This working paper describes main evolutions in household expenditure for transport in Belgium. Results are based on data from national accounts (National Accounts Institute, Eurostat) as well as data from Household budget surveys (Statbel).
Transport models used for long-term projections should reflect the impact of shared, automated and electric mobility modes. The objective of the current paper is to derive lessons from the existing literature on vehicle ownership modelling to find options to further improve the PLANET model, which is used for projections of transport demand in Belgium.
PLANET is already well equipped to represent the impacts of shared and automated cars on the opportunity cost of travel time, the load factors and the annual mileage of cars.
The PLANET model, developed by the Federal Planning Bureau within the framework of a cooperation agreement with the Federal Public Service Mobility and Transport, makes it possible to calculate the long-term evolution of transport demand in Belgium. Transport demand includes both passenger and freight transport and is broken down by mode of transport. For rail transport, demand is projected assuming constant average speed on the network over the whole projection period. The PLANET model does not take into account railway infrastructure capacity; in other words, it assumes that the network will be able to cope with any increase in demand without affecting the quality of service. Since the utilisation rate of some lines is already very high, there was a need to extend the scope of analysis of PLANET to estimate the impact of the future railway demand on the network utilisation rate. That analysis, performed at a detailed spatial level (the rail sections), is useful and pertinent, particularly for rail operators and public authorities within the context of the railway investment plans.
The main objective of the paper is to evaluate the development of the EV in a couple of selected energy scenarios, to address the influence climate policy and the presence of nuclear energy can have on this development and to estimate the impact of different EV penetration rates on electricity demand. Throughout the paper, it becomes clear that, in the absence of specific, dedicated EV public programmes, policies and measures aimed at curbing climate change spark off the penetration of EVs, especially on a longer time horizon (up to 2030): with post 2012 climate policy in place, the pure EV penetration in 2020 attains approximately 2% of the road vehicle fleet while in 2030, around 5% of the road vehicle fleet will be electrically propelled. In the time span up to 2020, the electricity consumption of the EVs is rather small: it ranges between 0.4 and 0.5 TWh. It isn’t until 2025 and 2030 that EVs start to have a more visible impact on electricity consumption, stretching out between 1.2 and 1.4 TWh which represents approximately 1% of the total final electricity demand in 2030. Nuclear energy can then be a modest incentive for EVs through, assuming perfect market functioning, a decrease in electricity prices, hence triggering a slightly higher EV penetration.
This paper assumes that no specific dedicated policies are in place to stimulate the upsurge of EVs. If policy makers decide they want to support and even intensify the expansion of EVs considering their positive impact on oil independency, climate change, transport efficiency and possibly job retention/creation, further policy measures (beyond climate policy) embedded in a long term national master plan are of utmost importance.