We explore cost-efficient scenarios for reaching climate targets for the transportation sectors in the EU and Sweden, covering both passenger and freight transport. Our focus is on the relative contributions to emissions reductions from electrification, biofuels and traffic reduction. Information about cost-efficient scenarios can serve as roadmaps to reach given targets. This is important not only for designing climate policies; it also has implications for traffic planning, biofuel production planning and the transformation of the vehicle manufacturing industry. Our results indicate that electrification is by far the most important factor for reaching the climate targets. With the recent EU vehicle regulations in place, it is possible to reach the climate targets at a moderate cost; with slow electrification, the long-run targets are virtually impossible to reach. Assuming that EU emission standards are binding, reaching the targets also requires considerable amounts of biofuel, especially in the short and medium term. Traffic reduction contributes only marginally to reduced emissions. Hence, other negative externalities from road traffic need to be handled by other policy measures; climate policy only makes a marginal contribution to reducing other traffic externalities.

We estimate the income and fuel price elasticities of household vehicle kilometres travelled (VKT) and car ownership. To model the VKT, we apply a discrete-continuous model on registrAy micro panel data, covering all Swedish households from 1999 to 2018. We model two joint choices: car ownership and VKT conditional on car ownership, where the elasticity of VKT from these two choices are obtained by using the Two-Part model. We account for unobserved household effects using a correlated random effects specification and take household adjustments into account by including lagged values of fuel prices and income. Our preferred model yields a long-run income elasticity of 0.45 for private VKT, where close to two-thirds of the effect comes from the income elasticity of car ownership. The long-run fuel price elasticity of private VKT is-1.03, with the response in VKT among car owners being larger than the response in car ownership.

It has proven difficult to reduce carbon emissions from the transport sector; in fact, emissions from this sector are still increasing worldwide. Reducing emissions by reducing road transport is challenging; therefore, a transition to a vehicle fleet with low or zero emissions seems essential. Many new cars in OECD countries are sold to firms as fringe benefit cars (sometimes called company cars in the literature). The generous taxation of such cars has been shown to have negative welfare effects because it increases the consumption of cars. However, it is sometimes justified since it speeds up the transition of the car fleet to lower-emission vehicles. The purpose of this paper is to analyze how fringe benefit cars impact carbon emissions, fuel type, weight, size, engine power, and market value of new cars. We apply micro register data including all adult Swedes and their cars, spanning the years 1999 to 2020. By using a matching model that combines Exact matching and Mahalanobis distance matching, the fuel consumption of the fringe benefit car is compared to the hypothetical new private car that the employee receiving the fringe benefit would have otherwise purchased. We find that new fringe benefit cars tend to be larger, heavier, and more powerful than the hypothetical new private cars that fringe benefit car recipients would have otherwise purchased, However, we also find that new fringe benefit cars sold in 2019-2020 consumed 1.2 L less fuel per 100 km compared to hypothetical new private cars, a decrease of 20 percent. The lower fuel consumption of the fringe benefit cars in these years results from a higher share of electric vehicles among them. We also find that the likelihood of the fringe benefit car being an alternative-fuelled vehicle is 6 percentage points higher than if it was bought as a private car.

Reducing traffic volumes is one way to reduce carbon emissions from the transport sector. Since increasing driving costs is often met with public resistance, high hopes are often pinned on the possibility to reduce traffic volumes by non-coercive policy measures, or “carrots”. Such measures include improvements of alternative modes, strategies that affect urban forms, and “soft measures” that aim to affect behaviour by providing information or changing norms and attitudes. This paper reviews the empirical evidence regarding such measures, focusing on their potential to reduce aggregate road traffic volumes in a national perspective. While such measures can yield significant other benefits, and may also reduce traffic volumes locally, our general conclusion is that their effects on aggregate traffic volumes appear small, especially from a climate policy perspective where emissions need to be cut radically and rapidly. While they are often motivated for several other reasons, overestimating their effects on aggregate traffic volumes may cause complacency, misallocations of scarce public resources and backlashes against climate policy.

This paper studies how vertical integration, subsidies, and interoperability regulation shape investment and welfare in electric vehicle (EV) public charging markets. We model the interaction between Charging Point Operators (CPOs), which invest in charging infrastructure, and Mobility Service Providers (MSPs), which sell charging services to end users. The model features indirect network externalities: denser infrastructure reduces range anxiety and increases EV adoption, while higher EV demand raises infrastructure profitability. In the benchmark laissez-faire environment, vertical integration creates a trade-off. Integrated CPOs-MSPs may foreclose rival MSPs and reduce downstream competition, but they also internalize the complementarity between infrastructure investment and EV demand, generating higher infrastructure density, EV adoption, consumer surplus, and welfare than vertical separation. This welfare ranking is established under foreclosure and is then generalized in a robustness extension with interoperability constraints. Minimum-margin rules keep the rival MSP active and soften foreclosure, while the integrated firm still retains part of its investment advantage. However, when interoperability entails compliance and coordination costs, stricter access regulation may further weaken infrastructure investment incentives. We also compare consumer subsidies and firm subsidies within the benchmark environment. Consumer subsidy dominates under vertical integration, while firm subsidy may dominate under separation only when network effects are sufficiently strong. A calibration to Italian metropolitan areas supports these mechanisms and highlights a fiscal trade-off: consumer subsidy generates larger welfare gains, whereas firm subsidy may deliver higher environmental benefits per euro spent.

This paper studies why women commute shorter distances than men and why the gap is smaller in dense labor markets. Using geocoded Swedish registry panels for 1998, 2005, and 2017 (about two million workers), we relate commuting distance and wages to household structure, sector of employment, and residential labor market potential. First-difference estimates show substantial spatial sorting: workers with time-invariant unobservables associated with longer commutes are disproportionately located in high-accessibility areas, with stronger sorting patterns among women. Women’s overrepresentation in the public sector further explains part of the commuting gap and its weaker gradient with labor market potential. Interpreting the wage–commuting gradient as a revealed value of commuting time, IV estimates are not statistically different from zero for fathers but positive for mothers. These IV wage regressions show positive wage compensation for commuting distance for both genders but substantially larger for men, while women in the private sector obtain larger wage gains from higher residential labor market potential. The commuting gap is largest among parents, consistent with child-related constraints, but sorting and sectoral employment are key drivers among workers without children.

We estimate how changes in transport supply affect vehicle kilometres travelled (VKT) and thereby transport energy demand. Specifically, we examine how changes in generalized travel times by car, generalized travel times by other modes (public transit, walking, and cycling), and destination density affect individual VKT. The analysis uses Swedish register panel data for 1998, 2005, and 2017, matched to historically reconstructed transport networks and destination distributions. We exploit within-individual first differences and use an instrumental-variable strategy based on changes around individuals’ initial residential locations to separate changes in transport conditions from changes induced by residential relocation. The results show that pooled cross-sectional models substantially overstate the effects. Moreover, the IV estimates are substantially smaller than the first-difference estimates, indicating that endogenous residential relocation and preference shifts may affect both experienced travel conditions and car use. Improvements in generalized travel times by other modes reduce VKT, but the implied effects are modest: a one-standard-deviation improvement reduces annual VKT by about 183 km, corresponding to 2.3% of mean VKT, in the preferred IV specification. The effects of changes in car generalized travel times and destination density are less robust.

This study analyzes how the willingness to pay (WTP) for a stated reduction in the risk of traffic accidents depends on the type of measure that delivers the reduction and on whether it is framed as a public or a private good. Building on previous studies, we designed and conducted a contingent valuation survey targeting a representative sample of the Swedish adult population. Our results suggest that WTP for risk reduction varies across measures, even when they are all of private good or all of a public good nature. Furthermore, for conventional safety measures, WTP for a stated risk reduction is higher in private-good settings. Still, for a measure based on a mobile app, the result is the opposite (in the full sample). These findings caution against uncritical use of a uniform unit value of risk reduction and suggest that some public hesitancy towards the use of digital instead of conventional safety-enhancing technologies remains.

The EU aims to achieve climate neutrality for trucks. This paper compares the user cost of diesel trucks, battery electric trucks, and trucks that rely on overhead lines in a decision context where the developments of battery costs and overhead line investment and maintenance costs are uncertain. The user costs contain the truck capital cost and the energy costs, the possible vehicle-to-grid benefits, driver costs, and other distance costs. User costs are compared for different distance profiles and optimized battery sizes. The possible user cost developments serve as input to an analysis of investment decisions in electric motorways (e-roads). The economics of e-roads is analyzed for two representations of the EU TEN-T network. In the first analysis, average EU truck flow (veh/h) and truck trip characteristics are used. In the second representation, we consider domestic and international truck transport between two neighbouring countries with strongly diverging average traffic flows and shares of international truck trips on their TEN-T network. This allows for the analysis of the non-cooperative and cooperative solutions of the two countries. The installation of e-roads appears to be a robust investment decision for the motorways of large countries that have dense truck traffic but not for less dense countries. Cooperation between countries may increase total benefits due to economies of scale.

Declining response rates to travel surveys are an increasing problem for the estimation of accurate transport forecasting models. In this paper we investigate the use of mobile phone network data as a sole data source for estimation of a domestic long-distance mode choice model. We address two data-related challenges: the difficulties of estimating a model when bus and car trips are both observed as 'road' in the dataset, and distinguishing trip purpose. We successfully estimate a nested logit model with a nest that accounts for the differences in utility between bus and car, and we estimate a nested latent class model with the aim of identifying trip purposes. We find that while the proposed methods improve model fit, the latent class model cannot distinguish trip purposes clearly based on mobile phone network data alone. The paper thus demonstrates the benefits and limitations of mobile phone data for demand forecasting.

This article estimates the external marginal costs of traffic accidents for light and heavy vehicles. We use microdata covering the Swedish national road network from 2004 to 2012. We estimate the risk elasticities for heavy and light vehicles by applying a set of count data models that explain how the number of fatalities, and of severely and slightly injured individuals, changes in response to changes in traffic flows. We analyse the impact of observed and unobserved heterogeneity by applying a range of different model specifications. The weighted average of the external marginal costs for light and heavy vehicles is computed for the full road network, taking into account the degree of risk internalization by vehicle type. The preferred model yields an external marginal cost of 0.28 cents per vehicle-kilometre (vkm) for light vehicles, and this result is robust across specifications. The estimates for heavy vehicles are more sensitive to the model specification; including or excluding traffic flows of light vehicles has a substantial effect. Our preferred estimate for the external marginal cost of heavy vehicles is 2.36 cents per vkm. We find negative risk elasticities for both light and heavy vehicles.

The European rail sector has united within the Shift2Rail programme to develop new technologies to increase the competitiveness of European rail. However, technological development, such as electrification, is also rapid in the road sector. Here, we model the effects of Shift2Rail innovations on rail market shares for three passenger transport use cases: high-speed, regional, and metro corridors. We find that Shift2Rail innovations could increase high-speed and regional rail market shares substantially in the scenario without the high penetration of electric cars, but only moderately when there is high penetration of electric cars. Since the competition from electric cars will increase over time, the best chance of sustaining or increasing the rail market share lies in the near future. Our results further suggest that electric cars are less competitive in congested metropolitan areas and hence pose less of a threat to metro corridors.

The accuracy of a transport demand model's predictions is inherently limited by the quality of the underlying data. This issue has been highlighted by the decline in response rates for transport surveys, which have traditionally served as the primary data source for estimating transport demand models. At the same time, mobile phone network data, not requiring active participation from subjects, have become increasingly available. However, some key trip and traveller characteristics enhancing the prediction power of the estimated models are not collected in mobile phone network data. In this paper we therefore investigate what can be gained from combining mobile phone network data with travel survey data, using the strengths of each data source, to estimate long-distance mode choice models. We propose and estimate a set of mode choice demand models on joint mobile phone network data and travel survey data. We show that combining the two data sources produces more credible estimates than models estimated on each data source separately. The travel survey should preferably include the variables: travel party size, cars per household licence, licence holding, in addition to origin, destination, mode, trip purpose, age, and gender of the respondent.

We estimate the income and fuel price elasticities of private car vehicle kilometres travelled (VKT) using fixed effects on registry micro panel data covering all Swedish households from 1999 to 2018. Such registry data, covering all individuals and cars in the country, are unique to Nordic countries and are comprehensive enough to allow fine segmentation of the population by both income groups and several municipality types. To address potential endogeneity arising if employees receive a wage compensation for long commutes, we apply the temporal changes in earned income tax credits as an instrumental variable. We find lower income and price elasticities (in absolute value) in the large cities, and larger elasticities in suburbs, other cities and in rural areas. We also find that the elasticities decrease with income, excluding the lowest income quartile, having the lowest elasticities. Specifically, we show theoretically and empirically that because the income elasticity varies considerably along the income distribution, the resulting income elasticity depends heavily on how the estimator assigns weight to different income groups, unless the specification explicitly allows for variation in the impact of income on VKT. Moreover, the impact of an income increase depends on to whom the income increase accrues to. For a uniform income increase, 0.2 is the preferred income elasticity. Our preferred long-run fuel price elasticity is −0.53. The short-run elasticities are lower. These elasticities apply to the full population and not only to car owners or drivers.