Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models

Abstract

The main objective of this paper is to provide an overview and a critical analysis of the recent literature on incorporating induced technical change in energy systems models. Special emphasis is put on surveying recent studies aimed at integrating learning-by-doing into bottom-up energy systems models through so-called learning curves, and on analyzing the relevance of learning curve analysis for understanding the process of innovation and technology diffusion in the energy sector. The survey indicates that this model work represents a major advance in energy research, and embeds important policy implications, not the least concerning the cost and the timing of environmental policies (including carbon emission constraints). However, bottom-up energy models with endogenous learning are also limited in their characterization of technology diffusion and innovation. While they provide a detailed account of technical options—which is absent in many top-down models—they also lack important aspects of diffusion behavior that are captured in top-down representations. For instance, they often fail in capturing strategic technology diffusion behavior in the energy sector as well as the energy sector's endogenous responses to policy, and they neglect important general equilibrium impacts (such as the opportunity cost of redirecting R&D support to the energy sector). Some suggestions on how innovation and diffusion modeling in bottom-up analysis can be improved are put forward.