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
Environmental & Resource Economics, 2020, vol. 76, issue 4, No 4, 553-580
Abstract:
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. in The augmented synthetic control method. University of California Berkeley, Mimeo, 2019. https://arxiv.org/pdf/1811.04170.pdf ) 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 NO $$_{2}$$ 2 concentrations fell by as much as 24 $$\upmu$$ μ g/m $$^3$$ 3 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 SO $$_{2}$$ 2 or CO. We calculate that the reduction of NO $$_{2}$$ 2 concentrations could have prevented as many as 496 deaths in Wuhan city, 3368 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)
Date: 2020
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Citations: View citations in EconPapers (40)
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DOI: 10.1007/s10640-020-00483-4
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