Several countries have set reduction targets for the transportation sector and the EU has an emission target for the ESR sector, which includes the transport sector. Carbon emission reduction targets in the road transport sector can be achieved through a combination of reduced traffic (VKT), an increased share of biofuels, and decreased vehicle emissions per kilometer. This paper explores cost-efficient contributions from different adaptation mechanisms by determining the optimal fossil fuel tax and biofuel share needed to meet climate emission targets in the EU and Sweden. Cost-efficient scenarios, or roadmaps, are important not only for for climate policy; they have implications for traffic planning, biofuel production and the transformation of the vehicle industry. 

We first forecast the traffic increase for cars, vans, and heavy vehicles from 2020 to 2050, as well as the average emissions per VKT for the future vehicle fleet. This is based on a vintage model that accounts for how new vehicles enter the fleet and how VKT vary by vehicle age. We consider the emission requirements for new vehicles as binding in the EU, though not in individual countries. Based on the forecasted transport demand and average emissions per VKT by year, we project emission reductions for 2030 and 2050 in the baseline scenario. We then calculate the optimal combination of a carbon tax increase and biofuel share that ensures the emission targets are met and determine the traffic response. 

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.

Achieving climate neutrality for heavy-duty trucks is, in some ways, more challenging than for cars, but it is necessary since they contribute nearly 30 percent of climate emissions from road transport. This paper compares the social costs of diesel trucks, battery-only trucks (BAT), and battery overhead line trucks (BOT), which can charge while driving via overhead lines on electric motorways (e-roads). We conduct this comparison within a decision-making context where the future developments of battery costs, as well as overhead line investment and maintenance costs, are uncertain.  

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.  

We examine the gender gap in commuting distance over time. We show that the gender gap in commuting distance has decreased less than the wage earnings gap. This holds true also for singles without children, where an uneven division of household duties should not be a factor. In 1998, the lion's share of the gender gap in commuting distance, conditional on effective job density, could be attributed to job specialization and women's higher marginal cost of commuting due to their higher share of unpaid work. However, by 2017, the influence of these factors has diminished, resulting in a growing “unexplained” gender gap in commuting distance.

Long-distance travel demand models have traditionally been estimated based only on national travel surveys (NTS). However, low response rate is an increasing issue in NTS data, calling into question the representativeness of the survey sample. In this paper we investigate which machine learning method (if any) is a suitable approach to model transportation mode choice for long-distance travel based on NTS data complemented by mobile phone network data. We find artificial neural network (ANN) to be the best candidate, and that for an ANN to be feasible as a mode choice model intended for policy development, the network architecture should fulfill a set of requirements: a utility function inspired network architecture, correct handling of non-available alternatives, and constraining the weights connected to the travel cost inputs to be the same for all modes. Furthermore, complementing NTS data with mobile phone network data provides more stable and feasible valuations of travel time compared to using NTS data only.

This study developed a multi-day electric truck trip cost model to analyse cost-minimising carrier use of stationary charging, battery swapping and catenary electric roads in the EU TEN-T road network setting. Each of these three charging options has its own advantages for carriers, thus motivating the analysis. The multi-day truck segment accounts for almost one-fifth of all truck tonne kilometres within the EU, making it important to consider in the quest to decarbonise this sector. Nevertheless, this segment has until now been sparsely analysed in the literature on truck electrification. Based on the scenario analysis, the carrier truck trip costs in the current state (year 2024) and future state (year 2035) was analysed. The current state analysis revealed that catenary electric roads are a cost-minimising option to carriers, whereas future state analysis revealed that catenary electric roads and swapping can perform on par while being cost-advantageous vis-à-vis stationary charging. The sensitivity of these findings to battery parameters, electric road user charges, swapping fee levels and charging alliances was reported. The implications of the findings for carriers, vehicle producers and policymakers were discussed.

Company cars, juridically owned cars possessed and used privately by employees, are common in most OECD countries. The cost of possessing and using these cars is generally lower than if the car was bought privately, which leads to welfare losses from increased car possession and expenditure. However, the previous literature has taken the productivity of the employee receiving a company car as given. We believe that there are two ways company cars can impact productivity. First, the lower cost of company cars than privately owned cars also implies a lower commuting cost. This may enable the employee to accept an employer further away, that may be a more productive match. Second, the employee would pay more in income tax if they received the monetary value of the company car as an increased wage rather than a company car. This decrease in the marginal tax rate may induce the employee to take on a more demanding position where they are more productive. 

To estimate the effect of company car possession on productivity (taken as gross wage earnings) and tax revenues, we use fixed effects models applied to Swedish registry micro panel data from 2005 to 2020. The rich dataset contains socio-demographic variables for all adult Swedish individuals, where we can observe the residential location and the firm location for all workers. We only consider employed individuals, yielding 54 million observations. We take gross wage earnings to include cash wage earnings, social service contributions, and potential car costs if the employee is given a company car. Tax revenues include labour tax on wage earnings, the fringe benefit value from the company car, and social service contributions.  

Many countries signed the Paris Agreement (2015) to reduce greenhouse gas emissions. However, the national commitments do not include international shipping. In a separate agreement, countries are negotiating specific targets to reduce emissions from international shipping by 20 to 30% in 2030 and by 70 to 80% in 2040, compared to emission levels in 2008. These targets exist, but sufficient global emission reduction policies are not yet in place. The updated greenhouse gas strategy stipulates a limitation on the greenhouse gas intensity of fuels, complemented by an economic instrument. Since the IMO does not possess legislative power over member states, the policy instruments it stipulates are more likely to be adopted if they are self-enforcing, ensuring that countries are better off with the policy measures than without. In this paper, we analyze three policy scenarios. The first best scenario uses the world social cost of carbon as a basis. Since full global cooperation is unrealistic due to unenforceable international agreements, this scenario serves as a benchmark. The second type of scenario is the non-cooperative scenario, where each country sets its own carbon tax based on its national climate damage. The third type of scenario is the bilateral cooperation scenario, where a pair of countries agrees on a common carbon policy. 

Several studies have found that cost-benefit analysis (CBA) of transport projects has a limited impact on project selection, even if decision-makers claim it has. One potential reason for this could be that CBA is conducted too late, making it politically difficult to filter out projects with a low NBCR (net benefit-cost ratio). A possible remedy for this problem is to identify general project-specific characteristics that affect social costs and benefits at early stages—during the front end of the project—when there is greater flexibility to take different courses of action and the economic and political costs of making changes are relatively low. However, to our knowledge, no previous research has identified such project-specific characteristics, despite the growing interest in this idea within project management literature. One possible reason for this gap in the literature could be a lack of data—few researchers have access to detailed data on a large number of transport project CBAs. This paper contributes to the literature by empirically identifying which project-specific characteristics, available at early stages, are associated with high or low NBCR in a project. It uses data from over one thousand road projects from four national transport investment plans in Sweden and Norway.