A Synthetic Difference-in-Differences Approach to Assess the Impact of Shanghai’s 2022 Lockdown on Ozone Levels

Yumin Li, Jun Wang, Yuntong Fan, Chuchu Chen (), Jaime Campos Gutiérrez, Ling Huang (), Zhenxing Lin, Siyuan Li and Yu Lei
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Yumin Li: SILC Business School, Shanghai University, Shanghai 201800, China
Jun Wang: SILC Business School, Shanghai University, Shanghai 201800, China
Yuntong Fan: SILC Business School, Shanghai University, Shanghai 201800, China
Chuchu Chen: State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-Control, Chinese Academy of Environmental Planning, Beijing 100041, China
Jaime Campos Gutiérrez: Faculty of Management and Economics, Universidad de Santiago de Chile, Santiago 8320000, Chile
Ling Huang: School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
Zhenxing Lin: SILC Business School, Shanghai University, Shanghai 201800, China
Siyuan Li: Department of Economics, Katholieke Universiteit Leuven, 3000 Leuven, Belgium
Yu Lei: State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-Control, Chinese Academy of Environmental Planning, Beijing 100041, China

Sustainability, 2025, vol. 17, issue 15, 1-15

Abstract: Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O 3 ) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O 3 ) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m 3 (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O 3 concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design.

Keywords: air quality management; synthetic difference-in-differences; ozone pollution; 2022 Shanghai lockdown; sustainable urban governance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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