Abstract Air pollution and climate change, driven by fine particulate matter (PM2.5) and carbon dioxide (CO2), present critical challenges to human survival. Understanding the interaction between PM2.5 control and carbon reduction‐specifically, how mitigating PM2.5 sources impacts CO2 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 PM2.5 and CO2, 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 CO2, while targeting VE more effectively reduces PM2.5. The IMSA framework provides critical insights for co‐beneficial strategies to improve air quality and mitigate climate change.

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