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Models and Analytical Approaches

Following the discussion of policymakers’ or end-users’ informational needs, the next panel provided a brief overview of several models and analytic approaches currently available. Not all models were represented (notably MARKAL, the technology-rich DOE-supported model), but collectively the panelists offered a reasonable glimpse of existing analytical capabilities. Panelists were John Conti, EIA; Richard Goettle, Northeastern University; Leon Clarke, Pacific Northwest National Laboratory; John Reilly, Massachusetts Institute of Technology; Dallas Burtraw, Resources for the Future (RFF); Jean-Marc Burniaux, Organisation for Economic Co-operation and Development (OECD); Tom Kram, Netherlands Environmental Assessment Agency (MNP); and Peter Evans, GE.

Rather than focusing on specific modeling results, panelists presented the capabilities and high-level structure of these approaches, advantages and disadvantages relative to other approaches, how decisionmakers are using these tools, and whether and how the approaches incorporate uncertainty. At the outset, several speakers noted that existing models are complements to one another—they each have unique strengths and are not interchangeable. Thus, discussions focused on perceived informational gaps within the collective community and any opportunities to enhance existing capability (e.g., through improved data). This chapter summarizes the key features of the analytical approaches presented at the workshop and the major themes addressed by presenters and participants. Specifically, discussions focused on existing models, their data limitations, and how the models characterize technologies, policy effects, major sources of uncertainty, behavior, and systems interactions.

Existing Models and Analytical Approaches

National Energy Modeling System

The National Energy Modeling System (NEMS), managed by EIA, is the direct result of a National Research Council study that provided recommendations to EIA on improving its modeling capability to better inform DOE’s national energy strategy (NRC, 1992). A partial equilibrium model of the U.S. energy economy, NEMS provides projections for imports, exports, conversion processes, and prices. One of its primary outputs, the Annual Energy Outlook, serves as a baseline for several other models. NEMS is linked to the Global Insight macroeconomic model and relies on EPA’s estimates for non-CO2 gases. Modules represent specific sectors (supply, conversion, end use). Depending on end-use sector, NEMS may utilize a number of different methodology paradigms, including adaptive expectations, myopic expectations, and perfect foresight. Outputs are regionally specific, roughly following the 10 U.S. census regions, and exhibit rich technological detail that is econometrically estimated and dependent on exhibited historical behavior.

Intertemporal General Equilibrium Model

The Intertemporal General Equilibrium Model (IGEM) was developed and is managed by Dale Jorgenson and Associates and is an intermediate-term (20-50 year) model of the growth and structure of the U.S. economy. It represents 35 producing sectors and utilizes three sources of primary inputs: capital, labor, and non-competing imports. Unlike dynamically recursive or myopic models, IGEM has perfect



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3 Models and Analytical Approaches Following the discussion of policymakers’ or end-users’ informational needs, the next panel provided a brief overview of several models and analytic approaches currently available. Not all models were represented (notably MARKAL, the technology-rich DOE-supported model), but collectively the panelists offered a reasonable glimpse of existing analytical capabilities. Panelists were John Conti, EIA; Richard Goettle, Northeastern University; Leon Clarke, Pacific Northwest National Laboratory; John Reilly, Massachusetts Institute of Technology; Dallas Burtraw, Resources for the Future (RFF); Jean- Marc Burniaux, Organisation for Economic Co-operation and Development (OECD); Tom Kram, Netherlands Environmental Assessment Agency (MNP); and Peter Evans, GE. Rather than focusing on specific modeling results, panelists presented the capabilities and high- level structure of these approaches, advantages and disadvantages relative to other approaches, how decisionmakers are using these tools, and whether and how the approaches incorporate uncertainty. At the outset, several speakers noted that existing models are complements to one another—they each have unique strengths and are not interchangeable. Thus, discussions focused on perceived informational gaps within the collective community and any opportunities to enhance existing capability (e.g., through improved data). This chapter summarizes the key features of the analytical approaches presented at the workshop and the major themes addressed by presenters and participants. Specifically, discussions focused on existing models, their data limitations, and how the models characterize technologies, policy effects, major sources of uncertainty, behavior, and systems interactions. Existing Models and Analytical Approaches National Energy Modeling System The National Energy Modeling System (NEMS), managed by EIA, is the direct result of a National Research Council study that provided recommendations to EIA on improving its modeling capability to better inform DOE’s national energy strategy (NRC, 1992). A partial equilibrium model of the U.S. energy economy, NEMS provides projections for imports, exports, conversion processes, and prices. One of its primary outputs, the Annual Energy Outlook, serves as a baseline for several other models. NEMS is linked to the Global Insight macroeconomic model and relies on EPA’s estimates for non-CO2 gases. Modules represent specific sectors (supply, conversion, end use). Depending on end-use sector, NEMS may utilize a number of different methodology paradigms, including adaptive expectations, myopic expectations, and perfect foresight. Outputs are regionally specific, roughly following the 10 U.S. census regions, and exhibit rich technological detail that is econometrically estimated and dependent on exhibited historical behavior. Intertemporal General Equilibrium Model The Intertemporal General Equilibrium Model (IGEM) was developed and is managed by Dale Jorgenson and Associates and is an intermediate-term (20-50 year) model of the growth and structure of the U.S. economy. It represents 35 producing sectors and utilizes three sources of primary inputs: capital, labor, and non-competing imports. Unlike dynamically recursive or myopic models, IGEM has perfect 8

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foresight.1 Like other CGE models, IGEM’s outcomes are largely influenced by household-level decisions on labor, consumption, and leisure. It is sometimes criticized for assuming perfectly mobile capital and labor, but its substitution effects are econometrically estimated based on market behavior observed over the past 50 years. IGEM’s reliance on observed behavior and empirical data makes it rich in detail and substitution possibilities; there are more than 12,000 substitution parameters, and so measurable variance-covariance matrices (which IGEM has) are increasingly important to help account for imprecision in the parameter estimates. This is a potential advantage of the econometric approach, since it can potentially put bounds on uncertainty; IGEM’s operators are just beginning to work in that direction, though such an effort requires significant time and resources. MiniCAM MiniCAM is an integrated assessment model (IAM) operated by Pacific Northwest National Laboratory. Like other IAMs, MiniCAM is a complete model in the sense that it models the entire globe and includes not only human and economic systems, but also their interactions with physical systems. It models out to the end of the 21st century; although it captures at a high level all of the interactions possible among agriculture, land use, climate, energy, and other systems, it is not able to provide regionally specific or short-term outcomes. MiniCAM operators also engage in integrated assessment research, in which they work with and incorporate detailed systems models (e.g., for ecosystems or carbon capture and storage) that then feed back into the IAM. These nested models can also help scale down impacts to a more regional level. MiniCAM’s focus is on energy, agriculture, and land use markets in 14 world regions, and it is fairly detailed from a technology standpoint. In this regard, much of its research has focused on drawing insights about the relative importance of different energy technologies. ADAGE ADAGE, a dynamic CGE model with foresight, is operated by RTI. Its coverage is both international and regional within the United States, and so it can be used to model regional energy production as well as international policies and their impacts on trade flows. It is flexible in that it can parse out a sector such as agriculture into its various components for further analysis. EPA is currently working with ADAGE operators to examine how climate change might affect sectors ranging from agriculture to human health. EPPA EPPA is a CGE model that is part of a broader integrated system operated by MIT. It represents and balances primary factors of production, the goods and services produced, and the income accrued and spent by households. Recently, operators have made additions to EPPA to account for perceived weaknesses or shortcomings of the model. Specifically, they have added household transportation with a variety of vehicle and fuel types, a recreation system that examines competition in land use demand between recreation and biofuels, and a health services sector that reflects damage caused by air pollution. ENV-Linkages The OECD utilizes a CGE model with world coverage (22 sectors and 12 regions) and it covers 50 years with recursive dynamics. OECD’s clients, its member countries, have tended to focus on medium-term (as opposed to longer-term) outcomes, and are interested in the policy responses to issues such as carbon leakage or trade and competitiveness. More recently, operators have begun work on modeling “hybrid” approaches that include some level of countrywide reductions, coupled with sectoral agreements for certain energy-intensive industries, as well as existing energy subsidies that affect 1 Perfect foresight does not imply that decisionmakers will precisely know the future, but it does assume that they are fully aware of all contingencies and the probabilities associated with these contingencies. 9

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mitigation cost. They have also started to analyze implications for worldwide emissions trading. In particular, they are modeling scenarios for varying carbon allocation rules. IMAGE The Netherlands Environmental Assessment Agency (MNP) operates a global coverage model similar to the MIT-EPPA model. IMAGE represents 26 regions at a resolution of 0.5 degree by 0.5 degree. It integrates energy, land use, food security, climate, and air quality in one framework and is used to analyze long-term (2050-2100) issues. From a technology perspective, IMAGE is relatively aggregated, though it does include advanced options such as biomass power plants with CCS. Climate change feedback is represented and affects land use and energy demand. The MNP is also now working to disaggregate regional data, with the hope of shedding some light on the positions of different countries— some countries will likely experience damages that exceed their expected GHG mitigation costs—and this information may be useful in formulating a more equitable global response to climate change. Haiku Resources for the Future (RFF) models the U.S. electric utility sector using the Haiku model. It solves for the electricity sector in 21 U.S. regions, and unlike some other electricity market models, it explicitly accounts for the structure of cost recovery in different regions of the country (whether it is market-based or based on regulation of the cost of service). It also includes a fuel price response to demand. Dallas Burtraw noted that, although the Haiku model may not handle uncertainty any better than other models, RFF is using the Delta method when looking at safety valves, and at the influence of a price cap and price floor on variability in allowance price transactions. RFF has also linked to an investor behavior model and is using stochastic dynamic optimization to analyze how those who invest in CCS would respond to equilibrium conditions, given the uncertainty in that market. Scenario Planning Within GE, analysts are using scenario planning to help the business units understand the social, economic, and political ramifications of climate change. Their primary interest is in technology change, both its rate of deployment and its effects on factors like energy efficiency. Evans provided three reasons that GE takes this approach: to deal with rapid change and uncertainty in national and international energy markets; to improve long-term technology investment decisionmaking within the company; and to enhance organizational learning. They use three scenario frameworks: Asia Rising, Changing Climates, and Global Rifts. Each scenario provides a unique framework for thinking about how the future could unfold and the technology pathways that might result. Data Sources and Limitations Baseline assumptions are the biggest source of uncertainty for several of the models. Assumptions about world oil prices, technological progress, or population growth can have important implications, particularly beyond 2030. These sorts of assumptions determine the “business as usual” scenario. In the shorter-term, even the changes in EIA’s Annual Energy Outlook 2008 (notably the Energy Independence and Security Act) affect the baseline for many models. John Conti noted that Congress would like NEMS to model at the state or district level, but sufficient data does not exist. End- use data and consumption surveys would aid the EIA in providing a richer level of detail, and from his perspective, industrial sector data is most in need of improvement. DOE has programs that extend NEMS results out to 2050, but it becomes difficult to fill in the detailed data assumptions that would drive the model beyond its 25-year timeframe. John Reilly stated that MIT is developing a U.S. regional model that 10

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would eventually support a state-level modeling system, but access to regional detail (especially plant- level) will be critical. IGEM, like other CGE models, is largely empirically based. Social accounting matrices come from the National Income of Product Accounts (U.S. Bureau of Economic Analysis). Many models also draw from the Global Trade Analysis Project (GTAP), a database containing bilateral trade information for more than 40 countries and 50 sectors. IGEM’s representations of price- and policy-induced technical change are based on observed behavior over 40-50 years, though its managers recognize that past performance is not necessarily indicative of what will happen in the future. IGEM does not represent feedback effects, and so “shocking the system” will not change the parameters themselves. Technology Characterization Marilyn Brown noted that the multiplicity of technology options available can sometimes be a deterrent to action—policymakers want to know what their best option is. Therefore, she wondered how models might be able to offer additional insight into the role of specific technologies, such as different low-carbon fuels, or tradeoffs between all-electric and plug-in hybrid cars. This level of technological detail often confines the timeframe for estimates. For technology-rich models like NEMS, assumptions about specific technologies that go beyond 25 years or so are both difficult to make and highly uncertain. MiniCAM attempts to draw out insights over a longer timeframe (100 years), but Leon Clarke cautioned that technology assumptions are even more influential in its model outcomes. In general, it is more difficult to get technology richness in the more top-down models. ADAGE and other CGE models can try to isolate the electricity sector and include specific technologies directly in the model, or, as EPA has done, it can link results from other modeling frameworks. Jay Braitsch of DOE noted that technology progress is among the most important of the assumptions made by the DOE in its modeling efforts. IMAGE and several other models use a simple learning-by-doing approach to represent technology learning. In the case of IMAGE, it is affected by resource depletion (including competition for biomass), and in the case of renewable resources, it is also influenced by distance from human settlements. As noted in the discussion on policymakers’ information needs, technology acceptance is also an important consideration that is not always being reflected in models as another uncertainty factor. Dallas Burtraw recalled that in the 1970s, few analysts would have forecast that the nuclear power industry would have such difficulty in siting and building new plants; a similar situation is playing out for many coal plants in the United States, and so the business as usual reference case may no longer apply. Representing Policy Effects Detailed and complex legislation must be characterized to fit into the models, and while it is not generally a problem to represent the instruments, there is a great deal of uncertainty surrounding what these policies would actually accomplish. Many CGE models capture rich substitution possibilities, based on observed behavior. However, for all models, it is less clear that historical behavior accurately represents the choices consumers would make today or in the future. Forward-looking models, which optimize over time, tend to yield lower macroeconomic costs than do myopic dynamic models, because actors know what will happen in the future and can plan accordingly. The models can use behavioral elasticities to reflect, for example, slow household response to price changes, but capturing explicit distortions is difficult. These substitution elasticities have an important bearing on costs, in both static and dynamic models. John Reilly also pointed out that credit systems like the Clean Development Mechanism (CDM) are not nearly as efficient as a cap-and-trade program, but the credit supply curves are generally based on some sort of cap. 11

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FIGURE 2. Relationships among energy security, air quality, and climate goals, as modeled under different scenarios for Western Europe. SOURCE: Tom Kram, Netherlands Environmental Assessment Agency, presentation given at the Workshop on Assessing Economic Impacts of Greenhouse Gas Mitigation, National Academies, Washington, D.C., October 2-3, 2008. NEMS is able to model many policies currently under consideration because it has a fairly detailed transportation module embedded in the system—as workshop participants noted, the transportation sector is often treated separately, and thus models must account for, among other things, specific fuel economy requirements. NEMS’s modularity also allows its managers to work on particular modules as specific programs (e.g., CAFE standards) are modified. IGEM has a rich and detailed tax structure, which allows it to examine a variety of revenue recycling schemes and alternatives to a particular climate policy. Tom Kram noted that in the Netherlands, MNP is receiving questions about economic impacts on low income groups; the Netherlands Economic Bureau no longer has a detailed sectoral model that might help analyze this, and so currently MNP is unable to assess specific impacts. He also noted that clients are interested in understanding the side benefits of taking action to reduce emissions, and that is represented in terms of import reductions and human health benefits as compared to direct costs (Figure 2). When considering targeted use for allowance values, Dallas Burtraw suggested that much more work is needed to examine incentives in the second-best construct. There will be administrative costs, free riders, and missed opportunities in efficiency programs, and he wondered whether or not there are insights into effective mechanism design to incentivize investment in efficiency. On the role of state and local governments, he noted that most analyses are not sufficiently considering the impact of these subnational policies and practices. Issues ranging from local land use ordinances to the Regional Greenhouse Gas Initiative (RGGI) will undoubtedly influence the kinds of action taken on the ground. He also noted that incidence analysis is important, and that his model finds, for example, that the way in which allowance values are recycled largely determines who bears the burden of the program. In other 12

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words, few existing models can incorporate subnational policies or analyze the incidence of different cap- and-trade program designs, but both are important for understanding federal options. Identifying and Quantifying Uncertainty Sensitivity analyses are useful to identify key areas of uncertainty within the modeling community. They are especially important for the largest models, to examine critical issues in some isolation, such as the Env-Linkages model did recently to look at carbon leakages. Janet Peace of the Pew Center on Global Climate Change did note that sensitivities tend to be conservative and do not often consider efficiency improvements or technology development occurring more rapidly than one would ordinarily expect. National models like NEMS require more sensitivity to international experiments. A more comprehensive analysis, the Monte Carlo simulation, is difficult for the large and complex models, even with low-cost computer time. John Reilly noted that MIT recently completed a Monte Carlo analysis of the EPPA model (Webster et al., forthcoming). IGEM is experimenting with the Delta method to exploit the standard errors in parameters and the standard error in, for example, the estimated energy input demand function, to try and create confidence intervals around the outcomes it produces—an approach that might be applicable to other large-scale models. Representing Behavior Building on discussions from the previous panel, several workshop participants remarked on the importance of behavior and how it is represented in the various models. Marilyn Brown pointed out that as social preferences evolve, there may be generational differences that are not currently captured by modeling, such as a preference for walkable (i.e., not car-dependent) communities, or a preference for locally produced foods. Tom Kram noted that the Netherlands Environmental Assessment Agency has embarked on a study to examine the relevance of dietary preferences, specifically moving from an animal-based diet to a more vegetarian-based diet. Many participants remarked that absence of a capability for modeling evolving preferences is currently a major limitation of most available analytical tools. Richard Goettle wondered whether the functional forms in the models actually represent the right way to look at the world. Models focus on things like maximizing utility, but that may not reflect real- world household decisionmaking. John Conti noted that NEMS tries to incorporate behavior in its estimates, by going beyond least- cost modules. Dallas Burtraw stated that the demand side of the electricity equation is where the Haiku model faces its biggest challenges now. One area is the role for efficiency, and attempts have been made to address this by looking for opportunities on a broad national scale, and combining those with incentive- based programs to promote energy efficiency. He emphasized that consumers consume electricity services, not kilowatt-hours, so how does incentive-driven behavior affect outcomes? Resources for the Future is now trying to model the effects of time-of-day pricing in the electricity sector. Peter Evans pointed out that international relations research has suggested that as countries interact, they may not act “rationally” in the economic sense by pursuing absolute gains, and instead might be concerned with relative gains. Several other participants noted that analyses are only beginning to take this in to account. On a similar point, Tom Kram explained that regional differences among countries in terms of preferences can be significant, but these are not captured in global models. Systems Interactions One important insight that IAMs provide in particular is human-Earth systems interactions. While this linkage makes detailed regional or inter-temporal results difficult, it does offer several advantages, 13

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particularly for examining international impacts. For example, if countries delay or stagger mitigation measures, this has important ramifications for global crop production. EPPA is also modeling interactions among land-use change, terrestrial carbon emissions, and related factors, which is critical in analyzing interactions between mitigation and adaptation: EPPA and other IAMs reflect not only economic impacts, but also longer-term impacts on natural systems, which in turn help estimate potential damage, or impacts avoided (Figure 3). Dallas Burtraw pointed out that there is a need for a better understanding and incorporation of temperature changes in models’ baseline scenarios, to analyze the effects on changes in electricity demand. Beyond the interactions of physical or natural systems, Peter Evans remarked that there is a need for more information on and understanding of the relationships between governments and markets. Evans discussed what he called “green industrial policy” through which governments are beginning to intervene in the market, in the name of climate change, and this will have important ramifications going forward. MIT EPPA, 16 Region, multi-sector CGE model GHG and Other Pollutants Land use shares for crops, from energy and agriculture/land use livestock, bioenergy, forestry GTAP land data/ Coupled Ocean, Spatial disaggregation Atmosphere algorithm Crop, pasture, CH4, N2O, Net CO2 Biogeophysical Land bioenergy, forest from land use Processes productivity Spatial data (.5º x .5º) for CO2, Tropospheric Ozone, land use Temperature, Precipitation, Nitrogen deposition Solar Radiation DYNAMIC TERRESTRIAL ECOSYSTEMS MODEL (TEM) FIGURE 3. Interaction of mitigation and adaptation through land/biofuels. SOURCE: John Reilly, Massachusetts Institute of Technology, presentation given at the Workshop on Assessing Economic Impacts of Greenhouse Gas Mitigation, National Academies, Washington, D.C., October 2-3, 2008. 14