Interaction Patterns between Climate Action and Air Cleaning in China: A Two-Way Evaluation Based on an Ensemble Learning Approach
Zeyuan Liu, Mengting Dong, Wenbo Xue, Xiufeng Ni, Zhulin Qi, Jiacheng Shao, Yingzhuang Guo, Mengying Ma, Qingyu Zhang, Wang Jinnan
Abstract
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.