The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach

Matthew Cole, Robert Elliott () and Bowen Liu
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Bowen Liu: University of Birmingham

Discussion Papers from Department of Economics, University of Birmingham

Abstract: We quantify the impact of the Wuhan Covid-19 lockdown on concentrations of four air pollutants using a two-step approach. First, we use machine learning to remove the confounding effects of weather conditions on pollution concentrations. Second, we use a new Augmented Synthetic Control Method (Ben-Michael et al. 2019) to estimate the impact of the lockdown on weather normalised pollution relative to a control group of cities that were not in lockdown. We find NO2 concentrations fell by as much as 24 ug/m3 during the lockdown (a reduction of 63% from the pre-lockdown level), while PM10 concentrations fell by a similar amount but for a shorter period. The lockdown had no discernible impact on concentrations of SO2 or CO. We calculate that the reduction of NO2 concentrations could have prevented as many as 496 deaths in Wuhan city, 3,368 deaths in Hubei province and 10,822 deaths in China as a whole.

Keywords: Air pollution; Covid-19; machine learning; synthetic control; health. (search for similar items in EconPapers)
JEL-codes: C21 C23 I15 I18 Q52 Q53 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2020-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-env and nep-hea
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Citations: View citations in EconPapers (41)

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Journal Article: The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:bir:birmec:20-09

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