Bayesian learning, growth, and pollution

David L. Kelly, Charles D. Kolstad

Research output: Contribution to journalArticlepeer-review

140 Scopus citations

Abstract

Environmental problems, such as climate change, have great uncertainties. Current expectations are that uncertainties about climate change will be resolved quickly. We examine this hypothesis theoretically and computationally. We consider Bayesian learning about the relationship between greenhouse gas levels and global mean temperature changes, a key uncertainty. Learning is non-trivial because of a stochastic shock to the realized temperature. We find theoretically the expected learning time, which is related to the variance of the shock and the emissions policy, implying a tradeoff between the benefits of controlling emissions and information. We imbed the learning model into an optimal growth model with a climate sector and solve the resulting dynamic program. We find computationally that learning takes on average over 90 yr, far longer than currently believed.

Original languageEnglish (US)
Pages (from-to)491-518
Number of pages28
JournalJournal of Economic Dynamics and Control
Volume23
Issue number4
DOIs
StatePublished - Feb 24 1999

Keywords

  • Bayesian learning
  • C61
  • C63
  • Climate change
  • D81
  • D83
  • Dynamic programming
  • E1
  • E61
  • Growth
  • H4
  • Pollution
  • Q25
  • Q28

ASJC Scopus subject areas

  • Economics and Econometrics
  • Control and Optimization
  • Applied Mathematics

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