Climatic Cost-benefit Analysis Under Uncertainty and Learning on Climate Sensitivity and Damages

Cost-benefit analyses on climate change have drawn considerable critique, primarily due to contestable choices of discounting and high uncertainties in climate sensitivity and climatic damages. Consequentially, it is argued that cost-benefit analysis can suggest mitigation rates that are nearly arbitrary. This article investigates how firm conclusions can be made from cost-benefit analysis if the main uncertainties are considered endogenously in the analysis and an extensive sensitivity analysis is carried out regarding the contestable assumptions. The SCORE model is used to calculate optimal emission pathways and carbon prices that hedge against climate sensitivity and damage risks using a wide range of plausible parametrizations. In a vast majority of cases the near-term emissions fall between 1.5 °C and 2 °C emission pathways, giving thus support for the Paris Agreement targets. Sequential decision-making allows hedging against uncertainties and readjusting mitigation efforts over time to reflect new information, leading to diverse stabilization temperatures in the long-term. Assumptions on mitigation costs, climate sensitivity and damages affect optimal near-term mitigation and long-term stabilization temperature more strongly than the discount rate choice. The consideration of parametric uncertainty on climate sensitivity and damages adds a substantial risk component to carbon pricing, while learning can induce significant price volatility in a decadal timescale.