunctive norm that promotes a low-carbon transport hierarchy, but excludes car sharing.  H2b: When car sharing is included in the hierarchy, fewer respondents favour the adoption of lowcarbon transport. However, complementarities (0=none; 3=high) between car sharing and lowcarbon transport options indicate perceived compatibility or substitution effects. This paper is organised as follows. Section 2 provides details of our first experiment, including its design, methodology and results. Section 3 describes our second experiment, based on the same structure. In Section 4, and drawing upon our findings and current knowledge, we provide an overall discussion of various policy and methodological issues. Finally, we outline some concluding remarks in Section 5. 2. Experiment 1: Descriptive and injunctive norms addressing CSS 2.1. Design and procedure As stated above, the first experiment aimed to assess and compared the impacts of descriptive and injunctive norms on CSS. Participants were given a brief outline of our study, answered some questions Pre-print version 9 about climate change and air pollution, and were presented with a description of CSS and its modalities (see Annex 1). Then they were randomly allocated to one of three groups (control, condition 1, condition 2). The control group was not exposed to any manipulation. In condition 1, participants were exposed to the following descriptive social norm. It aimed to describe an actual and collective behaviour. Data and statements supporting this dynamic descriptive normative message were extracted from Miljöbarometern 2030 3 (Miljöbarometern, 2019a): “In Sweden, conservative estimates show that there are at least 100,000 drivers who currently use nearly 2,000 cars provided by existing car sharing services. According to ‘Miljöbarometern 2030’, it is encouraging to see that the number of users of car sharing services and the number of bookings is accelerating”. In condition 2, participants were exposed to the following injunctive norm that addressed CSS and complementarities with low-carbon mobility as normative intervention artefacts. Data and statements supporting this normative message were extracted from official sources (see IEA, 2019; SCB, 2020; STEM, 2020; Trafikverket, 2013), including a recent public inquiry into CSS use (see SOU, 2020): “In Sweden, the transport sector accounts for almost half of energy-related CO2 emissions. To reduce air pollution and related health impacts, research shows that people need to use sustainable forms of transportation, including car sharing services. In Sweden, the transport sector needs to reduce its emissions by 70% in 2030 compared to 2010 levels. The adoption of car sharing services is an important part of reaching this goal.” After being exposed to the normative message, participants were asked to state how willing they were to adopt CSS on a four-point Likert-type scale (0=very unlikely, 3=very likely). Then they were asked to 3 This initiative collects official transport information across municipalities and national organizations. It supports the national policy effort known as ‘Fossil Freedom on the Road’ (‘Fossilfrihet på Väg’ in Swedish) and the climate target defined for the transport sector in the Climate Act (Klimatlagen). The initiative tracks a number of environmental indicators related to the transport sector (both national and municipal), including car sharing. For further information see https://2030.miljobarometern.se/. Pre-print version 16 In condition 2 (the injunctive norm), the initial model was also statistically significant (χ2 (27, 240)=129.57, p=.000) and explained between 41.7% (Cox & Snell R2) and 45.7% (Nagelkerke R2) of variance. In addition to the main predictors mentioned above, price and convenience were statistically significant factors. For example, a one unit increase in pricing (i.e. lower prices) changed the odds of CSS adoption by 2.38. The introduction of control variables (χ2 (42, 240)=170.19, p=.000) also increased the explanatory capability of the model (Cox & Snell R2=50.8%; Nagelkerke R2=55.7%). Here again, complementarities between CSS and low-carbon mobility, and the perceived environmental effectiveness of CSS were important predictors, with odds ratios of 8.02 and 23.28 for the ‘very likely’ condition, respectively (INSERT Table 6). Similarly, age was also a significant predictor: as age decreased by one year, people were 1.08 (=1/0.92) times more likely to adopt CSS. In this specific case, we once again found negative correlations between age and climate change awareness (r= ‒.18, p=.002), and age and household size (r= ‒.21, p=.001), which can help us to better understand these results. INSERT Table 6: Estimated coefficients and odds ratios for predictors that were significant in at least one outcome category. The model (bootstrapped based on 1,000 samples) predicted the likelihood of CSS adoption under the two conditions and with control variables. Reference category: ‘very unlikely’. *** p<0.01, ** p<0.05, * p<0.1. 3. Experiment 2: Injunctive norms and low-carbon mobility options 3.1. Design and procedure The aim of the second experiment was to assess the influence of injunctive norms on decisions regarding low-carbon transport options, with the inclusion or exclusion of CSS in the decision-making framework. As in Experiment 1, participants were briefly introduced to the purpose of the study. They were asked to answer questions about climate change and air pollution and presented with the following climate policy statement: Pre-print version 17 “Sweden's long-term climate target is to have zero net greenhouse gas emissions by 2045 at the latest. The target for the transport sector (excluding domestic aviation) is a reduction of 70% by 2030 compared to 2010 levels”. Then, participants were randomly allocated to treatments. The control group was not exposed to any intervention. In condition 1, participants were exposed to an injunctive norm that included both prescriptive and proscriptive elements (Farrow et al., 2017; Thøgersen, 2006), and the notion of the low-carbon transport hierarchy (LCTH). While the prescriptive component in the norm is expressed by the ‘appropriateness’ of the desirable behaviour (i.e. “to reduce emissions in the transport sector, citizens are encouraged to follow the Low-carbon Transport Hierarchy……. are the most sustainable options”), the proscriptive component is conveyed by the ‘inappropriateness’ of the undesirable behaviour (i.e. “car use and air travel are the least preferred options”). Several sources (Gota et al., 2019; Gravett & Mundaca, 2021; Miljöbarometern, 2019c; Nakamura & Hayashi, 2013; Rogelj et al., 2018; STEM, 2020; UK Energy Saving Trust, 2019) were used to construct this hierarchy, based on the environmental effectiveness of different transport options (i.e. their ability to reduce carbon emissions and air pollution) in the form of the following message: “To reduce emissions in the transport sector, citizens are encouraged to follow the Lowcarbon Transport Hierarchy. Walking, cycling and public transport are the most sustainable options, and single occupancy car use and air travel are the least preferred options” (see INSERT Figure 2, panel a). In condition 2, participants were exposed to an almost identical normative message and hierarchy, with the only difference being the inclusion of car sharing. Here, the message was as follows: “To reduce emissions in the transport sector, citizens are encouraged to follow the Lowcarbon Transport Hierarchy. Walking, cycling, efficient public transport and car sharing (where consumers share access to cars rather than owning a car themselves) are the most Pre-print version 18 sustainable options, and single occupancy car use and air travel are the least preferred options” (see INSERT Figure 2, panel b). INSERT Figure 2. The low-carbon transport hierarchy (LCTH) used in condition 1 ‘injunctive norm without car sharing’ (panel a), and condition 2 ‘injunctive norm with car sharing’ (panel b). Then participants were asked to state the willingness of adopting (or choosing) low-carbon transport (i.e. walking, cycling and public transport) rated on a four-point Likert-type scale (0=very unlikely, 3=very likely). The remainder of the method followed the procedure given for Experiment 1, including the attention check. The latter resulted in the exclusion of 29 (control group), 27 (condition 1), and 22 (condition 2) participants. Here again, the experiment was pre-registered on AsPredicted. 3.2. Participants and randomisation Participant selection and randomisation followed the method given in section 2.2. In this section, we outline differences to the procedure used in Experiment 1. In Experiment 2, 730 respondents were recruited and randomly allocated to: i) the control group (n=240); ii) the injunctive norm without car sharing in the LCTH (n=250); and iii) the injunctive norm with car sharing in the LCTH (n=240). In addition to the socioeconomic and demographic variables listed in INSERT Table 1, participants were asked about mobility patterns, perceived complementarities among low-carbon mobility options, their environmental effectiveness, the number of cars the household owned and their environmental awareness (INSERT Table 7). The latter was measured via two items (i.e. ‘tell us whether you agree with the following statement about: i) Climate change: Human influence on the climate system is clear, and recent anthropogenic emissions of greenhouse gases are the highest in history; and ii) Air pollution: Outdoor air pollution is a major threat to human health and the climate system’) (α=.75). As in Experiment 1, statistical analyses found no significant differences for the majority of variables. The only exception was car ownership (i.e. owned number of cars); however, this variable was not Pre-print version 19 found to be relevant in our analysis. Again, this indicated that the randomisation procedure was effective and cancelled any potentially confounding effects. 7 INSERT Table 7: Descriptive statistics in experiment 2. M: Mean; Mdn: Median; SD: Standard Deviation; DV: Dummy Variable; CV: Categorical Variable. 3.3. Data analysis See section 2.3 for details of the analysis. The vector of explanatory variables is drawn from the literature on the determinants of low-carbon transport options. They include mobility patterns (Schoenau & Müller, 2017), car ownership (Mattioli, 2014), environmental awareness (de las Heras- Rosas & Herrera, 2019), the perceived environmental effectiveness of transport options (Scheepers et al., 2014; Zachariadis, 2005), household location (Beaudoin et al., 2015), and socioeconomic and demographic factors such as income (Gota et al., 2019). In addition, given our interest in the role of CSS in the LCTH, we explored perceived complementarities between CSS and low-carbon transport options via three questions (‘How do you perceive complementarities between car sharing and: i) walking; ii) cycling; and iii) public transport?’) (α=.84). As in Experiment 1, all models were bootstrapped (based on 1,000 samples) and checked for goodness- of-fit. VIFs and odds ratios were also computed. INSERT Table 8: Variable definition in experiment 2. For socioeconomic and demographic variables, see Table 1. 3.4. Results 3.4.1. First-order effects Overall, participants stated a relatively high willingness (Mdn=2) to adopt low-carbon transport options (i.e. walking, cycling and public transport) across all groups (see Fig. 3 and Table 9). The Kruskal-Wallis test revealed an overall significant effect, K-W (2, 730)=9.99, p=.007, ɳ2=.014. The mean (M=1.92, 7 However, we must note that our sample is representative of the Swedish population for gender only (χ2 (1, 730)= .056, p=.81). Once again, tests showed statistical differences for income, household size, education level and age. This limitation is discussed in section.4. Pre-print version 20 n=240) was highest in condition 2 (with car sharing in the LCTH) compared to the other two groups (control group: M=1.76; n=240; condition 1: M=1.68, n=250). Post-hoc tests revealed that only condition 2 generated a significant effect, in particular compared to condition 1, U=25578, z=−3.04, p=.002, r=.14. We thus retain the null hypothesis under H2a. INSERT Figure 3: Results of experiment 2. Participants' stated willingness of adopting low-carbon mobility options, namely walking, cycling and public transport (0=very unlikely, 3=very likely) across the control and treatment groups. LCTH: Lowcarbon transport hierarchy. Error bars represent 95% confidence intervals. INSERT Table 9: Stated willingness of adopting low-carbon transport options for all groups. LCTH: Low-carbon transport hierarchy. Although the difference between the control group and condition 1 was marginal and insignificant, U=28691, z=−.90, p=.365, it was interesting to note that means were slightly higher. A detailed review of the data highlighted some subtle differences. For example, in the control group, the correlation between the likelihood of adopting low-carbon transport and the perceived environmental effectiveness of both active (r=.31, p=.000) and public (r=.25, p=.000) transport modes was higher compared to participants under condition 1 (r=.13, p=.029; r=.17, p=.006; respectively). We found similar, albeit marginally higher correlations between environmental awareness and the outcome variable for both active mobility (r=.23 in the control group vs r=.22 in condition 1) and public transport (r=.22 in the control group vs r=.19 in condition 1). We hypothesise that environmental awareness, which was marginally higher in the control group (see INSERT Table 7), could have driven this subtle (and non-significant) difference. That said, we also retained the null hypothesis in H2b. Contrary to concerns reported in the literature, our data showed that in condition 2, with the car sharing option in the LCTH, participants were more willing to adopt low-carbon transport options. In all groups, we observed a significant association between the likelihood of adopting low-carbon transport and perceived complementarities with CSS (χ2(9, 730)=262.3, p=.000), with a large effect size (Φ=.35). These aspects suggest that concerns about potential substitution effects between low-carbon mobility and CSS are not valid. On the contrary, Pre-print version 21 efforts to ensure effective synergies need to be underlined. In fact, we observed that the direction of perceived complementarities between low-carbon mobility and CSS was relatively high across all groups (Mdn=2) with no significant differences between them, K-W (2, 730)=.099, p=.95. 3.4.2. Main determinants of treatment conditions On the one hand, multinomial logistic regressions highlighted a set of statistically significant predictors across both conditions. They include environmental awareness and mobility patterns of active transport (α = .73). On the other hand, the results also highlighted (subtle) differences that explain specific outcomes across the two conditions. For example, while location and complementarities between CSS and low-carbon mobility played a significant role in condition 1, the effect of active transport patterns was much more pronounced in condition 2. No collinearity issues were identified (all VIF<5). See the Supplementary Material for full results. In condition 1, the initial model (with no control variables) was statistically significant (χ2(24, 250)=178.35, p=.000) and explained between 51.4% (Cox & Snell R2) and 56.6% (Nagelkerke R2) of the variance. The most significant determinants were complementarities between CSS and lowcarbon transport options (odds ratios between 2.01 and 8.47), mobility patterns of active transport (odds ratios between 2.43 and 6.05) and environmental awareness (odds ratios between 2.76 and 4.42). As most participants lived in urban areas (~70%), where accessing workplaces/ schools using public transport or active mobility options is plausible, location also played a significant role (odds ratios between 2.53 and 4.66). After the introduction of control variables (see INSERT Table 10), the model remained statistically significant (χ2 (39, 250)=186.49, p=.000) and its explanatory capability increased (Cox & Snell R2=53.0%; Nagelkerke R2=58.3%). The main determinants remained significant and their effect marginally increased (e.g. from 8.47 in the initial model, to 9.13 in the second model for ‘complementarities’ in the ‘very likely’ category). Interestingly, no significant effect was found for any of the socioeconomic or demographic variables. Pre-print version 22 In condition 2, the initial model contained a relatively small set of significant predictors. In particular, environmental awareness, active transport patterns, the perceived environmental effectiveness of active transport (α=.87), and complementarities between CSS and low-carbon mobility were significant. The initial model (χ2 (24, 240)=181.07, p=.000) and its explanatory capability (Cox & Snell R2=53.1%; Nagelkerke R2=58.9%) was slightly higher compared to condition 1. Notably, the effect of active transport patterns was much more significant compared to condition 1. The odds ratios tell us that as the frequency of using active transport increased, the change in the odds of being ‘likely’ or ’very likely’ to adopt sustainable mobility was 17.72 and 27.27, respectively. Unlike condition 1, location was not a significant predictor. Consistent with the results under experiment 1, in which location did not play a role, we hypothesise that location was insignificant because the majority of participants live in urban areas, where CSS is mostly already available and complementarities with public transport and active mobility were more present or salient. The model remained significant following the introduction of control variables (χ2 (39, 250)=198.42, p=.000) and its explanatory capability increased (Cox & Snell R2=56.4%; Nagelkerke R2=62.6%) (INSERT Table 10). A relatively limited set of significant predictors was identified, particularly for ‘unlikely’ and ‘likely’ outcome categories. However, the effect of active transport increased substantially, with odds ratios equal to 21.32 and 31.11 for ‘likely’ and ‘very likely’ adoption, respectively. With the exception of gender, none of the socioeconomic or demographic variables were significant. Finally, patterns for public transport were non-significant across both conditions and models (K-W (2, 730)=2.97, p=.22). This seems to be consistent with the COVID-19 pandemic and the fact that the majority of participants (e.g. 45% in condition 2) stated that they used public transportation ‘less frequent than before the coronavirus pandemic’ (Mdn=1). On the other hand, a majority (e.g. 53% in condition 2) used active travel ‘the same as before the coronavirus pandemic’ (Mdn=2), and the remainder (e.g. 20% in condition 2) stated that they used it ‘more than before the pandemic’, K-W (2, 730)=2.67, p=.26. Pre-print version 23 INSERT Table 10: Estimated coefficients and odds ratios for predictors that were significant in at least one outcome category. The model (bootstrapped based on 1,000 samples) predicted the likelihood of adopting low-carbon transport options (i.e. public and active transport) under the two conditions and with control variables. Reference category: ‘very unlikely’. LCTH: Low-carbon transport hierarchy. COMPL_LC_Mob_CSS: Complementarity between low-carbon mobility and car sharing services. *** p<0.01, ** p<0.05, * p<0.1. 4. Discussion Taken together, the results of both experiments confirmed that the use of social norms can have a positive impact on the willingness to adopt low-carbon transport options, including CSS. First-order effects indicated the presence of a limited, positive impact. In both experiments, we observed that the effect size was small (ɳ2=.01). In particular, we found that only injunctive norms had the potential to steer behaviour in the desired direction. The presence of main effects also suggested that concerns about potential substitution between low-carbon transport options and CSS are not valid. Despite these small effect sizes, our findings support the growing literature (see e.g. Farrow et al., 2017; Nyborg et al., 2016) on the effectiveness of social norms as potential policy interventions. They seem consistent with previous policy-oriented experiments that assess the effectiveness of social norms applied to energy use (Andor & Fels, 2018; Nisa et al., 2019; Schultz et al., 2007) and, importantly in our case, with transport (Cherchi, 2017; Liu et al., 2017; Raux et al., 2015; Riggs, 2017; Saleem et al., 2021; Zhang et al., 2015). Furthermore, results highlight the behavioural factors (or ‘anomalies’) that support the use of social norms in policy interventions from a theoretical perspective (e.g. they can act as a reference point in people’s dependent preferences (see Kahneman & Miller, 1986)), as earlier findings suggest that consumers use the behaviour of others, or the predominant group behaviour, to guide their decisions, particularly under uncertainty or information asymmetries (see Gelfand & Harrington, 2015). However, we must acknowledge that research has also shown insignificant effects of injunctive norms applied to energy use (Allcott, 2011) and active mobility Pre-print version 24 (Bourke et al., 2019). 8 This underlines that results are likely to be context-specific and policy generalisations must not be encouraged. Turning to our research questions, the following overarching policy answers emerge. The results of experiment 1 show that an injunctive norm has the potential to increase the willingness of CSS adoption. While a descriptive norm also has the same effect, the size is marginal and not statistically significant compared to the control group. The descriptive norm, even if it included some dynamic elements (i.e. people’s behaviour is changing over time) (Sparkman & Walton, 2017), appeared incapable of increasing the willingness to adopt CSS. Logistic regressions also reveal a more complex picture: i) the complementarities between CSS and low-carbon mobility options (i.e. public and active transport), and ii) the perceived environmental effectiveness of CSS are particularly important. These two determinants (in addition to specific product-service features, such as pricing) appear to be preconditions for an injunctive norm to be effective, and they have important policy implications, which are discussed below. Age is another key factor, and it seems that targeting younger people (prior to the purchase of a car) may be an appropriate measure to foster CSS use if the conditions mentioned above are met. For the specific role of car ownership, the results reported in the literature are mixed, which is also consistent with our findings. For example, some studies indicate that CSS contributes to reduced car ownership, with reduced mileage and emissions (Katzev, 2003; Lane, 2005; Meijkamp, 1998; Rabbitt & Ghosh, 2016). This appears to be consistent with the statement, ‘I have a car, but want to give up private car ownership’ (23.6% of respondents). However, the literature also reports that car owners are unwilling to give up their car, and that CSS mostly substitutes for a second or third car (Ferrero et al., 2018; Nijland & van Meerkerk, 2017). CSS can also introduce users to new vehicle technology (Zoepf & Keith, 2016) and potentially increase demand for car ownership in the future. These claims seem to be consistent with the 12.5% of respondents in our study who stated, ‘I have a car, but I would like to test different models before buying a new car’. 8 No impacts of injunctive social norms are also found for sustainable food choices (Salmivaara et al., 2021). Pre-print version 25 The results of experiment 2 show that an injunctive social norm may promote a LCTH. Importantly, we found no evidence of crowd out effects between low-carbon mobility options and CSS in particular. However, once again, perceived complementarities between low-carbon mobility and CSS played an important mediating role, reflected in a large effect size (Φ=.35). Furthermore, perceived environmental effectiveness and mobility patterns of active transport options were found to be relevant. Both of these aspects have significant policy implications (see below). As a whole, our findings regarding complementarities between low-carbon transport options and CSS in both experiments are very consistent not only with the literature (see e.g. Ferrero et al., 2018; Katzev, 2003; Plepys et al., 2015), but also with calls for further research on the integration of CSS with low-carbon mobility options (Becker et al., 2017; Cervero et al., 2007; Firnkorn & Müller, 2015; Kent, 2014). Our results also suggest that social norms are not a panacea. Rather, they strongly indicate that supportive policy instruments remain relevant pre-conditions for social norms to be effective. For example, perceived complementarities between CSS and public and active transport modes suggest that public investment in integrated low-carbon transport systems is a relevant policy instrument. Likewise, the perceived environmental effectiveness of CSS suggests that pricing mechanisms (e.g. congestion charging) and regulations (e.g. parking restrictions) are other relevant (local) policies that promote environmentally effective alternatives to private car use. The importance of certain product attributes (e.g. the type of fuel) highlight the relevance of regulatory approaches that address energy efficiency, zero-emission mandates and low-carbon fuel standards. Furthermore, our results underscore the importance of information programmes that focus on the synergies, pros and cons, or potential trade-offs between CSS and low-carbon transport options. Examples include the integration of CSS with active transport. Improving, monitoring and verifying the environmental credentials of CSS seems to be crucially important (e.g. via performance standards and labelling schemes) to support information programmes. 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Car sharing services take various forms, for example: - Two-way station-based, where available cars are parked in pick-up stations and trips must start and finish in the same place. - One-way station-based, which is similar to a two-way use but the journey does not necessarily need to end at the same station. - Free floating, where cars can be parked anywhere within an area served by an operator and the journey can start and end at any point within this area. Car sharing services can be offered by companies (where there is a for-profit operator which owns and provides access to a fleet of vehicles), individuals (where access to cars is facilitated by an online platform, cars are owned by peers and are lent from user to user, often for profit) or cooperatives (where members get financing together to sustain a joint ownership of vehicle(s), often non-for-profit). Car sharing is a membership-based service available to all qualified drivers. Among others, factors affecting its adoption include price, type of service, availability and features of shared cars and convenience (e.g. distance to car, parking availability, booking system).” Pre-print version 40 Tables Table 11: Variable definition in experiment 1 Variable Coding Definition/range Willingness to adopt CSS 0 Very unlikely 1 Unlikely 2 Likely 3 Very likely Product attributes a) Price, b) fleet, c) type of engine and d) convenience) 0 Not important 1 Slightly important 2 Important 3 Very important Complementarity between CSS and walking, cycling and public transport 0 None 1 Low 2 Moderate 3 High Environmental effectiveness of CSS 0 Not at all effective 1 Slightly effective 2 Effective 3 Very Effective Location 0 Rural area (below 50 inhabitants) 1 Small rural town (50-199 inhabitants) 2 Small urban area (200+ inhabitants) 3 Urban area (10,000-199,000 inhabitants) 4 Large urban area (+200,000 inhabitants) Environmental knowledge of CSS 0 None 1 Low 2 Moderate 3 High Car ownership 0 Car-free household 1 Household with a car Income level 0 Less than 20.000 SEK 1 20.000 – 29.999 SEK 2 30.000 – 39.999 SEK 3 40.000 – 49.999 SEK 4 50.000 – 59.999 SEK 5 60.000 – 69.999 SEK 6 70.000 – 79.999 SEK 7 80.000 – 89.999 SEK 8 90.000 – 99.999 SEK 9 100.000 SEK or up Household size 1 Person 2 People 3 People 4 People 5 People 6 People 7+ People Educational level 0 Less than high school Pre-print version 41 1 Graduate high school 2 Graduate trade/technical school 3 Bachelor degree 4 Master-level 5 Licentiate or Ph.D. degree Gender 0 Female 1 Male Table 12: Descriptive statistics in experiment 1. M: Mean; Mdn: Median; SD: Standard Deviation; DV: Dummy Variable; CV: Categorical Variable. Control group (n=240) Condition 1: Descriptive norm (n=240) Condition 2: Injunctive norm (n=240) Difference between groups M/Mdn SD M/Mdn SD M/Mdn SD Age (years) 48.3/48 18.37 45.6/44 17.00 45.4/44 17.57 F (2, 717)=1.91, p=.148 Gender (DV) 0.49/0 0.50 0.51/1 0.50 0.48/0 0.50 χ2(2, 720)=.54, p=.762 Income (CV) 1.78/1 1.81 1.87/2 1.67 1.85/2 1.85 χ2(2, 720)=1.70, p=.426 Household size (CV) 2.45/2 1.22 2.50/2 1.21 2.42/2 1.24 χ2(2, 720)=.86, p=.649 Education level (CV) 2.31/2 1.49 2.63/3 1.44 2.40/3 1.45 χ2(2, 720)=5.72, p=.057 Location (CV) 2.62/3 1.15 2.94/3 1.01 2.76/3 1.15 χ2(2, 720)=9.42, p=.009 Car ownership (DV) 0.84/1 0.36 0.83/1 0.37 0.83/1 0.38 χ2(2, 720)=.24, p=.887 Table 13: Stated willingness of adopting car sharing services across groups Control group Condition 1: Descriptive norm Condition 2: Injunctive norm Frequency % Frequency % Frequency % Very unlikely 97 40.4 85 35.4 80 33.3 Unlikely 97 40.4 94 39.2 88 36.7 Likely 39 16.3 50 20.8 62 25.8 Very likely 7 2.9 11 4.6 10 4.2 Total 240 100 240 100 240 100 Table 14: Reasons for ‘very unlikely’ and ‘unlikely’ adoption of CSS as mobility option Control group Descriptive norm Injunctive norm Frequency % Frequency % Frequency % R1: I already have a car 106 54.6 101 56.4 79 47 R2: It can be too expensive 3 1.5 2 1.1 4 2.4 R3: I’m concerned about air pollution 1 0.5 0 0 0 0 R4: It can create more traffic congestion 0 0 0 0 0 0 R5: Hygiene, cleanliness and health related issues 3 1.5 8 4.5 8 4.8 R6: Because long distance to reach the car 26 13.4 12 6.7 22 13.1 R7: Inconvenience related to parking availability and booking system 18 9.3 18 10.1 16 9.5 R8: It is no longer socially acceptable to drive or have more cars 1 0.5 0 0 1 0.6 R9: I prefer to use public transportation, walking and cycling as much as possible 19 9.8 12 6.7 17 10.1 R10: Other 17 8.8 26 14.5 21 12.5 Total 194 100 179 100 168 100