Energy efficiency and China's sustainable carbon neutrality target: evidence from novel research methods quantile on quantile regression approach

dc.contributor.authorBao, Zongke
dc.contributor.authorFerraz, Diogo [UNESP]
dc.contributor.authorNascimento Rebelatto, Daisy Aparecida do
dc.contributor.institutionZhejiang Univ Finance & Econ
dc.contributor.institutionFed Univ Ouro Preto UFOP
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniv Hohenheim
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2022-04-28T17:21:55Z
dc.date.available2022-04-28T17:21:55Z
dc.date.issued2022-03-16
dc.description.abstractSince the last few decades, scholars and policy-makers have been struggling to find ways to achieve carbon neutrality target or a low carbon economy. To contribute to the existing literature regarding the said issue, this study aims to investigate whether energy efficiency could lead to achieving carbon neutrality target in the case of China. Also, this study analyses the association of economic growth to energy-related greenhouse gas emissions while using quarterly data over the period from 1990Q1 to 2014Q2. Empirical findings of the study suggest the mixed order of integration and Cointegration between economic growth, energy efficiency, and energy-related greenhouse gas emissions. This study employed a Quantile-on-Quantile regression approach to examine the long-run association at various quantiles. The estimated results asserted that energy efficiency holds a weaker relationship in the lower and medium quantiles, while relatively higher association to energy-related emission in the upper quantiles. On the other hand, economic growth and its squared are found significantly and highly associated with enhancing energy-related emissions in the country. Besides, the frequency domain causality indicates a causal association running from energy efficiency and economic growth to energy-related greenhouse gas emissions. This study recommends revised policies for energy efficiency and suggests that economic growth could be used as a remedial measure for environmental recovery by enhancing investment in the renewable energy sector, energy efficiency, and structural transformation of the industrial sector.en
dc.description.affiliationZhejiang Univ Finance & Econ, Sch Accounting, Hangzhou, Peoples R China
dc.description.affiliationFed Univ Ouro Preto UFOP, Dept Econ DEECO, Rua Catete, Mariana, Brazil
dc.description.affiliationSao Paulo State Univ UNESP, Dept Prod Engn, Sao Paulo, Brazil
dc.description.affiliationUniv Hohenheim, Stuttgart, Germany
dc.description.affiliationUniv Sao Paulo, Dept Prod Engn, Ave Trabalhador Sao Carlense, Sao Carlos, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ UNESP, Dept Prod Engn, Sao Paulo, Brazil
dc.description.sponsorshipYouth Program of NSFC (National Natural Science Foundation of China)
dc.description.sponsorshipSoft Science Research Project of Zhejiang Soft Science of China Province
dc.description.sponsorshipIdYouth Program of NSFC (National Natural Science Foundation of China): 71704154
dc.description.sponsorshipIdSoft Science Research Project of Zhejiang Soft Science of China Province: 2021C35022
dc.format.extent23
dc.identifierhttp://dx.doi.org/10.1080/1331677X.2022.2054456
dc.identifier.citationEconomic Research-ekonomska Istrazivanja. Abingdon: Routledge Journals, Taylor & Francis Ltd, 23 p., 2022.
dc.identifier.doi10.1080/1331677X.2022.2054456
dc.identifier.issn1331-677X
dc.identifier.urihttp://hdl.handle.net/11449/218600
dc.identifier.wosWOS:000778668300001
dc.language.isoeng
dc.publisherRoutledge Journals, Taylor & Francis Ltd
dc.relation.ispartofEconomic Research-ekonomska Istrazivanja
dc.sourceWeb of Science
dc.subjectEnergy efficiency
dc.subjecteconomic growth
dc.subjectgreenhouse gas
dc.subjectemissions
dc.subjectquantile-on-quantile regression
dc.subjectfrequency domain causality
dc.titleEnergy efficiency and China's sustainable carbon neutrality target: evidence from novel research methods quantile on quantile regression approachen
dc.typeArtigo
dcterms.licensehttp://journalauthors.tandf.co.uk/permissions/reusingOwnWork.asp
dcterms.rightsHolderRoutledge Journals, Taylor & Francis Ltd

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