Renewable energy consumption, financial development, environmental pollution, and innovations in the ASEAN + 3 group: Evidence from (P-ARDL) model

Assi, Ala Fathi; Isiksal, Aliya Zhakanova; Türsoy, Turgut

doi:10.1016/j.renene.2020.11.052

Cited by 166 publications

(59 citation statements)

References 79 publications

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“…[16][17][18][19] The effect of various variables; especially, economic growth, prices, and pollution on RE is a widely studied issue in the literature. For example, financial development, [20][21][22][23][24][25] trade openness, 23,26,27 and globalization 28,29 are recently considered as determinants of RE deployment for small country groups and single countries. As clear from the literature review, only gross domestic product (GDP) (economic growth), oil price, and CO 2 (pollution) are available for a large country group.…”

Section: Introductionmentioning

confidence: 99%

Examining the determinants of renewable energy deployment: Does the choice of indicator matter?

2021

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Comprehending the determinants of renewable energy (RE) deployment has preoccupied the energy literature as well as policymakers internationally due to countries' overall shift away from fossil fuels in the energy mix.As stated in the literature, empirical studies that analyze the determinants of RE deployment use a number of different indicators for RE. The effect of an ambiguous choice of the proxy might produce various outcomes and thus create inconsistencies in the policy recommendations. This study aims at filling this gap in the literature comparing and contrasting not only the use of RE indicators but also, for robustness purposes, using indicators at aggregate and per capita forms for a global sample, developed countries, and developing countries. For the empirical purpose, this study employs two econometric techniques: the pooled ordinary least squares with robust SEs and the augmented mean group estimator, which account for cross- Highlights• This study uses a number of renewable energy indicators to analyze the determinants of renewable energy deployment.

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Section: Introductionmentioning

confidence: 99%

Examining the determinants of renewable energy deployment: Does the choice of indicator matter?

2021

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“…However, the effect of renewable energy on pollution is mixed, because biomass burning can negatively impact the environment. Empirical findings have confirmed the positive effects of renewable energy consumption on pollution (Destek and Aslan, 2020;Assi et al, 2021;Shahnazi and Shabani, 2021). Sharif et al (2020) also analyze the relationship between renewable energy utilization and environmental degradation on a dataset comprising the top 10-polluted countries and found a bidirectional causal link between renewable energy consumption and environmental degradation in most countries, except India, Indonesia, and China, where the relationship is inconclusive.…”

Section: Literature Reviewmentioning

confidence: 72%

“…For example, Shahnazi and Shabani (2021) showed that renewable energy consumption reduced the level of pollution in EU countries in the period 2000-2017 reducing environmental degradation. Environmental sustainability improved in ASEAN +3 countries due to renewable energy consumption in the period 1998-2018 (Assi et al, 2021). A particular type of renewable energy, for example, wind energy, diminished the GHG emissions in the G7 countries between 1991 and 2004 (Destek and Aslan, 2020).…”

Section: Discussionmentioning

confidence: 99%

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The Impact of Quality of Governance, Renewable Energy and Foreign Direct Investment on Sustainable Development in Cee Countries

Simionescu¹,

Szeles

Gavurová

et al. 2021

Front. Environ. Sci.

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Recent debates on the Environmental Kuznets Curve (EKC) in the context of the European Green Deal highlights that pollution could be controlled using channels other than economic development. The role of renewable energy consumption in reducing pollution should be correlated with quality of governance, which could support initiatives for a cleaner environment. This study considers the renewable Kuznets curve for 10 Central and Eastern European countries in the period 2006–2019, using economic indicators (index of economic freedom, foreign direct investment, domestic credit to private sector and labour productivity) as control variables and indicators reflecting the quality of governance (Worldwide Governance Indicators and Google Trend indexes associated to key-word corruption in each language). The research is based on two types of panel data models: panel dynamic OLS model (PDOLS) and panel autoregressive distributed lag models based on pooled mean group (PMG) estimator. The results indicate that government effectiveness reduces pollution in both the short-run and long-run, while control of corruption and rule of law supports environmental protection in the long-run. Google searches for corruption as a proxy for quality of governance indicate that tGHG emissions have immediate and practical implications in nowcasting pollution.

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“…Other factors, such as foreign direct investment (Essandoh et al, 2020;Le et al, 2020;Khan and Rana, 2021), financial development Bhattacharya et al, 2017;Zaidi et al, 2019;Wang et al, 2020), oil price (Brini et al, 2017;Shahbaz et al, 2021), renewable energy consumption (Vo et al, 2020;Assi et al, 2021;Magazzino et al, 2021) and innovation (Nguyen et al, 2020) can be largely classified as the secondary driving factors which affect the primary factors. For example, it has already been proven that foreign direct investment, financial development and innovation are all positively related to growth (Jones, 1995;Muhammad and Khan, 2019), while oil price and renewable energy consumption will affect energy consumption as a whole.…”

Section: Literature Reviewmentioning

confidence: 99%

The Major Driving Factors of Carbon Emissions in China and Their Relative Importance: An Application of the LASSO Model

Shum

Ma²,

Lin³

et al. 2021

Front. Energy Res.

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China is one of the biggest energy consumers and carbon emitters in the world. Understanding the factors affecting carbon emissions is critical for policymakers to control the rising trend of carbon emissions. This paper investigates the relative importance of carbon emissions drivers in China. Literature review has been carried out to determine a set of predominant independent variables; the LASSO model is then introduced to rank the relative importance among the set of independent variables. The results find that 1) carbon emissions were mainly driven by economic growth and energy consumption followed by population size and industrialization; and 2) income growth slowed down carbon emissions during the studied period, but it is the least significant factor among the other factors. The ranking allows policy makers to focus on the most critical contributors to carbon emissions and gives policymakers more flexibility in determining policy interventions.

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Renewable energy consumption, financial development, environmental pollution, and innovations in the ASEAN + 3 group: Evidence from (P-ARDL) model

Cited by 166 publications

References 79 publications

Examining the determinants of renewable energy deployment: Does the choice of indicator matter?

Examining the determinants of renewable energy deployment: Does the choice of indicator matter?

The Impact of Quality of Governance, Renewable Energy and Foreign Direct Investment on Sustainable Development in Cee Countries

The Major Driving Factors of Carbon Emissions in China and Their Relative Importance: An Application of the LASSO Model

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