The transportation demand module (TRAN) forecasts the consumption of transportation sector fuels by transportation mode, including the use of renewables and alternative fuels, subject to delivered prices of energy fuels and macroeconomic variables, including disposable personal income, gross domestic product, level of imports and exports, industrial output, new car and light truck sales, and population.
NEMS projections of future fuel prices influence the fuel efficiency, vehicle-miles traveled, and alternative-fuel vehicle (AFV) market penetration for the current fleet of vehicles. Alternative-fuel shares are projected on the basis of a multinomial logit vehicle attribute model, subject to State and Federal government mandates.
The Fuel Economy Submodule projects new light-duty vehicle fuel efficiency by 12 U.S. Environmental Protection Agency (EPA) vehicle size classes and 16 engine technologies (gasoline, diesel, and 14 AFV technologies) as a function of energy prices and income-related variables. There are 56 fuel-saving technologies which vary in cost and marginal fuel savings by size class. Technologies penetrate the market based on a cost-effectiveness algorithm which compares the technology cost to the discounted stream of fuel savings and the value of performance to the consumer. In general, higher fuel prices and/or lower income per capita lead to higher fuel efficiency estimates within each size class (i.e. lower performance/horsepower demanded), and a shift to a more fuel-efficient size class mix.
Vehicle sales from the macroeconomic activity module are divided into car and light truck sales based on demographic analysis. The remainder of the submodule is a simple accounting mechanism that uses endogenous estimates of new car and light truck sales and the historical regional vehicle sales adjusted for regional population trends to produce estimates of regional sales, which are subsequently passed to the alternative-fuel vehicle and the light-duty vehicle stock submodules.
The Alternative-Fuel Vehicle submodule projects the sales shares of alternative-fuel technologies as a function of time, technology attributes, costs, and fuel prices. Both conventional and new technology vehicles are considered. The alternative-fuel vehicle submodule receives regional new car and light truck sales by size class from the regional sales submodule.
The forecast of vehicle sales by technology requires a three-stage nested decision process. The first stage consists of endogenously calculating the sales shares between conventional and total alternative-fuel vehicles on a regional level, based on the following factors: regional fuel operating costs per mile (fuel price divided by fuel efficiency), vehicle price, range, regional fuel availability, commercial and size-class availability, and regional regulatory constraints.
Once the level of total alternative-fuel vehicles per region has been calculated, the second stage estimates shares among the alternative-fuel vehicle technologies within each region, based on the same regional factors and methodology used in the prior step to calculate the shares of conventional and total alternative-fuel vehicle sales. The third stage subdivides electric vehicle sales into individual electric vehicle technologies. TRAN includes the following alternative-fuel technologies: methanol flex-fueled, methanol neat (85 percent methanol), ethanol flex-fueled, ethanol neat (85 percent ethanol), compressed natural gas (CNG), CNG Bi-Fuel, liquefied petroleum gas (LPG), LPG Bi-Fuel, electric, electric hybrid, gas turbine gasoline, gas turbine CNG, fuel cell methanol, and fuel cell hydrogen.
The Light-Duty Vehicle Stock submodule specifies the inventory of light-duty vehicles from year to year. Separate car and light truck survival rates are applied to 10 vintages, and new vehicle sales are introduced into the vehicle stock through an accounting framework. The fleet of vehicles and their fuel efficiency characteristics are maintained through time as they are important to the translation of transportation services demand into fuel demand. Degradation factors are also applied to the new vehicle efficiencies to account for the differences between EPA estimated fuel economy and actual "on the road" fuel efficiencies. These degradation factors take into account over time increasing city to highway driving, rising congestion, and higher average highway speeds.
TRAN maintains a level of detail that includes ten vintage classifications and six passenger car and six light truck size classes corresponding to EPA interior volume classifications for all vehicles less than 8,500 pounds.
This submodule projects travel demand for automobiles and light trucks. VMT per capita estimates are based on the fuel cost of driving per mile, per capita disposable personal income, an index that reflects the aging of the population, and an adjustment for female-to-male driving ratios. Total VMT is calculated by multiplying VMT per capita by the driving age population.
This submodule generates estimates of the sales and stock of cars and light trucks used in business, government, and utility fleets. It also estimates travel demand, fuel efficiency, and energy consumption for the fleet vehicles prior to their transition to the private sector at predetermined vintages.
The commercial light truck submodule estimates sales, stocks, fuel efficiencies, travel, and fuel demand for all trucks greater than 8,500 pounds and less than 10,000 pounds.
This submodule estimates the demand for both passenger and freight air travel. Passenger travel is forecasted by domestic travel, which is disaggregated between business and personal travel, and international travel. Dedicated air freight travel is disaggregated between the total air freight demand and air freight carried in the lower hull of commercial passenger aircraft. In each of the market segments, the demand for air travel is estimated as a function of the cost of air travel (including fuel costs) and economic growth (GDP, disposable income, and merchandise exports).
This submodule forecasts the total stock and the average fleet efficiency of narrow body and wide body aircraft required to meet the projected travel demand. The stock estimation is based on the growth of travel demand and a logistic function that calculates the survival of the older planes. The overall fleet efficiency is determined by the weighted average of the surviving aircraft efficiency (including retrofits) and the efficiencies of the newly acquired aircraft. The efficiency improvements of the new aircraft are determined by technology choice (ultra-high bypass, propfan, hybrid laminar flow, advanced aerodynamics, weight-reducing materials, or thermodynamics) which depends on the trigger fuel price and the time in which the technology has been commercially viable.
This submodule translates NEMS estimates of industrial production into ton-miles traveled requirements for rail and ship travel, and into vehicle-miles traveled for trucks, then into fuel demand by mode of freight travel. The freight truck stock submodule is subdivided into medium and heavy-duty trucks. VMT freight estimates by truck size class and technology are based on matching freight needs, as measured by the growth in industrial output by Standard Industrial Classification (SIC) code, to VMT levels associated with truck stocks and new vehicles.
Rail and shipping ton-miles traveled are also estimated as a function of growth in industrial output. Freight truck fuel efficiency growth rates relative to fuel prices are tied to historical growth rates by size class and are also dependent on the maximum penetration, introduction year, fuel trigger price (based on cost-effectiveness) and fuel economy improvement of the technologies including alternative-fuel technologies. In the rail and shipping modes, energy efficiency estimates are structured to evaluate the potential of both technology trends and efficiency improvements related to energy prices.
This submodule projects the use of energy in military operations, mass transit vehicles, recreational boats, and automotive lubricants, based on endogenous variables within NEMS (e.g., vehicle fuel efficiencies) and exogenous variables (e.g., the military budget).
11Energy Information Administration, The National Energy Modeling System: An Overview 1998, DOE/EIA-0581(98) (Washington, DC, February 1998).
Appendix C: Request for Service Report
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