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This paper uses the formal concept of learning curves to analyse regular behaviour of performance improvements in various energy technologies. The concept allows the estimation of a single indicator of technological progress, the learning rate, which expresses the constant percentage improvement (usually in terms of cost reductions) in a technology for each doubling of the technology's cumulative installed capacity. We present 42 energy-related learning rates, either calculated directly from available data or assembled from the literature. We elaborate briefly on eight of these to illustrate issues addressed by technology assessments to convert these raw historical learning rates into prospective learning rate distributions for use in long-term energy models. The paper includes a sensitivity analysis of policyrelevant variables with respect to learning rates, a discussion of possible extensions and limitations of the approach and an outlook on future work in the field. Keywords Leaming curves and technology assessmentincluding the analysis of the role of research and development in enhancing technological progress.

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This paper estimates investment cost targets for future nuclear power plants to be competitive in mid-21st century energy markets and beyond. The point of departure is the nuclear market shares derived from the Special Report on Emissions Scenarios (SRES) of the Intergovernmental Panel on Climate Change. One provocative result is that substantial nuclear expansion does not seem to require big reductions in nuclear investment costs, largely explained by the difference between cost reductions consistent with long-term energy system optimisation based on perfect foresight, and cost reductions necessary to attract private investment in today's deregulating and uncertain energy markets.