Technological Learning in the Energy Sector
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Technological Learning in the Energy Sector

Lessons for Policy, Industry and Science

Edited by Martin Junginger, Wilfried van Sark and André Faaij

Technological learning is a key driver behind the improvement of energy technologies and subsequent reduction of production costs. Understanding how and why production costs for energy technologies decline, and whether they will continue to do so in the future, is of crucial importance for policy makers, industrial stakeholders and scientists alike. This timely and informative book therefore provides a comprehensive review of technological development and cost reductions for renewable energy, clean fossil fuel and energy-efficient demand-side technologies.
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Chapter 20: Methodological Lessons and Recommendations for Scientists and Modellers

Wilfried van Sark, Martin Junginger and André Faaij


Wilfried van Sark, Martin Junginger and André Faaij 20.1 INTRODUCTION The valuable use of experience curve analyses has been amply demonstrated in the previous technology overview chapters, as summarized in Chapter 19. For both energy supply and energy demand technologies, technological learning as expressed through experience curves can be used in energy models. However, several lessons have been learned in using experience curves. In this chapter, we will discuss methodological lessons, and from those we will formulate recommendations for further scientific development of experience curve analysis. 20.2 20.2.1 LESSONS LEARNED Prices Versus Costs For reasons of data availability, prices are commonly used to construct experience curves, assuming that a certain relation exists between prices and costs, with a constant relative difference (see Figure 3.1). However, such a relation depends on the market maturity of the product or technology under study, and on a healthy balance between supply and demand, at least over the timeframe investigated. Fluctuations in price can clearly be seen in many experience curves, which illustrates the different stages of a specific technology in the market. Although the scientific community is well aware of this, prices are used as a proxy for cost, because cost data are very hard to get. The majority of studies scrutinized in Chapters 6–18 do not perform such an analysis, or only when stable or increasing prices are found, suggesting no learning takes place. However, especially if only 262 Methodological lessons for scientists and modellers 263 short time periods (i.e. limited numbers...

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