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Geodemographic analysis and estimation of early plug-in hybrid electric vehicle adoption

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  • Saarenpää, Jukka
  • Kolehmainen, Mikko
  • Niska, Harri

Abstract

Electric vehicles and hybrids are expected to become increasingly common in the coming years. The implications of growing adoption depend on its geographical extent. For instance, vehicles that are chargeable from the electrical grid, such as plug-in hybrids, can introduce problems for the distribution network especially if the vehicle adoption is spatially concentrated. In this paper, the adoption of hybrid electric vehicles is analysed in heterogeneous areas. The main purpose is to study the interrelationships between early hybrid electric vehicle adoption and different demographic and socio-economic characteristics of the areas. It is further discussed how the results can be applied to estimate the upcoming plug-in hybrid adoption. As there is a vast amount of information in the various registers of the society, slowly being opened for free usage but not fully utilised so far, it is also of interest to study and demonstrate the usability of public register data in this context. Our analysis suggests that certain characteristics of the areas strongly correlate with the hybrid electric vehicle adoption. The results of this study could be relevant, e.g., for electric distribution network planning, targeting policies to support cleaner vehicle adoption, marketing hybrid vehicles and locating charging stations.

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  • Saarenpää, Jukka & Kolehmainen, Mikko & Niska, Harri, 2013. "Geodemographic analysis and estimation of early plug-in hybrid electric vehicle adoption," Applied Energy, Elsevier, vol. 107(C), pages 456-464.
    • Handle: RePEc:eee:appene:v:107:y:2013:i:c:p:456-464
    DOI: 10.1016/j.apenergy.2013.02.066
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    9. Mejia, Mario A. & Melo, Joel D. & Zambrano-Asanza, Sergio & Padilha-Feltrin, Antonio, 2020. "Spatial-temporal growth model to estimate the adoption of new end-use electric technologies encouraged by energy-efficiency programs," Energy, Elsevier, vol. 191(C).
    10. Heymann, Fabian & Miranda, Vladimiro & Soares, Filipe Joel & Duenas, Pablo & Perez Arriaga, Ignacio & Prata, Ricardo, 2019. "Orchestrating incentive designs to reduce adverse system-level effects of large-scale EV/PV adoption – The case of Portugal," Applied Energy, Elsevier, vol. 256(C).
    11. 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.
    12. Sanguinetti, Angela & Favetti, Matthew & Hirschfelt, Kate & Kong, Nathaniel & Chakraborty, Debapriya & Alston-Stepnitz, Eli & Ma, Howard, 2023. "Developing a Vehicle Cost Calculator to Promote Electric Vehicle Adoption Among TNC Drivers," Institute of Transportation Studies, Working Paper Series qt1v44b5kp, Institute of Transportation Studies, UC Davis.
    13. Poullikkas, Andreas, 2015. "Sustainable options for electric vehicle technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1277-1287.
    14. Prateek Bansal & Akanksha Sinha & Rubal Dua & Ricardo Daziano, 2019. "Eliciting Preferences of Ridehailing Users and Drivers: Evidence from the United States," Papers 1904.06695, arXiv.org.
    15. Morton, Craig & Lovelace, Robin & Anable, Jillian, 2017. "Exploring the effect of local transport policies on the adoption of low emission vehicles: Evidence from the London Congestion Charge and Hybrid Electric Vehicles," Transport Policy, Elsevier, vol. 60(C), pages 34-46.
    16. Morton, Craig & Anable, Jillian & Yeboah, Godwin & Cottrill, Caitlin, 2018. "The spatial pattern of demand in the early market for electric vehicles: Evidence from the United Kingdom," Journal of Transport Geography, Elsevier, vol. 72(C), pages 119-130.
    17. Moon, HyungBin & Park, Stephen Youngjun & Woo, JongRoul, 2021. "Staying on convention or leapfrogging to eco-innovation?: Identifying early adopters of hydrogen-powered vehicles," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    18. Tikka, Ville & Haapaniemi, Jouni & Räisänen, Otto & Honkapuro, Samuli, 2022. "Convolutional neural networks in estimating the spatial distribution of electric vehicles to support electricity grid planning," Applied Energy, Elsevier, vol. 328(C).
    19. Mu, Yunfei & Wu, Jianzhong & Jenkins, Nick & Jia, Hongjie & Wang, Chengshan, 2014. "A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles," Applied Energy, Elsevier, vol. 114(C), pages 456-465.
    20. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    21. Sadeghi-Barzani, Payam & Rajabi-Ghahnavieh, Abbas & Kazemi-Karegar, Hosein, 2014. "Optimal fast charging station placing and sizing," Applied Energy, Elsevier, vol. 125(C), pages 289-299.
    22. De Gennaro, Michele & Paffumi, Elena & Martini, Giorgio, 2015. "Customer-driven design of the recharge infrastructure and Vehicle-to-Grid in urban areas: A large-scale application for electric vehicles deployment," Energy, Elsevier, vol. 82(C), pages 294-311.
    23. McCoy, Daire & Lyons, Sean, 2014. "The diffusion of electric vehicles: An agent-based microsimulation," MPRA Paper 54560, University Library of Munich, Germany.
    24. Sorrentino, Marco & Rizzo, Gianfranco & Sorrentino, Luca, 2014. "A study aimed at assessing the potential impact of vehicle electrification on grid infrastructure and road-traffic green house emissions," Applied Energy, Elsevier, vol. 120(C), pages 31-40.
    25. Brown, Marilyn A. & Kale, Snehal & Cha, Min-Kyeong & Chapman, Oliver, 2023. "Exploring the willingness of consumers to electrify their homes," Applied Energy, Elsevier, vol. 338(C).

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