Population, uncertainty, and learning in climate change decision analysis
Brian C. O'Neill, International Institute for Applied Systems Analysis (IIASA)
Warren C. Sanderson, International Institute for Applied System Analysis
The question of whether to act now or wait to learn more is central to the climate change issue. Previous work has reached no firm conclusions on either the direction or the magnitude of the effect on optimal emissions reductions of incorporating the potential for learning in climate change decision analysis. Here we use a well known, simple integrated assessment model to investigate how learning about the outlook for future population growth could affect optimal climate policy. We draw on recent work showing that, because population growth is path dependent, we can learn about the long term outlook for population size by waiting to observe how population changes in the short term. We find that learning about population growth can affect optimal policy, and that the timing and scope for learning are a key determinant of the magnitude of the effect.
Presented in Session 6: Environment, climate and population