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Factors Affecting the Preference of Buying Hybrid and Electric Vehicles

Electric Vehicles is regarded as an important solution for emission reduction. But, the adoption to it is still a problem in many countries. With survey data containing demographic and attitude factors of respondents, this paper proposes two classification models: logistic regression and random forest using the Multiple Correspondence Analysis (MCA) as an intermediate step to identify the factors affecting the willingness of electric vehicles purchase. The analysis shows that the addition of MCA does enhance the explanatory power while it takes a low cost on prediction performance, and the results reveal that characteristics such as frequency of using modern transport services, car-sharing subscription, living place, mode of frequent trip do have a significant impact on EV purchases.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-447231
Date January 2021
CreatorsZhao, Zhenyu
PublisherUppsala universitet, Statistiska institutionen
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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