2014
DOI: 10.1016/j.eneco.2014.05.001
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Renewable and non-renewable energy consumption and economic activities: Further evidence from OECD countries

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Cited by 278 publications
(112 citation statements)
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References 54 publications
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“…Furthermore, a significant body of literature analyses the relationships among electricity use, air pollution levels, and economic growth (Chandran et al, 2010;Silva et al, 2011;Bélaïd and Abderrahmani, 2013;Salim et al, 2014;Khalid, 2015). prove the existence of EKCs between economic growth, electricity consumption and CO2 emissions.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Furthermore, a significant body of literature analyses the relationships among electricity use, air pollution levels, and economic growth (Chandran et al, 2010;Silva et al, 2011;Bélaïd and Abderrahmani, 2013;Salim et al, 2014;Khalid, 2015). prove the existence of EKCs between economic growth, electricity consumption and CO2 emissions.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Lee and Chang [15] utilized panel data of 16 Asian economies from 1971 to 2002 to examine the causal interactions between energy consumption and real GDP, they found that in the long-run conservation hypothesis existed between energy consumption and GDP, but not vice versa. Taking into consideration panel heterogeneity, Akkemik and Goksal [16] examined the causality between energy consumption and GDP using panel Granger causality methodology developed by Hurlin and Venet [17], in which four different causal relationships, homogeneous (non-) causality, heterogeneous (non-) causality were presented, and so on [18][19][20].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Thereafter, studies considering structural breaks but not cross-sectional dependence were published [42]. Recently, most studies have been conducted using dependent panel analysis [17,22,23,[43][44][45]. Some have considered cross-sectional dependence and structural breaks simultaneously [22,23].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, most studies have been conducted using dependent panel analysis [17,22,23,[43][44][45]. Some have considered cross-sectional dependence and structural breaks simultaneously [22,23]. In particular, Apergis and Payne [15] used a panel smooth transition vector error correction model (VECM), which can handle structural breaks.…”
Section: Introductionmentioning
confidence: 99%