Expert elicitations are now frequently used to characterize uncertain future technology outcomes. However, their usefulness is limited, in part because: estimates across studies are not easily comparable; choices in survey design and expert selection may bias results; and overconfidence is a persistent problem. The authors provide quantitative evidence of how these choices affect experts' estimates.

Characterizing the future performance of energy technologies can improve the development of energy policies that have net benefits under a broad set of future conditions. In particular, decisions about public investments in research, development, and demonstration (RD&D) that promote technological change can benefit from (1) an explicit consideration of the uncertainty inherent in the innovation process and (2) a systematic evaluation of the tradeoffs in investment allocations across different technologies. To shed light on these questions, over the past five years several groups in the United States and Europe have conducted expert elicitations and modeled the resulting societal benefits. In this paper, the authors discuss the lessons learned from the design and implementation of these initiatives.

Characterization of the anticipated performance of energy technologies to inform policy decisions increasingly relies on expert elicitation. Knowledge about how elicitation design factors impact the probabilistic estimates emerging from these studies is, however, scarce. We focus on nuclear power, a large-scale low-carbon power option, for which future cost estimates are important for the design of energy policies and climate change mitigation efforts. We use data from three elicitations in the USA and in Europe and assess the role of government research, development, and demonstration (RD&D) investments on expected nuclear costs in 2030.

This article reviews the concept of an energy technology innovation system (ETIS). The ETIS is a systemic perspective on innovation comprising all aspects of energy transformations (supply and demand); all stages of the technology development cycle; as well as all the major innovation processes, feedbacks, actors, institutions, and networks.