Examining the linkages among electricity consumption, income and environmental pollution in Saudi Arabia: from a spectral wavelet analysis to the Granger Causality test

Al-Rdaydeh, Mahmoud; Schneider, Nicolas; Matar, Ali

doi:10.1080/00207233.2021.1893479

Cited by 15 publications

(4 citation statements)

References 88 publications

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“…[25][26][27][28] Moreover, the correlation measure in continuous wavelet transforms (CWT) developed by Rua 30 offers the background for the causal association between variables under study. Recently, Adebayo, 37 Al-Rdaydeh et al, 38 and Haseeb et al, 1 have performed a wavelet-based investigation to analyze the lead-lag relationship between energy consumption, environmental pollution, and economic growth. In this section, we briefly introduce on wavelet framework.…”

Section: Wavelet Methodologymentioning

confidence: 99%

Effect of economic indicators, biomass energy on human development in China

Hung

2021

Energy & Environment

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Previous studies ignored the distinction between short, medium, and long term by decomposing macroeconomic variables and human development index at different time scales. We re-visit the causal association between biomass energy (BIO), economic growth (GDP), trade openness (TRO), industrialization (IND), foreign direct investment (FDI), and human development (HDI) in China on a quarterly scale by scale basis for the period 1990 to 2019 using the tools of wavelet, i.e., wavelet correlation, wavelet coherence and scale by scale Granger causality test. The main findings uncover that IND, TRO, GDP, and BIO positively drive the HDI at low and medium frequencies, while FDI negatively impacts HDI during the sample period. Additionally, there is a bidirectional relationship between GDP and HDI at different time and frequency domains. Specifically, we discover that the positive co-movement is more robust in the aftermath of the global financial crisis, particularly for HDI, BIO, GDP, and TRO at medium frequencies throughout the period under research. Our empirical insights have significant implications for achieving human development sustainability in China.

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Section: Wavelet Methodologymentioning

confidence: 99%

Effect of economic indicators, biomass energy on human development in China

Hung

2021

Energy & Environment

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“…Only the use of fossil fuels can meet this energy requirement for preparing fuel for nuclear reaction, and the burning of fossil fuels emits greenhouse gases, especially CO 2 emissions. 50,51 The study conducted by Al-Rdaydeh et al 52 is a debate on the influences of electricity generation from nuclear on CO 2 emissions. This study reveals that electricity from nuclear itself does not cause CO 2 emissions, but it is fuel for nuclear reaction and construction of nuclear reactors that cause CO 2 emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of non-renewable energy production and energy usage on carbon emissions: Evidence from China

Zhang

Sadiq

et al. 2023

Energy & Environment

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A high level of carbon dioxide (CO2) emission has become a global issue due to extensive production and energy usage that needs regulators’ attention and researchers’ emphasis. Hence, the study aims to explore the effectiveness of energy production and usage on carbon dioxide emission in China. The current study has taken the electricity production from oil, coal and nuclear as the measurement of energy production, while fossil fuel energy consumption and energy use have been taken as the measurement of energy usage, and energy import has been taken as the control variable. The secondary data has been gathered using a secondary source like World Bank from 1991 to 2020. The Quantile Autoregressive Distributed Lag (QARDL) was employed by the study to examine the linkage among variables, while the Augmented Dickey–Fuller (ADF) test was adopted to check the stationarity. The results revealed that electricity production from oil, coal and nuclear has a positive connection with CO2 emission. The findings also exposed that the FFEC, energy import, and energy use also have a positive linkage with CO2 emission. This study monitors the policymakers while establishing regulations to control CO2 emissions by limiting the country's energy production and usage.

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“…Following the studies by Hung ( 2020 ), Al-Rdaydeh et al ( 2021 ), and Chien et al ( 2021 ), we employ wavelet-based Granger causality analysis by utilizing the time–frequency band of the wavelet transform to capture the causal association between the examined indexes. In this regard, we decompose the data into eight levels spanning different holding periods using the continuous wavelet method.…”

Section: Resultsmentioning

confidence: 99%

Time–frequency return co-movement among asset classes around the COVID-19 outbreak: portfolio implications

Athari

Hung

2022

J Econ Finan

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This study explores the time–frequency return connectedness of the four most relevant asset classes namely, equity, digital assets, commodity, and fixed income. To do so, we use the novel proxies of the S&P500 Index for equity, the S&P Cryptocurrency MegaCAP Index for digital assets, the S&P Goldman Sachs Commodity Index for commodity, and the S&P Global Developed Sovereign Bond Index for fixed income, and also employ the wavelet analysis for daily data over the period 2017: M02 to 2021: M09. In contrast to the pre-COVID-19 period, our findings indicate that the interdependence between the selected asset classes has intensified across all time scales and frequency bands during the COVID-19 crisis, proving the lack of hedging opportunities. Besides, the findings reveal that there is a significant lead-lag relationship between time series at medium and low frequencies during the research period, and the directional connectedness among asset classes is sensitive to frequencies. Especially, the co-movements among the pairs are pronounced during the COVID-19 outbreak. Remarkably, the wavelet-based Granger causality test corroborates the wavelet results and underscores there is a significant causal link between the variables during COVID compared to pre-COVID. Moreover, the results of the portfolio risk analysis by employing the value at risk (VaR) measure indicate that portfolio diversity advantages vary among frequency and across time. The results of the present study provide insight and might help foreign portfolio investors diversify their portfolios across different asset classes.

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Examining the linkages among electricity consumption, income and environmental pollution in Saudi Arabia: from a spectral wavelet analysis to the Granger Causality test

Cited by 15 publications

References 88 publications

Effect of economic indicators, biomass energy on human development in China

Effect of economic indicators, biomass energy on human development in China

The impact of non-renewable energy production and energy usage on carbon emissions: Evidence from China

Time–frequency return co-movement among asset classes around the COVID-19 outbreak: portfolio implications

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