H

Modeling

H.1 MODELING THE TRANSITION TO ALTERNATIVE FUELS AND VEHICLES USING VISION

H.1.1 The VISION Model

The VISION model was developed by Argonne National Laboratory as a means of extending the transportation sector component of the Energy Information Adminstration’s (EIA’s) National Energy Modeling System (NEMS) model to longer-term projections of U.S. energy use and greenhouse gas (GHG) emissions. The model is available to the public as a downloadable Excel file and is updated each year to incorporate recent results from NEMS and the EIA Annual Energy Outlook (AEO) report.1 VISION calculates energy use and greenhouse gas emissions for light, medium, and heavy-duty vehicles using simple algebraic energy balance equations and input assumptions about vehicle fleet mix, efficiency of vehicles, fuel characteristics, and vehicle miles traveled (VMT) out to the year 2050 and beyond. Although the calculations are conceptually simple, the model is complicated in that it incorporates a wide range of data and conversion factors to explicitly track multiple vehicle vintages, fuel types, and other trends on an annual basis. Singh et al. (2003) and Ward (2008) provide documentation and a user’s guide for the VISION model.

VISION does not include any market feedback effects over time within the model or between the transportation sector and other sectors of the economy.2 Fuel and vehicle prices are exogenous to the model and must be specified by the user. Any responses to changes in those prices would also have to be specified by the user. So, if, for example, deployment of more efficient vehicles in the VISION model reduces demand for petroleum fuels, there is no feedback to the global petroleum market and subsequent changes to gasoline and diesel fuel prices. Default values in VISION are calibrated to transportation sector results from the NEMS model, which does account for interactions between global and domestic energy markets. What VISION can assess are the effects on energy use and GHGs when there are different shares of vehicle types and fuel types over time. Vehicle shares, efficiencies, fuel volume constraints, and fuel intensities are the major inputs to the model. VISION uses the GREET 1-2011 model for assumptions about the GHG emissions rates of different fuels,3 but the analysis in this study relies on the judgment of the committee for GHG intensity rates.

VISION was used to explore the range of possible vehicle and fuel combinations that could attain the goals of this study and their associated costs. The committee modified VISION in a number of ways to add capability for the purposes of this study. The revised VISION model, referred to here as the VISION-NRC model, includes the most up-to-date assumptions from the committee about vehicle efficiencies, fuel availability, and GHG emissions of specific fuels. The sections below review the scenarios developed for the committee using VISION-National Research Council (NRC) (Section H.1.2)

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1 See http://www.transportation.anl.gov/modeling_simulation/VISION/.

2 VISION does include a demand elasticity function to adjust VMT in response to fuel price change assumption; however, this function was not used in the present study.

3 Features of GREET1_2011 are listed at http://greet.es.anl.gov/.



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H Modeling H.1 MODELING THE TRANSITION TO ALTERNATIVE FUELS AND VEHICLES USING VISION H.1.1 The VISION Model The VISION model was developed by Argonne National Laboratory as a means of extending the transportation sector component of the Energy Information Adminstration’s (EIA’s) National Energy Modeling System (NEMS) model to longer-term projections of U.S. energy use and greenhouse gas (GHG) emissions. The model is available to the public as a downloadable Excel file and is updated each year to incorporate recent results from NEMS and the EIA Annual Energy Outlook (AEO) report. 1 VISION calculates energy use and greenhouse gas emissions for light, medium, and heavy-duty vehicles using simple algebraic energy balance equations and input assumptions about vehicle fleet mix, efficiency of vehicles, fuel characteristics, and vehicle miles traveled (VMT) out to the year 2050 and beyond. Although the calculations are conceptually simple, the model is complicated in that it incorporates a wide range of data and conversion factors to explicitly track multiple vehicle vintages, fuel types, and other trends on an annual basis. Singh et al. (2003) and Ward (2008) provide documentation and a user’s guide for the VISION model. VISION does not include any market feedback effects over time within the model or between the transportation sector and other sectors of the economy. 2 Fuel and vehicle prices are exogenous to the model and must be specified by the user. Any responses to changes in those prices would also have to be specified by the user. So, if, for example, deployment of more efficient vehicles in the VISION model reduces demand for petroleum fuels, there is no feedback to the global petroleum market and subsequent changes to gasoline and diesel fuel prices. Default values in VISION are calibrated to transportation sector results from the NEMS model, which does account for interactions between global and domestic energy markets. What VISION can assess are the effects on energy use and GHGs when there are different shares of vehicle types and fuel types over time. Vehicle shares, efficiencies, fuel volume constraints, and fuel intensities are the major inputs to the model. VISION uses the GREET 1-2011 model for assumptions about the GHG emissions rates of different fuels, 3 but the analysis in this study relies on the judgment of the committee for GHG intensity rates. VISION was used to explore the range of possible vehicle and fuel combinations that could attain the goals of this study and their associated costs. The committee modified VISION in a number of ways to add capability for the purposes of this study. The revised VISION model, referred to here as the VISION-NRC model, includes the most up-to-date assumptions from the committee about vehicle efficiencies, fuel availability, and GHG emissions of specific fuels. The sections below review the scenarios developed for the committee using VISION-National Research Council (NRC) (Section H.1.2) 1 See http://www.transportation.anl.gov/modeling_simulation/VISION/. 2 VISION does include a demand elasticity function to adjust VMT in response to fuel price change assumption; however, this function was not used in the present study. 3 Features of GREET1_2011 are listed at http://greet.es.anl.gov/. 331

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and major modifications made to the original VISION model in developing VISION-NRC (Section H.1.3). For more information on the VISION model and to download the model itself, see the attached Appendix H VISION Model Spreadsheet. H.1.2 VISION-NRC Scenarios To explore possible paths to attain the goals, VISION-NRC was run for a range of cases. The predominant characteristic of these runs was to focus on a market dominated by a particular vehicle type and alternative fuel (i.e., battery electric vehicles (BEVs), fuel-cell vehicles). To assess the range of possibilities, the committee looked both at runs that used the midrange vehicle efficiencies for these advanced vehicles as well as runs that used the optimistic efficiencies to represent technological breakthroughs, as described in Chapter 2 and summarized in Table 2.11. From the fuels side, the committee considered both business-as-usual (BAU) production of a fuel (gasoline, hydrogen, or electricity) as well as a low-GHG fuel supply technologies, as described in Chapter 3 (low-net-GHG biofuels, H2 generation with carbon capture and storage (CCS), or a low-GHG electric grid). Some of the key assumptions throughout all of the runs are listed below. • There are two “reference cases” in the committee’s analysis. There is the BAU Case, which is basically the AEO 2011 assumptions, and then there is the Committee Reference Case, which includes, instead of the AEO assumptions, all of the committee assumptions about vehicle efficiencies, fuel carbon intensity, and effects in the future of existing regulations (see below). • All runs of the model, except the AEO BAU Case, use the committee’s assumptions on vehicle efficiencies, GHG impact of the fuels supplied, and availability of resources. Committee estimates of vehicle fuel efficiencies can be found in Table 2.12 of Chapter 2. • Total new vehicle sales each year are drawn from the AEO 2011 Reference Case and do not change with the different runs, only the mix of vehicles changes; VMT per vehicle is from AEO Reference Case forecast and falls over time as vehicles age; total VMT of the fleet is the same for each run and is consistent with the AEO 2011 assumptions about total VMT over time (see Table H.1). • Oil prices are taken from AEO 2011 and are expected to gradually increase to $125/barrel by 2035, resulting in a pre-tax gasoline price of $3.16 in that year. Gasoline prices are then extrapolated out to 2050, assuming the compound rate of growth modeled in AEO 2011 from 2030-2035. The current gasoline tax of $0.42/gallon is assumed to hold true out to 2050. • VMT per year for battery electric vehicles (BEVs) are assumed to be two-thirds that of other vehicles, due to battery range limitations. • The shares of new vehicles sales by type of vehicle (hybrid electric vehicle [HEV], plug-in hybrid electric vehicle [PHEV 4], fuel cell electric vehicle [FCEV], etc.) are from AEO Reference Case for our BAU run; for the committee scenarios, shares are assumed to change as specified in Table H.1. In the scenarios where alternative vehicles are assumed to enter the fleet in large numbers, it is assumed that new vehicle shares never increase by more than 5 percentage points of the new vehicle stock in any given year. • Only one PHEV, a PHEV-30, with a real world all-electric driving range of 25 miles—this yields a utility factor of 46 percent is included. • GHGs from biofuels include both direct emissions from production and also emissions from indirect effects on land use (see Chapter 3). 4 BEVs and PHEVs are collectively known as plug-in vehicles (PEVs). 332

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TABLE H.1 Assumptions Taken from AEO 2011; These Hold for All VISION Cases 2005 2030 2050 Total LDV sales, 1000s/year 16,766 18,502 22,219 Stock of LDVs, millions 234.6 282.2 365.2 Share of cars, percent of total fleet Total VMT, trillion VMT 2.69 3.76 5.05 a Average VMT, VMT/LDV 11,455 13,316 13,822 a Average VMT is assumed to two-thirds of this for BEVs. A detailed overview of the different VISION cases is provided below, with Table H.2 summarizing the differences. For more information on fuel efficiency assumptions of vehicles, see Table 2.12. For more information on the carbon rates of different fuels, see Table 3.4 in Chapter 3. • AEO BAU Case. Uses AEO 2011 Reference Case assumptions on VMT, vehicle shares, vehicle efficiencies, fuels shares, and fuel GHG impacts. AEO forecast only is made to 2035. VMT was extrapolated to 2050 assuming a 1.5 percent growth rate from 2036 to 2050. Corporate Average Fuel Economy (CAFE) standards are only assumed to be specified through the 2016 model year, but not beyond. This case assumes a small amount of coal to liquid (CTL) fuel and gas to liquid (GTL) fuel is introduced by 2035. • Committee Reference Case. The Committee defines its own reference case that includes all of the midrange assumptions about vehicle efficiencies, fuel availability, and GHG impact developed by the committee (summarized in Chapters 2 and 3). In addition, this case assumes that the recently finalized 2025 CAFE and GHG standards for fuel efficiency of light-duty vehicles (LDVs) will be met, and the standards will then stay at that level through 2050. The standards are interpreted to require that new vehicles in 2025 must have on-road fuel economy averaging about 41 mpg (given a fleetwide CAFE rating of 49.6 mpg). New vehicle sales shares are adjusted to meet this standard—primarily, advanced internal combustion engine vehicle (ICEV) and HEV shares are increased. After 2025, there is a very small annual improvement in average fuel consumption (~0.3 percent), which is consistent with the AEO2011 projection. This case also assumes that the federal Renewable Fuels Standard (RFS2) will be met by 2030. As a result, corn ethanol sales rise to about 10 billion gallon of gasoline equivalent (gge) per year by 2015 and stay at that level through the period. And, based on the analysis in Chapter 3, it is assumed that all cellulosic biofuels will be thermo-chemically derived drop-in fuels. The RFS2 requirements result in production of 14 billion gge per year of such biofuels by 2030, and it is assumed that they remain roughly constant after that time. • Emphasis on ICE Vehicle Efficiency. A set of model runs that continue the focus on light duty fuel efficiency improvements through the period to 2050. Shares of advanced ICEVs and HEVs increase to just over 80 percent of new vehicles by 2050. Two runs are included that differ only in their assumptions about the fuel efficiency improvements of vehicles over time. The first assumes the midrange assumptions for fuel efficiency for all technologies (Chapter 2, Table 2.12), and the second assumes optimistic fuel efficiency for ICEs and HEVs, while maintaining midrange values for the small numbers of other types of vehicles in the fleet. It is assumed that the RFS2 requirements described above (under the Committee Reference Case) are still in place, bringing in some corn ethanol and cellulosic biofuels. These increased vehicle efficiency cases require much less liquid fuel over time, and it is assumed that the fuel backed out is gasoline. • Emphasis on ICE Vehicle Efficiency and Biofuels. Two runs are similar to the Committee Reference Case and the emphasis on efficiency case, with the difference that more biofuels are brought into the market after 2030. The amount of biofuel brought to the market rises to the limit specified by the 333

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committee in Chapter 3, which is 45 billion gge/year and assumes 703 million dry tons per year of cellulosic feedstock. The two runs of the model both assume this additional biofuel, largely in the form of drop-in gasoline components that displace petroleum, and the difference in the two runs is just the assumption on the fuel efficiency of vehicles. As in the case above, the first run assumes all vehicles are at the midrange efficiency. In this run, the share of petroleum-based gasoline as a liquid fuel falls to about 25 percent by 2050. The second run assumes optimistic fuel efficiency for ICEVs and HEVs. In this case, bio-based ethanol, bio-based gasoline, and a small amount of CTL and GTL, make up all liquid fuel, with almost no petroleum-based gasoline. • Emphasis on fuel cell vehicles. This case also has four different runs of VISION to capture variation in both vehicle efficiency and fuel carbon content. In all of these runs, the share of fuel cell vehicles (FCVs) increases to about 25 percent of new car sales by 2030 and then to 80 percent by 2050, modeled on the maximum practical deployment scenario from Transition to Alternative Transportation Technologies: A Focus on Hydrogen (NRC, 2008). There are two runs with the midrange vehicle fuel efficiencies, each with a different assumption about the GHG impact of the hydrogen production. Finally, there are two additional runs with optimistic assumptions about the fuel efficiency of FCVs, each with the different assumptions for the GHG emissions from hydrogen production. The hydrogen produced from a mix of low-GHG-emitting sources is assumed to come from production facilities, because they might operate under a sufficiently high carbon price. The CO2 emissions are about one-fifth of those from the alternative, low-cost hydrogen fuel generation (2.6 g CO2e/gge H2 compared to 12.2 g CO2e/gge H2; see Table 3.15). • Emphasis on electric vehicles. There are four VISION runs for this case that account for differences in assumptions about vehicle efficiency as well as the GHG emissions of the fuel. It is assumed in all runs that the share of BEVs and PHEVs increases to about 35 percent of new car sales by 2030 and 80 percent of new car sales by 2050, in line with the rates put forth in Transitions to Alternative Transportation Technologies: Plug-In Hybrid Electric Vehicles (NRC, 2010), and this case assumes relatively greater sales of PHEVs than BEVs in all years. The first two runs assume midrange vehicle efficiency, each with a different assumption about GHG emissions from the electricity grid. These forecasts for the make-up of the grid are derived from the two cases put forth in AEO 2011 (EIA, 2011). The first is the BAU Case, and the second is the GHG price economy-wide case, where a low-GHG emissions grid is achieved by a tax on carbon that is first assessed in 2013 and increases at 5 percent per year (further details of the two grid scenarios can be found in Chapter 3). The second set of runs both use the optimistic assumptions about vehicle efficiency for the BEVs and PHEVs, again, with the two differing only in their assumptions about the GHG emissions from the grid. The low-GHG emissions grid is assumed to emit 111 g CO2 per kWh of generated power by 2050, reduced to just 21 percent of the BAU grid (541 gCO2e/kWh; see Table 3.8 and discussion). • Emphasis on natural gas vehicles. This case has a set of runs that assumes an increasing penetration of compressed natural gas (CNG) vehicles into the market. The new car sales of CNG vehicles are assumed to be 25 percent by 2030 and 80 percent by 2050, as in the case for HFCVs due to a comparable level of current technological deployment. In the first run, the committee assumed that all vehicles attain the midrange efficiencies. The second run assumes optimistic fuel efficiency for CNG vehicles and midrange for the other vehicles in the fleet. CNG fuels rise over time to fuel the vehicles, and very little liquid fuel is needed by 2050. The committee continued to assume that RFS2 must be met by 2030, so the liquid fuel that is used is primarily biofuels in both of these runs. So little liquid fuels are needed in these runs that the committee assumed no CTL and GTL comes into the market—the plants are never built. CO2 levels are about 82 percent of conventional gasoline, on an energy basis (gCO2e/MJ, see Chapter 3). 334

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TABLE H.2 VISION Run Assumptions Cases Vehicle Efficiencies Fuel Assumptions Shares of New Vehicles AEO BAU AEO assumptions AEO 2011 AEO assumptions Committee Reference Case Midrange Committee assumptions, Small increase in HEVs TCC biofuel available 13 above AEO in order to bgge/year by 2030 meet CAFE Emphasis on ICE Vehicle 1. Midrange all vehicles 1. Reference 90% HEV share by 2050 Efficiency 2. Optimistic for ICEs, 2. Emphasis on HEVs, midrange others biofuels, thermochemical conversion increases to 45 bgge/year by 2050 Emphasis on Fuel 1. Midrange all vehicles 1. Low cost hydrogen 25% HFCVs in 2030 Cells/Hydrogen 2. Optimistic for FCVs 2. Low-CO2 hydrogen 80% HFCVs in 2050 Emphasis on Electric 1. Midrange all 1. AEO 2011 grid 35% PEVs in 2030 Vehicles 2. Optimistic PHEV, 2. Low-CO2 grid 80% PEVs in 2050 BEV Emphasis on Natural Gas 1. Midrange Committee assumptions 25% CNGVs in 2030 ICEVs 2. Optimistic 80% CNGVs in 2050 H.1.3 Major Changes to the Original Vision Model to Develop VISION-NRC The VISION-NRC model was developed from the “VISION_2010_AEO_Base_Case” version of the VISION model, which includes EIA’s AEO 2010 projections to 2035 and GHG and upstream energy use rates from GREET 1.8d.1. The sections below review the major modifications made to this original Excel model to develop the VISION-NRC model. H.1.3.1 Changes to the Model Input Sheet The Model Input worksheet has been modified to store multiple scenario assumptions. Sets of inputs can be changed for each scenario by changing the value of the “CS” named variable, located in cell B5. Alternates of each scenario can be chosen by changing the values in cells I9:I12. The actual input values for each scenario are provided in the columns to the right of the main input columns, columns A through N. This is also where the scenario values themselves can be modified, though changes in one parameter can influence the implications of other parameters. For example, if fuel economy or VMT assumptions are changed, the fuel split parameters, expressed in percentages of total fuel (such as percent of ethanol as corn ethanol), would need to be modified to maintain the same absolute volume of a particular fuel type. H.1.3.2 Updates to AEO 2011 Data Key model inputs were updated to the revised data used in the VISION-2011 AEO BAU Case model. These are indicated in the Auto-LTs worksheet and include the following: annual auto and light truck sales, LDV stock values, and baseline new vehicle miles per gallon gasoline equivalent values. 335

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TABLE H.3 Data for Baseline GHG Emissions to Which 2050 Levels Are Compared AEO 2007 2005 Metrics Units 2005, All LDVs Source Energy Use (HHV) trillion Btu 16,227 AEO 2007, Table 35, Transportation Sector Energy Use by Mode bgge (LHV) 139.89 Total Energy use converted to gallon gasoline equivalents Vehicle Miles Traveled million miles 2,687,058 AEO 2007, Table 50, LDV Miles Traveled by Tech. Type Average mpg mpgge 19.21 Calculated as total VMT / Total Energy Average FCI gCO2e/MJ 94.73 Calculated from fuel energy and FCI values below Greenhouse gas emissions MMTCO2e 1,514.23 Calculated as Total Energy × Average FCI Energy Use by Fuel Type Motor Gasoline bgge 123.76 AEO 2007, Table 36, Transportation Sector Energy Use by Mode Ethanol bgge 4.77 Includes 4.757 BGGEs, and subtracted from above Compressed Natural Gas bgge 0.06 Same as above Liquefied Petroleum Gases bgge 0.04 Same as above Electricity bgge 0.01 Same as above Distillate Fuel Oil (diesel) bgge 1.99 Same as above Total bgge 130.61 Same as above Fuel Carbon Intensity (FCI, LHV) Motor Gasoline gCO2e/MJ 91.27 NRC Fuels Committee (2010 FCI Value) Ethanol gCO2e/MJ 44.63 NRC Fuels Committee (2010 FCI Value) Compressed Natural Gas gCO2e/MJ 74.88 NRC Fuels Committee (2010 FCI Value) Liquefied Petroleum Gases gCO2e/MJ 79.48 GREET value from VISION model Electricity gCO2e/MJ 165.25 NRC Fuels Committee (2010 FCI Value) Distillate Fuel Oil (diesel) gCO2e/MJ 90.04 NRC Fuels Committee (2010 FCI Value) Average FCI gCO2e/MJ 94.73 Calculated as fuel energy-weighted average Greenhouse Gas Emissions Motor Gasoline MMTCO2e 1,382.41 Ethanol MMTCO2e 26.03 Compressed Natural Gas MMTCO2e 0.51 Liquefied Petroleum Gases MMTCO2e 0.40 Electricity MMTCO2e 0.11 Distillate Fuel Oil (diesel) MMTCO2e 21.89 iLUC from ethanol production MMTCO2e 82.88 Total MMTCO2e 1,514.23 Conversion Factors Btu/gal gasoline (HHV) 124,238 AER 2010, Table A3 p367, 2005 value Btu/gal gasoline (LHV) 116,000 NRC Fuels Committee Btu/MJ 947.8 H.1.3.3 The New “NRC Results” Sheet Key output values and graphs are located in a new tab, “NRC Results,” and the values for most of these graphs are contained in the columns to the right of the graphs themselves. H.1.3.4 Calibrating the 2005 GHG Baseline Table H.3 summarizes data used to determine the baseline GHG emissions in 2005. H.1.3.5 Changes to the LDV Stock Sheets The vehicle stock sheets for each vehicle type have been modified to incorporate various scenario assumptions. For example, VMT for BEVs can be adjusted downwards and redistributed to other vehicle types in the revised stock sheets (see explanation below). In addition, new correction factors have been incorporated and fuel carbon intensity values have been linked directed to the stock sheets in a new column. 336

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H.1.3.6 Changes to the Carbon Coefficients Sheet The Carbon Coefficients worksheet has been modified to incorporate the unique fuel carbon intensity values used in the scenarios. Calculations to capture the accounting used for indirect land use change (iLUC) emissions are also included in this worksheet. H.1.3.7 Calculation of iLUC GHG Emissions as a Result of Increased Biofuels Production The additional GHG emissions associated with expanding biofuels production, due to iLUC, is calculated as a function of new production capacity established in any given year: GHGiLUC = Qnew × F fuel Where new production capacity, Qnew, has units of bgge/year, and the emissions factor for a particular fuel, Ffuel, has units of MMTCe /(bgge/year). For corn ethanol, FCornEthanol = 29.9 MMTCe/(bgge/year), and for thermochemical biofuels, FThermochem = 15.3 MMTCe/(bgge/year). These values are determined from committee data in the Carbon Coefficients worksheet, and then added to the total GHG emissions in the NRC Results worksheet. In years where no new production capacity is installed, no additional iLUC GHGs are emitted. H.1.3.8 Redistribution of BEV VMT to Remainder of LDV Fleet It is estimated that 33 percent of the BEV VMT that would have been driven are redistributed to all other LDV cars or light trucks, using Equation H.1. N i ,n 1  VMTi ,n = VMTi ,on +  VMTi = BEV ,n  (H.1) N Total Cars or L.Trucks ,n  3  Where i = vehicle type (ICE, PHEV, etc.) and n = year. The equation applies for cars and light trucks separately. In other words, for i = ICE, the ratio of ICE cars in year n (NICE,n) to total cars (Ncars) would be multiplied to one-third of VMT from BEV cars in year n (VMTi = BEV,n). This equation can be interpreted as an equal distribution of all “displaced” BEV car or light truck VMT (from any vintage) across all cars or all light trucks (of any vintage). Note that in VISION fuel use is determined by multiplying total VMT for any platform type (e.g., BEV cars) to the VMT-weighted fuel economy of all vehicles (of all vintages, which have distinct VMT/year) on the road. In the calculation, it is just the total VMT that increases proportional to the percent of cars or light trucks on the road. Another way of calculating this redistribution might be to allocate proportional to the VMT of any platform type divided by all VMT by cars or light trucks. With scenarios that have newer vehicles being much higher fuel economy than older vehicles, this approach would result in lower fuel demand than distributing by the percent of total on-road vehicles. However, this approach implies BEV VMT would tend to be preferentially transferred to newer vehicles (which have higher VMT/year) compared to older vehicles (with lower VMT/year, as older vehicles are driven less). This allocation seems less realistic, considering that households purchasing a new BEV would probably not also have a new LDV of another type. Conceivably, an algorithm could be developed to determine the degree to which VMT would tend to be transferred to vehicles of a different vintage than the on-road fleet average vintage. In theory, for example, a household purchasing a BEV may not necessarily have a second or third vehicle with a 337

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vintage equal to the fleet average. It may be more wealthy households with second vehicles slightly newer than the fleet average. Given that BEVs will be introduced into the LDV fleet gradually over time, and that newer more efficient vehicles would mostly likely also be achieving greater market share over the same period, the effort of differentiating VMT distribution more realistically by vintage would likely result in a small change in fuel use compared to the vehicle share allocation described above. H.2 LIGHT-DUTY ALTERNATIVE VEHICLE ENERGY TRANSITIONS MODEL: WORKING DOCUMENTATION AND USER’S GUIDE The Light-Duty Alternative Vehicle Energy Transitions (LAVE-Trans) Model described in this section was developed by David L. Greene, Oak Ridge National Laboratory and University of Tennessee; Changzheng Liu, Oak Ridge National Laboratory; and Sangsoo Park, University of Tennessee. The committee agreed by consensus to use this model for its analysis. See the attached Appendix H LAVE- Trans Model Spreadsheet. H.2.1 Purpose The transition from a motor vehicle transportation system based on ICEs powered by fossil petroleum to low-GHG-emission vehicles poses an extraordinary problem for public policy. The chief benefits sought are public goods: environmental protection, energy security, and sustainability. As a consequence, market forces alone cannot be relied on to drive the transition. Securing these benefits may require replacing a conventional vehicle technology that has been “locked-in” by a century of innovation and adaptation with an enormous infrastructure of physical and human capital. The time constants for transforming the energy basis of vehicular transport are reckoned in decades rather than years. A comprehensive, rigorous, and durable policy framework is needed to guide the transition. The LAVE-Trans model was developed to quantify the private and public benefits and costs of transitions to electric drive vehicles under a variety of future scenarios, making use of the best available information in a rigorous mathematical framework. At present, knowledge of the key factors affecting LDV energy transitions is incomplete. As a consequence, the model’s outputs should not be considered accurate predictions of how the market will evolve. Rather, the LAVE-Trans model provides a framework for integrating available knowledge with plausible assumptions and analyzing the implications for benefits, costs and public policies. The transition to electric drive vehicles faces the following six major economic barriers that help to lock in petroleum-powered vehicles: 1. Technological limitations, 2. The need to accomplish learning by doing, 3. The need to achieve scale economies, 4. Consumers’ aversion to the risk of novel products, 5. Lack of diversity of choice, and 6. Lack of an energy supply infrastructure. Each of the six barriers can be viewed either as a transition cost or as a positive external benefit created by adoption of the novel technology. Modern economics recognizes “network externalities,” positive external benefits that one user of a commodity can produce for another. Each of these barriers has been incorporated in the model so that the costs of overcoming them, and alternatively the external benefits of policies that break them down, can be measured, subject to the limits of current knowledge. 338

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Policies Parameters Innovators Technical Attributes Baseline & Preferences & Majority & Prices of Vehicles Projections Vehicle Choice Vehicle Utility & Calibration Choice Model Vehicle Sales Energy Infrastructure Vehicle Stock & Costs Results Summary Vehicle sales, stock & use GHG emissions Vehicle Use Energy use Petroleum use Energy Use PHEV Energy Use Cost/Benefit Comparisons Net Present Value GHG Emissions All feedback loops are recursive rather than simultaneous and are indicated by a dashed red line --- FIGURE H.1 Diagrammatic representation of the LAVE-Trans Model. This report provides an overview of the LAVE-Trans model structure, explains how it functions, and provides instructions for operating it. Section H.2.2 provides an overview of the model structure and the components and how they are linked together. Section H.2.3 describes each component, including the key equations that control its operation. Section H.2.4 describes the inputs (parameters and data) that must be supplied to the model, and Section H.2.5 describes model outputs. Section H.2.6 is a brief users’ guide to executing a model run. H.2.2 Model Structure The LAVE-Trans model is an Excel spreadsheet model comprised of 25 worksheets. Figure H.1 illustrates the relationships between the major components of the model. The areas where exogenous inputs enter the model are shown as blue boxes. A relatively large amount of exogenous information is required to carry out a model run. Baseline projections of vehicle sales and energy prices are required to 2050. Technical attributes of advanced technology vehicles, including fuel consumption per kilometer, on-board energy storage, and retail price equivalent (RPE) at full scale and learning, must be specified for current and certain future years. Parameters that describe consumers’ willingness to pay for vehicles and 339

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their attributes must also be provided. The model translates these into coefficients for the vehicle choice model. Capital, operating, and input costs of both electric, hydrogen, and natural gas infrastructure or alternative price projections must also be provided. The model can be automatically calibrated to specified vehicle sales and vehicle use projections. At that point the Vehicle Choice model estimates the shares of ICE, HEV, PHEV, BEV, and FCV or CNG technologies for passenger cars and light trucks and for Innovator and Majority market segments. The market shares are multiplied by the passenger car and light truck sales totals in the Vehicle Sales worksheet. Sales are passed to the Vehicle Stock worksheet, which retires vehicles as they age and keeps track of the number of vehicles of each technology type by model year for every forecast year. Vehicle kilometers by age and vehicle type are calculated in the Vehicle Use worksheet. In the Energy Use worksheet, energy use is calculated for all but PHEVs by multiplying vehicle kilometers by the number of vehicles and by on-road energy consumption per kilometer. PHEV energy use, electricity and gasoline, is calculated in a separate worksheet. GHG emissions factors are applied to energy use in the GHG Emissions worksheet. In a BAU run, the total passenger car and total light truck sales will exactly match the input projections. The technology and price assumptions of the BAU Case should match the baseline projection to which the model’s vehicle sales and vehicle use have been calibrated. Next, a Base Case, reflecting alternative technology and price assumptions can be run. In the Base Case, vehicle sales, vehicle use, energy use, and GHG emissions will change due to the new technology and price assumptions. Once a Base Case run has been made, it is transferred to the Base Case worksheet by clicking on a button in the Current Case worksheet. A policy run may then be created by specifying vehicle or fuel subsidies or taxes, exogenous investments in fuel infrastructure, or by changing assumptions about vehicle or fuel technologies. In a policy run, sales may be higher or lower than the Base Case, depending on the specific policy assumptions. The results of a policy case are stored in a Current Case worksheet, which also contains built-in graphical displays. The impacts of the Current Case relative to the Base Case are calculated in the Costs-Benefits worksheet. A standard set of tables and graphs summarizing the BAU Case, Base Case, and Current Case are stored in an Output worksheet. There are several important feedback loops in the model. Feedbacks are recursive (with a 1-year lag) rather than simultaneous. This simplifies the solution of the model greatly but is also generally more representative of how changes can be made in the motor vehicle industry. Cumulative vehicle sales generate learning-by-doing effects that lower vehicle prices over time. Sales are accumulated in the Vehicle Sales worksheet, and learning effects are calculated there, as well. Current sales affect the availability of different makes and models, i.e., the diversity of choice available for both advanced and conventional ICE technologies. A diversity of choice metric is passed to the Vehicle Attributes worksheet. Current sales also affect next year’s vehicle prices via scale effects, also computed in the Vehicle Sales worksheet. Both learning and scale effects are passed to the Vehicle Production worksheet, where RPEs are calculated for each technology in each future year. These adjusted prices are then passed to the Vehicle Attributes worksheet. Demand for electricity, hydrogen, and natural gas, plus exogenous assumptions about the supply of refueling/recharging infrastructure, are passed to the Fuel Input worksheet, where the quantities and costs of infrastructure are calculated. For hydrogen, these costs also depend on the model user’s assumptions about how hydrogen will be produced and delivered to vehicles in the future. These assumptions also affect the cost of hydrogen and its GHG emissions per kilogram. The availability of refueling/recharging infrastructure is passed to the Vehicle Attributes model and influences the choice among alternative technologies. The following is a list of the model’s 25 worksheets along with a brief description of their functions: a. Flow Chart—contains the diagram of the model structure shown in Figure H.1. b. Scenario Assumptions—contains alternative data sets describing vehicle and fuel technologies, as well as a table in which the different data sets can be conveniently selected to construct fuel and 340

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vehicle technology scenarios. Alternative social values for reducing petroleum use and GHG mitigation may also be selected. c. Parameter Input—contains most of the key assumptions of the model that a user will want to change in creating a new run. d. CO2 Cost—holds the alternative estimates of the social value of reducing carbon emissions from the U.S. government’s interagency assessment of the social costs of carbon (Interagency Working Group, 2010). e. Hydrogen Stations—contains the multinomial logit model used to estimate a smooth transition from a user-specified initial distribution of types of hydrogen stations to a user-specified long- run configuration as a function of the total volume of hydrogen production for LDVs. f. VISION—used storing output from the Argonne National Laboratory’s VISION model. The LAVE-Trans model can be forced to match the market shares of a VISION run. In that mode, it calculates the costs and benefits of achieving the particular VISION scenario. g. Vehicle Attributes—contains the key vehicle attributes, by year, from 2010 to 2050. Most are derived from data contained in the Parameter Input worksheet. h. Risk Groups—contains assumptions and calculations about innovators and majority adopters. At present these are the only two classes of consumers. i. Choice Parameters—where the coefficients of the nested multinomial logit (NMNL) model for predicting choices among technologies are calculated. j. Vehicle Production—where learning-by-doing, scale effects, and rates of exogenous technological progress are applied to the prices of technologies to estimate RPEs by year. k. Vehicle Choice—the above factors come together to estimate market shares for each technology for new vehicles, as well as household’s decisions to buy or not buy a new vehicle in a given year. Consumers’ surplus is calculated here as well. Also calculated here are the cost components (i.e., cost of lack of fuel availability, cost of lack of diversity of choice, etc.) that also comprise the positive network externalities generated during the transition. l. Vehicle Sales—the choices are applied to total vehicle sales (which will vary by time as the buy/no-buy decision changes each year) to produce estimates of sales by passenger cars and light trucks, by technology type and for innovators and majority. Also calculated in this worksheet are cumulative production, learning-by-doing, scale economies, and choice diversity. Next come a series of large worksheets that depend on vehicle stock turnover. m. Vehicle Stock—adds new vehicles to the existing fleet and scraps older vehicles by vintage. There are 10 tables (PC versus LT) × (five technologies). n. Vehicle Use—multiplies kilometers per vehicle by vehicle age by the number of vehicles in the vehicle stock to estimate vehicle kilometers traveled (VKT) by vehicle type, technology type and 25 vintages. A rebound effect is built in to represent the tendency of vehicle use to increase when fuel cost per kilometer declines. o. Energy Use—uses the vehicle efficiency estimates by vintage together with an on-road adjustment factor to estimate energy use by the same 250 categories for all years 2010 to 2050. p. PHEV Energy—divides PHEV energy use into electricity and gasoline. q. GHG Emissions—applies fuel specific well-to-wheel GHG factors to estimate emissions in CO2 equivalents, again for all vehicle types, technology types, vintages, and years. r. Fuel Input—contains information about the capital, operating, and delivery costs of alternative LDV energy sources and their lifecycle GHG emissions. s. Input USA—where the projections of U.S. vehicle sales by vehicle type and technology type are stored. In addition to vehicle projections there are U.S. VKT projections, energy price projections, value of time projections (related to income per capita) and demographic projections (e.g., numbers of households). 341

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3. Hydrogen: R (low-cost hydrogen), CCS (low-carbon hydrogen with carbon sequestration) and L (very low carbon hydrogen), 4. CNG: At present only one scenario has been defined, and 5. Thermochemical biofuel: R (default assumption for biofuel price and carbon content), CCS (biofuel with carbon sequestration). One of the four social costs of carbon emissions produced by the Interagency Working Group (2010) may be chosen. At present there is only one social cost of petroleum projection; it begins at $25/bbl and declines to $20 per barrel by 2050. Next is a section dedicated to policy options. Various policies are considered in the model, including indexed highway user fee, existing alternative fuel tax credits, feebates that reflect social cost of oil dependence and GHG emissions, feebates reflecting additional fuel savings that are not considered in consumer purchase decisions, and carbon tax (i.e., the fuel tax reflecting social cost of oil dependence and GHG emissions). The user can choose the starting year for all these policies. All policies, except indexed highway user fee, are phased in over a period of 5 years. H.2.8.2 User Control The model has several control buttons which are associated with Excel Macros. 1. Controls in Scenario Assumption Sheet (rows 32-41): • Clear. The corresponding macro simply zeros out any subsidies (including vehicle, infrastructure and fuel subsidies) that may have been entered previously. • Clear and calibration. The corresponding macro first zeros out all subsidies and then calibrates the total LDV sales to match the AEO projection located in the Input USA worksheet. The current version of the model contains projections from the 2011 AEO Reference Case. The macro also saves results in Current Case worksheet to Base Case worksheet. • VKT calibration. The corresponding macro calibrates total VKT to match the AEO projection of total LDV travel. It then saves results in Current Case worksheet to BAU Case worksheet. 2. Controls in Current Case worksheet (rows 1-7): • Save Base Case. The corresponding macro copies all the results in Current Case worksheet to the Base Case worksheet. Later, when the user changes data and assumptions, the difference between Current Case and saved Base Case is reflected in Costs-Benefits worksheet. • Save BAU Case. The corresponding macro copies all the results in Current Case worksheet to BAU Case worksheet. Some of BAU Case results are used in Output worksheet. 3. Controls in Costs and Benefits worksheet (rows 1-5 and 165-174): • Update NPV. This button needs to be clicked in order to get correct net present value estimates when fuel taxes are being used. The corresponding Macro calculates consumer surplus loss due to fuel taxes (carbon tax and the indexed highway user fee). • Five “Match” buttons. These controls are used to match market shares from VISION model. For example, the Match PHEV&EV button works to match PHEV and EV shares from VISION; the Match ADV button works to match PHEV, EV, and FCEV shares from VISION. 368

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H.2.8.3 Step-by-Step Instructions 1. Scenario Definition Define a scenario according to the instructions in Section a. 2. BAU Case Calibration Skip this step if the case you are creating is not the BAU Case. The model must be calibrated if you are creating a new or revising an existing BAU Case. Go to the Parameter Input worksheet. Choose subsidy values for HEV, PHEV, and BEV cars in rows 276 to 278 to match historic sales. Go to the Scenario Assumption worksheet. First click on the Clear & Calibrate button and then the VKT Calibration button. These controls will calibrate BAU total LDV sales and VKT to match the 2011 AEO Reference Case. The model will generally not reach full convergence after calibrating sales and VKT only once. These two calibrations should be repeated 1 more time to insure that both BAU total sales and VKT equal AEO sales and VKT. After calibration, results in the Current Case, Base Case, and BAU Case worksheets are identical. Total net present value (NPV) in cell B46 of the Costs-Benefits worksheet should be zero. 3. Run a Non-BAU Case Click Clear button in Scenario Assumption worksheet to remove all subsidies (including vehicle, infrastructure and fuel subsidies). The summary results are presented in the Current Case worksheet. Summary tables and graphs are also presented in the Output worksheet, in a format similar to VISION model output. Clicking on the Save Base Case button copies summary model results in the Current Case worksheet to the Base Case worksheet. Check that the total NPV in cell B46 of the Costs- Benefits worksheet is zero. This means that both the Base Case and the Current Case are identical, and one is ready to specify a Policy Case. Changes to the model will now affect the Current Case but not the Base Case. The user may now change the value of subsidies for vehicles or fuels, or specify the provision of additional infrastructure for alternative fuel vehicles (relevant Excel ranges: Car Subsidies, Truck Subsidies, Fuel Subsidies, “H2_Station_Subsidies, NG_Station_Subsidies”). The effects of these policies will be calculated by the model and shown in the Current Case worksheet. Users may also want to check other worksheets (e.g., Vehicle Choice, Vehicle Sales, Vehicle Stock, Vehicle use, Energy use) for raw and intermediate results. The Costs-Benefits worksheet contains calculations reflecting the differences between the Current Case and Base Case results and also calculates GHG emissions and petroleum consumption reductions compared to BAU Case. 4. Match VISION Market Shares This capability makes use of the goal seek function of Excel. Users are required to input market shares to be matched, generally taken from VISION model outputs. The model calculates the vehicle subsidies needed to match the specified shares. Enter the shares to be matched in range D93:G97 (named range: Share_Target) of the Costs-Benefits worksheet. Click on the appropriate Match Case button (e.g., Match FCEV) in the Costs-Benefits worksheet to match FCEV shares. Vehicle subsidies are calculated and shown in C255:C265 (named range: Car Subsidies, Truck Subsidies) of the Parameter Input worksheet. This routine will estimate required subsidies only for the technology(ies) whose shares are being matched. It is possible to manually input subsidy values for other technologies (not being matched) and then run the Match routine to match VISION market shares for a specific technology. For example, users can specify the number of subsidized hydrogen refueling stations in the Parameter Input worksheet (named range: H2_Station_Subsidies) and then run Match FCEV routine. Another example is to tax ICE vehicles and subsidize FCEVs. Users can predefine a 369

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relationship between the tax and subsidy by entering equations in the relevant cells and then running the Match FCEV routine. 5. Solve an Optimization Model to Maximize Social Value A licensed and installed version of @Risk solver software is required to run this step. Users can define an optimization model using the Evolver module of the @risk software. One example is to maximize the net present value of a Policy Case (the value in cell B46 of the Costs-Benefits worksheet) by having the solver find optimal subsidies to PHEVs and BEVs. H.3 FUEL INVESTMENT COST SUMMARY—LAVE-TRANS MODEL Shown below are alternative fuel infrastructure investment costs for the LAVE-Trans model scenarios outlined in Chapter 5. The costs shown reflect only the investment costs that involve building a new form of infrastructure needed to use the fuel as a transportation fuel, not those for expanding an already large and functioning infrastructure associated with its more traditional use. They are based on costs summarized in Table 3.3 in Chapter 3 and described in greater detail in Appendix G. Biofuels investment costs are those necessary to expand the number of biofuel conversion facilities. Hydrogen investment costs represent the costs of the fueling infrastructure as well as production and distribution of the fuel. CNG costs include only the infrastructure necessary to deliver CNG to the vehicle. Electric charger investment costs include both public and private infrastructure. Table H.6 represents the annual investment costs in each scenario during the middle of the transition period. Table H.7 depicts the undiscounted sum of annual investment costs from 2010-2050 for each scenario. Table H.8 shows the cumulative fuel infrastructure investment costs in 2050 based on an annual discount rate of 2.3 percent. 370

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TABLE H.6 Annual Investment Costs (2009$) for 2030 Annual investment cost in 2030 ($ millions, undiscounted) CTL with Electric Scenario Hydrogen Biofuel CCS GTL CNG Chargers TOTAL BAU 0 0 2,017 0 0 0 2,017 Reference 0 7,626 175 133 0 10 7,945 Eff+FBSC 0 7,626 175 133 0 21 7,955 Eff+FBSC+IHUF 0 7,626 175 133 0 22 7,956 Eff+Bio+FBSC+IHUF 0 10,022 175 133 0 21 10,351 Eff+Bio w/CCS+FBSC+IHUF Investment costs unavailable for Biofuels w/CCS — Eff+Intensive Pricing+LCe 0 7,626 175 133 0 14,910 22,844 PEV+FBSC+IHUF+Trans+AEOe 0 7,626 175 133 0 4,367 12,302 PEV+FBSC+IHUF+Trans+LCe 0 7,626 175 133 0 4,465 12,400 PEV(later)+FBSC+IHUF+Trans+Lce 0 7,626 175 133 0 4,757 12,691 PEV+Bio+FBSC+IHUF+Trans+Lce 0 10,022 175 133 0 4,325 14,655 FCV+FBSC+IHUF+Trans+L$H2 11,094 7,626 175 133 0 13 19,042 FCV+FBSC+IHUF+Trans+H2CCS 11,931 7,626 175 133 0 12 19,878 FCV+FBSC+IHUF+Trans+LCH2 11,874 7,626 175 133 0 12 19,821 FCV+Bio+FBSC+IHUF+Trans+LCH2 11,218 10,022 175 133 0 12 21,560 CNGV+FBSC 0 7,626 175 133 0 21 7,955 CNGV+FBSC+IHUF+Trans 0 7,626 175 133 9,003 13 16,950 CNGV+Bio+FBSC+IHUF+Trans 0 7,626 175 133 8,660 12 19,002 Eff(Opt)+FBSC 0 7,626 175 133 0 14 7,948 Eff(Opt)+Bio+FBSC+IHUF 0 10,022 175 133 0 13 10,343 PEV(Opt)+FBSC 0 7,626 175 133 0 286 8,221 PEV(Opt)+FBSC+IHUF+Trans+Lce 0 7,626 175 133 0 5,933 13,867 FCV(Opt)+FBSC 0 7,626 175 133 0 21 7,955 FCV(Opt)+FBSC+IHUF+Trans+LCH2 17,959 7,626 175 133 0 5 25,898 PEV+FCV+FBSC+IHUF+Trans+LCe+LCH2 12,590 7,626 175 133 0 1,760 22,284 PEV+FCV+Bio+FBSC+IHUF+Trans+LCe+LCH2 12,123 10,022 175 133 0 1,658 24,111 371

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TABLE H.7 Sum Total of Annual Investment Costs (2009$) Out to 2050 Cumulative Investment Cost in 2050 ($ billions, discounted) CTL with Electric Scenario Hydrogen Biofuel CCS GTL CNG Chargers TOTAL BAU 0.0 0.0 55.5 0.0 0.0 0.2 55.8 Reference 0.0 114.7 21.0 23.9 0.0 0.6 160.2 Eff+FBSC 0.0 114.7 21.0 23.9 0.0 49.8 209.4 Eff+FBSC+IHUF 0.0 114.7 21.0 23.9 0.0 57.6 217.2 Eff+Bio+FBSC+IHUF 0.0 382.2 21.0 23.9 0.0 40.4 467.5 Eff+Bio w/CCS+FBSC+IHUF Investment costs unavailable for Biofuels w/CCS — Eff+Intensive Pricing+LCe 0.0 114.7 21.0 23.9 0.0 187.1 346.7 PEV+FBSC+IHUF+Trans+AEOe 0.0 114.7 21.0 23.9 0.0 128.2 287.8 PEV+FBSC+IHUF+Trans+LCe 0.0 114.7 21.0 23.9 0.0 139.0 298.6 PEV(later)+FBSC+IHUF+Trans+Lce 0.0 114.7 21.0 23.9 0.0 144.6 304.2 PEV+Bio+FBSC+IHUF+Trans+Lce 0.0 382.2 21.0 23.9 0.0 112.1 539.3 FCV+FBSC+IHUF+Trans+L$H2 214.6 114.7 21.0 23.9 0.0 14.3 388.4 FCV+FBSC+IHUF+Trans+H2CCS 243.4 114.7 21.0 23.9 0.0 9.3 412.3 FCV+FBSC+IHUF+Trans+LCH2 244.5 114.7 21.0 23.9 0.0 8.5 412.6 FCV+Bio+FBSC+IHUF+Trans+LCH2 210.1 382.2 2.6 2.0 0.0 5.8 602.7 CNGV+FBSC 0.0 114.7 21.0 23.9 0.0 49.8 209.4 CNGV+FBSC+IHUF+Trans 0.0 114.7 21.0 23.9 128.2 19.0 306.9 CNGV+Bio+FBSC+IHUF+Trans 0.0 382.2 15.8 12.0 116.7 11.0 537.6 Eff(Opt)+FBSC 0.0 114.7 21.0 23.9 0.0 20.8 180.4 Eff(Opt)+Bio+FBSC+IHUF 0.0 382.2 21.0 23.9 0.0 14.5 441.6 PEV(Opt)+FBSC 0.0 114.7 21.0 23.9 0.0 193.4 353.0 PEV(Opt)+FBSC+IHUF+Trans+Lce 0.0 114.7 21.0 23.9 0.0 287.9 447.5 FCV(Opt)+FBSC 0.0 114.7 21.0 23.9 0.0 49.8 209.4 FCV(Opt)+FBSC+IHUF+Trans+LCH2 278.9 114.7 2.6 2.0 0.0 2.3 400.4 PEV+FCV+FBSC+IHUF+Trans+LCe+LCH2 222.0 114.7 21.0 23.9 0.0 67.7 449.2 PEV+FCV+Bio+FBSC+IHUF+Trans+LCe+LCH2 195.5 349.1 2.6 2.0 0.0 59.0 608.1 372

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TABLE H.8 Cumulative Investment Costs (2009$) Out to 2050, Discounted Annually at 2.3 Percent Cumulative Investment Cost in 2050 ($ billions, discounted) CTL with Electric Scenario Hydrogen Biofuel CCS GTL CNG Chargers TOTAL BAU 0.0 0.0 35.3 0.0 0.0 0.2 35.5 Reference 0.0 85.8 12.2 13.1 0.0 0.4 111.5 Eff+FBSC 0.0 85.8 12.2 13.1 0.0 21.9 133.0 Eff+FBSC+IHUF 0.0 85.8 12.2 13.1 0.0 25.4 136.5 Eff+Bio+FBSC+IHUF 0.0 234.8 12.2 13.1 0.0 17.8 277.9 Eff+Bio w/CCS+FBSC+IHUF Investment costs unavailable for Biofuels w/CCS — Eff+Intensive Pricing+LCe 0.0 85.8 12.2 13.1 0.0 88.9 200.0 PEV+FBSC+IHUF+Trans+AEOe 0.0 85.8 12.2 13.1 0.0 71.8 182.9 PEV+FBSC+IHUF+Trans+LCe 0.0 85.8 12.2 13.1 0.0 76.0 187.1 PEV(later)+FBSC+IHUF+Trans+Lce 0.0 85.8 12.2 13.1 0.0 76.6 187.7 PEV+Bio+FBSC+IHUF+Trans+Lce 0.0 234.8 12.2 13.1 0.0 63.4 323.5 FCV+FBSC+IHUF+Trans+L$H2 122.1 85.8 12.2 13.1 0.0 6.3 239.5 FCV+FBSC+IHUF+Trans+H2CCS 137.3 85.8 12.2 13.1 0.0 4.2 252.6 FCV+FBSC+IHUF+Trans+LCH2 137.8 85.8 12.2 13.1 0.0 3.8 252.7 FCV+Bio+FBSC+IHUF+Trans+LCH2 120.6 234.8 2.0 1.5 0.0 2.7 361.5 CNGV+FBSC 0.0 85.8 12.2 13.1 0.0 21.9 133.0 CNGV+FBSC+IHUF+Trans 0.0 85.8 12.2 13.1 83.0 8.3 202.4 CNGV+Bio+FBSC+IHUF+Trans 0.0 234.8 9.7 7.4 76.4 4.9 333.2 Eff(Opt)+FBSC 0.0 85.8 12.2 13.1 0.0 9.1 120.2 Eff(Opt)+Bio+FBSC+IHUF 0.0 234.8 12.2 13.1 0.0 6.4 266.5 PEV(Opt)+FBSC 0.0 85.8 12.2 13.1 0.0 94.6 205.7 PEV(Opt)+FBSC+IHUF+Trans+Lce 0.0 85.8 12.2 13.1 0.0 156.3 267.4 FCV(Opt)+FBSC 0.0 85.8 12.2 13.1 0.0 21.9 133.0 FCV(Opt)+FBSC+IHUF+Trans+LCH2 164.3 85.8 2.0 1.5 0.0 1.1 254.6 PEV+FCV+FBSC+IHUF+Trans+LCe+LCH2 134.2 85.8 12.2 13.1 0.0 44.4 289.7 PEV+FCV+Bio+FBSC+IHUF+Trans+LCe+LCH2 120.6 219.0 2.0 1.5 0.0 40.3 383.4 373

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H.4 MODELING OPTIMISTIC TECHNOLOGY SCENARIOS USING THE LAVE-TRANS MODEL Optimistic technology scenarios imply breakthrough advancement of a given technology. These are taken to represent roughly a 20 percent likelihood occurrence in technological development for the respective technology. Although such advancement is less likely than the midrange assumptions, if it occurs, it changes the landscape for adoption of a technology, both in its costs and its benefits. H.4.1 Plug-in Electric Vehicles If the optimistic technology projections for PEVs are used together with the midrange technology projections for other vehicles, and the same policy assumptions are maintained (transitional subsidies + social cost feebates + IHUF), PEVs maintain an ever-growing share of the market, comprising two-thirds of all vehicles sold in 2050, with over half of all vehicles being BEVs (Figure H.12). The effect of the BEVs’ one-third lower usage rates can be seen by comparing Figures H.13 and H.14. BEVs comprise 43 percent of the stock of vehicles on the road but account for 32 percent of VMT. 11 While this reduces the BEVs’ impact on petroleum use and GHG emissions, it also causes a small reduction in total vehicle travel. Assuming decarbonization of the grid, the transition to PEVs in this policy case reduces petroleum consumption by an estimated 35 percent in 2030 and petroleum consumption and GHG emission by 89 and 76 percent in 2050, respectively, versus 2005 levels (Figure H.15). FIGURE H.12 Vehicle sales by vehicle technology assuming optimistic PEV technology estimates. 11 Although it is assumed that, all else equal, BEVs will be driven two-thirds as much as ICEs, usage is also affected by vehicle age and energy costs per kilometer. 374

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FIGURE H.13 Vehicle stock by vehicle technology assuming optimistic PEV technology estimates. FIGURE H.14 Vehicle miles traveled by vehicle technology assuming optimistic PEV technology estimates. FIGURE H.15 Changes in petroleum use and GHG emissions compared to 2005 assuming optimistic PEV technology estimates and a low-GHG electric grid. 375

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H.4.2 Hydrogen Fuel Cell Vehicles Using the optimistic projections of FCEV technology and the midrange assumptions for all others makes it easier to introduce FCEVs (lower implicit subsidies) and increases their ultimate, sustainable market share to 75 percent. Total implied subsidies begin at $17,500 per vehicle in 2015 but can be decreased immediately thereafter. Transition subsidies can be eliminated by 2022, leaving social cost feebates in place. Sales of FCEVs are 12,000 in 2015, 22,000 in 2016 and 33,000 in 2017. In 2025, sales exceed 4 million units (Figure H.16). This transition exceeds the speed limit for transitions, with 10.0 percent of the market converting to FCEVs in 2026. Assuming the low-carbon production of hydrogen but not advanced biofuels, this case appears to be able to meet all goals. Due to the rapid introduction of FCEVs and the substantial increase in energy efficiencies of all vehicles, petroleum use in 2030 is 50 percent lower than it was in 2005. By 2050, petroleum use by LDVs has been eliminated (replaced by 13.5 billion GGE of thermochemical biofuel and 7 billion gge of corn ethanol). GHG emissions are down 90 percent (Figure H.17). FIGURE H.16 Vehicle sales by vehicle technology assuming optimistic fuel cell electric vehicle (FCEV) technology estimates and policies that promote FCEV use. FIGURE H.17 Changes in petroleum use and GHG emissions compared to 2005 assuming optimistic FCEV technology estimates and policies that promote FCEV use. 376

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