Report#:DOE/EIA-0554(99)
Introduction
Transportation
Demand
Electricity Market
Oil and Gas
Supply
Natural Gas Transmission & Distribution
Petroleum Market
Coal Market
Renewable Fuels
Download a Comleted Copy in PDF
Format
Assumptions to the AEO99
Interactive Data Queries to the AEO99
Supplemental Tables to
the AEO99
NEMS Conference
To Forecasting Home Page
EIA Homepage
[1] Energy Information Administration, Annual Energy Outlook 1999 (AEO99), DOE/EIA-0383(99), (Washington, DC, December 1998).
[2] NEMS documentation reports are available on the EIA CD-ROM and the EIA Homepage (http://www.eia.doe.gov/bookshelf.html). For ordering information on the CD-ROM, contact STAT-USA's toll free order number: 1-800-STAT-USA or by calling (202) 482-1986.
[3] Energy Information Administration, The National Energy Modeling System: An Overview 1998, DOE/EIA-0581(98), (Washington, DC, February 1998).
[4] The underlying macroeconomic growth cases use DRI/McGraw-Hill’s August 1998 T250898 and February TO250298 and TP250298.
[5] EIA, International Energy Outlook 1998, DOE/EIA-0484(98) (Washington DC, April 1998).
[6] EIA, EIA Model Documentation: World Oil Refining Logistics Demand Model, “WORLD” Reference Manual, DOE/EIA-M058, (Washington, DC, March 1994).
[7] Oil & Gas Journal, World Wide Refinery Survey, (data as of January 1, 1996).
[8] The Model Documentation Report contains additional details concerning model structure and operation. Refer to Energy Information Administration, Model Documentation Report: Residential Sector Demand Module of the National Energy Modeling System, DOE/EIA M065(99), ( January 1999).
[9] Among the explanations often mentioned for observed high average implicit discount rates are: market failures, (i.e., cases where incentives are not properly aligned for markets to result in purchases based on energy economics alone); unmeasured technology costs (i.e., extra costs of adoption which are not included or difficult to measure like employee down-time); characteristics of efficient technologies viewed as less desirable than their less efficient alternatives (such as equipment noise levels or lighting quality characteristics); and the risk inherent in making irreversible investment decisions. Examples of market failures/barriers include: decision makers having less than complete information, cases where energy equipment decisions are made by parties not responsible for energy bills (e.g., landlord/tenants, builders/home buyers), discount horizons which are truncated (which might be caused by mean occupancy times that are less than the simple payback time and that could possibly be classified as an information failure), and lack of appropriate credit vehicles for making efficiency investments, to name a few. The use of high implicit discount rates in NEMS merely recognizes that such rates are typically found to apply to energy-efficiency investments.
[10] U.S. Bureau of Census, Characteristics of New Housing, C25/95-A.
[11] The high technology assumptions are based on Energy Information Administration, Technology Forecast Updates-Residential and Commercial Building technologies-Advanced Adoption Case (Arthur D. Little, Inc., September 1998).
[12] Energy Information Administration, A Look at Commercial Buildings in 1995: Characteristics, Energy Consumption, and Energy Expenditures, DOE/EIA-0625(95), (Washington, DC, October 1998).
[13] The fuels accounted for by the commercial module are electricity, natural gas, distillate fuel oil, residual fuel oil, liquefied petroleum gas (LPG), coal, motor gasoline, and kerosene. In addition to these fuels the use of solar energy is projected based on an exogenous forecast.
[14] The end-use services in the commercial module are heating, cooling, water heating, ventilation, cooking, lighting, refrigeration, PC and non-PC office equipment and a category denoted other to account for all other minor end uses.
[15] The 11 building categories are assembly, education, food sales, food services, health care, lodging, large offices, small offices, mercantile/services, warehouse and other.
[16] Minor end uses are modeled based on penetration rates and efficiency trends.
[17] The detailed documentation of the commercial module contains additional details concerning model structure and operation. Refer to Energy Information Administration, Model Documentation Report: Commercial Sector Demand Module of the National Energy Modeling System, DOE/EIA M066(99), (Forthcoming January 1999).
[18] The floorspace from the Macroeconomic Activity Model is based on the Data Resources Incorporated (DRI) floorspace estimates which are approximately 15 percent lower than the estimate obtained from the CBECS used for the Commercial module. The DRI forecast is developed using the F.W. Dodge data on commercial floorspace. See F.W. Dodge, Building Stock Database Methodology and 1991 Results, Construction Statistics and Forecasts, F.W. Dodge, McGraw-Hill.
[19] The commercial module performs attrition for 9 vintages of floorspace developed from the CBECS 1995 stock estimate and historical floorspace additions data from F.W. Dodge data.
[20] In the event that the computation of additions produce a negative value for a specific building type, it is assumed to be zero.
[21] “Other office equipment” includes copiers, fax machines, typewriters, cash registers, and other miscellaneous office equipment. A tenth category denoted other includes equipment such as elevators, medical, and other laboratory equipment, communications equipment, security equipment, and miscellaneous electrical appliances. Commercial energy consumed outside of buildings and for cogeneration is also included in the “other” category.
[22] Based on updated estimates using CBECS 1995 data and the methodology described in End-Use Energy Consumption Estimates for U.S. Commercial Buildings, 1992, Belzer, D.B., and Wrench, L.E., Pacific Northwest Laboratories, PNNL-11514, Prepared for the U.S. DOE under Contract DE-AC06-76RLO-1830, (Richland, WA, March, 1997).
[23] The proportion of equipment retiring is inversely related to the equipment life.
[24] The sensitivity parameter assumes that a 10 percent change in relative prices results in a 1 percent change in Cogeneration activity.
[25] Energy Information Administration, State Energy Data Report 1995, DOE/EIA-0214(95), (Washington, D.C., August 1998).
[26] Energy Information Administration, Manufacturing Consumption of Energy 1994, DOE/EIA-0512(94), (Washington, D.C., December 1997).
[27] Primary aluminum is excluded because they use only electricity in the process and assembly component.
[28] Energy Information Administration, Manufacturing Energy Consumption Survey: Consumption of Energy 1994, DOE/EIA-0512(94), (Washington, D.C., December 1994).
[29] These assumptions are based in part on Arthur D. Little, “Aggressive Technology for the NEMS model,” (September 1998).
[30] U.S. Department of Transportation, National Highway Traffic and Safety Administration, Mid-Model Year Fuel Economy Reports from Automanufacturers, (1997).
[31] Maples, John D., The Light-Duty Vehicle MPG Gap: Its Size Today and Potential Impacts in the Future, University of Tennessee Transportation Center, Knoxville, TN, (May 28, 1993, Draft).
[32] Decision Analysis Corporation of Virginia, Fuel Efficiency Degradation Factor, Final Report, Prepared for Energy Information Administration (EIA), (Vienna, VA, August 3, 1992).
[33] U.S. Department of Transportation, Federal Highway Administration, New Perspectives in Commuting, (Washington, DC, July 1992).
[34] U.S. Department of Transportation, Federal Highway Administration, Highway Statistics 1996, FHWA-PL-96-023, (Washington, DC, 1996).
[35] Decision Analysis Corporation of Virginia, NEMS Transportation Sector Model: Re-estimation of VMT Model, Prepared for Energy Information Administration (EIA), (Vienna, VA, June 30, 1995).
[36] Energy Information Administration, Describing Current and Potential Markets for Alternative Fuel Vehicles, DOE/EIA-0604(96), (Washington, DC, March 1996).
[37] Energy Information Administration, Alternatives to Traditional Transportation Fuels 1996, DOE/(EIA-0585(96), (Washington, DC, December 1997).
[38] Bobbit, The Fleet Fact Book, Redondo Beach, (California, 1995).
[39] U.S. Department of Commerce and Bureau of Census, Truck Inventory and Use Survey 1992, TC-92-T-52, (Washington, DC, May 1995).
[40] U.S. Department of Energy, Office of Policy, Assessment of Costs and Benefits of Flexible and Alternative Fuel Use in the U.S. Transportation Sector, Technical Report Fourteen: Market Potential and Impacts of Alternative-Fuel Use in Light-Duty Vehicles: A 2000/2010 Analysis, (Washington, DC, 1995).
[41] California Resources Board, Proposed Regulations for Low-Emission Vehicles and Fuels, Staff Report, (August 13, 1990).
[42] Oak Ridge National Laboratory, Fleet Vehicles in the United States: Composition, Operating Characteristics, and Fueling Practices, Prepared for the Department of Energy, Office of Transportation Technologies and Office of Policy, Planning, and Analysis, (Oak Ridge, TN, May 1992).
[43] U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, prepared by Interlaboratory Working Group, Scenarios of U.S. Carbon Reductions: Potential Impacts of Energy Technologies by 2010 and Beyond, (Washington DC, 1998).
[44] U.S. Department of Energy, Office of Transportation Technologies and Energy Efficiency and Renewable Energy, Alternative-Fuel Vehicle Model, 1998.
[45] Energy and Environmental Analysis, Changes to the Fuel Economy Module, Final Report, Prepared for Energy Information Administration (EIA), (June 1998).
[46] U.S. Department of Energy, Office of Transportation Technologies, and Argonne National Laboratory, National Alternative Fuel Vehicle Survey, Draft, August 21, 1998.
[47] Decision Analysis Corporation of Virginia, Re-estimation of Freight Adjustment Coefficients, Report Prepared for Energy Information Administration (EIA), (February 28, 1995).
[48] Reebie Associates, TRANSEARCH Freight Commodity Flow Database, (Greenwich, CT, 1992).
[49] U.S. Department of Energy, Office of Heavy Vehicle, Technologies (OHVT), OHVT Technology Roadmap, DOE/OSTI-11690, (October 1997).
[50] Energy Information Administration, State Energy Data Report 1995, DOE/EIA-0214(95), (Washington, DC, December 1997).
[51] U.S. Department of Transportation, Federal Railroad Administration, 1989 Carload Waybill Statistics; Territorial Distribution, Traffic and Revenue by Commodity Classes, (September 1991 and prior issues).
[52] Argonne National Laboratory, Transportation Energy Demand Through 2010, (Argonne, IL, 1992).
[53] Army Corps of Engineers, Waterborne Commerce of the United States, (Waterborne Statistics Center: New Orleans, LA, 1993).
[54] Transportation Research Board, Forecasting Civil Aviation Activity: Methods and Approaches, Appendix A, Transportation Research Circular Number 372, (June 1991).
[55] Decision Analysis Corporation of Virginia, Re-estimation of NEMS Air Transportation Model, Prepared for the Energy Information Administration (EIA), (Vienna, VA, 1995).
[56] Air Transport Association of America, Air Travel Survey, (Washington DC, 1990).
[57] U.S. Department of Transportation, Air Carrier Traffic Statistics Monthly, (December 1996).
[58] U.S. Department of Transportation, Federal Aviation Administration, FAA Aviation Forecasts Fiscal Years 1996-2008, (Washington, DC, March 1997, and previous editions).
[59] Oak Ridge National Laboratory, Energy Efficiency Improvement of Potential Commercial Aircraft to 2010, ORNL-6622, (Oak Ridge, TN, June 1990), Oak Ridge National Laboratory, Air Transport Energy Use Model, Draft Report, (Oak Ridge, TN, April 1991).
[60] California Air Resources Board, Proposed Regulations for Low Emission Vehicles and Clean Fuels, Staff Report, (August 13, 1990).
[61] DeCicco, John, and Marc Ross, An Updated Assessment of the Near-Term Potential for Improving Automotive Fuel Economy, American Council for an Energy Efficient Economy, November 1993.
[62] U.S. Department of Energy, Office of Energy Efficiency and Renewables, Office of Transportation Technologies, OTT Program Analysis Methodology: Quality Metrics 99, (December, 1997)
