Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation: Special Report 307 (2011)

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Factors Driving Modal Energy Use 4 and Emissions As reported in Chapter 1, the U.S. Department of Energy’s Annual Energy Outlook for 2010 (AEO 2010)1 projects that motor vehicles will continue to be the transportation sector’s largest contributor to energy use and greenhouse gas (GHG) emissions two to three decades from now (see Figures 1-4 and 1-5). The nation’s cars, light trucks, and medium- and heavy-duty trucks and buses contribute more than 85 percent of the sector’s petroleum use and associated carbon dioxide (CO2) emissions. This dominance is largely because such vehicles serve most of the nation’s transportation activity, which is not expected to change fundamentally over the course of two to three decades. AEO 2010, therefore, projects that motor vehicles will continue to contribute nearly 80 percent of the sector’s energy use and emissions during the 2030s. The remaining 20 percent will be split among commercial airplanes and all other freight and passenger modes combined. The AEO 2010 projections imply that progress in curbing energy use and emissions from the fleet of light- and heavy-duty motor vehicles, and to a lesser extent commercial airplanes, will be central in making deep cuts in transportation energy use and emissions during the next half century. Accordingly, Chapter 4 examines some of the key factors that are likely to influence trends in the amount of energy used and GHGs emitted from these modes. In particular, the discussion focuses on 1 http://www.eia.doe.gov/oiaf/archive/aeo10/index.html. 101 

102 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation (a) the cars and light trucks that are owned and operated by households, since they make up about 90 percent of the light-duty motor vehicle fleet; (b) freight-carrying trucks, which consume most of the fuel used by the nation’s heavy-duty vehicles; and (c) passenger airlines, which are the main users of energy in commercial aviation. The chapter contains more detail on freight-carrying trucks, largely because this mode has received less attention than the other two with respect to energy- and emissions- saving trends and opportunities. For each mode, key factors that are likely to drive trends in energy and emissions are identified, and energy use and emissions projections are made to illustrate their effects. Presumably, policies that seek to reduce transportation energy use and emissions will need to modify or counter these driving factors. Factors Influencing Trends in Light-Duty Vehicles Trends in energy use and emissions by light-duty vehicles (LDVs) are largely a function of trends in the number of miles traveled by these vehicles and the energy-efficiency gains of new vehicles entering the fleet each year. How these two factors can influence trends in LDV energy use and emis- sions is described and illustrated, along with assumptions about changes in the carbon characteristics of the LDV fuel supply. role of changes in household travel According to the Federal Highway Administration’s (FHWA’s) Highway Statistics,2 LDV vehicle miles of travel (VMT) grew by more than 3 percent per year from 1970 to 1990. During the 1990s, it grew at a more modest rate of 2 percent per year. As explained in Chapter 2, this rate of growth has slowed even more during the past decade for a number of reasons, including stabilizing household size (following decades of decline), stabiliz- ing female labor force participation rates (following decades of increase), and the transitioning of many members of the large baby boom cohort past their peak travel years.3 The AEO 2010 reference case projects VMT 2 http://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.cfm. 3 The economic recession has slowed VMT growth even more in recent years. This is generally thought to be temporary and similar to what has occurred in past recessions. 

103 Factors Driving Modal Energy Use and Emissions to grow by only 1.6 percent per year from now until 2030.4 The anticipated moderation in VMT growth is one reason why cars and light trucks are expected to contribute a slightly smaller share of transportation’s total energy use and emissions over the next 20 years. Nevertheless, because LDVs will continue to account for most of the sector’s energy use and emissions in 2030, even larger reductions in the mode’s VMT growth may be required if deep reductions in total sector energy use and emis- sions are to be achieved. Most of the nation’s fleet of cars and light trucks consists of private vehicles that are owned and operated by households. Consequently, trends in household demographics and associated trip-making patterns will significantly influence total LDV travel. To illustrate how changes in household trip making can influence VMT, the scenario in Table 4-1 posits alternative rates of growth in the average number of person trips per household by trip purpose (e.g., shopping, commuting). While such trips are made by various modes, they are dominated by LDVs. For the sake of simplicity, the scenario in Table 4-1 assumes that LDVs account for a constant share (60 percent) of all household person trips for the period 2010 to 2030 but that the factors driving growth in person trips are increased trip making for shopping, family errands, and other personal business. The assumed growth in importance of these purposes of house- hold travel is consistent with trends observed in national household travel surveys over the past several decades (see Chapter 2). The surveys suggest that person trips will grow about three times faster for shopping, family, and personal business than for commuting to and from work, which is often perceived incorrectly as the main reason for household trip making. Should this scenario hold, average VMT per household will increase by 10 to 15 percent over the next 20 years. The above scenario implies that strategies aimed at reducing LDV use for commuting, such as support for carpooling and public transit, may not be as effective in tempering growth in household VMT as would policies aimed at reducing vehicle use for shopping and other non-work-related reasons for personal travel. Another important factor 4 http://www.eia.doe.gov/oiaf/aeo/excel/aeotab_7.xls. 

104 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation table 4-1 Scenario of Changing Household Travel Patterns, 2010–2030 Trip Purpose Shopping, Personal, Commuting Family Business All Other Total Annual growth in person trips 0.25 0.75 0.50 0.79 per household, 2010–2030 (%) Vehicle trips for every person trip, 0.85 0.63 0.48 0.60 2010 and 2030 (held constant) Miles per vehicle trip, 2010 and 2030 12 7.1 12.1 10.3 (held constant) Person Trips per Household 2010 548 1,528 1,426 3,502 2030 597 1,838 1,660 4,095 Share of Household Person Trips (%) 2010 16 44 40 100 2030 15 44 41 100 Vehicle Trips per Household 2010 466 963 685 2,113 2030 507 1,158 784 2,449 VMT per Household 2010 5,592 6,837 8,288 20,717 2030 6,084 8,222 9,489 23,795 NOTE: The specific figures in this scenario are derived as follows: Total LDV VMT, projected to be 2.75 trillion in 2010 by AEO 2010, is multiplied by 0.9, the historic share of LDV VMT by households. The result, 2.48 trillion vehicle miles, is divided by the Census Bureau forecast of 119.6 million households in 2010, which yields an average household VMT of 20,717. In the 2001 National Household Travel Survey, commuter, shopping, personal, family, and all other vehicle trips accounted for 27, 33, and 40 percent of household VMT, respectively. The number of vehicle trips is computed by dividing VMT by the 2001 average trip length for each trip purpose. Person trips are then computed by applying the ratio of vehicle trips to person trips. in total growth in VMT will be the rate of growth in the number of households. For the next three decades, Yi et al. (2006) forecast that the total number of U.S. households will grow by 0.7 to 1.1 percent per year, depending on assumptions about overall population growth and changes in family size, age structure, and marriage rates (Table 4-2). The higher growth rate is considered more likely if households continue 

105 Factors Driving Modal Energy Use and Emissions table 4-2 Projected Ranges in the Number of U.S. Households, Total and One-Person Total One-Person Percentage of Households Households All Households Having Year (millions) (millions) One or Two Persons 2010 121–122 33–34 26–28 2020 133–137 36–41 27–30 2030 143–153 38–48 27–31 2040 150–172 42–54 28–31 Percentage annual growth 0.7–1.1 0.8–1.5 SOURCE: Yi et al. 2006. to become smaller and the U.S. population grows by 0.93 percent per year, as forecast by the U.S. Bureau of the Census.5 Persons living in smaller households average more VMT than do persons living in larger households. In the latter case, the vehicle miles for a household errand, such as shopping for groceries, are spread among a larger number of household members (Hu and Reuscher 2004). The assumption that the total number of U.S. households grows by 1 percent per year from 2010 to 2030 results in an estimate of about 145 million households by 2030, compared with about 120 million today. Even if average VMT per household were to remain static over the next two decades, total household VMT will increase by at least 20 percent. Thus, household demographic trends must be considered as a factor influencing future transportation energy use and emissions. Because of the growth in U.S. population, a reduction in total household VMT would likely require major changes in the number, structure, and size of households, which are outcomes that transportation policy making alone cannot bring about. Nevertheless, transportation policies that can help reduce VMT per household may be able to amplify the effect of fuel taxes and other policies in curbing growth in transportation energy use and emissions. 5 http://www.census.gov/population/www/projections/summarytables.html. 

106 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation 14,000 New Vehicle Sales (thousands) 12,000 10,000 8,000 6,000 4,000 2,000 0 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 06 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 Gasoline ICE vehicles TDI diesel ICE Ethanol-flex fuel ICE Electric vehicle Plug-in hybrid electric Hybrid electric Natural gas Fuel cell figure 4-1 Forecast new light-duty automobile sales by technology type, 2006–2030 (AEO 2010 reference case). ICE = internal combustion engine; TDI = turbocharged direct injection. role of vehicle efficiency performance In 2006, the U.S. LDV fleet averaged 20.6 miles per gallon of gasoline (mpg), up almost 9 percent from 1990.6 The effects of even faster increases in fuel economy on energy use and emissions are worth considering. As a result of tighter federal fuel economy standards and new standards for vehicle GHG performance (as explained in Chapter 3), AEO 2010 projects that the combined mpg for new cars and light trucks will grow by 2.75 percent per year from 2010 to 2020. For the period extending to 2030, AEO 2010 projects an average improvement of 1.8 percent per year in the mpg of the fleet. As shown in Figure 4-1, the AEO 2010 projections assume that new cars and light trucks sold between 2010 and 2030 will consist largely of vehicles powered by gasoline, although they will have increasingly efficient engines and other fuel-saving systems. By 2030, only about two-thirds of all new vehicles are expected to be solely gasoline powered. Diesel, 6 In this section, “miles per gallon” is in reference to a gallon of gasoline. The referenced 20.6 mpg is from FHWA 2007, Table VM-1. (See http://www.fhwa.dot.gov.) 

107 Factors Driving Modal Energy Use and Emissions 4,500 4,000 Miles Traveled (billions) 3,500 3,000 2,500 2,000 1,500 1,000 500 0 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 06 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 Gasoline ICE vehicles TDI diesel ICE Ethanol-flex fuel ICE Electric technology Fuel cell technology Natural gas technology figure 4-2 VMT by LDV technology set, 2006–2030 (AEO 2010 reference case). ethanol, hybrid electric, and plug-in hybrid electric vehicles are projected to make up the remaining one-third of new vehicle sales. By 2030, the latter vehicles, having entered the fleet in large numbers during the 2020s, are projected to account for about 25 percent of the miles traveled by all LDVs (Figure 4-2). An average rate of growth of 1.8 percent per year in fleet fuel economy is high compared with trends over the past 25 years, but the impact of the higher mpg on total LDV energy use would be largely offset by growth in household VMT, as discussed above. Indeed, the projection of a 1.6 percent per year rate of growth in LDV travel in AEO 2010 implies that nearly all of the fuel savings from the annual 1.8 percent increase in vehicle efficiency will be countered by increased vehicle use. Table 4-3 illustrates how an average increase of 1.8 percent per year in the fuel economy of the fleet translates into changes in mpg for new vehicles (for all vehicles combined and for passenger cars and light trucks separately). This example assumes that light trucks account for about 54 percent of miles traveled by new vehicles. The average mpg of new vehicles entering the fleet would increase from 22.3 today to 31.6 by 2030, or by 41 percent. Of course, if light trucks become less popular, the 

108 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation table 4-3 Growth in LDV Miles per Gallon, 2010–2030 2010 Values 2030 Values New car miles per gallon 25.3 35.8 New light truck miles per gallon 19.8 28.0 Light truck share of new-vehicle VMT (%) 54.0 54.0 Combined new-vehicle miles per gallon weighted by VMT (assumes that light trucks account for 54% of new-vehicle VMT) 22.3 31.6 LDV fleet (on-road) average miles per gallon 20.7 29.6 NOTE: Vehicle miles per gallon values are intended to represent actual experience on the road. The figures shown are lower than the EPA test values by 20%. improvements in the average mpg of new vehicles could be lower and still achieve the same result. Thus, policies that discourage interest in SUVs and other light trucks may deserve consideration. resulting trends in ldv energy use and ghg emissions The factors affecting future LDV energy use and GHG emissions dis- cussed in this section are household VMT and vehicle fuel economy. If trends in VMT and fuel economy were independent of one another, total LDV fuel consumption might be expected to fall by about 0.2 per- cent per year (the anticipated 1.6 percent annual growth in VMT would be more than offset by the 1.8 percent annual growth in fleet mpg result- ing from current legislation). However, VMT and fuel economy are not fully independent of one another because increases in vehicle fuel economy will cause the fuel-related cost of driving to go down in the absence of higher fuel prices. The reduction in fuel operating cost (that is, the reduction in fuel expenditures per mile driven) lowers the “price” of driving an additional mile and will thus prompt some addi- tional motorist demand for driving. The increase in travel demand is widely known as the “rebound effect.” The size of the rebound effect associated with stricter fuel economy standards has been a topic of debate for decades. The literature contains a range of rebound effects associated with increases in vehicle fuel econ- omy. In the recent literature, Small and Van Dender (2007), who examined a pooled cross section of U.S. states for 1966 to 2004, found rebound 

109 Factors Driving Modal Energy Use and Emissions effects of 4 and 21 percent for the short and longer runs, respectively.7 Other researchers have reported similarly low values (Schipper and Grubb 2000), while a few have reported much higher values [for example, Frondel (2004) found an increase exceeding 50 percent]. On the basis of a review of dozens of studies from the 1980s and 1990s, Graham and Glaister (2002) report short- and long-run rebound effects of 10 and 30 percent, respectively. This range is the one most commonly cited in the literature. Small and Van Dender observed that the effect of fuel costs on total demand for driving is becoming smaller as household income rises. Their analysis of data from 2000 to 2004 suggests that the rebound effect has diminished to between 1 and 6 percent, with the higher value represent- ing the longer-term response. Others, such as Hughes et al. (2006) and Basso and Oum (2007), have observed similar declines over time. Small and Van Dender surmise that higher household incomes have rendered fuel costs per mile less significant relative to other costs associated with more travel, particularly the value of travel time. On the basis of this evidence, the assumption that each 10 percent increase in vehicle fuel economy will produce about a 1 percent increase in VMT appears reasonable. This value aligns with recent lower estimates for longer-run responses while remaining within the range of rebound values traditionally cited. Thus, if fleet mpg is assumed to increase by an average of 1.8 percent per year over the next 20 years, VMT will like- wise increase by nearly 1.8 percent per year. The total increase in VMT (including the small addition from the rebound effect) would cancel most of the fuel savings that would otherwise have been achieved from the higher vehicle fuel economy. Figure 4-3 shows the resulting trend line, which is similar to projections of LDV fuel use in the AEO 2009 reference case.8 In considering trends in GHG emissions, changes that may occur in the GHG characteristics of the energy used by the LDVs must be taken into account. Of course, how LDV energy supplies will change over time is unknown. As a reference case, however, the assumption that gasoline will 7 Long-run responses to changes in the fuel cost per mile of driving are greater because consumers have more time to make changes, such as in their commuting distance. 8 When these analyses were performed, AEO 2009 was the latest available AEO forecast. 

110 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation 160.0 140.0 120.0 100.0 Billion gallons 80.0 60.0 AEO 2009 LDV fuel use 40.0 Reference LDV fuel use 20.0 0.0 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 figure 4-3 Projections of total LDV fuel use, 2010–2030, chapter illustrative case compared with AEO 2009 reference case. remain the dominant fuel used by the LDV fleet for at least the next two decades, and probably for much longer, appears reasonable. Indeed, this assumption is consistent with Argonne National Laboratory’s VISION model, whose reference case projections of the LDV energy supply are shown in Table 4-4.9 The VISION model assumes that in 2010 gasoline and ethanol will account for 94 and 5.4 percent, respectively, of LDV energy used, with the small remainder (<1 percent) consisting mostly of diesel. By 2030, the model assumes that gasoline will account for only 88 percent of LDV energy use, diesel for 2.5 percent, and ethanol for 8.8 percent. 9 The VISION model was developed by Argonne National Laboratory to provide estimates of the potential energy use, oil use, and carbon emission impacts of advanced LDV and heavy-duty vehicle technologies and alternative fuels through 2100. The model consists of two Excel workbooks: a base case of U.S. highway fuel use and carbon emissions to 2100 and a copy of the base case that can be modified to reflect alternative assumptions about advanced vehicle and alternative fuel market penetration. The VISION model uses VMT projections from AEO 2009. http://www.transportation. anl.gov/modeling_simulation/VISION/index.html. 

111 Factors Driving Modal Energy Use and Emissions table 4-4 Projected Shares of LDV Energy Use by Fuel Type, 2010–2030, from Argonne VISION Model (base case) Compressed Year Gasoline Diesel Natural Gas F-T Diesel Biodiesel Ethanol Total 2010 94.0 0.5 0.1 0.0 0.0 5.4 100 2015 92.6 0.8 0.0 0.0 0.0 6.5 100 2020 90.7 1.2 0.0 0.1 0.0 7.8 100 2025 89.0 1.8 0.0 0.3 0.0 8.8 100 2030 88.2 2.5 0.0 0.4 0.1 8.8 100 NOTE: Shares are percentages. F-T = Fischer–Tropsch. Translating these various trends in VMT, vehicle efficiency, and fuel supply composition into projections of LDV GHG emissions trends presents additional uncertainties. The burning of a gallon of gasoline creates about 19 pounds of CO2. However, estimation of the net effect of the substitution of ethanol for some gasoline on GHG emissions requires calculations of life-cycle emissions of each fuel, including emissions from fuel production and distribution. This is a complicated and controversial step, which, given the relatively small changes projected in the fuel supply (that is, ethanol increasing from 5.5 to 8.8 percent of energy use), is not merited. Thus, the fuel consumption trends shown in Figure 4-3 assume that gasoline will remain the dominant energy source for LDVs until 2030. If such trends play out, CO2 emissions from the burning of fuel by LDVs will remain steady over the next 20 years, holding at about 1,125 million metric tons per year (19 pounds of CO2 per gallon × 130,000 billion gallons/ 2,200 pounds per metric ton). Heavy-Duty Trucks As in the case of LDVs, the key factors influencing energy use and emis- sions by large trucks are growth in vehicle travel and energy efficiency. However, gauging energy efficiency trends in trucking can be complicated because the item of interest is the total amount of energy used to move a given amount of freight over a distance, not the mpg of individual vehicles. Thus, trends in total energy use by large trucks will depend on many factors, 

112 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation 400 Billions of VMT—100 million tons of freight AEO 2009 truck VMT forecast (2.5% annual growth) 350 300 Interstate tons (FAF) (2.6% annual growth) 250 Intrastate tons (FAF) (1.8% annual growth) Metropolitan tons (FAF) (1.9% annual growth) FHWA truck freight forecast 200 (tons) (2.0% annual growth) 150 100 50 0 10 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 20 22 2 2 2 2 2 2 22 22 2 2 2 2 2 2 2 2 figure 4-4 FHWA truck freight forecasts for 2010–2030. from growth in freight demand to trends in truck payloads, average ship- ping distances, and vehicle efficiency characteristics. role of truck travel and its determinants The major determinant of truck travel is the demand for freight, which is driven largely by growth in the national economy. Two other important factors are changes in average truck payload size and length of haul. The latter factors are, in turn, affected by changes in the overall geographic pattern of freight demand, particularly the rate of growth in long-distance versus local freight traffic. The main metric used for measuring freight movement is tonnage. FHWA projects trucked freight tonnage for the next 20 years largely on the basis of assumptions about economic growth. For the period 2010 to 2030, FHWA expects tonnage to grow by an average of 2 percent per year, increasing from 13.2 billion tons to nearly 20 billion tons, or by about 50 percent (Figure 4-4). If the average truck payload and length of haul are assumed not to change over this time span, a 50 percent increase in freight tonnage would translate into an equivalent increase in truck VMT. However, the FHWA projections assume that long-distance 

113 Factors Driving Modal Energy Use and Emissions (interstate) freight tonnage will grow faster than tonnage moved within state (intrastate) and locally (metropolitan) (Figure 4-4). A disproportion- ate increase in long-distance trucking will cause larger increases in VMT relative to the increase in total freight tonnage. A few simplifying assumptions based on FHWA’s freight tonnage forecasts can be made to illustrate how disproportionate growth in long- distance trucking can influence overall trends in truck travel (and in energy use). The FHWA tonnage forecasts distinguish between local, intrastate, and interstate freight. Each differs in average truck payload and distance traveled. In the case of local trucking, most trucks consist of single-unit vehicles carrying small loads. Because trucks often travel empty or with partial loads, these vehicles are assumed to average 3 tons of freight payload over a distance of 25 miles. Intrastate trucking tends to consists of a mix of single-unit and combination trucks. These trucks are assumed to average 8 tons of freight payload and 100 miles per haul. Nearly all longer-distance interstate trucking consists of combination trucks, which are assumed to average 14 tons of freight payload and 500 miles per haul. These assumptions are applied to FHWA’s freight tonnage projections in Table 4-5. The table illustrates a division of freight tonnage, truck VMT, and ton-miles across the segments of the trucking business. Table 4-6 shows how truck VMT would trend for the single-unit and combination fleets on the basis of these divisions and additional assumptions about the share of local, interstate, and intrastate traffic moved by single-unit versus combination trucks. On the basis of numerous simplifying assumptions, this scenario illustrates how changes in the nature and structure of freight markets and the trucking industry can affect trends in the overall energy performance of trucking, regardless of changes in the energy efficiency of the vehicles themselves. If FHWA’s projections of faster growth in long-distance freight demand are borne out, then VMT by combination trucks will grow faster (by 2.4 percent per year in this scenario) than VMT by single- unit trucks (1.9 percent). This trend, by itself, would lower the average mpg of trucks on the road because of the greater number of combination trucks in service. However, the same trend would reduce the average amount of energy used per ton-mile of trucked freight, since the larger combination trucks carry more tonnage per mile. 

114 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation table 4-5 Scenario Projections of Truck Freight, VMT, and Ton-Miles, 2010–2030 Local Intrastate Interstate Hauls Hauls Hauls Total Tons Number, 2010 (billions) 4.7 5.5 3.0 13.2 Share, 2010 (%) 36 42 22 100 Number, 2030 (billions) 6.9 7.9 5.0 19.7 Share, 2030 (%) 35 40 25 100 Annual growth rate, 2010–2030 (%) 1.9 1.8 2.6 2.0 VMT Number, 2010 (billions) 39.3 92.1 105.7 237.1 Share, 2010 (%) 17 39 45 100 Number, 2030 (billions) 57.2 131.5 177.2 365.9 Share, 2030 (%) 16 36 48 100 Annual growth rate, 2010–2030 (%) 1.9 1.8 2.6 2.2 Ton-Miles Number, 2010 (billions) 118 553 1,480 2,151 Share, 2010 (%) 5 26 69 100 Number, 2030 (billions) 172 789 2,480 3,441 Share, 2030 (%) 5 23 72 100 Annual growth rate, 2010–2030 (%) 1.9 1.8 2.6 2.4 role of vehicle energy efficiency According to FHWA statistics for 2006,10 combination and single-unit trucks averaged 5.9 mpg of diesel fuel burned.11 In the AEO 2010 reference case, the mpg of the heavy-duty truck fleet is projected to reach 6.0 in 2010 and 6.9 by 2030, an improvement of 0.6 percent per year (Figure 4-5). The AEO 2010 projections do not assume that significant state or federal policies are put into effect to increase truck energy efficiency between 2010 and 2030. The 0.6 percent annual improvement in vehicle mpg is expected to occur largely as a result of industry efforts to save on 10 http://cta.ornl.gov/data/chapter5.shtml. 11 Miles per gallon figures in this section assume the fuel is diesel fuel. 

115 Factors Driving Modal Energy Use and Emissions table 4-6 Allocation of Projected VMT by Single-Unit and Combination Trucks, 2010–2030 2010 2030 Share of Share of Annual VMT Total VMT VMT Total VMT Growth (billions) (%) (billions) (%) Rate (%) Local hauls (85% single unit, 39.3 17 57.2 16 1.9 15% combination) Single unit 33.5 14 48.6 13 Combination 5.8 2 8.6 2 Intrastate hauls (50% single unit, 92.1 39 131.5 36 1.8 50% combination) Single unit 46.1 19 65.7 18 Combination 46.1 19 65.7 18 Interstate hauls (15% single unit, 105.7 45 177.2 48 2.6 85% combination) Single unit 5.3 2 8.8 2 Combination 100.4 42 168.3 46 Total 237.1 100 365.9 100 2.2 Single unit 84.8 36 123.3 34 1.9 Combination 152.4 64 242.7 66 2.4 350 10 9 300 8 Miles per Gallon 250 VMT (billions) 7 6 200 5 150 4 3 100 2 50 1 0 0 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 Medium-sized truck VMT Heavy-sized truck VMT Medium-sized truck average Heavy-sized truck average miles per gallon miles per gallon figure 4-5 Freight truck VMT and fuel economy forecasts, 2006–2030 (AEO 2010). 

116 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation Tax and license 4% Insurance Communications 6% 1% Maintenance 10% Labor 44% Depreciation 11% Fuel 24% figure 4-6 Truckload carrier operating expenses, 2006. SOURCE: Global Insight, Inc. fuel by way of changes in technology and operations. Although federal legislation (the Energy Independence and Security Act of 2007) now requires the setting of fuel economy standards for medium and heavy trucks, how these standards will be designed to affect vehicle efficiency levels remained unclear as the analyses for this chapter were being undertaken. The AEO assumption that the trucking industry, acting largely in its self-interest, will continue to pursue improvements in energy performance is based on the importance of diesel fuel costs to motor carrier operations and profitability. Figure 4-6 shows the major operating expenses from 2006 for the “truckload” segment of the trucking industry. The truck- load segment consists of many trucking companies who haul freight over long distances. During 2006, diesel fuel prices were starting to rise, but they were still lower than they are today. Even then, diesel fuel purchases were the second-largest operating expense for these trucking companies, accounting for about one-quarter of truckload carrier operating expenses. Managing these costs, therefore, is crucial to the ability of the carriers to remain competitive with one another and with other transportation modes such as freight rail. 

117 Factors Driving Modal Energy Use and Emissions table 4-7 Opportunities for Fuel-Saving Technology and Design Changes in New Combination Trucks, 2010–2030 Miles per Gallon Improvement Resulting Miles per Relative to 2010 2030 Gallon Change in Trucks (averaging Penetration New Trucks from Area of Opportunity 5.5 mpg) (%) (%) 2010 to 2030 (%) Diesel combustion efficiency 3 85 2.6 Waste heat recovery 3 75 2.3 Oxides of nitrogen after-treatment 2 80 1.6 Additional engine friction reduction 2 90 1.8 Engine auxiliaries (water/oil pump) 2 75 1.5 Transmission and driveline 1 80 0.8 efficiency Power train integration 5 75 3.8 Cooling optimization 1 50 0.5 Improvements to other auxiliaries 2 75 1.5 Additional aerodynamic 3 80 2.4 improvements Weight reduction 1 75 0.8 Mild long-haul electric hybrid 2 50 1.0 Full electric hybrid 30 5 1.5 Truck designs better optimized for 25 5 1.3 cargo and capacity needs New truck fuel economy (starting at 6.75 mpg (up 23% 5.5 mpg for 2010 trucks) from 5.5 mpg) Average annual improvement 1.1 The progress that will be made by the trucking industry in controlling fuel expenditures over the next two decades will almost certainly be driven by real and anticipated trends in the price of diesel fuel. Thus, some plausible scenarios for achieving energy savings in the trucking industry are considered below. Opportunities to Increase the Energy Efficiency of New Trucks Table 4-7 shows a number of opportunities for changes in truck technologies and designs to increase the energy efficiency of the new combination trucks 

118 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation entering the fleet each year over the next two decades. These fuel-saving opportunities—which were first identified by the committee and then verified against the technologies examined in recent National Research Council reports (NRC 2010a; NRC 2010b)—range from improving diesel combustion and driveline efficiency to the use of electric hybrid systems and aerodynamic designs. In each case, the committee estimated how deeply the change in technology or design could penetrate the combina- tion truck fleet to increase the average mpg of new trucks. The estimates recognize that different technologies are likely to have different levels of applicability to individual segments of the combination truck fleet. For example, trucks that are used mainly for long-distance travel are less likely to benefit from full hybridization than are combination trucks used locally in urban (stop-and-go) traffic conditions. On the other hand, the former trucks are more likely to benefit from improvements in vehicle aerodynamics and advances in auxiliary power units that can reduce engine idling in sleeper mode. The many fuel-saving design and technology opportunities shown in Table 4-7 suggest the potential for significant improvements in the energy efficiency of new combination trucks that enter the fleet from 2010 to 2030. On the basis of the assumption that trucks entering the fleet in 2010 averaged 5.5 mpg, the scenario in Table 4-7 suggests that exploiting these technology and design changes could lead to new trucks averaging 6.75 mpg by 2030, an average increase of 1.1 percent per year. Table 4-8 identifies a similar list of opportunities for technology and design changes to increase fuel economy in the single-unit fleet, again checked against opportunities examined in the other National Research Council reports. The fuel-saving opportunities differ somewhat from those identified for combination trucks because of differences in how the two kinds of trucks are used. Hybrid electric technology, in particular, represents a major fuel-saving opportunity for single-unit trucks operat- ing mainly in urban environments. This technology, therefore, contributes significantly to the faster improvements in mpg estimated for single-unit trucks relative to combination trucks. Overall, the estimates of market penetration and impact on fuel econ- omy in the scenario in Table 4-8 imply a plausible rate of mpg improvement of 1.4 percent per year. On the basis of the assumption that single-unit 

119 Factors Driving Modal Energy Use and Emissions table 4-8 Opportunities for Fuel-Saving Technology and Design Changes in New Single-Unit Trucks, 2010–2030 Miles per Gallon Improvement Maximum Penetration Resulting Miles per Relative to 2010 (increase among Gallon Change in Trucks (averaging new trucks relative New Trucks from Area of Opportunity 8.5 mpg) (%) to 2010 trucks) (%) 2010 to 2030 (%) Fuel combustion 3 60 1.8 efficiency Waste heat recovery 3 20 0.6 Oxides of nitrogen 2 70 1.4 after-treatment Additional engine 2 50 1.0 friction reduction Engine auxiliaries 2 75 1.5 (water/oil pump) Transmission and 1 60 0.6 driveline efficiency Power train integration 5 50 2.5 Cooling optimization 1 20 0.2 Improvements in other 2 25 0.5 auxiliaries Additional aerodynamic 3 10 0.3 improvements Weight reduction 1 50 0.5 Full hybrid 30 40 12.0 Trucks designed to 25 40 10.0 optimize cargo and capacity New truck fuel economy 11.3 mpg (up 33% (starting at 8.5 mpg) from 8.5 mpg) Average annual 1.4 improvement trucks averaged 8.5 mpg in 2010, a 1.4 percent annual rate of increase would result in an average of 11.3 mpg by 2030. Implications for Fleetwide Fuel Efficiency The extent to which the improvements in new truck energy performance estimated above would translate into fleetwide mpg increases would 

120 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation 25 Share of VMT (within each truck fleet)(%) 20 Combination fleet 15 Single-unit fleet 10 5 0 <1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Age of Truck (years) figure 4-7 VMT distribution of trucks by age. SOURCE: 2002 Vehicle Inventory and Use Survey. depend on how fast the newer fuel-efficient trucks enter the fleet. Fleet turnover tends to occur at a slower pace for single-unit trucks than for combination trucks, primarily because of the much higher annual mileage accrued by the latter. Figure 4-7 shows the differences in age distribution of combination and single-unit trucks relative to their VMT. Because combination trucks require a high level of reliability for long-haul service, they are replaced by newer trucks when they reach high mileage (which can occur within 4 or 5 years). The fleetwide effects of increasing new-truck fuel economy will therefore occur more rapidly for combination trucks than for single-unit trucks, which average more years in service. Applying the age distributions in Figure 4-7 to the estimates above for annual increases in new truck mpg leads to an average mpg growth of 1 percent per year for the entire single-unit fleet and 0.8 percent per year for the entire combination fleet. Other Fuel-Saving Opportunities in Truck Operations and Maintenance The rate of increase in mpg per year for the truck fleet in the illustrative scenario given here, 0.8 to 1 percent per year, exceeds the 0.6 percent 

121 Factors Driving Modal Energy Use and Emissions annual improvement in AEO 2010. The reason for the difference is that the scenario given here assumes that a wider array of opportunities for new technologies and design changes are all successfully exploited. In addition to these technological opportunities, there are opportunities for saving fuel through changes in truck operations and maintenance practices. A number of such opportunities are shown in Table 4-9. They include reducing the incidence of engine idling, limiting operations at higher travel speeds, more direct routing of shipments, and improving trailer aero- dynamics and overall maintenance. Some of the opportunities shown in Table 4-9 are already being exploited by large motor carriers that have invested in sophisticated fleet energy management systems. More widespread utilization will likely occur during the next 20 years if diesel fuel prices continue to be a significant operating expense. Combination trucks would be the primary candi- dates for more aggressive energy-saving improvements in operations and maintenance, but comparable opportunities may exist for carriers who operate single-unit fleets. Table 4-9 suggests that fleet mpg can be increased by 0.9 and 0.4 percent per year for the combination and single- unit fleets, respectively. Total Increase in Trucking Fuel Efficiency Unless the price of diesel fuel increases substantially, the expectation that all of the fuel-saving opportunities identified in the scenarios developed here will be exploited to their full potential over the next two decades may be optimistic. If they are exploited, however, the trucking industry would experience an increase in mpg on the order of 1.7 and 1.4 percent per year for the combination and single-unit fleets, respectively (see Figure 4-8). The result would be an increase from 5.5 to 7.8 mpg (41 percent) for the combination fleet and 8.5 to 11.3 mpg (33 percent) for the single-unit fleet. trends in trucking energy use and ghg emissions The truck travel scenario in Table 4-6 assumes that VMT will increase by an average of 1.9 percent per year for single-unit trucks and 2.4 percent per year for combination trucks from 2010 to 2030. Additional scenarios assume that the average mpg of the single-unit and combination fleets will increase 1.4 percent and 1.7 percent per year, respectively. Combining 

122 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation table 4-9 Opportunities for Saving Fuel Through Changes in Truck Operations and Maintenance, 2010–2030 Maximum Penetration Fleetwide (increase within Resulting Change Operations and Maintenance Improvement in fleet relative to in Fleet Miles Practice Miles per Gallon (%) 2010 fleet) (%) per Gallon (%) Combination Trucks Lower rolling resistance 3 60 1.8 replacement tires Trailer gap controls/ 2 20 0.4 vortex stabilizer Smart navigation 2 70 1.4 Driver training 4 50 2.0 Idle reduction or 6 75 4.5 elimination Road maximum speed 10 60 6.0 reduced about 7 mph (from assumed 65 mph) Trailer maintenance and 7 50 3.5 system compatibility with respect to tires, weight, aerodynamics (e.g., adding skirting and changes in trailer design) Total fleet improvement 19.6 in miles per gallon Average annual 0.9 improvement Single-Unit Trucks Lower rolling resistance 3 75 2.3 tires Smart navigation 2 25 0.5 Driver training 4 50 2.0 Idle reduction or 6 10 0.6 elimination Road maximum speed 10 25 2.5 reduced about 7 mph (from 65 mph) Total improvement in 7.9 miles per gallon Average annual 0.4 improvement 

123 Factors Driving Modal Energy Use and Emissions 12.00 11.00 10.00 9.00 Miles per Gallon 8.00 7.00 6.00 5.00 4.00 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 10 11 12 13 14 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 Combination fleet miles per gallon without operations improvements (0.8% per year) Single-unit fleet miles per gallon without operations improvements (1% per year) Miles per gallon of new combination trucks (1.1% per year) Miles per gallon of new single-unit trucks (1.4% per year) Total miles per gallon of combination fleet (1.7% per year) Total miles per gallon of single-unit fleet (1.4% per year) figure 4-8 Projected growth (miles per gallon) for new trucks and the overall fleet of single-unit and combination trucks, 2010–2030. these VMT and mpg projections implies that total fuel consumption from 2010 to 2030 will increase by 9 percent for the single-unit fleet and 12 percent for the combination-vehicle fleet. The trucking industry as a whole would experience an 11 percent increase in fuel consumption from 2010 to 2030 (Figure 4-9). If diesel remains the dominant fuel for trucking over this period, the effects on GHG emissions can be calculated by assuming the emission of 22 pounds of CO2 from the burning of each gallon of diesel fuel. Accord- ingly, CO2 emissions would increase at the same rate as the projected 

124 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation 60.0 Single-unit truck fuel use Combination truck fuel use Total AEO 2009 50.0 40.0 Billion Gallons of Diesel 30.0 20.0 10.0 0.0 10 11 12 13 14 15 16 17 20 21 22 23 24 25 26 27 28 29 30 18 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 figure 4-9 Projected consumption of diesel fuel by heavy-duty trucks, 2010–2030. 11 percent increase in diesel fuel consumption, from 380 million met- ric tons in 2010 to 425 million metric tons in 2030. As in the calcula- tions for the effects of LDV gasoline consumption on GHG emissions, these truck calculations do not include any upstream emissions of CO2 or other GHGs associated with diesel fuel production and distribution. Such estimates would be important in comparing the benefits of switching to alternative fuels. Although it is highly probable that diesel fuel will remain dominant in trucking for the next 20 years at least, alternative fuels may make inroads in reducing diesel fuel consumption. The base case of the aforementioned Argonne VISION model assumes that diesel accounts for 94 percent of truck energy use in 2010 (with nearly all of the remaining 6 percent of energy supplied by gasoline). However, by 2030, the VISION model projects that diesel’s share of trucking energy will fall to 80 percent, with gasoline 

125 Factors Driving Modal Energy Use and Emissions continuing to account for about 5 percent. Biodiesel and synthetic diesels are projected to account for 15 percent (2 and 13 percent, respectively). Whether these alternative fuels result in increased or decreased life-cycle emissions of GHGs will be an important question. Air Passenger Transportation Commercial airlines provide both passenger and cargo transportation service, with the former accounting for about 90 percent of aircraft miles and fuel consumption. The demand for air passenger service is positively correlated with income: wealthier individuals, who have greater mobility demands in general, seek out faster modes of transportation and are willing and able to pay for more expensive air travel. Increasing affluence and economic development globally are expected to contribute to growing demand for air passenger service, both domestically and internationally. Nearly all passenger airplanes use turbine engines powered by jet fuel. Most of the fuel is burned while at cruise, followed by the takeoff, taxi, and landing phases of the flight. The percentage burned in each phase depends on the design and weight of the aircraft and its engines, the distance traveled, and the manner in which the aircraft is operated. In addition, parked aircraft operate auxiliary power units that consume energy and emit CO2 to varying degrees, depending on how the units are powered. At the airport, the vehicles and equipment that service aircraft contribute to the transportation sector’s emission of GHGs, mainly from the production of CO2 from the use of gasoline and diesel fuel. Energy use and emissions from these service vehicles are not well documented, and some of their fuel use may be included in energy figures for motor vehicles.12 The energy used by other airport vehicles, such as shuttle buses, is included in the totals for motor vehicles. In contrast to other modes, a large portion of emissions occurs at altitude: in the lower troposphere during aircraft ascent and descent and in the upper troposphere and lower stratosphere during cruise. Whereas 12 The Transportation Research Board’s Airport Cooperative Research Program has completed a Guidebook on Preparing Airport Greenhouse Gas Emissions Inventories (Kim et al. 2009), which is intended to help in conducting inventories and thus may clarify airport ground emissions. http:// onlinepubs.trb.org/onlinepubs/acrp/acrp_rpt_011.pdf. 

126 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation altitude is not particularly relevant for CO2 emissions, it can be important for other substances, including water vapor, aerosols, oxides of nitrogen, black carbon, and sulfur oxides. When these substances are released at higher altitudes, they can cause changes in atmospheric chemistry and in physical processes (such as contrail and cloud formation) that enhance radiative forcing. Unlike CO2, emissions of these substances, and the physical and chemical effects that ensue, are influenced by factors other than total fuel consumption. Their impacts can vary considerably depending on where in the atmosphere the fuel is burned, atmospheric conditions, the efficiency of combustion, and numerous other factors. As a result, it has proved difficult to translate emissions from aviation fuel consumption into CO2–equivalent values that are normalized for global warming potential. In the case of passenger air service, the following are key factors that influence trends in energy use and CO2 emissions: engine efficiency), and Higher passenger demand will generally lead to more flights and thus more fuel consumption. Higher passenger demand, however, can also increase aircraft load factors (occupancy rates), leading to a reduction in energy consumed per passenger mile. Increased demand can also lead to the use of larger aircraft, which usually consume less energy per passenger mile than smaller aircraft when they maintain high occupancy. Because the taxi, takeoff, and climb phases of flight are the most fuel-intensive, shorter flights tend to consume more fuel per passenger mile than longer flights involving longer distances in cruise.13 Finally, newer aircraft tend to be more energy efficient than older aircraft because of technology improve- 13 This relationship, in which larger aircraft and longer flight distances lead to reduced fuel consump- tion per passenger mile, can weaken for large aircraft flying very long distances because these trips will require more fuel storage that adds weight and leads to more fuel burn. 

127 Factors Driving Modal Energy Use and Emissions ments. Thus, trends in any of these key factors—such as shorter or longer flight lengths, the use of larger or smaller aircraft, changes in takeoff and landing procedures and cruise speeds, and the more rapid development and diffusion of newer aircraft into the fleet—can have major implications for fuel consumption and CO2 emissions from air transportation. Trends in the system-level CO2 impacts of fuel, including the increased avail- ability and use of alternatives with lower life-cycle emissions per unit of energy, are another factor (Kar et al. 2010). Each year the Federal Aviation Administration (FAA) publishes long-range forecasts for aviation demand, typically covering a 15- to 20-year period.14 Included in the forecasts are projections of average aircraft load factors, flight distances, seating capacity, and fuel consumption. The forecasts, which make various assumptions about economic growth and structural changes in the aviation industry, provide a reasonable basis for projecting modal energy use and CO2 emissions. faa traffic and fuel use forecasts FAA projects that total passenger enplanements on domestic airlines will increase by 2.7 percent per year from 2010 to 2025 (Table 4-10).15 The total miles traveled by the enplaned passengers are forecast to increase at an even faster rate of 3.4 percent per year, owing largely to an expected increase in the average trip length. Even with these assumptions of growth in travel, passenger airline fuel consumption is projected to grow by only 1.9 percent per year from 2010 to 2025, implying a reduction of 1 to 2 percent per year in the average amount of energy consumed per passenger mile. According to Lee et al. (2001), energy-efficiency improvements of this magnitude are consistent with historical precedent and with other estimates that consider the prospects of increases in energy efficiency of new aircraft and changes in operational procedures, such as more direct routing. These researchers estimate 1.2 to 2.2 percent annual improve- ments in energy efficiency over the next two decades. They discuss the potential for new technologies, materials, and practices to achieve higher engine efficiencies (e.g., through higher temperatures and pressures), 14 http://www.faa.gov/data_statistics/aviation/aerospace_forecasts/2008-2025/. 15 https://www.faa.gov/data_research/aviation/aerospace_forecasts/2009-2025/media/2009%20Forecast %20Doc.pdf. 

128 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation table 4-10 FAA Forecast of Airline Passenger Traffic and Fuel Use, 2010–2025 Gallons of Jet Revenue Passenger Revenue Fuel Consumed Enplanements Passenger Load Average Seats per Year (millions) (millions) Miles (billions) Factors Aircraft Flight 2010 11,435 638.9 555.8 79.5 120.6 2011 11,706 665.6 584.8 80.5 120.8 2012 12,131 698.6 620.4 81.2 120.8 2013 12,584 732.1 656.8 81.6 120.5 2014 12,837 752.4 680.1 81.6 120.2 2015 13,050 770.0 700.7 81.5 120.3 2016 13,247 789.1 723.2 81.7 120.5 2017 13,440 807.3 745.0 81.7 120.8 2018 13,634 823.9 765.1 81.5 121.0 2019 13,831 840.3 785.3 81.2 121.1 2020 14,032 857.8 806.7 81.0 121.3 2021 14,236 875.7 828.7 80.8 121.5 2022 14,444 894.0 851.4 80.6 121.6 2023 14,656 912.9 874.8 80.4 121.8 2024 14,873 932.2 898.9 80.2 121.9 2025 15,093 952.1 923.7 80.1 122.1 Annual 1.9 2.7 3.4 0.1 0.1 change (%) reductions in weight (e.g., by the use of composites), and more efficient operations (e.g., Global Positioning System–based navigation and sepa- ration control). Literature sources consistently report that two-thirds or more of the potential for long-term improvements will derive from technological improvements, such as new airframe designs and engines (Kar et al. 2010). Operational improvements are generally reported to account for the remainder. Barriers to faster deployment of energy- and emissions-saving tech- nologies and operations in commercial aviation include the high capital costs of aircraft and the time-consuming processes for the safety certi- fication of new designs, technologies, and operating procedures. In the 

129 Factors Driving Modal Energy Use and Emissions case of low-GHG fuels for aviation, these barriers are especially high, and they are accompanied by the limited availability of suitable energy- intensive fuels (Kar et al. 2010). projections of energy use and ghg emissions FAA forecasts that fuel use by air passenger transportation will increase from 11.4 billion gallons in 2010 to 15.1 billion gallons in 2025, an annual growth rate of 1.9 percent. One gallon of jet fuel emits about 21.1 pounds of CO2. Thus, extrapolating the FAA energy projections for an additional 5 years implies that jet fuel use will reach 16.6 billion gallons by 2030 and that CO2 emissions will reach 159 million metric tons. Other Modes The modes of transportation not covered above contribute less than 5 percent of the sector’s energy use and CO2 emissions. The relatively small contribution results from a combination of higher energy effi- ciency and lower traffic activity. Thus, focusing on these modes to achieve reductions in total transportation energy use and emissions will provide marginal gains at best. Collectively, for example, the nation’s public transit systems—buses and rail—account for less than 1 percent of passenger miles and less than 1 percent of transport energy use and GHG emissions. Freight railroads account for a large share of long-haul freight traffic (about 38 percent of ton-miles), but they already operate with a level of energy efficiency, especially compared with trucks. Rail freight averages more than 400 ton-miles per gallon of diesel, compared with about 70 ton-miles per gallon for combination trucks. As noted in Chapter 2, railroads are striving to raise this figure over the next decade. However, the total energy and emissions saved would be minimal in light of the mode’s already low energy demand. Summary Assessment Figure 4-10 shows the various projections in this chapter for energy- related CO2 emissions by cars, trucks, and passenger airlines. In addition, trends for other modes (which already contribute little to sector energy 

130 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation 2,250 General aviation (gasoline) 2,000 Motorcoach (diesel) 1,750 Transit bus Millions of Metric Tons of CO2 and van (diesel) Business aviation 1,500 (jet fuel) Domestic 1,250 water (diesel) Air cargo 1,000 (jet fuel) Rail freight 750 (diesel) Single-unit truck (diesel) 500 Air passenger (jet fuel) 250 Combination truck (diesel) 0 LDV (gasoline) 20 0 20 1 12 20 3 20 4 20 5 20 6 20 7 18 20 9 20 20 1 22 20 3 20 4 20 5 20 6 27 20 8 20 9 30 1 2 1 1 1 1 1 1 1 2 2 2 2 2 2 20 20 20 20 20 20 figure 4-10 Reference projections of CO2 emissions from the U.S. transportation sector. use and emissions) are shown under the simplifying assumptions that they will grow at historical rates and maintain existing levels of energy efficiency. Currently, passenger cars and light trucks (LDVs) account for about two-thirds of transportation energy use and emissions. Largely because of increases in vehicle efficiency standards, these vehicles are projected to account for about 57 percent of energy use and emissions in 2030. Heavy trucks, which contribute about 22 percent of the sector’s energy use and emissions, are projected to account for the same share in 2030. Finally, passenger airlines are projected to increase their share from 6 to 8 percent. The factors considered in projecting these trends suggest where opportunities may lie for reducing transportation energy use and emis- sions over the next two to three decades. For cars and light trucks, these opportunities are likely to include 2020 in an attempt to exceed the goal of 35 miles per gallon required in current legislation; 

131 Factors Driving Modal Energy Use and Emissions particularly for the fastest-growing reasons for personal trip making, such as discretionary trips for shopping and services; and favor energy sources whose production and consumption both result in lower emissions of GHGs. For freight-carrying trucks, the opportunities are likely to include designs and technologies, energy-efficient operations and maintenance practices, and energy sources whose production and consumption both result in lower emissions of GHGs. For passenger airlines, the opportunities are likely to include aircraft that are more efficient in using energy and produce fewer emissions and of improved air traffic management procedures and systems. Policy approaches that seek to exploit these and other opportunities are considered in Chapter 5. References abbreviations FHWA Federal Highway Administration NRC National Research Council Basso, L. J., and T. H. Oum. 2007. Automobile Fuel Demand: A Critical Assessment of Empirical Methodologies. Transport Reviews, Vol. 27, No. 4, pp. 449–484. FHWA. 2007. Highway Statistics 2006. U.S. Department of Transportation, Washington, D.C. 

132 Policy Options for Reducing Energy Use and Greenhouse Gas Emissions from U.S. Transportation Frondel, M. 2004. Empirical Assessment of Energy-Price Policies: The Case for Cross-Price Elasticities. Energy Policy, Vol. 32, No. 8, pp. 989–1000. Graham, D., and S. Glaister. 2002. Review of Income and Price Elasticities of Demand for Road Traffic. Working paper. Center for Transport Studies, Imperial College of Science, Technology, and Medicine, London. Hu, P. S., and T. R. Reuscher. 2004. Summary of Travel Trends: 2001 National Household Travel Survey. Prepared for the U.S. Department of Transportation and Federal Highway Administration. http://nhts.ornl.gov/2001/pub/STT.pdf. Hughes, J. E., C. R. Knittel, and D. Sperling. 2006. Evidence of a Shift in the Short-Run Price Elasticity of Gasoline Demand. National Bureau of Economic Research Working Paper W12530. http://ssrn.com/abstract=931375. Kar, R., P. A. Bonnefoy, and R. J. Hansman. 2010. Dynamics of Implementation of Mitigating Measures to Reduce CO2 Emissions from Commercial Aviation. Report ICAT-2010-01. International Center for Air Transportation, Massachusetts Institute of Technology, Cambridge, June. http://dspace.mit.edu/bitstream/ handle/1721.1/56268/Kar_et_al_ICAT_Report.pdf?sequence=1. Kim, B., I. A. Waitz, M. Vigilante, and R. Bassarab. 2009. ACRP Report 11: Guidebook on Preparing Airport Greenhouse Gas Emissions Inventories. Transportation Research Board of the National Academies, Washington, D.C. Lee, J. J., S. P. Lukachko, I. A. Waitz, and A. Schäfer. 2001. Historical and Future Trends in Aircraft Performance, Cost, and Emissions. Annual Review of Energy and the Environment, Vol. 26, pp. 167–200. NRC. 2010a. Real Prospects for Energy Efficiency in the United States. National Academies Press, Washington, D.C. NRC. 2010b. Technologies and Approaches to Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles. National Academies Press, Washington, D.C. Schipper, L., and M. Grubb. 2000. On the Rebound? Feedback Between Energy Intensities and Energy Uses in IEA Countries. Energy Policy, Vol. 28, pp. 367–388. Small, K. A., and K. Van Dender. 2007. Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound Effect. Energy Journal, Vol. 28, No. 1, pp. 25–51. Yi, Z., K. C. Land, Z. Wang, and D. Gu. 2006. U.S. Family Household Momentum and Dynamics: An Extension and Application of the ProFamy Method. Population Research and Policy Review, Vol. 25, No. 1, pp. 1–41.