Horizon CDT Research Highlights

Research Highlights

Energy Security Versus Fuel Poverty: Protecting Vulnerable Households During an Energy Crisis

  Torran Semple (2021 cohort)

My PhD aims to analyse current experiences of fuel poverty and energy security in the UK, with the ultimate aim of generating local and national policy recommendations to remedy the most severe cases of fuel poverty. In the literature, there is a general consensus that household income, household composition, building efficiency and energy prices are the main contributing factors affecting fuel poverty risk; however, the Low Income Low Energy Efficiency (LILEE) definition of fuel poverty, which is currently adopted in England, contradicts this set of criteria, and places a (potentially) unwarranted level of focus on Energy Performance Certificate (EPC) ratings instead.

The first study of my PhD, entitled An Empirical Critique of the Low Income Low Energy Efficiency (LILEE) Approach to Measuring Fuel Poverty, showed that the current fuel poverty metric in England (LILEE) significantly underestimates the true rate of on-the-ground fuel poverty (see http://dx.doi.org/10.2139/ssrn.4583384 for preprint). These findings are the result of a two-stage analysis of fuel poverty in Nottingham and London: stage 1 involved the estimation of financially vulnerable households omitted from fuel poverty statistics according to the flawed criteria of the LILEE metric; while stage 2 analysed survey data collected in London to show that energy insecurity (which refers to a household’s self-reported ability/inability to afford sufficient energy) was around 145% higher than the LILEE fuel poverty rate for London in winter 2022. Given the findings of the first study, the research questions for the remainder of my PhD are as follows:

Research Question I: How feasible is the introduction of a new/amended fuel poverty metric in England?

Research Question II: How do different types of health problems or disabilities affect risk of fuel poverty/energy security?

Research Question III: Can a post-hoc machine learning (ML) method be developed to enhance the interpretability and understanding of heterogenous variables in discrete outcome modelling frameworks?

At this point, most focus will be afforded to the design, feasibility and proposal of a new or amended fuel poverty metric in England (as per RQI). The findings of this study can be used to show the degree of LILEE’s underestimation, as well as improving the identification of truly fuel poor (i.e., energy insecure) households. Following the proposal of a new metric, political impact routes will be explored, with a view to shaping the future approach to measuring fuel poverty in England.