cost projections, and, specifically, to identify gaps in the underlying economic research and modeling. The current workshop, as Weyant described it, aimed to focus on a limited number of key analytic challenges that emerged from the first workshop. Weyant pointed out the extensive ties to the first workshop—the planning group chair for that event was Richard Newell, one of the introductory keynote speakers for the second workshop. Marilyn Brown, John Weyant, and William Nordhaus also served on the planning committee for or as a speaker at each workshop.
Richard Duke followed Weyant with a discussion of the motivation for the present workshop. After underscoring how much Secretary Steven Chu had hoped to be delivering the welcoming remarks himself, Duke provided some thoughts on the agenda from the perspective of someone with experience with both abatement supply curves and learning curves as well as someone involved in climate policy at DOE. He noted that, when attempting to model the long-term energy system transformations that are necessary to address climate change, it is important to try to capture speculative technology changes—and yet this is so difficult to do. He mentioned the potential for insights through marginal abatement supply curves, but also that these curves contain hidden assumptions that are fundamental to their construction. He noted the importance of the offsets and story line issues being discussed in the final session. Duke finished with a description of some recent legislative and international initiatives to address climate change, including Secretary Chu’s international outreach activities.
Richard Newell followed with remarks intended to set the stage for the rest of the workshop. Newell noted that he was the chair for the planning committee that put together the first workshop in this series. He also noted that the EIA’s analyses and forecasts are independent of DOE and that his views should not be construed as representing those of DOE or the Administration. He began his talk by framing two major considerations in the economic modeling of greenhouse gas mitigation. The first is establishing a baseline picture of what the future may look like without any particular greenhouse gas policy. Newell pointed out that the baseline provides a counterfactual description of the future in the absence of some policy, but that baseline itself is subject to considerable economic, technological, and policy uncertainty. The baseline is not nearly as pure as is often imagined in textbooks and includes a significant number of technology, economic, and policy assumptions. Second, in estimating the nature of a future with greenhouse gas policies, the interest of policymakers is not just the allowance prices for carbon, impacts on gross domestic product, or the total cost of the policy, but potentially much more detailed impacts as well, such as the production and consumption of specific fuels, the level of deployment of specific technologies, emission levels, and other sectoral and regional impacts. Additionally, he noted that, although modelers want to understand the effect of policy relative to the baseline, it is important to remember that many people in the world do not think in those terms. They are interested instead, for example, in what will be the trajectory of natural gas prices and use with climate policy, not in how the trajectory of both change as one moves from the baseline to the policy case. Newell cautioned that these kinds of demands emerging from the policy process need to be kept in mind when models are being developed. Modelers need to be conscious that, just because certain categories of results are desired, it does not necessarily mean that such results can always be provided.
Newell then went on to provide some thoughts on the four topics of the workshop and how they relate to baseline energy-economic modeling as well as policy analysis against the baseline. First, with bottom-up marginal abatement supply curves, Newell reminded the workshop audience of the long-running debate attempting to reconcile the large technical potential for reduction of energy use and emissions through energy efficiency with the relatively low acceptance of these technologies in the marketplace. There is an ongoing discourse about the extent to which this lack of acceptance of energy-efficient technologies is explainable by real-world costs and benefits or whether it is attributable to market imperfections owing to principal-agent problems or imperfect information. There is also the possibility of inconsistent behavior on the part of households and firms, namely that they do not minimize costs as often as is assumed in economic models. With regard to learning curves, Newell noted that there is a strong empirical observation of technical learning as indicated by the relationship between cumulative production experience and manufacturing cost reductions. This relationship is a key feature of the process of technological change that comes up in almost every conversation with industry representatives—thus appearing to Newell and most people to be a real phenomenon.
One of the modeling issues associated with learning curves is the potential for double counting—for example, including cost reductions associated with cumulative production experience and increasing R&D expenditures separately in a model. Another learning curve issue is the selective incorporation of learning, including learning-