This paper presents a framework for estimating the future N^th-of-a-kind (NOAK) cost of advanced low-carbon technologies that are currently at early pre-commercial stages of development. It identifies two types of question that commonly motivate a cost analysis: “What If” questions about the hypothetical future cost of a technology that meets specified R&D goals or requirements; and “What Will” questions regarding the true expected cost of an advanced technology once it is mature and widely deployed. The latter type of question is the focus of this paper. It addresses shortcomings in the “bottom up” engineering-economic method current used to estimate NOAK costs. It describes a more rigorous hybrid costing method that combines a bottom-up analysis of the first-of-a-kind (FOAK) commercial cost of an advanced technology with an empirical model employing experience curves to project its future cost. Guidelines are presented for all phases of the analysis.

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Uncertainty analysis is a key element of sound techno-economic analysis (TEA) of CO2 capture and storage (CCS) technologies and systems, and in the communication of TEA results. Many CCS technologies are novel concepts, that are still in the early (pre-commercial) stages of development. Therefore, uncertainties in their technology performance and cost are often substantial, making it imperative that they be characterized, and their impacts reported. Although uncertainty analysis itself is not novel, with some methods already frequently used by the CCS TEA community, a document that provides a comprehensive overview of methods and approaches, as well as guidance on their selection and use, is still lacking. Given its importance, we seek to fill this gap by providing a critical review of uncertainty analysis methods along with guidance on the selection and use of these methods for CCS TEAs, highlighting good practice and examples from the CCS literature. There are many opportunities to bring the use of uncertainty analysis to a higher level than currently practiced. This review of and guidance on available methods is intended to help accelerate continued methods development and their application to more robust and meaningful CCS performance and cost studies.@en