Analysis on the determinants of EV purchase intention in Sweden
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This study uses a discrete choice experiment embedded in a survey to explore the
determinants of Swedish consumers’ choice between electric vehicles (EVs) and
internal combustion engine (ICE) vehicles. A total of 373 respondents, resulting in
7,266 valid choice observations, were collected and analyzed using binary logistic
regression models. Model specifications include both vehicle-specific attributes (e.g.,
price, range, maintenance costs, charging time, charging convenience, and emissions)
and demographic characteristics (e.g., age, gender, education, income, and family
structure).
The results show that economic and infrastructure considerations dominate consumers’
decision-making process. Specifically, vehicle price, maintenance costs, and the
availability of home charging infrastructure are significant attributes of EV adoption.
The existence of a home charger is a particularly important driver, increasing the
probability of choosing an EV by nearly 60 percentage points on average. In contrast,
attributes such as range and charging time, while in the direction of theoretical
expectations, are not statistically significant in the current sample. The probability
analysis also highlights that in the absence of home charging facilities, the impact of
price cuts is relatively limited, suggesting that policymakers should increase investment
in EV charging facilities.
The study provides practical insights for policymakers aiming to accelerate the adoption
of EVs in Sweden. In addition to targeted financial incentives, efforts should focus on
improving private and public charging infrastructure. The findings also contribute to a
broader understanding of how practical and infrastructure factors influence low-carbon
transport choices in European markets.
Beskrivning
Ämne/nyckelord
Electric Vehicle, Vehicle Attributes, Discrete Choice Modeling