Technological Learning in the Energy Sector

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.

Chapter 5: The Use of Experience Curves in Energy Models

Sander Lensink, Sondes Kahouli-Brahmi and Wilfried van Sark

Subjects: economics and finance, energy economics, environment, energy policy and regulation, innovation and technology, technology and ict

Extract

Sander Lensink, Sondes Kahouli-Brahmi and Wilfried van Sark Forecasting, the principal aim of many energy models, inherently implies uncertainty. The more complex the system that is being modelled, the more difficult forecasting future developments becomes. To manage the uncertainty that is related to future events one can change exogenous, predescribed cost reductions into endogenous, model-driven cost reductions by using the experience curve tool which allows for better handing of the complexity of realizable cost reductions. Typically, models that do not use the concept of experience curves for endogenous learning do take future cost reductions of technologies into account. Indeed, the costs of technologies change in time following an autonomous, exogenous decline path. On an aggregated level in macroeconomic analyses, this has long been an acceptable approach. These models, which are often named top-down models, are typically general equilibrium models in which technological change is a substitution effect that is driven by price changes of the input factors. For all non-price driven improvements in technologies, an Autonomous Energy Efficiency Improvement (AEEI) parameter exogenously describes the cost decline. Although such an approach was frequently used in energy modelling, it was argued in the energy economics literature that it suffers from some drawbacks, especially with regard to the evaluation of policy measures’ impacts on feasible future cost reduction. Therefore, models that endogenously model the cost reduction via the experience curve approach, often named bottom-up models, enable better evaluation of such policy measures’ impacts. If one acknowledges the new uncertainties that are introduced by...

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