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Energy biased technology change: Focused on Chinese energy-intensive industries

dc.contributor.authorZha, Donglan
dc.contributor.authorKavuri, Anil Savio
dc.contributor.authorSi, Songjian
dc.date.accessioned2022-05-17T05:42:50Z
dc.date.issued2017
dc.date.updated2020-12-27T07:32:15Z
dc.description.abstractTechnical change bias has predominately been measured through two-factor models. Resulting from the rising importance of energy we devised a framework to estimate technical change bias for three input factors. The framework involves an estimation of elasticity and technical change parameters for a constant elasticity of substitution function with capital, labour and energy, which is derived from the elasticities and marginal output. We apply the framework to investigate eleven Chinese energy-intensive industries. The optimal nested structure for eight Of the industries is for capital and energy to be combined first at the composite level and then with labour to form total output. Between 1990 and 2012, six of the industries were energy biased, three were towards capital, one towards labour and one mixed. The results show that recent Chinese energy intensity reduction programs are not sufficient to induce energy efficient development. The policy recommendations target specifically the energy biased industries to achieve desired energy savings in the future.en_AU
dc.description.sponsorshipThe authors are also grateful to the financial support provided by the China Natural Science Funding No. 71673134, Qing Lan Projecten_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn0306-2619en_AU
dc.identifier.urihttp://hdl.handle.net/1885/265497
dc.language.isoen_AUen_AU
dc.publisherPergamon Pressen_AU
dc.rights© 2017 The authorsen_AU
dc.sourceApplied Energyen_AU
dc.subjectEnergy biased technologyen_AU
dc.subjectEnergy-intensive industriesen_AU
dc.subjectElasticity of substitutionen_AU
dc.titleEnergy biased technology change: Focused on Chinese energy-intensive industriesen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.lastpage1089en_AU
local.bibliographicCitation.startpage1081en_AU
local.contributor.affiliationZha, Donglan, Nanjing University of Aeronautics and Astronauticsen_AU
local.contributor.affiliationKavuri, Anil Savio, College of Asia and the Pacific, ANUen_AU
local.contributor.affiliationSi, Songjian, Nanjing University of Aeronautics and Astronauticsen_AU
local.contributor.authoruidKavuri, Anil Savio, u5078201en_AU
local.description.embargo2099-12-31
local.description.notesImported from ARIESen_AU
local.identifier.absfor140200 - APPLIED ECONOMICSen_AU
local.identifier.ariespublicationu4485658xPUB1162en_AU
local.identifier.citationvolume190en_AU
local.identifier.doi10.1016/j.apenergy.2016.11.001en_AU
local.identifier.scopusID2-s2.0-85009905331
local.identifier.thomsonID000395959100088
local.publisher.urlhttps://www.sciencedirect.com/en_AU
local.type.statusPublished Versionen_AU

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