R&D in clean technology: A project choice model with learning

Abstract

A c c e p t e d M a n u s c r i p t Highlights of "R&D in Clean Technology: A Project Choice Model with Learning"• A model of many-step R&D on environmental technology with researcher's learning about the potential of its R&D project.• The optimal R&D subsidy with consideration of learning is higher than in the no-learning case.• The R&D subsidy regime is superior to the Pigouvian tax regime unless suppliers have sufficient incentives to continue cost-reduction efforts after the new technology successfully replaces the old one.• When there are multiple R&D projects, a uniform subsidy is more socially desirable than a selective subsidy. AbstractIn this study, we investigate the qualitative and quantitative effects of an R&D subsidy for a clean technology and a Pigouvian tax on a dirty technology on environmental R&D when it is uncertain how long the research takes to complete. The model is formulated as an optimal stopping problem, in which the number of successes required to complete the R&D project is finite and learning about the probability of success is incorporated. We show that the optimal R&D subsidy with the consideration of learning is higher than that without it. We also find that an R&D subsidy performs better than a Pigouvian tax unless suppliers have sufficient incentives to continue costreduction efforts after the new technology successfully replaces the old one. Moreover, by using a two-project model, we show that a uniform subsidy is better than a selective subsidy. JEL classification codes: D83, O33, Q55, Q58 $ We are grateful to Hideki Konishi, Takeshi Ogawa, and the seminar and conference participants at GRIPS, Hitotsubashi University, Tohoku University, the JEA meeting, and the SEEPS meeting for their helpful comments. We also thank an anonymous reviewer for his or her insightful comments and suggestions.

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