En model for godstransportens udvikling - DCE - Nationalt Center for ...

numbers are provided from which it is impossible to find the figures
for the single trip, and hence find the correct tonne kilometres.
It is now possible to calculate the energy consumption and the emissions
from the different types of vehicles and types of transport (own
and haulage transport) using emission factors. The factors are fixed,
apart from changes due to EU regulations on the emissions of particulate
matter from vehicles to be introduced in the future. The importance
of these regulations is major, and is actually the one element
having the largest impact on the final size of the emissions.
Data and information
Two primary data-sets have been used in the analysis. The first dataset
contains information on the production in the different economic
sectors as well as in the different types of goods both measured in
DKK and in tonnes. This data-set covers the period from 1981 to 1992
for the values in DKK, and from selected years concerning the production
in tonnes.
The second data-set contains information on the road freight transport
in Denmark in the period from 1980 to 1997. The information in
this data-set covers the amounts transported in 23 types of goods
(including trips without load and a mixed goods category), the size of
the vehicle, who is the performer of the transport (own or haulage),
the number of trips, the vehicle kilometres and the tonnes kilometres.
All of this information is on an annual basis. The data-set is made up
of a sampled survey. It is therefore expected that it contains some
errors in the categorisation of the goods and in the amount transported.
These errors influence the estimations, and are in some cases
the reason why it is not possible to give a reasonable description of a
particular coefficient. However, this problem is not of such magnitude
that it makes it impossible to use the data.
The production has in the mentioned time-period shown a general
increase in value, which is also seen when disaggregated on the different
types of goods. Looking at the different types of goods it becomes
clear that neither the level nor the size of the increase is the
same. This indicates the potential benefit from using the high level of
detail in the calculations in this part of the model. It is also possible to
find a positive trend in the transport performed. However, this picture
becomes less pronounced when looking at the different categories
used in the model. In some categories (goods, mode and size) a
decline can be found, especially when looking at small company
owned vehicles. Some of the differences between the different categories
can be seen in Figure 2. Here a distribution of the amounts transported
and the traffic performance on the size and the ownership of
the vehicles are shown.
Similar differences can be observed when looking at the average trip
lengths and the average load per trip depending on the type of
goods, mode and ownership. All these findings support the general
idea of the model, namely to split the calculations into separate calculations
for the many individual categories. The separation will give
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