NDLI: Understanding the impact of public charging infrastructure on the consideration to purchase an electric vehicle in California (Dataset)

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Understanding the impact of public charging infrastructure on the consideration to purchase an electric vehicle in California (Dataset)

Content Provider	Dryad
Author	Hoogland, Kelly Kurani, Kenneth Hardman, Scott Chakraborty, Debapriya
Abstract	This research tests an implicit assumption on investment in plug-in electric vehicle (PEV) charging infrastructure: people who are not already interested in PEVs will see public PEV charging and become interested in PEVs. Data from a survey of car-owning households in California are combined with data on public PEV charging and PEV registrations to estimate a structural equation model of the extent to which participants have considered acquiring a battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV), and whether participants report seeing PEV charging. The model controls for socio-economic and demographic measures and participants’ awareness, knowledge, and assessments of PEVs, and the known correlation between PEV registrations and public charging locations. Using logistic ordinal regression we also assess whether charger density near workplaces affects the results. All results contradict the assumption that people will see charging infrastructure and that more charging—rather than more people seeing charging—means more people will consider purchasing a BEV or PHEV. Rather, prior interest and positive assessments of PEVs, allow people to see PEV charging. In short, interest in PEVs is a prerequisite to people seeing PEV charging.
File Size	2256986
File Format	HTM / HTML
ISSN	20587546
DOI	10.25338/B8035D
Alternate Webpage(s)	https://datadryad.org/stash/downloads/file_stream/2211656
Alternate Webpage(s)	https://datadryad.org/stash/downloads/file_stream/2211655
Language	English
Publisher Date	2023-04-07
Access Restriction	Open
Subject Keyword	Structural Equation Model Electric Vehicle Adoption Electric Cars Charging Station Inattentional Blindness
Content Type	Text
Resource Type	Data Set
Subject	Renewable Energy, Sustainability and the Environment Fuel Technology Electronic, Optical and Magnetic Materials Energy Engineering and Power Technology

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