Many governments and international finance organisations use a carbon price in cost-benefit analyses, emissions trading schemes, quantification of energy subsidies, and modelling the impact of climate change on financial assets. The most commonly used value in this context is the so-called “social cost of carbon”. Users of the social cost of carbon include the US, UK, German, and other governments, as well as organisations such as the World Bank, the International Monetary Fund, and Citigroup. Consequently, the social cost of carbon is a key factor driving worldwide investment decisions worth many trillions of dollars.
The social cost of carbon is derived using so-called "integrated assessment models” that combine simplified models of the climate and the economy. One of three dominant models used in the calculation of the social cost of carbon is the Dynamic Integrated model of Climate and the Economy, or DICE. DICE contains approximately 70 parameters as well as several “exogenous” driving signals such as population growth and a measure of technological progress.
Given the quantity of finance tied up in a figure derived from this simple highly parameterised model, understanding uncertainty in the model and capturing its effects on the social cost of carbon is of paramount importance.
Indeed, in late January this year the US National Academies of Sciences, Engineering, and Medicine released a report calling for discussion on “the various types of uncertainty in the overall SCC estimation approach” and addressing “how different models used in SCC estimation capture uncertainty."
This talk, which focuses on the DICE model, essentially consists of two parts.
In Part One, I will describe the social cost of carbon and the DICE model at a high-level, and will present some interesting preliminary results relating to uncertainty and the impact of realistic constraints on emissions mitigation efforts. Part one will be accessible to a broad audience and will not require any specific technical background knowledge. In Part Two, I will provide a more detailed description of the DICE model, describe precisely how the social cost of carbon is calculated, and indicate ongoing developments aimed at providing better estimates of the social cost of carbon.
About the speaker
Christopher M. Kellett received the Bachelor of Science in Electrical Engineering and Mathematics from the University of California, Riverside and the Master of Science and Doctor of Philosophy in Electrical and Computer Engineering from the University of California, Santa Barbara. He subsequently held research positions with the Centre Automatique et Systemes at Ecole des Mines de Paris, the Department of Electrical and Electronic Engineering at the University of Melbourne, Australia, and the Hamilton Institute at the National University of Ireland, Maynooth. Since 2006, Chris has been with the School of Electrical Engineering and Computer Science at the University of Newcastle, Australia where he is currently an Associate Professor.
Chris is an Associate Editor for the European Journal on Control and Mathematics of Control, Signals and Systems, as well as a member of the IEEE Control Systems Society Conference Editorial Board. He has been the recipient of an ARC Future Fellowship, an Alexander von Humboldt Research Fellowship, and the 2012 IET Control Theory and its Applications Premium Award. Chris' research interests are broadly in the area of systems and control, with specific emphases on stability and robustness properties for nonlinear systems, high speed model predictive control, applications in electricity distribution networks, and applications in social systems such as carbon pricing and opinion dynamics.