Greenhouse gas emissions and their trends over the last three decades across Africa
Authors: Mostefaoui Mounia , Ciais Philippe , McGrath Matthew Joseph , Peylin Philippe , Patra Prabir K. , Ernst Yolandi 
Published: July 2023

Abstract. A key goal of the Paris Agreement (PA) is to reach net-zero Greenhouse Gasses (GHG) emissions by 2050 globally, which requires mitigation efforts from all countries. Africa’s rapidly growing population and GDP makes this continent important for GHG emission trends. In this paper, we study the emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) in Africa over three decades (1990–2018). We compare the relative merits of data products from bottom-up approaches including UNFCCC national inventories, FAO, PRIMAP-hist, process-based ecosystem models for CO2 fluxes in the Land Use, Land Use Change and Forestry (LULUCF) sector, and global atmospheric inversions. Our database is available from Zenodo at: https://doi.org/10.5281/zenodo.7347077 (Mostefaoui et al., 2022). For inversions, we applied different methods to separate anthropogenic CH4 emissions. The bottom-up (BU) inventories show that over the decade 2010–2018, less than ten countries represented more than 75 % of African fossil CO2 emissions. With a mean of 1373 MtCO2 yr-1, total African fossil CO2 emissions over 2010–2018 represent only 4 % of global fossil emissions. Yet, these emissions grew by +34 % from 1990–1999 to 2000–2009 and by +31 % over 2000–2009 to 2010–2018, more than doubling in 30 years. This growth rate is more than twice faster than the global growth rate of fossil CO2 emissions. The anthropogenic emissions of CH4 grew by 5 % from 1990–1999 to 2000–2009 and by 14.8 % from 2000–2009 to 2010–2018. The N2O emissions grew by 19.5 % from 1990–1999 to 2000–2009; and by 20.8 % from 2000–2009 to 2010–2018. When using the mean of estimates from UNFCCC reports (including the land use sector), with corrections from outliers, Africa was a mean source of greenhouse gasses of MtCO2e yr-1 from all bottom-up estimates (figures into brackets indicating min-max range uncertainties), and of MtCO2e yr-1 from top-down methods, during their overlap period from 2001 to 2017. Although the mean values are consistent, the range of top-down estimates is larger than the one of bottom up, indicating that sparse atmospheric observations and transport model errors do not allow us to use inversions to reduce the uncertainty of bottom-up estimates. A main source of uncertainty comes from CO2 fluxes in the land-use sector (LULUCF) for which the spread across inversions is larger than 50 %, especially in Central Africa. Moreover, estimates from national UNFCCC communications differ widely depending on whether the large sinks in a few countries are corrected to more plausible values using more recent national sources following the methodology of Grassi et al. (2022) The median of CH4 emissions from inversions based on satellite retrievals and in situ surface networks are consistent with each other within 2 % at continental scale. The inversion ensemble also provides consistent estimates of anthropogenic CH4 emissions with bottom-up inventories such as PRIMAP-hist. For N2O, inversions systematically show higher emissions than inventories, on average about 4.5 times more than PRIMAP-hist, either because natural N2O sources cannot be separated accurately from anthropogenic ones in inversions, or because bottom-up estimates ignore indirect emissions and under-estimate emission factors. Future improvements can be expected thanks to a denser network for monitoring atmospheric concentrations. This study helps to introduce methods to enhance the scope of use of various published datasets and allows to compute budgets thanks to recombinations those data products. Our results allow to understand uncertainty and trends of emissions and removals in a region of the world where few observations exist and most inventories are based on default IPCC guidelines values. The results can therefore serve as a support tool for the Global Stocktake (GST) of the Paris Agreement. The referenced datasets related to figures are available at: https://doi.org/10.5281/zenodo.7347077 (Mostefaoui et al., 2022).