This data collection includes a User Guide and the anonymised transcripts of 30 semi-structured interviews of #60-90 minutes each, with 31 high household energy consumers, about their homes, appliances, infrastructures, vehicles, and everyday life and travel practices that generate household (domestic and travel-related) energy demand. It also includes the anonymised transcripts of 4 3-hour deliberatively workshops of ~8 public participants and 2 facilitators, one recruited from the interviewees, and the others recruited to represent different levels of domestic and travel-related energy consumption, held to discuss the validity, fairness, effectively and acceptability of four broad policy approaches to reduce (particularly high levels of) household energy consumption: Rationing; Economic (Dis)Incentives; Structural Change; and Behaviour Change.Increased electrification of heating and transport may result in localised strain on the electricity grid. How can these potential costly upgrades be avoided or the costs of these infrastructure investments be fairly distributed? Tackling ‘over consumption’ is a potentially efficient and equitable approach to reducing energy demand. Achieving this will rely on understanding the reasons for high energy use and the structural, social, cultural and economic influences on these behaviours. This project uses novel datasets of domestic and mobility-related consumption data (for example, see www.motproject.net) plus primary quantitative and qualitative data, to develop and test a methodology for identifying, characterising and assessing locations that have disproportionately high levels of energy consumption (i.e. gas, electricity and car based mobility). The findings are considered in the context of political theory and theories of consumption to structure definitions of high use consumers and to develop and assess approaches to equitable radical reductions. This understanding is informing another project to model electricity networks at a local level. What we are asking How can we meaningfully identify, assess and characterise households or locations with disproportionately high levels of energy consumption? What is energy demand being used for in the highest consuming households? To what extent is high energy demand for domestic use correlated with high energy demand from mobility? When is income a principal determinant of excess demand, and when is it not? What is the relationship between energy poverty and excess demand? To what extent do those who consume most energy also have the greatest social and economic capital to reduce consumption?

Semi-structured qualitative interviews. Deliberative Workshops. The data was sampled in order to provide a set of naturalistic explanations of high-energy-consuming lifestyles – involving both high domestic energy consumption, and high travel energy consumption. In order to sample this, area-based analysis identified a short-list of 33 Lower Super Output Areas in the top 10% for Gas or Electricity or Driving; in England; in urban areas; with more than 50% of people within 15 minutes of a town centre by public transport (thus eliminating the ‘traditional’ understanding of car dependent areas); and having above average Energy Performance Certificate ratings (thus hoping to exclude high heating due to poor insulation). These were then further filtered to focus on areas where more flights were expected, using the mean number of household flights by Super Output Area classifications. Consumer data and contacts in the 8 super-shortlisted LSOAs were located and purchased, and further recruitment criteria were applied by professional recruiters to ensure a diverse sample. The workshops were organised to explore the fairness, acceptability, and effectiveness of different ways of using policy to reduce (especially high) household energy consumption. They were recruited by the same company using altered criteria to ensure participants had High/Low energy consumption domestically or related to travel. Workshop One was recruited directly from the highest consuming interviewees.