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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. Matthew A. Cole & Ceren Ozgen & Eric Strobl, 2020. "Air Pollution Exposure and Covid-19 in Dutch Municipalities," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 581-610, August.
    2. Muhammad, Shahbaz & Avik, Sinha & Muhammad Ibrahim, Shah, 2021. "Differential Impacts of US-China Trade War and Outbreak of COVID-19 on Chinese Air Quality," MPRA Paper 110040, University Library of Munich, Germany, revised 2021.
    3. Mónica Amador-Jiménez & Naomi Millner & Charles Palmer & R. Toby Pennington & Lorenzo Sileci, 2020. "The Unintended Impact of Colombia’s Covid-19 Lockdown on Forest Fires," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 1081-1105, August.
    4. Yu, Bo & Tran, Trang & Lee, Wang-Sheng, 2021. "Green Infrastructure and Air Pollution: Evidence from Highways Connecting Two Megacities in China," IZA Discussion Papers 14900, Institute of Labor Economics (IZA).
    5. Florence Bouvet & Roy Bower & Jason C. Jones, 2022. "Currency Devaluation as a Source of Growth in Africa: A Synthetic Control Approach," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 48(3), pages 367-389, June.
    6. Roy Cerqueti & Raffaella Coppier & Alessandro Girardi & Marco Ventura, 2022. "The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy [Using synthetic controls: Feasibility, data requirements, and methodological aspects]," Econometrics Journal, Royal Economic Society, vol. 25(1), pages 46-70.
    7. Hung-Hao Chang & Chad Meyerhoefer & Feng-An Yang, 2020. "COVID-19 Prevention and Air Pollution in the Absence of a Lockdown," NBER Working Papers 27604, National Bureau of Economic Research, Inc.
    8. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    9. Hayakawa, Kazunobu & Keola, Souknilanh, 2021. "How is the Asian economy recovering from COVID-19? Evidence from the emissions of air pollutants," Journal of Asian Economics, Elsevier, vol. 77(C).
    10. Augusto Cerqua & Roberta Di Stefano, 2022. "When did coronavirus arrive in Europe?," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 181-195, March.
    11. Cui, Zhiwei & Fu, Xin & Wang, Jianwei & Qiang, Yongjie & Jiang, Ying & Long, Zhiyou, 2022. "How does COVID-19 pandemic impact cities' logistics performance? An evidence from China's highway freight transport," Transport Policy, Elsevier, vol. 120(C), pages 11-22.
    12. Mohan Sarkar & Anupam Das & Sutapa Mukhopadhyay, 2021. "Assessing the immediate impact of COVID-19 lockdown on the air quality of Kolkata and Howrah, West Bengal, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 8613-8642, June.
    13. Souknilanh Keola & Kazunobu Hayakawa, 2021. "Do Lockdown Policies Reduce Economic and Social Activities? Evidence from NO2 Emissions," The Developing Economies, Institute of Developing Economies, vol. 59(2), pages 178-205, June.
    14. Kuosmanen, Timo & Zhou, Xun & Eskelinen, Juha & Malo, Pekka, 2021. "Design Flaw of the Synthetic Control Method," MPRA Paper 106328, University Library of Munich, Germany.
    15. 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).
    16. 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, vol. 12(17), pages 1-17, September.
    17. Matthew A Cole & Ceren Ozgen & Eric Strobl, 2020. "Air Pollution Exposure and Covid-19," Discussion Papers 20-13, Department of Economics, University of Birmingham.
    18. Xiao Ke & Cheng Hsiao, 2022. "Economic impact of the most drastic lockdown during COVID‐19 pandemic—The experience of Hubei, China," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 187-209, January.

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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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