Use and limitations of learning curves for energy technology policy: A component-learning hypothesis

By F. Ferioli, K. Schoots, Bob van der Zwaan. 2009

284 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/b6b8ff93d742dfd065554a3f1a017681cfa3831c

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Citations in the corpus

Up-scaling, formative phases, and learning in the historical diffusion of energy technologies (2012, background): “The price of production inputs, as well as profit margins (if price is used in lieu of cost as the performance measure) may change over time (Ferioli et al., 2009).”
Learning curve analysis of concentrated photovoltaic systems (2015, background): “The results of this paper enable the calculation of evolving LCOEs for CPV, taking into account future trends in deployment volumes and system costs which will be of value to system developers, installers and government policy makers [15].”
Renewable Electricity Futures Study. Volume 1. Exploration of High-Penetration Renewable Electricity Futures (2012, background): “As technologies mature, however, diminishing returns in cost reduction might be expected, and the drivers that have impacted cost reductions in the past may not continue into the future (e.g., Arrow 1962; Ferioli et al. 2009).”
The dynamics of solar PV costs and prices as a challenge for technology forecasting (2013, background): “2%, this more than doubled, to 190 GW [25].”
Expert elicitation survey on future wind energy costs (2016, methodology): “In addition, using historical data to generate learning rates that are then extrapolated into the future implicitly assumes that future trends will replicate past ones [14, 20-21].”
The experience curve theory and its application in the field of electricity generation technologies – A literature review (2018, background): “Critics of this approach emphasise that it should not be taken for granted that past experience curves can simply be extrapolated [53,65,72].”
An innovation-focused roadmap for a sustainable global photovoltaic industry (2014, background): “5A), and this uncertainty in LR will significantly affect the projected timing and cost of reaching the cost-reduction milestones (van der Zwaan and Rabl, 2004; Ferioli et al., 2009) and GHG mitigation targets.”