Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30495
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dc.contributor.authorOdam, Neilen_UK
dc.contributor.authorde Vries, Frans Pen_UK
dc.date.accessioned2019-11-22T16:49:01Z-
dc.date.available2019-11-22T16:49:01Z-
dc.date.issued2019-11-21en_UK
dc.identifier.urihttp://hdl.handle.net/1893/30495-
dc.description.abstractLearning curves are frequently cited to justify the subsidization of new technologies to facilitate market competitiveness. The main literature has focused on improving the specification of the basic learning curve model by augmenting it to control for technological development measured by public R&D expenditures. In addition to employing R&D expenditures, the purpose of this paper is to assess the robustness of an augmented multi-factor learning curve model by estimating learning rates in a panel framework utilising patent data on relevant wind power technologies in Germany, Denmark, Spain and the UK. Results indicate that both innovation proxies are qualitatively identical and generate consistent learning estimates. The paper also aims at exploring the presence of unit roots in learning curves and alerts to the possibility of spurious estimations. Renewable energy policy guided by learning curve estimates should therefore be implemented with caution.en_UK
dc.language.isoenen_UK
dc.publisherElsevier BVen_UK
dc.relationOdam N & de Vries FP (2019) Innovation Modelling and Multi-Factor Learning in Wind Energy Technology. Energy Economics. https://doi.org/10.1016/j.eneco.2019.104594en_UK
dc.rightsThis item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectTechnical changeen_UK
dc.subjectR&Den_UK
dc.subjectLearning curvesen_UK
dc.subjectRenewablesen_UK
dc.subjectPatentsen_UK
dc.subjectKnowledge stocken_UK
dc.subjectUnit rootsen_UK
dc.titleInnovation Modelling and Multi-Factor Learning in Wind Energy Technologyen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2021-05-22en_UK
dc.rights.embargoreason[Innovation_Learning_Wind_Energy.pdf] Publisher requires embargo of 18 months after formal publication.en_UK
dc.identifier.doi10.1016/j.eneco.2019.104594en_UK
dc.citation.jtitleEnergy Economicsen_UK
dc.citation.issn0140-9883en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderEconomic and Social Research Councilen_UK
dc.author.emailf.p.devries@stir.ac.uken_UK
dc.citation.date21/11/2019en_UK
dc.description.notesOutput Status: Forthcoming/Available Onlineen_UK
dc.contributor.affiliationEconomicsen_UK
dc.identifier.wtid1485637en_UK
dc.contributor.orcid0000-0003-0462-5035en_UK
dc.date.accepted2019-11-17en_UK
dc.description.refREF Compliant by Deposit in Stirling's Repositoryen_UK
dc.date.filedepositdate2019-11-22en_UK
Appears in Collections:Economics Journal Articles

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