Estimating the uncertainty of the greenhouse gas emission accounts in Global Multi-Regional Input-Output analysis

摘要: Global multi-regional input-output (GMRIO) analysis is the standard tool to calculate consumption-based carbon accounts at the macro level. Recent inter-database comparisons have exposed discrepancies in GMRIO-based results, pinpointing greenhouse gas (GHG) emission accounts as the primary source of variation. A few studies have delved into the robustness of GHG emission accounts, using Monte-Carlo simulations to understand how uncertainty from raw data propagates to the final GHG emission accounts. However, these studies often make simplistic assumptions about raw data uncertainty and ignore correlations between disaggregated variables. Here, we compile GHG emission accounts for the year 2015 according to the resolution of EXIOBASE v3, covering CO2, CH4 and N2O emissions. We propagate uncertainty from the raw data, namely the United Nations Framework Convention on Climate Change (UNFCCC) and EDGAR inventories, to the GHG emission accounts, and then further to the GHG footprints. We address both limitations from previous studies. First, instead of making simplistic assumptions, we utilise authoritative raw data uncertainty estimates from the National Inventory Reports (NIR) submitted to the UNFCCC and a recent study on uncertainty of the EDGAR emission inventory. Second, we account for inherent correlations due to data disaggregation by sampling from a Dirichlet distribution. Our results show a median coefficient of variation (CV) for GHG emission accounts at the country level of 4 % for CO2, 12 % for CH4, and 33 % for N2O. For CO2, smaller economies with significant international aviation or …