IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v83y2022ics0038012122000064.html
   My bibliography  Save this article

COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts

Author

Listed:
  • Gao, Mingyun
  • Yang, Honglin
  • Xiao, Qinzi
  • Goh, Mark

Abstract

This paper proposes a novel grey spatiotemporal model and quantitatively analyzes the spillover and momentum effects of the COVID-19 lockdown policy on the concentration of PM2.5 (particulate matter of diameter less than 2.5 μm) in Wuhan during the COVID-19 pandemic lockdown from 23 January to 8 April 2020 inclusive, and the post-pandemic period from 9 April 2020 to 17 October 2020 inclusive. The results suggest that the stringent lockdowns lead to a reduction in PM2.5 emissions arising from a momentum effect (9.57–18.67%) and a spillover effect (7.07–27.60%).

Suggested Citation

  • Gao, Mingyun & Yang, Honglin & Xiao, Qinzi & Goh, Mark, 2022. "COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:soceps:v:83:y:2022:i:c:s0038012122000064
    DOI: 10.1016/j.seps.2022.101228
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012122000064
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2022.101228?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lu, Ya-Nan & Li, Sai-Ping & Zhong, Li-Xin & Jiang, Xiong-Fei & Ren, Fei, 2018. "A clustering-based portfolio strategy incorporating momentum effect and market trend prediction," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 1-15.
    2. M. Ha-Duong & M. J. Grubb & J.-C. Hourcade, 1997. "Influence of socioeconomic inertia and uncertainty on optimal CO2-emission abatement," Nature, Nature, vol. 390(6657), pages 270-273, November.
    3. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    4. Qian Liu & Wei Liu & Dexuan Sha & Shubham Kumar & Emily Chang & Vishakh Arora & Hai Lan & Yun Li & Zifu Wang & Yadong Zhang & Zhiran Zhang & Jackson T. Harris & Srikar Chinala & Chaowei Yang, 2020. "An Environmental Data Collection for COVID-19 Pandemic Research," Data, MDPI, vol. 5(3), pages 1-13, August.
    5. Liu, Lianyi & Wu, Lifeng, 2020. "Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    6. Tønnessen, Øystein & Dhir, Amandeep & Flåten, Bjørn-Tore, 2021. "Digital knowledge sharing and creative performance: Work from home during the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    7. Zhang, Hanyuan & Song, Haiyan & Wen, Long & Liu, Chang, 2021. "Forecasting tourism recovery amid COVID-19," Annals of Tourism Research, Elsevier, vol. 87(C).
    8. Quan Lu & Zehao Cai & Bin Chen & Tao Liu, 2020. "Social Policy Responses to the Covid-19 Crisis in China in 2020," IJERPH, MDPI, vol. 17(16), pages 1-14, August.
    9. Xue-Bo Jin & Nian-Xiang Yang & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction," Mathematics, MDPI, vol. 8(2), pages 1-17, February.
    10. Ocampo, Lanndon & Yamagishi, Kafferine, 2020. "Modeling the lockdown relaxation protocols of the Philippine government in response to the COVID-19 pandemic: An intuitionistic fuzzy DEMATEL analysis," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    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. Duan, Huiming & Nie, Weige, 2022. "A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    2. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    3. Anagh Chattopadhyay & Soudeep Deb, 2024. "A spatio-temporal model for binary data and its application in analyzing the direction of COVID-19 spread," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(4), pages 823-851, December.
    4. Ding, Qi & Xiao, Xinping & Kong, Dekai, 2023. "Estimating energy-related CO2 emissions using a novel multivariable fuzzy grey model with time-delay and interaction effect characteristics," Energy, Elsevier, vol. 263(PE).
    5. Duan, Huiming & Liu, Yunmei & Wang, Guan, 2022. "A novel dynamic time-delay grey model of energy prices and its application in crude oil price forecasting," Energy, Elsevier, vol. 251(C).

    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. Ali Zackery & Joseph Amankwah-Amoah & Zahra Heidari Darani & Shiva Ghasemi, 2022. "COVID-19 Research in Business and Management: A Review and Future Research Agenda," Sustainability, MDPI, vol. 14(16), pages 1-32, August.
    2. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    3. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    4. Jean Charles Hourcade & Antonin Pottier & Etienne Espagne, 2011. "The environment and directed technical change : comment," CIRED Working Papers hal-00866435, HAL.
    5. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    6. Antoine GODIN & Emanuele CAMPIGLIO & Eric KEMP-BENEDICT, 2017. "Networks of stranded assets: A case for a balance sheet approach," Working Paper d51a41b5-00ba-40b4-abe6-5, Agence française de développement.
    7. Hamdi-Cherif, Meriem & Waisman, Henri & Guivarch, Céline & Hourcade, Jean-Charles, 2012. "Mitigation costs in second-best economies: time profile of emission reductions and sequencing of accompanying measures," Conference papers 332206, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    8. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    9. Chung-Wei Kuo, 2021. "Can We Return to Our Normal Life When the Pandemic Is under Control? A Preliminary Study on the Influence of COVID-19 on the Tourism Characteristics of Taiwan," Sustainability, MDPI, vol. 13(17), pages 1-17, August.
    10. Ying Shu & Chengfu Ding & Lingbing Tao & Chentao Hu & Zhixin Tie, 2023. "Air Pollution Prediction Based on Discrete Wavelets and Deep Learning," Sustainability, MDPI, vol. 15(9), pages 1-19, April.
    11. Adam Michael Bauer & Cristian Proistosescu & Gernot Wagner, 2023. "Carbon Dioxide as a Risky Asset," CESifo Working Paper Series 10278, CESifo.
    12. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    13. Vogt-Schilb, Adrien & Hallegatte, Stephane & de Gouvello Christophe, 2014. "Long-term mitigation strategies and marginal abatement cost curves : a case study on Brazil," Policy Research Working Paper Series 6808, The World Bank.
    14. Hanyuan Zhang & Jiangping Lu, 2022. "Forecasting hotel room demand amid COVID-19," Tourism Economics, , vol. 28(1), pages 200-221, February.
    15. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    16. Castaldo, Sandro & Ciacci, Andrea & Penco, Lara & Profumo, Giorgia, 2024. "Which trust layer better counterbalances the risk impact on travel intentions in a crisis scenario?," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    17. Fereydooni, Ali & Barak, Sasan & Asaad Sajadi, Seyed Mehrzad, 2024. "A novel online portfolio selection approach based on pattern matching and ESG factors," Omega, Elsevier, vol. 123(C).
    18. Jean-Charles Hourcade & Philippe Ambrosi & Patrice Dumas, 2009. "Beyond the Stern Review: Lessons from a risky venture at the limits of the cost–benefit analysis," Post-Print hal-00716769, HAL.
    19. Xue-Bo Jin & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su, 2022. "PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    20. Adrien Vogt-Schilb & St�phane Hallegatte & Christophe de Gouvello, 2015. "Marginal abatement cost curves and the quality of emission reductions: a case study on Brazil," Climate Policy, Taylor & Francis Journals, vol. 15(6), pages 703-723, November.

    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:eee:soceps:v:83:y:2022:i:c:s0038012122000064. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    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.