An Activity‐Based Inversion Framework for Estimating Sectoral Anthropogenic CO 2 Emissions Using Multi‐Pollutant Satellite Observations

Abstract This study develops an activity‐based inversion framework to quantify sectoral carbon dioxide (CO 2 ) emissions using observations of co‐emitted air pollutants. It leverages distinct sectoral co‐emission profiles of nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), and carbon monoxide (CO) to attribute sources and optimize sectoral activity‐rate scaling factors, which are applied to bottom‐up CO 2 emissions to estimate posterior emissions. Results show that nitrogen dioxide (NO 2 ) informs energy and transportation adjustments, while SO 2 and CO constrain industrial and residential adjustments. Pseudo‐observation tests with known solutions show that the posterior reduces normalized mean square error (NMSE) by 26%–68% and normalized mean bias (NMB) by 10%–63% relative to the prior when evaluated against true CO 2 emissions. Posterior simulations show improved agreement with Greenhouse Gases Observing Satellite (GOSAT) CO 2 dry column mixing ratios, but gains are limited where CO model‐observation mismatches drive large residential emission adjustments, highlighting the need to reduce and better characterize CO‐related errors.