| ID | 162347 |
| Title Proper | Rare breakthroughs vs. incremental development in R&D strategy for an early-stage energy technology |
| Language | ENG |
| Author | Fertig, Emily |
| Summary / Abstract (Note) | 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. |
| `In' analytical Note | Energy Policy Vol. 123; Dec 2018: p.711-721 |
| Journal Source | Energy Policy 2018-12 123 |
| Key Words | R&D ; Real Options ; Stochastic Dynamic Programming ; Carbon Capture and Sequestration ; Managerial Flexibility ; Endogenous Technological Change |