Practical Policy Applications of Uncertainty Analysis for National Greenhouse Gas Inventories

Abstract

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).

Compliance for uncertain inventories via probabilistic/fuzzy comparison of alternatives

“…It states that only a small enough α-th part of an inventory distribution may lie above target K. This approach is called undershooting, see Gillenwater et al 2007;Godal et al 2003;Nahorski and Horabik 2010;Nahorski et al 2003, and it is illustrated in Fig. 1c.…”

Comparison of preparatory signal analysis techniques for consideration in the (post-)Kyoto policy process

“…The tacit assumptions underlying this approach are that human impact on nature, the unaccounted-for remainder under the Protocol, is irrelevant and inventory uncertainty matters from only a relative point of view over space and time, not an absolute one. But such an approach is highly problematic because biases Trend uncertainty Total uncertainty Intra-systems view Intra-systems view but suited to support inter-systems (top-down) view Emissions difference (between t 1 and t 2 or at t 2 ) Emissions gradient (between t 1 and t 2 ) Detectability of when an emission signal outstrips total uncertainty Undershooting Upward adjustment of reported emissions Risk with reference to the concept of significance Risk with reference to the concept of detectability Sources: Jonas et al (2004a: (Gusti and Jęda 2002); 2 verification time concept (Jonas et al 1999); 3 undershooting concept (Nahorski et al 2003); 4 undershooting and verification time concepts combined (Jonas et al 2004a); 5 Gillenwater, Sussman and Cohen #1 concept (Gillenwater et al 2007); 6 Gillenwater, Sussman and Cohen #2 concept (Gillenwater et al 2007) (i.e., discrepancies between true and reported emissions), typically resulting from partial accounting, are not uniform across space and time. In addition, human impact on nature need not be constant or negligible.…”

“…Depending on whether or not excess emissions are accepted and favorable compliance conditions exist a priori, the modified GSC #1 concept of Gillenwater et al (2007) comprises three cases (see Fig. 5).…”

“…It allows useful information to be generated in advance as to how great uncertainties can be dependent on the level of confidence of the emission signal or the signal one wishes to detect, and on the risk that one is willing to tolerate in not meeting an agreed emission limitation or reduction commitment. We are aware of at least six different preparatory signal analysis techniques, some of which were presented at the 1st International Workshop on Uncertainty in GHG Inventories (Gillenwater et al 2007;Jonas and Nilsson 2007;Nahorski et al 2007). These techniques need to be scrutinized further, now in a comparative mode, before a discussion on which of them to select can take place.…”

Dealing with Uncertainty in GHG Inventories: How to Go About It?

“…The state-of-the-art of preparatory signal analysis is well summarized by [23] (see also [21,[24][25][26]), who compare six of the most widely discussed techniques. 2 In addition, preparatory signal analysis also allows monitoring the success of a country in reducing its emissions along a prescribed emissions target path between its base year and commitment year/period.…”