Wind power learning rates: A conceptual review and meta-analysis☆
By Åsa Lindman, Patrik Söderholm. 2012
147 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/f645ded8fab91ef046ce492ec9fda4f63919b010
How researchers see this paper
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In Economics and Econometrics
- Tobias S. Schmidt
- Sonia Yeh
- J. Huenteler
- Martin Kalthaus
- Inês Lima Azevedo
- P. Jaramillo
- Erin Baker
- David J. Wright
In Management Science and Operations Research
- Charlie Wilson
- Edward S. Rubin
- Martin Kalthaus
- Inês Lima Azevedo
- P. Jaramillo
- Erin Baker
- E. Lantz
- J. Huenteler
Citations in the corpus
- A review of learning rates for electricity supply technologies (2015, background): “Overall rates span a very large range, from –11% to 35% (Edenhofer et al., 2011; Ibenholt, 2002; Junginger et al., 2005; Kahouli-Brahmi, 2008; Lindman and Söderholm, 2012; McDonald 0% 2% 4% 6% 8% 10% 12% 1980 1985 1990 Le ar ni ng R at e Period McDonald and Schrattenholzer,…”
- Learning curve analysis of concentrated photovoltaic systems (2015, methodology): “We can investigate the relationship between CPV deployment volume and system price using a learning curve analysis (also known as experience curve analysis), which has been used to model prices of many technologically intensive products including automobiles, aircraft, military equipment [35] and en”
- The Effect of Local and Global Learning on the Cost of Renewable Energy in Developing Countries (2016, background): “The geographical dispersion of value chains leads to cost and cost trends that differ significantly between components (e.g., Lindman and Söderholm, 2011).”
- Inventor Networks in Renewable Energies: The Influence of the Policy Mix in Germany (2016, background): “This creates strong incentives for firms to expand production capacity to serve demand whereby they appropriate economies of scale and learning effects that allow developing more efficient production processes or new machinery (Arrow 1962, Peters et al. 2012, Lindman and Söderholm 2012).”
- Up-scaling, formative phases, and learning in the historical diffusion of energy technologies (2012, methodology): “knowledge spillovers, turbine-specific costs, and public R&D expenditures had a statistically significant effect on estimated learning rates, up-scaling did not (Lindman and Soderholm, 2012).”
- Expert elicitation survey on future wind energy costs (2016, background): “Estimated learning rates span an enormous range, from a 33% cost decline with each doubling of cumulative production (LR = 33%) to a cost increase of 11% for each doubling (LR = -11%) [2, 12-13].”