Are fuel poverty metrics fit for purpose? An assessment using behavioural microsimulation. (June 2022)

• Record Type: Journal Article
• Authors: Tovar Reaños, Miguel A.; Lynch, Muireann Á.
• Is Part Of: Sustainable cities and society. Volume 81(2022)
• Publication Date: 2022-06
• Subjects: H23 -- Q18 -- H31; Household energy demand -- Microsimulation -- Energy taxes -- Fuel poverty; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2022.103817
• Languages: English
• ISSNs: 2210-6707

Abstract

Abstract: This paper contributes to the literature on fuel poverty measurement by analysing the ability of different metrics to identify fuel poor households. We consider existing expenditure-based metrics and recently-developed metrics for multidimensional poverty, and compare three aspects: (a) Their ability to identify households at high risk of experiencing fuel poverty, (b) their ability to identify low income households with a large carbon tax burden, (c) their ability to measure changes in fuel poverty under carbon taxes and compensatory measures, including increases in fuel efficiency. We employ a fully flexible model to quantify demand responses to changes in fuel prices and energy expenditure for residential heating. We find that in general all analysed metrics perform well at identifying the household types frequently mentioned in the literature as fuel poor. Regarding the second aspect (b), we find that in general, the metrics performed badly at identifying vulnerable households with the largest tax burden. Finally, we show that using a multidimensional metric that includes energy efficiency can track changes in fuel poverty under the analysed scenarios, and it generally is a promising approach to measuring fuel poverty. Highlights: EASI demand system used to parameterise a microsimulation model. The incidence of carbon taxes is analysed. Welfare losses are compared with metrics of fuel poverty. Multidimensional deprivation methods have the best performance. Cross price effects are also included when measuring fuel poverty.