Overview of EMF 24 Policy Scenarios.

• Author: Fawcett, Allen A.
• Position: Report

1. INTRODUCTION

   In the absence of comprehensive legislation to curb greenhouse gas (GHG) emissions in the United States, policymakers have been pursuing climate change mitigation through sector or technology-specific regulatory measures. Comprehensive climate policies would cover most or all sources of GHG emissions and potentially incentivize reductions at least cost through a market mechanism--such as a carbon tax, cap-and-trade system, or hybrid mechanism--by achieving an equalization of marginal abatement costs across participants (Metcalf, 2009). Sectoral and regulatory measures, by contrast, require that GHG emissions reductions be achieved through compliance with sector-specific technology or efficiency targets. The policy scenarios of the EMF 24 exercise are based on combinations of three different types of national policy instruments: an economy-wide cap-and-trade policy, a transportation policy representing a Corporate Average Fuel Economy (CAFE) standard for light-duty vehicles (LDV), and a clean or renewable energy standard for electricity. These policy scenarios do not reflect any specific legislative or administration policy proposals, but instead are intended to represent more generic versions of economy wide and sector specific policies. Questions that are addressed are: (1) what are the potential implications of transportation and electric sector regulatory approaches to emissions reductions that are roughly consistent with widely discussed goals for the reduction of greenhouse gas emissions? (2) How do the separate regulatory policies behave on their own, and how do they interact with an economy-wide climate policy meant to meet this goal? (3) What are the costs of different policy architectures? (4) How might technological improvements and technological availability influence the answers to the above questions?

   The EMF 24 study explores these questions through a comparison of results from seven modeling teams across seven standardized climate policy scenarios. Each modeling team was required to provide results related to economics, emissions, and energy systems for reference and policy scenarios. Policy assumptions are combined with two sets of coordinated technology assumptions for each individual or group of technologies: one set with pessimistic-technology assumptions representing evolutionary improvements in a technology, and a second set of optimistic-technology assumptions representing plausibly optimistic improvements. Modelers were free to make their own decisions on demographics, baseline GDP growth and energy consumption, and technology availability.

   The remainder of this paper proceeds as follows. Section 2 details the study design and includes a list of modeling teams and scenarios. Section 3 and 4 provide results from the study on emissions pathways and the cost-effectiveness of climate policies considered here, as well as an exploration of differences in results across models and various cost and emissions metrics. Section 5 summarizes the results.

2. OVERVIEW OF THE STUDY DESIGN

   2.1 Scenario Design

   The scenarios in this study are built from combinations of technology assumptions and policy assumptions. Table 1 summarizes the scenarios. The "Technology Overview of EMF 24" (Clarke et al., 2013) in this volume describes the technology assumptions used in this study, and the policy assumptions are described below. Two of the policy assumptions, the baseline and the 50 percent cap-and-trade scenarios, are run for all of the technology assumptions, and are further explored in Clarke et al. (2013). This paper explores the full set of policy assumptions, which are modeled for two specific sets of technology assumptions, a "optimistic CCS / nuclear" set of technology assumptions that allow carbon capture and storage (CCS) and Nuclear technologies, and have pessimistic assumptions about renewable energy (RE); and a "optimistic RE" set of technology assumptions that do not allow CCS, phase out nuclear power, and have optimistic assumptions about bioenergy, wind and solar. (1) Both of these sets of assumptions include optimistic assumptions about end use technology.

   Seven policy architectures are explored in this study: (1) baseline or reference scenarios with no policy, (2) cap-and-trade scenarios of varying stringency, (3) combined electricity and transportation regulatory scenarios, (4) electricity and transportation regulatory scenarios combined with a cap-and- trade policy, (5) isolated transportation sector policy scenarios, (6) isolated electricity sector policy scenarios with a renewable portfolio standard (RPS), and finally (7) isolated electricity sector policy scenarios with a clean energy standard (CES). Each of the scenarios is described by the set of policies of which it is comprised. These are discussed in detail in Table 2.

   2.2 Modeling Teams

   Though nine models participated in the EMF24 study, seven modeling teams participated in the full extensive menu of policy scenarios of the EMF 24, and the results of these models are the focus of this paper. The models include: the Applied Dynamic Analysis of the Global Economy model (ADAGE), from Research Triangle Institute; the Environment Canada Integrated Assessment Model (EC-IAM), from Environment Canada; the Future Agricultural Resources Model (FARM), from U.S. Department of Agriculture; the Global Change Assessment Model (GCAM), from the Pacific Northwest National Laboratory/Joint Global Change Research Institute; the NewERA model, from NERA Economic Consulting;; the U.S. Regional Economy, GHG, and Energy Model (US-REGEN), from the Electric Power Research Institute; and the U.S. Regional Energy Policy (USREP) model, from the MIT Joint Program on the Science and Policy of Global Change. These seven models were able to report policy cost metrics that are the focus of the analysis presented here. (2)

   2.3 Limitations of this Study

   It is important to note some of the limitations of this study. First, while these scenarios comprise a broad set of different climate policies and span a wide range emissions reductions targets, many uncertainties have yet to be explored, and implementation details, such as permit allocation, cost containment mechanisms, and revenue recycling issues, were not addressed in the comparisons. Some, but not all, of these uncertainties have been addressed by modeling teams in their individual papers. Second, fully harmonizing technology cost assumptions across all models proved inherently difficult as there are significant differences in model structure, in particular with respect to how technology choice is represented in each model. Third, models have not been fully harmonized with respect to their representation of the U.S. fiscal system, in particular if and how they represent existing taxes (for example, income and payroll taxes, corporate income tax). This implies that the interaction of a given climate policy instrument with pre-existing fiscal (tax) distortions may differ across models. More generally, it should be noted that the rank-ordering of policy instruments depends significantly on how the rents from a cap-and-trade program are used. While we assume a per-capita based lump-sum recycling of the revenue, it is well-known from the literature (for example, Goulder et al., 1999) that using the carbon revenue to lower pre-existing distortionary taxes may yield substantial efficiency gains. Due to model differences in the representation of the fiscal system, this study is not able to explore this dimension further, but it is important to bear in mind that the estimated cost for the cap-and-trade policies presented below should be interpreted as an upper bound, i.e. cost may be smaller if the carbon revenue would be recycled by lowering marginal tax rates, and the welfare ranking vis-a-vis the regulatory policy choices may be altered. Fourth, the scenario design and model baselines were locked down in early 2012, so the baselines do not reflect policies that were later adopted (e.g. the light duty vehicle and corporate average fuel economy standards that were published in October 2012). Additionally, developments in energy markets such as the shale gas boom have altered baseline emissions projections since the EMF 24 scenarios were developed (e.g. the Energy Information Administration's Annual Energy Outlook (AEO) for 2013 projects 2020 CO2 emissions to be 6 percent lower than the then current AEO 2011 projections). Despite the various limitations and uncertainties, clear insights emerged from this study.

3. EMISSIONS PATHWAYS

   Figure 1 shows historic U.S. C[O.sub.2] emissions covered by the policies modeled and projected reference scenario emissions for each model. (3) The reference case emissions pathways show a wide range of emissions projections across models, which is likely an important factor in explaining differences in costs among the participating models. Differing levels of emissions in the reference case imply different amounts of abatement required to meet the cap established in the cap-and-trade policies and the reductions targets implicitly specified in the sectoral regulatory approaches. Note that for most models, 2010 is a modeled year, and thus different input assumptions across models give rise to modest deviations from historic emissions in 2010. For a first group of models (US-REGEN, ADAGE, and GCAM) total U.S. C[O.sub.2] emissions in the reference case remain relatively flat over the 2010-2050 period while a second group of models models (USREP, NewERA, EC-IAM, and FARM) predict that emissions rise at roughly similar and constant rates reaching levels in 2050 that are 3-29 percent higher than emissions in 2010. Modeling teams in the first group expect significant reductions in carbon emissions per dollar of gross domestic product (GDP) even without focused climate policy, reflecting different baseline assumptions about recent and anticipated non-climate related...