Interaction Patterns between Climate Action and Air Cleaning in China: A Two-Way Evaluation Based on an Ensemble Learning Approach

China will attempt to achieve its
simultaneous goals in 2060, whereby
carbon neutrality will be accomplished and the PM2.5 (fine
particulate matter) level is expected to remain below 10 μg/m3. Identifying interaction patterns between air cleaning and
climate action represents an important step to obtain cobenefits.
Here, we used a random sampling strategy through the combination of
chemical transport modeling and machine learning approach to capture
the interaction effects from two perspectives in which the driving
forces of both climate action and air cleaning measures were compared.
We revealed that climate action where carbon emissions were decreased
to 1.9 Bt (billion tons) could lead to a PM2.5 level of
12.4 μg/m3 (95% CI (confidence interval): 10.2–14.6
μg/m3) in 2060, while air cleaning could force carbon
emissions to reach 1.93 Bt (95% CI: 0.79–3.19 Bt) to achieve
net carbon neutrality based on the potential carbon sinks in 2060.
Additional controls targeting primary PM2.5, ammonia, and
volatile organic compounds were required as supplements to overcome
the partial lack of climate action. Our study provides novel insights
into the cobenefits of air-quality improvement and climate change
mitigation, indicating that the effect of air cleaning on the simultaneous
goals might have been underestimated before.