Abstract
Introduction
Urban planners often prescribe compact, walkable development as one solution to reduce the negative impacts of car travel. As higher density, mixed-use neighbourhoods with connected street networks and frequent transit are associated with less car ownership and use (Ewing and Cervero, 2010; Stevens, 2017), land use regulations that encourage or require such compact development patterns can help reduce greenhouse gas emissions and other forms of pollution (Ewing et al., 2008). Many urban planners and designers writing about urban sprawl do not even mention the role of prices and taxes, preferring to focus on physical design and regulatory solutions (e.g. Talen, 2015).
Policies that promote higher densities may be as much about removing existing distortionary regulations that limit heights or mandate parking as introducing new ones. Indeed, scholars across disciplines have highlighted the role of exclusionary zoning and other land use restrictions that hinder infill development in existing urban neighbourhoods (Glaeser and Ward, 2009; Levine, 2005).
Economists, however, tend to emphasise price-based rather than regulatory approaches. To the extent that carbon emissions, local air pollution, or congestion are excessive, the appropriate solution is to raise prices to marginal social cost through a carbon tax, increased gasoline taxes, or congestion pricing (Brueckner et al., 2001; Glaeser and Kahn, 2004). In turn, higher prices for car travel would soften consumer demand for lower-density, car-oriented housing.
Several studies point to the synergies between pricing and the built environment. For example, pricing may have greater impacts on travel behaviour in mixed-use and higher-density neighbourhoods with good public transportation where people have a wider range of realistic alternatives to the private car (Boarnet, 2010; Guo et al., 2011).
A further potential synergy between pricing and regulation has attracted less attention and is the subject of this paper: how land use patterns affect the feasibility of taxing car travel. Urban economists tend to sidestep the question of where taxes come from, but there is no benevolent social planner who sets taxes on gasoline, carbon and congestion at the socially efficient level. Rather, such taxes are determined by elected officials, who in turn are subject to a variety of political pressures from their constituents, political parties, and campaign contributors, or by voters directly through referenda and ballot initiatives.
The political economy of carbon taxes and cap-and-trade has attracted considerable attention, particularly at the US federal level. For example, Holland et al. (2015) seek to explain why cap-and-trade or Pigouvian taxes are shunned in favour of less efficient alternatives such as subsidies for biofuels. They find the answer lies in the distribution of gains and losses: both cap-and-trade and biofuel subsidies spread their costs fairly evenly throughout the population, but biofuel subsidies also concentrate gains in agriculturally oriented Congressional districts – creating a strong constituency behind that policy. Other research shows that Congressional representatives are more likely to support cap-and-trade legislation if their districts have lower carbon footprints, presumably because their constituents would bear lower costs (Cragg et al., 2013). But there is little equivalent to this work in the transportation domain, where local and state gasoline taxes are typically more substantial than a carbon price. The few exceptions (e.g. Holian and Kahn, 2015) are discussed below.
In this paper, we respond to the call of Boarnet (2010: 588) who bemoans the ‘caricatured nature of current policy debate’ that pits pricing and regulatory approaches against each other rather than recognising their complementarities. We take a step towards reconciling the two approaches, and suggest that the willingness of a community to tax car travel depends on its land use patterns and transportation systems. Specifically, we hypothesise that elected officials and voters will be more willing to increase gasoline taxes in compact, walkable neighbourhoods that are well served by transit. When land use regulation promotes less car-oriented development, Pigouvian taxes on car travel become more feasible.
The land use/pricing connection
Transportation costs, particularly for gasoline, have a short-run impact on vehicle travel and emissions through influencing household decisions on whether, where, and how to travel. While elasticities are often modest in the short run, they are larger in the long run (Graham and Glaister, 2002; Small and Van Dender, 2007). This makes sense as households have more margins of adjustment over longer time frames, such as buying a car with greater fuel efficiency or moving closer to work.
Transportation costs also influence household travel through housing and land markets. The principle follows from canonical models of urban form, such as the Alonso–Mills–Muth model, which place transportation costs at the heart of the analysis. A well-known result is that at the city boundary, agricultural land rents are equal to urban land rents, which in turn are a function of transportation costs. The lower the transportation costs, the larger the radius of the city for a given population size, and thus the lower the density. Numerous studies support this result empirically, showing that higher gasoline taxes are associated with more compact and denser cities (McGibany, 2004; Molloy and Shan, 2013; Tanguay and Gingras, 2012). In turn, compactness and density are associated with less vehicle travel (Stevens, 2017).
In principle, however, there is a two-way relationship between urban form and transportation prices if urban form affects the willingness of voters and elected officials to support increased driving taxes – whether directly through increased gasoline taxes or road-user charges, or indirectly through carbon pricing. The more that a voter drives, the greater the costs they will incur from increased taxes – particularly if alternatives to driving are slower or more expensive. Thus, voters in places with lower per-capita vehicle travel and higher accessibility by public transportation, walking, and cycling would be expected to support higher taxes on car use, either directly through referenda or indirectly through voting for elected officials who promise to raise such taxes.
The relationship between urban form and political support for driving taxes is complicated by several factors. First, fuel efficiency affects the costs that a household incurs from a carbon or gasoline tax, albeit in a way that reinforces the effects of urban form: denser urban areas tend to have more fuel-efficient cars as well as less vehicle travel (Cook et al., 2015). Second, not only the costs but also the benefits of increased driving taxes through reducing congestion and air pollution externalities will vary spatially. Third, political support for driving taxes will depend on the proposed use of revenue. A tax that funds highway expansion might attract more support in car-oriented, low-density neighbourhoods, while one that funds public transportation might gain more backing in transit-oriented, high-density places. Similarly, road pricing might attract more political support were the revenue to be distributed to the cities through which the tolled freeways pass (King et al., 2007).
In short, a reasonable assumption is that the costs of driving taxes fall disproportionately on the lower-density neighbourhoods where people drive the most, while it is harder to generalise about the benefits. Empirically, the willingness of voters and elected officials to increase the cost of driving appears to be greatest in places where vehicle travel is limited. Anecdotal evidence comes from road-user pricing, which, despite the prescriptions of economists since the days of Vickrey (1963), has only been implemented at scale by four major cities – Singapore, London, Stockholm, and Oslo. Not surprisingly, all four have unusually low rates of car ownership and/or driving.
More systematic empirical studies are scarce, but two studies are notable. In Stockholm’s congestion pricing referendum, ideology partly determined voting choices, but support for the tolls was greatest among voters who would benefit the most from reduced congestion and face the lower monetary costs (Hårsman and Quigley, 2010). Meanwhile, Holian and Kahn (2015) study California’s Proposition 23, which would have effectively abolished the state’s cap-and-trade programme. They find that support for the voter initiative varied with political ideology but also with urban form, with suburban voters facing higher costs from a carbon price. Electoral precincts with higher residential density and that are closer to the city centre saw more votes in favour of carbon pricing (i.e. against Proposition 23). However, the authors were unable to distinguish between the effect of energy costs (larger suburban homes require more energy for heating and cooling) and transportation costs.
In contrast, other studies centre the role of ideology and the related concept of partisanship in explaining transportation policy preferences for taxes and expenditures. Manville and Cummins (2015) compile survey data on support for public transit funding, and find that attitudes on broader policy issues such as the environment are more predictive than whether an individual rides transit themselves. The survey findings of Nixon and Agrawal (2019) offer a similar conclusion: partisan identification is the dominant factor affecting support for a gasoline tax increase. Using data on local referenda, Congressional roll call votes, and public opinion surveys, Nall (2018) finds that density has little explanatory power once political partisanship, income, and race are accounted for. Support for highways is broad-based and bipartisan and varies little with urban form, while Democratic voters disproportionately favour pedestrian and public transit improvements.
Klein et al. (2022), meanwhile, take the partisan divide as a starting point and use survey data to explore the pathways that connect partisanship with policy preferences, including self-interest and transportation-related values. Independent of partisanship, they find a strong role for self-interest (proxied by travel behaviour) in affecting views of whether mode shift to transit, walking, and cycling should be a transportation policy goal.
Taken together, these studies indicate that ideology, or at least partisanship, is a dominant influence on support for gasoline and carbon taxes. But is there a role for urban form and public transportation service as well? Most of the studies that consider urban form (e.g. Klein et al., 2022; Nall, 2018) focus on broader aspects of transportation policy such as mode shift goals and highway and public transit spending, sometimes supported by local sales taxes. We now turn our focus to the relationship between urban form and policies such as carbon or gasoline taxes that would increase the cost of driving. We use the term ‘urban form’ in its broad sense, to encompass public transit service and transportation infrastructure as well as the physical layout of land uses. This is in line with the broader literature, which considers access to public transit as one aspect of the built environment (e.g. Ewing and Cervero, 2010).
Research design
Causal pathways
We examine the impact of urban form on support for two California ballot measures – Proposition (Prop) 23 in 2010 and Proposition 6 in 2018 – that would have repealed carbon pricing (Prop 23) and gasoline tax increases (Prop 6) that had been enacted by the state legislature. For simplicity, we refer to both as ‘driving taxes’ in the remainder of the paper. We pay particular attention to separating out the impact of urban form from that of ideology, given the confounding influence of liberal voters tending to cluster in larger, denser cities (Nall, 2018; Rodden, 2019). Specifically, we model the impact of urban form variables on support for each of the two propositions, after controlling for vote share in the Governor’s race and on a separate ballot measure that would have increased non-transportation spending or made it more difficult to raise taxes. Our multi-pronged identification strategy, discussed in more detail below, relies on controlling for ideology, as observed through voting on different contests in the same election.
Figure 1 shows the causal pathways that are considered (solid lines) and omitted (dashed lines) from the study. Urban form influences ideology through contextual effects; for example, social interactions with neighbours, housing tenure, and collective consumption change people’s attitudes and political identification (Martin and Webster, 2020; Pattie and Johnston, 2000; for broader discussions, see Weaver, 2014; Williamson, 2008). Such contextual effects are likely to be weak (e.g. Walks, 2006), but to the extent they exist, they will bias our estimates downwards because we do not capture the upper path in Figure 1 which runs through ideology. For example, people moving to a denser neighbourhood may become less conservative (Martin and Webster, 2020), perhaps because they are influenced by the political beliefs of their neighbours or due to social pressure to conform (Brown, 2023). This means that our results can be interpreted as a lower bound. A stronger effect of urban form on ideology comes through residential sorting (i.e. self-selection): people move to neighbourhoods that match their political leanings and preferences for collective versus individual service provision (Nall, 2018). We address the confounding impacts of self-selection through careful attention to controlling for ideology, as discussed below.
Causal path diagram.
In the long run, ideology also affects urban form (e.g. Kahn, 2011). We are unable to capture this effect, which also means that our estimates of the impacts of urban form on driving taxes may be biased downwards since we control for or match on ideology. However, this effect of ideology on urban form is most likely to be pronounced in the long run; much of urban California was built up at a time when ideological alignments and spatial patterns of political preferences were very different from those of today. While future work might be able to untangle these multiple potential pathways, our objective in this paper is more modest – to explore the existence of a direct connection, independent of ideology, between urban form and support for increased driving taxes.
Another causal path shown in Figure 1 is through the proposed uses of revenue. However, we assume that these are independent of urban form for the two measures we study here. Opposition to both taxation measures focussed on the cost of gasoline and energy, while support emphasised clean energy (in the case of Proposition 23) and road maintenance (in the case of Proposition 6).
Dependent variables: Two California ballot measures
Proposition 23, rejected by the state’s voters in November 2010 by a margin of 62–38%, would effectively have abolished the state’s cap-and-trade programme and much of its other climate policy apparatus (Biber, 2013). At the time of the election, cap-and-trade had not yet taken effect, but California Air Resources Board projections at the time indicated that it would increase gasoline prices by 6% or $0.18 per gallon (Burtraw et al., 2012). 1 Transportation was one of the most salient aspects of the Proposition 23 campaign, and oil companies provided almost all of the campaign funding on the ‘Yes’ side (Biber, 2013). While the spatial variation in Proposition 23 support has been previously studied by Holian and Kahn (2015), among others, here we provide richer measures of urban form and more systematic controls for political ideology.
Proposition 6, which was on the ballot in November 2018, was also rejected by the state’s voters, this time by a margin of 57% to 43%. It would have effectively repealed the $0.12 per gallon gasoline tax increase that had been approved by the state legislature a year earlier, along with other fees and taxes on transportation fuels and vehicles that were part of Senate Bill 1. The revenue is primarily dedicated to maintenance of local streets and roads, with smaller amounts earmarked for public transportation and bicycle and pedestrian facilities. In disclosure, Senate Bill 1 also funds university transportation research, including some of the authors’.
Treatment variables: Measures of urban form
Our treatment variables consist of five dimensions of urban form: residential density, job density, the working population/jobs equilibrium index (which we call the population–jobs index for short in the remainder of this paper), transit frequency, and street connectivity. A large literature shows that higher values for all five variables are associated with less vehicle travel (Barrington-Leigh and Millard-Ball, 2020; Ewing and Cervero, 2010; Salon et al., 2012; Stevens, 2017), likely because of shorter travel distances that enable walking and cycling, the economies of scale of public transportation, and higher parking costs in dense neighbourhoods. We take the natural log of residential density, job density, and transit frequency (the other two variables are not as heavily right-skewed and no transformation is necessary). We standardise all five variables, and allow for non-linear relationships through including a squared term. The Appendix defines each variable and provides summary statistics.
Each of these five variables provides an incomplete picture of urban form, and the theoretical relevance of some of them has been questioned in previous research. For example, the population–jobs index, which captures the balance between the working age population and jobs, addresses only one aspect of the ultimate goal of increasing accessibility, and accessibility itself may be more important for providing households with more residential and transportation choices, rather than reducing car use and congestion (Levine, 1998). Our interest here is less in the specific contribution of each individual measure, and more about whether we can trace a link between any (or all) aspects of urban form and political support for driving taxes.
Data sources
The raw vote counts, precinct boundaries, and geographic conversion files are taken from the California Statewide Database (available at https://statewidedatabase.org). Four of our urban form measures are from the EPA Smart Location Database (available at https://www.epa.gov/smartgrowth/smart-location-mapping#SLD). Street connectivity is calculated based on the method in Barrington-Leigh and Millard-Ball (2019); we multiply their measure of street disconnectivity (SNDi) by −1 in order to align it with our other measures of urban form.
The scale of our analysis is the voter precinct. 2 The four urban form measures from US EPA are reported by census block group, which we rescale to precincts based on population weights. 3 Voting data and street connectivity are already aggregated to the precinct level. While an individual-level analysis would offer considerable advantages in avoiding ecological inference challenges, such data are not available. Moreover, the pitfalls of ecological inference are more limited in our analysis since voters in the same precinct experience similar urban form regardless of individual demographic characteristics. (A similar ecological approach is employed by Hårsman and Quigley (2010) in their precinct-level study of the congestion pricing referendum in Stockholm.)
Regression specifications
The primary consideration in our regression specifications is to separate out the influence of urban form on support for driving tax increases from the influence of political ideology. Our first two specifications respectively include linear and cubic controls for ideology, as measured through both the Governor’s race in that year and a voter initiative on a non-transportation spending measure: Proposition 1 in 2018, which would have issued $4 billion in bonds for housing and veterans’ programmes, and Proposition 26 in 2010, which would have introduced a two-thirds supermajority requirement for most future voter-approved taxes. A “yes” vote on Proposition 1 and a “no” vote on Proposition 26 indicate a more fiscally liberal ideology, and capture the overall willingness of voters to support tax increases. The gubernatorial vote share provides a multidimensional measure of ideology.
Our third specification uses a propensity score matching algorithm. Matching methods aim to achieve covariate balance and reduce the importance of parametric assumptions, but are typically used in the context of a binary treatment. Because our treatment (urban form) is continuous, we use the covariate balancing generalised propensity score method proposed by Fong et al. (2018). We use the CBPS R package to match on two different treatments: residential density and job density. In essence, we weight each observation to construct a sample that minimises the weighted correlation between the treatment (residential density or job density) and other covariates. The major advantage of matching is to increase robustness to nonlinear relationships and other forms of model misspecification. While matching cannot control for unobserved confounders, our primary concern is to control for ideology, which we
Our fourth specification allows the urban form of neighbouring precincts to influence voting decisions through a spatial lag. For example, a voter’s travel costs and thus willingness to support tax increases could be affected by the mix of uses and densities in nearby blocks, not just the characteristics of their home precinct.
We also conduct a series of robustness tests that add additional covariates: voting on two additional races (California Secretary of State and US Senate), and/or demographics (gender, race as measured by the percentage of white people, median age, percentage of the population that are children, and median income). We report these specifications in the Appendix, but our results are not qualitatively affected, likely because the impact of these variables on voting behaviour is already captured by ideology.
We do not wish to control for car ownership and other transportation outcomes because those decisions are partly a result of ideology and partly a result of urban form (in addition to other factors such as income and job location). The ideological aspects of car ownership are already addressed through controlling for ideology. To the extent that car ownership is also a product of urban form, controlling for car ownership would bias our estimates of the impacts of urban form.
As shown below, our regression specifications provide broadly similar results, indicating that our findings are robust to different functional forms and non-parametric relationships. However, in all four specifications, our measures of ideology are limited to those expressed through the Governor’s race and the non-driving tax-related propositions, and may not capture other dimensions of voter preferences. In short, we cannot rule out biases caused by unobservable aspects of ideology.
Results
Figure 2 shows the distribution of support for tax increases across the state. (Recall that a ‘no’ vote equates to support for leaving the tax in place.) The patterns reflect the political geography of California, with coastal areas, particularly in the north, strongly opposed to both measures, and the interior of the state offering more support. Our urban form metrics (Figure 3) show a slightly different pattern: while the coastal metropolitan areas of San Francisco and Los Angeles stand out for their higher residential and job densities, frequent transit, and connected streets, the same is not true of more rural coastal areas, even though the entire coast tended to vote against both propositions. Moreover, the population–jobs index (which measures the balance between working-age population and jobs) is greatest in the interior Central Valley and sparsely populated rural areas, perhaps as a result of more localised labour markets.
Voting outcomes from Proposition 6 (2018) and Proposition 23 (2010). Results are plotted at the precinct level.
Spatial variation in urban form metrics. Results are plotted at the precinct level. Variables are log-standardised, that is, they represent
The scatter plot matrices (Figures 4 and 5) confirm this picture. Visually, there is a strong association between each proposition and ideology, as measured through voting in the gubernatorial race and the non-transportation tax related measures on the ballot (Proposition 26 in 2010 and Proposition 1 in 2018). While there is also a bivariate association between voting on our two propositions and each of our measures of urban form, there is more variation (reflected in the scatter around the 45-degree line), and the strength of the association varies across the measures.
Pairwise relationships between voting on Proposition 6 (top row) and ideological and log-standardised urban form variables.
Pairwise relationships between voting on Proposition 23 (top row) and ideological and log-standardised urban form variables.
We now consider the relationship between each measure of urban form and voting on the two propositions, while controlling for the effects of ideology as discussed above. Figure 6 plots the coefficients for the five models discussed in the Methods section, each of which offers a different way of controlling for the influence of ideology; the spatial lag model also captures some spatial spillovers. The Appendix provides the same results in tabular form.
Coefficient plots. We show five different model specifications, discussed in the text, that control for the influence of ideology in different ways.
The first point to emphasise is the general consistency between the five models in terms of the sign and magnitude of the coefficients. In the spatial lag models, some of the effect of each treatment variable is picked up through the spatially lagged term, indicating the importance of urban form in the broader neighbourhood, not just the voter’s precinct of residence. The Akaike Information Criterion (AIC) scores (reported in Tables A-4 and A-5) favour the models with flexible controls, but AIC is a measure of fit whereas our primary interest is in separating out the influence of urban form.
Second, residential and job density have the largest consistent effects, predicting support for taxes in both the Proposition 6 and 23 referenda. Transit frequency and the population–jobs index have predictive power for Proposition 6 but not 23, while street connectivity has a small negative effect.
Third, nonlinear effects are apparent, especially in the Proposition 6 models; the squared terms for residential and job density and for transit service are positive and significant, even though in most cases the linear coefficient is negative or indistinguishable from zero. For example, higher residential density and transit frequency are only associated with opposition to Proposition 6 above about 3.5 housing units per acre and 5.7 peak-hour transit trips per square mile. Plausibly, an increase from low to moderately low densities and transit service has little impact on mode choice or travel costs and thus support for driving taxes, with the impact only felt at moderate to higher levels.
An alternative and more intuitive way of examining the impacts of urban form on voting is through the predictive plots shown in Figure 7. In each precinct, we predict Proposition 6 and 23 vote shares using our base model and the actual values of each urban form metric, but with ideology adjusted to one of two predefined levels. The blue markers and associated lowess fit show an election in which every precinct voted for the Governor and the non-transportation taxation measure (Proposition 1 in 2018 and Proposition 26 in 2010) according to the statewide means. The orange markers and lowess fit show an election in which there was a 50:50 split in voting for the gubernatorial candidate and the non-transportation taxation measure.
Predictive plots. Each point represents our model’s predicted outcome for that precinct, with our ideological measures (e.g. the Governor’s race) adjusted to match the statewide means (blue) or a 50:50 election (orange).
The plots in Figure 7 are a useful complement to the coefficient plots (Figure 6) for two reasons. First, the plots in Figure 7 make any non-linearities readily apparent. Second, they account for the correlations between our measures of urban form. Residential and job density, for example, often covary: low-density residential areas rarely have much in the way of employment. Density and street connectivity are also associated with transit service, given that limited demand makes it hard to provide frequent transit to low-density neighbourhoods with culs-de-sac and other disconnected street patterns. Indeed, the land use-transportation literature often emphasises the synergistic effects of different aspects of urban form (e.g. Ewing and Cervero, 2010). Thus, rather than seeking to isolate the contribution of each measure of urban form, Figure 7 accounts for their combined impacts.
Two major conclusions are evident from the predictive plots in Figure 7. First, the ideological gap – represented by the difference between the blue and orange lines – is striking in the case of Proposition 6, but almost absent in the case of Proposition 23. Proposition 6 failed because of the clear ideological leaning of the electorate; the Democratic gubernatorial candidate, Gavin Newsom, won with 62% of the vote. Had the Governor’s race and Proposition 1 been evenly split (orange line), Proposition 6 would have passed in the majority of precincts. Proposition 23, in contrast, would have failed in almost every precinct under such a 50:50 election. This may be due to a legacy of bipartisan support for climate policy in California that remained through 2010; after all, AB 32, which Proposition 23 would have effectively repealed, was signed into law by Republican governor Arnold Schwarzenegger.
Second, the population–jobs index and street connectivity appear to be less useful predictors of support for driving taxes, at least in a consistent manner across the two propositions that we study. This is somewhat surprising given that the literature implies that these measures should have similar effects to density and transit service. In contrast, residential density, employment density, and transit frequency do emerge as useful predictors. For example, moving from the 1st percentile of residential density to the 99th percentile increases opposition to Proposition 6 from 57% to 60%, holding our ideological measures at their statewide means. For transit frequency, the effect of a shift from the 1st to the 99th percentile is an increase in opposition from 56% to 60%. However, Figure 7 indicates that the impact is not necessarily linear; the major gains in support come from moving from moderately high to very high levels of density and transit service.
Conclusion
Compact development and transportation pricing have often been considered as separate policy approaches for addressing the negative impacts of car travel. Manville (2017) laments the focus of planners and planning researchers on how compact development affects driving, calling instead for an emphasis on pricing reforms such as eliminating subsidies for driving through free parking and free roads.
However, cities have withstood more than 50 years of economic consensus about the desirability of congestion pricing. Once approved, pricing may be a rapid and efficient way to force drivers to internalise the safety, environmental, and other externalities of driving. But the day of that approval may never come.
Much has been written about how auto-oriented urban form locks in car dependency through path dependencies in land use patterns and public transportation systems. From a political economy perspective, such patterns may also lock in cheap driving, or at least make it more difficult to price roads and gasoline correctly. The literature already shows how higher gasoline taxes lead to more compact development. Here, we demonstrate that the relationship is two-way: compact development and frequent transit can also lead to voter support to increase taxes on driving. The precise contributions of residential density, employment density, frequent transit, and so on are hard to separate out empirically, but in practice, these measures of urban form and public transportation are highly correlated and mutually reinforcing.
The magnitude of the effect is modest, but could make the difference in a close election. Moreover, we might expect small effects in our study because voters were choosing whether to keep already enacted taxation increases. The taxes may also have had relatively low salience, being bundled into the price at the pump rather than a separate charge as with, for example, a congestion pricing scheme.
While our results are limited to two California referenda, it is reasonable to infer that the effect also exists where taxes are determined legislatively rather than through referenda. To some extent, council members and other elected officials respond to the preferences of their constituents.
The policy implications of our findings are indirect, but reinforce the case for creating urban neighbourhoods where people have a range of travel options, not just the private car. Our results imply that the voluminous research on land use and transportation underestimates the long-run impacts of compact development and public transportation on driving. Such studies are typically unable to capture the indirect effect of compact development on increasing political support for taxes on driving.
Such taxes are already politically salient, as in the case of mass protests against fuel taxes in places such as France and the United Kingdom. However, the political economy of driving taxes is likely to become more critical in the coming years with the rapid adoption of electric vehicles, which do not pay the driving taxes levied on gasoline. While the technology was still embryonic, there was a case for subsidies, but as electric vehicles become mainstream, taxing travel based on miles driven becomes more urgent. One rationale relates to replacing the lost revenue from gasoline taxes (Wachs, 2009). Another is to price externalities; while electric vehicles are generally preferable from a climate policy and air quality perspective, their greater weight exacerbates road safety problems (Shaffer et al., 2021), and they of course congest the roads just as much as vehicles powered by internal combustion.
New taxes on vehicle travel might attract limited support at the national scale, where car-dependent rural and suburban residents might understandably object given that they have little alternative to driving. But in the same way that congestion pricing has gained most traction in places where public transportation is frequent and reliable and walking is an option for most trips, mileage fees or other replacements for gasoline taxes might be rolled out first as pilots in urban centres. Our results point to a geographic strategy to roll out new pricing schemes: target pockets of support in cities that are more dense, walkable and transit-oriented.
Footnotes
Declaration of conflicting interests
Funding
ORCID iDs
