A thread that runs through much of the discussion of costs is that of uncertainty. The whole exercise of estimating mitigation costs is confounded by imprecise information about baselines, and the costs of mitigation and adaptation measures (especially future costs). It is critical that such uncertainties be recognized and conveyed to the policymakers in the most effective manner possible.

As discussed above, uncertainty about baselines is best dealt with by taking more than one baseline and reporting cost estimates for multiple baselines. Hence costs should not be given as single values, but as ranges based on the full set of plausible baselines.

Technological uncertainty is another key area. As noted in Section 7.3.4.1, the autonomous rate of improvement in the energy-to-GDP ratio that underlies almost all models of climate economics is a clear example of an exogenous parameter currently subject to uncertainty. This is not easy to overcome by endogenizing technical change, as practical models currently available have difficulties in dealing with endogenous technical change. Thus, the way firms develop new technologies is probably an issue surrounded by a greater uncertainty than uncertainty on the consumer side. There is a moderate degree of consensus in the literature on these issues. As with baselines, a scenario approach is essential and results have to be reported for both “optimistic” and “pessimistic” development paths.

Taking a different approach, the way consumers adopt existing lower carbon technologies and firms develop new ones can be viewed as key sources of uncertainty in costing methodologies. These assumptions are crucial, as different valuations are likely to affect the conclusions. However, the ways in which guidance and information about these two crucial issues are provided are radically different. Two different options are available from the consumer side. First, energy oriented macroeconometric models can provide a price elasticity to show how changes in the fuel mix are driven by relative prices. No specific direction of technological change can be derived from this class of model. However, differences in the results in terms of different energy structures (and different carbon impacts) could easily emerge. Second, engineering studies can provide some indications about available lower energy technologies to show the impact on energy demand and carbon emissions. Hence, from the point of view of uncertainty there is no a priori reason to choose between bottom-up and top-down models.

Finally there are uncertainties in the estimated costs as well as in the estimation of the ancillary benefits and/or co-benefits. As the literature on potential ancillary benefits is continues to develop, current estimates of the net social impacts of various mitigation policies are necessarily incomplete. Private cost figures are generally more certain than the external ones, but some imprecision remains. As with baselines, a scenario approach is recommended, with estimates prepared for a “low value”, a “mid value”, and a “high value”. Uncertainty about the external costs is well recognized. As with the private costs, again a scenario approach that gives a range from low, through mid, to high values is recommended. In both cases the scenario approach provides a sensitivity analysis for the costing exercise.

In the crosscutting paper on uncertainty (Moss and Schneider, 2000), a number of scales are proposed to assess the level of imprecision in the reported impacts, costs, etc. One that has frequently been used for costing exercises is the three-point scale that seeks to evaluate the degree of confidence in a particular result using a scale of: low, medium, and high confidence levels. This has been expanded to a five-point scale, which asks the researcher to select one of the following:

• “very high confidence” (over 95% certain);
  
• “high confidence” (67%–95% certain);
  
• “medium confidence” (33%–67% certain);
  
• “low confidence” (5%–33% certain); and
  
• “very low confidence” (below 5% certain).

This has not been applied to cost estimates, but it would useful to establish whether it could be applied and, if so, whether it would provide policymakers with better guidance as to the reliability of the results.

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