The Impact of the COVID-19 Pandemic on Ambient Air Quality in China: A Quasi-Difference-in-Difference Approach
Abstract
:1. Introduction
2. Data and Empirical Methodology
2.1. Datasets
2.2. Quasi-DID Identification
2.2.1. Identification Strategy
2.2.2. The Average Effect on Air Pollution
2.2.3. Dynamic Impacts on Air Pollution
2.3. Summary Statistics
3. The Average Effect on Air Quality
4. Discussion: The Dynamic Patterns of the Lockdown Effects
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Panel A: 2019 Sample | |||||||
(1) | (2) | (3) | (4) | (5) | (6) | (7) | |
Obs. | Mean | p10 | p25 | p50 | p75 | p90 | |
All | 45718 | 77.32 | 36.33 | 47.58 | 64.21 | 89.63 | 134.85 |
pre: [−22, −1] | 7944 | 92.40 | 41.46 | 55.75 | 78.94 | 113.88 | 158.82 |
post: [0, 93] | 33444 | 71.55 | 35.92 | 46.46 | 61.08 | 81.42 | 116.79 |
Panel B: 2020 Sample | |||||||
(1) | (2) | (3) | (4) | (5) | (6) | (7) | |
Obs. | Mean | p10 | p25 | p50 | p75 | p90 | |
All | 41960 | 67.86 | 27.96 | 39.21 | 56.58 | 79.05 | 116.71 |
pre: [−22, −1] | 7955 | 90.25 | 31.90 | 46.96 | 72.71 | 116.94 | 179.36 |
post: [0, 93] | 33644 | 62.32 | 27.38 | 37.92 | 54.25 | 73.46 | 99.28 |
Panel C: Mean difference of city-level air pollutants | |||||||
(1) | (2) | (3) | (1) | (2) | (3) | ||
pre:2019 | post:2019 | post-pre:2019 | pre:2020 | post:2020 | post-pre:2020 | ||
AQI | 92.40 | 71.55 | −20.85 *** | 90.25 | 62.32 | −27.92 *** | |
Type: | |||||||
SO2 | 16.42 | 11.42 | −5.00 *** | 14.21 | 10.48 | −3.73 *** | |
NO2 | 36.36 | 26.56 | −9.80 *** | 37.24 | 22.21 | −15.03 *** | |
CO | 1.15 | 0.80 | −0.34 *** | 1.15 | 0.74 | −0.41 *** | |
O3 | 75.31 | 103.77 | 28.46 *** | 69.24 | 100.84 | 31.6 *** | |
PM2.5 | 63.43 | 41.97 | −21.45 *** | 65.18 | 38.52 | −26.66 *** | |
PM10 | 99.53 | 76.79 | −22.73 *** | 86.60 | 66.58 | −20.02 *** |
Dependent Variable | AQI | ||||
---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | |
Treat*Post | −7.125 *** | −5.604 *** | −4.749 ** | −5.831 *** | −4.884 ** |
(1.605) | (1.842) | (1.945) | (1.827) | (1.944) | |
Treat | −1.412 | −4.176 *** | −4.226 *** | −4.034 ** | −4.172 *** |
(1.452) | (1.598) | (1.596) | (1.588) | (1.602) | |
Wind Speed | −0.424 | −0.559 * | −0.372 | −0.478 | |
(0.296) | (0.292) | (0.296) | (0.293) | ||
L.Wind Speed | −6.094 *** | −6.033 *** | −6.088 *** | −6.072 *** | |
(0.461) | (0.462) | (0.460) | (0.459) | ||
L2. Wind Speed | −5.146 *** | −5.172 *** | −5.151 *** | −5.163 *** | |
(0.311) | (0.309) | (0.311) | (0.307) | ||
L3. Wind Speed | −2.759 *** | −2.799 *** | −2.763 *** | −2.865 *** | |
(0.286) | (0.280) | (0.286) | (0.283) | ||
L4. Wind Speed | −2.037 *** | −2.199 *** | −2.054 *** | −2.219 *** | |
(0.314) | (0.294) | (0.314) | (0.293) | ||
Temperature (Minimum) | 0.093 | 0.120 | |||
(0.098) | (0.110) | ||||
Temperature (Highest) | 0.385 ** | 0.396 ** | |||
(0.166) | (0.178) | ||||
Sunny | 1.189 *** | 1.296 *** | |||
(0.433) | (0.487) | ||||
Constant | 100.713 *** | 112.625 *** | 112.971 *** | 111.668 *** | 117.206 *** |
(1.937) | (3.152) | (3.078) | (3.173) | (4.102) | |
Date Dummy | Y | Y | Y | Y | Y |
City Dummy | Y | Y | Y | Y | Y |
Groups | 367 | 335 | 335 | 335 | 335 |
Sample | 83,710 | 71,597 | 71,597 | 71,597 | 71,597 |
adj R2 | 0.127 | 0.141 | 0.143 | 0.141 | 0.144 |
(1) | (2) | (3) | (4) | (5) | (6) | |
---|---|---|---|---|---|---|
SO2 Concentration | NO2 Concentration | CO Concentration | O3 Concentration | PM2.5 Concentration | PM10 Concentration | |
Treat*Post | 1.679 *** | −5.113 *** | −0.105 *** | 3.881 *** | −5.772 *** | 2.721 |
(0.343) | (0.402) | (0.012) | (0.794) | (1.526) | (2.198) | |
Treat | −3.073 *** | −0.156 | 0.019 | −3.134 *** | 1.077 | −11.821 *** |
(0.425) | (0.427) | (0.013) | (0.605) | (1.347) | (1.647) | |
Wind Speed | −0.149 ** | −0.120 * | −0.005 ** | 1.945 *** | −0.645 *** | 0.725 * |
(0.067) | (0.065) | (0.002) | (0.190) | (0.209) | (0.387) | |
L.Wind Speed | −1.391 *** | −4.008 *** | −0.075 *** | 0.584 *** | −5.363 *** | −0.563 |
(0.106) | (0.113) | (0.004) | (0.177) | (0.294) | (0.645) | |
L2. Wind Speed | −1.239 *** | −3.363 *** | −0.084 *** | −2.013 *** | −7.452 *** | −6.733 *** |
(0.094) | (0.105) | (0.004) | (0.183) | (0.346) | (0.476) | |
L3. Wind Speed | −0.458 *** | −0.879 *** | −0.038 *** | −2.036 *** | −3.954 *** | −4.282 *** |
(0.056) | (0.069) | (0.003) | (0.173) | (0.295) | (0.428) | |
Temperature (Minimum) | 0.129 *** | 0.270 *** | −0.003 *** | 2.105 *** | −0.242 *** | 0.179 |
(0.020) | (0.021) | (0.001) | (0.076) | (0.087) | (0.139) | |
Temperature (Highest) | −0.241 *** | −0.362 *** | −0.002 * | −0.835 *** | 0.314 ** | 0.649 ** |
(0.027) | (0.031) | (0.001) | (0.081) | (0.151) | (0.304) | |
No-rain | −0.322 *** | −0.735 *** | −0.029 *** | 0.234 | −0.735 ** | 0.711 |
(0.076) | (0.101) | (0.003) | (0.329) | (0.351) | (0.662) | |
Constant | 22.881 *** | 54.813 *** | 1.648 *** | 57.874 *** | 108.913 *** | 135.890 *** |
(1.059) | (1.119) | (0.040) | (2.182) | (3.177) | (5.310) | |
Groups | 335 | 335 | 335 | 335 | 335 | 335 |
Sample | 72,281 | 72,281 | 72,281 | 72,281 | 72,281 | 72,281 |
adj R2 | 0.153 | 0.403 | 0.347 | 0.418 | 0.208 | 0.059 |
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Zhang, T.; Tang, M. The Impact of the COVID-19 Pandemic on Ambient Air Quality in China: A Quasi-Difference-in-Difference Approach. Int. J. Environ. Res. Public Health 2021, 18, 3404. https://doi.org/10.3390/ijerph18073404
Zhang T, Tang M. The Impact of the COVID-19 Pandemic on Ambient Air Quality in China: A Quasi-Difference-in-Difference Approach. International Journal of Environmental Research and Public Health. 2021; 18(7):3404. https://doi.org/10.3390/ijerph18073404
Chicago/Turabian StyleZhang, Tuo, and Maogang Tang. 2021. "The Impact of the COVID-19 Pandemic on Ambient Air Quality in China: A Quasi-Difference-in-Difference Approach" International Journal of Environmental Research and Public Health 18, no. 7: 3404. https://doi.org/10.3390/ijerph18073404