Can Building Subway Systems Improve Air Quality? New Evidence from Multiple Cities and Machine Learning

Lunyu Xie (), Tianhua Zou (), Joshua Linn () and Haosheng Yan ()
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Lunyu Xie: Renmin University of China
Tianhua Zou: Renmin University of China
Joshua Linn: University of Maryland
Haosheng Yan: Central University of Finance and Economics

Environmental & Resource Economics, 2024, vol. 87, issue 4, No 5, 1009-1044

Abstract: Abstract Public investments in subway systems are often motivated by improving local air quality. Recent studies, however, have reached different conclusions on the air quality benefits of subway investment. To reconcile these findings, this paper examines the air quality effects of all 359 subway line openings in China between 2013 and 2018. The machine learning method adopted in this paper substantially improves the consistency and precision of the estimates by purging seasonality, volatility, and the nonlinear effects of meteorological conditions in air quality data. The empirical results suggest an insignificant short-term effect and a significant long-term effect, which is expected as the adjustment of commuting mode takes time. Using the causal forest approach, the heterogeneity analysis find that a city that is experiencing rapid economic growth from a lower income level and currently has fewer subway lines is more likely to experience statistically significant improvements in air quality from a subway opening. These findings help reconcile the different findings in the literature and shed light on air pollution reduction as one of the objectives of public transit investment.

Keywords: Air quality; Heterogenous effect; Machine learning method; Urban rail transit (search for similar items in EconPapers)
JEL-codes: L92 Q53 R41 R53 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10640-024-00852-3

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