How to foster electric vehicle market penetration? – A model based assessment of policy measures and external factors

Panel: 4. Mobility, transport, and smart and sustainable cities

This is a peer-reviewed paper.

Authors:
Patrick Plötz, Fraunhofer Institute for Systems and Innovation Research ISI, Germany
Till Gnann, Fraunhofer Institute for Systems and Innovation Research ISI, Germany
Martin Wietschel, Fraunhofer Institute for Systems and Innovation Research ISI
Sandra Ullrich, Fraunhofer Institute for Systems and Innovation Research ISI, Germany

Abstract

Electric vehicles (EVs) have the potential to reduce green house gas emissions from the transport sector. However, the future market evolution of EVs strongly depends on several influencing factors such as battery and oil prices as well as their future evolution. The effect of these and other influencing factors as well as the resulting future market evolution are uncertain, yet policy makers need an empirical basis to take decisions impacting the future market evolution. Here, we study the market evolution of EVs in Germany until 2020 and perform a model-based assessment of influencing factors and different monetary policy measures. We use an agent-based model with a utility maximising decision function for several thousand individual private and commercial vehicle owners.

Our results reveal a great deal of uncertainty in the market evolution of EVs due to external conditions and the users' willingness-to-pay for this new technology. Energy prices have a large impact on EV market evolution as a 25% increase in fuel prices would double the number of EVs in stock by 2020 compared to a reference scenario. We find a special depreciation allowance for commercial vehicles and a subsidy of 1,000 Euro per vehicle to be the most effective and efficient monetary policy options. Furthermore, the high uncertainty of framework conditions and the EV market evolution implies that policies to foster market diffusion of EVs should be dynamically adaptable to react to changing framework conditions.