Learning as a Guard against Substantial Climate Change Impacts

The project has been specified to deal withThe Race Against Time: Optimal Climate Policies and Costly Inaction”. Climate targets define boundaries beyond which unacceptable environmental change is considered to be very likely. A growing body of scientific evidence shows the increasing risk of these thresholds being exceeded if global mitigation efforts are delayed further.

This paper aims to contribute to our understanding of how the decision to adopt climate policy is influenced by this limited time to act. To this end, we depart from the commonly used expected utility approach and adopt a methodology that is able to explain the delay of some decisions. Our real options analysis explains that the interaction of intrinsic uncertainty and irreversibility of investment provides an incentive to wait for new information to arrive instead of taking action now.

The novel feature is the incorporation of this limited time to act into a non-perpetual real options framework analysing optimal climate policy under two kinds of uncertainty: stochasticity in the climate damage costs and in the temperature evolution. In both cases, simulations show that the knowledge of having a closing window of opportunity should accelerate climate policy adoption, in particular if the time runs out very soon. However, a sensitivity analysis covering crucial parameters such as the projected temperature increase and the discount rate reveals that the magnitude of this effect is rather small. This indicates that the urgency to act is not likely to trigger significant climate policy action.