Suboptimal Scenarios and Quantitative Targets

Bob Shackleton described what he called the “unimportance of a quantitative target” with regard to climate policy analyses—there are so many uncertainties in terms of program implementation and climate response, that even if a specific target is achieved, such as stabilizing CO2 concentration, there is always a possibility that costs will greatly exceed what was projected, or that temperature change will be higher or lower than expected. He commented that analyses have often focused on quantitative targets and first-best scenarios, but increasingly decisionmakers are requesting suboptimal (i.e., realistic) scenarios, and more attention needs to be paid to how researchers communicate the uncertainty of their results (e.g., by bounding estimates). He further pointed out that many projections are based on concerted global action, but individual countries, particularly the large energy consumers, can substantially shift potential temperature outcomes, and thus analysts ought to pay more attention to communicating how these individual actions can shift the outcome.

Howard Gruenspecht agreed that there has been too much emphasis on first best scenarios, and he emphasized, as did several subsequent participants, that policymakers are interested in the nth best scenario, not only the first or second. As noted earlier, he drew a distinction between policymakers’ interests (their “wants”) and their needs, and he encouraged economic analysts to explain the limits of existing analytical tools, but also to make efforts in their own analyses to go beyond ideal scenarios. Gruenspecht argued that analyses generally took a cost engineering approach, but like the question of technology acceptance, policymakers are just as interested in what might be feasible or expedient, even if it is not the most economically efficient. John Weyant also urged analysts to keep in mind that the models provide insights, not numbers—policymakers have sometimes had a tendency to give modelers a number, e.g., 2 percent of GDP, and then ask modelers to “fill in the rest,” but this may not be feasible or desirable. As an example, he noted that one analysis of California’s bill to regulate GHGs (AB32) concluded that a 100 percent regulatory approach could yield the same results as a cap-and-trade program (in terms of GDP), but this sort of analysis left out two of the most important benefits of a cap-and-trade approach—its ability to handle uncertainty and its flexibility in handling heterogeneous costs among sectors.

Modeling Policy Interactions

Bob Shackleton pointed out that complicated interactions will inevitably occur between climate policies and policies developed to address a variety of other issues. This reality signals a need to analyze complementary measures, especially in the transportation sector (e.g., a low-carbon fuel standard). He urged more attention to understanding how policies interact, because the reality is that a suite of policies and approaches will be utilized. He noted that some analysts had begun to consider interactions among price floors, price ceilings, and banking/borrowing credits (e.g., Murray et al., 2008), but that more work needs to be done in this area. He also reminded participants that policies would not be static and would be revised in light of new information, further underscoring the importance of analyzing their interactions.

Participants identified several gaps between what policymakers were asking and what analysts were considering with regard to policy interactions. Nat Keohane categorized these as policy gaps and methodological gaps. Policy gaps include complementary measures (e.g., a low-carbon fuel standard), trade measures, and revenue recycling. Methodological gaps include price volatility or cost containment, energy efficiency measures as captured in models, and baselines for renewables, especially in the electricity sector. Several other participants suggested that price volatility is an important concern among policymakers, but it is difficult to capture since models tend to predict a particular price path, which may not be realistic. There was also discussion of how energy efficiency is represented in models. Economists are not necessarily as optimistic as engineers about energy efficiency opportunities (e.g., Paul et al., 2008), and Howard Gruenspecht pointed out that many efficiency improvements are assumed in the models, thus there is a danger of double counting. This factor must be teased out further so as to provide insights to policymakers about how policy can most effectively help drive improvements in energy efficiency.

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