I can’t believe your attitude: a joint estimation of best worst attitudes and electric vehicle choice
Matthew J. Beck (),
John Rose () and
Stephen P. Greaves ()
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Matthew J. Beck: University of Sydney
Stephen P. Greaves: University of Sydney
Transportation, 2017, vol. 44, issue 4, No 6, 753-772
Abstract:
Abstract The number of conventionally fuelled motor vehicles in use is increasing worldwide despite warnings about finite fossil fuel and the detrimental impacts of burning such fuels. While electric vehicles, the subject of much research, generate far less emissions and offer the potential for power from renewable sources, they are yet to significantly penetrate the market. Tangible barriers such as price and vehicle range still exist, but consumer attitudes also drive behaviour. This paper examines attributes in a framework relatively new to transportation and energy policy; best–worst scaling. This method is widely considered an improvement over traditional methods of eliciting attitudes and beliefs, where respondents select attitudes they find best or worst from a set of attitudinal statements. To avoid potential endogeneity bias, we jointly model attitudes and choice for the first time with best–worst data. It is found that energy crisis, air quality and climate change concerns influence behaviour with respect to vehicle range and that travel behaviour change and forms of government incentives are needed influences on behaviour with respect to vehicle emissions. It is argued that correctly modelling attitudes reduces the error term of the vehicle choice model and provides policy makers with an improved lens for assessing behaviour. Additionally, the methods described within can easily be adapted to other policy scenarios.
Keywords: Electric vehicle; Stated preference; Environmental attitudes; Alternative fuel; Vehicle choice (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (22)
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Persistent link: https://EconPapers.repec.org/RePEc:kap:transp:v:44:y:2017:i:4:d:10.1007_s11116-016-9675-9
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DOI: 10.1007/s11116-016-9675-9
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