Nexus of COVID-19 and carbon prices in the EU emission trading system: evidence from multifractal and the wavelet coherence approaches

Ghazani, Majid Mirzaee; Khosravi, Reza; Barak, Sasan

doi:10.1007/s11356-021-18304-6

Cited by 9 publications

(5 citation statements)

References 45 publications

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“…This aim of this part of our study was to explore the correlation between AQI, and PM 2.5 and O 3 _8h using the wavelet coherence method. The time scale is provided on the horizontal axis, whereas the frequency scale is represented on the vertical axis (Ghazani et al 2022 ). The frequency range spans high frequency (0–16), medium frequency (16–128) and low frequency (128–512), and we used color coding (blue to red, low to high) to define the coherent wavelet spectral intensity (Cheng et al 2021 ; Fareed et al 2021 ).…”

Section: Resultsmentioning

confidence: 99%

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Assessment of air quality before and during the COVID-19 and its potential health impacts in an arid oasis city: Urumqi, China

Sun

Aishan

Halik

et al. 2022

Stoch Environ Res Risk Assess

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As a key node city of the “Silk Road Economic Belt” Urumqi has been listed as one of the ten most polluted cities in the world, posing a serious threat to the urban environment and residents' health. This study analyzed the air quality before and during the COVID-19 (Coronavirus disease 2019) pandemic and its potential health effects based on the data of PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 _8h levels from 10 air quality monitoring stations in Urumqi from January 1, 2017, to December 31, 2021. As per the results, the concentrations of the air pollutants PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 _8h in Urumqi from 2017 to 2021 showed a cyclical trend, and the implementation of COVID-19 prevention and control measures could effectively reduce the concentration(ρ) of air pollutants. The mean value of ρ(PM 2.5 ) decreased from 2017 to 2021, whereas ρ(O 3 _8h) showed a waveform change trend (increased in 2017–2018, decreased in 2018–2020, and increased after 2020). Meanwhile, the maximum annual average values of ρ(PM 2.5 ) and ρ(O 3 _8h) for the six monitoring stations during 2017–2021 occurred at sites S2 (74.37 µg m −3 ) and S6 (91.80 µg m −3 ), respectively; rapid industrialization had a greater impact on PM 2.5 and O 3 _8h concentrations compared to commercial and residential areas. In addition, the air quality index data series can characterize the fluctuation trend of PM 2.5 . The high pollution levels (Class IV and V) of the air pollutants PM 2.5 and O 3 _8h in Urumqi have been decreasing annually, and good days can account for 80–95% of the total number of days in the year, indicating that the number of days with a potential threat to residents' health is gradually decreasing. Therefore, more attention should be paid in controlling and managing air pollution in Urumqi.

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Section: Resultsmentioning

confidence: 99%

“…Arrows pointing to the right (→) (in-phase) and left (←) (out-of-phase) indicate the positive and negative correlations, respectively. The up (↑) and down (↓) arrows indicate that there is a leading and lagging relationship between the two factors, respectively (Ghazani et al 2022 ).…”

Section: Resultsmentioning

confidence: 99%

Assessment of air quality before and during the COVID-19 and its potential health impacts in an arid oasis city: Urumqi, China

Sun

Aishan

Halik

et al. 2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…However, these traditional approaches cannot be robust if the data are non-stationary. Indeed, significant structural breakdown(s) in datasets has (have) harmed the outcomes of standard time-domain analysis (Ghazani et al 2022). Meanwhile, wavelet techniques, which permit one-dimensional time data to be split into the two-dimensional time-frequency domain, have been suggested as a critical innovation to overcome these constraints (Kirikkaleli and Gokmenoglu 2020).…”

Section: Wavelet Analysismentioning

confidence: 99%

“…Additionally, the existence of timevarying fluctuation over time justifies a need for implementing methods that account for a lead-lag structure among the variables, which most studies have overlooked. Significant structural breakdowns in the data are another issue observed in financial data, and using traditional methods in this context does not lead to a favorable result (Ghazani et al 2022).…”

mentioning

confidence: 99%

Analyzing time–frequency connectedness between cryptocurrencies, stock indices, and benchmark crude oils during the COVID-19 pandemic

Ghazani,

Malekshah,

Khosravi

2024

Financ Innov

Self Cite

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We used daily return series for three pairs of datasets from the crude oil markets (WTI and Brent), stock indices (the Dow Jones Industrial Average and S&P 500), and benchmark cryptocurrencies (Bitcoin and Ethereum) to examine the connections between various data during the COVID-19 pandemic. We consider two characteristics: time and frequency. Based on Diebold and Yilmaz’s (Int J Forecast 28:57–66, 2012) technique, our findings indicate that comparable data have a substantially stronger correlation (regarding return) than volatility. Per Baruník and Křehlík’ (J Financ Econ 16:271–296, 2018) approach, interconnectedness among returns (volatilities) reduces (increases) as one moves from the short to the long term. A moving window analysis reveals a sudden increase in correlation, both in volatility and return, during the COVID-19 pandemic. In the context of wavelet coherence analysis, we observe a strong interconnection between data corresponding to the COVID-19 outbreak. The only exceptions are the behavior of Bitcoin and Ethereum. Specifically, Bitcoin combinations with other data exhibit a distinct behavior. The period precisely coincides with the COVID-19 pandemic. Evidently, volatility spillover has a long-lasting impact; policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks (e.g., the COVID-19 pandemic) and simultaneously reduce its side effects.

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“…The pollutants PM 2.5 , PM 10 , SO Wavelet-based analysis of the coherence of AQI with ρ(PM 2.5 ) and ρ(O _8h) This section of the article is suitable for exploring the correlation between AQI with PM 2.5 and O 3 _8h through the wavelet coherence method. The time scale is provided on the horizontal axis, while the frequency scale is represented by the vertical axis (Ghazani et al 2022). The frequency range spans high frequency (0-16), medium frequency (16-128) and low frequency (128-512), meanwhile, we use color coding (blue to red, low to high) to de ne the coherent wavelet spectral intensity (Cheng et Figure 3(a) shows the WTC between AQI and PM 2.5 at site S1.…”

Section: Spatiotemporal Variation Of Air Pollutant Concentrations And...mentioning

confidence: 99%

Assessment of air quality before and during the COVID-19 and its potential health impacts in an arid oasis city: Urumqi, China

Sun

Aishan

Halik

et al. 2022

Preprint

View full text Add to dashboard Cite

Air pollution has become one of the major environmental concerns to academia and urban residents. As a key megacity city of the "Silk Road Economic Belt", Urumqi has been listed as one of the ten most polluted cities in the world, posing a serious threat to the urban environment and residents' health. Therefore, the assessment of air pollution and its health impact on urban residents is urgently required for promoting the sustainable development of the city. This study analyzed the temporal and spatial changes in the concentration of major air pollutants in Urumqi and its response to epidemic prevention and control measures based on the data of PM2.5, PM10, SO2, NO2, CO, and O3_8h from 10 air quality monitoring stations in Urumqi from January 1, 2017, to December 31, 2021, mainly focusing on the temporal and spatial variation characteristics of PM2.5 and O3_8h concentrations, changes in pollution levels and their potential risks on human health. The results showed that the concentrations of air pollutants PM2.5, PM10, SO2, NO2, CO, and O3_8h at various air quality monitoring sites from 2017 to 2021 showed a cyclical trend, and the implementation of the government's strict COVID-19 prevention and control measures had a significant effect on the reduction of air pollutant concentrations. The mean value of ρ(PM2.5) decreased from 2017 to 2021, while ρ(O3_8h) showed a waveform change trend (increased in 2017–2018, decreased in 2018–2020, and increased after 2020 ). In terms of the distribution area, the variation of ρ(PM2.5) and ρ(O3_8h) were greater in industrial areas than in commercial and residential areas. Continuous wavelet coherence exists between AQI and PM2.5 and O3_8h at each site during the period from January 2018 to December 2020, and AQI and PM2.5 were positively correlated. The high pollution levels (grades IV and V) of air pollutants PM2.5 and O3_8h in Urumqi decreased year by year, indicating that the number of days with potential threats to residents' health is gradually decreasing. Therefore, more attention should be paid to the control and governance of air pollution in Urumqi.

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Nexus of COVID-19 and carbon prices in the EU emission trading system: evidence from multifractal and the wavelet coherence approaches

Cited by 9 publications

References 45 publications

Assessment of air quality before and during the COVID-19 and its potential health impacts in an arid oasis city: Urumqi, China

Assessment of air quality before and during the COVID-19 and its potential health impacts in an arid oasis city: Urumqi, China

Analyzing time–frequency connectedness between cryptocurrencies, stock indices, and benchmark crude oils during the COVID-19 pandemic

Assessment of air quality before and during the COVID-19 and its potential health impacts in an arid oasis city: Urumqi, China

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