Time–frequency co-movement between COVID-19, crude oil prices, and atmospheric CO2 emissions: Fresh global insights from partial and multiple coherence approach

Yasir Habib (), Enjun Xia (), Zeeshan Fareed () and Shujahat Haider Hashmi ()
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Yasir Habib: Beijing Institute of Technology
Enjun Xia: Beijing Institute of Technology
Zeeshan Fareed: Huzhou University
Shujahat Haider Hashmi: Huazhong University of Science and Technology

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 6, No 67, 9397-9417

Abstract: Abstract This paper endeavors to analyze and provide fresh global insights from the asymmetric nexus between the recent outbreak of COVID-19, crude oil prices, and atmospheric CO2 emissions. The analysis employs a unique Morlet’s wavelet method. More precisely, this paper implements comprehensive wavelet coherence analysis tools, including continuous wavelet coherence, partial wavelet coherence, and multiple wavelet coherence to the daily dataset spanning from December 31, 2019 to May 31, 2020. From the frequency perspective, this paper finds significant wavelet coherence and vigorous lead and lag connections. This analysis ascertains significant movement in variables over frequency and time domain. These results demonstrate strong but varying connotations between studied variables. The results also indicate that COVID-19 impacts crude oil prices and the most contributor to the reduction in CO2 emissions during the pandemic period. This study offers practical and policy implications and endorsements for individuals, environmental experts, and investors. Graphic abstract

Keywords: COVID-19; Crude oil prices; Atmospheric CO2 emissions; Partial and multiple wavelet coherence; Global (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-020-01031-2

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