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Weather, Pollution, and Covid-19 Spread: A Time Series and Wavelet Reassessment

In: Energy Transition, Climate Change, and COVID-19

Author

Listed:
  • Olivier Damette

    (University of Lorraine, University of Strasbourg
    Climate Economic Chair Paris Dauphine)

  • Stéphane Goutte

    (Université Paris-Saclay, CEMOTEV)

Abstract

Faced with the global pandemic of Covid-19, we need to better understand the links between meteorological factors, air quality, and the virus. In the vein of a recent empirical literature, we reassess the impact of weather factors like temperatures, humidity, and air quality indicators on Covid-19 daily cases in China for both Wuhan and Beijing. Using a consistent number of observations (104), we compute, for the first time, correlations but also Granger causality and, above all, a spectral analysis using Wavelet methods. Our results go further of the previous studies and reveal the complexity of the studied relationships when both time and frequency domains are taken into account. Wavelet analysis enables us to go further of the usual correlation analysis. Though negative humidity impact on Covid-19 cases was expected to be relatively clear regarding previous literature based on correlations, we do not find evidence of such a result. The controversial effect of warmer temperatures on the Covid-19, often difficult to identify or sometimes identified as surprisingly positive, can negatively emerge via wavelet analysis for some periods only. This result is however clear-cut for the Hubei Province but for the Beijing one. Finally, our results reveal a bi-directional causality between air quality and the number of infected people. Short-run causality from Covid-19 to air quality (better induced air quality) via lockdown policies disappear in a medium-run and turns to become a significant causal link from induced air quality improvement to Covid-19 daily cases (reduction of infected people). The study reassesses the link between local weather, air quality, and Covid-19 epidemic in China using time series (TS) with 104 observations for China. Wavelet analysis enables us to go further of the usual correlations and TS analysis and reveal the complexity of the relationships. Wavelet analysis about temperature and humidity effects shows that the relationship between weather and Covid-19 is complex and not uniform regarding time and frequency. Humidity impact on Covid-19 epidemic seems to be not clear. Temperature positive effect of Covid-19 epidemic turns to be negative for some periods only. Short-run causality from Covid-19 via lockdown disappears in a medium-run and turns to become a significant causal link from induced air quality improvement to Covid-19 daily cases.

Suggested Citation

  • Olivier Damette & Stéphane Goutte, 2021. "Weather, Pollution, and Covid-19 Spread: A Time Series and Wavelet Reassessment," Springer Books, in: Fateh Belaïd & Anna Cretì (ed.), Energy Transition, Climate Change, and COVID-19, pages 95-106, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-79713-3_5
    DOI: 10.1007/978-3-030-79713-3_5
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    References listed on IDEAS

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    1. Marco Gallegati, 2018. "A systematic wavelet-based exploratory analysis of climatic variables," Climatic Change, Springer, vol. 148(1), pages 325-338, May.
    2. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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