Investigating car users' attitudes to climate change using multiple correspondence analysis

In recent years climate change and environmental issues have attracted worldwide attention and local, regional and national authorities, as well as policy makers, have accelerated their efforts to identify suitable emission reduction policies and measures. According to the UK Department for Transport, around a quarter of domestic carbon dioxide and other greenhouse gas emissions can be attributed to transport. Therefore, the UK Government has mandated reductions of 67% by 2050 over 2010 levels. This paper investigates car users' attitudes to and awareness of climate change to gain further understanding of how those attitudes relate to their travel behaviour. A comprehensive analysis of the British Social Attitude Survey (BSA) datasets of 2011 and 2014 was achieved by using a multi-faceted analytical approach which included Exploratory Factor Analysis (EFA), Multiple Correspondence Analysis (MCA), Hierarchical Cluster analysis (HCA) and Multinomial Logistic Regression (MLR). The dimension reduction process by means of the EFA has resulted in three main factors: “traffic awareness”, “perceptions and actions, and “environmentally sensitive”. The HCA and MCA resulted in seven clusters that were based on car users socio–demographic and travel behaviour variables. The outcomes from the EFA, MCA and HCA were analysed further using MLR methods to investigate respondents' perceptions in greater depth and to relate them to their travel behaviour. These were used as main variables in the MLR analysis. Although 81% of respondents agreed that human actions are partly responsible for climate change impact as per the initial descriptive analysis, the MLR analysis revealed that the group described as ‘middle-aged males and females in full time employment’ and ‘older-aged males in retirement’ in 2014 exhibited an increased likelihood of holding a positive attitude towards “perceptions and actions” regarding climate change than the similarly described cohort in 2011. Intervention mechanisms to encourage mode shift to sustainable modes would be more likely to achieve success if tailored to this group.