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The study explores inter-relations between the geopolitical risk index and renewable energy volatility index at frequency dimensions from April 4, 2019, to June 13, 2022, using novel multivariate wavelet analysis approaches, such as partial wavelet coherency and partial wavelet gain. Our method allows us to study these interlinkages at various time frequencies. We also consider the influences of uncertain events like the COVID-19 pandemic and Ukraine-Russia conflicts on their interconnectedness. The multiple coherencies between the geopolitical risk index and the green energy sector suggest four cycles in the low-frequency range (50-130 days) from March 2020 to October 2021 and from February 2022 to June 2022. The partial coherency between the geopolitical risk index and renewable energy volatility index suggests connectedness between renewable energy dynamics and geopolitical risks during the COVID-19 duration and the Russia-Ukraine conflict. The partial wavelet coherency of the volatility of green bonds and geopolitical risks suggests that alterations in green bonds caused alterations in geopolitical risks, and the association is negative from February 2021 to April 2021. Both indicators are in-phase with geopolitical risks pushing from February 2020 to April 2020 and from October 2021 to the end of the sample. The partial coherence between clean energy and geopolitical risk suggests geopolitical risks pushing anti-phase connectedness from September 2020 to September 2022. Our findings help policymakers design the most effective policies to lessen the vulnerabilities of these indicators and reduce the spread of risk or uncertainty across them by having insightful knowledge about the primary antecedents of the contagions among these indicators.

Titel:	A wavelet analysis of dynamic connectedness between geopolitical risk and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts.
Autor/in / Beteiligte Person:	Ha, LT
Link:	Full Text Finder (Volltext)
Zeitschrift:	Environmental science and pollution research international, Jg. 31 (2024-03-01), Heft 12, S. 17994-18009
Veröffentlichung:	<2013->: Berlin : Springer ; <i>Original Publication</i>: Landsberg, Germany : Ecomed, 2024
Medientyp:	academicJournal
ISSN:	1614-7499 (electronic)
DOI:	10.1007/s11356-023-26033-1
Schlagwort:	Humans Pandemics Ukraine epidemiology Wavelet Analysis Renewable Energy Russia COVID-19 epidemiology
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Environ Sci Pollut Res Int] 2024 Mar; Vol. 31 (12), pp. 17994-18009. <i>Date of Electronic Publication: </i>2023 Mar 14. MeSH Terms: COVID-19* / epidemiology ; Humans ; Pandemics ; Ukraine / epidemiology ; Wavelet Analysis ; Renewable Energy ; Russia References: Adebayo TS, Oladipupo SD, Adeshola I, Rjoub H (2022) Wavelet analysis of impact of renewable energy consumption and technological innovation on CO2 emissions: evidence from Portugal. Environ Sci Pollut Res 29(16):23887–23904. https://doi.org/10.1007/s11356-021-17708-8. (PMID: 10.1007/s11356-021-17708-8) ; Aguiar-Conraria L, Martins MMF, Soares MJ (2018) Estimating the Taylor rule in the time-frequency domain. J Macroecon 57:122–137. https://doi.org/10.1016/j.jmacro.2018.05.008. 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A wavelet analysis of dynamic connectedness between geopolitical risk and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts.