A Vehicle Transition Model

Abstract

This research deals with identifying policies that can stimulate the transition of private combustion vehicles to electric vehicles in Amsterdam, the Netherlands by 2040. As the problem owner, the Municipality of Amsterdam is responsible for improving the air quality of the city. Since road traffic has the largest share in pollution (i.e. 68%), the Municipality has defined short and long term objectives to switch from combustion to electric vehicles (e.g. by 2015, 2020 and 2040) and has set aside a budget of 20 million Euros to subsidize the transition until 2015. This study suggests that the public is most sensitive to the driving range, the recharging time and the price of a vehicle when they make a purchasing decision. The study also suggests that the availability of infrastructure is necessary for the transition but does not ensure the acceptance of electric vehicles by the consumers. To identify and quantify the factors that can influence the transition, system dynamics method is used to construct a model. The model is built with a simulation software, named Vensim. These factors are identified using the literature, questionnaires with 130 residents of Amsterdam and by interviews with the Municipality of Amsterdam and a private electricity supplier. The model -as a base case- shows the causal relationships between three sub-systems; required infrastructure, consumers’ choice and the flow of vehicles (i.e. from combustion to electric vehicles or vice versa) between 2009 and 2040. Uncertainty analysis and dominance analysis are performed to check whether the parameters influence the system behavior and to understand how the feedback loops influence the observed behavior. The model also shows the potential influence of the external factors on various policies under different contextual scenarios (e.g. optimistic, pessimistic, average etc.). Nine individual policies are identified and classified in three groups: (1) capital intensive policies, (2) low capital intensive policies and (3) a promising policy combination, which is further improved by taking into account the time factor (e.g. as opposed to introducing all policies at the same time). Overall, the model suggests that reaching the 2015 objective is not likely and the objectives for 2020 and 2040 are ambitious, unless there are significant technical developments in the battery technology for the electric vehicles. Technical developments between now and 2040 can lead to recharging vehicles faster (e.g. 5 times) and/or increasing the driving range (e.g. from 130 km to 350 km) and/or lowering the price of electric vehicles (e.g. by one third). This can make it more attractive for the consumers in their choices and help reaching the short and long term objectives. The model suggests that the transition takes place in three stages: slow increase in number of electric vehicles, fast increase in number of electric vehicles and slow increase in number of electric vehicles. The promising policy combination obtained from the model suggests that subsidizing the purchasing price of the electric vehicles at the end of the second stage leads to a higher number of electric vehicles in the system. Until that time it is suggested for the Municipality to continuously invest in creating public awareness and trust while making sure that there is sufficient capacity of infrastructure -whether with fast or slow recharging units. Another implication for the Municipality is to be ready to implement a large number of recharging units in a relatively short time in the second stage. This may have additional cost implications such as planning where to install the recharging units, the ability to produce and install these units and arranging sufficient logistics and human resources for the installation. Last but not least, the Municipality can monitor the consumer preferences against changes over time and regularly review the effectiveness of implemented policies.