IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i12p6478-d575418.html
   My bibliography  Save this article

The Dynamic Impact of the COVID-19 Pandemic on Air Quality: The Beijing Lessons

Author

Listed:
  • Chenlu Tao

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

  • Gang Diao

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

  • Baodong Cheng

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

Abstract

Air pollution is one of the major environmental problems that endanger human health. The COVID-19 pandemic provided an excellent opportunity to investigate the possible methods to improve Beijing’s air quality meanwhile considering Beijing’s economic impact. We used the TVP-VAR model to analyze the dynamic relationship among the pandemic, economy and air quality based on the daily data from 1 January to 30 August 2020. The result shows that the COVID-19 pandemic indeed had a positive effect on air governance which was good for human health, while doing business as usual would gradually weaken this effect. It shows that the Chinese authority’s production restriction effectively deals with air pollution in a short period of time since the pandemic is just like a quasi-experiment that suddenly suspended all the companies. However, as the limitation stops, the improvement decreases. It is not sustainable. In addition, a partial quarantine also has a positive impact on air quality, which means a partial limitation was also helpful in improving air quality and also played an important role in protecting people’s health. Second, the control measures really hurt Beijing’s economy. However, the partial quarantine had fewer adverse effects on the economy than the lockdown. It is supposed to be a reference for air governance and pandemic control. Third, the more the lag periods were, the smaller their impact. Thus, restrictions on production can only be used in emergencies, such as some international meetings, while it is hard to improve the air quality and create a healthy and comfortable living environment only by limitation in the long-term.

Suggested Citation

  • Chenlu Tao & Gang Diao & Baodong Cheng, 2021. "The Dynamic Impact of the COVID-19 Pandemic on Air Quality: The Beijing Lessons," IJERPH, MDPI, vol. 18(12), pages 1-12, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6478-:d:575418
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/12/6478/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/12/6478/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chang-Jin Ma & Gong-Unn Kang, 2020. "Air Quality Variation in Wuhan, Daegu, and Tokyo during the Explosive Outbreak of COVID-19 and Its Health Effects," IJERPH, MDPI, vol. 17(11), pages 1-12, June.
    2. Antonakakis, Nikolaos & Gabauer, David & Gupta, Rangan, 2019. "International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression," International Review of Financial Analysis, Elsevier, vol. 65(C).
    3. Balli, Esra & Nazif Çatık, Abdurrahman & Nugent, Jeffrey B., 2021. "Time-varying impact of oil shocks on trade balances: Evidence using the TVP-VAR model," Energy, Elsevier, vol. 217(C).
    4. Schwert, G William, 2002. "Tests for Unit Roots: A Monte Carlo Investigation," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 5-17, January.
    5. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    6. Luis Alberiko Gil-Alaña & Carlos Pestana Barros & Zhongfei Chen, 2016. "The persistence of air pollution in four mega-cities of China," NCID Working Papers 04/2016, Navarra Center for International Development, University of Navarra.
    7. Kang, Sang Hoon & Islam, Faridul & Kumar Tiwari, Aviral, 2019. "The dynamic relationships among CO2 emissions, renewable and non-renewable energy sources, and economic growth in India: Evidence from time-varying Bayesian VAR model," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 90-101.
    8. Antonakakis, Nikolaos & Gabauer, David & Gupta, Rangan & Plakandaras, Vasilios, 2018. "Dynamic connectedness of uncertainty across developed economies: A time-varying approach," Economics Letters, Elsevier, vol. 166(C), pages 63-75.
    9. Guojun He & Yuhang Pan & Takanao Tanaka, 2020. "The short-term impacts of COVID-19 lockdown on urban air pollution in China," Nature Sustainability, Nature, vol. 3(12), pages 1005-1011, December.
    10. Gabauer, David & Gupta, Rangan, 2018. "On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach," Economics Letters, Elsevier, vol. 171(C), pages 63-71.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xue Jin & Ussif Rashid Sumaila & Kedong Yin & Zhichao Qi, 2021. "Evaluation of the Policy Effect of China’s Environmental Interview System for Effective Air Quality Governance," IJERPH, MDPI, vol. 18(17), pages 1-20, August.
    2. Nai Yang & Xin Sun & Yi Chao, 2022. "Analysis of Spatial and Temporal Changes of AQI in Wuhan City under the Urban Blockade of COVID-19 Pandemic," IJERPH, MDPI, vol. 19(14), pages 1-21, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    2. Mahdi Ghaemi Asl & Oluwasegun B. Adekoya & Muhammad Mahdi Rashidi, 2023. "Quantiles dependence and dynamic connectedness between distributed ledger technology and sectoral stocks: enhancing the supply chain and investment decisions with digital platforms," Annals of Operations Research, Springer, vol. 327(1), pages 435-464, August.
    3. Stenfors, Alexis & Chatziantoniou, Ioannis & Gabauer, David, 2022. "Independent policy, dependent outcomes: A game of cross-country dominoes across European yield curves," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    4. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2021. "Volatility spillovers during market supply shocks: The case of negative oil prices," Resources Policy, Elsevier, vol. 74(C).
    5. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Forecasting Stock Market Returns in Advanced Economies over a Century," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
    6. Balcilar, Mehmet & Gabauer, David & Umar, Zaghum, 2021. "Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach," Resources Policy, Elsevier, vol. 73(C).
    7. Ko, Hyungjin & Son, Bumho & Lee, Yunyoung & Jang, Huisu & Lee, Jaewook, 2022. "The economic value of NFT: Evidence from a portfolio analysis using mean–variance framework," Finance Research Letters, Elsevier, vol. 47(PA).
    8. Mudassar Hasan & Muhammad Abubakr Naeem & Muhammad Arif & Syed Jawad Hussain Shahzad & Safwan Mohd Nor, 2020. "Role of Economic Policy Uncertainty in the Connectedness of Cross-Country Stock Market Volatilities," Mathematics, MDPI, vol. 8(11), pages 1-17, October.
    9. Christophe Andre & David Gabauer & Rangan Gupta, 2020. "Time-Varying Spillovers between Housing Sentiment and Housing Market in the United States," Working Papers 202091, University of Pretoria, Department of Economics.
    10. Liew, Ping-Xin & Lim, Kian-Ping & Goh, Kim-Leng, 2022. "The dynamics and determinants of liquidity connectedness across financial asset markets," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 341-358.
    11. Dong, Zibing & Li, Yanshuang & Zhuang, Xintian & Wang, Jian, 2022. "Impacts of COVID-19 on global stock sectors: Evidence from time-varying connectedness and asymmetric nexus analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    12. Zhang, Yulian & Hamori, Shigeyuki, 2021. "Do news sentiment and the economic uncertainty caused by public health events impact macroeconomic indicators? Evidence from a TVP-VAR decomposition approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 145-162.
    13. Liang Xie & Xianzhong Mu & Kuanyuting Lu & Dongou Hu & Guangwen Hu, 2023. "The time-varying relationship between CO2 emissions, heterogeneous energy consumption, and economic growth in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 7769-7793, August.
    14. Yao Xiao & Zibing Dong & Shihua Huang & Yanshuang Li & Jian Wang & Xintian Zhuang & Stefan Cristian Gherghina, 2023. "Time-Frequency Volatility Spillovers among Major International Financial Markets: Perspective from Global Extreme Events," Discrete Dynamics in Nature and Society, Hindawi, vol. 2023, pages 1-20, May.
    15. André, Christophe & Gabauer, David & Gupta, Rangan, 2021. "Time-varying spillovers between housing sentiment and housing market in the United States☆," Finance Research Letters, Elsevier, vol. 42(C).
    16. Śmiech, Sławomir & Papież, Monika & Shahzad, Syed Jawad Hussain, 2020. "Spillover among financial, industrial and consumer uncertainties. The case of EU member states," International Review of Financial Analysis, Elsevier, vol. 70(C).
    17. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
    18. Wan, Yang & He, Shi, 2021. "Dynamic connectedness of currencies in G7 countries: A Bayesian time-varying approach," Finance Research Letters, Elsevier, vol. 41(C).
    19. Chatziantoniou, Ioannis & Gabauer, David & Stenfors, Alexis, 2020. "From CIP-deviations to a market for risk premia: A dynamic investigation of cross-currency basis swaps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 69(C).
    20. Dai, Zhifeng & Tang, Rui & Zhang, Xiaotong, 2023. "A new multilayer network for measuring interconnectedness among the energy firms," Energy Economics, Elsevier, vol. 124(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6478-:d:575418. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.