A receding horizon control approach to estimating the social cost of carbon in the presence of emissions and temperature uncertainty

In this paper, we consider estimates of the dollar value of carbon dioxide (CO2) emissions due to anthropogenic climate damages, known as the social cost of carbon (SCC). Estimates of SCC are produced using integrated assessment models (IAMs) in which simplified models of the global climate system are in feedback connection with highly stylized models of global economic behavior. Approaches to optimal emissions reduction pathways are conventionally based on solutions to open-loop optimal control problems, which do not easily accommodate key uncertainties in the dynamic response of the climatic and economic sub-systems. In this paper we develop a receding horizon implementation of DICE (Dynamic Integrated model of Climate and the Economy), the most widely used IAM. We use this receding horizon control framework to compute the social cost of carbon in the presence of uncertainty in CO2 emissions and measurements of global mean surface temperature, and to investigate the effect on the SCC of a hard constraint limiting global temperature rise to no more than 2 °C over pre-industrial levels.