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RR-01-14.pdf

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/60854 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/60854 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/60854 archive 351 disk0/00/00/65/22 2016-01-15 02:13:54 2021-08-27 17:17:40 2016-01-15 02:13:54 monograph show 1 McDonald A. 757 Schrattenholzer L. AL1189 pub iis_ene prog_ecs Technological learning, i.e., cost reductions as technology manufacturers accumulate experience, is increasingly being incorporated in models to assess long-term energy strategies and greenhouse gas emissions. Most of these applications use learning rates based on studies of non-energy technologies, or sparse results from a few energy studies. This report is a step towards a larger empirical basis for choosing learning rates (or learning rate distributions) of energy conversion technologies of energy models. We assemble data on experience accumulation and cost reductions for a number of energy technologies, estimate learning rates for the resulting 26 data sets, analyze their variability, and evaluate their usefulness for applications in long-term energy models. 2001 published RR-01-014. Reprinted from Energy Policy, 29:255-261 [2001]. 10.1016/S0301-4215(00)00122-1 RP-01-014 Reprinted from Energy Policy; 29:255-261 [2001] [doi:10.1016/S0301-4215(00)00122-1] 10 1442 2366 public research_reprint Energy Policy 29 IIASA, Laxenburg, Austria 255-261 11 TRUE 0301-4215 FALSE info:eu-repo/semantics/book