Benchmarking and tracking domestic gas and electricity consumption at the local authority level

Abstract

Government, local authority and industry initiatives to improve the energy efficiency of housing stocks are central to national and international commitments to reduce carbon dioxide emissions. To be effective, initiatives need to target homes which, given their location, size, fuel type and occupancy, use more energy than expected. This paper illustrates how energy efficiency benchmarks can be developed that account for these factors and highlights the shortcomings of relying on simple energy consumption statistics. The study uses existing data (with national coverage) and the measured electricity and gas consumption of groups consisting of, on average, 500-700 households to benchmark and track domestic gas and electricity consumption across England. Multiple regression models, which account for 65 % of the variation in domestic gas consumption and 73 % of domestic electricity variation, are used to derive the benchmarks. The actual gas and electricity consumption of each group of homes is compared against the derived benchmark and an energy efficiency index presented. The approach enables changes in energy efficiency to be tracked temporally, for example to assess the effectiveness of government, local authority or industry initiatives. National and city-scale patterns of energy efficiency are also discussed.

Uncovering hidden geographies and socio-economic influences on fuel poverty using household fuel spend data: A meso-scale study in Scotland

“…SIMD are reported as data zones which contain, on average, around 350 households (around 800 people), and correspond to administrative and political boundaries. The datazones are similar to the super output areas used in England, meaning the results of analyses using them are broadly comparable with English studies (such as Morris et al, 2016) 88 . However, as they are population-based, the datazones vary significantly in size, meaning that low density rural data zones covering multiple small population centres would be expected to be less homogenous than the small datazones in dense urban areas -a problem which is more significant in Scotland due to its more distinct urban-rural divide and the nature and distributions of rural and island populations.…”

A Study on the Factors Affecting China’s Direct Household Carbon Emission and Comparison of Regional Differences

“…In the long run, the analysis of China's household carbon emissions can deepen our understanding of the diversity of the influencing factors. Through an analysis of the UK's situation, Brand et al found that employment status, car ownership, and commuting distance are the most important factors affecting household carbon emissions, followed by family income, property quantity, education level, and other factors [38] Morris et al point out the significant influence of climate, income, and the ratios of electricity to gas meters on household carbon emissions [39]. Although income has a positive impact on household carbon emissions, low-income, unemployed, and elderly groups may produce more carbon emissions, considering that the elderly group may feel colder and they are not constrained by carbon tax [40].…”

An empirical investigation into students' experience of fuel poverty

“…However, current UK policy is focused on directing a compulsory levy imposed on electricity suppliers towards those areas ranking highest for income deprivation, such as the Energy Company Obligation (ECO) and its predecessor, the Community Energy Saving Programme (CESP) (HM Government, 2009;Rosenow et al, 2013). Such schemes do not necessarily reach those most in need, due to the highly variable nature of household energy consumption which is strongly influenced by socio-economic factors (Morris et al, 2016).…”

“…In this context, the Green Deal was launched in 2011 to 'support the retrofit of 1.4 million homes' by 2020, focused on creating markets for energy efficiency measures and aimed at incentivising owners to invest in measures and receive pay-back from reduced energy bills (Rosenow et al, 2013;Hope and Booth, 2014;Morris et al, 2016). In principle, the Green Deal's payback mechanism, combined with the 2011 Energy Act which prevented landlords from refusing 'reasonable' requests from tenants for energy efficiency improvements should have helped overcome the split-incentive problem that persists in the private rented sector, where upgrading the energy efficiency of the dwelling is the responsibility of the landlord, yet tenants receive the benefit through lower energy bills and increased internal warmth (Ambrose, 2015;Leicester and Stoye, 2016).…”