08 March 2010

Bias in IPCC WGIII? A Guest Post by Richard Tol, Part III

This post is Part III of Richard Tol's look at Chapter 11 of the IPCC AR4 WGIII. Part I is here. Part II is here.

Richard Tol is a research professor at ESRI in Ireland, one of the top 175 economists in the world and a contributor to the work of the Intergovernmental Panel on Climate Change (IPCC), where his work is widely cited. In this guest post, the third of a series, Richard takes a look at parts of the IPCC AR4 Working Group III, which has largely escaped scrutiny in recent months. In this Part III he discusses "cherry-picked results" and "misleading" tables.

Please have a look at Richard's full discussion below. If you have questions or criticisms of Richard's analysis please submit them in the comments, I am sure that Richard will be happy to engage.
Selective Results in the SPM of IPCC AR4 WGIII

Table SPM.4 summarizes the costs of emission reduction in 2030. The title comes with a footnote: “GDP reduction would increase over time in most models after 2030”. Deep cuts in emissions would come after 2030, and the real costs of emission reduction would therefore be felt later. The cost estimates in Table SPM.4 are low by construction, not because emission reduction is cheap.

Table SPM.6 shows the costs of emission reduction in 2050. This table does not warn that the bulk of emission reduction and its costs will be in the second half of the century. There is no table on the costs of emission reduction in 2100.

Tables SPM.4 and SPM.6 show the reduction in economic growth for three alternative targets, averaged over a number of studies. For 2050 (SPM.6), the results are a loss of economic growth of 0.05% per year if greenhouse concentrations are stabilized between 590-710 ppm CO2eq; and 0.10% if the target is between 535-590 ppm CO2eq. That is, costs double if the target becomes considerably more stringent. However, the economy slows down by 0.12% per year if the target is between 445 and 535 ppm CO2eq. Although the target becomes substantially more stringent, costs increase by only a little bit!

This is an amazing result. The models assessed by the IPCC all have that abatement costs grow and accelerate as targets become more stringent. Typically, doubling the rate of emission reduction would lead to a quadrupling of costs. The cost curve in SPM.6 (and SPM.4) bends the wrong way: Incremental costs fall as policy become stricter.

This was not picked up by the referees of the SPM because neither Table SPM.4 nor Table SPM.6 appeared in the drafts circulated for comment.

This travesty is partly explained in footnote g: “The number of studies that report GDP results is relatively small and they generally use low baselines.”

Table SPM.5 specifies the numbers: 118 studies estimated the costs of stabilizing atmospheric concentrations between 590 and 710 ppm CO2eq; 21 between 535 and 590 ppm CO2eq; and 24 between 445 and 535 CO2eq.

There are a large number of models that estimate the costs of emission reduction. Some have high costs, and others have low costs. Modelers self-censor their results, or are censored by referees and editors. If a relatively lenient target implies already relatively high costs, then there is no reason to show the results for more stringent targets. More stringent targets would lead to unacceptably high costs. Why waste journal pages on unrealistic scenarios?

This implies that only the “cheap models” ran the most stringent scenarios. The “expensive models” did not report the results, did not try to run these scenarios, or tried and failed. Clarke et al. (2009) investigate this matter, as do Tavoni and Tol (2009).

Furthermore, footnote g reveals that even the “cheap models” could only meet the most stringent targets if the no-policy scenario has benignly low emissions to start with.

In other words, the numbers in Tables SPM.4 and SPM.6 cannot and should not be compared to one another. The results for the relatively lenient targets are representative for the literature. The results for the relatively stringent targets suffer from selection bias.

Tables SPM.4 and SPM.6 cherry-pick results. These tables are misleading.