Assumptions to the Annual Energy Outlook 2002
Footnotes
[1] Energy Information Administration, Annual Energy Outlook 2002 (AEO2002), DOE/EIA-0383(2002), (Washington, DC, December 2001).
[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 2000, DOE/EIA-0581(2000), (Washington, DC, March 2000).
[4] The underlying macroeconomic growth cases use DRI-WEFA July 2001 T250701 and February TO250299 and TP250299.
[5] PennWell Publishing Co., International Petroleum Encyclopedia, (Tulsa, OK, 2001).
[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, 2001).
[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(2001), (December 2000).
[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, Series C25 Data from various years of publications.
[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., October 2001).
[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 of projected solar photovoltaic system installations under the Million Solar Roofs program, State and local incentive programs, and the potential endogenous penetration of solar photovoltaic systems and solar thermal water heaters.
[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(2002), (December 2001).
[18] The floorspace from the Macroeconomic Activity Model is based on the DRI-WEFA floorspace estimates which are approximately 15 percent lower than the estimate obtained from the CBECS used for the Commercial module. The DRI-WEFA estimate 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, mainframe computers, and other miscellaneous office equipment. A tenth category denoted other includes equipment such as elevators, medical, and other laboratory equipment, communications equipment, security equipment, transformers 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 Estimation of Energy End-Use Intensities, web site www.eia.doe.gov/emeu/cbecs/tech_end_use.html.
[23] The proportion of equipment retiring is inversely related to the equipment life.
[24] For current DOE technology characterizations for photovoltaic systems see web site www.eren.doe.gov/pv/pvmenu.cgi?site=pv&idx=2&body=newsinfo.html
[25] Energy Information Administration, State Energy Data Report 1999, DOE/EIA-0214(99), (Washington, D.C., May 2001).
[26] Energy Information Administration, Manufacturing Energy Consumption Survey, web site www.eia.doe.gov/emeu/mecs/mecs98/datatables/contents.html.
[27] Aluminum is excluded due to its almost exclusive reliance on electricity in the process and assembly component.
[28] Energy Information Administration, Manufacturing Energy Consumption Survey, web site www.eia.doe.gov/emeu/mecs/mecs98/datatables/contents.html.
[29] These assumptions are based in part on Arthur D. Little, Industrial Model: Update on Energy Use and Industrial Characteristics (September 2001).
[30]U.S. Department of Transportation, National Highway Traffic and Safety Administration, Mid-Model Year Fuel Economy Reports from Automanufacturers, (1999).
[31] Goldberg, Pinelopi Koujianou, Product Differentiation and Oligopoly In International Markets: The Case of The U.S. Automobile Industry,” Econometrica, Vol. 63, No.4 (July, 1995), 891-951.
[32] 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); Decision Analysis Corporation of Virginia, Fuel Efficiency Degradation Factor, Final Report, Prepared for Energy Information Administration (EIA), (Vienna, VA, August 3, 1992); U.S. Department of Transportation, Federal Highway Administration, New Perspectives in Commuting, (Washington, DC, July 1992); U.S. Department of Transportation, Federal Highway Administration, Highway Statistics 1999, FHWA-PL-00-020, (Washington, DC, November 1, 2000); and Green, Tamara, “Re-estimation of Annual Energy Outlook 2000 Degradation Factors,” prepared for the Energy Information Administration, unpublished paper, August 18, 1999, Washington, D.C.
[33] U.S. Department of Transportation, op.cit., Note 32.
[34] 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).
[35] Energy Information Administration, Describing Current and Potential Markets for Alternative Fuel Vehicles, DOE/EIA-0604(96), (Washington, DC, March 1996).
[36] Energy Information Administration, Alternatives to Traditional Transportation Fuels http://www.eia.doe.gov/cneaf/solar.renewables/alt-trans-fuel98/table14.html.
[37] Bobbit, The Fleet Fact Book, Redondo Beach, (California, 1995).
[38] U.S. Department of Commerce, Bureau of Census, Truck Inventory and Use Survey 1992, TC-92-T-52, (Washington, DC, May 1995).
[39] 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).
[40] California Resources Board, Proposed Regulations for Low-Emission Vehicles and Fuels, Staff Report, (August 13, 1990).
[41] 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).
[42] Energy Information Administration, op.cit., Note 34.
[43] Energy Information Administration, op.cit., Note 35.
[44] Davis, Stacy C., Lorena F. Truett, “Investigation of Class 2B Trucks (Vehicles of 8,500 to 10,000 LBS GVWR),” Oak Ridge National Laboratory, DRAFT ORNL/TM, Table 1, page 11, (Oak Ridge, TN, September 27, 2001).
[45] Schmoyer, Rick, “Scrappage of Heavy Trucks-Estimates for the Year 2000,” Oak Ridge National Laboratory, DRAFT, (Oak Ridge, TN, June 2001).
[46] Davis, Stacy C., “Memorandum on the Distribution of Trucks by Age and Weight: 2000 Truck Population,” Oak Ridge National Laboratory, (Oak Ridge, TN, November 2001).
[47] Davis, Stacy C., Transportation Energy Data Book Edition 20, Center For Transportation Analysis, Oak Ridge National Laboratory, Table 6.7, page 6-9, ORNL-6958, (Oak Ridge, TN, October, 2000).
[48] Davis, Stacy C., Lorena F. Truett, “Investigation of Class 2B Trucks (Vehicles of 8,500 to 10,000 LBS GVWR),” Oak Ridge National Laboratory, DRAFT ORNL/TM, Table 13, page 23, (Oak Ridge, TN, September 27, 2001).
[49] Greene, David L. and S.M. Chin, “Alternative Fuels and Vehicles (AFV) Model Changes,” Center for Transportation Analysis, Oak Ridge National Laboratory, page 1, (Oak Ridge, TN, November 14, 2000).
[50] 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).
[51] Energy and Environmental Analysis, Inc., Updates to the Fuel Economy Model (FEM) and Advanced Technology Vehicle (ATV) Module of the National Energy Modeling System (NEMS) Transportation Model, Prepared for the Energy Information Administration (EIA), (Arlington, VA, October 23, 2000).
[52] Energy Information Administration, op.cit., Note 34.
[53] Energy Information Administration, op.cit., Note 35.
[54] Energy Information Administration, State Energy Data Report 1999, DOE/EIA-0214(99), (Washington, DC, May 2001).
[55] Decision Analysis Corporation of Virginia, “Re-estimation of Freight Adjustment Coefficients,” Report Prepared for the Energy Information Administration (EIA), (February 28, 1995).
[56] Reebie Associates, TRANSEARCH Freight Commodity Flow Database, (Greenwich CT, 1992).
[57] U.S. Department of Commerce, Bureau of Census, Vehicle Inventory and Use Survey Data 1997, EC-97-TV-US, (Washington, DC, October 1999).
[58] Vyas, A., C. Saricks, and F. Stodolsky, “Projected Effect of Future Energy Efficiency and Emissions Improving Technologies on Fuel Consumption of Heavy Trucks,” Argonne National Laboratory, (Argonne, Illinois, 2001).
[59] Davis, Stacy C., Memorandum on the Average Annual Miles Traveled per Truck: 1997 Vehicle Inventory and Use Survey, (November 2001).
[60] Davis, Stacy C., Memorandum on the Distribution of Trucks by Age and Weight: 2000 Truck Population, Oak Ridge National Laboratory, (November 2001).
[61] Schmoyer, Rick, “Scrappage of Heavy Trucks-Estimates for the Year 2000,” Oak Ridge National Laboratory, DRAFT, (Oak Ridge, TN, June 2001).
[62] Decision Analysis Corporation of Virginia, op.cit., Note 49.
[63] Reebie Associates, op.cit., Note 50.
[64] Argonne National Laboratory, Transportation Energy Demand Through 2010, (Argonne, IL, 1992).
[65] 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).
[66] Energy Information Administration, op.cit., Note 53.
[67] Decision Analysis Corporation of Virginia, op.cit., Note 49.
[68] Reebie Associates, op.cit., Note 50.
[69] Army Corps of Engineers, Waterborne Commerce of the United States, (Waterborne Statistics Center: New Orleans, LA, 1993).
[70] Energy Information Administration, op.cit., Note 53.
[71] Transportation Research Board, Forecasting Civil Aviation Activity: Methods and Approaches, Appendix A, Transportation Research Circular Number 372, (June 1991).
[72] Decision Analysis Corporation of Virginia, Re-estimation of NEMS Air Transportation Model, Prepared for the Energy Information Administration (EIA), (Vienna, VA, 1995).
[73] Air Transport Association of America, Air Travel Survey, (Washington DC, 1990).
[74] U.S. Department of Transportation, Air Carrier Traffic Statistics Monthly, (December 1996).
[75] U.S. Department of Transportation, Federal Aviation Administration, FAA Aviation Forecasts Fiscal Years 1996-2008, (Washington, DC, March 1997, and previous editions).
[76] Ibid.
[77] 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).
[78] Ibid.
[79] Energy Information Administration, op.cit., Note 34.
[80] Energy Information Administration, op.cit., Note 35.
[81] Bobbit, op.cit., Note 36.
[82] U.S. Department of Commerce, Bureau of Census, op.cit., Note 37.
[83] U.S. Department of Energy, Office of Policy, op.cit., Note 38.
[84] U.S. Department of Energy, Office of Policy, op.cit., Note 38.
[85] State of California Air Resources Board, Staff Report: Initial Statement of Reasons, Proposed Amendments to California Exhaust and Evaporative Emissions Standards and Test Procedures For Passenger Cars, Light-Duty Trucks and Medium-Duty Vehicles -”LEV II” and Proposed Amendments to California Motor Vehicle Certification, Assembly-Line and In-Use Test Requirements -”CAP 2000,” Mobile Source Control Division, El Monte, CA, September 18, 1998.
[86] Http://www.epa.gov/fedrgstr/EPA-AIR/1999/May/Day-13/a11384a.htm.
[87] U.S. EPA, Office of Mobile Sources, Exhaust Emission Certification Standards, EPA 420-B-98-001, (March 24, 1998).
[88] http://www.epa.gov/oms/tr2home.htm.
[89] National Research Council, Review of the Research Program of the Partnership for a New Generation of Vehicles: Fifth Report, National Academy Press, Washington, D.C., 1999.
[90] DeCicco, John, and Marc Ross, “An Updated Assessment of the Near-Term Potential for Improving Automotive Fuel Economy,” (November 1993).
[91] Vyas, A., C. Saricks, and F. Stodolsky, “Projected Effect of Future Energy Efficiency and Emissions Improving Technologies on Fuel Consumption of Heavy Trucks,” Argonne National Laboratory, (Argonne, Illinois, 2001).
[92] National Research Council, Aeronautics and Space Engineering Board, 1992. Aeronautical Technologies for the Twenty-First Century, National academy Press, Washington, D.C.
URL: http://www.eia.doe.gov/oiaf/aeo/footnote.html