|Appears in Collections:||Economics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Innovation Modelling and Multi-Factor Learning in Wind Energy Technology|
de Vries, Frans P
|Citation:||Odam N & de Vries FP (2020) Innovation Modelling and Multi-Factor Learning in Wind Energy Technology. Energy Economics, 85, Art. No.: 104594. 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.|
|Rights:||This 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. Accepted refereed manuscript of: Odam N & de Vries FP (2020) Innovation Modelling and Multi-Factor Learning in Wind Energy Technology. Energy Economics, 85, Art. No.: 104594. DOI: https://doi.org/10.1016/j.eneco.2019.104594 © 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/|
|Innovation_Learning_Wind_Energy.pdf||Fulltext - Accepted Version||412.33 kB||Adobe PDF||Under Embargo until 2021-05-22 Request a copy|
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