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DOE/EIA-0554(2001) Report
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Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Key Assumptions Macroeconomic Sector Inputs Macroeconomic sector inputs used in the NEMS Transportation Demand Module (Table 25) consist of the following: gross domestic product (GDP), industrial output by Standard Industrial Classification code, personal disposable income, new car and light truck sales, total population, driving age population, total value of imports and exports, and the military budget. The share of total vehicle sales that represent light truck sales is assumed to approach fifty percent by 2020. Table 25. Macroeconomic Inputs to the transportation Module Light-Duty Vehicle Assumptions The light duty vehicle Fuel Economy Module includes 58 fuel saving technologies with data specific to cars and light trucks including incremental fuel efficiency improvement, incremental cost, first year of introduction, and fractional horsepower change. These assumed technology characterizations are scaled up or down to approximate the differences in each attribute for 6 EPA size classes of cars and light trucks (Tables 26 and 27). Table 26. Standard Technology Matrix For Cars Table 27. Standard Technology Matrix For Trucks The vehicle sales share module holds vehicle sales shares by import and domestic manufacturers constant within a vehicle size class at the 1999 level from the National Highway Traffic and Safety Administration data.29 EPA size class sales shares are projected as a function of income per capita, fuel prices, and average predicted vehicle prices based on endogeous calculations within the Fuel Economy Module.30 The fuel economy module utilizes 58 new technologies for each size class and origin of manufacturer (domestic or foreign) based on the cost-effectiveness of each technology and an initial availability year. The discounted stream of fuel savings is compared to the marginal cost of each technology. The fuel economy module assumes the following:
Degradation factors (Table 28) used to convert Environmental Protection Agency-rated fuel economy to actual “on the road” fuel economy are based on application of a logistic curve to the projections of three factors: increases in city/highway driving, increasing congestion levels, and rising highway speeds.31 Degradation factors are also adjusted to reflect the percentage of reformulated gasoline consumed. Table 28. Car and Light Truck Degradation Factors
Figure 4. VMT per Driver by Age-Group (Vehicles-Miles Traveled) Commercial Light-Duty Fleet Assumptions With the current focus of transportation legislation on commercial fleets and their composition, the Transportation Demand Module has been redesigned to divide commercial light-duty fleets into three types of fleets: business, government, and utility. Based on this classification, commercial light-duty fleet vehicles vary in survival rates and duration in the fleet, before being combined with the personal vehicle stock (Table 29). Sales shares of fleet vehicles by fleet type also remain constant over the forecast period. Automobile fleets are divided into the following shares: business (87.39%), government (7.42%), and utilities (5.19%). Light truck fleets are divided into the following shares: business (83.50%), government (14.1%), and utilities(2.40%)34,35. Both car (23.70%) and light truck (28.57%) fleet sales are assumed to be a constant fraction of total car and light truck sales. Table 29. The Average Length of Time Vehicles Are Kept Before they are Sold to Others Alternative-fuel shares of fleet sales by fleet type are initially set according to historical shares (business (0.36%), government (2.21%), utility (2.64%))36,37 then compared to a minimum constraint level of sales based on legislative initiatives, such as the Energy Policy Act of 1992 and the Low Emission Vehicle Program.38,39 Size class sales shares of vehicles are held constant at anticipated levels (Table 30).40,41 Individual sales shares of alternative-fuel fleet vehicles by technology type are assumed to remain at anticipated levels for utility, government, and for business fleets in accordance with the technology shares implied from EIA surveys42,43 (Table 31). Table 30. Commercial Fleet Size Class Shares by fleet and Vehicle Type, 1992 Table 31. Anticipated Purchases of Alternative-Fuel Vehicles by Fleet Type and Technology Type Annual VMT per vehicle by fleet type stays constant over the forecast period based on the Oak Ridge National Laboratory fleet data. Fleet fuel economy for both conventional and alternative-fuel vehicles is assumed to be the same as the personal new vehicle fuel economy and is subdivided into six EPA size classes for cars and light trucks. The Light Commercial Truck Module The Light Commercial Truck Module of the NEMS Transportation Model is constructed to represent trucks that weight 8501 lbs. to 10,000 lbs. These vehicles are assumed to be used for commercial freight purposes. The primary source of data for this model is the microdata file of the 1992 Truck Inventory and Use Survey (TIUS), which provides numerous details on truck stock and usage patterns at a high level of disaggregation. The data derived from this source are used to allocate and sort the summary truck data presented in the Federal Highway Administration’s annual publication of highway statistics, which constitute the baseline from which the NEMS forecast is made (Figure 5). TIUS data are also used to distribute estimated sales of trucks, obtained from the Macroeconomic Model, among the affected models according to their weight class (Figure 6). Finally, the TIUS microdata set is used to construct a characterization of these Light Commercial Trucks. Figure 5. Distribution of FHWA Single-Unit Truck Stocks Figure 6. Distribution of Light Truck Sales Truck characterizations comprised of their average annual miles of travel, fuel economy, and distribution among several aggregate industrial groupings chosen for their correspondence with output measures currently being forecast by NEMS (Tables 32 and 33). It is expected that projected growth in industrial output will provide a useful proxy for the growth in demand for the services of light commercial trucks. VMT for light commercial trucks is a function of industrial output for agriculture, mining, construction, trade, utilities, and personal VMT. Forecasted fuel efficiencies are assumed to increase at the same annual growth rate as light-duty trucks (<8500 lbs.). Table 32. Annual Miles by Major Use, 1992 Table 33. Average Miles Per Gallon, 1992 Alternative-Fuel Vehicle Technology Choice Assumptions Fuel vehicle (AFV) technology choice module utilizes a discrete choice specification, which uses vehicle attributes as inputs and forecasts vehicle sales shares among the following 16 light-duty technologies: gasoline internal combustion engine (ICE), direct injection diesel ICE, ethanol flex, ethanol neat, methanol flex, methanol neat, electric dedicated (uses only electricity), diesel electric hybrid, gasoline electric hybrid, compressed natural gas (CNG), CNG bi-fuel, LPG, LPG bi-fuel, fuel cell gasoline, fuel cell methanol, and fuel cell liquid hydrogen.44 Direct injection gasoline technologies are included in the conventional gasoline ICE technologies. Listed in Table 34 are a few examples of the input variables that correspond to the vehicle attributes used in the analysis. With the exception of vehicle fuel economy, fuel price, vehicle price, vehicle range, 0-60 second acceleration times, and fuel availability, all other attributes are exogenously set, based on offline analysis.45 Table 34. Alternative-Fuel Vehicle Attribute Inputs For Compact Cars For Two Stage Logit Model Vehicle attributes vary by six EPA size classes for cars and light trucks, and fuel availability varies by Census Division. The logit model coefficients vary by three car sizes and four light truck sizes. Vehicle prices are assumed to follow exponential curves of economies of scale in production dependent upon the volumes and cost curves which vary by AFV technologies. Where applicable, AFV fuel efficient technology attributes are calculated relative to conventional gasoline miles per gallon. It is assumed that many fuel efficiency improvements to conventional vehicles will be transferred to alternative-fuel vehicles. Specific individual alternative-fuel technological improvements are also dependent upon the AFV technology type, cost, research and development, and availability over time. Make and model availability estimates are assumed values according to a logistic curve based on the initial technology introduction date and are based on current offerings. Coefficients summarizing consumer valuation of vehicle attributes were derived from assumed economic valuation compared to vehicle price elasticities. Initial AFV vehicle stocks are set according to EIA surveys.46,47 A fuel switching algorithm based on the relative fuel prices for AF compared to gasoline is used to determine the percentage of total VMT represented by AF in bi-fuel and flex-fuel alcohol vehicles. An upper limit of 5 percent and a lower limit of 25 percent is assumed for the percentage of the vehicle-miles traveled using the alternative fuel. Freight Truck Assumptions The freight truck stock module converts industrial output in dollar terms to an equivalent measure of volume by using a freight adjustment coefficient.48,49These freight truck adjustment coefficients vary by NEMS Standard Industrial Classification (SIC) code, gradually diminishing their deviation over time toward parity and are estimated from historical freight data. Freight truck load factors (ton-miles per truck) by SIC code are constants formulated from historical load factors.50 All freight trucks are subdivided into medium and heavy-duty trucks. New freight truck fuel efficiency is dependent on the maximum penetration, introduction year, cost-effectiveness based on fuel price and capital costs, and fuel economy improvement of the technologies including alternative fuel technologies (Table 35).51 VMT freight estimates by size class and technology are based on matching freight needs as measured by the growth in industrial output by SIC code to VMT levels associated with truck stocks and new vehicles. Fuel consumption by freight trucks is regionalized by Census Division according to the State Energy Data Report distillate regional shares.52 Table 35. Diesel Technology Characteristics for the Freight Truck Model Initial freight trucks are obtained by the Federal Highway Administration (FHWA) and are distributed by Truck and Inventory Use Survey (TIUS) shares. Freight and Transit Rail Assumptions The freight rail module receives industrial output by SIC code measured in real 1987 dollars and converts these dollars into an adjusted volume equivalent. Specific NEMS coal production from the Coal Module is also used to adjust coal rail travel. Freight rail adjustment coefficients, which are used to convert dollars into volume equivalents, remain constant and are based on historical data.53,54 Initial freight rail efficiencies are based on the freight model from Argonne National Laboratory.55 The distribution of rail fuel consumption by fuel type remains constant and is based on historical data.56 Regional freight rail consumption estimates are distributed according to the State Energy Data Report 1997.57 Freight Domestic and International Shipping Assumptions The freight domestic shipping module also converts industrial output by SIC code measured in dollars, to a volumetric equivalent by SIC code.58,59 These freight adjustment coefficients are based on analysis of historical data and remain constant throughout the forecast period. Domestic shipping efficiencies are based on the freight model by Argonne National Laboratory. The energy consumption in the freight international shipping module is a function of the total level of imports and exports. The distribution of domestic and international shipping fuel consumption by fuel type remains constant throughout the analysis and is based on historical data.60 Regional domestic and international shipping consumption estimates are distributed according to the State Energy Data Report residual oil regional shares.61 Air Travel Demand Assumptions The air travel demand module calculates the ticket price for travel as a function of fuel cost. Similar to the light-duty vehicle module, the air travel fuel price elasticity rises from -0.05 to -0.2 if jet fuel prices exceed reference case levels. A demographic index based on the propensity to fly was introduced into the air travel demand equation.62 The propensity to fly was made a function of the age and gender distribution over the forecast period63,64 The air travel demand module assumes that these relationships between the groups and their propensity to fly remain constant over time. International revenue passenger miles are based on historical data.65 The revenue ton miles of air freight are based on merchandise exports and gross domestic product. Aircraft Stock/Efficiency Assumptions The aircraft stock and efficiency module consists of a stock model of both wide and narrow body planes by vintage. The shifting of passenger load between narrow and wide body aircraft is assumed to occur at a constant historical annual 1-percent rate.66 The available seat-miles per plane, which measure the carrying capacity of the airplanes by aircraft type, remain constant and are based on holding the seat-miles and the number of planes constant within an aircraft type.67 The difference between the seat-miles demanded and the available seat-miles represents newly purchased aircraft. Aircraft purchases in a given year cannot exceed historical annual growth rates, a constraint that sets an upper limit on the application of new aircraft to meet the gap between seat-miles demanded and available seat-miles. With a constraint on new aircraft purchases, it is assumed that when the gap exceeds historical aircraft sales levels, planes that have been temporarily stored or retired will be brought back into service. Technological availability, economic viability, and efficiency characteristics of new aircraft are based on the technologies listed in the Oak Ridge National Laboratory Air Transport Energy Use Model. (See Table 39)68 Fuel efficiency of new aircraft acquisitions represents, at a minimum, a 5-percent improvement over the stock efficiency of surviving airplanes.69 Maximum growth rates of fuel efficiency for new aircraft are based on a future technology improvement list consisting of an estimate of the introduction year, jet fuel price, and an estimate of the proposed marginal fuel efficiency improvement. Regional shares of all types of aircraft fuel are assumed to be constant and are consistent with the State Energy Data Report estimate of regional jet fuel shares. Legislation Energy Policy Act of 1992 (EPACT) Fleet alternative-fuel vehicle sales necessary to meet the EPACT regulations were derived based on the mandates as they currently stand and the Commercial Fleet Vehicle Module calculations. Total projected AFV sales are divided into fleets by government, business, and fuel providers (Table 36). Business fleet EPACT mandates are not included in the projections for AFV sales pending a decision on a proposed rulemaking. Table 36. EPACT Legislative Mandates for Percentage AFV Purchases by Fleet Type, Year Because the commercial fleet model operates on three fleet type representations (business, government, and utility), the federal and state mandates were weighted by fleet vehicle stocks to create a composite mandate for both. The same combining methodology was used to create a composite mandate for electric utilities and fuel providers based on fleet vehicle stocks.70,71 Fleet vehicle stocks by car and light truck were disaggregated to include only fleets of 50 or more (in accordance with EPACT) by using a fleet size distribution function based on The Fleet Factbook and the Truck and Inventory Use Survey.72,73 To account for the EPACT regulations which stipulate that “covered” fleets (which refer to fleets bound by the EPACT mandates) include only fleets in the metropolitan statistical areas (MSA’s) of 250,000 population or greater, 90 percent of the business and utility fleets were included and 63 percent were included for government fleets.74 EPACT covered fleets were to only include those fleets that could be centrally fueled, which was assumed to be 50 percent of the fleets for all fleet types, and only fleets of 50 or more that had 20 vehicles or more in those MSA’s of 250,000 or greater population; it was assumed that 90 percent of all fleets were within this category except for business fleets, which were assumed to be 75 percent.75 Low Emission Vehicle Program (LEVP) The LEVP, which began in California, was later instituted in New York and Massachusetts, and most recently by Maine and Vermont has now been rolled back to begin in 2003 at the original 10 percent mandate for California, Massachusetts and New York. The following Zero Emission Vehicle (ZEV) sales percentage numbers (Table 37) come from the California Air Resources Board.76 All of the ULEV sales were assumed to meet the ULEV air standards with reformulated gasoline and a heated catalytic converter. On November 5, 1998, the California Air Resources Board (CARB) amended the original LEVP to include ZEV credits for advanced technology vehicles. According to CARB these advanced technology vehicles must be capable of achieving "extremely low levels of emissions on the order of the power plant emissions that occur from charging battery-powered electric vehicles, and some that demonstrate other ZEV-like characteristics such as inherent durability and partial zero-emission range."77 There are three components to calculating the ZEV credit, a baseline ZEV allowance, a zero-emission vehicle-miles traveled (VMT) allowance, and a low fuel-cycle emission allowance. Using these advanced vehicles in place of ZEV's in order to comply with the LEVP mandates requires assessment of each vehicle characteristic relative to the three criteria allowances. The baseline ZEV allowance potentially can provide up to .2 credits if the advanced technology vehicle meets the: a) Super Ultra Low Emission Vehicle (SULEV) standards contained in the originial LEVP proposal; b) on-board diagnostics requirements (OBD) which illuminates indicators on the dashboard when vehicles are out of emissions compliance levels; c) 150,000 mile emission equipment warranty; and d) evaporative emissions requirements in California which prevent emissions during refueling. SULEV emissions standards approximate the emissions from powerplants associated with recharing electric vehicles. The second criteria, zero-emission VMT allowance, will allow a maximum .6 credit if the vehicle is capable of some all-electric operation which was fueled by off-vehicle sources (i.e. no on-board fuel reformers), or if the vehicle has ZEV-like equipment on-board such as regenerative braking, advanced batteries, or an advanced electric drivetrain. An emission allowance was also made for low fuel-cycle vehicle fuels used in the advanced technology vehicles. A maximum of .2 credit is provided for vehicles which use fuel that has less than or equal to .01 NMOG grams per mile emissions based on the grams per gallon and the fuel efficiency of the vehicle. Overall, large volume manufacturers can apply ZEV credits up to a maximum of 60 percent of the original 10 percent ZEV mandate; the original ZEV mandate required that all (100 percent) of the 10 percent of all light-duty vehicle sales must be ZEVs (defined only as dedicated electric vehicles) beginning with the 2003 model year. The remaining 40 percent of the ZEV mandates must still come from electric vehicles, or variants of cell vehicles, which have extremely low emissions such as a hydrogen fuel cell vehicle. The AFV sales module compares these legislatively mandated sales to the results from the AFV logit market-driven sales shares. The legislatively mandated sales serve as a minimum constraint to AFV sales. According to the EPA federal register, EPA's Tier II proposed regulations for light-duty vehicles below 6000 pounds must meet a sales weighted average of 0.07 grams/mile NOx emissions standard by 2004 and approximately a 0.01 to 0.02 grams/mile standard for particulates.78 The previous Clean Air Act 1990 Tier I emissions standards were set at 0.6 grams/mile for NOx and 0.1 grams/mile for particulates.79 EPA has estimated the costs to consumers range from $100 per car to $200 per light-truck.80 However, recently the U.S. Circuit Court ruling determined that EPA was not authorized to set new standards without indicating the benefits of the new regulations. In the National Research Council=s (NRC) Fifth Annual Review of Partnership for a New Generation of Vehicles (PNGV)81, the NRC committee commented,"..the most difficult technical challenge facing the CIDI (compression ignition direct injection diesel) engine program will be meeting the standards for NOx and particulate emissions. In addition, meeting an even more stringent research objective (0.01 grams/mile) for particulate matter instead of the 0.04 grams/mile PNGV target would require additional technological breakthroughs." The NRC has stated their concern that the Tier II regulations may affect the commercial viability of many advanced vehicles. Meeting the Tier II proposed standards may: require trading-off emissions levels for fuel economy by redesigning engines; add significant cost to a technology due to exhaust catalyst systems and their potential lack of effectiveness; stifle development of diesel technologies as a result of the unknown health effects of particulates; and result in new specifications for diesel fuel or development of advanced low emission fuels. Energy Efficiency Programs There are four energy efficiency programs related to transportation—reform Federal subsidy for employer-provided parking, adopt a transportation system efficiency strategy, promote telecommuting, and develop fuel economy labels for tires. The combined effect of the Federal subsidy, system efficiency, and telecommuting policies was a reduction in VMT of 1.6 percent in 2010, representing a decline in consumption of approximately 310 trillion Btu with a net carbon dioxide reduction of 6.0 million metric tons carbon equivalent. The fuel economy tire labeling program improved fuel efficiency by 4 percent among vehicles that switched to low rolling resistance tires in pre-1999 vehicles. Therefore there are no new fuel or carbon dioxide savings from this program. High Technology and 2001 Technology Cases In the high technology case, the light-duty vehicle assumptions for alternative fuel vehicles are presented in Table 38 and are based on the yearly U.S. Department of Energy Office of Energy Efficiency and Renewables Office of Transportation Technologies (OTT) Program Analysis82 The conventional fuel saving technology characteristics come from a study by the American Council For an Energy Efficient Economy.83 In the high technology case, fuel efficiency improvements from new technology more than offset the increasing travel in each transportation mode. As a result, the total energy consumption in the transportation sector was 10.2 percent lower (3.95 quadrillion Btu) than in the reference case by 2020. Tables 40 and 41 summarize the High Technology matrix for cars and trucks. Table 39. Future New Aircraft Technology Improvement List Table 40. High Technology Matrix For Trucks Table 41. High Technology Matrix For Cars The 2001 technology case assumes that new fuel efficiency technologies are held constant at 2001 levels over the forecast. As a result, the energy use in the transportation sector was 6.7 percent higher (2.59 quadrillion Btu) than in the reference case by 2020. Both cases were run with only the transportation demand module rather than as a fully integrated NEMS run. Consequently, no potential macroeconomic feedback on travel demand, or fuel economy was captured. Freight trucks in the high technology case were constructed in accordance with the assumptions from a Department of Energy (DOE) study.84 The following technologies were made commercially available and cost effective within the forecast period: advanced transmissions, light weight materials, synthetic gear lube, advanced hires, advanced drag reduction, electronic engine controls, advanced engine, turbo compounding, hybrid powertrain, port-injection, and reduced empty travel. Additionally, maximum market penetration periods are reached earlier, and technology prices were made more cost-effective. The air model in the high technology case assumed efficiency from new aircraft could improve by 40 percent from the 1992 level based on the conclusion from the Aeronautics and Space Engineering Board of the National Research Council.85
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