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progress. For example, Grubler and Gritsevskii [109] used a simple optimization model with endogenous technological change represented by a traditional log-linear experience curve, but added uncertainty in the learning rate, represented by a lognormal distribution function around the mean value. They showed that when the rate of learning was certain (i.e., perfect foresight), the optimal solution was to invest heavily and early in the “winning” technology. Barreto and Klaassen [59] found similar results. However, when learning rates were uncertain (as in the real world), the optimal solution also became less certain. As a result, there were broader investments in a portfolio of technologies, with slower diffusion and market entry of any particular technology. Messner et al. [110] also incorporated uncertainties in future technology performance and found that it tended to spread risk over a larger number of options to cope with uncertainties in technology development paths.

Over the longer term, continued research into the underlying factors that govern or influence technological innovations may yield improved models that can reliably forecast the implications of proposed energy and environmental policy measures. In the meantime, more concerted efforts are needed to explore, understand and display the consequences of uncertainties in current formulations of technology experience curves (or other models) used to project the future cost of technology in energy-economic modeling and policy analysis.


We gratefully acknowledge the contributions of our colleagues Profs. Margaret Taylor (University of California, Berkeley) and David Hounshell (Carnegie Mellon University) to an earlier version of this paper (available at <>), as well as to several of the references cited below, which were invaluable to the foundations of this paper.


[1] S. Kypreos, CO2 emission control in Switzerland using mathematical programming. INFOR, 30 (1992) 194-206.

[2] A. Manne and R. Richels, Buying Greenhouse Insurance: The Economic Costs of CO2 Emission Limits. MIT, Cambridge, MA, 1992.

[3] W.D. Nordhaus, Managing the Global Commons: The Economics of Climate Change. MIT Press, Cambridge, 1994.

[4] R. Prinn, et al., Integrated global system model for climate policy assessment: Feedbacks and sensitivity studies. Climatic Change, 41 (1999) 469-546.

[5] C. Azar and H. Dowlatabadi, A review of technical change in assessment of climate policy. Annual Review of Energy and the Environment, 24 (1999) 513-544.

[6] L. Clarke, J. Weyant and A. Birky, On the sources of technological change: Assessing the evidence. Energy Economics, 28 (2006) 579-595.

[7] B.C.C. van der Zwaan, et al., Endogenous technological change in climate change modeling. Energy Economics, 24 (2002).

[8] K. Gillingham, R. G.Newell and W.A. Pizer, Modeling endogenous technological change for climate policy analysis. Energy Economics, 30 (2008) 2734-2753.

[9] F. Ferioli and B.C.C. van der Zwaan, Learning in Times of Change: A Dynamic Explanation for Technological Progress. Environmental Science and Technology, 43 (2009) 4002-4008.

[10] W. Nordhaus, The Perils of the Learning Model For Modeling Endogenous Technological Change. 2009, National Bureau of Economic Research Working Paper Series.

[11] T.P. Wright, Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3 (1936) 122-128.

[12] Boston Consulting Group, Perspectives on Experience. Boston Consulting Group Inc., 1968.

[13] K. Arrow, The Economic Implications of Learning-by-doing. Review of Economic Studies, 29 (1962) 155-173.

[14] L.E. Yelle, The Learning Curve: Historical Review and Comprehensive Survey. Decision Sciences, 10 (1979) 302-328.

[15] J.M. Dutton and A. Thomas, Treating Progress Functions as a Managerial Opportunity. Academy of Management Review, 9 (1984) 235-247.

[16] L. Argote and D. Epple, Learning curves in manufacturing. Science, 247 (1990) 920-924.

[17] R.A. Thornton and P. Thompson, Learning from experience and learning from others. An exploration of learning and spilllovers in wartime shipbuilding. The American Economic Review, 91 (2001) 1350-1368.

[18] R.M. Bell and D. Scott-Kemmis, The mythology of learning-by-doing in World War II airframe and ship production. 1990, Science Policy Research Unit, University of Sussex.

[19] F.M. Bass, The relationship between diffusion rates, experience curves and demand elasticities for consumer durable technological innovations. The Journal of Business, 53 (1980) S51-S67.

[20] IEA/OECD, Experience Curves for Energy Technology Policy. 2000, International Energy Agency: Paris, France.

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