IDEAS home Printed from https://ideas.repec.org/a/igg/joris0/v12y2021i4p1-17.html
   My bibliography  Save this article

The Socio-Technical Transition to Electric Vehicle Mobility in Turkey: A Multi-Level Perspective

Author

Listed:
  • Şükrü İmre

    (Istanbul Technical University, Turkey)

  • Fatih Canıtez

    (Istanbul Technical University, Turkey)

  • Dilay Çelebi

    (Istanbul Technical University, Turkey)

Abstract

The adoption of Electric Vehicles (EVs) has been examined in various settings, yet the issue has rarely been addressed for less developed settings in terms of transport institutions, policies and practices. Turkey, with its rapidly growing emerging economy, presents such a setting for the adoption of EVs. There are various reasons for why the adoption of EVs is still considerably limited in Turkey. A multi-dimensional and multi-actor analysis of the EV landscape can help us better understand the dynamics of transition to EVs. In this paper, a Multi-Level Perspective (MLP) framework is used to examine the current state of EV adoption in Turkey and to interpret the prospects of a possible transition to EVs. Our study shows that a potential transition to EVs in Turkey presents many socio-technical challenges to overcome including current policies, institutions, market dynamics, technological infrastructure, and social limitations. The insights from this review can be used for settings where policies and institutions are not developed enough to achieve a transition to EVs.

Suggested Citation

  • Şükrü İmre & Fatih Canıtez & Dilay Çelebi, 2021. "The Socio-Technical Transition to Electric Vehicle Mobility in Turkey: A Multi-Level Perspective," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 12(4), pages 1-17, October.
  • Handle: RePEc:igg:joris0:v:12:y:2021:i:4:p:1-17
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJORIS.294117
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mitchell, P. L., 1997. "Misuse of regression for empirical validation of models," Agricultural Systems, Elsevier, vol. 54(3), pages 313-326, July.
    2. Miao, Shuwei & Xie, Kaigui & Yang, Hejun & Tai, Heng-Ming & Hu, Bo, 2017. "A Markovian wind farm generation model and its application to adequacy assessment," Renewable Energy, Elsevier, vol. 113(C), pages 1447-1461.
    3. Jack P. C. Kleijnen & Bert Bettonvil & Willem Van Groenendaal, 1998. "Validation of Trace-Driven Simulation Models: A Novel Regression Test," Management Science, INFORMS, vol. 44(6), pages 812-819, June.
    4. Michael Sony & Subhash Naik, 2011. "Successful implementation of Six Sigma in services: an exploratory research in India Inc," International Journal of Business Excellence, Inderscience Enterprises Ltd, vol. 4(4), pages 399-419.
    5. Sony Michael & V. Mariappan, 2012. "Strategic role of capacity management in electricity service centre using Markovian and simulation approach," International Journal of Business and Systems Research, Inderscience Enterprises Ltd, vol. 6(1), pages 59-88.
    6. Michael Sony & V. Mariappan, 2019. "Stochastic Model for Preventing Blackouts: A Live Case," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 10(1), pages 41-55, January.
    Full references (including those not matched with items on IDEAS)

    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. Tedeschi, Luis Orlindo, 2006. "Assessment of the adequacy of mathematical models," Agricultural Systems, Elsevier, vol. 89(2-3), pages 225-247, September.
    2. Alagarswamy, G. & Singh, P. & Hoogenboom, G. & Wani, S. P. & Pathak, P. & Virmani, S. M., 2000. "Evaluation and application of the CROPGRO-Soybean simulation model in a Vertic Inceptisol," Agricultural Systems, Elsevier, vol. 63(1), pages 19-32, January.
    3. van Schaik, F.D.J. & Kleijnen, J.P.C., 2001. "Sealed-Bid Auctions : Case Study," Other publications TiSEM dd93bf40-d790-4fd1-ac2c-f, Tilburg University, School of Economics and Management.
    4. Qiongfang Li & John Gowing, 2005. "A Daily Water Balance Modelling Approach for Simulating Performance of Tank-Based Irrigation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(3), pages 211-231, June.
    5. Bouman, B.A.M. & van Laar, H.H., 2006. "Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions," Agricultural Systems, Elsevier, vol. 87(3), pages 249-273, March.
    6. Correndo, Adrian A. & Hefley, Trevor J. & Holzworth, Dean P. & Ciampitti, Ignacio A., 2021. "Revisiting linear regression to test agreement in continuous predicted-observed datasets," Agricultural Systems, Elsevier, vol. 192(C).
    7. Legato, Pasquale & Mazza, Rina M., 2001. "Berth planning and resources optimisation at a container terminal via discrete event simulation," European Journal of Operational Research, Elsevier, vol. 133(3), pages 537-547, September.
    8. M. Sezer & S. Tarhan, 2005. "Model parameters of growth curves of three meat-type lines of Japanese quail," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 50(1), pages 22-30.
    9. Schönhart, Martin & Schmid, Erwin & Schneider, Uwe A., 2009. "CropRota – A Model to Generate Optimal Crop Rotations from Observed Land Use," Discussion Papers DP-45-2009, University of Natural Resources and Life Sciences, Vienna, Department of Economics and Social Sciences, Institute for Sustainable Economic Development.
    10. Kleijnen, Jack P. C. & Sargent, Robert G., 2000. "A methodology for fitting and validating metamodels in simulation," European Journal of Operational Research, Elsevier, vol. 120(1), pages 14-29, January.
    11. Ilan Halachmi & Yitzhak Simon & Noam Mozes, 2014. "Simulation of the shift from marine netcages to inland recirculating aquaculture systems," Annals of Operations Research, Springer, vol. 219(1), pages 85-99, August.
    12. Kleijnen, Jack P.C. & van Schaik, Frans D.J., 2011. "Sealed-bid auction of Netherlands mussels: Statistical analysis," International Journal of Production Economics, Elsevier, vol. 132(1), pages 154-161, July.
    13. McCall, D. G. & Bishop-Hurley, G. J., 2003. "A pasture growth model for use in a whole-farm dairy production model," Agricultural Systems, Elsevier, vol. 76(3), pages 1183-1205, June.
    14. Tedeschi, Luis Orlindo & Fox, Danny G. & Guiroy, Pablo J., 2004. "A decision support system to improve individual cattle management. 1. A mechanistic, dynamic model for animal growth," Agricultural Systems, Elsevier, vol. 79(2), pages 171-204, February.
    15. Bockstaller, C. & Girardin, P., 2003. "How to validate environmental indicators," Agricultural Systems, Elsevier, vol. 76(2), pages 639-653, May.
    16. Chatterjee, Sheshadri & Chaudhuri, Ranjan & González, Vanessa Izquierdo & Kumar, Ajay & Singh, Sanjay Kumar, 2022. "Resource integration and dynamic capability of frontline employee during COVID-19 pandemic: From value creation and engineering management perspectives," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    17. Guillaume Coqueret & Romain Deguest, 2024. "Unexpected opportunities in misspecified predictive regressions," Post-Print hal-04595355, HAL.
    18. Reis dos Santos, Pedro M. & Isabel Reis dos Santos, M., 2009. "Using subsystem linear regression metamodels in stochastic simulation," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1031-1040, August.
    19. Miao, Shuwei & Yang, Hejun & Gu, Yingzhong, 2018. "A wind vector simulation model and its application to adequacy assessment," Energy, Elsevier, vol. 148(C), pages 324-340.
    20. J. Sales, 2009. "The error associated with the prediction of digestible protein contents of fish diets from tabulated values," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 54(11), pages 498-509.

    More about this item

    Statistics

    Access and download statistics

    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:igg:joris0:v:12:y:2021:i:4:p:1-17. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.