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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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          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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document} concentrations fell by as much as 24  \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document} g/m \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^3$$\end{document} 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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document} or CO. We calculate that the reduction of NO \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document} 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.

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          Most cited references26

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          Comparative Politics and the Synthetic Control Method

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            Machine Learning: An Applied Econometric Approach

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              Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models

              Yiqing Xu (2017)
              Difference-in-differences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, we propose a method that not only relaxes this often-violated assumption, but also unifies the synthetic control method (Abadie, Diamond, and Hainmueller 2010) with linear fixed effects models under a simple framework, of which DID is a special case. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method has several advantages. First, it allows the treatment to be correlated with unobserved unit and time heterogeneities under reasonable modeling assumptions. Second, it generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. Third, with a built-in cross-validation procedure, it avoids specification searches and thus is easy to implement. An empirical example of Election Day Registration and voter turnout in the United States is provided.
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                Author and article information

                Contributors
                m.a.cole@bham.ac.uk
                r.j.elliott@bham.ac.uk
                BXL720@student.bham.ac.uk
                Journal
                Environ Resour Econ (Dordr)
                Environ Resour Econ (Dordr)
                Environmental & Resource Economics
                Springer Netherlands (Dordrecht )
                0924-6460
                1573-1502
                10 August 2020
                : 1-28
                Affiliations
                GRID grid.6572.6, ISNI 0000 0004 1936 7486, Department of Economics, , University of Birmingham, ; Birmingham, UK
                Author information
                http://orcid.org/0000-0002-3966-2082
                Article
                483
                10.1007/s10640-020-00483-4
                7416596
                32836865
                603d84ee-7c8f-4f71-becb-8300ca975008
                © Springer Nature B.V. 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 13 July 2020
                Categories
                Article

                air pollution,covid-19,machine learning,synthetic control,health,q53,q52,i18,i15,c21,c23

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