Determining the Ability of Satellite CO2 Observations to Verify Emissions Trends and Resolve Discrepancies Between Emissions Inventories

Bottom-up emissions inventories face unresolved discrepancies due to differences in reporting requirements and data collection methods between jurisdictions and agencies, reducing their ability to verify emissions mitigation on a policy-relevant scale or detect emissions leakage between jurisdictional areas. For example, power plant CO₂ emissions estimates from the Environmental Protection Agency’s Clean Air Markets Program Data (CAMPD) and the Department of Energy’s Energy Information Administration (EIA) are created with different scopes and methodologies that result in geographically correlated differences, even for facilities for which they overlap. This study assesses the ability of column CO₂ observations from the Orbiting Carbon Observatory-2 (OCO-2) mission and a hypothetical wide-swath space-borne instrument to detect trends and resolve geographical biases in inventories, with a focus on the Northeast United States. Datasets agree that power plant CO₂ emissions from this region have broadly decreased in the past two decades, but geographical discrepancies between inventories make it difficult to quantify emissions shifts between jurisdictional areas. We propagate EIA and CAMPD emissions, as well as emissions scenarios that exaggerate various discrepancies between these inventories, through the atmosphere alongside biospheric fluxes using the GEOS model. We create pseudo-observations using these simulated CO₂ concentrations for OCO-2 and the hypothetical instrument. To determine whether such observations could verify decadal regional emissions trends for either inventory in the presence of realistic variability, we quantify the relative contributions of power plant emissions trends and variability from short-term power plant emissions fluctuations, biospheric fluxes, and atmospheric transport to the pseudo-observations. To determine whether such observations could detect discrepancies between inventories and therefore resolve biases, we quantify the differences in pseudo-observations resulting from the different emissions scenarios relative to overall variability. The results will inform recommendations for future space-borne greenhouse gas observations to improve bottom-up inventories.