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

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  • Matthew A Cole

    (University of Birmingham)

  • Robert J R Elliott

    (University of Birmingham)

  • Bowen Liu

    (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.

Suggested Citation

  • Matthew A Cole & Robert J R Elliott & Bowen Liu, 2020. "The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach," Discussion Papers 20-09, Department of Economics, University of Birmingham.
  • Handle: RePEc:bir:birmec:20-09
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    Cited by:

    1. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. López-Cazar, Ibeth & Papyrakis, Elissaios & Pellegrini, Lorenzo, 2021. "The Extractive Industries Transparency Initiative (EITI) and corruption in Latin America: Evidence from Colombia, Guatemala, Honduras, Peru, and Trinidad and Tobago," Resources Policy, Elsevier, vol. 70(C).
    3. Veronika Harantová & Ambróz Hájnik & Alica Kalašová, 2020. "Comparison of the Flow Rate and Speed of Vehicles on a Representative Road Section before and after the Implementation of Measures in Connection with COVID-19," Sustainability, MDPI, Open Access Journal, vol. 12(17), pages 1-17, September.
    4. Roy Cerqueti & Raffaella Coppier & Alessandro Girardi & Marco Ventura, 2021. "The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy," Papers 2101.11901, arXiv.org.
    5. Matthew A Cole & Ceren Ozgen & Eric Strobl, 2020. "Air Pollution Exposure and Covid-19," Discussion Papers 20-13, Department of Economics, University of Birmingham.

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    More about this item

    Keywords

    Air pollution; Covid-19; machine learning; synthetic control; health.;
    All these keywords.

    JEL classification:

    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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