Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market
Electric vehicles (EVs) continue to penetrate passenger vehicle markets worldwide. Most current EV markets remain in nascent stages, with buyers being categorised as early adopters or pioneers. However, if electric vehicles are to successfully contribute to the decarbonisation of transportation, the...
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Online Access: | https://pure.au.dk/portal/da/publications/who-will-buy-electric-vehicles-after-early-adopters-using-machine-learning-to-identify-the-electric-vehicle-mainstream-market(3613bf54-b264-4b5c-a345-264165c79083).html https://doi.org/10.1016/j.energy.2019.01.114 https://pure.au.dk/ws/files/144290932/Who_will_buy_electric_vehicles_after_early_adopters_Using_machine_learning_to_identify_the_electric_vehicle_mainstream_market.pdf |
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ftuniaarhuspubl:oai:pure.atira.dk:publications/3613bf54-b264-4b5c-a345-264165c79083 2023-05-15T16:50:53+02:00 Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market Zarazua de Rubens, Gerardo 2019 application/pdf https://pure.au.dk/portal/da/publications/who-will-buy-electric-vehicles-after-early-adopters-using-machine-learning-to-identify-the-electric-vehicle-mainstream-market(3613bf54-b264-4b5c-a345-264165c79083).html https://doi.org/10.1016/j.energy.2019.01.114 https://pure.au.dk/ws/files/144290932/Who_will_buy_electric_vehicles_after_early_adopters_Using_machine_learning_to_identify_the_electric_vehicle_mainstream_market.pdf eng eng info:eu-repo/semantics/openAccess Zarazua de Rubens , G 2019 , ' Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market ' , Energy , vol. 172 , no. April , pp. 243-254 . https://doi.org/10.1016/j.energy.2019.01.114 article 2019 ftuniaarhuspubl https://doi.org/10.1016/j.energy.2019.01.114 2021-04-07T22:44:27Z Electric vehicles (EVs) continue to penetrate passenger vehicle markets worldwide. Most current EV markets remain in nascent stages, with buyers being categorised as early adopters or pioneers. However, if electric vehicles are to successfully contribute to the decarbonisation of transportation, they must reach mainstream consumer segments. To investigate the underlying causes of EV interest and to determine the potential next wave of EV buyers, this study draws data from an original dataset (n = 5067) across the five Nordic countries of Denmark, Finland, Iceland, Norway and Sweden. A machine learning model, based on the k-means method, is used for the analysis, creating six consumer segments around prospective EV adoption. The study finds that three consumer clusters, that account for 68% of the (sampled) population, are primed for EV adoption and represent the near-term mainstream EV market. The findings corroborate that price is a main determinant in reaching these mainstream consumers, while suggesting that vehicle-to-grid can contribute to the attractiveness of EVs and their uptake. The study also highlights that EV deployment strategy should focus on the technological and status aspects of EVs, as opopsed to only their environmental and financial attributes. Finally, the study stresses the importance that policy and industry decision-makers must create an equally competitive market place for EVs developing strategies and policy that considers the characteristics and interests of mainstream EV customers. Article in Journal/Newspaper Iceland Aarhus University: Research Norway Energy 172 243 254 |
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Aarhus University: Research |
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ftuniaarhuspubl |
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English |
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Electric vehicles (EVs) continue to penetrate passenger vehicle markets worldwide. Most current EV markets remain in nascent stages, with buyers being categorised as early adopters or pioneers. However, if electric vehicles are to successfully contribute to the decarbonisation of transportation, they must reach mainstream consumer segments. To investigate the underlying causes of EV interest and to determine the potential next wave of EV buyers, this study draws data from an original dataset (n = 5067) across the five Nordic countries of Denmark, Finland, Iceland, Norway and Sweden. A machine learning model, based on the k-means method, is used for the analysis, creating six consumer segments around prospective EV adoption. The study finds that three consumer clusters, that account for 68% of the (sampled) population, are primed for EV adoption and represent the near-term mainstream EV market. The findings corroborate that price is a main determinant in reaching these mainstream consumers, while suggesting that vehicle-to-grid can contribute to the attractiveness of EVs and their uptake. The study also highlights that EV deployment strategy should focus on the technological and status aspects of EVs, as opopsed to only their environmental and financial attributes. Finally, the study stresses the importance that policy and industry decision-makers must create an equally competitive market place for EVs developing strategies and policy that considers the characteristics and interests of mainstream EV customers. |
format |
Article in Journal/Newspaper |
author |
Zarazua de Rubens, Gerardo |
spellingShingle |
Zarazua de Rubens, Gerardo Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market |
author_facet |
Zarazua de Rubens, Gerardo |
author_sort |
Zarazua de Rubens, Gerardo |
title |
Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market |
title_short |
Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market |
title_full |
Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market |
title_fullStr |
Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market |
title_full_unstemmed |
Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market |
title_sort |
who will buy electric vehicles after early adopters? using machine learning to identify the electric vehicle mainstream market |
publishDate |
2019 |
url |
https://pure.au.dk/portal/da/publications/who-will-buy-electric-vehicles-after-early-adopters-using-machine-learning-to-identify-the-electric-vehicle-mainstream-market(3613bf54-b264-4b5c-a345-264165c79083).html https://doi.org/10.1016/j.energy.2019.01.114 https://pure.au.dk/ws/files/144290932/Who_will_buy_electric_vehicles_after_early_adopters_Using_machine_learning_to_identify_the_electric_vehicle_mainstream_market.pdf |
geographic |
Norway |
geographic_facet |
Norway |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
Zarazua de Rubens , G 2019 , ' Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market ' , Energy , vol. 172 , no. April , pp. 243-254 . https://doi.org/10.1016/j.energy.2019.01.114 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1016/j.energy.2019.01.114 |
container_title |
Energy |
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172 |
container_start_page |
243 |
op_container_end_page |
254 |
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1766040997046779904 |