R&D in clean technology: A project choice model with learning
Data(s) |
01/09/2015
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Resumo |
In 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 cost-reduction efforts after the new technology success-fully replaces the old one. Moreover, by using a two-project model, we show that a uniform subsidy is better than a selective subsidy. |
Identificador | |
Publicador |
Elsevier BV |
Relação |
DOI:10.1016/j.jebo.2015.06.015 Oikawa, Koki & Managi, Shunsuke (2015) R&D in clean technology: A project choice model with learning. Journal of Economic Behavior and Organization, 117, pp. 175-195. |
Direitos |
Copyright 2015 Elsevier B.V. |
Fonte |
QUT Business School; School of Economics & Finance |
Palavras-Chave | #Environmental technology #Learning #R&D subsidy #Pigouvian tax |
Tipo |
Journal Article |