Decarbonising at scale: Extracting strategic thinking from EPC and deprivation data

Decarbonising the built environment is at the heart of many nations' route to net zero. This leads to policies that target specific technologies. Within such policies, there is a natural instinct to combine the need to reduce carbon emissions with solving other issues, such as fuel poverty. Here, we examine for the first time if, from a carbon perspective, this is optimal. By assembling energy performance certificates and household economic deprivation data, we use fuzzy matching techniques to produce a single statistically robust dataset of 44,300 households. Then, through secondary data analysis, we closely examine the carbon impact and cost of energy retrofits. Overall, upgrading to band C is the most viable strategy. However, the results demonstrate that households belonging to the least deprived 20% present more than double the carbon saving potential compared to those in the most deprived 20% (2.7 and 1.2tCO 2 /yr, respectively), and offer the best return in CO 2 savings on money spent. This highlights the need for retrofitting policy to be cognisant of both building stock and deprivation and the disproportionate role in climate change played by the more affluent. The results offer important new insights for governments and suggest a rethinking of retrofit initiatives. Practical Application: This study is the first to employ such data to identify retrofit strategies for governments and offers three key practical applications. (i) It shows how by combining such data one can start to develop policy that is tuned to the demographics and stock, and that by disaggregating the data a lot can be learnt prior to the development of local or national policy. (ii) It clearly puts to bed the idea that attacking fuel poverty is the most effective way towards carbon reductions. (iii) It suggests a new way of thinking about targeted interventions that optimise carbon reduction in a cost-effective way.