An assessment of UK drivers' attitudes regarding the forthcoming ban on the sale of petrol and diesel vehicles

Bennett, Roger and Vijaygopal, Rohini (2018). An assessment of UK drivers' attitudes regarding the forthcoming ban on the sale of petrol and diesel vehicles. Transportation Research Part D: Transport and Environment, 62 pp. 330–344.

DOI: https://doi.org/10.1016/j.trd.2018.03.017

Abstract

The purpose of the study was to predict how drivers of petrol or diesel cars might vote in a ‘Yes/No’ referendum concerning the UK government’s decision to ban from 2040 onwards the sale of all new non-electric vehicles. Five main factors were hypothesised to influence voting intention: a person’s (i) level of environmental concern, (ii) attitude towards electric cars (measured via an Implicit Association Test), (iii) belief about the importance of air pollution, (iv) driving requirements, and (v) reaction to the cost of the ban to the individual (assessed using a contingency valuation approach). The study also examined possible determinants of attitudes to electric vehicles, e.g., whether an individual was a ‘technology enthusiast’, had prior knowledge of and searched for knowledge about electric vehicles, and whether a person had played an online game where the player assumed the identity of an electric car driver. A structural equation model was developed and tested on a sample of 675 UK drivers, none of whom had ever owned or driven an electric car. The results suggested a good fit of the model to the data, except that neither environmental concern nor belief in the importance of clean air affected attitude to electric cars. Also, high levels of environmental concern did not motivate people to search for knowledge about electric vehicles. Social marketing campaigns that will be needed to precede the ban should focus on its health benefits, and not target particular age groups, gender, or whether a participant had children.

Viewing alternatives

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

Recommendations