User Adoption of Electrified Powertrains: Identification of Factors Through Discrete Choice Modelling
This study identified the main factors affecting car selection decisions through discrete choice experiments based on a large dataset collected in four European countries in 2023 using stated choice questionnaires. The choice set includes six current and popular car powertrains with factors related...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Future Transportation |
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| Online Access: | https://www.mdpi.com/2673-7590/5/1/16 |
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| author | Lorenzo Sica Angela Carboni Francesco Paolo Deflorio Filippo Fappanni Cristiana Botta |
| author_facet | Lorenzo Sica Angela Carboni Francesco Paolo Deflorio Filippo Fappanni Cristiana Botta |
| author_sort | Lorenzo Sica |
| collection | DOAJ |
| description | This study identified the main factors affecting car selection decisions through discrete choice experiments based on a large dataset collected in four European countries in 2023 using stated choice questionnaires. The choice set includes six current and popular car powertrains with factors related to vehicle features, user characteristics, and specific geographical contexts, which can influence the adoption of vehicles with electrified powertrains. An easily applicable multinomial logit model was first proposed to explore the effects of selected attributes and the model’s ability to reproduce user preferences with different incentive policies, geographical contexts, and energy prices. A mixed logit model and a segmented multinomial logit model were introduced to consider the sample’s heterogeneity. The first captures the preference dispersion among respondents related to incentives and operational costs. The second, which specifically classifies users based on car market segments, showed a greater variation in factors related to the purchase cost and battery range. The models estimate the weight of nine factors, offering support for targeted policy recommendations. Cost-related factors confirm their relevance in choices, and the analysis shows that users who want to enhance their vehicle range by 1 km are willing to pay approximately EUR 80. |
| format | Article |
| id | doaj-art-1dd0a69af56d4da68d5d9c184dafbb27 |
| institution | Kabale University |
| issn | 2673-7590 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Transportation |
| spelling | doaj-art-1dd0a69af56d4da68d5d9c184dafbb272025-08-20T03:43:22ZengMDPI AGFuture Transportation2673-75902025-02-01511610.3390/futuretransp5010016User Adoption of Electrified Powertrains: Identification of Factors Through Discrete Choice ModellingLorenzo Sica0Angela Carboni1Francesco Paolo Deflorio2Filippo Fappanni3Cristiana Botta4Department of Environment, Land and Infrastructure Engineering, CARS@POLITO, Politecnico di Torino, Duca degli Abruzzi 24, 10129 Turin, ItalyDepartment of Environment, Land and Infrastructure Engineering, CARS@POLITO, Politecnico di Torino, Duca degli Abruzzi 24, 10129 Turin, ItalyDepartment of Environment, Land and Infrastructure Engineering, CARS@POLITO, Politecnico di Torino, Duca degli Abruzzi 24, 10129 Turin, ItalyLINKS Foundation, Via Pier Carlo Boggio 61, 10138 Turin, ItalyLINKS Foundation, Via Pier Carlo Boggio 61, 10138 Turin, ItalyThis study identified the main factors affecting car selection decisions through discrete choice experiments based on a large dataset collected in four European countries in 2023 using stated choice questionnaires. The choice set includes six current and popular car powertrains with factors related to vehicle features, user characteristics, and specific geographical contexts, which can influence the adoption of vehicles with electrified powertrains. An easily applicable multinomial logit model was first proposed to explore the effects of selected attributes and the model’s ability to reproduce user preferences with different incentive policies, geographical contexts, and energy prices. A mixed logit model and a segmented multinomial logit model were introduced to consider the sample’s heterogeneity. The first captures the preference dispersion among respondents related to incentives and operational costs. The second, which specifically classifies users based on car market segments, showed a greater variation in factors related to the purchase cost and battery range. The models estimate the weight of nine factors, offering support for targeted policy recommendations. Cost-related factors confirm their relevance in choices, and the analysis shows that users who want to enhance their vehicle range by 1 km are willing to pay approximately EUR 80.https://www.mdpi.com/2673-7590/5/1/16discrete choice experimentelectric vehiclesstated preferenceEV adoption |
| spellingShingle | Lorenzo Sica Angela Carboni Francesco Paolo Deflorio Filippo Fappanni Cristiana Botta User Adoption of Electrified Powertrains: Identification of Factors Through Discrete Choice Modelling Future Transportation discrete choice experiment electric vehicles stated preference EV adoption |
| title | User Adoption of Electrified Powertrains: Identification of Factors Through Discrete Choice Modelling |
| title_full | User Adoption of Electrified Powertrains: Identification of Factors Through Discrete Choice Modelling |
| title_fullStr | User Adoption of Electrified Powertrains: Identification of Factors Through Discrete Choice Modelling |
| title_full_unstemmed | User Adoption of Electrified Powertrains: Identification of Factors Through Discrete Choice Modelling |
| title_short | User Adoption of Electrified Powertrains: Identification of Factors Through Discrete Choice Modelling |
| title_sort | user adoption of electrified powertrains identification of factors through discrete choice modelling |
| topic | discrete choice experiment electric vehicles stated preference EV adoption |
| url | https://www.mdpi.com/2673-7590/5/1/16 |
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