In this week’s deep dive into last summer’s contrarian US Department of Energy climate report we look at Section 11.2, Models of the Social Cost of Carbon or SCC. The report emphasizes that one does not “measure” the SCC the way one could measure, for example, the inflation rate or average income. Those kinds of things can be measured directly, albeit only approximately, because they are based on prices and quantities in the economy, which economists can observe directly. In consequence, the more data they collect, the better the estimate will be. (For instance to know the “price of bread” it is better to visit 200 stores in 30 states than six stores in two states, and better still to visit 2,000 stores in 50 states.) But, they note, “there are no market data available to measure many, if not most, of the marginal damages or benefits believed to be associated with CO2 emissions, so these need to be imputed using economic models.” Imputed meaning feed an assumption into a computer and then call it a calculation when it comes back out intact. And of course running more models with more and more assumptions doesn’t guarantee a more accurate or truthful estimate, of SCC or anything else. Instead “Social Cost of Carbon” estimates simply reflect the assumptions of the people doing the estimate. You want a high SCC? Read on and we’ll give you the formula.

As the report notes:

“No amount of data collection can change the fact that many components of the SCC are unknown and rely on judgment and opinion based on knowledge of the underlying literature on the physical effects of climate change. SCC calculations are thus best thought of as “if-then” statements: if the following assumptions hold, then the SCC is $X per tonne.”

The key assumptions include the discount rate (how much or how little to shrink dollar amounts in the future to take account of the effects of interest rates), Equilibrium Climate Sensitivity (how many degrees Celsius a doubling of atmospheric CO2 is expected to cause), damage function coefficients, which is model-speak for how much harm any given change in conditions is expected to do to, say crop yields (or benefit functions if CO2 fertilization is allowed, which you will not be surprised to hear is not always the case in these models despite its obvious real-world existence), and assumptions about future emission trends because the higher the amount of CO2 emitted, the higher the assumed harm from it, aka the Social Cost of Carbon. (Why “social cost”? Why not “economic cost”? Because it sounds worse and more anti-social, that’s why.) So if you want a high SCC estimate, use a low discount rate, a high ECS, high damage parameters and RCP8.5. Et voila: you pull a tiger out of the hat.

The report then summarizes some of the indirect evidence against a high SCC value. Data-driven ECS estimates, for example, have been lower than models assumed, and CO2 fertilization rates have been higher, both of which imply the SCC is likely smaller.

P.S. The report also discusses so-called Tipping Points, another tool scientivists use to try and claim the SCC is very high, but the probabilities of these events are so low that even when they get added to models the numbers don’t change much.

Next week: US emissions and the scale problem