Improving the analytical framework for quantifying technological progress in energy technologies

Abstract

This article reviews experience curve applications in energy technology studies to illustrate best practices in analyzing technological learning. Findings are then applied to evaluate future performance projections of three emerging offshore energy technologies, namely, offshore wind, wave & tidal, and biofuel production from seaweed. Key insights from the review are: First, the experience curve approach provides a strong analytical construct to describe and project technology cost developments. However, disaggregating the influences of individual learning mechanisms on observed cost developments demands extensive data requirements, e.g., R&D expenditures, component level cost information, which are often not publicly available/readily accessible. Second, in an experience curve analysis, the LR estimate of the technology is highly sensitive towards the changes in model specifications and data assumptions. Future studies should evaluate the impact of these variations and inform the uncertainties associated with using the observed learning rates. Third, the review of the literature relevant to offshore energy technology developments revealed that experience curve studies have commonly applied single-factor experience curve model to derive technology cost projections. This has led to an overview of the role of distinct learning mechanisms (e.g., learning-by-doing, scale effects), and factors (site-specific parameters) influencing their developments. To overcome these limitations, we propose a coherent framework based on the findings of this review. The framework disaggregates the technological development process into multiple stages and maps the expected data availability, characteristics, and methodological options to quantify the learning effects. The evaluation of the framework using three offshore energy technologies signals that the data limitations that restrict the process of disaggregating the learning process and identifying cost drivers can be overcome by utilizing detailed bottom-up engineering cost modeling and technology diffusion curves; with experience curve models.