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Demand Subsidies Versus R&D: Comparing the Uncertain Impacts of Policy on a Pre-commercial Low-carbon Energy Technology

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  • Gregory F. Nemet
  • Erin Baker

Abstract

We combine an expert elicitation and a bottom-up manufacturing cost model to compare the effects of R&D and demand subsidies. We model their effects on the future costs of a low-carbon energy technology that is not currently commercially available, purely organic photovoltaics (PV). We find that: (1) successful R&D enables PV to achieve a cost target of 4c/kWh, (2) the cost of PV does not reach the target when only subsidies, and not R&D, are implemented, and (3) production-related effects on technological advanceÑlearning-by-doing and economies of scaleÑare not as critical to the long-term potential for cost reduction in organic PV than is the investment in and success of R&D. These results are insensitive to two levels of policy intensity, the level of a carbon price, the availability of storage technology, and uncertainty in the main parameters used in the model. However, a case can still be made for subsidies: comparisons of stochastic dominance show that subsidies provide a hedge against failure in the R&D program.

Suggested Citation

  • Gregory F. Nemet & Erin Baker, 2009. "Demand Subsidies Versus R&D: Comparing the Uncertain Impacts of Policy on a Pre-commercial Low-carbon Energy Technology," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 49-80.
  • Handle: RePEc:aen:journl:2009v30-04-a02
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    1. Mark A. Moore & Anthony E. Boardman & Aidan R. Vining & David L. Weimer & David H. Greenberg, 2004. "“Just give me a number!” Practical values for the social discount rate," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 23(4), pages 789-812.
    2. Robert T. Clemen & Robert L. Winkler, 1999. "Combining Probability Distributions From Experts in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 19(2), pages 187-203, April.
    3. Miketa, Asami & Schrattenholzer, Leo, 2004. "Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes; first results," Energy Policy, Elsevier, vol. 32(15), pages 1679-1692, October.
    4. Nikolaos Kouvaritakis & Antonio Soria & Stephane Isoard, 2000. "Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 104-115.
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