Technology and U.S. Emissions Reductions Goals: Results of the EMF 24 Modeling Exercise.

AuthorClarke, Leon E.
PositionReport
  1. INTRODUCTION AND BACKGROUND

    It is now well understood that technology cost, performance, and availability can have a substantial impact on the macroeconomic costs, and the challenge more generally, of meeting long-term global climate goals as well as national mitigation goals such as those that have been considered in the United States. Although a number of individual studies have specifically explored the role of technology in meeting climate goals in the U.S. (see, for example, Kyle 2009 and Kyle 2011 among others), there exists no coordinated study that explores this space across multiple models and using a coordinated set of model assumptions. The EMF 24 scenarios fill this gap. Nine models produced scenarios for this study, based on three mitigation goals for the United States: no emissions reductions (reference scenarios), a 50% reduction in emissions by 2050 relative to 2005 levels, and an 80% reduction relative to 2005 levels. These emissions pathways correspond to those explored in the EMF 22 multi-model study (Clarke et al., 2009) and its predecessor (Paltsev et al., 2008). The EMF 24 scenarios then combine these mitigation goals with various assumptions about the availability, cost, and performance of C[O.sub.2] capture and storage (CCS), nuclear power, wind and solar power, bioenergy, and energy end use.

    This study is motivated by three primary questions. First, how might technological improvements and technological availability influence the character of the U.S. energy system transition associated with 2050 climate mitigation goals? Second, what are the macroeconomic mitigation cost and carbon price implications of meeting 2050 climate mitigation goals, and how are these influenced by different futures of technology availability, cost, and performance? Finally, can 50% and 80% reduction goals for the United States be met largely through the implementation of limited technology portfolios? In particular, can these goals be met based exclusively through end-use measures and renewable energy--that is, without the use of nuclear power and CCS--and vice versa?

    The remainder of this paper proceeds as follows. Section 2 introduces the study design for the EMF 24 Technology Scenarios. Section 3 then discusses the nature of the emissions and energy system transitions in the reference scenarios. Section 4 then discusses the economic, emissions, and technological characteristics of the mitigation scenarios. Section 5 sums up and discusses directions for future research suggested by the results of this study.

  2. STUDY DESIGN

    2.1 Overview of the study design

    The EMF24 Technology Scenarios were designed to assess how the cost and availability of low-carbon technologies and energy end-use measures might affect the U.S economy and energy system under policies that reduce GHG emissions. The matrix of scenarios in the study consists of a technology dimension and a policy dimension (Table 1). The technology dimension captures variations in technology cost, performance, and availability. The policy dimension captures the two 2050 mitigation goals for the study.

    The suite of technologies examined in the study includes end-use energy reduction technologies, CCS, nuclear power, wind and solar power, and bioenergy. For each class of technologies, optimistic and pessimistic sensitivities were specified (Table 2). For nuclear power and CCS, the sensitivities are meant to capture the influence of factors that might affect the availability of these technologies. Hence, the pessimistic sensitivities restrict the deployment of these technologies whereas the optimistic sensitivities allow for expansion. No variation in cost and performance is assumed for these technologies. Based on similar reasoning, bioenergy sensitivities represent variations in the supply of bioenergy. In contrast, sensitivities for wind and solar power capture variations in the cost and performance of solar and wind power. No explicit limitations on expansion were specified for the scenarios. Finally, sensitivities in end-use are meant to capture changes in technology and deployment that would lower end-use energy demands. Because many models do not have structural representations of the end-use sector, the end-use assumptions were specified simply in terms of a reduction in final energy consumption. The means of achieving this reduction was left ambiguous, which raises interpretation issues that are discussed below.

    The EMF 24 Technology Scenarios (Table 1) represent different combinations of technology sensitivities (Table 2). They are bracketed by Optimistic Technology and Pessimistic Technology assumptions, which hold all technologies at their respective optimistic and pessimistic sensitivities. A set of three single technology sensitivities test the effect of switching from optimistic assumptions about end-use, CCS, and nuclear to pessimistic assumptions while maintaining optimistic assumptions for all other technologies. Three combined sensitivities, Pessimistic CCS/Nuc, Pessimistic Renewable, and Pessimistic End-Use Energy and Renewable Energy (EERE) examine the effect of limiting the energy system transition to pathways that rely on particular combinations of technologies. Scenarios based on Pessimistic CCS/Nuc assumptions rely exclusively on end-use reductions and renewable sources, because deployment of CCS and nuclear energy is constrained. Scenarios based on the Pessimistic Renewable assumptions assume the availability of CCS and nuclear energy, but uses less optimistic assumptions about renewable technologies. The Pessimistic EERE technology assumptions add pessimistic assumptions about end-use energy to the Pessimistic Renewable assumptions.

    Several observations are important for interpretation of these scenarios. First, although the assumptions across technology categories were chosen to be roughly comparable, in practice this is an imprecise and subjective decision. It is difficult, for example, to assess the likelihood of the end-use energy reductions assumed in this study relative to the constraints on CCS or nuclear energy. Second, with the exception of nuclear and CCS assumptions, the precise of specifications of many of the technology assumptions (e.g., for renewable power) were left to the individual modeling teams, who undoubtedly chose different values. This means that it is difficult to consistently ascertain the implications of, for example, more optimistic wind and solar assumptions. One reason for this decentralized approach was that the models have very different methods of representing these technologies. Third, the costs of achieving Optimistic Technology assumptions are not specified for any of the scenarios. For example, research, development, and demonstration (RD&D) costs are not specified. This means that the cost difference between scenarios based on Pessimistic Technology and Optimistic Technology assumptions is biased toward overestimation in all cases by the additional investment that would be required to reach the Optimistic Technology assumptions. The treatment of end-use measures is particularly ambiguous in this regard. Improvements in end-use efficiency could involve a mix of both improvements in technology and changes in policy--for example, appliance efficiency standards--to spur adoption. The precise role of each of these is unspecified. To interpret the end-use assumptions in a manner that is consistent with the supply-side assumptions, it is necessary to assume that all of the energy end-use reductions occurred because of the availability of new technology with higher efficiency but without additional cost. In addition to the ambiguity of the source of end-use energy reductions, there are known market failures in markets for end-use efficiency that further complicate the welfare costs of implementing energy end-use measures.

    All told, then, the differences in results arising from differences between technology assumptions in this study should be interpreted carefully and precisely. On the one hand, it is possible to draw some conclusions about the implications of different technologies at a broad level. On the other hand, these results are highly dependent on assumptions and may miss underlying costs, so precision is limited.

    The policy dimension of these scenarios is based on an economy-wide carbon price leading to linear reductions in cumulative emissions of greenhouse gases over the period from 2012 through 2050. Reductions are specified as reaching either 50% below 2005 levels or 80% below 2005 levels in 2050. Banking of allowances is allowed, but borrowing of allowances is not permitted. In cases where models found banking to be cost-effective, the linear pathway was not sufficient to characterize the scenarios, so a cumulative total was required. The emissions cap covers all Kyoto gases in all sectors of the economy that the particular model represents, with the exception of C[O.sub.2] emissions from land use and land use change, which are excluded from the analysis. This means that non-C[O.sub.2] land use and land use change emissions and emissions of GHGs not covered under many U.S. climate bills are still included in the cap. It is important to note that different models have different capabilities to represent emissions from different sources and sectors (see Table 3), so individual models were asked to define the full scope of their targets to fit the capabilities of the models. In general, this meant that there was a distinction between those models that represent non-CO2 substances and those that don't.

    The balance between the technology and policy dimensions of the study was made by conducting a full evaluation of technology variations for the 50% scenarios and then producing both 50% and 80% reductions for two specific combinations of technology assumptions. To manage the burden on the modelers, it was not feasible to produce the full range of technology variations for both...

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