Discounting is both a critical and pervasive issue in CBA, and this is nowhere more so than in environmental applications. On the one hand, this is a technical matter arising from the standard assumption in CBA that the social or shadow price of a unit of consumption in the future is lower than the price of a unit of consumption today. The discount rate simply measures the rate of change of the shadow price. This simplicity is, of course, a matter of extent. While the theory of social discounting shows clearly how the social discount rate should be defined, in practice numerous questions arise especially when considering actions with implications for generations in the far distant future: intergenerational projects and policies. Not only do the assumptions underpinning conventional discounting become problematic but also the ethical underpinnings of discounting become extremely important and influential. As a result, the chapter discusses how the parameters of the discount rate for social CBA are determined as well as their ethical and practical content. This involves a discussion of the problems introduced to the conventional discounting approach by intergenerational projects such as climate change and the strengthening of theoretical and empirical support for schedule of discount rates that decline with time.

Methods of dealing with uncertainty – specifically probabilistic risks – in CBA have typically focused on expected utility theory which provides a strong theoretical basis for deviating from the simple use of expected values in a deterministic framework, towards estimating welfare corrections for use in CBA. However, estimating the resulting certainty equivalent values requires assumptions about the nature of society’s utility function, and some demanding estimates of the probability distributions of the risky quantities associated with any given project. Even so, practitioners are increasingly prepared to use these methods, given emerging evidence about the errors associated with simpler approaches. That said, more ad hoc ways of addressing this such as sensitivity analysis and Monte Carlo simulations have their place, and the chapter shows how a nuclear power project appraisal might utilise and interpret a Monte Carlo analyses. Nor should a focus on formal economics ignore the fact that there are many other principles that could be applied in CBA to make decisions in the face of uncertainty, such as “safety first” and “precaution”.

Another aspect of uncertainty is quasi-option value (QOV) where the notion of precaution is made more formal. Again the starting point here is that costs and benefits are almost never known with certainty. But the insight in QOV is that uncertainty can be reduced in some situations by gathering information. Any decision made now which commits resources or generates costs that cannot subsequently recovered or reversed is an irreversible decision. In this context of both uncertainty and irreversibility it may pay to delay making a decision to commit resources. The value of the information gained from that delay is the QOV. This chapter explains how QOV arises, what it adds to the approaches outlined in and addresses some of the terminological issues that have arisen in the literature. The concept of QOV can make a significant difference to decision-making especially as it serves as a reminder that such decisions should be based on maximum feasible information about the costs and benefits involved, and that includes “knowing that we do not (currently) know”. If this ignorance cannot be resolved then there is nothing to be gained by delay. But if further information can resolve it, then delay can improve the quality of the decision. How large is this gain is an empirical question.

Conventional CBA for the most part continues to regard (intra-generational) distributional or equity concerns as having little or no place in making its recommendations about policy formulation or investment projects. Identifying this oversight is one thing, responding to it is more controversial especially where this involves weighting costs and benefits according to equity criteria. But this usefully might just involve simply identifying the costs and benefits of individuals and groups on the basis of differences in the characteristic of interest. Perhaps this sounds unambitious but given the starting position (where this seldom happens), more routine cataloguing of this type surely would be useful. Moreover, this could involve not only cataloguing how costs and benefits are distributed across people but also how particular environmental goods and bads (such as air quality, unwanted land uses and so on) are distributed. One catalyst almost certainly could be demand from policy makers. That is, the observation that too much practical CBA neglects distributional concerns may not just be a supply problem (a single-minded focus of cost-benefit practitioners on efficiency), it is also likely to be an issue about demand: perhaps, for example, policy makers perhaps have not required this information in the terms of reference guiding that practical work. Taking this further might involve weighting costs and benefits and scrutinising proposals on the basis of a distributional cost-benefit test. While a long-standing analytical option, there is no easy answer to the equally long-standing question about what value these weights should take. Nevertheless, exploring this question has led to some interesting empirical insights about inequality aversion generally and for specific goods and bads (such as health risks).