COVID-19, Urban Transportation, and Air Pollution
37 Pages Posted: 10 Jun 2021 Last revised: 1 Aug 2022
Date Written: June 3, 2021
Urban air pollution has severe negative effects on health and the economy, especially in developing and industrializing countries, such as China and India. Although the transportation sector is widely acknowledged as among the largest contributors to urban air pollution, quantifying its causal effects on air pollution is challenging, as decisions to travel are endogenous with air quality. The spread of COVID-19 offers a unique opportunity for causal identification, as the pandemic directly affects decisions to travel but has little direct effect on air pollution. Leveraging the number of COVID-19 infections and COVID-19-related queries to online search engines as instruments for decisions to travel, controlling for two-way fixed effects, we quantify the effects of three public transportation subsectors (buses, railways, and taxis) and private vehicles on six primary air pollutants (CO, NO2, O3, PM2.5, PM10, and SO2) of 36 central cities of China, using two-stage ridge regression and double/debiased machine-learning models. The results demonstrate that the negative effects of urban transportation on air quality are likely to be significantly underestimated without addressing endogeneity in the observational data. After addressing endogeneity, the findings show that every 1% increase in the passenger volume of public transportation and in the congestion index results in about a 0.039% and 0.368% increase in the synthesized air pollution index. Further, our estimates indicate heterogeneous effects across transportation modes and air pollutants. Notably, our work shows that air pollution shifts the demand from mass transportation (i.e., buses) to taxis, which aggravates pollution further. These findings contribute to the literature on the COVID-19 pandemic, transportation, and air pollution, and have implications for sustainable transportation planning, operation, and policy evaluation.
Keywords: Air pollution; transportation; COVID-19; double/debiased machine learning
JEL Classification: O14
Suggested Citation: Suggested Citation