Learning rates for energy technologies

Abstract Technological learning, i.e., cost reductions as technology manufacturers accumulate experience, is increasingly being incorporated in models to assess long-term energy strategies and related 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 for 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.

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

[2]  Paul L. Joskow,et al.  The Effects of Technological Change, Experience, and Environmental Regulation on the Construction Cost of Coal-Burning Generating Units , 1985 .

[3]  Nebojsa Nakicenovic,et al.  Global energy : perspectives , 1998 .

[4]  Paul D. Maycock,et al.  Business analysis of solar photovoltaic energy conversion , 1975 .

[5]  J. Houghton,et al.  Climate change 1995: the science of climate change. , 1996 .

[6]  Arnulf Grubler,et al.  A Model of Endogenous Technological Change Through Uncertain Returns on Learning (R&D and Investments) , 1997 .

[7]  C. Harmon,et al.  Experience Curves of Photovoltaic Technology , 2000 .

[8]  G. Watts,et al.  Climate Change 1995 , 1998 .

[9]  Vicki Norberg-Bohm,et al.  Technology policy and renewable energy: public roles in the development of new energy technologies , 1999 .

[10]  Pantelis Capros,et al.  Technology evolution and energy modelling: overview of research and findings , 2000 .

[11]  C. Wene.  Experience Curves for Energy Technology Policy , 2000 .

[12]  L. Goulder,et al.  Optimal Co2 Abatement in the Presence of Induced Technological Change , 1998 .

[13]  P. Capros,et al.  Energy Technology Dynamics and Advanced Energy System Modelling , 1998 .

[14]  J. Dutton,et al.  Treating Progress Functions as a Managerial Opportunity , 1984 .

[15]  Gary W. Yohe,et al.  Exercises in hedging against extreme consequences of global change and the expected value of information , 1996 .

[16]  J. Fisher,et al.  Energy crises in perspective , 1974 .

[17]  Y. Iwafune.  Technology Progress Dynamics of Compact Fluorescent Lamps , 2000 .

[18]  L. Argote.  Organizational Learning: Creating, Retaining and Transferring Knowledge , 1999 .

[19]  Lena Neij.  Experience curves for wind turbines , 1999 .

[20]  L. Argote,et al.  Learning Curves in Manufacturing , 1990, Science.

[21]  Niclas Mattsson,et al.  Internalizing technological development in energy systems models , 1997 .

[22]  S. Messner,et al.  Endogenized technological learning in an energy systems model , 1997 .

[23]  José Goldemberg.  The evolution of ethanol costs in Brazil , 1996 .

[24]  J. Edmonds,et al.  Economic and environmental choices in the stabilization of atmospheric CO2 concentrations , 1996, Nature.