Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/65545
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dc.contributor.authorJianxu Liuen_US
dc.contributor.authorZihe Lien_US
dc.contributor.authorChangrui Dongen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2019-08-05T04:35:12Z-
dc.date.available2019-08-05T04:35:12Z-
dc.date.issued2019-01-01en_US
dc.identifier.issn16113349en_US
dc.identifier.issn03029743en_US
dc.identifier.other2-s2.0-85064223128en_US
dc.identifier.other10.1007/978-3-030-14815-7_20en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85064223128&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/65545-
dc.description.abstract© Springer Nature Switzerland AG 2019. The study aims to test the long-run cointegration relationship and causality among China’s carbon emissions, economic growth, energy consumption and trade openness for the period 1971–2013. Autoregressive Distributed Lag (ARDL) model incorporating with structural breaks and Vector Error Correction Model (VECM) Granger causality test are applied in this study. The empirical results reveal that inverted-U shape relationship exists between carbon emissions and economic growth in the long-run, but it doesn’t hold in short-run, proving that Environmental Kuznets Curve (EKC) is a long-run phenomenon rather than short-run. Moreover, energy consumption and trade openness are found to have positive impacts on carbon emissions in the long-run and short-run. As for causality test the result showed that bi-directional causal relationship exists between energy consumption and carbon emissions in the long-run. In the short-run, unidirectional causality is found running from trade openness to carbon emissions.en_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleThe impact of economic growth, energy consumption and trade openness on carbon emissions: An empirical analysis in Chinaen_US
dc.typeBook Seriesen_US
article.title.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
article.volume11471 LNAIen_US
article.stream.affiliationsShandong University of Finance and Economicsen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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