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

Koki Oikawa, Shunsuke Managi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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 successfully replaces the old one. Moreover, by using a two-project model, we show that a uniform subsidy is better than a selective subsidy.

Original languageEnglish
Pages (from-to)175-195
Number of pages21
JournalJournal of Economic Behavior and Organization
Volume117
DOIs
Publication statusPublished - Sep 1 2015

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics
  • Organizational Behavior and Human Resource Management

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