A new local energy mapping approach for targeting urban energy renovations

Large-scale energy renovations need to be better targeted, more cost-effective and result in a higher uptake to alleviate fuel poverty and reduce energy use. This paper presents a localised Geographical Information System (GIS) based approach using publicly-available national and local datasets on housing and energy to plan mass renovation and provide targeted low carbon measures across UK cities. The study used these datasets to first, spatially identify an area for energy renovation (high energy use and/or fuel poor), and then applied a bottom-up carbon mapping model (called DECoRuM) to estimate energy use and potential for reduction on a house-by-house level, and aggregated to urban scale. To identify an appropriate neighbourhood case study area (covering over 600 households), publicly available datasets were assessed for the town of Bicester, UK, which included Ordnance Survey Mastermap and Address-point data (to identify dwelling characteristics e.g. built form), Energy performance Certificate data (EPC) and sub-national energy statistics. When the modelled EPC data were compared with the actual sub-national data, the EPC mean energy use was found to be over 3,000 kWh/yr more than the sub-national figure at lower layer super output area. Following this initial analysis, an area with both high fuel poverty and energy use was selected for deeper investigation. Following this rapid energy assessment, a community engagement campaign was led by the local authority to gather detailed data from householders on their dwellings, to improve the energy model. House-level energy assessment in the selected area showed that a package based approach comprising fabric improvement, heating system upgrade and solar photovoltaics emerged as the most effective in reducing CO2 emissions. Costs and payback periods however, hinder this success in specific dwellings. Energy improvements in older dwellings such as 1930–1949 semi-detached (dominant type in the area) demonstrated shorter payback periods due to large baseline energy. The online GIS visualisation of the results is considered particularly helpful for local authorities and community groups in planning local energy action