He also emphasized, as did other participants, that analyses should be transparent to the lay reader. However, participants did not explore what was meant by “transparent” or how transparency might be accomplished.
John Conti of EIA responded that in EIA’s case, its model is quite transparent with hundreds of pages of documentation easily accessible, but this does not mean that a lay reader would understand it. Keohane suggested that analysts be prepared to be able to explain key assumptions to lay readers like congressional staff. He noted that Stanford University’s Energy Modeling Forum (EMF) exercises, which engage many from the analytical community, are valuable but are still not transparent to non-analysts, or as he later put it, “The mechanics understand what is going on under the hood, and so the next step is to educate the driver.” Tim Profeta suggested that modelers consider designing sensitivities (e.g., discount rates) and certain assumptions with more stakeholders at the table, as a way to make them more transparent. Adele Morris of the Brookings Institution suggested that the media and broader public should be made more aware of these underlying assumptions as well, and she offered the example of policymakers citing the benefits to be gained from a stringent scenario and the costs arising from a lax scenario, which is of course misleading.
Finally, Peter Evans of GE pointed out that although several corporations have developed their own ways to analyze and plan for climate change impacts, they are also avid consumers of formal model outputs. An important consideration for modelers, particularly when they are thinking about reporting on outputs, is thus that in addition to the policy community, the business community is quite interested in learning about analytical work on designing models to estimate economic impacts.
Decisionmaker behavior and its representation in models was a subject of discussion throughout the workshop. Howard Gruenspecht raised several related issues and stressed that in general, analysts need to focus more on behavior. He cautioned that simply taking underlying preferences as given was likely not a solid assumption, and that there had been too much attention to cost engineering and not enough attention to the behavior embedded in modeling assumptions. Other participants echoed this notion that economists have focused on costs whereas policymakers are perhaps more interested in feasibility. In other words, policymakers want to know how quickly energy efficiency would have to improve, and with what degree of certainty they might expect to see that happen.
Some models also assume the adoption of technologies that may not be universally accepted, and this has major ramifications in many of the modeling results . As has been the case with nuclear power in the United States, community and public acceptance in general will have an influence on the siting and construction of many technologies that models assume will be adopted based on cost alone. Several participants raised questions about how nuclear power and carbon capture and sequestration (CCS) were handled in models, because these two technologies have a large influence on the costs of programs but are not certain to be widely accepted. One suggestion was that analysts should be able to separate these types of technologies out and communicate the results of not using them versus using them (e.g., estimate costs of 3 percent of GDP without such technologies, and 1.5 percent if adopted). Such reporting of results could help bound expectations of costs, as well as signal to policymakers that they may need to expend additional efforts to generate support for or reduce opposition to technologies that are otherwise cost-effective. In this regard, Peter Evans of GE noted that there appears to be a need for some sort of “grand bargain” around coal, given its importance in economic projections, and wondered how modeling results might influence public campaigns or other ways to shape national or even international interest. Dallas Burtraw of Resources for the Future (RFF) seconded this idea, and Kerry King of the University of Texas remarked that his research team is beginning to conduct survey sampling in Texas to understand the level of community acceptance of CCS, as Texas is likely to be a test bed for the new technology in the coming years.