Investigating the Influence of a New Ride-Hailing Policy on Air Quality Using Regression Discontinuity Design

Abstract

With the increase in the number of ride-hailing, traffic congestion and environmental pollution have been exacerbated. A new ride-hailing policy was formed as a means of administrative control to reduce the negative externalities. However, the improvement in air quality caused by the new ride-hailing policy has not been effectively verified. This study employed regression discontinuity design (RDD) to explore the causal relationship between the air quality in Beijing and the new ride-hailing policy to reduce the endogeneity problems. The main results show that the implementation of the new policy reduces air quality index (AQI), particulate matter 10 (PM10), and ozone (O3) by 14%, 12%, and 16%, respectively, in the ordinary least squares (OLS) regression analysis. After applying the RDD, this study finds that the implementation of the new policy reduces AQI, PM10, and O3 by 43%, 30%, and 42%, respectively. The regression value of RDD is higher than that of OLS regression. Additionally, the robustness test reveals that RDD is reasonable. The findings suggest that the implementation of the new ride-hailing policy exerts an improvement effect on air quality. Policy implications to further promote the new ride-hailing policy are hence discussed.