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This paper discusses the challenges inherent in developing benefitcost analysis (BCAs) of climate change. Challenges are explored from three perspectives: meeting the foundational premises for conducting BCA within the framework of welfare economics, methodological considerations that affect the application of the tools and techniques of BCA, and practical limitations that arise out of resource constraints and the nature of the question, project, or policy being evaluated. Although economic analysts frequently face -and overcome -conceptual and practical complications in developing BCAs, climate change presents difficulties beyond those posed by more conventional environmental problems. Five characteristics of the climate system and associated impacts on human and natural systems are identified that pose particular challenges to BCA of climate change, including ubiquity of impacts, intangibility, non-marginal changes, long timeframes, and uncertainty. These characteristics interact with traditional economic challenges, such as valuing non-market impact, addressing non-marginal changes, accounting for low-probability but high-impact events, and the eternal issue of appropriately discounting the future. A mapping between the characteristics of climate change and traditional economic challenges highlights the difficulties analysts are likely to encounter in conducting BCA. Despite these challenges, the paper argues that the fundamental ability of economic analysis to evaluate alternatives and tradeoffs is vital to decision making. Climate-related decisions span a wide range in terms of their scope, complexity, and depth, and for many applications of economic analyses the issues associated with climate change are tractable. In other cases it may require improved economic techniques or taking steps to ensure uncertainty is more fully addressed. Augmenting economic analysis with distribution analysis or an account of physical effects, and exploring how economic benefit and cost estimates can be incorporated into broader decision making frameworks have also been suggested. The paper concludes that there are opportunities for BCA to play a key role in informing climate change decision-making.

International policy makers and climate researchers use greenhouse gas emissions inventory estimates in a variety of ways. Because of the varied uses of the inventory data, as well as the high uncertainty surrounding some of the source category estimates, considerable effort has been devoted to understanding the causes and magnitude of uncertainty in national emissions inventories. In this paper, we focus on two aspects of the rationale for quantifying uncertainty: (1) the possible uses of the quantified uncertainty estimates for policy (e.g., as a means of adjusting inventories used to determine compliance with international commitments); and (2) the direct benefits of the process of investigating uncertainties in terms of improving inventory quality. We find that there are particular characteristics that an inventory uncertainty estimate should have if it is to be used for policy purposes: (1) it should be comparable across countries; (2) it should be relatively objective, or at least subject to review and verification; (3) it should not be subject to gaming by countries acting in their own self-interest; (4) it should be administratively feasible to estimate and use; (5) the quality of the uncertainty estimate should be high enough to warrant the additional compliance costs that its use in an adjustment factor may impose on countries; and (6) it should attempt to address all types of inventory uncertainty. Currently, inventory uncertainty estimates for national greenhouse gas inventories do not have these characteristics. For example, the information used to develop quantitative uncertainty estimates for national inventories is often based on expert judgments, which are, by definition, subjective rather than objective, and therefore difficult to review and compare. Further, the practical design of a potential factor to adjust inventory estimates using uncertainty estimates would require policy makers to (1) identify clear environmental goals; (2) define these goals precisely in terms of relationships among important variables (such as emissions estimate, commitment level, or statistical confidence); and (3) develop a quantifiable adjustment mechanism that reflects these environmental goals. We recommend that countries implement an investigation-focused (i.e., qualitative) uncertainty analysis that will (1) provide the type of information necessary to develop more substantive, and potentially useful, quantitative uncertainty estimates-regardless of whether those quantitative estimates are used for policy purposes; and (2) provide information needed to understand the likely causes of uncertainty in inventory data and thereby point to ways to improve inventory quality (i.e., accuracy, transparency, completeness, and consistency).