Empirical essays on consumer engagement and heterogeneity in residential energy demand

Residential energy demand is a key priority area for energy policy in the European Union, especially for the engagement of its citizens in a so-called \u27Energy Union\u27 that encourages consumers to take ownership of the energy transition to a low carbon economy. Consumers can engage in energy markets in several ways and there can be significant differences in the nature and depth of this engagement amongst individuals and households. More generally, variations in the demand for energy and in the energy related behaviour of households are also important for policy. In this context, the objective of this thesis is to examine issues relating to consumer engagement and heterogeneity in residential energy demand. It addresses four specific goals aligned with this objective in four separate empirical essays. A better understanding of the determinants of residential gas demand can support the development of policy measures intended to engage consumers and change aspects of their behaviour relating to household gas use. This information is particularly important when designing demand side management (DSM)programmes which encourage households to play a more active role in managing their energy use. Thus, the first essay of this thesis examines the determinants of residential gas demand using a random effects model of daily gas consumption panel data from a large scale smart metering trial in Ireland. It analyses the effects of the socio-economic and dwelling characteristics of households, as well as the impact of weather-related variables, on residential gas use. In addition, the analysis employs a quasi-experimental methodology, through difference-in-differences estimation, to identify the effectiveness of DSM in engaging households to reduce their gas consumption. To help realise the full potential of DSM, the second essay of this thesis explores the heterogeneous effects of DSM on residential gas demand across different groups of households categorised by their socio-economic, household level and dwelling characteristics. A random effects model is utilised where the average treatment effects are allowed to vary systematically across the different characteristics to determine which household factors are more or less responsive to the programme. In addition, a fixed effects model is employed where the overall effect is allowed to vary across billing cycles to establish if DSM programmes induce habit formation in households, while a quantile regression model is employed to explore the variability in effects across the distribution of daily gas consumption. The elasticity at mean income is usually the parameter of most interest when examining the relationship between energy expenditure and income. However, not all policy is concerned with the average household and is more likely to target low or high energy consumption households. To this end, the third essay examines the variation in the income elasticity of household energy demand across the distribution of energy expenditure. The analysis is based on the application of a two stage instrumental variable quantile regression methodology to five independent cross-sections and the pooled sample of the Irish Household Budget Survey (HBS) to estimate elasticities across the distribution of energy expenditure. The elasticities are compared across high and low energy consumption pro les and to the benchmark constant mean elasticity estimated separately using a two stage least squares method. Consumer switching is understood to play a key role in creating competitive markets and it is important that consumers actively engage in switching to help maintain competitive pressure on energy providers. Using a pooled cross-section from a pan-European market monitoring survey, the fourth essay explores the role of consumers\u27 socio-demographic characteristics, together with their attitudes to the main features of the market, on the propensity to switch in energy markets across Europe. The analysis estimates a binary logit model for overall switching across 14 European markets, as well as separate models for switching in both the electricity and natural gas markets for comparison. In an extension to the analysis, another model is considered to provide evidence on whether switching in other non-energy markets influences switching in energy markets, while finally, separate country models are estimated to compare the heterogeneous effects of the different influential factors on switching across countries.2019-12-1