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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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    1. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    2. He, Guojun & Fan, Maoyong & Zhou, Maigeng, 2016. "The effect of air pollution on mortality in China: Evidence from the 2008 Beijing Olympic Games," Journal of Environmental Economics and Management, Elsevier, vol. 79(C), pages 18-39.
    3. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    4. Lagravinese, R. & Moscone, F. & Tosetti, E. & Lee, H., 2014. "The impact of air pollution on hospital admissions: Evidence from Italy," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 278-285.
    5. Tatyana Deryugina & Garth Heutel & Nolan H. Miller & David Molitor & Julian Reif, 2019. "The Mortality and Medical Costs of Air Pollution: Evidence from Changes in Wind Direction," American Economic Review, American Economic Association, vol. 109(12), pages 4178-4219, December.
    6. Eli Ben-Michael & Avi Feller & Jesse Rothstein, 2018. "The Augmented Synthetic Control Method," Papers 1811.04170, arXiv.org, revised Jul 2020.
    7. Maddison, David, 2005. "Air pollution and hospital admissions: an ARMAX modelling approach," Journal of Environmental Economics and Management, Elsevier, vol. 49(1), pages 116-131, January.
    8. Christian Dustmann & Uta Schönberg & Jan Stuhler, 2017. "Labor Supply Shocks, Native Wages, and the Adjustment of Local Employment," The Quarterly Journal of Economics, Oxford University Press, vol. 132(1), pages 435-483.
    9. Eduardo Cavallo & Sebastian Galiani & Ilan Noy & Juan Pantano, 2013. "Catastrophic Natural Disasters and Economic Growth," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1549-1561, December.
    10. Noémi Kreif & Richard Grieve & Dominik Hangartner & Alex James Turner & Silviya Nikolova & Matt Sutton, 2016. "Examination of the Synthetic Control Method for Evaluating Health Policies with Multiple Treated Units," Health Economics, John Wiley & Sons, Ltd., vol. 25(12), pages 1514-1528, December.
    11. Yu Qin & Hongjia Zhu, 2018. "Run away? Air pollution and emigration interests in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 31(1), pages 235-266, January.
    12. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    13. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    14. Henrik Jacobsen Kleven & Camille Landais & Emmanuel Saez, 2013. "Taxation and International Migration of Superstars: Evidence from the European Football Market," American Economic Review, American Economic Association, vol. 103(5), pages 1892-1924, August.
    15. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    16. Fang, Hanming & Wang, Long & Yang, Yang, 2020. "Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China," Journal of Public Economics, Elsevier, vol. 191(C).
    17. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    18. Andrew C. Johnston & Alexandre Mas, 2018. "Potential Unemployment Insurance Duration and Labor Supply: The Individual and Market-Level Response to a Benefit Cut," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2480-2522.
    19. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2015. "Comparative Politics and the Synthetic Control Method," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 495-510, February.
    20. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    21. Cheung, Chun Wai & He, Guojun & Pan, Yuhang, 2020. "Mitigating the air pollution effect? The remarkable decline in the pollution-mortality relationship in Hong Kong," Journal of Environmental Economics and Management, Elsevier, vol. 101(C).
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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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