The main aim of this dissertation is to estimate fuel demand elasticities for the UK road transport sector. Despite being extensively studied, there is a renewed need for the estimation of fuel demand elasticities so that they might be more reflective of recent trends and changes in consumption patterns. At present, understanding the fuel demand sensitivities is especially important for policy making purposes.
A review of the empirical literature on fuel demand revealed three important areas of concern. First, fuel demand estimates tend to vary greatly in magnitude. The effect-size differences observed are probably related to the diversity of study-characteristics and data factors. Second, the reliability of past estimates may be questionable due to shortcomings in the modelling methodology employed. Obtaining reliable estimates does not only require the use of recent data but, beyond that, it is also important for the model to be based on sound methodological and theoretical foundations. Third, studies have often relied on the elasticity of petrol to define road transport fuel demand, assuming the absence of fuel type heterogeneity among road transport fuels. This is severely restrictive, however, since demand sensitivities are likely to vary between the respective fuels.
This thesis undertakes a series of empirical analyses aimed at improving the current understanding of fuel demand for the UK road transport sector. Through meta-regression analysis, this research examines the underlying factors that can help explain the between-study variations found in the literature. The research then examines the sensitivity of fuel demand through the use of both time series and panel data econometric models. Special attention has been given to methodological issues and the use of recent econometric techniques to ensure the reliability of the estimates. In addition, this thesis does not assume that demand elasticity is homogenous for each respective transport fuels. To that end, fuel demand elasticities are estimated separately for each fuel type.