Buildings account for a significant portion of global GHG emissions, yet the building sector lacks high spatial and temporal resolution emissions estimates that could help drive emissions reduction actions. To address this limitation, we propose several methodologies for super-resolving lower-resolution GHG estimates. To examine our proposed disaggregation methods, we utilize the Emissions Database for Global Atmospheric Research (EDGAR) v8.0 gridded emissions data—which exist at 0.1° × 0.1° (approximately 11 × 11 km 2 ) spatial resolution—and subdivide those emissions data into residential and non-residential subsectors to account for critical differences in energy consumption behavior. EDGAR v8.0 gridded data are provided annually from 2015 to 2023, which we spatially super-resolve to a 30″ (approximately 1 × 1 km 2 ) grid and temporally super-resolve using heating degree days to allocate the time-varying portion of emissions at quarterly intervals. To evaluate the accuracy of our proposed disaggregation methods, we use our spatially super-resolved gridded emissions data from EDGAR v8.0 and estimate municipal-level direct onsite CO 2 emissions from buildings across 19,998 municipalities for which emissions data are available. Our spatial super-resolution method provides a two-order-of-magnitude increase in spatial resolution compared to a 0.1° × 0.1° grid cell while decreasing the weighted absolute percentage error from small to large cities as compared to using unmodified EDGAR v8.0 data. These data allow for municipal-level analysis of onsite building emissions for any municipality in the world, revealing that 10% of global direct onsite building emissions are concentrated within only 38 functional urban areas (i.e., a city and the surrounding commuting area), and 25% of these emissions are concentrated within 259 functional urban areas, globally. These municipality-level observations can inform prioritization of subnational emissions reduction actions, particularly in regions that cannot access other forms of emissions inventories.
Markakis et al. (Thu,) studied this question.