Notes

1 National Research Council, Commission on Engineering and Technical Systems, Energy Engineering Board, Committee on the National Energy Modeling System, The National Energy Modeling System (Washington, DC: National Academy Press, 1992).

2 Energy Information Administration, Annual Energy Outlook 1994, DOE/EIA-0383(94) (Washington, DC, January 1994).

3 Energy Information Administration, Electricity Prices in a Competitive Environment: Marginal Cost Pricing of Generation Services and Financial Status of Electric Utilities, DOE/EIA-0614 (Washington, DC, August 1997); An Analysis of Carbon Mitigation Cases, SR-OIAF(96-01) (Washington, DC, June 1996); and Analysis of Carbon Stabilization Cases, SR-OIAF/97-01 (Washington, DC, October 1997).

4 A.S. Kydes and S.H. Holte, “The National Energy Modelling System: Policy Analysis and Forecasting at the U.S. Department of Energy,” in Systems Modelling for Energy Policy, eds. D.W. Bunn and E.R. Larsen (New York, NY: John Wiley and Sons, 1997), pp. 9-30.

5 Energy Information Administration, Annual Energy Outlook 1998, DOE/EIA-0383(98) (Washington, DC, December 1997).

6 Energy Information Administration, The National Energy Modeling System: An Overview 1998, DOE/EIA-0581(98) (Washington, DC, February 1998).

7 The DRI macroeconomic model is a Keynesian model of the U.S. economy, which is characterized by a system of estimated nonlinear equations.

8 See Energy Information Administration, Residential Energy Consumption Survey: Housing Characteristics 1993, DOE/EIA-0314(93) (Washington, DC, June 1995), and Residential Energy Consumption Survey: Household Energy Consumption and Expenditures 1993, DOE/EIA-0321(93) (Washington, DC, June 1995).

9 Information published for the RECS, like the Commercial Buildings Energy Consumption Survey described later, is developed through two sequential surveys over a 2-year period. These surveys are conducted every 3 to 4 years. The 1997 RECS—the next update of the survey—completed the initial buildings characteristics survey for 1997. The data were quality reviewed and updated in the second quarter of 1998. The corresponding energy consumption survey portion of the 1997 RECS will not begin until early 1999. Responses are expected from May through August 1999. Consequently, the final 1997 RECS report will not be available until the beginning of the fourth quarter of 1999. The next edition of RECS is scheduled for 2001 and probably will be published in 2003.

10 Among the sources of the shipment data are the Association of Home Appliance Manufacturers, FACT Book; Air Conditioning and Refrigerator Institute; Gas Supply Manufacturers Association; and Lawrence Berkeley National Laboratory, Energy Data Sourcebook for the U.S. Residential Sector, LBL-40297 (Berkeley, CA, May 1997), and Appliance Data Assumptions and Methodology for Residential End-Use Forecasting with EPRI-REEPS (1993) (Berkeley, CA, September 1993).

11 The primary source of data for the technology menu is Arthur D. Little, Inc., EIA—Technology Forecast Updates, Reference No. 41615 (June 20, 1995).

12 For example, the logit function for the efficiency choice for each combination of fuel and technology (electric heat pumps, gas furnaces, etc.) is based on a function of the form:



where FC_i is the installed cost, OM_i is the operation and maintenance cost of efficiency choice i, and n is the number of efficiency choices available for selection. The parameters a and b are specific to the combination being considered. Their values are set so that modeled choices closely track actual purchased efficiencies. The approximate implicit discount rate for the efficiency decision is the ratio of a to b. For a further description of the choice methodology and equations, see Energy Information Administration, Model Documentation Report: Residential Sector Demand Module of the National Energy Modeling System, DOE/EIA-M067(98) (Washington, DC, January 1998).

13 Among the reasons often cited for relatively high apparent discount rates for energy efficiency choices are uncertainty about future energy prices and thus about the returns from an energy-efficiency investment; lack, or high cost, of good information on efficiency and savings; short tenure, causing some of the gains for energy efficiency investments to be lost to the purchaser; renter/owner incentive differences, such as master metering of apartments, so that energy savings do not accrue to the tenant; and builder incentives to minimize construction costs of speculative housing. For a discussion of potential market failures and the economics of energy efficiency decisions, see A. Jaffe and R. Stavins, “Energy Efficiency Investments and Public Policy,” The Energy Journal, Vol. 15, No. 2 (1994), pp. 43-65.

14 The cost and performance characteristics of residential (and other end-use) appliances reflected our best estimates of the equipment and installation costs of complete new systems when the AEO98 projection was developed. Replacement units will often cost less than complete systems when only a portion of the system fails and is being replaced. Equipment costs are being reviewed and updated for AEO99.

15 Using appropriate discounting would lengthen the payback period in each case, and for economic reasons the efficiency upgrade would not be made. The internal rate of return over 12 years is under 7 percent. At a price of 16 cents per kilowatthour, the payback period for the 9.5 HSPF technology relative to the 8 HSPF decreases from 8 years to just over 7 years—still not sufficient to trigger wide market preference. Based on the implicit discount rate of 20 percent in the RDM, the installed cost difference between the 8 HSPF and 9.5 HSPF heat pumps would have to be just under $600 before a 4.5-year payback would be achieved and significant market share for the higher efficiency unit would be projected in the Doubling Case.

16 The CDM is currently being updated to the 1995 CBECS; the energy consumption data became available in January 1998 due to the lengthy time required to survey and process the data. Like RECS, CBECS is updated once every 3 to 4 years. For the AEO98 sources, refer to Energy Information Administration, Commercial Buildings Energy Consumption Survey: Commercial Buildings Characteristics 1992, DOE/EIA-0246(92) (Washington, DC, April 1994) and Commercial Buildings Energy Consumption Survey: Commercial Buildings Energy Consumption and Expenditures 1992, DOE/EIA-0318(92) (Washington, DC, April 1995). The next edition of CBECS will be for 1999, and the data probably will be published in 2001.

17 The three primary sources of data for the technology menu are Arthur D. Little, Inc., EIA—Technology Forecast Updates, Reference No. 41615, prepared for the U.S. Department of Energy under Contract DE-AC01-92EI21946 (Washington, DC, June 1995); Decision Analysis Corporation of Virginia, Lighting Systems Technology Characterizations for the NEMS Commercial Sector Demand Module, prepared for the Energy Information Administration under Contract DE-AC01-92EI21946 (Washington, DC, August 1996); and Decision Analysis Corporation of Virginia, Ventilation Systems Technology Characterizations for the NEMS Commercial Sector Demand Module, prepared for the Energy Information Administration, Contract DE-AC01-92EI21946 (Washington, DC, August 1996).

18 The coverage of commercial floorspace in the two sources is different, with CBECS probably covering smaller buildings more completely. For example, the DRI-Dodge estimates for the commercial sector were developed from construction costing $50,000 or more, whereas CBECS includes all buildings larger than 1,000 square feet.

19 Ignoring adjustments for building shell efficiency, price elasticity of demand, historical weather, and other effects, energy consumption (energy input) is derived after dividing service demand for output by the equipment efficiency (energy output divided by energy input).

20 New construction also has limitations on choices. As an example, a “same fuel” restriction would allocate new floorspace on the basis of existing shares by fuel type.

21 Refrigeration includes lower, medium, and higher temperature applications, the shares of which would change inappropriately if full competition across temperature ranges were permitted.

22 The use of current-year energy prices is referred to as “myopic” cost-minimizing behavior. That is, it is assumed that commercial-sector decisionmakers are not incorporating energy price projections that differ from present prices in their equipment choice decisions.

23 The service demands illustrated include growth in commercial floorspace by region and building type. For the graphs shown here, no adjustments were made for the effects of elasticities (responses to price changes) or rebound (responses to efficiency changes) on the demand for service. Adjustments are made for these effects when the CDM computes energy consumption.

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Modeling Technological Change and Diffusion in the Buildings Sector