[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i3p656-d488506.html
   My bibliography  Save this article

Stimulating E-Mobility Diffusion in Germany (EMOSIM): An Agent-Based Simulation Approach

Author

Listed:
  • Tobias Buchmann

    (Center for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW), 70563 Stuttgart, Germany)

  • Patrick Wolf

    (Center for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW), 70563 Stuttgart, Germany)

  • Stefan Fidaschek

    (Center for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW), 70563 Stuttgart, Germany)

Abstract
The German Climate Action Plan targets an electric vehicle fleet of 6 million by 2030. However, from today’s perspective, we are far away from a path that is steep enough to reach this goal. In order to identify how different policy instruments can stimulate e-mobility diffusion in Germany, we build and calibrate an agent-based simulation model (ABM). The model allows for the consideration of the rich dynamics of social influence as well as the heterogeneity of actors and is flexible enough to be applied with other technologies. We simulate different policy scenarios against a business as usual (BAU) scenario. We show that with the currently implemented set of policies (BAU scenario), it is very unlikely that the envisaged goals in terms of e-mobility diffusion can be reached. Moreover, we suggest additional measures such as a carbon tax on fuel, more charging points, and higher direct subsidies, which are as a combined package likely to have a significantly positive effect on the diffusion of electric cars.

Suggested Citation

  • Tobias Buchmann & Patrick Wolf & Stefan Fidaschek, 2021. "Stimulating E-Mobility Diffusion in Germany (EMOSIM): An Agent-Based Simulation Approach," Energies, MDPI, vol. 14(3), pages 1-25, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:656-:d:488506
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/3/656/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/3/656/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gnann, Till & Plötz, Patrick & Kühn, André & Wietschel, Martin, 2015. "Modelling market diffusion of electric vehicles with real world driving data – German market and policy options," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 95-112.
    2. Werker, C. & Brenner, T., 2004. "Empirical calibration of simulation models," Working Papers 04.13, Eindhoven Center for Innovation Studies.
    3. Smith, Adrian & Stirling, Andy & Berkhout, Frans, 2005. "The governance of sustainable socio-technical transitions," Research Policy, Elsevier, vol. 34(10), pages 1491-1510, December.
    4. Mariana Mazzucato & Carlota Perez, 2014. "Innovation as Growth Policy: the challenge for Europe," SPRU Working Paper Series 2014-13, SPRU - Science Policy Research Unit, University of Sussex Business School.
    5. Matteo Richiardi & Roberto Leombruni & Nicole J. Saam & Michele Sonnessa, 2006. "A Common Protocol for Agent-Based Social Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(1), pages 1-15.
    6. Sun, Lishan & Huang, Yuchen & Liu, Shuli & Chen, Yanyan & Yao, Liya & Kashyap, Anil, 2017. "A completive survey study on the feasibility and adaptation of EVs in Beijing, China," Applied Energy, Elsevier, vol. 187(C), pages 128-139.
    7. Querini, Florent & Benetto, Enrico, 2014. "Agent-based modelling for assessing hybrid and electric cars deployment policies in Luxembourg and Lorraine," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 149-161.
    8. Javid, Roxana J. & Nejat, Ali, 2017. "A comprehensive model of regional electric vehicle adoption and penetration," Transport Policy, Elsevier, vol. 54(C), pages 30-42.
    9. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-131, March.
    10. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    11. Katz, Michael L & Shapiro, Carl, 1986. "Technology Adoption in the Presence of Network Externalities," Journal of Political Economy, University of Chicago Press, vol. 94(4), pages 822-841, August.
    12. Pepijn Schreinemachers & Thomas Berger & Aer Sirijinda & Suwanna Praneetvatakul, 2009. "The Diffusion of Greenhouse Agriculture in Northern Thailand: Combining Econometrics and Agent‐Based Modeling," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 513-536, December.
    13. Martin Achtnicht, 2012. "German car buyers’ willingness to pay to reduce CO 2 emissions," Climatic Change, Springer, vol. 113(3), pages 679-697, August.
    14. Ravi Shankar & James Marco & Francis Assadian, 2012. "The Novel Application of Optimization and Charge Blended Energy Management Control for Component Downsizing within a Plug-in Hybrid Electric Vehicle," Energies, MDPI, vol. 5(12), pages 1-32, November.
    15. Shafiei, Ehsan & Thorkelsson, Hedinn & Ásgeirsson, Eyjólfur Ingi & Davidsdottir, Brynhildur & Raberto, Marco & Stefansson, Hlynur, 2012. "An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland," Technological Forecasting and Social Change, Elsevier, vol. 79(9), pages 1638-1653.
    16. Church, Jeffrey & Gandal, Neil, 1993. "Complementary network externalities and technological adoption," International Journal of Industrial Organization, Elsevier, vol. 11(2), pages 239-260, June.
    17. Münzel, Christiane & Plötz, Patrick & Sprei, Frances & Gnann, Till, 2019. "How large is the effect of financial incentives on electric vehicle sales? – A global review and European analysis," Energy Economics, Elsevier, vol. 84(C).
    18. Caiani, Alessandro & Godin, Antoine & Caverzasi, Eugenio & Gallegati, Mauro & Kinsella, Stephen & Stiglitz, Joseph E., 2016. "Agent based-stock flow consistent macroeconomics: Towards a benchmark model," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 375-408.
    19. Gnann, Till & Stephens, Thomas S. & Lin, Zhenhong & Plötz, Patrick & Liu, Changzheng & Brokate, Jens, 2018. "What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 158-164.
    20. Giorgio Fagiolo & Paul Windrum & Alessio Moneta, 2006. "Empirical Validation of Agent Based Models: A Critical Survey," LEM Papers Series 2006/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    21. Maxwell Brown, 2013. "Catching the PHEVer: Simulating Electric Vehicle Diffusion with an Agent-Based Mixed Logit Model of Vehicle Choice," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(2), pages 1-5.
    22. Sierzchula, William & Bakker, Sjoerd & Maat, Kees & van Wee, Bert, 2014. "The influence of financial incentives and other socio-economic factors on electric vehicle adoption," Energy Policy, Elsevier, vol. 68(C), pages 183-194.
    23. Kaifang Zheng & Suling Jia, 2017. "Promoting the Opportunity Identification of Industrial Symbiosis: Agent-Based Modeling Inspired by Innovation Diffusion Theory," Sustainability, MDPI, vol. 9(5), pages 1-24, May.
    24. 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.
    25. Farrell, Joseph & Saloner, Garth, 1986. "Installed Base and Compatibility: Innovation, Product Preannouncements, and Predation," American Economic Review, American Economic Association, vol. 76(5), pages 940-955, December.
    26. Dijk, Marc & Orsato, Renato J. & Kemp, René, 2013. "The emergence of an electric mobility trajectory," Energy Policy, Elsevier, vol. 52(C), pages 135-145.
    27. Gert Berckmans & Maarten Messagie & Jelle Smekens & Noshin Omar & Lieselot Vanhaverbeke & Joeri Van Mierlo, 2017. "Cost Projection of State of the Art Lithium-Ion Batteries for Electric Vehicles Up to 2030," Energies, MDPI, vol. 10(9), pages 1-20, September.
    28. Mark Grinblatt & Matti Keloharju & Seppo Ikäheimo, 2008. "Social Influence and Consumption: Evidence from the Automobile Purchases of Neighbors," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 735-753, November.
    29. Raul Bagni & Roberto Berchi & Pasquale Cariello, 2002. "A Comparison of Simulation Models Applied to Epidemics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-5.
    30. Langbroek, Joram H.M. & Franklin, Joel P. & Susilo, Yusak O., 2016. "The effect of policy incentives on electric vehicle adoption," Energy Policy, Elsevier, vol. 94(C), pages 94-103.
    31. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    32. Carlo Bianchi & Pasquale Cirillo & Mauro Gallegati & Pietro Vagliasindi, 2007. "Validating and Calibrating Agent-Based Models: A Case Study," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 245-264, October.
    33. Wang, Shanyong & Li, Jun & Zhao, Dingtao, 2017. "The impact of policy measures on consumer intention to adopt electric vehicles: Evidence from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 14-26.
    34. Wolf, Ingo & Schröder, Tobias & Neumann, Jochen & de Haan, Gerhard, 2015. "Changing minds about electric cars: An empirically grounded agent-based modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 269-285.
    35. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
    36. Franco Malerba & Luigi Orsenigo, 2000. "Towards a History Friendly Model of Innovation, Market Structure and Regulation in the Dynamics of the Pharmaceutical Industry: the Age of Random Screening," KITeS Working Papers 124, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Jan 2001.
    37. Geels, Frank W., 2002. "Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study," Research Policy, Elsevier, vol. 31(8-9), pages 1257-1274, December.
    38. Eppstein, Margaret J. & Grover, David K. & Marshall, Jeffrey S. & Rizzo, Donna M., 2011. "An agent-based model to study market penetration of plug-in hybrid electric vehicles," Energy Policy, Elsevier, vol. 39(6), pages 3789-3802, June.
    39. Nicholas Stern, 2008. "The Economics of Climate Change," American Economic Review, American Economic Association, vol. 98(2), pages 1-37, May.
    40. Malerba, Franco, et al, 1999. "'History-Friendly' Models of Industry Evolution: The Computer Industry," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 8(1), pages 3-40, March.
    41. Tobias Buchmann, 2015. "The Evolution of Innovation Networks," Springer Books, Springer, edition 127, number 978-3-658-10383-5, December.
    42. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.
    43. Axsen, Jonn & Orlebar, Caroline & Skippon, Stephen, 2013. "Social influence and consumer preference formation for pro-environmental technology: The case of a U.K. workplace electric-vehicle study," Ecological Economics, Elsevier, vol. 95(C), pages 96-107.
    44. Silvia, Chris & Krause, Rachel M., 2016. "Assessing the impact of policy interventions on the adoption of plug-in electric vehicles: An agent-based model," Energy Policy, Elsevier, vol. 96(C), pages 105-118.
    45. Nicholas Kaldor, 1961. "Capital Accumulation and Economic Growth," International Economic Association Series, in: D. C. Hague (ed.), The Theory of Capital, chapter 0, pages 177-222, Palgrave Macmillan.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Leonardo Bitencourt & Tiago Abud & Rachel Santos & Bruno Borba, 2021. "Bass Diffusion Model Adaptation Considering Public Policies to Improve Electric Vehicle Sales—A Brazilian Case Study," Energies, MDPI, vol. 14(17), pages 1-19, September.
    2. 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).
    3. Elham Allahmoradi & Saeed Mirzamohammadi & Ali Bonyadi Naeini & Ali Maleki & Saleh Mobayen & Paweł Skruch, 2022. "Policy Instruments for the Improvement of Customers’ Willingness to Purchase Electric Vehicles: A Case Study in Iran," Energies, MDPI, vol. 15(12), pages 1-17, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Liu, Junbei & Zhuge, Chengxiang & Tang, Justin Hayse Chiwing G. & Meng, Meng & Zhang, Jie, 2022. "A spatial agent-based joint model of electric vehicle and vehicle-to-grid adoption: A case of Beijing," Applied Energy, Elsevier, vol. 310(C).
    3. Papachristos, George, 2017. "Diversity in technology competition: The link between platforms and sociotechnical transitions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 291-306.
    4. Gnann, Till & Stephens, Thomas S. & Lin, Zhenhong & Plötz, Patrick & Liu, Changzheng & Brokate, Jens, 2018. "What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 158-164.
    5. Giovanna Devetag & Enrico Zaninotto, 2001. "The imperfect hiding: Some introductory concepts and preliminary issues on modularity," ROCK Working Papers 010, Department of Computer and Management Sciences, University of Trento, Italy, revised 13 Jun 2008.
    6. Wolbertus, Rick & van den Hoed, Robert & Kroesen, Maarten & Chorus, Caspar, 2021. "Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas: An agent-based simulation study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 262-285.
    7. Peng, Ruoqing & Tang, Justin Hayse Chiwing G. & Yang, Xiong & Meng, Meng & Zhang, Jie & Zhuge, Chengxiang, 2024. "Investigating the factors influencing the electric vehicle market share: A comparative study of the European Union and United States," Applied Energy, Elsevier, vol. 355(C).
    8. Zhang, Junjie & Jia, Rongwen & Yang, Hangjun & Dong, Kangyin, 2022. "Does electric vehicle promotion in the public sector contribute to urban transport carbon emissions reduction?," Transport Policy, Elsevier, vol. 125(C), pages 151-163.
    9. Foxon, Timothy J., 2011. "A coevolutionary framework for analysing a transition to a sustainable low carbon economy," Ecological Economics, Elsevier, vol. 70(12), pages 2258-2267.
    10. Kenneth Arrow, 2000. "Increasing returns: historiographic issues and path dependence," The European Journal of the History of Economic Thought, Taylor & Francis Journals, vol. 7(2), pages 171-180.
    11. Luo, Qi & Yin, Yunlei & Chen, Pengyu & Zhan, Zhenfei & Saigal, Romesh, 2022. "Dynamic subsidies for synergistic development of charging infrastructure and electric vehicle adoption," Transport Policy, Elsevier, vol. 129(C), pages 117-136.
    12. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2018. "IRPsim: A techno-socio-economic energy system model vision for business strategy assessment at municipal level," Contributions of the Institute for Infrastructure and Resources Management 02/2018, University of Leipzig, Institute for Infrastructure and Resources Management.
    13. Mo Chen & Rudy X. J. Liu & Chaochao Liu, 2021. "How to Improve the Market Penetration of New Energy Vehicles in China: An Agent-Based Model with a Three-Level Variables Structure," Sustainability, MDPI, vol. 13(21), pages 1-17, November.
    14. Sen, Burak & Noori, Mehdi & Tatari, Omer, 2017. "Will Corporate Average Fuel Economy (CAFE) Standard help? Modeling CAFE's impact on market share of electric vehicles," Energy Policy, Elsevier, vol. 109(C), pages 279-287.
    15. Mirzadeh Phirouzabadi, Amir & Savage, David & Blackmore, Karen & Juniper, James, 2020. "The evolution of dynamic interactions between the knowledge development of powertrain systems," Transport Policy, Elsevier, vol. 93(C), pages 1-16.
    16. Huotari, Pontus & Järvi, Kati & Kortelainen, Samuli & Huhtamäki, Jukka, 2017. "Winner does not take all: Selective attention and local bias in platform-based markets," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 313-326.
    17. Safarzyńska, Karolina & Frenken, Koen & van den Bergh, Jeroen C.J.M., 2012. "Evolutionary theorizing and modeling of sustainability transitions," Research Policy, Elsevier, vol. 41(6), pages 1011-1024.
    18. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2019. "A review of designing empirically grounded agent-based models of innovation diffusion: Development process, conceptual foundation and research agenda," Contributions of the Institute for Infrastructure and Resources Management 01/2019, University of Leipzig, Institute for Infrastructure and Resources Management.
    19. Seiho Kim & Jaesik Lee & Chulung Lee, 2017. "Does Driving Range of Electric Vehicles Influence Electric Vehicle Adoption?," Sustainability, MDPI, vol. 9(10), pages 1-15, October.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:656-:d:488506. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.