Abstract
The purpose of this paper is to provide a quantitative analysis of innovation and diffusion in the European wind power sector. We derive a simultaneous model of wind power innovation and diffusion, which combines a rational choice model of technological diffusion and a learning curve model of dynamic cost reductions. These models are estimated using pooled annual time series data for four European countries (Denmark, Germany, Spain and the United Kingdom) over the time period 1986–2000. The empirical results indicate that reductions in investment costs have been important determinants of increased diffusion of wind power, and these cost reductions can in turn be explained by learning activities and public R&D support. Feed-in tariffs also play an important role in the innovation and diffusion processes. The higher is the feed-in price the higher is, ceteris paribus, the rate of diffusion, and we present some preliminary empirical support for the notion that the impact on diffusion of a marginal increase in the feed-in tariff will differ depending on the support system used. High feed-in tariffs, though, also have a negative effect on cost reductions as they induce wind generators to choose high-cost sites and provide fewer incentives for cost cuts. This illustrates the importance of designing an efficient wind energy support system, which not only promotes diffusion but also provides continuous incentives for cost-reducing innovations.
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Acknowledgements
An earlier version of this paper was presented at the 12th Annual Conference of the European Association of Environmental and Resource Economists, Bilbao, Spain, June 28–30, 2003. At the time the research for this paper was carried out both authors were affiliated with the International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria. Financial support from the Kempe Foundations and the Swedish Environmental Protection Agency’s COPE program is gratefully acknowledged, as are valuable comments and help from Frank den Butter, Jim Griffin, Madhu Khanna, David Pearce, Marian Radetzki, John Tilton, the researchers at the Environmentally Compatible Energy Strategies Project, IIASA, Laxenburg, Austria, and an anonymous reviewer. Any remaining errors, however, reside solely with the authors. Finally, the paper reflects the personal opinions of the authors and does not in any way reflect the official position of the European Commission on the results obtained.
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Appendices
Appendix A Tests for Different Support System Impacts
Appendix B. Learning curve estimates with a 6% depreciation rate.
Appendix C. Learning curve estimates with international R&D spillovers.
Appendix D. Estimates for the traditional learning curve specification
Coefficients | Estimates | t-ratios |
|---|---|---|
Constant | 7.651 | 142.011** |
Cumulative capacity | − 0.071 | − 7.659** |
DK-dummy | 0.034 | 115.212** |
GE-dummy | 0.238 | 108.476** |
UK-dummy | 0.163 | 139.020** |
R2 = 0.67 |
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Söderholm, P., Klaassen, G. Wind Power in Europe: A Simultaneous Innovation–Diffusion Model. Environ Resource Econ 36, 163–190 (2007). https://doi.org/10.1007/s10640-006-9025-z
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DOI: https://doi.org/10.1007/s10640-006-9025-z