Incorporating Greenhouse Gas Emissions into the Collaborative Decision-Making Process (2012)

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8C h a p t e r 2 Greenhouse Gas emissions GHGs include water vapor, ozone, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride (SF6). Of the GHGs, CO2 is one of the most important human-influenced contribu- tors to climate change, accounting in 2008 for almost 83% of U.S. GHG emissions. Within this broad context, transportation- related GHG emissions can be viewed from different perspec- tives. This first part of this chapter examines the relative contribution to GHG emissions of different economic sectors, the contribution of the transportation sector by mode, and the contribution over the lifespan of a project. The second part of the chapter describes some of the key factors that will likely influence GHG emissions in the future. Emissions by Sector The U.S. Environmental Protection Agency’s (EPA) Inventory of Greenhouse Gas Emissions and Sinks provides historic data on GHG emissions from transportation and other sectors (U.S. Environmental Protection Agency 2010a). Direct trans- portation emissions from on-road sources accounted for approximately 23% of total inventoried U.S. GHG emissions in 2008. When considering all transportation sources (includ- ing aircraft, marine, rail, and pipeline), this figure increases to about 29%. As shown in Figure 2.1, industry is the only eco- nomic sector with higher GHG emissions; however, recent trends show transportation and industry emissions con- verging to represent an almost equal share of U.S. GHG emissions, with transportation soon to surpass (or already surpassing) industrial emissions. The figures presented in this section reflect only inventoried GHGs with agreed-on 100-year global warming potentials. Recent studies have sug- gested that other pollutants, such as ozone, black carbon, organic carbon, sulfates, and aerosols, are significant climate change agents. Some of these pollutants contribute to global warming, and some counteract it by reflecting solar radiation or destroying GHGs. However, scientific consensus does not yet support including these gases in national GHG inventories (U.S. Environmental Protection Agency 2010a). As discussed later in this chapter, the industrial sector also is responsible for some transportation-related emissions, including those asso- ciated with vehicle manufacture, fuels production, and the production of cement and other materials for transportation facilities. The growth in transportation GHG emissions between 1990 and 2008 was primarily caused by an increase in person and vehicle miles traveled (VMT) and stagnation of fuel effi- ciency across the U.S. vehicle fleet. Person miles traveled in light-duty vehicles (LDVs) increased 36% between 1990 and 2008, ton-miles carried by medium- and heavy-duty trucks increased 55% between 1990 and 2007, and passenger miles traveled by aircraft increased 63% between 1990 and 2008 (Bureau of Transportation Statistics 2010a). Although average fuel economy for the LDV fleet during this period increased slightly because of the retirement of older vehicles, average fuel economy among new vehicles sold actually declined between 1990 and 2004. The decline in new vehicle average fuel economy reflected the increasing market share of light-duty trucks, which grew from about one-fifth of new vehicle sales in the 1970s to slightly over half of the market by 2004. Both the trends of increasing VMT and declining fuel effi- ciency have reversed themselves, at least temporarily, in recent years. Average new vehicle fuel economy improved in 2008 and 2009 as the market share of passenger cars increased. Growth in passenger vehicle miles traveled slowed from an annual rate of 2.6% over the period 1990 to 2004 to an average annual rate of 0.7% from 2004 to 2007, and in 2008 it decreased for the first time since 1980 (due primarily to the economic turndown). The U.S. Department of Energy’s Annual Energy Outlook (AEO) provides forecasts of carbon dioxide (CO2) emissions by sector through 2030, referred to as the AEO reference case Understanding GHG Emissions and Energy Consumption

9(Energy Information Administration 2009). The AEO reports only CO2 emissions, but the historic data from the EPA inventory include all GHG emissions. Since CO2 makes up over 95% of all inventoried transportation GHGs, the data from the two sources can be considered roughly comparable for this sector. The difference is greater in the industrial sec- tor, which is why the AEO forecasts show the transportation sector having higher CO2 emissions than the industrial sector in both present and future years (Bureau of Transportation Statistics 2010b). Under the AEO reference case, transportation is forecast to be the economic sector with the largest contribution to total GHG emissions from the present until at least 2035 (Figure 2.2). The AEO forecasts transportation energy usage and GHG emissions based on projections of activity and fuel efficiency for each mode. The 2011 AEO reference case projects that for LDVs between 2009 and 2035, fuel economy gains are almost entirely offset by increases in VMT (Energy Information Administration 2011). LDVs include passenger cars, motor- cycles, and light trucks less than an 8,500-pound gross vehicle weight rating, most of which are used primarily for personal travel. Light trucks include almost all four-tire, two-axle vehicles, such as SUVs, minivans, and pickup trucks. The AEO LDV forecasts consider the underlying factors that drive vehicle purchases and use, such as how income per capita, population forecasts, and fuel costs affect the growth of personal travel and VMT. Forecasts for other modes con- sider different factors, such as how increases in industrial out- put increase heavy-duty vehicle (truck) activity as well as rail, marine, and air transport. Emissions by Mode Figures 2.3 and 2.4 present an inventory of transportation- related GHG emissions sources for both historic trends and forecasted scenarios. LDVs make up the largest portion of GHG emissions, followed by heavy-duty vehicles and air- craft. This is true for both the historic and forecasted inven- tories. When considering the breakdown of transportation GHG emissions by transportation mode in 2008, passenger modes made up about 71%, with freight modes constituting the remaining 29%. It is likely that the AEO forecasts overstate future GHG emissions, at least for LDVs. If VMT growth slows below 1.5% annually and vehicle efficiency standards continue to be increased beyond requirements that currently extend through model year 2016, emissions from LDVs will decrease in the future. Figure 2.1. Historic trends in GHG emissions by sector.

10 Figure 2.2. Forecasted CO2 emissions by sector. Figure 2.3. Inventory of transportation-related GHG emissions by mode.

11 Figures 2.5 and 2.6 show contributions to GHG emissions by both passenger and freight modes. As shown in Figure 2.5, the vast majority of passenger transportation GHG emis- sions come from LDVs, accounting for 87% of the passenger transportation GHG contribution and 62% of total GHG transportation emissions in 2008. Domestic air travel made up most of the remaining emissions (10% of passenger transportation emissions and 7% of total emissions). Travel by bus, motorcycle, rail, and ship accounted for the very small remaining balance of passenger transportation and total emissions. Figure 2.6 shows that about three-quarters of freight- related GHG emissions (21% of all transportation GHG emissions) come from trucks. Freight rail accounted for 9% Figure 2.4. Future inventory of transportation-related GHG emissions by mode. Figure 2.5. Contribution to GHG emissions, passenger modes.

12 of freight-related GHG emissions and 2.6% of total transpor- tation GHG emissions, with GHG emissions from air, marine, and pipeline operations making up less than 2% each of total transportation GHG emissions. Perhaps of greatest interest in freight-related GHG emis- sions is that the amount of such emissions from heavy-duty trucks has increased rapidly since 1990, growing at three times the rate of emissions from LDVs. This is the product of decreasing fuel efficiency (as measured per ton-mile carried) and increasing demand for freight movement by trucks. Over the 1990 to 2007 period, CO2 emissions per ton-mile carried increased almost 12%, while ton-miles carried increased 55%. The changes were driven by an expansion of freight trucking after economic deregulation of the trucking industry in the 1980s, widespread adoption of just-in-time manufacturing and retailing practices by business shippers and receivers, increasing highway congestion, and structural changes in the economy that produced higher-value, lower-weight, and more time-sensitive shipments that were best served by trucking. Life-Cycle Emissions Most transportation GHG emissions are the direct result of burning gasoline and diesel fuel to power engines in cars, trucks, locomotives, aircraft, and ships. But GHG emissions are also generated in the process of constructing and maintaining road, rail, port, and airport infrastructure; manufacturing and main- taining vehicles; and extracting and refining transportation fuels; in other words, GHGs are emitted over the life cycle of an asset. A recent study estimated that direct emissions from vehicle operations account for only 60% of the total GHG emissions associated with LDVs (Chester 2008). As shown in Figure 2.7, the extracting and refining of fuels accounts for 10% of emissions, vehicle manufacturing for 12%, and constructing and maintaining roads used by these vehicles for an estimated 17%. Therefore, only 70% of a vehicle’s total GHG emissions (including fuel production and vehicle operations) is directly proportional to distance driven (VMT). GHG emissions not directly associated with vehicle operation are not included in the figures for transportation sector GHG emissions provided earlier in this section; therefore, the overall contribution of the transportation sector is actually significantly larger than its direct contribution of 29% of U.S. GHG emissions. Other transportation modes show different results, but most tend to show a significant increase in GHGs when all compo- nents of the vehicle and system’s operation are accounted for. In particular, the nonoperational life-cycle components of urban rail transit (i.e., vehicle manufacturing, track and station construction, station operations, and maintenance) account for about 50% of total life-cycle GHG emissions for that mode (Chester 2008). Context Factors Influencing Transportation GHG Emissions The AEO reference case presented above is just one potential future scenario for transportation GHG emissions. GHG emissions may be affected by a wide range of factors, some under varying degrees of influence by transportation agen- cies, such as speed, congestion, infrastructure investment, Figure 2.6. Contribution to GHG emissions, freight modes.

13 and pricing; and some over which transportation agencies have little or no influence, such as population growth and vehicle and fuel technologies. As shown in Figure 2.8, GHG emissions from passenger and freight travel are affected by four primary factors: total travel activity, the fuel efficiency of vehicles, the operational efficiency of drivers and the system (e.g., congestion, aggressive driving), and the carbon content of fuels. In addition, energy is consumed in the construction and maintenance of transportation facilities and in transpor- tation agency operations. Table 2.1 presents an overview of key contextual factors that could influence GHG emissions and surface transporta- tion energy use. Table 2.1 also identifies which of the compo- nents of transportation GHG emissions (as identified above) each factor will likely affect. Additional discussion is provided in the following sections on some of the most important factors that are most directly relevant to GHG planning and analysis. These factors include • Population and economic growth; • Passenger and truck VMT; • Vehicle technology and fuel efficiency; • Trends in the management and operation of transporta- tion infrastructure; • Future scenarios for energy use, supply, and costs; and • Potential federal policy initiatives directed at GHG reduc- tion, both economywide (e.g., cap-and-trade, carbon tax) and for the transportation sector in particular (e.g., trans- portation planning regulations, funding, and vehicle and fuel standards). Population and Economic Growth Forecasts The U.S. Bureau of the Census releases national population forecasts every 4 years using the cohort-component method, which is based on assumptions about future births, deaths, and net international migration. A 2008 Census release proj- ects that the U.S. population will increase from 310 million people in 2010 to 374 million people in 2030, a growth of about 20%, or 0.93% per year. Out of this increase of 64 mil- lion people, 29 million (46%) are expected to be immigrants (U.S. Census 2008). This is important to travel trends because immigrants are usually already working age and need to travel to work, unlike those born in the United States who will not reach working age until many years after birth. The per- centage of the population aged 65 and older will also increase, with people 65 and older making up 19% of the population in 2030 compared with 13% in 2010. This will potentially reduce the demand for personal travel and especially work- related travel. Figure 2.7. GHG emissions sources from a life-cycle perspective. Figure 2.8. Different components of transportation-related GHG emissions.

14 Table 2.1. Context Factors That Could Influence GHG Emissions and Surface Transportation Energy Use Factor Category Factors Influence Transportation costs and pricing •  Congestion pricing •  Parking pricing •  User fees (gas taxes, VMT fees, excise taxes) •  Cost of fuel •  Vehicle insurance and registration fees A, E, S, F Population and economic activity •  Overall population growth, nationally and by region •  Aging population •  Increasing immigration •  Continuing internal (to the U.S.) migration •  Changing levels of affluence •  Economic growth or stagnation •  Service versus industrial economy •  Magnitude and patterns of consumption •  Tourism and recreational activity patterns •  Patterns and variations in values, priorities, and political beliefs of the population •  International trade and travel •  Fiscal conditions for state DOTs, transit operators, and local transportation agencies A, E, S Land use and urban form •  Urban and rural land use patterns •  Developing megaregions •  Continuing and emerging challenges in rural and nonmetropolitan areas •  Quality of schools as it affects locational choices •  Crime and security as they affect locational choices •  Comparative cost of housing and other services in different land use settings •  Comparative fiscal and economic conditions in different local jurisdictions and statewide A Operational efficiency of drivers  and system managers •  Congestion •  Intelligent transportation systems •  Eco-driving and other driving behaviors •  Speed (speed limits, speed enforcement, design speeds, flow management, traffic signal  timing and synchronization, and use of roundabouts) •  Freight routing, border-crossing procedures for freight, urban freight consolidation centers,  urban goods movement policies, and other freight logistics S, A Passenger and truck VMT •  Magnitude and type of costs and pricing for transportation use (e.g., cost of fuel, cost of  vehicles, and user fees) •  Passenger VMT per capita •  Freight and logistics patterns and overall freight demand •  Extent of use of telecommuting and alternative work schedules •  Potential shifts to pay-as-you-drive insurance •  Parking supply management and pricing A Policies and regulations •  Emerging national approaches (cap-and-trade, taxation, and conformity) •  Statewide and metropolitan surface transportation planning legislation and regulations •  National Environmental Policy Act (NEPA) A, E, S, F, C Vehicle technology and fuel  efficiency •  Fuel economy: CAFE standards and California Pavley standards and consumer purchase  decisions •  Emerging alternative propulsion systems (hybrid and electric) and characteristics E, F Carbon intensity of transportation  fuels •  Corn ethanol •  Cellulosic fuels •  Algae-based fuels •  Electricity as a vehicle power source (including differential of carbon  intensity of electric  power sources over time and across regions and states) •  Low-carbon fuel standards and policies F Future scenarios for energy use,  supply and cost •  Price of energy (especially petroleum) •  Conservation incentives and education A, E, F (continued on next page)

15 Construction and maintenance  agency operations •  Extent of new construction and type of construction (tunnels versus at-grade) •  Energy intensity and carbon intensity of construction equipment and practices •  Energy intensity of materials used in construction and maintenance (including extent of  use of recycled materials) •  Roadway lighting •  Vegetation management along right-of-way (including vegetation choices and mowing  practices) •  Snow-plowing practices •  Vehicles and fuels used in agency fleets •  Paving frequency, pavement type, paving practices •  Work zone management (as it affects traffic tie-ups and idling) •  Energy efficiency of agency buildings and facilities •  Asset management practices affecting energy and carbon generation •  Increasing requirements for energy-efficient construction S, C Note: A = influences travel activity; E = influences vehicle fuel efficiency; S = influences system and driver efficiency; F = influences carbon content of fuels; C = influences  GHGs from construction, maintenance, and agency operations; DOT = department of transportation; CAFE = corporate average fuel economy. Table 2.1. Context Factors That Could Influence GHG Emissions and Surface Transportation Energy Use (continued) Factor Category Factors Influence Economic growth also affects transportation demand, since a growing economy will involve the production of more goods and services, many of which need to be transported. The Con- gressional Budget Office, which produces 10-year economic forecasts, projects that gross domestic product will grow by about 3.5% annually between 2010 and 2015 (in real terms), and 2.3% annually between 2016 and 2019 (Congressional Budget Office 2009). A recent report for the U.S. Chamber of Commerce notes that international trade has continued to grow faster than the U.S. economy, increasing the volume of freight moving through international gateways, as well as along domestic trade corridors (Cambridge Systematics et al. 2008). All of these economic forecasts assume recovery from the economic downturn that began in 2008. Passenger and Truck VMT Forecasts Multiple sources have developed VMT forecasts for passen- gers and trucks. As noted above, the VMT growth rate assumption used in the AEO reference case works out to be an average of 1.5% per year between now and 2030, which is lower than the previous rate of 1.8%. A recent Bottom Line report (Cambridge Systematics and Pisarski 2009) and the Moving Cooler study (Cambridge Systematics 2009) of trans- portation GHG reduction strategies use a growth rate of 1.4% growth in VMT per year. However, some experts have come to view even this rate as too high. They suggest that fac- tors such as rising fuel prices, saturation of the workforce, aging population, and a lower rate of transportation invest- ment will further reduce VMT growth rates in the future. Since 2000, the annual VMT growth rate was only 1.4%, with an absolute decline occurring in 2008. The early release of the 2011 AEO projects an annual growth in truck VMT averaging 1.9% between 2011 and 2020, moderating to 1.4% through 2035 (Energy Informa- tion Administration 2011). The long-term growth rate is in line with the Bottom Line report, which forecasts truck VMT growth at the same 1.4% annual rate as LDV VMT (Cam- bridge Systematics and Pisarski 2009). The forecast is based on the observation that freight VMT has recently been grow- ing at about the same rate as passenger VMT. For example, between 1995 and 2006, passenger car and other two-axle, four-tire vehicle traffic grew by 24.4%, while combination truck traffic grew by 23.6%, and all truck traffic grew by 25.2%. In contrast to light-duty VMT, which is primarily affected by socioeconomic, demographic, and land use fac- tors, truck VMT is closely related to overall economic activity, as well as to the structure of how industries produce and ship goods. At first glance this seems to contradict the earlier observation that GHG emissions have increased more rapidly from trucks than from cars since 1990. This can be explained by two factors: first, the greatest increase in freight volumes occurred in the early part of this period (1990 to 1995); and second, the productivity of freight movement (ton-miles per VMT) has continued to decrease. Vehicle Technology and Fuel Efficiency Forecasts Significant increases in fuel economy standards for LDVs, coupled with higher prices and investments in alternative fuels infrastructure, are likely to have a dramatic impact on the development and sales of alternative fuel and advanced technology LDVs. The AEO reference case includes a sharp increase in sales of unconventional vehicle technologies, such as flex-fuel, hybrid, and diesel vehicles. For example, hybrid vehicle sales of all varieties increase from 2% of new LDV sales in 2007 to 40% in 2030; diesel vehicles account for 16%

16 of new LDV sales, and flex-fuel vehicles for 13%. Dramatic shifts away from spark- and compression-ignited engines are not anticipated in the next 20 years because it is not antici- pated that battery-powered electric or fuel cell vehicles will be able to replace the petroleum-based fleet in this time period. In addition to the shift to unconventional vehicle technol- ogies, the AEO reference case shows a shift in the LDV sales mix between cars and light trucks. Driven by rising fuel prices and the cost of corporate average fuel economy (CAFE) com- pliance, the market share of new light trucks is expected to decline. In 2007, light-duty truck sales accounted for approxi- mately 50% of new LDV sales. In 2030, their share is esti- mated to be 36%, mostly as a result of a shift in LDV sales from SUVs to midsize and large cars. For the first time in 20 years, the 2007 Energy Independence and Security Act (EISA) required a change in federal fuel econ- omy standards. In May 2010, EPA and the National Highway Traffic Safety Administration adopted a set of new light-duty fuel economy standards through 2016 consistent with the GHG emissions standards adopted by California (U.S. Environmental Protection Agency and National Highway Traffic Safety Admin- istration 2010a). In October 2010, the agencies announced their intent to propose more stringent light-duty fuel efficiency stan- dards for the 2017 through 2025 model years (U.S. Environ- mental Protection Agency and National Highway Traffic Safety Administration 2010b). One of the uncertainties in future year motor vehicle tech- nology and fuel efficiency forecasts is whether U.S. LDV sales will return to historic levels after the economic recession is over. Recent annual LDV sales have been near 16 million units, but the 2009 AEO forecast for 2030 is for sales near 20 million units per year. Some analysts believe that the most recent his- toric sales are artificially high, for a number of reasons, and that near-term vehicle sales will be closer to 12 million than 16 million. If this occurs, the penetration of new technologies and more fuel-efficient vehicles will be slower than expected, and baseline GHG emissions will be above expected values. This would make it more difficult to meet GHG emissions reduction targets. Unlike LDVs, heavy-duty vehicles are not currently subject to fuel efficiency standards. However, the 2007 EISA required that the EPA evaluate fuel efficiency standards for trucks. In October 2010 EPA and NHTSA announced proposed GHG and fuel efficiency standards for heavy-duty trucks (U.S. Environmental Protection Agency and National Highway Traffic Safety Administration 2010c). The proposed stan- dards would reduce energy consumption and GHG emis- sions by 7% to 20% for combination tractors, heavy-duty pickups and vans, and vocational vehicles by model year 2019 compared with a 2010 baseline. (The reduction compared with the AEO reference case would be somewhat lower since this projection already assumes modest increases in fuel efficiency over this time period.) The proposed standards are less aggressive than light-duty standards (as measured by the percentage improvement in fuel efficiency, as for LDVs), largely because market forces have already fostered more aggressive development and adoption of fuel economy improvements for U.S. trucks compared with LDVs. Trends in Management and Operation of Transportation Infrastructure As gas tax revenues fall and the highway trust fund realizes severe shortfalls, state and local agencies are facing significant budget constraints that affect their ability to operate the trans- portation system. This fiscal stress, along with constrained right-of-way, community impacts, and environmental con- cerns, limits major expansions of the transportation system as a solution to ease traffic congestion. Many agencies, in particu- lar state departments of transportation (DOTs), have begun to use incident and congestion management strategies (e.g., intel- ligent transportation systems [ITS], real-time information, managed lanes, and pricing) to maintain an adequate level of service as transportation demand outpaces infrastructure investment. This trend is likely to continue in the future. Given that the United States consumed an additional 2.9 billion gal- lons of fuel in 2005 due to congestion, a substantial increase from 0.5 billion gallons in 1982 (Schrank and Lomax 2007), the success of such strategies in reducing delay and easing traf- fic congestion could help reduce GHG emissions as fuel is used more efficiently. Conversely, if VMT continues to increase without corresponding infrastructure or operational improve- ments, then congestion, delay, and associated emissions will continue to increase. The application of dynamic technology, specifically ITS, is becoming a relatively common strategy for improving the oper- ational efficiency of the transportation system. Examples include ramp meters that control the volume of drivers entering a highway, electronic signage that informs drivers of upcoming travel conditions, and traffic signalization that can encourage steady vehicular flow along a specific corridor (Lockwood 2008). ITS technology also allows for traffic management cen- ters to respond promptly to roadway incidents, thereby lessen- ing delay and potentially reducing GHG emissions. Taking the traffic management center and ITS concept one step further, lane management is a strategy that allows a transportation agency to actively manage travel lanes in real time for optimal flow conditions. High-occupancy toll lanes allow carpools to ride for free, but charge other vehicles a toll that varies by time of day and traffic conditions. Conceptu- ally, a high-occupancy toll lane increases highway efficiency by allowing additional vehicles to use an underutilized high- occupancy vehicle lane. The U.S. DOT’s Urban Partnership Program provided funds for selected metropolitan areas to

17 demonstrate different aspects of managed lanes operation. It is expected that the experiences of these metropolitan areas with the managed lane concept will provide the impetus for other metropolitan areas to adopt similar strategies. Over the long run, however, GHG reductions that result from fuel savings from management and operational strategies are likely to be at least partially—if not completely—offset by induced demand, or the increase in travel that results from improved travel conditions. The Moving Cooler study con- cluded that when measured cumulatively through 2050, addi- tional GHGs from induced travel in response to transportation improvements (including capacity expansion and operational improvements) would come close to offsetting the GHG emis- sions reduction benefits of reduced congestion (Cambridge Systematics 2009). The magnitude of the induced demand effect is subject to considerable uncertainty, and it is possible that under some assumptions, the increase in GHG emissions from induced travel could outweigh the congestion benefits. This may become particularly true in the future, as vehicle tech- nologies (such as hybrids or electric vehicles) that are more effi- cient in low-speed operation become more widely adopted. Even without considering these effects, the efficiency benefits of congestion reduction will decline over time in proportion to increases in CAFE standards, as well as the adoption of less carbon-intensive fuels, as baseline GHG emissions decrease. Real-time management of parking facilities, or perfor- mance parking, follows the same concept as managed lanes by varying the price of parking according to usage; that is, more demand for parking will yield a higher price. The price, which would vary in real time, is intended to maintain an 85% occupancy rate. Although only a few cities have success- fully implemented parking management strategies, a recently proposed California Senate bill has called for statewide park- ing reform, with performance parking as a major component. The bill’s purpose is to help California meet its GHG reduc- tion goal of 1990 levels by 2020, as introduced by Assembly Bill 32 in 2006. In Senate Bill 518 of the California State Assembly, performance parking is identified as a strategy to communicate the true cost of parking to travelers and ulti- mately reduce vehicle trips and GHGs. It is hard to say whether performance parking will take hold in other regions of the United States. It seems likely, however, that pricing in all forms will be a much more important strategy for transportation officials in the future. Future Scenarios for Energy Use, Supply, and Costs Since the vast majority of transportation energy in the United States comes from petroleum, importing oil is going to remain a political necessity for decades into the future. This requires ceding a certain level of political influence and control to oil-exporting nations. Many of these oil-producing nations are among the most politically unstable in the world, which necessarily results in unavoidable uncertainty with regard to the oil supply. Furthermore, although overall worldwide sup- plies of petroleum are nowhere near exhaustion, it is likely that the ability to expand oil supply capacity cheaply is nearing its peak, and that in the near future it will become more diffi- cult to expand oil production beyond current levels. When this occurs, energy production will expand to nonpetroleum sources, most of which are likely to reduce life-cycle GHG emissions. During the transition period, there will also be pres- sure to extract petroleum from sources that were not previ- ously economical, such as tar sands. Such production methods are more energy intensive and their use may result in increased life-cycle GHG emissions per unit of fuel produced. Several technologies are available or in development that could potentially reduce gasoline consumption and GHG emis- sions in the transportation sector. Many of these options, such as hydrogen fuel cells, would require a dramatic infrastructure investment before the technology could be implemented on a large scale. Biofuels and electrification require far more modest infrastructure investments, and therefore are more likely to be implemented in the foreseeable future. Biofuels require feed- stocks that can be produced with very little energy input in order to reduce overall carbon emissions. However, concerns have been raised that the demand for biofuel feedstocks may reduce agricultural land for other purposes while increasing pressure to convert nonagricultural lands (such as forests) to agricultural production, which could cause sequestered carbon to be released. Likewise, plug-in electric vehicles require elec- tricity production from low-carbon sources such as wind, solar, nuclear, and biomass to significantly decrease emissions. The United States invests billions of dollars every year to promote energy efficiency, expand the energy supply, develop energy technologies, and reduce energy costs. Over $16 billion was spent on energy subsidies in 2007 (Energy Information Administration 2008). The 2007 Renewable Fuels Standard (RFS), signed into law as part of EISA, mandates that 36 billion gallons of biofuels will be used in the United States in the year 2022. In March 2010, EPA updated the RFS to encourage the production of low-GHG biofuels (U.S. Environmental Protec- tion Agency 2010b). These changes include a higher standard in the short term to reflect existing production surpluses. In addition, the standards for advanced biofuels and biomass- based diesel have been modified to be stronger and more flex- ible. The RFS will result in a dramatic increase in the amount of ethanol being sold in the country over the next 15 years and could potentially reduce overall gasoline consumption. The impact of any of these alternative fuels on transporta- tion GHG emissions will range from modest to significant depending on the fuel and how it is produced. Figure 2.9, which is based on the Department of Energy’s GREET

18 (Greenhouse Gas, Regulated Emissions and Energy use in Transport) model (Version 1.8b), shows relative GHG emis- sions, including full fuel-cycle emissions, for a variety of transportation fuels (Cambridge Systematics and Eastern Research Group 2010). Compared with gasoline, emissions reductions range from about 16% for an 85% corn ethanol blend (E85) to 57% to 84% for ethanol from various cellu- losic feedstocks. A 20% blend of soy-based biodiesel provides roughly an 18% reduction, and natural gas results in a reduc- tion in the range of 16% to 30%. (Note that the model does not reflect the latest research on biofuel impacts as reported for the 2010 RFS2 rulemaking [U.S. Environmental Protec- tion Agency 2010c]). Electricity shows roughly a 33% reduc- tion with today’s technology and electricity generation mix. Benefits of hydrogen vary greatly depending on the produc- tion method. The net impact of any of these fuels on total GHG emissions will depend not only on the per vehicle ben- efit but also the rate of market penetration, which will depend on a host of uncertain factors such as technology advance- ment, fuel supply, policy choices that may encourage or dis- courage specific fuels, and the relative prices of different fuels. Conclusion This chapter has identified the important role that the trans- portation sector plays in the U.S. GHG emissions inventory. If the United States is serious about reducing the amount of GHG emissions entering the atmosphere, the transportation sector will have to be a part of the national strategy because of its significant place as a major source of such emissions. This chapter also identified many population trends and likely characteristics of future transportation system manage- ment that could lead to improved and perhaps less polluting system operations. However, as noted, some studies argue that the growth in VMT will negate any possible benefits of reduced GHG emissions associated with improved system management: there will simply be more people traveling and goods moving. Thus, it is important in any discussion of incorporating GHG emissions into decision making that some understanding of the level of effectiveness associated with different GHG-reducing transportation strategies be part of the discussion. The next chapter presents such information. Figure 2.9. Relative GHG emissions from different fuels using GREET model.