The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption isthe sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, 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 

Light-Duty Vehicle Assumptions 

The light duty vehicle Manufacturers Technology Choice Model (MTCM) includes 63 fuel saving technologies with data specific to cars and light trucks (Tables 25 and 26) 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 Environmental Protection Administration (EPA) size classes of cars and light trucks. 

The vehicle sales share module holds the share of vehicle sales by import and domestic manufacturers constant within a vehicle size class at 1999 levels based on National Highway Traffic and Safety Administration data.³⁴ 

EPA size class sales shares are projected as a function of income per capita, fuel prices, and average predicted vehicle prices based on endogenous calculations within the MTCM³⁵ 

The MTCM utilizes 63 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: 

•  All fuel saving technologies have a 3-year payback period. 
•  The real discount rate remains steady at 15 percent. 
• Corporate Average Fuel Efficiency standards remain constant at 27.5 mpg for cars and rise from a level of 20.7 mpg in 2004 to 22.2 mpg in 2007 for light trucks, and then remain constant throughout the forecast period. 
• Expected future fuel prices are calculated based on an extrapolation of the growth rate between a five year moving average of fuel price 3 years and 4 years prior to the present year. This assumption is founded upon an assumed lead time of 3 to 4 years to significantly modify the vehicles offered by a manufacturer. 

Degradation factors (Table 27) 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.³⁶  Baseline degradation factors are then adjusted to reflect the percentage of reformulated gasoline consumed. 

The vehicle miles traveled (VMT) module forecasts VMT as a function of the cost of driving per mile, and disposable personal income per capita. Coefficients were re-estimated for AEO2006. Based on output from the model, the fuel price elasticity rises to a maximum of -0.4 as fuel prices rise above reference case levels  in each year. 

Commercial Light-Duty Fleet Assumptions 

With the current focus of transportation legislation on commercial fleets and their composition, the Transportation Demand Module is designed to divide commercial light-duty fleets into three types: business, government, and utility. Based on this classification, commercial light-duty fleet vehicles vary in survival rates and duration in fleet use before being sold for use as personal vehicles (Table 28). While the total number of vehicles sold to fleets can vary over time, the share of total fleet sales by fleet type is held constant at 2000 levels in the Transportation Demand Module. Of total automobile sales to fleets, 91.1 percent are used in business fleets, 6.4 percent in government fleets, and 2.4 percent in utility fleets. Of total light truck sales to fleets, 56.8 percent are used in business fleets, 12.3 percent in government fleets, and 31.0 percent in utility fleets.³⁷ Both the automobile and light truck shares by fleet type are held constant from 2002 through 2025.  In 2003, 19.7 percent of all automobiles sold and 12.8 percent of all light trucks sold were for fleet use.  The share of total automobile and light truck sales to fleet remains constant at these levels over the entire forecast period. 

Alternative-fuel shares of fleet sales by fleet type are held constant at year 2000 levels (business (4.78 percent), government (7.91 percent), utility (0.84 percent)),³⁸ but compared to a minimum level of sales based on legislative initiatives, such as the Energy Policy Act of 1992 and the Low Emission Vehicle Program.³⁹,⁴⁰ Size class sales shares of vehicles are held constant at anticipated levels (Table 29).⁴¹  Individual sales shares of alternative-fuel fleet vehicles by technology type are assumed to remain constant for utility, government, and for business fleets⁴² (Table 30). 

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 Model 

The Light Commercial Truck Module of the NEMS Transportation Model is constructed to represent light trucks that weigh 8,501 to 10,000 pounds gross vehicle weight (Class 2B vehicles).   These vehicles are assumed to be used primarily for commercial purposes. 

The module implements a twenty-year stock model that estimates vehicle stocks, travel, fuel efficiency, and energy use by vintage. Historic vehicle sales and stock data, which constitute the baseline from which the forecast is made, are taken from a recent Oak Ridge National Laboratory study.⁴³ The distribution of vehicles by vintage, and vehicle scrappage rates is derived from R.L. Polk company registration data.^44,45 Vehicle travel by vintage was constructed using vintage distribution curves and estimates of average annual travel by vehicle.^46,47 

The  growth  in  light  commercial  truck  VMT  is  a  function  of industrial  output for  agriculture,  mining, construction, trade, utilities,  and personal travel.  These industrial groupings were chosen for their correspondence with output measures being forecast by NEMS.  The overall growth in VMT reflects a weighted average based upon the distribution to total light commercial truck VMT by sector. Forecasted fuel efficiencies are assumed to increase at the same annual growth rate as light-duty trucks (<8,500 pounds gross vehicle weight). 

Consumer Vehicle Choice Assumptions 

The Consumer Vehicle Choice Module (CVCM) utilizes a nested multinomial logit (NMNL) model that predicts sales shares based on relevant vehicle and fuel attributes. The nesting structure first predicts the probability of fuel choice for multi-fuel vehicles within a technology set. The second level nesting predicts penetration among similar technologies within a technology set (i.e., gasoline versus diesel hybrids). The third level choice determines market share among the different technology sets.⁴⁸ The technology sets include: 

• Conventional fuel capable (gasoline, diesel, bi-fuel and flex-fuel), 
•   Hybrid (gasoline and diesel), 
•   Dedicated alternative fuel (CNG, LPG, methanol, and ethanol), 
•   Fuel cell (gasoline, methanol, and hydrogen), and 
•   Electric battery powered (lead acid, nickel-metal hydride, lithium polymer)⁴⁹ 

The vehicle attributes considered in the choice algorithm include: price, maintenance cost, battery replacement cost, range, multi-fuel capability, home refueling capability, fuel economy, acceleration and luggage space.   With the exception of maintenance cost, battery replacement cost, and luggage space, vehicle attributes are determined endogenously.⁵⁰ The fuel attributes used in market share estimation include availability and price. Vehicle attributes vary by six EPA size classes for cars and light trucks and fuel availability varies by Census division. The NMNL model coefficients were developed to reflect purchase decisions for cars and light trucks separately. 

Where applicable, CVCM fuel efficient technology attributes are calculated relative to conventional gasoline miles per gallon.   It is assumed that many fuel efficiency improvements in conventional vehicles will be transferred to alternative-fuel vehicles. Specific individual alternative-fuel technological improvements are also dependent upon the CVCM technology type, cost, research and development, and availability over time. Make and model availability estimates are assumed according to a logistic curve based on the initial technology introduction date and current offerings. Coefficients summarizing consumer valuation of vehicle attributes were derived from assumed economic valuation compared to vehicle price elasticities. Initial CVCM vehicle stocks are set according to EIA surveys.⁵¹ A fuel switching algorithm based on the relative fuel prices for alternative fuels compared to gasoline is used to determine the percentage of total VMT represented by alternative fuels in bi-fuel and flex-fuel alcohol vehicles. 

Freight Truck Assumptions 

The freight truck module estimates vehicle stocks, travel, fuel efficiency, and energy use of three size classes: light medium (Class 3), heavy medium (Classes 4 –6), and heavy (Classes 7-8).  Within the size classes, the stock model structure is designed to cover twenty vehicle vintages and estimate energy use by four fuel types: diesel, gasoline, LPG, and CNG. Fuel consumption estimates are reported regionally (by Census Division) according to the distillate fuel shares from the State Energy Data Report. ⁵² The technology input data specific to the different types of trucks including the year of introduction, incremental fuel efficiency improvement, and capital cost of introducing the new technologies, is shown in Table 31. 

The freight module uses projections of dollars of industrial output to estimate growth in freight truck travel. The industrial output is converted to an equivalent measure of volume output using freight adjustment coefficients. ⁵³,⁵⁴ These freight adjustment coefficients vary by North American Industrial Classification System (NAICS) code with the deviation diminishing gradually over time toward parity. Freight truck load-factors (ton-miles per truck) by NAICS code are constants formulated from historical data. ⁵⁵ 

Fuel economy of new freight trucks is dependent on the market penetration of various emission control technologies and advanced technology components.⁵⁶ For the advanced technology components, market penetration is determined as a function of technology type, cost effectiveness, and introduction year. Cost effectiveness is calculated as a function of fuel price, vehicle travel, fuel economy improvement, and incremental capital cost. Emissions control equipment is assumed to enter the market to meet regulated emission standards. 

Heavy truck freight travel is estimated by class size and fuel type based on matching projected freight travel demand (measured by industrial output) to the travel supplied by the current fleet. Travel by vintage and size class is then adjusted so that total travel meets total demand. Initial heavy vehicle travel, by vintage and size class, is derived using Vehicle Inventory and Use Survey (VIUS) data. ⁵⁷

Initial freight truck stocks by vintage are obtained from R. L. Polk Co. and are distributed by fuel type using VIUS data. ⁵⁸ Vehicle scrappage rates are also estimated using R. L. Polk Co. data. ⁵⁹ 

Freight and Transit Rail Assumptions 

The freight rail module uses the industrial output by NAICS code measured in real 1987 dollars and converts these dollars into an adjusted volume equivalent. Coal production from the NEMS Coal Market Module is used to adjust coal rail travel. Freight rail adjustment coefficients (used to convert dollars to volume equivalents) are based on historical data and remain constant. ^60,61  Initial freight rail efficiencies are based on the freight model from Argonne National Laboratory. ⁶² The distribution of rail fuel consumption by fuel type is also based on historical data and remains constant. ⁶³ Regional freight rail consumption estimates are distributed according to the State Energy Data Report 1999. ⁶⁴ 

Domestic and International Shipping Assumptions 

As done in the previous sub-module, the domestic freight shipping module uses the industrial output by NAICS code measured in real 1987 dollars and converts these dollars into an adjusted volume equivalent. 

The freight adjustment coefficients (used to convert dollars to volume equivalents) are based on historical data and remain constant throughout the forecast period. Domestic shipping efficiencies are based on the model developed by Argonne National Laboratory. The energy consumption in the 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 is based on historical data and remains constant throughout the analysis. ⁶⁵ Regional domestic shipping consumption estimates are distributed according to the residual oil regional shares in the State Energy Data Report. ⁶⁶ 

Air TravelDemand Assumptions 

The air travel demand module calculates the domestic and international ticket prices for travel as a function of fuel cost. The ticket price is constrained to be no lower than the lowest cost per mile, adjusted by load factor.   Domestic and international revenue passenger miles are based on historic data,⁶⁷ per capita income, and ticket price.   The revenue ton miles of air freight are based on merchandise exports, gross domestic product, and fuel cost.⁶⁸ 

Airport capacity constraints based on the FAA’s Airport Capacity Bench mark Report 2004 are incorporated into the air travel demand module using airport capacity measures.⁶⁹ Airport capacity is defined by the maximum number of flights per hour airports can routinely handle, the amount of time airports operate at optimal capacity, and passenger load factors. Capacity is expected to increase over time due to planned infrastructure improvements. If the projected demand in air travel exceeds the capacity constraint, demand is reduced to match the constraint. 

Aircraft Stock/Efficiency Assumptions 

The aircraft stock and efficiency module consists of a stock model of wide body, narrow body, and regional jets by vintage. Total aircraft supply for a given year is based on the initial supply of aircraft for model year 2003, new passenger sales, and the survival rate by vintage (Table 32).⁷⁰ New passenger sales are a function of revenue passenger miles and gross domestic product. 

Older planes, wide and narrow body planes over 25 years of age are placed as cargo jets according to a cargo percentage varying from 50 percent of 25 year old planes to 100 percent of those aircraft 30 years and older.   The available seat-miles per plane, which measure the carrying capacity of the airplanes by aircraft type, vary over time, with wide bodies remaining constant and narrow bodies increasing.⁷¹ The difference between the seat-miles demanded and the available seat-miles represents potential newly purchased planes. If demand is less than supply, then passenger aircraft is parked, starting with twenty nine year old aircraft, at a pre-defined rate. Aircraft continues to be parked until equilibrium is reached. If supply is less than demand planes that have been temporarily stored, or parked, are 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 (Table 33)⁷² Fuel efficiency of new aircraft acquisitions represents, at a minimum, a 5-percent improvement over the stock efficiency of surviving airplanes.⁷³ 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 Reportestimate of regional jet fuel shares.⁷⁴ 

Legislation 

The Energy Policy Act of 2005 

The Energy Policy Act of 2005 provides tax credits for the purchase of vehicles that have a lean burn engine or employ a hybrid or fuel cell propulsion system.  The amount of the credit received for a vehicle is based the vehicle’s inertia weight, improvement in city tested fuel economy relative to an equivalent 2002 base year value, emissions classification, and type of propulsion system.  The tax credit is also sales limited by manufacturer for vehicles with lean burn engines or hybrid propulsion systems.  After December 31, 2005, the first calendar quarter a manufacturer’s sales of lean burn or hybrid vehicles reaches 60,000 units, the phase out period begins.  Reduction of credits begins in the second calendar quarter following the initial quarter the sales maximum was reached.  For that quarter and the following quarter, the applicable tax credit will be reduced by 50 percent.  For the subsequent third and fourth calendar quarters, the applicable tax credit is reduced to 25 percent of the original value.   These tax credits are included in the AEO2006. 

Energy Policy Act of 1992 (EPACT) 

Fleet alternative-fuel vehicle sales necessary to meet the EPACT regulations are 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 34). Business fleet EPACT mandates are not included in the projections for AFV sales pending a decision on a proposed rulemaking. 

Because the commercial fleet model operates on three fleet type representations (business, government,  and utility), the federal and state mandates are weighted by fleet vehicle stocks to create a composite mandate for both. The same combining methodology is used to create a composite mandate for electric utilities and fuel providers based on fleet vehicle stocks.⁷⁵ Fleet vehicle stocks by car and light truck are 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.^76,77  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 are included and 63 percent are included for government fleets.⁷⁸ EPACT covered fleets only include those fleets that can be centrally fueled, which is 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 is assumed that 90 percent of all fleets are within this category except for business fleets, which are assumed to be 75 percent.⁷⁹ 

Low Emission Vehicle Program (LEVP) 

The LEVP was originally passed into legislation in 1990 in the State of California.   It began as the implementation of a voluntary opt-in pilot program under the purview of Clean Air Act Amendments of 1990 (CAAA90), which included a provision that other States could opt in to the California program to achieve lower emissions levels than would otherwise be achieved through CAAA90.  Connecticut, Maine, Massachusetts, New Jersey, New York, Rhode Island, Vermont, and Washington have elected to adopt the California LEVP. 

The LEVP is an emissions-based policy, setting sales mandates for 6 categories of low-emission vehicles: low-emission vehicles (LEVs), ultra-low-emission vehicles (ULEVs), super-ultra low emission vehicles (SULEVs), partial zero-emission vehicles (PZEVs), advanced technology partial zero emission vehicles (AT-PZEVs), and zero-emission vehicles (ZEVs).  The LEVP requires that in 2005 10 percent of a manufacturer’s sales are ZEVs or equivalent ZEV earned credits, increasing to 11 percent in 2009, 12 percent in 2012, 14 percent in 2015, and 16 percent in 2018 where it remains constant thereafter. In August 2004, CARB enacted further amendments to the LEVP that place a greater emphasis on emissions reductions from PZEVs and AT-PZEVs and requires that manufacturers produce a minimum number of fuel cell and electric vehicles.  In addition, manufacturers are allowed to adopt alternative compliance requirements for ZEV sales that are based on cumulative fuel cell vehicle sales targets for vehicles sold in all States participating in California’s LEVP. Under the alternative compliance requirements, ZEV credits can also be earned by selling battery electric vehicles. Currently, all manufacturers have opted to adhere to the alternative compliance requirements.  The mandate still includes phase-in multipliers for pure ZEVs and allows 20 percent of the sales requirement to be met with AT-PZEVs and 60 percent of the requirement to be met with PZEVs. AT-PZEVs and PZEVs are allowed 0.2 credits per vehicle. EIA assumes that credit allowances for PZEVs will be met with conventional vehicle technology, hybrid vehicles will be sold to meet the AT-PZEV allowances, and that hydrogen fuel cell vehicles will be sold to meet the pure ZEV requirements under the alternative compliance path. 

The vehicle sales module compares the legislatively mandated sales to the results from the consumer driven sales shares.  If the consumer driven sales shares are less than the  legislatively  mandated  sales requirements, then the legislative requirements serve as a minimum constraint for the hybrid, electric, and fuel cell vehicle sales.  

High Technology and 2005 Technology Cases 

In the high technology case, the conventional fuel saving technology characteristics came from a study by the American Council for an Energy Efficient Economy.⁸⁰  Tables 35 and 36 summarize the High Technology matrix for cars and light trucks. High technology case assumptions for heavy trucks reflect the optimistic values, with respect to efficiency improvement, for advanced engine and emission control technologies as reported by ANL.⁸¹

The 2006 technology case assumes that new fuel efficiency technologies are held constant at 2005 levels over the forecast.   As a result, the energy use in the transportation sector was 5.8 percent higher (2.31 quadrillion Btu) than in the reference case by 2030.   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.

Transportation Tables
Transportation Notes