Satellite-Based Differentiation of Anthropogenic and Forest Fire CO2 Emission Regions

The release of CO₂ from human activities and forest fires constitutes the two primary and most significant contributors to the global warming process. Identifying distinct types of CO₂ emission sources is crucial for developing targeted emission reduction measures tailored to varying intensities. In this study, an integrated methodology framework has been proposed to identify abnormal carbon release zones, clarify human-induced versus natural emissions, and quantify their respective contributions. To achieve this, Greenhouse Gases Observing Satellite XCO₂, NO₂, and CO data were analyzed across the globe using cluster-based segmentation and vector autoregressive modeling. Specifically, primary regions of anthropogenic and forest fire CO₂ emissions were identified based on these datasets. Furthermore, influencing factors affecting anthropogenic CO₂ emissions in China were quantified. Notably, human activities or forest fire regions were effectively distinguished from each other by K-means clustering of XCO₂(anomaly) alongside NO₂ and CO data. In China, anthropogenic CO₂ emission patterns exhibit significant regional variability due to factors such as the output value of secondary and tertiary industries, residential energy consumption, and motor vehicle ownership. This study offers substantial insights into identifying distinct types of carbon emission sources while providing a foundation for developing targeted emission reduction strategies based on their varying intensities.