R&D in Clean Technology : A Project Choice Model with Learning

In this study, we investigate the qualitative and quantitative effects of an R&D subsidy for 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 which incorporates learning about the probability of success. 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 the government can induce suppliers to make cost reduction efforts even after the new technology successfully replaces the old one. Moreover, by a two-project model, we show that a uniform subsidy is better than a selective subsidy