Technology learning in the presence of public R&D : the case of European wind power

The objective of this paper is to analyze the role of technology learning in European wind power generation in the presence of public R&D. A Cobb-Douglas cost function is employed to derive a learning curve model for wind power, thus illustrating how the investment costs for this technology are influenced by global learning-by-doing, scale effects, and a European R&D-based knowledge stock. We assume that public R&D expenses targeting wind power add to the above stock, and these R&D outlays are in turn hypothesized to be influenced by technology cost levels, the opportunity cost of public R&D as well as by government budget constraints. We estimate the learning and the R&D model, respectively, using a panel data set covering five European countries over the time period 1986-2002. The empirical results confirm the importance of both learning-by-doing and public R&D support in the cost reduction process, and governments' R&D expenses have declined in response to lowered investment costs. This is efficient in the sense that public funds are best targeted at technologies which are far from being commercial. The results also illustrate that governments in Europe have been sensitive to the opportunity cost of public R&D in the energy R&D budget process.

[1]  Dennis Anderson,et al.  Induced Technical Change in Energy and Environmental Modeling: Analytic Approaches and Policy Implications , 2002 .

[2]  Socrates Kypreos,et al.  Endogenizing R&D and Market Experience in the "Bottom-Up" Energy-Systems ERIS Model , 2004 .

[3]  J. Hausman Specification tests in econometrics , 1978 .

[4]  Antonio Soria,et al.  Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching , 2000 .

[5]  Leo Schrattenholzer,et al.  Learning rates for energy technologies , 2001 .

[6]  Ernst R. Berndt,et al.  The Practice of Econometrics: Classic and Contemporary. , 1992 .

[7]  Asami Miketa,et al.  The impact of R&D on innovation for wind energy in Denmark, Germany and the United Kingdom , 2005 .

[8]  Patrik Söderholm,et al.  Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models , 2006 .

[9]  Martin Kumar Patel,et al.  A review of experience curve analyses for energy demand technologies , 2010 .

[10]  William D. Nordhaus,et al.  The Perils of the Learning Model for Modeling Endogenous Technological Change , 2009 .

[11]  Richard G. Newell,et al.  Modeling endogenous technological change for climate policy analysis , 2008 .

[12]  Patrik Söderholm,et al.  Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies , 2007 .

[13]  William A. Pizer,et al.  Endogenizing Technological Change: Matching Empirical Evidence to Modeling Needs , 2007 .

[14]  Andreas Löschel,et al.  Technological Change in Economic Models of Environmental Policy: A Survey , 2002 .

[15]  Kenneth J. Arrow,et al.  A Statement on the Appropriate Role for Research and Development in Climate Policy , 2008 .

[16]  Ian Sue Wing,et al.  Representing induced technological change in models for climate policy analysis , 2006 .

[17]  Antonio Soria,et al.  Technical change dynamics: evidence from the emerging renewable energy technologies , 2001 .

[18]  Elhanan Helpman,et al.  International R&D spillovers , 1995 .

[19]  R. Newell Climate Technology Deployment Policy , 2007 .

[20]  Andrew R. Henderson,et al.  Offshore Wind Energy in Europe , 2001 .

[21]  Patrik Söderholm,et al.  Wind Power in Europe: A Simultaneous Innovation–Diffusion Model , 2007 .

[22]  David Popp,et al.  Innovation in climate policy models: Implementing lessons from the economics of R&D , 2006 .

[23]  Martin Junginger,et al.  Technological learning in the energy sector : lessons for policy, industry and science , 2010 .