Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30495
Appears in Collections:Economics Journal Articles
Peer Review Status: Refereed
Title: Innovation Modelling and Multi-Factor Learning in Wind Energy Technology
Author(s): Odam, Neil
de Vries, Frans P
Contact Email: f.p.devries@stir.ac.uk
Keywords: Technical change
R&D
Learning curves
Renewables
Patents
Knowledge stock
Unit roots
Issue Date: 21-Nov-2019
Citation: Odam N & de Vries FP (2019) Innovation Modelling and Multi-Factor Learning in Wind Energy Technology. Energy Economics. https://doi.org/10.1016/j.eneco.2019.104594
Abstract: Learning 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.
DOI Link: 10.1016/j.eneco.2019.104594
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Notes: Output Status: Forthcoming/Available Online

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