Many climate change-related building frameworks are designed to improve environmental performance by requiring reduced net energy demand, as it is widely assumed that energy demand (e.g. delivered/final, primary, primary non-renewable) is a good proxy for carbon emissions. However, energy grids are becoming less carbon intensive, meaning that the climate change mitigation value of renewably generated energy is not static, and is likely to decrease. In this research, a global integrated building carbon and energy model was created to explore how assessed building performance responded to stepwise variation in multiple building features. Operational and embodied metrics were measured concurrently on the basis of carbon emissions and delivered final energy demand, and included renewable energy generation (via roof-mounted photovoltaics), resulting in two 12.3 million-point data sets. Logistic regression was used to identify patterns in the data sets using binary building classifications (zero or non-zero energy or carbon). The results demonstrate that the profiles of the energy and carbon metric data sets do not mirror each other, indicating that a delivered energy demand assessment is not necessarily a good proxy for carbon emissions. The divergence of these metrics is likely to grow in future as energy grids are increasingly decarbonised.