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Spatial diffusion of electric vehicles in the German metropolitan region of Stuttgart

Author

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  • Susanne Linder

Abstract

At the moment, interest in electric vehicles (EVs) is increasing worldwide, mainly due to concerns about climate change and rising prices of fossil fuels. EVs still have some significant drawbacks compared to gasoline-powered cars. However, a small part of the population is expected to adopt this technology already within the next years, because higher purchase costs and lower driving range are of less concern to them. They are called the "Early Adopters" of EVs. In this study we developed scenarios for the spatial diffusion of EVs up to 2020 in private households in the municipalities and urban districts of the metropolitan region of Stuttgart in Germany. First, hypotheses of Early Adopters of EVs were constructed based on social mobility profiles and the demands of car drivers. Secondly, the number of these potential adopters was calculated with statistical data for each municipality and urban district. In a third step, we developed a Bass diffusion model with System Dynamics to simulate the spatial diffusion of EVs in the region of Stuttgart. The increase of EV-ownership in each Early Adopter-type in a single municipality depends on the chosen values of the parameters "Advertisement effectiveness", "Contact Rate" and "Adoption Fraction" of the Bass model. Furthermore, neighbourhood effects were modeled such that the increase of EVs in one municipality also depends on the increase of EVs in the neighbouring municipalities. In the baseline scenario, significant spatial differences in the diffusion of EVs up to 2020 become apparent: the highest number of EV-holders will be found in the urban areas of the region. There exist also differences in the number of EVs present at each Early Adopter-type: The "Urban trend-setter" is prevalent in the central districts of Stuttgart, while the "Multi-car family" is mostly located in the more rural municipalities of the region of Stuttgart. The "Dynamic senior citizen" is almost equally distributed in the urban and rural areas. The results of the spatial distribution of potential adopters of EVs can be used for the automobile industry's marketing campaigns as well as to identify the regional demand for EV charging infrastructure.

Suggested Citation

  • Susanne Linder, 2011. "Spatial diffusion of electric vehicles in the German metropolitan region of Stuttgart," ERSA conference papers ersa11p557, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa11p557
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    References listed on IDEAS

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    1. Azadeh Ahkamiraad & Yong Wang, 2018. "An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City," Energies, MDPI, vol. 11(3), pages 1-17, March.
    2. Mohammadreza Zolfagharian & Bob Walrave & A. Georges L. Romme & Rob Raven, 2020. "Toward the Dynamic Modeling of Transition Problems: The Case of Electric Mobility," Sustainability, MDPI, vol. 13(1), pages 1-23, December.
    3. Alderete Peralta, Ali & Balta-Ozkan, Nazmiye & Longhurst, Philip, 2022. "Spatio-temporal modelling of solar photovoltaic adoption: An integrated neural networks and agent-based modelling approach," Applied Energy, Elsevier, vol. 305(C).
    4. Zhuge, Chengxiang & Wei, Binru & Shao, Chunfu & Shan, Yuli & Dong, Chunjiao, 2020. "The role of the license plate lottery policy in the adoption of Electric Vehicles: A case study of Beijing," Energy Policy, Elsevier, vol. 139(C).
    5. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    6. McCoy, Daire & Lyons, Sean, 2014. "The diffusion of electric vehicles: An agent-based microsimulation," MPRA Paper 54560, University Library of Munich, Germany.

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