Assessing the Impacts of Birmingham’s Clean Air Zone on Air Quality: Estimates from a Machine Learning and Synthetic Control Approach
研究使用随机森林和增强合成控制法，评估英国伯明翰清洁空气区政策实施第一年对NO2、NOx和PM2.5的因果影响，发现该政策显著但适度降低了路边监测点的NO2和NOx浓度。
Abstract We apply a two-step data driven approach to determine the causal impact of the clean air zone (CAZ) policy on air quality in Birmingham, UK. Levels of NO 2 , NO x and PM 2.5 before and after CAZ implementation were collected from automatic air quality monitoring sites both within and outside the CAZ. We apply a unique combination of two recent methods: (1) a random forest machine learning method to strip out the effects of meteorological conditions on air pollution levels, and then (2) the Augmented Synthetic Control Method (ASCM) on the de-weathered air pollution data to isolate the causal effect of the CAZ. We find that, during the first year following the formal policy implementation, the CAZ led to significant but modest reductions of NO 2 and NO X levels measured at the roadside within (up to 3.4% and 5.4% of NO 2 and NO X , respectively) and outside (up to 6.6% and 11.9%) the zone, with no detectable changes at the urban background site outside the CAZ. No significant impacts of the CAZ were found on concentrations of fine particulates (PM 2.5 ). Our analysis demonstrates the short-term effectiveness of CAZ in reducing concentrations of NO 2 and NO X .