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When perceived fairness and acceptance go hand in hand–Drivers of regulatory and economic policies for low-carbon mobility


Restrictive measures are indispensable to achieving sustainable and low-carbon mobility. At the same time, these are often not implemented due to concerns that public support will not materialize; therefore, they are relegated to the background in the debate on policy measures that can be applied to change mobility behavior. In this national study (N = 1,083), we used structural equation modeling (SEM) to examine the drivers of and differences between regulatory and economic transport policies. We find that policy-specific beliefs and, in particular, perceived fairness are key drivers of public acceptance. Our results indicate that policies must be perceived as fair, effective, and minimally intrusive for them to be accepted by the public and thus implementable. No major differences were found between the two types of policies examined, namely, regulatory and economic policies. Overall, public acceptance of the proposed measures is low. We discuss these results in terms of the study content and methodology and conclude by describing their implications for transport policy design.

1. Introduction

The need for immediate emission reductions to combat anthropogenic climate change has never been more urgent [1]. Currently, a substantial emission gap exists, because several countries–including the majority of G20 member states–have failed to act in order to meet their emission targets [2]. A particularly problematic area contributing to this prevailing emission gap is that of transport, which is responsible for 25% of the global energy-related greenhouse gas (GHG) emissions and, more specifically, road transport due to the ever-increasing demand and its emissions [2, 3]. The latest IPCC report highlighted the important role played by demand-side mitigation and behavior change, dedicating for the first time a specific chapter to “demand, services and social aspects of mitigation” [1]. With regard to transportation, the A-S-I concept (avoiding, shifting, and improving transport) summarizes strategies that can be applied to reduce carbon emissions [4], and recent studies show that several behaviors that are among those with the highest potential to reduce per capita emissions stem from the passenger transport domain [5, 6]. Because prevalent carbon lock-in, which is described by Unruh [7] as “interlocking technological, institutional and social forces that can create policy inertia towards the mitigation of global climate change”, makes self-driven behavioral change in passenger transport highly unlikely, ambitious policy making and demand-side measures are needed to guide the way.

These necessary emission reduction measures are often restrictive in nature, making it difficult to gain public support for them. Moreover, public resistance to policy instruments often makes policy makers and authorities reluctant to implement these policies [8, 9]. Therefore, promoting more public acceptance and supporting restrictive transport policies, such as regulations or taxes, may crucially impact whether the much-needed demand-side behavioral change will be attained and, in turn, enable climate change to be tackled effectively.

Restrictive measures, one of a variety of policy instruments available, are often also summarized under the umbrella term of “push measures”. These measures are critical, as they are applied “to change people’s behavior by explicitly making their current behavior more difficult” [9]. The two dominant categories of restrictive measures are regulatory and economic measures, also referred to as command-and-control and market-based measures. The former category includes measures that are often applied to encourage people to switch fuels and may include measures such as low-carbon fuel standards, zero emission vehicle (ZEV) mandates, or internal combustion engine car bans [10]. In 2022, the European Union announced its plans to introduce such a registration ban on fossil-fueled vehicles by 2035 [11]. The latter category of economic measures, on the contrary, includes financial incentives that encourage behavioral change by pricing, for example, carbon or road use [10]. In terms of public acceptance, economic measures usually meet more opposition than regulatory measures [10, 12], especially regarding carbon taxation [13]. For example, Wang et al. [14] compared both types of policies by using congestion charging and driving restrictions as examples in Latin American cities and found that driving restrictions were consistently more popular than congestion charging, despite the differences among the cities. While restrictive push measures are necessary to reduce emissions, they clearly need to be embedded in a comprehensive set of transport policies, which are often referred to as policy packages. These also need to include incentives or pull measures to further increase public support [1517].

In a recent meta-analysis, Bergquist et al. [18] examined predictors of climate policy acceptance and found policy fairness to be the most decisive factor (r = .65). However, the authors pointed out that fairness comprises several dimensions, and the effect of distributional fairness (e.g., how taxes are distributed among societal groups) is the strongest. Slightly smaller effects were found for perceived policy effectiveness (r = .54), again by distinguishing between dimensions, whereby policies addressing climate change mitigation (e.g., through taxes and laws) were more strongly correlated to policy acceptance than policies that attempted to change the individuals’ behavior. Both fairness and effectiveness had stronger effects on the acceptance of regulatory policies than economic ones.

In addition, factors that influence climate change evaluations, such as an individuals’ worry about climate change or their belief in climate change or negative consequences thereof, had medium to strong correlations with policy acceptance (r = .48 to r = .23). Greater trust in institutions was also positively correlated with policy acceptance, whereby this effect was stronger regarding trust in institutions responsible for implementing policies (r = .42) as compared to trust in political institutions (r = .21). In addition to trust, other psychological factors were identified as important, such as self-enhancement or self-transcendent values, where the latter (such as biospheric or altruistic values) positively correlated (r = .26) with the acceptance of policies. While the results of Bergquist et al. [18] rest on a broad range of climate policies (e.g., measures for energy or transport), previous studies conducted specifically on transport policies paint a similar picture, finding that policy-specific beliefs, such as fairness, effectiveness, or intrusiveness, can be important drivers behind public acceptance [19]. In addition, transport-specific studies also identified trust–typically specifically related to the government [20]–as well as biospheric values [21, 22] as relevant drivers.

Based on the literature cited above, this study was carried out to examine drivers of and differences between regulatory and economic policies related to low-carbon passenger transport. We asked the following research questions: Which differences exist between economic and regulatory measures regarding public support? How are they assessed regarding their effectiveness, fairness and intrusiveness? Which additional factors influence their policy acceptance? For both types of policy measures (regulatory and economic), we hypothesize that public acceptance increases when transport measures are perceived as being fair and effective. Correspondingly, we assumed that public acceptance decreases if measures are evaluated as being intrusive. The results of previous studies show varying levels of support of regulatory and economic measures. Therefore, we hypothesize that the public acceptance of regulatory measures (e.g., driving bans on fossil fuel cars) will be higher than acceptance of economic measures (e.g., fuel price increases), which is predominantly influenced by fairness, formulating the following secondary hypotheses:

  • H1: Perceived effectiveness increases policy acceptance.
  • H2: Perceived fairness increases policy acceptance.
  • H3: Perceived intrusiveness decreases policy acceptance.
  • H4: Economic measures are perceived as less fair and therefore receive lower public acceptance than regulatory measures.

We also assumed that both biospheric values and the belief in climate change increase policy support directly, as well as indirectly via the person’s willingness to make sacrifices for the sake of the climate. Furthermore, we assumed that higher levels of trust in the government increase policy support. Finally, we assumed that a higher level of perceived intrusiveness negatively affects fairness and effectiveness. The results of this study contribute to the highly relevant debate on how to increase public acceptance of restrictive measures by increasing our understanding of the driving forces and distinguishing between different types of policies.

2. Materials and methods

2.1. Data collection

A pre-test was conducted in October 2021 using a convenience student sample (n = 118) to improve the online survey (implemented in LimeSurvey, version 3, [23]) and to clarify any wording. Data for the present study were subsequently collected between November 4–18, 2021. Before the survey launch, the study was pre-registered on the Open Science Framework (OSF). Recruitment of participants was administered by the market research institute Talk Online to attain a quota-representative sample of the Austrian population in terms of their age (five age groups between 16 and 69 years), sex, education (with/without high school diploma) and size of the residential municipality in which they lived (below/above 10,000 residents) (see S1 Text for further information).

2.2. Questionnaire and variables

At the beginning of the survey, participants gave their written informed consent to voluntarily participate and to the use of their data. The questionnaire consisted of several parts relevant to this study. Additional variables were also included that are not within the scope of this study. Detailed information about these variables can be found in the supplementary document on the OSF page. First, we collected information for relevant sociodemographic variables (e.g., age, sex, and education). We then introduced the main part of the survey, namely the assessment of eight different transport policies, including four regulatory policies (i.e., registration ban for fossil fuel cars, an environmental zone for low-emission cars, an inner-city car ban, and the reduction of parking space) and four economic policies (i.e., fuel and parking price increases, a city toll, and road pricing). See Table A in S2 Text for full descriptions of the eight policies. Participants were asked to rate their support for each policy, the perceived fairness of the policy, the perceived effectiveness of the policy in terms of reducing GHG emissions, and the perceived intrusiveness of the policy in their daily life on a seven-point scale [19]. After the main part of the survey, two questions were asked to assess the participants’ biospheric values; these questions were formulated based on the question formats used by Schwartz et al. [24] and enabled us to determine whether the participants valued environmental protection and the well-being of plants and animals. Climate change beliefs were measured with four items defined by Van Boven et al. [25], which addressed the consequences and causes of climate change and enabled us to assess whether the participants think that climate change is happening. Four items from the International Social Survey Program were adapted to fit the present study context and to measure the participants’ willingness to sacrifice. This allowed us to determine whether they would be willing to pay higher prices or taxes and would be willing to accept cuts in their standard of living to reduce greenhouse gas emissions [26]. The final part of the survey included questions that enabled us to collect additional sociodemographic information (e.g., income and political orientation) and to assess the level of the participants’ trust in the Austrian government on an eleven-point scale, ranging from 0 (no trust at all) to 10 (a lot of trust) [20].

2.3. Statistical analyses

To test the hypotheses described above, we ran several structural equation models (SEMs), focusing on the relevant path coefficients. We averaged the values obtained for acceptance, fairness, effectiveness, and intrusiveness for each of the four regulatory and economic measures in order to analyze the models for each policy type. First, we computed the hypothesized SEMs, which yielded unsatisfactory results with respect to model fits. Therefore, we modified the original version of the SEM to improve model performance by excluding beliefs about climate change and trust levels; instead, we changed the paths related to the more general variables from acceptance to fairness. We also added free correlations and report the results for both the original and the revised model for transparency. See Fig 1 for an overview of the original and revised models. To test whether differences in the acceptance and fairness assessments existed between the economic and regulatory measures, we also conducted t-tests for paired samples. All steps taken, ranging from data cleaning to data analyses, were performed using R software [27] and especially the open source R software package ‘lavaan’ [28].

Fig 1.

Original (A) and revised (B) models for regulatory and economic measures; detailed information about the variables used in both models is presented in Table 1; bold arrows represent main hypotheses H1, H2 and H3.

3. Results

3.1. Sample description and variable overview

The final sample consisted of n = 1,083 responses. This sample size was deemed as sufficient to spot effects (i.e., standardized estimates of .10 and larger and sufficient statistical power, see preregistration). With regard to the sociodemographic values used to define the quotas, 49.9% were female, and three identified as non-binary or did not wish to indicate their gender. On average, respondents were 44 years old (SD = 14.7, min = 16, max = 69). In total, 344 participants (31.9%) indicated that they had at least a high school diploma (Austrian Matura), four participants did not wish to indicate their education level. Additionally, 57.1% of those who answered this question live in areas with less than 10,000 residents. The sample is representative for gender, education, and age (except for the oldest age group). Regarding the size of the residential area, rural areas (< 10,000) are slightly overrepresented (see S1 Text for further information).

Table 1 gives an overview of the descriptive statistics and Cronbach’s alpha values for mean scales of the relevant variables for the SEM models. In a direct comparison, the regulatory measures (i.e., registration ban for fossil fuel cars, environmental zone for low-emission cars, inner-city car ban, reduction of parking space) performed better in terms of acceptance, fairness, and effectiveness than the economic measures (i.e. fuel and parking price increases, city toll, road pricing). However, the mean value of intrusiveness is slightly higher for regulatory measures. Regarding hypothesis 4, the paired t-tests showed significant but small effects between the two policy types regarding acceptance (Cohen’s d = 0.16, p < .001) and fairness (Cohen’s d = 0.12, p < .001). Participants display rather low values both for a belief in climate change as well as a willingness to sacrifice something, whereas their biospheric values are quite high. Trust in government seems to be particularly low; at the same time, these values show a high standard deviation.

Table 1. Relevant variables and descriptive statistics for the SEM.

3.2. Original models

In total, we performed four SEMs, first for the original models and then for the revised models with regard to the regulatory and economic measures, respectively. Table 2 gives an overview of the calculated models. We also performed a random sample split to determine whether the main results were stable. Since this was the case, we decided to report the full sample in the results section.

Regarding the original models, we hypothesized that the perceived fairness and effectiveness of the measures would increase their acceptance, while a higher perceived intrusiveness would have a negative effect on their acceptance. All of these relationships were confirmed for both the regulatory and economic models (p < .001), with perceived fairness being the most important predictor. In addition, we found significant but negligible effects of trust in the government and a willingness to sacrifice something in the regulatory model (p < .01), while trust was not a significant predictor in the economic model. We identified strong effects of intrusiveness on both fairness and effectiveness (p < .001) for the regulatory and economic models. In the regulatory model, we did not observe direct effects of biospheric values or climate change beliefs on public acceptance, but a significant (p < .05) but negligible effect of biospheric values was detected in the economic model. Both biospheric values and climate change beliefs seem to influence the willingness to sacrifice something (p < .001), with the latter showing a strong positive effect. In addition, we found significant but negligible effects of trust in the government and a willingness to sacrifice something on acceptance in the regulatory model (p < .01), while only the latter was a significant predictor in the economic model (p < .001). In terms of the explained variance for the dependent variables, the R2 for acceptance is exceptionally high due to the close relationships with the policy-specific variables, and especially perceived fairness. Most of the variance can be explained by the three predictors of fairness, effectiveness, and intrusiveness. In terms of model fit statistics, the original models performed poorly for both the absolute and the incremental indicators (CFI & TLI < .9; RMSEA & SRMR > .1).

3.3. Revised models

Given the poor model fits, we decided to revise the original models. We found extraordinarily high correlations between fairness and acceptance as well as between fairness and the other psychological predictors (see Table A in S3 Text). For this reason, we discussed several ways to reasonably restructure the model and decided to redirect the paths from a willingness to sacrifice something and biospheric values toward fairness (see Fig 1B). We proceeded as follows: First, we excluded climate change beliefs due to its very low standardized estimate (i.e., close to zero) and applied the revised models with direct path coefficients only. Upon checking these models, we found somewhat improved model fit indices, but still very low standardized effects for trust (p ≥ .104) for both dependent variables. Second, after dropping trust from the model and adding medium-sized residual correlations (r >.30, see preregistration), the fit indices improved further (see Table 2, lower part).

As in the original models, applying these revised models revealed the significant effects that policy-specific beliefs, namely fairness, effectiveness, and intrusiveness, had on acceptance, whereby fairness has by far the largest effect. In both models, effectiveness and intrusiveness were significant (p < .001) predictors for fairness, as was the willingness to sacrifice something (p < .001), although the effect was small. Regarding biospheric values, we only found a small effect (p < .001) for predicting fairness in the regulatory model. Both investigated free correlations were significant (p < .001) and of medium size, where the larger was a negative correlation between the willingness to sacrifice something and intrusiveness. With regard to the indirect effects, we found a large effect, ranging from intrusiveness to fairness and on to acceptance (p < .001), but only a small one ranging from intrusiveness to effectiveness and on to acceptance (p < .001). The revised models show satisfying incremental fits with CFI and TLI > .94 and weak absolute fits with RMSEA < .15 and SRMR < .12, leading us to conclude a mediocre overall performance.

4. Discussion

In this study, we examined the drivers of and the differences between regulatory and economic policies for low-carbon passenger transport. With respect to our main hypotheses, all three policy-specific beliefs (i.e., perceived fairness, effectiveness, and intrusiveness) emerged as significant predictors, with perceived fairness being by far the most important predictor of public acceptance both in the original and the revised models. This finding is consistent with that of Bergquist et al. [18], whose meta-study found that perceived fairness was the most important predictor of public support. This result was also reported by Dreyer et al. [29] for a transport policy, namely US fuel standards. Because we only examined perceived personal fairness, we cannot comment on distributional fairness, which has been shown to be even more relevant to public support [18]. Our results, however, show that the high importance of perceived fairness is the same in both the regulatory and economic models.

For both policy types, perceived effectiveness positively influences acceptance, and perceived intrusiveness negatively influences acceptance. Regardless of the actual acceptance of a policy approach, the direct effect of perceived effectiveness and intrusiveness on acceptance is equally low for both the regulatory and economic policies as compared to perceived fairness. On the one hand, the negative relationship between intrusiveness and public acceptance, also reported in other studies [19, 30], is certainly more relevant for the investigated restrictive push measures than for the pull measures, which are typically perceived as less intrusive. With regard to effectiveness, on the other hand, where we identified a small positive effect of the policies on public acceptance, it is less clear whether the participants’ assessment of the general effectiveness of the respective policy measures is well-founded. In any case, the results indicate that the direct influence of effectiveness as well as intrusiveness on acceptance is only weak.

However, this finding is somewhat relativized by the identified indirect effects. The intrusiveness of the two policy approaches has an equally negative effect on the perceived fairness, as well as on the perceived effectiveness of the policies. Due to the great importance of the fairness factor described above, intrusiveness has a medium indirect negative effect on the acceptance via fairness. Again, the effect size is similar for both policy approaches, regardless of the respective level of public acceptance. This may be interpreted as evidence that restrictive measures are perceived as less fair when they seem to be highly intrusive, which, in turn, leads to lower acceptance. Hence, highlighting the non-intrusive aspects of future regulations and pricing policies could support their perception as being fairer and, in consequence, more acceptable.

The participants’ willingness to sacrifice something seems to be positively influenced by biospheric values and, in the original model, also by climate change beliefs. However, their willingness to sacrifice something has only a very small effect on the acceptance or perceived fairness of the policy measures. The effects of biospheric values and climate change beliefs were even less relevant than the willingness to sacrifice something in terms of how they influenced the acceptance of policy approaches. This is interesting, because one would assume that underlying values favoring the environment would encourage people to support policies to achieve this goal. Ultimately, these broader values seem to be overridden by aspects of perceived fairness or intrusiveness, as they are closer to the concrete policy measure.

Taken together, these findings indicate that restrictive policies have a higher chance of being publicly accepted if they are perceived to be fair, effective, and minimally intrusive. While the first aspect can be tackled by creating additional policy measures or compensatory elements for economic measures [10], the latter is particularly problematic with respect to restrictive transport policies. The extent to which people feel that their freedom is being limited by a certain policy is highly subjective; this might also depend on the proximity of the policy (i.e., whether the people currently use their car on a daily basis or not) [20] and whether they are willing to change their current behavior. Our results touch upon these aspects, as we observe negative correlations between the intrusiveness of the policy and the participants’ willingness to sacrifice something, which also suggest that the people are more likely to consider policies as intrusive if they are less willing to make sacrifices in their daily lives for the sake of climate protection. Considering that the issue of passenger transport and, in particular, policies that address motorized individual transport often evoke highly emotional responses, the situation becomes even more challenging.

Regarding the differences between regulatory and economic measures, our findings show that regulatory measures are rated slightly higher both in terms of fairness and acceptance. However, in the models, the effects are similar for both policy types. Therefore, we do not find strong differences between the two policy types in this study. This finding contrasts with the findings of, for example, Wicki et al. [31], who found higher support for regulatory measures than for economic measures. However, in that study, the authors took a policy packaging approach by combining multiple measures in a choice experiment. In our study, however, we examined individual policy assessments that we then aggregated for each of the regulatory and economic measures. As Fesenfeld [32] showed, response behavior can differ between research designs involving individual policies and policy packages. Irrespective of the policy type, the mean values for acceptance are low, indicating problems with implementing such restrictive policies.

As in any study, this study also had certain limitations. While a strong relationship between fairness and acceptance is frequently reported in the literature, the exceptionably high correlations between the variables might also be partly explained by the questionnaire design. We presented one policy proposal per page and subsequently asked participants to rate the policies in terms of their acceptance, fairness, effectiveness, and intrusiveness. This high correlation, therefore, might be the result of bias due to the order of the questions. One could argue that asking respondents to first indicate whether they would support a specific policy might influence their fairness rating. However, another–and maybe even more reasonable–explanation would be that, given that fairness is a multi-dimensional concept [18], individuals might have difficulties properly separating the acceptance of a policy from the perceived personal fairness if both are measured with a single item each. This difficulty in distinguishing between the two concepts was also discussed by Larsson et al. [33]. Additionally, while we focused explicitly and solely on restrictive measures due to their particularly challenging role with respect to public acceptance, no single policy can and should be introduced; instead, policy packages are needed [15]. The results of this study, therefore, should be considered in the context of how restrictive measures can best be designed to complement other policy measures, such as incentives and mobility offers. Finally, in this study we did not calculate the impact of the proposed measures on the reduction of GHG emissions. Such a simulation model based on the different measures and their expected public acceptance could be an interesting focus for future research.

5. Conclusions

Public acceptance of restrictive policies is critical to achieving the mobility and behavioral changes needed to dramatically reduce emissions in the fight against anthropogenic climate change. In our national survey study, we find that policy-specific beliefs, and particularly perceived fairness, are the main drivers of acceptance of restrictive regulatory and economic transportation measures. We did not find strong differences in the acceptance of the two types of measures, and overall public support is low, even though this is one prerequisite for their implementation. Although policy measures should be perceived as effective and minimally intrusive, perceived fairness was found to be especially crucial to promoting acceptance of these policies by the general public. Acceptance could be increased through careful policy design and transparent communication thereof, emphasizing the aspects related to (different dimensions of) perceived fairness. One solution might be to focus on how to embed the necessary restrictive measures in a comprehensive package that includes incentives and compensatory measures for vulnerable and highly affected social groups. Nevertheless, more attention needs to be paid to the specific design of restrictive measures, as they are fundamental to achieving emission reduction targets, but are still often being sidelined. Reducing the prevailing dependence on cars through courageous restrictive measures, while certainly difficult to implement initially, opens up the possibility for a more inclusive and sustainable transport system.

Supporting information

S1 Text. Supplementary material 1: Pre-test, control question, and sample description.


S2 Text. Supplementary material 2: Policy descriptions.


S3 Text. Supplementary material 3: Correlation matrix.



We thank Dr. Sonal Mobar Roy and Dr. V. Suresh Babu for their valuable comments, and Sara Crockett for proofreading.


  1. 1. IPCC. Climate Change 2022: Mitigation of Climate Change; 2022.
  2. 2. UNEP. Emission Gap Report; 2022.
  3. 3. Brand C, Götschi T, Dons E, Gerike R, Anaya-Boig E, Avila-Palencia I, et al. The climate change mitigation impacts of active travel: Evidence from a longitudinal panel study in seven European cities. Global Environmental Change 2021;67:102224.
  4. 4. Creutzig F, Roy J, Lamb WF, Azevedo IML, Bruine de Bruin W, Dalkmann H, et al. Towards demand-side solutions for mitigating climate change. Nature Clim Change 2018;8(4):260–3.
  5. 5. Ivanova D, Barrett J, Wiedenhofer D, Macura B, Callaghan M, Creutzig F. Quantifying the potential for climate change mitigation of consumption options. Environ. Res. Lett. 2020;15(9):93001.
  6. 6. Wynes S, Nicholas KA. The climate mitigation gap: education and government recommendations miss the most effective individual actions. Environ. Res. Lett. 2017;12(7):74024.
  7. 7. Unruh GC. Understanding carbon lock-in. Energy Policy 2000;28(12):817–30.
  8. 8. Eriksson L, Garvill J, Nordlund AM. Acceptability of single and combined transport policy measures: The importance of environmental and policy specific beliefs. Transportation Research Part A: Policy and Practice 2008;42(8):1117–28.
  9. 9. Keizer M, Sargisson RJ, van Zomeren M, Steg L. When personal norms predict the acceptability of push and pull car-reduction policies: Testing the ABC model and low-cost hypothesis. Transportation Research Part F: Traffic Psychology and Behaviour 2019;64:413–23.
  10. 10. Axsen J, Plötz P, Wolinetz M. Crafting strong, integrated policy mixes for deep CO2 mitigation in road transport. Nat. Clim. Chang. 2020;10(9):809–18.
  11. 11. EC. Zero emission vehicles: first ‘Fit for 55’ deal will end the sale of new CO2 emitting cars in Europe by 2035 [Internet]; 2022. Available from:
  12. 12. Axsen J, Wolinetz M. Taxes, tolls and ZEV zones for climate: Synthesizing insights on effectiveness, efficiency, equity, acceptability and implementation. Energy Policy 2021;156:112457.
  13. 13. Douenne T, Fabre A. French attitudes on climate change, carbon taxation and other climate policies. Ecological Economics 2020;169:106496.
  14. 14. Wang X, Rodríguez DA, Mahendra A. Support for market-based and command-and-control congestion relief policies in Latin American cities: Effects of mobility, environmental health, and city-level factors. Transp Res Part A Policy Pract 2021;146:91–108. pmid:34295022; PubMed Central PMCID: PMC7611337.
  15. 15. Thaller A, Posch A, Dugan A, Steininger K. How to design policy packages for sustainable transport: Balancing disruptiveness and implementability. Transportation Research Part D: Transport and Environment 2021;91:102714.
  16. 16. Dugan A, Mayer J, Thaller A, Bachner G, Steininger KW. Developing policy packages for low-carbon passenger transport: A mixed methods analysis of trade-offs and synergies. Ecological Economics 2022;193:107304.
  17. 17. Wicki M, Fesenfeld L, Bernauer T. In search of politically feasible policy-packages for sustainable passenger transport: insights from choice experiments in China, Germany, and the USA. Environ. Res. Lett. 2019;14(8):84048.
  18. 18. Bergquist M, Nilsson A, Harring N, Jagers SC. Meta-analyses of fifteen determinants of public opinion about climate change taxes and laws. Nat. Clim. Chang. 2022;12(3):235–40.
  19. 19. Huber RA, Wicki ML, Bernauer T. Public support for environmental policy depends on beliefs concerning effectiveness, intrusiveness, and fairness. Environmental Politics 2020;29(4):649–73.
  20. 20. Huber RA, Wicki M. What explains citizen support for transport policy? the roles of policy design, trust in government and proximity among Swiss citizens. Energy Research & Social Science 2021;75:101973.
  21. 21. Long Z, Axsen J, Kitt S. Public support for supply-focused transport policies: Vehicle emissions, low-carbon fuels, and ZEV sales standards in Canada and California. Transportation Research Part A: Policy and Practice 2020;141:98–115.
  22. 22. Kitt S, Axsen J, Long Z, Rhodes E. The role of trust in citizen acceptance of climate policy: Comparing perceptions of government competence, integrity and value similarity. Ecological Economics 2021;183:106958.
  23. 23. LimeSurvey Development Team. LimeSurvey, version 3 [Internet]; 2012.
  24. 24. Schwartz SH, Breyer B, Danner D. Human Values Scale (ESS) [Internet]; 2015.
  25. 25. van Boven L, Ehret PJ, Sherman DK. Psychological Barriers to Bipartisan Public Support for Climate Policy. Perspect Psychol Sci 2018;13(4):492–507. pmid:29961412.
  26. 26. Höllinger F, Hadler M, Aschauer W. International Social Survey Programme: Environment IV—ISSP 2020 [Internet]; 2020.
  27. 27. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria; 2020. Available from:
  28. 28. Rosseel Y. lavaan: An R package for structural equation modeling [Internet]; 2012.
  29. 29. Dreyer SJ, Teisl MF, McCoy SK. Are acceptance, support, and the factors that affect them, different? Examining perceptions of U.S. fuel economy standards. Transportation Research Part D: Transport and Environment 2015;39:65–75.
  30. 30. Hagman W, Andersson D, Västfjäll D, Tinghög G. Public Views on Policies Involving Nudges. Rev.Phil.Psych. 2015;6(3):439–53.
  31. 31. Wicki M, Huber RA, Bernauer T. Can policy-packaging increase public support for costly policies? Insights from a choice experiment on policies against vehicle emissions. J. Pub. Pol. 2020;40(4):599–625.
  32. 32. Fesenfeld LP. The effects of policy design complexity on public support for climate policy. Behav. Public Policy 2022:1–26.
  33. 33. Larsson J, Matti S, Nässén J. Public support for aviation policy measures in Sweden. Climate Policy 2020;20(10):1305–21.