<p>Monitoring spatiotemporal changes in anthropogenic CO₂ is crucial for informing international climate change policy initiatives, but also challenging due to the absence of national inventories and statistical data during such conflicts. Currently, nighttime light (NTL) remote sensing data is often used for spatial disaggregation of CO₂ emission statistics, while the construction of existing anthropogenic CO₂ emission datasets relies on ground observation data, which are difficult to apply rapidly and accurately in the context of a geopolitical conflict. This study introduces a novel model for monitoring monthly changes in anthropogenic CO₂ emissions based on NTL data collected by the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Global Gridded Daily CO₂ Emission Dataset (GRACED). The proposed model integrates the monthly changes in NTL caused by the conflict with the monthly mean CO₂ emissions of various sectors before the conflict for near-real-time monitoring through spatial aggregation and statistical analysis using Google Earth Engine (GEE) and ArcGIS software. As a case study, we consider the Russia-Ukraine war to analyze the monthly CO₂ emission changes in Ukraine, across various scales. The results demonstrate that the residential consumption, ground transport, and industry sectors respectively have CO₂ emission changes of 413 kt, 106 kt, and 324 kt (six months after the war began), and of 136 kt, 33 kt, and 139 kt (one year after the war began) in Ukraine. Significant consistency between the estimated and reference CO₂ emission changes can be observed for each month during the war, with the R² ranging from 0.61–0.87, 0.51–0.74, and 0.69–0.93 for the residential consumption, ground transport, and industry sectors, respectively. Overall, this study contributes new insights into the monitoring of near-real-time changes in anthropogenic CO₂ emissions under geopolitical conflicts, and help to enhance the understanding of the environmental governance and climate accountability.</p>