A GIS-based statistical approach to prioritize the retrofit of housing stocks at the urban scale

Cities are responsible for about 70% of the overall primary energy consumption in Europe and play a major role in addressing carbon mitigation. In this respect, the housing s ector has been identified as a key sector for its high energy savings potential achievable by implementing retrofit measures. However, a detailed characterization of the housing energy consumption profile and spatial distribution is needed to properly asse ss the energy saving potential at the urban scale and further support sustainable urban planning and energy policies. This study focused on a statistical approach based on Geographical Information Systems (GIS) developed to identify the energy consumption profile of urban housing stocks, the energy savings potential achievable by implementing retrofit measures and their respective spatial distribution across one entire city. The final energy consumption of individual dwellings was predicted by running a mul tiple linear regression model based on measured energy consumption available at aggregated level (post - code area level) and GIS data about characteristics of buildings and household. Energy savings potential and cost - effectiveness of standard retrofit meas ures were subsequently calculated and results were finally displayed as maps for decision support in sustainable urban planning. The methodology was applied to the exemplary housing stock of Rotterdam city, consisting of almost 300,000 units. Relevant res ults were provided to prioritize retrofit measures implementation according to energy savings potential and cost - effectiveness. Different types of maps were produced to show energy consumption and energy saving potential patterns across the city. The metho dology is generically applicable to other contexts and provides an effective tool for decision support in carbon mitigation policies of housing stocks at the urban scale