CO2 emissions from C40 cities: citywide emission inventories and comparisons with global gridded emission datasets

Abstract

Abstract Under the leadership of the C40 Cities Climate Leadership Group (C40), approximately 1,100 global cities have signed to reach net-zero emissions by 2050. Accurate greenhouse gas emission calculations at the city-scale have become critical. This study forms a bridge between the two emission calculation methods: 1) the city-scale accounting used by C40 cities —the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) and 2) the global-scale gridded datasets used by the research community —the Emission Database for Global Atmospheric Research (EDGAR) and Open‐Source Data Inventory for Anthropogenic CO2 (ODIAC). For the emission magnitudes of 78 C40 cities, we find good correlations between the GPC and EDGAR (R2 = 0.80) and the GPC and ODIAC (R2 = 0.72). Regionally, African cities show the largest variability in the three emission estimates. For the emission trends, the standard deviation of the differences is ±4.7 %/year for EDGAR vs. GPC and is ±3.9 %/year for ODIAC vs. GPC: a factor of ~2 larger than the trends that many C40 cities pledged (net-zero by 2050 from 2010, or −2.5%/year). To examine the source of discrepancies in the emission datasets, we assess the impact of spatial resolutions of EDGAR (0.1°) and ODIAC (1km) on estimating varying-sized cities’ emissions. Our analysis shows that the coarser resolution of EDGAR can artificially decrease emissions by 13% for cities smaller than 1,000 km2. We find that data quality of emission factors used in GPC inventories vary regionally: the highest quality for European and North American and the lowest for African and Latin American cities. Our study indicates that the following items should be prioritized to reduce the discrepancies between the two emission calculation methods: 1) implementing local-specific/up-to-date emission factors in GPC inventories, 2) keeping the global power plant database current, and 3) incorporating satellite-derived CO2 datasets (i.e., NASA OCO-3).