Spatial disaggregation of urban greenhouse gases emission scenarios: Empirical analysis based on an expanded Kaya identity

• The study proposes a GIS-based application of the Kaya Identity ( C O 2 e = Population * GDP Population × Energy GDP × C O 2 e Energy ) for the spatialization and disaggregation of the GHG inventory of a city. • The proposed methodology illustrates an expansion of the Kaya Identity including level of income and socioeconomic stratification as scaling factors in the spatialization of the GHG emissions. • The results are contrasted with the assessment of different urban sustainability indicators to discuss the implications of spatialized GHG emissions inventories in the climate justice of the cities. Cities are increasingly recognized as critical nodes in the global carbon footprint. Accordingly, there is an increasing interest in the measurement of urban emissions to guide climate change mitigation in cities. Traditional greenhouse gas (GHG) inventories often provide a macroscopic view of emissions, obscuring the heterogeneity of cities. Recognizing this gap, this study proposes an expanded Kaya identity to spatially disaggregate the carbon emissions inventory and depicts future urban GHG emissions scenarios. Including additional variables to expand the traditional Kaya identity allows to retrieve a spatialized approximation of the emissions inventory that considers the heterogeneity of the city‘s socioeconomic structure within the model. To contextualize this framework, Bogota, Colombia, was chosen as case study. Following the spatial extent of the planning units of the city as disaggregation extent of the GHG emissions, this article offers a view beyond the limitations of the traditionally aggregated data. Such disaggregation underscores the nature of urban emissions, influenced by the distribution of the population, income levels, and socioeconomic strata. This approach allowed to retrieve a spatialization of the GHG emissions scenarios provided by the local administration with a higher level of granularity and an explicitly spatial methodology. The results facilitate targeted policy interventions for effective urban climate action and the cities' sustainability transition.