Ross McKittrick writes on his personal web page: (h/t to David L. Hagen)
I have just released a working paper with Kevin Dayaratna and David Kreutzer of the Heritage Foundation in Washington DC which recomputes standard Social Cost of Carbon (SCC) estimates using updated empirical estimates of the equilibrium climate sensitivity (ECS).
- Dayaratna, Kevin, Ross McKitrick and David Kreutzer (2016) Empirically-Constrained Climate Sensitivity and the Social Cost of Carbon. SSRN Discussion Paper 2759505.
We applied the 2015 Lewis and Curry ECS distribution to the widely-used DICE and FUND Integrated Assessment Models. Previously the developers of these models (and others) have relied on model-simulated distribution of ECS values, especially from a 2007 paper by Roe and Baker. The Roe-Baker distribution underpins the US government’s current SCC values used for regulatory purposes. We critique this aspect of SCC computation, explaining why the Roe-Baker distribution is unsuitable. A major reason is that simulated ECS distributions have been superseded by a suite of empirically-estimated distributions. Using a recent, well-constrained empirical ECS distribution we find the estimated SCC drops substantially in both the DICE and FUND models, and in the latter there is a large probability it is no longer even positive.
EMPIRICALLY-CONSTRAINED CLIMATE SENSITIVITY AND THE SOCIAL COST OF CARBON Kevin Dayaratna Heritage Foundation Washington DC Ross McKitrick Department of Economics, University of Guelph Frontier Centre for Public Policy David Kreutzer Heritage Foundation Washington DC
Integrated Assessment Models (IAMs) require parameterization of both economic and climatic processes. The latter include Ocean Heat Uptake (OHU) efficiency, which represents the rate of heat exchange between the atmosphere and the deep ocean, and Equilibrium Climate Sensitivity (ECS), or the surface temperature response to doubling of CO2 levels after adjustment of the deep ocean. Due to a lack of adequate data, OHU and ECS parameter distributions in IAMs have been based on simulations from climate models. In recent years, new and sufficiently long observational data sets have emerged to support a growing body of empirical ECS estimates, but the results have not been applied in IAMs. We incorporate a recent observational estimate of the ECS distribution conditioned on observed OHU efficiency into two widely-used IAMs. The resulting Social Cost of Carbon (SCC) estimates are much smaller than those from models based on simulated parameters. In the DICE model the average SCC falls by 30-50% depending on the discount rate, while in the FUND model the average SCC falls by over 80%. The span of estimates across discount rates also shrinks considerably, implying less sensitivity to this parameter choice”
“Substituting an empirical ECS distribution from LC15 yields a mean 2020 SCC of $19.52, a drop of 48%. The same exercise for the FUND model yields a mean SCC estimate of $19.33 based on RB07 and $3.33 based on the LC15 parameters—an 83% decline. Furthermore the probability of a negative SCC (implying CO2 emissions are a positive externality) jumps dramatically using an empirical ECS distribution. Using the FUND model, under the RB07 parameterization at a 3% discount rate there is only about a ten percent chance of a negative SCC through 2050, but using the LC15 distribution, the probability of a negative SCC jumps to about 40%. Remarkably, replacing simulated climate sensitivity values with an empirical distribution calls into question whether CO2 is even a negative externality. The lower SCC values also cluster more closely together across difference discount rates, diminishing the importance of this parameter.”