Development of the GreenREFORM sub-model for Danish air, sea and land transportation is described below.

The transportation sub-model describes the traffic volume and fuel consumption of firms and households and the associated emission of greenhouse gases and other pollutants as well as external congestion effects. There are four main requirements for the transportation sub-model: First of all, the sub-model must be able to describe the most important means of private and public transportation and the choice between these. Secondly, it should be able to describe the gradual transition towards electric vehicles and the use other renewable fuels in transportation. In light of these two requirements, the sub-model must also be able to explain the effect of changes to taxes and subsidies related to different transportation means and vehicle types, including the effect on the emissions of greenhouse gases and other pollutants from fuel consumption. Finally, the model should explain stylized effects of infrastructure investments on congestion and the demand for alternative fuels, such as electricity.

Sub-model for transportation

The following paper is the first published working paper about the transportation sub-model. The paper gives an introduction to and an overview of the transportation sub-model and highlights a number of observations, which have been important in the development of the model-design. The paper has some overlap with the following text, but provides a more detailed description.

The sub-model for transportation

The modelling of externalities from transportation focus on emissions and congestion. This means that other externalities, such as accidents, noise and wearing of infrastructure are not included in the sub-model. While the emission of greenhouse gasses and other pollutants are the result of fuel consumption, accidents, noise, wearing of infrastructure and congestion are mostly caused by the traffic volume. The reason for including congestion, and not the other externalities cause by traffic volume, is that firms and households not only face a direct monetary cost of transportation, but also the cost related to spending time in traffic. This is, for example, important when analyzing the effect of environmental and climate-related tax reforms, since households’ labor supply is closely related to their time spent commuting.

The modelling of emissions from transportation focus on energy-related emissions of greenhouse gasses and other pollutants. Upstream emissions from the production of vehicles are not modelled explicitly in the transportation sub-model. This is due to the fact that most vehicles in Denmark are imported from other countries, implying that emissions from the associated production process are not counted as emissions from Danish economic activity. The chosen energy-related emissions are:

  • Greenhouse gasses (methane, nitrous oxide and carbon dioxide)
  • Particulate pollution
  • NOx (nitric oxide and nitrogen dioxide)
  • Sulphur dioxide

Finally, the sub-model does not account for international transit through Denmark, since there is not sufficient data to to say anything about the scope of this. The transportation sub-model does, however, include emissions from Danish-owned vehicles overseas. These are included because a significant share of emissions from Danish-owned vehicles occurs outside of Denmark, due to the size of the Danish shipping industry. 

The sub-model uses a range of data sources from Statistics Denmark, the Danish Energy Agency, the Danish Centre for Environment and Energy and the transportation model Landstrafikmodellen, which is based at the Danish Technical University. An explanation of the data used to separate the transportation sector into multiple subsectors, as well as how this accommodates the first modelling requirement, can be found below. This will be followed by an explanation of the data used to categorize vehicle types, as well as the data outlining future vehicle and fuel technologies, which households and firms will be able to invest in between now and 2050. This paragraph also described how the second and third model requirements are implemented in the sub-model. The final paragraph summarized the data from Landstrafikmodellen, which is primarily used to determine the baseline forecast and to describe congestion effects.

As the basis for the categorization of different methods of transportation, an even more detailed division of transportation subsectors is used than in the 117-sector version of the National Accounts. This means that the transportation sector in the sub-model is split into 12 transportation subsectors, rather than seven in the 117-sector version of the National Accounts. This division of subsectors is intended to more adequately describe the most important transportation methods and to introduce the distinction between passenger and freight transportation. The subsectors are outlined in the following table.

Main category Transportation method Subsector(s)
Land transport Trains/metro Freight transportation by train
    Passenger transportation by regional or long-distance trains
    Passenger transportation by S-trains, local trains and metro
Land transport Busses Passenger transportation by bus (short- and long-distance)
Land transport Road vehicles excl. busses Passenger transportation by taxi
    Road freight transportation
    Postal services under the Universal Postal Obligation
    Other postal and courier services
Sea transport Ships Freight transportation by ship
    Passenger transportation by ship
Air transport Planes Freight transportation by plane
    Passenger transportation by plane


The National Accounts only include aggregates for the capital stock of vehicles owned by firms and households. In order to estimate the environmental and climate-related consequences from the use of this stock, it is necessary to determine which vehicle types are being used. The vehicle capital stock aggregates are therefore categorized using an inventory of the stock of different vehicle types made available by Statistics Denmark. To limit dimensionality, it is assumed that land vehicles only differ in terms of overall category, i.e. passenger car, bus, cargo truck and delivery van, and fuel type, i.e. petrol, diesel and electricity. As such, the sub-model does not explicitly account for vehicle size or make-model. The inventory from Statistics Denmark is then linked to emission coefficients and fuel efficiency coefficients, made available by the Danish Centre for Environment and Energy (DCE).

Future transportation technologies, which households and firms can invest in, are based on the Danish Energy Agency’s model for alternative fuel technologies (Alternativ Drivmiddelmodel), which includes a technology catalogue of representative vehicle technologies until 2050. The model also includes a technology catalogue for the plants that produce fuel as well as the infrastructure required for the fuel to reach consumers. By using the Danish Energy Agency’s technology catalogues for alternative fuel technologies , the transportation sub-model can account for the technological and economic potential in the entire value chain, from the production of fuel to the consumption of fuel in various vehicle types. 

Finally, the transportation sub-model uses simulations of Landtrafikmodellen (the National Traffic Model) up until 2030. There are three reasons for using this data. First, the simulations are used to determine the sub-model’s baseline forecast, such that GreenREFORM describes and replicates the expected traffic volume until 2030, as forecasted by Landstrafikmodellen. Secondly, the data is used to categorize the traffic volume by purpose, such as commuting and leisure. Finally, GreenREFORM replicates the effect of infrastructure investments on congestion, as predicted by Landstrafikmodellen. Since the transportation sub-model lacks a spatial dimension, estimates from LTM data will enter into GreenREFORM exogenously and will be used to calibrate the effect of infrastructure investments. The goal is thereby for the transportation sub-model to be able to explain, in a stylized manner, the effects of infrastructure investments.