2.3.3 Costs, benefits and uncertainties
In spite of scientific progress, there is still much uncertainty about future climate change and its mitigation costs. Given observed risk attitudes, the desirability of preventive efforts should be measured not only by the reduction in the expected (average) damages, but also by the value of the reduced risks and uncertainties that such efforts yield. The difficulty is how to value the societal benefits included in these risk reductions. Uncertainty concerning mitigation costs adds an additional level of difficulty in determining the optimal risk-prevention strategies, since the difference between two independent uncertain quantities is relatively more uncertain than related to the individual.
How can we decide whether a risk is acceptable to society? Cost-benefit analysis alone cannot represent all aspects of climate change policy evaluation, and Section 2.2 on Decision-making discusses a variety of tools. In the private sector, another practical way to deal with these risks has been to pay attention to the Value-At-Risk (VAR): in addition to using the mean and the variance of the outcome, a norm is set on the most unfavourable percentile (usually 0.05) of the distribution of outcomes at a given future date.
However, in the language of cost-benefit analysis, an acceptable risk means that its benefits to society exceed its costs. The standard rule used by public and private decision-makers in a wide variety of fields (from road safety to long-term investments in the energy sector) is that a risk will be acceptable if the expected net present value is positive. Arrow and Lind (1970) justify this criterion when the policy’s benefits and costs have known probabilities, and when agents can diversify their own risk through insurance and other markets. For most of the economic analysis of climate change, these assumptions are disputable, and have been discussed in the economic literature.
First, risks associated with climate change cannot easily be diversified using insurance and financial instruments. Atmospheric events are faced by everyone at the same time in the same region. This reduces the potential benefit of any mutual risk-sharing agreement. A solution would be to share risks internationally, but this is difficult to implement, and its efficiency depends upon the correlation of the regional damages. Inability to diversify risks, combined with the risk aversion observed in most public and private decision-makers, implies that there is an additional benefit to preventive efforts coming from the reduced variability of future damages. If these monetized damages are expressed as a percentage of GDP, the marginal benefit of prevention can be estimated as the marginal expected increase in GDP, with some adjustments for the marginal reduction in the variance of damages.
Second, in most instances, objective probabilities are difficult to estimate. Furthermore, a number of climate change impacts involve health, biodiversity, and future generations, and the value of changes in these assets is difficult to capture fully in estimates of economic costs and benefits (see Section 2.4 on costs). Where we cannot measure risks and consequences precisely, we cannot simply maximize net benefits mechanically. This does not mean that we should abandon the usefulness of cost-benefit analysis, but it should be used as an input, among others in climate change policy decisions. The literature on how to account for ambiguity in the total economic value is growing, even if there is no agreed standard.
Finally, Gollier (2001) suggests that a sophisticated interpretation of the Precautionary Principle is compatible with economic principles in general, and with cost-benefit analyses in particular. The timing of the decision process and the resolution of the uncertainty should be taken into account, in particular when waiting before implementing a preventive action as an option. Waiting, and thereby late reactions, yield a cost when risks happen to be worse than initially expected, but yield an option value and cost savings in cases where risks happen to be smaller than expected. Standard dynamic programming methods can be used to estimate these option values.