Co‐Benefits Between PM _2.5 Control and Carbon Reduction Revealed by Interpretable Machine Learning Methods

Abstract Air pollution and climate change, driven by fine particulate matter (PM 2.5 ) and carbon dioxide (CO 2 ), present critical challenges to human survival. Understanding the interaction between PM 2.5 control and carbon reduction‐specifically, how mitigating PM 2.5 sources impacts CO 2 levels and vice versa‐is essential for effective policy‐making. To address this, we developed an Interpretable machine learning (ML) and source apportionment (IMSA) framework. The framework screens pollutant sources for PM 2.5 and CO 2 , and calculates their contributions, revealing that industrial emissions (IE) (11%, 29%), vehicle exhaust (VE) (13%, 19%), and coal combustion (19%, 15%) are major shared sources. By integrating interpretable ML methods, IMSA uncovers interaction effects, showing that reducing IE significantly lowers CO 2 , while targeting VE more effectively reduces PM 2.5 . The IMSA framework provides critical insights for co‐beneficial strategies to improve air quality and mitigate climate change.