 Rare breakthroughs vs. incremental development in R&D strategy for an early-stage energy technologyEmily Fertig Energy Policy, 2018, vol. 123, issue C, 711-721 Abstract: Uncertainty in technological learning is a crucial factor in planning research, development, and demonstration (RD&D) strategies. Nevertheless, most previous work either models technological change as deterministic or accounts for uncertainty without fully capturing the recourse feature of the problem. This paper improves upon these approaches by developing a real options-based stochastic dynamic programming method for valuing and planning low-carbon energy RD&D investment and is the first of its kind to disaggregate the effects of R&D and learning-by-doing. This simplified model captures the relevant features of the problem and provides general insights on RD&D strategy under technological uncertainty. Results indicate that imminent deployment, high cost, lower exogenous cost reductions, and lower program funds all promote R&D spending over learning-by-doing, since under these circumstances a breakthrough, rather than slow and consistent cost reductions, will render the program successful. Keywords: Real options; R&D; Stochastic dynamic programming; Carbon capture and sequestration; Managerial flexibility; Endogenous technological change (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:enepol:v:123:y:2018:i:c:p:711-721 DOI: 10.1016/j.enpol.2018.08.019 Access Statistics for this article Energy Policy is currently edited by N. France More articles in Energy Policy from Elsevier |
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