## APPENDIX B. FORECASTING MODELS

B.1 LBL RESIDENTIAL ENERGY MODEL B.1.1 Overview The LBL Residential Energy Model (LBL-REM) provides projections ofthe important characteristics of the residential appliance market. The LBL-REM utilizes a database of significant detenninants of the current residential appliance market, as well as parameters characterizing marketdecisions thatwill affectthe energy consumption offuture appliances. This appendix describes the LBL-REM, the types ofdata it uses, the calculations itperfonns, and the results the LBL-REM provides. The LBL-REM projects numbers of households by house type, energy-related building shellcharacteristics, average energy efficiency ofappliances, fraction ofhouseholds owning each appliance, usage behavior, and turnover of appliances. These characteristics are projected for each year from 1980 to 2030. The LBL-REM combines these factors to project energy consumption and expenditures by fuel type for each end use, and monetary expenditures for equipment (both replacement and new installations) and for fuels. The results are reported in several fonnats, for convenience, including: annualenergyconsumptionandexpenditures, annualequipmentshipments,cumulative energy consumption and expenditures overaperiod (e.g., from policy implementationdate to the end of the projection period), and net present values of equipment and energy expenditures, discounted and summed over time. The basic equation in the simulation model that defines residential use offuel i for end use 

### k in housing type 

### m during year 

### tis:
(B.1)
where STOKC is the stock ofoccupied housing units, C is the saturation (percent ofhouseholds with this equipment): EU is the unit energy consumption, U is the usage behavior factor, and TIis the thermal integrity factor (for space conditioning only). Households respond to changes in operatingexpenses in threedifferent ways. In the short run (see B.1.6, "Usage Behavior"), they change the way they operate existing equipment and structures (e.g., lower winter thermostat settings). In the long run, they also change equipment by switching from one fuel to another (see B.1.5, "Market Shares"), by improving the efficiency B-1 

## APPENDIX B. FORECASTING MODELS

B.1 LBL RESIDENTIAL ENERGY MODEL B.1.1 Overview The LBL Residential Energy Model (LBL-REM) provides projections of the important characteristics of the residential appliance market. The LBL-REM utilizes a database of significant detenninants of the current residential appliance market, as well as parameters characterizingmarketdecisions thatwill affecttheenergyconsumptionoffuture appliances. This appendix describes theLBL-REM, the types ofdata it uses, the calculations itperfonns, andthe results the LBL-REM provides. The LBL-REM projects numbers of households by house type, energy-related building shellcharacteristics, average energyefficiencyofappliances, fraction ofhouseholdsowningeach appliance, usage behavior, and turnover of appliances. These characteristics are projected for each yearfrom 1980 to 2030. The LBL-REM combines these factors to project energy consumption and expenditures by fuel type for each enduse, and monetary expenditures for equipment (bothreplacement and new installations) and for fuels. The results are reported in several fonnats, for convenience, including: annualenergyconsumptionandexpenditures,annualequipmentshipments,cumulative energy consumption andexpenditures overa period (e.g., from policyimplementationdateto the end of the projection period), and net present values of equipment and energy expenditures, discounted and summed over time. The basic equation in the simulation model that defines residential use offuel i for end use 

### k in housing type m during year tis:
(B.l)
where STOKC is the stock ofoccupied housing units, C is the saturation (percent ofhouseholds with this equipment): 

### EU is the unit energy consumption, 

### U is the usage behavior factor, and TIis the thermal integrity factor (for space conditioning only). Householdsrespondtochangesin operatingexpenses inthreedifferentways. In the short run (see B.1.6, "Usage Behavior"), they change the way they operate existing equipment and structures (e.g., lower winter thermostat settings). In the long run, they also change equipment by switchingfrom one fuel to another (see B.1.5, "Market Shares"), by improving the efficiency B-1of their equipment (e.g., purchasing a water heater with more jacket insulation (see B.1.4, "Equipment Efficiency"), or both. Thus, the elasticity of demand for a particular fuel with respect to the price ofthat fuel can be thought ofas being separated into three elements-ausage elasticity (E u)'a technical efficiency elasticity (E e

), and an equipment/fuel choice (market share) elasticity (Ems): (B.2)

Infact, the LBL-REM also includes "cross-price" effects in the elasticity ofmarket share Ems' Le., the effect of changes in the operating expense of one fuel on the market shares of competing fuels and technologies. 

### B.t.2 Housing The housing submodel uses: (1) population projections, which are more stable than projections ofhousing completions because ofthe comparatively larger influence ofeconomics on housing completions. In the long run, the number ofhousing units available is expected to agree with the need for housing units, which is determined by the size and composition ofthe population; (2) population projections for states by house type; and (3) uses projections ofthe number ofpeople in each age group in future years to estimate changes in the distribution of house types. 

### B.t.2.t Number ofHouseholds The methodfordetermining the numberofhouseholds ofeach type in any future yearhas several steps. First, the 0.1% sample ofthe 1980 Census is analyzed to provide the fraction of each of four age cohorts (0-19, 20-39, 40-59, 60+) living in each of four house types
.. -. (single-family detached, single-family attached, multifamily with five ormore units perbuilding, and mobile homes). Second, a projection of future population by state and by age cohort is multiplied by the fraction ofthose people who live in each house type to derive a projection of population in each house type in future years. Third, the number ofpersons per household by house type and by state is determined from the 1980 Census 0.1% sample. Fourth, the number ofhouses required ofeach type is obtainedby dividing the numberofpersons in each house type by the persons per household appropriate for that state and house type. The number ofpersons per household in each state and housing type is assumed to remain constant over time at 2.76 persons per household (based on 1980 data). (Assuming declining persons per household produces inconsistency between the number ofhouseholds required by the projectedpopulation and the projected number of new housing units. Accepting the projection of new construction requires rejection ofeither the population projection, the household projection, or the projected decline in persons per household.)

B-2 of their equipment (e.g., purchasing a water heater with more jacket insulation (see B.1.4, "Equipment Efficiency"), or both. Thus, the elasticity of demand for a particular fuel with respect tothe price ofthat fuel can be thought ofas being separated into three elements-ausage elasticity (E u)' a technical efficiency elasticity (E e), and an equipment/fuelchoice (market share) elasticity (Ems): (B.2)

Infact, the LBL-REMalso includes "cross-price" effects in the elasticity ofmarket share Ems' Le., the effect of changes in the operating expense of one fuel on the market shares of competing fuels and technologies
. 

### B.l.2 Housing The housing submodel uses: (1) population projections, which are more stable than projections ofhousing completions because ofthe comparatively larger influence ofeconomics on housing completions. In the long run, the number ofhousing units available is expected to agree with the need for housing units, which is determined by the size and composition ofthe population; (2) population projections for states by house type; and (3) uses projections of the number ofpeople in each age group in future years to estimate changes in the distribution of house types. 

### B.l.2.1 Number ofHouseholds Themethodfordeterminingthe numberofhouseholds ofeach typein anyfuture yearhas several steps. First, the 0.1% sample ofthe 1980 Census is analyzed to provide the fraction
. of each of four age cohorts (0-19, 20-39, 40-59, 60+) living in each of four house types
.-.- . (single-familydetached, single-familyattached,multifamilywith five ormoreunitsperbuilding, and mobile homes). Second, a projection of future popUlation by state and by age cohort is multiplied by the fraction ofthose people who live in each house type to derive a projection of population in each house type in future years. Third, the number ofpersons perhousehold by house type and by state is determined from the 1980 Census 0.1% sample. Fourth, the number ofhousesrequired ofeach type is obtainedby dividing the numberofpersons ineachhouse type by the persons perhousehold appropriate for that state and house type. Thenumberofpersons per household in each state and housing type is assumed to remain constant over time at 2.76 persons per household (based on 1980 data). (Assuming declining persons per household produces inconsistency between the number ofhouseholds required by the projectedpopulation and the projected number of new housing units. Accepting the projection of new construction requires rejection ofeither the population projection, the household projection, or the projected decline in persons per household.)

B-2B.1.2.2 Housing Starts Thelong-termprojection ofhouseholds, describedabove, is basedon demographic trends, including changes in the age distribution ofthe population and projected decline in the number of persons per household. Because houses are added to the stock not only to accommodate population increases but also because ofvoluntary turnoveror replacement ofhouses, projected population increases are only one factor in estimating housing starts for a fixed period. In addition, houses will be constructed to replace losses to the inventory. Economic factors, such as interest rates, will play a significant role in determining the actual number of houses constructedin future years. To account for economic factors, the LBL estimate ofhousing starts by state is scaled to agree with a recent projection of regional housing starts that includes economic factors. B.1.3 Building Shell Thermal Integrity The equation used in the Residential Energy Model to represent the thermal performance/cost tradeofffor structures is:

TIN TINF 

# r TBETA 

# ]TALFA[ TINF] TINa 

## = TINa + LTBETA+C 1 - TINa (B.3)

where TIN is the thermal performance (relative heating energy normalized to 1980 existing houses); C is the change in the initial cost ofthe structure due to the change in TIN; and TINF, TALFA, and TBETA are parameters. Table B.l shows values ofthe parameters TINF/TIN O

' TALFA, and TBETA. Table B.1 Parameters ofThermal Integrity/Cost Curves by House Type
TINF/TINO TALFA TBETA Single-Family 0.280 12 9600 Multifamily 0.434 4 1887 Mobile Homes 0.440 9 2291 The thermal integrity factor (TI) is the relative energy consumption corresponding to a set of building shell characteristics (e.g., ceiling insulation, floor insulation, glazing, floor insulation, infiltration rate), associated with an incremental cost (C). For space heating, it is 

### calculated from the relationship each year to minimize the life-cycle cost, given current energy prices, using a discount rate consistent with historical market tradeoffs. The thermal integrity factor for air conditioning depends on, and differs from, the thermal integrity factor for heating. B-3 B.1.2.2 Housing Starts Thelong-termprojectionofhouseholds,describedabove,is basedondemographictrends, including changes in the age distribution ofthe population andprojected decline in the number of persons per household. Because houses are added to the stock not only to accommodate population increases but also because ofvoluntary turnoverorreplacement ofhouses, projected population increases are only one factor in estimating housing starts for a fixed period. In addition, houses will be constructed to replace losses to the inventory. Economic factors, such as interest rates, will play a significant role in determining the actual number of houses constructedinfuture years. To accountfor economic factors, theLBLestimate ofhousing starts by state is scaled to agree with a recent projection of regional housing starts that includes economic factors. B.1.3 Building Shell Thermal Integrity The equation used in the Residential Energy Model to represent the thermal performance/cost tradeofffor structures is:

TIN TINF 

# r TBETA 

# ]TALFA [ TINF] TINO 

## = TINo + LTBETA+C 1 - TINO (B.3)

where TIN is the thermal performance (relative heating energy normalized to 1980 existing houses); C is the change in the initial cost ofthe structure due to the changein TIN; and TINF, TALFA, and TBETA are parameters. Table B.l shows values ofthe parameters TINPITIN O

' TALPA, and TBETA. Table B.1 Parameters ofThermal Integrity/Cost Curves by House Type
TINF/TINo TALFA TBETA Single-Family 0.280 12 9600 Multifamily 0.434 4 1887 Mobile Homes 0.440 9 2291 The thermal integrity factor (TI) is the relative energy consumption corresponding to a set of building shell characteristics (e.g., ceiling insulation, floor insulation, glazing, floor insulation, infiltration rate), associated With an incremental cost (C). For space heating, it is 

### calculated from the relationship each year to minimize the life-cycle cost, given current energy prices, using a discount rate consistent with historical market tradeoffs. The thermal integrity factor for airconditioning depends on, and differs from, the thermal integrity factor for heating. B-3

### Thermal integrity is largely irrelevant in the analysis oftelevisions, water heaters, pool heaters, and ranges and ovens. The only interaction between energy consumption of these products and the building shell characteristics is through internal loads generated by these products. Those internal loads interact with space-conditioning requirements. 

### 8.1.4 Equipment Efficiency TheLBL-REM projects the average efficiency orunitenergy consumption for anew unit purchased in each future year. The method involves characterization of historical market behavior, in the form ofan implicit market discount rate. The observed average efficiency of the unit purchased is characterized as a function of the range of designs available from manufacturers, equipment prices (set by manufacturers and distributors), currentresidential fuel prices, and trade-off decisions made by purchasers (including homebuilders, contractors, landlords, and homeowners). The range of available designs and their prices are from the Engineering Analysis. Historical average efficiencies arereportedby industry trade associations. Taking the average efficiency of units purchased as the minimum life-cycle cost defmes the implicit market discount rate. The average efficiency in future years is projected based on the range ofengineering designs available (characterized by unitprice and efficiency) andprojected residential energy prices, given the implicit market discount rate. 

### Bol.S 

### Market Shares The number of potential purchasers is calculated for two markets: housing starts and potentialreplacement/retrofitinexistinghousing. Totalproductsales foreachyeararedetermined by applyingmarket shareelasticities toprojectedequipmentprice, operatingexpense, andincome to determine the fraction ofpotential purchasers who will actually purchase the product. The market share elasticities include cross-price variables, where appropriate, to capture inter-fuel competition. (An 

### example ofinter-fuel competition is the choice between water heaters fueled by electricity or by natural gas). The market share elasticities are constant over the projection period. The market share for each class in future years is described in Section 3.2 ofVolume B through 1. 

### Bolo6 

### Usage Behavior The usage elasticities for televisions, water heaters, and pool heaters are assumed to be zero. Projectedusage is assumed constant at the currentvalues. The usage elasticities forroom airconditioners, directheating equipment, mobile homefurnaces, andranges andovens are given in Appendix B ofVolumes B through 1.

B-4 

### Thermal integrity is largely irrelevant in the analysis oftelevisions, water heaters, pool heaters, and ranges and ovens. The only interaction between energy consumption of these products and the building shell characteristics is through internal loads generated by these products. Those internal loads interact with space-conditioning requirements. 

### B.1.4 Equipment Efficiency TheLBL-REMprojects the average efficiencyorunitenergyconsumptionfor anew unit purchased in each future year. The method involves characterization of historical market behavior, in the form ofan implicit market discount rate. The observed average efficiency of the unit purchased is characterized as a function of the range of designs available from manufacturers, equipmentprices (set by manufacturers and distributors), currentresidential fuel prices, and trade-off decisions made by purchasers (including homebuilders, contractors, landlords, and homeowners). The range of available designs and their prices are from the EngineeringAnalysis. Historical average efficiencies arereportedbyindustrytrade associations. Taking the average efficiency of units purchased as the minimum life-cycle cost defmes the implicit market discount rate. The average efficiency in future years is projected based on the range ofengineering designs available (characterized by unitprice and efficiency) andprojected residential energy prices, given the implicit market discount rate. 

### B.1.S Market Shares The number of potential purchasers is calculated for two markets: housing starts and potentialreplacement!retrofitinexistinghousing. Totalproductsalesforeachyeararedetermined by applyingmarket shareelasticities toprojectedequipmentprice, operatingexpense, andincome to determine the fraction ofpotential purchasers who will actually purchase the product. The market share elasticities include cross-price variables, where appropriate, to capture inter-fuel competition. (An 

### example ofinter-fuel competition is the choice between water heaters fueled by electricity orby natural gas). The market share elasticities are constant over the projection period. The market share for each class in future years is described in Section 3.2 ofVolume B through 1. 

### B.1.6 Usage Behavior The usage elasticities for televisions, water heaters, and pool heaters are assumed to be zero. Projectedusage is assumed constant at the currentvalues. The usage elasticities forroom airconditioners,directheatingequipment, mobile homefurnaces, andranges andovens are given in Appendix B ofVolumes B through 1.

B-4

### B.1.7 Equipment Turnover Tables 2.1 ofVolumes B through I show average equipment lifetimes. These lifetimes are assumed to be constant overthe projection period. The retirement functions (the percent of products which survives, as a function of years after purchase) are shown in Appendix B of Volumes B through I. 

### B.1.8 Outputs The LBL-REM produces annual projections of:
.

• number ofhouses in stock by house type;
• housing starts by house type;
• thermal integrity factors for stock and new houses by house type;
• average unitenergy consumption ofnew units sold (by class and by end use/fuel);
• ave,rage unit equipment price ofnew units sold (by class and by end use/fuel);
• fraction ofexisting houses having each end use;
• fraction ofnew houses having each end use;
• usage behavior factor, by end use/fuel;
• fuel consumption by housing type, by fuel, and total by end use;
• total equipment expenditures by end use; and
• total fuel expenditures. The LBL-REM produces cumulative values for a period (1996-2030):

• fuel use by end use and fuel type;
• discounted fuel expenditures by end use and fuel type;
• discounted equipment expenditures; and
• discounted total (fuel and equipment) expenditures. The LBL-REM produces cumulative values for new units purchased in a period (1996- 2030):

• discounted fuel expenditures by end use and fuel type;
• discounted equipment expenditures by end use and fuel type;
• discounted total (fuel and equipment) expenditures by end use and fuel type;
• units installed by end use and fuel type; and
• total discounted life-cycle expenditures per unit by end use and fuel type. Product types usually correspond to end uses.

B-5

. ..., 

### B.I.7 Equipment Turnover Tables 2.1 ofVolumes B through I show average equipment lifetimes. These lifetimes are assumed to be constant overthe projection period. Theretirement functions (the percent of products which survives, as a function of years after purchase) are shown in Appendix B of Volumes B through I. 

### B.I.S Outputs TheLBL-REM produces annual projections of:
.
• number ofhouses in stock by house type; • housing starts by house type; • thermal integrity factors for stock and new houses by house type; • average unitenergyconsumption ofnew units sold (by class and by enduse/fuel); • ave, rage unit equipment price ofnew units sold (by class and by end use/fuel); • fraction ofexisting houses having each end use; • fraction of new houses having each end use; • usage behavior factor, by end use/fuel; • fuel consumption by housing type, by fuel, and total by end use; • total equipment expenditures by end use; and • total fuel expenditures
. TheLBL-REM produces cumulative values for a period (1996-2030):

• fuel use by end use and fuel type; • discounted fuel expenditures by end use andfuel type; • discounted equipment expenditures; and • discounted total (fuel and equipment) expenditures
. TheLBL-REM produces cumulative values for new units purchased in a period (1996- 2030):

• discounted fuel expenditures by end use and fuel type; • discounted equipment expenditures by end use and fuel type; • discounted total (fuel and equipment) expenditures by end use and fuel type; • units installed by end use and fuel type; and • total discounted life-cycle expenditures per unit by end use and fuel type
. Product types usually correspond to end uses.

B-5

### B.I.9 Changes Since Previous Technical Support Document The previous Technical Support Document was concerned with dishwashers, clotheswashers, and clothes dryers. (1) The principal changes since 1990 are: (1) The set ofproducts being analyzed has changed. (2) The set ofdesign options, efficiencies and cost data have changed (see Volumes B through I, Chapter 1). (3) Housing start projections have been revised downward, consistent with recent trends (see Volume A, Chapter 5). (4) Energypriceprojections have beenupdated (toAnnualEnergy Outlook 1991) 

### (see Volume A, Chapter 5). (5) Equipment lifetimes have been re-estimated for water heaters, to distinguish among fuel types (see Volume F, Appendix B). (6) Initial stock and new market shares for space heating and cooling are revised, basedupon 1987ResidentialEnergy ConsumptionSurvey, 

### industry shipmentsdata, and (where applicable) revised equipment lifetimes (see Volumes H and I, Appendix B). B.I.IO Input Data The complete input listing is available on electronic medium in ASCII fonnat. The input database includes demographic, economic, and engineering data. The input listing for products in the current proposed rule is reproduced in Appendix B in the respective volumes for each product. B.2 COMMEND END-USE PLANNING SYSTEM· The COMMEND end-use planning system provides a framework for organizing and analyzing commercial-sector market data. COMMEND was developed by the Electric Power Research Institute (EPRI) for use by its member utilities. The main analysis uses are load forecasting forpower system planning, demand side managementplanning, andmarketplanning. In 

### recognition of the importance of the commercial buildings sector, EPRI initiated a research project (RPI216) in 1981 to develop and transfer end-use analysis tools, market infonnation, and data-gathering strategies to the industry. At the core of this effort is the COMMEND framework, the COMMEND programs, and their supporting databases. The COMMEND Framework. The COMMEND framework segments the commercial market by building type and end use. The framework is illustrated in Figure B-1. The detailed focus is driven naturally by emerging market issues and analysis needs.

B-6 

### B.l.9 Changes Since Previous Technical Support Document The previous Technical Support Document was concerned with dishwashers, clotheswashers, and clothes dryers. (1) The principal changes since 1990 are: (1) The set ofproducts being analyzed has changed
. (2) The set ofdesign options, efficiencies and cost data have changed (see Volumes B through I, Chapter 1). (3) Housing start projections have been revised downward, consistent with recent trends (see Volume A, Chapter 5). (4) Energypriceprojections have been updated (toAnnualEnergy Outlook1991) (see Volume A, Chapter 5). (5) Equipment lifetimes have been re-estimated tor water heaters, to distinguish among fuel types (see Volume F, Appendix B). (6) Initial stock and new market shares for space heating and cooling are revised, basedupon 1987ResidentialEnergyConsumptionSurvey, industryshipmentsdata, and (where applicable) revised equipment lifetimes (see Volumes H and I, Appendix B). 

### B.l.10 Input Data Thecompleteinputlisting is available on electronic medium in ASCIIformat. Theinput database includes demographic, economic, and engineering data. The input listing for products in the current proposed rule is reproduced in Appendix B in the respective volumes for each product. B.2 COMMEND END-USE PLANNING SYSTEM · The COMMEND end-use planning system provides a framework for organizing and analyzing commercial-sector market data. COMMEND was developed by the Electric Power Research Institute (EPRI) for use by its member utilities. The main analysis uses are load forecasting forpower systemplanning, demand side managementplanning, andmarketplanning. In recognition of the importance of the commercial buildings sector, EPRI initiated a research project (RP1216) in 1981 to develop and transfer end-use analysis tools, market information, and data-gathering strategies to the industry. At the core of this effort is the COMMEND framework, the COMMEND programs, and their supporting databases. The COMMEND Framework. The COMMEND framework segments the commercial market by building type and end use. The framework is illustrated in Figure B-l. The detailed focus is driven naturally by emerging market issues and analysis needs
.

B-6The primary use ofthe COMMEND framework is long-tenn forecasting. However, the market databases that result from model implementation are vital inputs to a wide variety of planning and analysis activities. Figure 

### B.l COMMEND Framework

Uses for Market Data

• Forecasting

• Demand-Side Planning

• Integrated Planning

• Marketing

BUILDINGS Small Ottlce Luce Ottlce Restaurant. Retail Grocery Warehouse Schools Colleees

. HealUl**Lod:in~ ~Uscellaneous**  END USES Space Heat Cootinc Ventilation Wat.er Heat Coold.nC Refriceration UChUnC Miscellaneous FUELS Electricit.y Natural Gas Fuel Oil

The COMMEND Models. The COMMEND 2.0 model was amainframemodel, whichhas been distributed to over 80 utilities in the U.S. and abroad. COMMEND-PC 3.0 became available in 1988, and has been distributed to over 100 utilities in the U.S. and abroad. In 1990, Version 3.1 became available. It contained minor changes to Version 3.0. Version 3.2 was released in April 1992. COMMEND Data 

### Bases. COMMEND-PC is distributed with a national data base, which is refined and updated as new information becomes available. B.2.1 Discussion ofthe COMMEND Market Framework The COMMEND model provides a conceptual framework for organlZtng market information. The purpose of the following discussion is to describe this framework, and to introduce the main analysis concepts. The focus is on the description of current energy-use patterns.

B-7 The primary use ofthe COMMEND framework is long-tenn forecasting. However, the market databases that result from model implementation are vital inputs to a wide variety of planning and analysis activities. Figure 

### B.l COMMEND Framework

BUILDINGS Small Ottlce Luce OCfice Restaurant. Retail Grocery Warehouse Schools Colleges

. Health

Lod:in:**~Uscellaneous** END USES Space Heat Coollnc Ventilation Water Heat CookiDC RetrlceratioD. UghUng Miscellaneous Uses for Market Data
• Forecasting • Demand-Side Planning • Integrated Planning
• Marketing

FUELS Electricity Natural Gas Fuel Oil

TheCOMMENDModels. TheCOMMEND2.0modelwas amainframemodel, whichhas been distributed to over 80 utilities in the U.S. and abroad. COMMEND-PC 3.0 became available in 1988, and has been distributed to over 100 utilities in the U.S. and abroad. In 1990, Version 3.1 became available. It contained minor changes to Version 3.0. Version 3.2 was released in April 1992. COMMEND Data 

### Bases. COMMEND-PC is distributed with a national data base, which is refined and updated as new information becomes available. B.2.1 Discussion ofthe COMMEND Market Framework The COMMEND model provides a conceptual framework for organlZtng market information. The purpose of the following discussion is to describe this framework, and to introduce the main analysis concepts. The focus is on the description of current energy-use patterns.

B-7

### Market Segments The purpose ofsegmenting a market is to group customers into segments with common properties. Across groups, the customers shouldhave differentproductrequirements ordifferent marketattitudes andpreferences. Within groups, theserequirements and attitudes shouldbemore homogeneous. The COMMEND framework uses a two-way primary segmentation scheme. The dimensions are building type and end use. Building-Type Segments. Building types define the primary market segments. This approach is useful because energy-use patterns differ strongly across building types. These differences reflect: Different operating hours, Different types ofenergy-using activities, Different types ofenergy-using equipment, and Different energy-using technologies. The building type has great intuitive appeal. For example, we all know what a high-rise office building looks like, andwe are unlikely to confuse it with a fast-food restaurant. Further, the linkage with energy-use patterns is clear. Offices have different operating hours and house a different mixture ofenergy-using equipment than do restaurants, hospitals, or warehouses. However, there are ambiguities that arise in applying the building type concept. Much of this derives from the following facts: The term building type 

### refers to the use ofthe internal space as well as the characteristics ofthe structure itself. Further, the use may change over time or a single structure may have mixed uses at a point in time. Reflecting these facts, many analysts refer to the segments as building/activity types. End-Use Segments. An energy end use is the ultimate service delivered by energy-using equipment. In 

### COMMEND 3.2 the end-use categories are: Space heat Cooling Ventilation Water heating Cooking Refrigeration Exterior lighting Interior lighting Office equipment Miscellaneous.
B-8 

### Market Segments The purpose ofsegmenting a market is to group customers into segments with common properties. Across groups, the customers shouldhave differentproductrequirements ordifferent marketattitudes andpreferences. Withingroups, theserequirements andattitudes shouldbemore homogeneous. The COMMEND framework uses a two-way primary segmentation scheme. The dimensions are building type and end use. Building-Type Segments. Building types define the primary market segments. This approach is useful because energy-use patterns differ strongly across building types. These differences reflect: Different operating hours, Different types ofenergy-using activities, Different types ofenergy-using equipment, and Differentenergy-using technologies. The building type has great intuitive appeal. For example, we all know what a high-rise office building looks like, andwe are unlikely to confuse it with a fast-food restaurant. Further, the linkage with energy-use patterns is clear. Offices have different operating hours and house a different mixture ofenergy-using equipment than do restaurants, hospitals, or warehouses. However, there are ambiguities that arise in applying the building type concept. Much of this derives from the following facts: The term building type 

### refers to the use ofthe internal space as well as the characteristics ofthe structure itself. Further, the use may change over time or a single structure may have mixed uses at a point in time. Reflecting these facts, many analysts refer to the segments as building/activity types. End-Use Segments. An energy end use is the ultimate service delivered by energy-using equipment. In

### COMMEND 3.2 the end-use categories are: Space heat Cooling Ventilation Waterheating Cooking Refrigeration Exterior lighting Interiorlighting Office equipment Miscellaneous.
B-8

### These segments are defined in terms ofthe final service being provided by energy inputs. Within each end-use segment, these classes ofdecisions will impactthe type offuel andthe level ofenergy use: 

### Fuel choice refers to decisions among alternative equipment that provide the same service but use differenttypes offuel. The main competitive uses are heating andcooling and the main fuels are electricity, natural gas, and fuel oil. 

### Efficiency decisions refer to decisions aboutequipmentfeatures and structure features that determine how much energy is required to deliver a given level ofend-use service. 

### Utilization refers to the frequency and duration ofequipment usage. This is impacted by occupant behavior and equipment operating controls. Typically, all ofthe end-use segments appear in each ofthe building types. For example, space conditioning and lighting are important end uses in all building categories. Others, such as cooking and refrigeration, are more concentrated in particular building types. Fromtheperspective ofthe equipmentproducers and distributors, the end-use segments are separate markets. For example, heating, ventilation, and air conditioning (HVAC) equipment manufacturers do not view lighting appliance manufacturers as competitors. This perspective could also be adopted here, in which case we would refer to the heating market rather than the heating segment ofthe commercial energy market. 

### Other Segments. The COMMEND framework also tracts buildings according to the year of construction, referred to as the building vintage. 

### This allows fuel and efficiency decisions to be analyzed separately for new construction versus retrofits and replacements. In many applications, building types are further split on the basis of size. The most common example ofthis is the separate treatment oflarge versus small office buildings. This separate treatment is prompted by the fact that large buildings have different thermal properties and tend to utilize different types ofHVAC technologies than do smallerbuildings. Central Energy Equation 

### The COMMEND framework provides an analysis structure for describing energy-use patterns. The primitive concepts in the framework are as follows: Floor stock (square feet ofbuilding space), Energy intensity (energy per square foot), Fuel share (percent ofarea served by an end use and fuel type), Energy-use index (energy per square foot for an end use), Peak-day fractions (share ofannual energy), and End-use load profiles.

B-9 

### These segments are defined in terms ofthe final service being provided by energy inputs. Withineachend-use segment, these classes ofdecisions will impactthe type offuel andthe level ofenergy use: 

### Fuel choice refers to decisions among alternative equipment that provide the same service butuse differenttypes offuel. The main competitive uses are heating andcooling andthe main fuels are electricity, natural gas, and fuel oil. 

### Efficiency decisions refer to decisions aboutequipmentfeatures and structure features that determine how much energy is required to deliver a given level ofend-use service. 

### Utilization refers to the frequency and duration ofequipment usage. This is impacted by occupant behavior and equipment operating controls. Typically, all ofthe end-use segments appear in each ofthe building types. Forexample, space conditioning and lighting are important end uses in all building categories. Others, such as cooking and refrigeration, are more concentrated in particular building types. Fromtheperspectiveofthe equipmentproducers and distributors, theend-use segments are separate markets. For example, heating, ventilation, and air conditioning (HVAC) equipment manufacturers do not view lighting appliance manufacturers as competitors. This perspective could also be adopted here, in which case we would refer to the heating market rather than the heating segment ofthe commercial energy market. 

### Other Segments. The COMMEND framework also tracts buildings according to the year of construction, referredto as the building vintage. 

### This allows fuel and efficiency decisions to be analyzed separately for new construction versus retrofits and replacements. In many applications, building types are further split on the basis of size. The most common example ofthis is the separate treatment oflarge versus small office buildings. This separate treatment is prompted by the fact that large buildings have different thermal properties and tend to utilize different types ofHVAC technologies than do smallerbuildings. Central Energy Equation 

### The COMMEND framework provides an analysis structure for describing energy-use patterns. The primitive concepts in the framework are as follows: Floor stock (square feet ofbuilding space), Energy intensity (energy per square foot), Fuel share (percent ofarea served by an end use and fuel type), Energy-use index (energy per square foot for an end use), Peak-day fractions (share ofannual energy), and End-use load profiles.

B-9

### These are the key concepts used in commercial-sectorenergy analysis. By developing data for these concepts, a complete profile ofthe commercial sector can be produced. Foreachmarketsegment, the central energy equation

### inCOMMEND defines currentenergy use as the produce of three factors. These are floor stock, fuel share, and energy use index (EUI). For a single building/end-use segment, the central equation is:
Annual Energy Use 

## =EUI *S * F

where: F 

### is square footage offloor stock, S

### is average share ofspace served by the end use and fuel, and EUI

### is average energy use for served space. (B.4)



### In this definition, the floor is the total amount across all building vintages, and the share of EUI values are average across buildings of all vintages. As an average, the EDI value embodies both average equipmentefficiencies and average usage levels across the customerbase in the segment. As an example ofthis equation: Fifty million square feet ofoffice space With 25% electric space heated, and An electric heating EDI value of 10 kWh/foot/year, gives Annual energy use of 125 gWh (50 million feet 

## * .25 

## * 10 kWh/foot). Thecentral energyequationis adefinition ofenergy use. Otherdefinitions arepossible and are sometimes used. For example, one alternative is to use employment times energy use per employee. Another is to use a measure of output times energy per use unit of output. These alternative definitions are valid, but for the commercial sector have not proven as useful as the floor stock approach. Floor 

### Stock. Floor Stock 

### provides the basis for energy-using equipment and activities in 

### the commercial sector. In new construction, energy-using technologies are an integral part of building design. In fast-growth areas with high construction levels, many energy equipment decisions are being made and new technologies can penetrate the market rapidly. In slowgrowing areas, relatively few equipment decisions are made, and they are restricted to replacement and retrofit in the existing stock. 

### Energy Intensity. The term energy intensity 

### applies to total energy use per square foot for all end uses. For example:

B-10 These are the key concepts usedin commercial-sectorenergy analysis. Bydeveloping data for these concepts, a complete profile ofthe commercial sector can be produced. Foreachmarketsegment,the centralenergyequation

### inCOMMENDdefinescurrentenergy use as the produce of three factors. These are floor stock, fuel share, and energy use index (EUI). For a single building/end-use segment, the central equation is:
Annual Energy Use 

## =EUI *S * F

where: F 

### is square footage offloor stock, S

### is average share ofspace served by the end use and fuel, and EUI

### is average energy use for served space. (B.4)



### In this definition, the floor is the total amount across all building vintages, and the share of EUI values are average across buildings of all vintages. As an average, the EUI value embodies both average equipmentefficiencies and average usage levels across the customerbase in the segment. As an example ofthis equation: Fifty million square feet ofoffice space With 25% electric space heated, and An electric heating EUI value of 10 kWh/foot/year, gives Annual energy use of 125 gWh (50 million feet 

## * .25 

## * 10 kWh/foot). Thecentralenergyequationis adefinition ofenergyuse. Otherdefinitions arepossible and are sometimes used. For example, one alternative is to use employment times energy use per employee. Another is to use a measure of output times energy per use unit of output. These alternative definitions are valid, but for the commercial sector have not proven as useful as the floor stock approach. Floor 

### Stock. Floor Stock 

### provides the basis for energy-using equipment and activities in 

### the commercial sector. In new construction, energy-using technologies are an integral part of building design. In fast-growth areas with high construction levels, many energy equipment decisions are being made and new technologies can penetrate the market rapidly. In slowgrowing areas, relatively few equipment decisions are made, and they are restricted to replacement and retrofit in the existing stock. 

### Energy Intensity. The term energy intensity 

### applies to total energy use per square foot for all end uses. For example:

B-10

### A typical office building intensity of 18 kWh/foot for electricity and 45 kBtu/foot for natural gas. A typical restaurant intensity is 36 kWh/foot for electricity and 140 kBtu/foot for natural gas. The numerator in these intensity ratios is annual energy use. The denominator is total square footage. Trends in energy intensItIes reflect changes in fuel shares, changes in equipment efficiencies, and changes in usage levels. At apointin time, the efficiency and usage factors are captured by the average EDI value. Fuel Shares. Fuel shares 

### indicate the share ofbuilding space that is served by a particularend use and fuel type. The term is used to indicate both stock and flow concepts. The stock concept refers to the share ofall buildings existing at a point in time. This is sometimes referredto as apenetrationormarket saturation. Wecall this the average share. 

### The flow concept refers to the share of current decisions in new construction and replacements. This corresponds more closely to an equipment supplier's concept of the share ofcurrent shipments. We call this the marginal share. 

### The share concept used in COMMEND is applied to total floor stock rather than the penetrated portion ofthe stock. Forexample, if90% offloor space is in buildings with heating, the shares will add up to 90% across fuels. Two types of share definitions are commonly used. The first is the "whole-building" approach. This approach measures shares ofspace in buildings with an enduseregardless ofthe portion of each building that is served or conditioned by the end use. The second is the "conditioned-space" approach, which accounts forthe fraction ofeachbuildingthatis conditioned by the end use. Energy Use Index (EUI). The term energy use intensity 

### refers to a measure ofaverage annual energy use per square foot offloor space in buildings that are served by an end use. In theresidential sector, a similarconceptis used, calledunitenergyconsumption, orUEC. This measures annual energy use by an average household appliance unit. This approach is not suitable for the commercial sector due to the wide range ofbuilding sizes and equipment types that are usedin these buildings. By focusing on

# atypical square foot, the EDI is a standardized concept. EDI values embody an average level of service and average equipment efficiency. There are several options for units ofmeasurement. The standard approach is to develop electric values in kWh/foot and fossil fuel values in kBtu/foot.
B-l1 

### A typical office building intensity of 18 kWh/foot for electricity and 45 kBtu/foot for natural gas. A typical restaurant intensity is 36 kWh/foot for electricity and 140 kBtu/foot for natural gas. The numerator in these intensity ratios is annual energy use. The denominator is total square footage. Trends in energy intensItIes reflect changes in fuel shares, changes in equipment efficiencies, and changes in usage levels. At apointin time, the efficiency and usage factors are captured by the average EUI value. Fuel Shares. Fuel shares

### indicate the share ofbuilding space that is served by a particularend use and fuel type. The term is used to indicate both stock and flow concepts. The stock concept refers to the share ofall buildings existing at a point in time. This is sometimesreferredto as apenetrationormarket saturation. Wecallthis the average share. 

### The flow concept refers to the share of current decisions in new construction and replacements. This corresponds more closely to an equipment supplier's concept of the share ofcurrent shipments. We call this the marginal share. 

### The share concept used in COMMEND is applied to total floor stock rather than the penetrated portion ofthe stock. Forexample, if90% offloor space is in buildings with heating, the shares will add up to 90% across fuels. Two types of share definitions are commonly used. The first is the "whole-building" approach. This approach measures shares ofspace in buildings with an enduseregardless ofthe portion of each building that is served or conditioned by the end use. The second is the "conditioned-space" approach, which accountsforthe fraction ofeachbuildingthatis conditioned by the end use. Energy Use Index (EUI). The term energy use intensity 

### refers to a measure ofaverage annual energy use per square foot offloor space in buildings that are served by an end use. In theresidential sector, a similarconceptis used, calledunitenergyconsumption, orDEC. This measures annual energy use by an average household appliance unit. This approach is not suitable for the commercial sector due to the wide range ofbuilding sizes and equipment types that are usedin these buildings. By focusing on

# atypical square foot, the EUIis a standardized concept. EUI values embody an average level of service and average equipment efficiency. There are several options for units ofmeasurement. The standard approach is todevelop electric values in kWh/foot and fossil fuel values in kBtu/foot.
B-l1For each end use, EDI values will differ across building types and across fuels. For example, for space heating in offices, suppose that: The electric EDI is 20 kEtu/foot (about 6 kWh/foot) and The gas EDI is 50 kEtu/foot. This difference in EDI values across fuels reflects differences in equipment efficiencies, in the thermal features ofbuildings using gas and electricity, and in usage levels. Differences in usage levels may reflect fuel price differences as well as technology-related factors. Usage Levels. Usage level is the most difficult of the COMMEND concepts to quantify. Ideally, 

### it would be measured in terms ofenergy services delivered. Examples are: Delivered heat in Btu for space heating Heat removed in tons for air conditioning Lighting delivered in lumen hours. Given these measures, usage is determined by occupant behavior, equipment controls, and other factors. Usage levels would change, for example, if thermostat settings are changed, comfort levels are altered, lighting fixtures are changed, or operating hours are altered. 

### LoadShapes The discussion thus far has focused on annual energy use. The COMMEND framework also deals with daily energy use and with peak-day load shapes. The approach used relies on fixed fractions. The first set offractions indicates the share ofannual energy use that occurs on the winter and summerpeak days. These are referred to aspeak-dayfractions. The second set offractions contains loadprofiles for eachelectric end use. These fractions are used to spread annual energy use from the daily total to hours of the day. Combined, these values allow the translation of annual energy usage levels to peak-day loads. 

### Example of MarketProfiles The market data described above represent a COMMEND data base. Figure B.2 provides an illustration ofthe types ofresults that can be obtained from a COMMEND implementation. The datapresentedhere reflect actualresults that have been developedfrom the COMMEND 3.1 national data base. These data have been scaled down to reflect about 1% ofthe U.S. total.

B-12 For each end use, EUI values will differ across building types and across fuels. For example, for space heating in offices, suppose that: The electric EUI is 20 kBtu/foot (about 6 kWh/foot) and The gas EUI is 50kBtu/foot. This difference inEUIvalues across fuels reflects differences inequipmentefficiencies, in the thermal features ofbuildings using gas and electricity, and in usage levels. Differences in usage levels may reflect fuel price differences as well as technology-related factors. Usage Levels. Usage level is the most difficult of the COMMEND concepts to quantify. Ideally, 

### it would be measured in terms ofenergy services delivered. Examples are: Delivered heat in Btu for space heating Heatremoved in tons for air conditioning Lighting delivered in lumen hours. Given these measures, usage is determined by occupant behavior, equipmentcontrols, and other factors. Usage levels would change, for example, if thermostat settings are changed. comfort levels are altered, lighting fixtures are changed, or operating hours are altered. 

### LoadShapes The discussion thus far has focused on annual energy use. The COMMEND framework also deals with daily energy use and with peak-day load shapes. The approach used relies on fixed fractions. Thefirst set offractions indicates the share ofannual energy use that occurs onthe winter and summerpeak days. These arereferredto aspeak-dayfractions. The second setoffractions contains loadprofilesforeachelectric enduse. Thesefractions are usedto spreadannualenergy use from the daily total to hours of the day. Combined, these values allow the translation of annual energy usage levels to peak-day loads. 

### Example of MarketProfiles Themarket data described above represent a COMMEND data base. Figure B.2provides an illustration ofthe types ofresults that can be obtainedfrom a COMMEND implementation. Thedatapresentedherereflect actualresults thathave been developedfrom the COMMEND 3.1 national data base. These data have been scaled down toreflect about 1 % ofthe U.S. total.

B-12Figure B.2 1986 Commercial Energy Sales by End Use (l% ofU.S. Total)

Cool

Light ELECTRICITY (Total 

## =7.6 TWll) Space Heat
Space Heat

Misc. Cool 11.Hel\t NATURAL GAS (Total 

## =22.4 TBTU) Spl\ce Hent "CHest FUEL OIL (Tolal .= 3.4 TBTU) Misc.

B.2.2 COMMEND Forecasting Framework For the base year, the market profiles discussed above provide a detailed depiction of energy-use patterns at the end-use level. The purpose ofthe COMMEND forecasting framework is to project these detailed profiles into the future. By forecasting at the end-use level, it is possible to isolate the influences on energy sales ofeconomic growth, changes in fuel shares, changes in efficiencies, and changes in usage levels. This approach allows consideration ofkey issues in future markets, such as fuel competition, technology competition, building standards, and customer behavior. 

### Central Energy Equation As discussed above, enduses within building types arereferredto as market segments. The COMMEND forecast framework applies separately to each segment. As aresult, itis appropriate to think ofCOMMEND as a matrix ofmodels, as depicted in Figure B.3.

B-13 Figure B.2 1986 Commercial Energy Sales by End Use (1% ofU.S. Total)

Cool

Light ELECTRICITY (Total 

## =7.6 TWll) Space Heat

Misc.

Spl\ce Hent Space Heat

Cool l'f'.He1\t

NATURAL GAS (Total 

## =22.4 TBTU)

W.Heat FUEL OIL (Total .= 3.4 TBTU)

B.2.2 COMMEND Forecasting Framework Misc.

For the base year, the market profiles discussed above provide a detailed depiction of energy-use patterns at the end-use level. The purpose ofthe COMMEND forecasting framework is to project these detailed profiles into the future. By forecasting at the end-use level, it is possible to isolate the influences on energy sales ofeconomic growth,changes in fuel shares, changes inefficiencies, andchanges in usagelevels. This approach allows consideration ofkey issues in future markets, such as fuel competition, technology competition, building standards, and customer behavior. 

### Central Energy Equation As discussedabove,enduses withinbuilding types arereferredto as market segments. The COMMENDforecast framework applies separately to each segment. As aresult, 

### itis appropriate to think ofCOMMEND as a matrix ofmodels, as depicted in Figure B.3.

B-13

### Figure B.3 COMMEND Framework for Long-Tenn Forecasting

Market Decisions 

# @ sage 

# /

....·"·"l

)f " 

## 1 " 

# o o o End-Use· Segment

I I I! I Market Framework



### Within each market segment or model cell, COMMEND computes energy sales using the central energy equation. 

### This equation sums across all building vintages as follows: (B.5) 

### This equation defines annual energy sales for each fuel (j) 

### as the sum across vintages (v) 

### ofthe product offour factors. Starting from the right-hand end, these factors are: Floor stock 

### ofvintage v (F), 

### The share 

### ofvintage v space using fuel

### f equipment (Sj'v)' EDI for fuel 

### f equipment in vintage v space (Ej'v)' 

### and Utilization rate for fuel

### f equipment in vintage space (Uj'v). 

### This definition holds in each forecast year for each fuel. All end-use models use this type ofdefinition as a starting point. The definition is not a static one, since each ofthe model components will change over time. These changes reflect economic decisions in the commercial market, such as the decision to build, the choice of
B-14 

### Figure B.3 COMMEND Framework for Long-Term Forecasting

Market Framework End-Use· Segment Market Decisions



# o o o 

# @ sage 

### /

....·..·..1
)f " 

## 1 II



### Within each market segment ormodel cell, COMMEND computes energy sales using the central energy equation. 

### This equation sums across all building vintages as follows: (B.5) 

### This equation defines annual energy sales for each fuel (j) 

### as the sum across vintages (v) 

### ofthe product offour factors. Starting from the right-hand end, these factors are: Floor stock 

### ofvintage v (F), 

### The share 

### ofvintage v space using fuel

### f equipment (Sf)' 

### EUI for fuel 

### f equipment in vintage v space (Ejv)' 

### and Utilization rate for fuel 

### f equipment in vintage space (Ujv). 

### This definition holds in each forecast year for each fuel. All end-use models use this type ofdefinition as a starting point. The definition is not a static one, since each ofthe model components will change over time. These changes reflect economic decisions in the commercial market, such as the decision to build, the choice of
B-14construction materials, the type of energy-using equipment to install, and the eventual usage pattern ofthis equipment. The challenge in end-use modeling is to provide an abstract model that captures the main influences on these decisions and that projects over time the basic trends in each component COMMEND's general framework is presented in Figure B.4. The remainder of this discussion focuses on Version 3.2 and briefly describes each model component, forecast logic, and forecast results
.. 

### Figure 8.4 COMMEND Forecast Framework

Cenlrnl Energy Equalion Floor Slock
Fuel Sllares

Ehislicities Energy Prices Employmenl. Popululion•...
Energy Prices Technology Dala Discounl Rales 1---1

_______

Elasticities



### FloorStock The floor stock component of COMMEND is used to organize infonnation about the existing floor stock and to forecast future stock levels. The floor stock outlook embodies the utility planning assumptions about growth in economic activity for the commercial sector. This outlook will be tightly linked to population growth, employment growth, and regional income.

Data about historical stock is input to the model. The key input values are: 

### Base yearfloor stock (e.g., 1987). 

### A historical floorstockseries from a distantyearto the base year (e.g., from 1941 to 1987). This series can be developed in the model using historical additions, scale variables (such as employment or population), or a combination. 

### Survivalfunctions describing building survival and decay over time.
B-15 construction materials, the type of energy-using equipment to install, and the eventual usage pattern ofthis equipment. The challenge in end-use modeling is to provide an abstract model that captures the main influences on these decisions and thatprojects over time the basic trends in each component COMMEND's general framework is presented in Figure B.4. The remainder of this discussion focuses on Version 3.2 and briefly describes each model component, forecast logic, and forecast results
.. Figure B.4 COMMEND Forecast Framework

Employmenl. Popululion•... Floor Slock
Fuel Energy Prices Technology Dala Discount Rales**~** Sllares
1---1

_______ Elasticities**~**
Energy Prices Ehislicities Level


### FloorStock CenlrOoI Energy Equation

The floor stock component of COMMEND is used to organize infonnation about the existing floor stock and to forecast future stock levels. The floor stock outlook embodies the utility planning assumptions about growth in economic activity for the commercial sector. This outlook will be tightly linked to population growth, employment growth, and regional income.

Data about historical stock is input to the model. The key input values are: 

### Base yearfloor stock (e.g., 1987). 

### Ahistorical floorstockseries from a distantyearto the base year (e.g., from 1941 to 1987). This series can be developed in the model using historical additions, scale variables (such as employment orpopulation), or a combination. 

### Survivalfunctions describing building survival and decay over time.
B-15

### A flexible forecasting framework is provided. Two general approaches can be used. In theflow approach, 

### annual building construction is projected directly. The stock is inferred as the old stock, having survived for one or more years, plus the new additions. In the stock approach, 

### the final stock is projected directly. Additions are inferred as the
. amount ofconstruction required to produce the projected stock value. With either approach, the user provides forecasting equations, including estimated coefficients andexogenous variable forecasts. Typically, the exogenous variables come directly from a service territory economic model. Variables that are used are (1) 

### employment in the economic SIC codes, (2) population by age group, (3) regional income, and (4) construction industry conditions, such as interestrates. Within this general framework, simple and complex forecast approaches can be implemented. Modeling Share, EUI, and Usage Decisions 

### The remaining three items in the central energy equation are fuel share, EUI values, and usage levels. Fuel shares and EUI values both reflect the outcome of choices among energy technologies. These choices are investment decisions made by building owners, designers, and contractors at the time ofconstruction or equipment replacement. Decisions involved include: The decision to use a generic technology 

### (such as an electric heatpump or a gas furnace). This determines the fuel share for each fuel. The decision to select a specific technology 

### (an equipment brand and model), along with structure characteristics and initial usage patterns. This determines the EUI for each fuel. Once a building is constructed and equipment is in place, changes in 

### usage levels reflect daily decisions about the frequency and intensity of equipment use.' These decisions are determined by the behavior ofbuilding managers and occupants. A variety of approaches have been used to model these decisions. The focus of these approaches is on the impact offuel prices on market decisions. These impacts are: Fuel Choice. An increase in one fuel price may cause switching away from that fuel to other fuels. For example, an increase in electric prices will cause a switch to fossil fuels. An increase in gas prices will cause a switch to electric technologies. Technology Choice. An
.increase in 

### a fuel price may cause switching to more efficient technologies. This can involve either more efficient equipment models or the addition of energy-conserving features.

B-16 

### A flexible forecasting framework is provided. Two general approaches can be used. In theflow approach, 

### annual building construction is projected directly. The stock is inferred as the old stock, having survived for one or more years, plus the new additions. In the stock approach, 

### the final stock is projected directly. Additions are inferred as the
. amount ofconstruction required to produce the projected stock value. With either approach, the user provides forecasting equations, including estimated coefficients andexogenous variableforecasts. Typically, the exogenous variables come directly from a service territory economic model. Variables that are used are (1) 

### employment in the economic SIC codes, (2) population by age group, (3) regional income, and (4) construction industry conditions, such as interestrates. Within this general framework, simple and complex forecast approaches can be implemented. Modeling Share, EUI, and Usage Decisions 

### The remaining three items in the central energy equation are fuel share, EUI values, and usage levels. Fuel shares and EUI values both reflect the outcome of choices among energy technologies. These choices are investment decisions made by building owners, designers, and contractors at the time ofconstruction or equipment replacement. Decisions involved include: The decision to use a generic technology 

### (such as an electric heatpump or a gas furnace). This determines the fuel share for each fuel. The decision to select a specific technology 

### (an equipment brand and model), along with structure characteristics and initial usage patterns. This determines the EUIfor each fuel. Once a building is constructed and equipment is in place, changes in 

### usage levels reflect daily decisions about the frequency and intensity of equipment use.' These decisions are determined by the behavior ofbuilding managers and occupants. A variety of approaches have been used to model these decisions. The focus of these approaches is on the impact offuel prices on market decisions. These impacts are: Fuel Choice. An increase in one fuel price may cause switching away from that fuel to otherfuels. For example, an increase in electric prices will cause a switch to fossil fuels. An increase in gas prices will cause a switch to electric technologies. Technology Choice. An
. increase in 

### a fuel price may cause switching to more efficient technologies. This can involve either more efficient equipment models or the addition of energy-conserving features.

B-16Usage Behavior. An increase in a fuel price may cause a reduction in the usage level through changes in the behaviorofbuilding occupants. Examples are reducedlighting level and more conservative thermostat settings. COMMEND 3.2 uses aprobabilistic choice approachfor fuel andefficiency choice. Inthis application, the model outcome is the proability that a specific system is installed in a particular building. The proability will depend on the following: The capital cost of all system options, The operating costs ofall system options, and Characteristics ofthe building and other relevant factors. The probabilistic approach is appealing because it is not possible to observe all the factors that affect equipment decisions. Therefore, itis not possible to predict these decisions perfectly. Thisphilosophycontrasts with the life-cyclecost (LCC) minimization approach, whichposits that each choice is known precisely, based on a complete set'Ofcost information and pure economic optimization. The proability approach does not have the knife's edge property associated with LCe minimization. Forexample, achange in fuel prices alters operating costs, which in turn reorients the probabilities. These shifts will be sudden and dramatic only ifestimated parameters suggest a high sensitivity to operating costs. Key inputs to the modeling process are grouped into technology data, economic data, and standards and DSM data. These are described below. Technology Data. Technology data center on equipment costs and efficiencies. The main technology inputs are: Heat Pump Data. Heat pump shares and relative efficiencies are needed to unbundle the overall electric heating EUI and share into resistance and heat pump components. Equipment Costs. Average installed system costs for all end uses by building type are entered in $/square foot. Efficiency Ranges. For each generic technology, the range of available suboptions is described. The range for each system is described as a curve segment. Parameters ofthe segmentare EDI range percentages and a tradeoffelasticity between outlay andenergy use. Theimplied costrange is computedinternally. This is referred to as the generic technology curve approach. These data describe the opportunity for price-induced efficiency changes. Efficiency and Costs Trends. For each generic technology, trend values that alter equipmentefficiencies and installedcosts may be specified. These impacts can be used to evaluate the impacts ofnaturally-occurring technology improvements.

B-17 Usage Behavior. An increase in a fuel price may cause a reduction in the usage level throughchangesinthe behaviorofbuildingoccupants. Examples arereducedlightinglevel andmore conservative thermostat settings. COMMEND 3.2uses aprobabilistic choice approachfor fuel andefficiencychoice. Inthis application, the model outcome is the proability that a specific system is installedin a particular building. The proability will depend on the following: The capital cost of all system options, The operating costs ofall system options, and Characteristics ofthe building and other relevant factors. Theprobabilistic approach is appealing because itis notpossible to observe all the factors thataffect equipmentdecisions. Therefore, itis not possible to predictthese decisionsperfectly. Thisphilosophycontrastswiththelife-cyclecost (LCC)minimization approach, whichpositsthat each choice is knownprecisely, based on a complete set'Ofcost information and pure economic optimization. The proability approach does not have the knife's edge property associated with LCe minimization. Forexample, achange in fuelprices alters operatingcosts, whichinturn reorients the probabilities. These shifts will be sudden and dramatic only ifestimatedparameters suggest a high sensitivity to operating costs. Key inputs to the modeling process are groupedinto technology data, economic data, and standards and DSM data. These are described below. Technology Data. Technology data center on equipment costs and efficiencies
. The main technology inputs are: Heat Pump Data. Heatpump shares andrelative efficiencies are needed to unbundle the overall electric heating EUI and share into resistance and heatpump components. Equipment Costs. Average installed system costs for all end uses by building type are entered in $/square foot. Efficiency Ranges. For each generic technology, the range of available suboptions is described. The range for each system is described as a curve segment. Parameters ofthe segmentare EDIrangepercentages and a tradeoffelasticitybetween outlay andenergyuse. Theimplied costrangeis computedinternally. Thisisreferredto as the generic technology curve approach. These data describe the opportunityfor price-inducedefficiency changes. Efficiency and Costs Trends. For each generic technology, trend values that alter equipmentefficiencies andinstalledcosts may be specified. These impacts can be usedto evaluate the impacts ofnaturally-occurring technology improvements.

B-17Thermal Interactions. Thermal interaction elasticities are used to describe the impact of changes in lighting and miscellaneous loads on HVAC energy use. Separate parameters give the impact ofchanges in building thermal characteristics on HVAC energy use. The equipment cost data determine the relationship between capital costs and operating costs, which is importantin determining the importance ofenergy prices in equipment decisions. Economic Data. The economic data describe decision-makers and decision rules. These data are defined as follows: Decision-Maker Data. Decision-makers are described by a block distribution ofdiscount rates. These distributions may differ across building types. The decision-makers have price expectations which are basedon a single distributedlag adjustment mechanism. This implies that price expectations are formed on the basis ofpast price events. Efficiency Option Elasticities. These parameters give the sensitivity ofmarket shares to life-cycle cost, where life-cycle cost includes both initial equipment cost and the present value ofoperating costs. These sensitivities are used to model efficiency choice for all end uses. Share Option Elasticities. Like the efficiency option elasticities, theseparameters give the sensitivity of market shares to life-cycle cost, where life-cycle cost includes both initial equipment cost and the present value ofoperating costs. These sensitivities are to model market shares ofcompeting fuels and technologies. Automatic Calibration. The technology data and decision data are combined to compute implied efficiency elasticities and to calibrate fuel-choice equations. These equations are calibrated to marginal shares in new construction. Utilization Elasticities. These parameters indicate the sensitivity ofequipment usage to energy prices, as well as weather data, operating hours, vacancy rates and other factors. These parameters are used to simulate changes in usage levels over time. Replacement Factors. Fuel share inertial parameters apply to fuel choice decisions in appliaIlcce replacement. They reflect the presence of barriers to fuel conversion when equipment is replaced. Eur inertial factors apply to efficiency changes at the time of equipment replacement Office Equipment and Miscellaneous EquipmentEUI Growth. Theseparameters allow office equipment and miscellaneous equipment EUrs to grow independently for each building type in the forecast period. Standards and DSM Data. This section includes data related to equipment efficiency standards, thermal efficiency standards and DSM program impacts. These inputs are described briefly below.
B-18 Thermal Interactions. Thermal interaction elasticities are used to describe the impact of changes in lighting and miscellaneous loads on HVAC energy use. Separate parameters give the impact ofchanges in building thermal characteristics on HV AC energy use. The equipment cost data determine the relationship between capital costs and operating costs, whichis importantin determining the importance ofenergy pricesinequipmentdecisions. Economic Data. The economic data describe decision-makers and decision rules. These data are defined as follows: Decision-MakerData. Decision-makers are describedby a block distribution ofdiscount rates. These distributions may differ across building types. The decision-makers have price expectations which arebasedon a single distributedlag adjustmentmechanism. This implies that price expectations are formed on the basis ofpastprice events. Efficiency Option Elasticities. These parameters give the sensitivity ofmarket shares to life-cycle cost, where life-cycle cost includes both initial equipment cost and the present value ofoperating costs. These sensitivities are used tomodel efficiency choiceforall end uses. ShareOptionElasticities. Liketheefficiencyoptionelasticities, theseparameters givethe sensitivity of market shares to life-cycle cost, where life-cycle cost includes both initial equipment cost and the present value ofoperating costs. These sensitivities are to model market shares ofcompeting fuels and technologies. Automatic Calibration. The technology data anddecision data are combinedtocompute implied efficiency elasticities and to calibrate fuel-choice equations. These equations are calibratedto marginal shares in new construction. Utilization Elasticities. These parameters indicate the sensitivity ofequipment usage to energy prices, as well as weather data, operating hours, vacancy rates and other factors. Theseparameters are used to simulate changes in usage levels over time. Replacement Factors. Fuel share inertial parameters apply to fuel choice decisions in**appli~ce** replacement. They reflect the presence of barriers to fuel conversion when equipment is replaced. EUr inertial factors apply to efficiency changes at the time of equipment replacement OfficeEquipmentandMiscellaneous EquipmentEUIGrowth. Theseparameters allow office equipment and miscellaneous equipment EUrs to grow independently for each building type in the forecast period. Standards and DSM Data. This section includes data related to equipment efficiency standards, thermal efficiency standards andDSM program impacts. These inputs are described briefly below.
B-18Efficiency Standards. This section contains data that identify the timing of efficiency standards andthatdescribe the impactofthese standards on (1) equipmentefficiencyranges and (2) the level ofthermal efficiency in new construction. Efficiency Incentives. This section allows introduction ofincentive orrebatepayments for equipment that meets specific efficiency requirements. SpecificDSM ProgramImpacts. This section allows impositionofDSMprogramimpacts by building type, and end use and fuel. General DSM Program Impacts. This section allows imposition ofimpacts by building and fuel. Specific end uses are not identified. 

### Forecast Logic Given the model parameters, the key steps in the forecast logic are summarized as follows: Compute price forecast, Compute floor stock forecast, Compute efficiency/cost changes, Trends and standards move curves, Simulated elasticities give changes along curves, Compute share changes, Compute replacement impacts, Shares, Average EUIs, Compute utilization impacts, and Apply central energy equation. 

### ForecastResults COMMEND 3.2 forecast results are summarized below. Price forecast, Floor stock forecast, Energy sales forecast, Sales forecast by building type, Sales forecast by end use, Summer peak demand forecast, and Winter peak demand forecast. Figure B.5 presents an example offorecast results using the COMMEND 3.1 national 

### data base.

B-19 Efficiency Standards. This section contains data that identify the timing of efficiency standards andthatdescribe the impactofthese standardson (1)equipmentefficiencyranges and (2) the level ofthermal efficiency in new construction. EfficiencyIncentives. This section allows introduction ofincentive orrebatepayments for equipment that meets specific efficiency requirements. SpecificDSMProgramImpacts. This section allowsimpositionofDSMprogramimpacts by building type, and end use and fuel. General DSM Program Impacts. This section allows imposition ofimpacts by building and fuel. Specific end uses are not identified. 

### Forecast Logic Given the model parameters, the key steps in the forecast logic are summarizedas follows: Compute price forecast, Compute floor stock forecast, Compute efficiency/cost changes, Trends and standards move curves, Simulated elasticities give changes along curves, Compute share changes, Compute replacement impacts, Shares, Average EUIs, Compute utilization impacts, and Apply central energy equation. 

### ForecastResults COMMEND 3.2 forecast results are summarized below. Price forecast, Floor stock forecast, Energy sales forecast, Sales forecast by building type, Sales forecast by end use, Summerpeak demand forecast, and Winter peak demand forecast. FigureB.5 presents an example offorecast results using the COMMEND 3.1 national 

### data base.

B-19

### Figure 

### 8.S Forecast ofElectricity Sales by End Use (gWh) (l% 

### ofU.S. Total)

200 199 2500 GWH 3500,----------------------
.... 3000

2000
1500

Cool ll'.He.t Cook Retrig Licht. Mille

B-20 Figure B.S Forecast ofElectricity Sales by End Use (gWh) (1% ofU.S. Total)

GWH 3500**r---------------------------------------------~** 200 3000 199 2500 

# , 2000
1500 

### .::**:~ ~** 

# ':::~t~::**;~ :** __

# ~;~I**~**__

# ;!**~~ II·** **~**__

# ~~~_W~~~L_~.~.**L_~~~ S.He:a~** **Ven~** Cool lI".Heat Coole Retrig**Li,h~** Mille

B-20

## APPENDIX C. LBL MANUFACTURER IMPACT MODEL

C.I OVERVIEW OF MANUFACTURER IMPACT MODEL C.I.I Purpose The Lawrence Berkeley Laboratory Manufacturer Impact Model (LBL-MIM) collects into one spreadsheet all the calculations necessary to determine the impact ofa change in appliance efficiency standards on an industry's profitability and scale ofoperation. The spreadsheetdesign makes itpossible to quickly analyze the effects ofuncertainties in input values and to efficiently incorporate new data into the calculations. The model has a control panel, which makes the analysis of uncertainty partiCUlarly straightforward for nine of the model's crucial inputs. Through this panel, the model provides the user not only with the best estimates offinancial impacts, but also with the uncertainty ofthese estimates and a sensitivity analysis attributing that uncertainty to certain input variables. 

### C.I.2 The Economic Approach The impact of efficiency standards on manufacturers depend on the interaction of three factors: (1) 

### the costs imposed by the change in standards, (2) the price elasticity of a single typical finn, 

### and (3) the industry's price and operating cost elasticities of demand. The LBL-MIM integrates and analyzes these four factors as they apply to a single typical finn. 

### A change in standards will generally create three types ofcosts for a firm. First, variable costs of goods sold (yeaS) will·be affected by new parts and labor requirements. Second, some engineering will be required to design, test, and plan the manufacturing ofnew products. And

__.'. third, new products willrequire re-tooling and new equipment (capital) costs. The incorporation ofyeaS is straightforward, butengineering and capital costs mustbe amortized to be converted from one-time costs to annual costs for a typical-year model. Markups from manufacturing costs to ex-factory prices dependon the single-firm price elasticity. Because it 

### is difficult to 

### find data on single-firm price elasticities, the LBL-MIM infers them from the firm's fiXed costs and return on equity (ROE). The markup is over variable cost and notfiXed; hence it 

### must be greaterthan one ifafirm with fixed costs is to recoverits fiXed costs and turn 

### a profit. The industry has two elasticities ofinterest: its price elasticity and its operating cost elasticity. Although individual finns must calculate according to their own elasticities of demand, it is ultimately the industry elasticities that determine the level ofdemand experienced by each firm. This apparentparadox arises because the model assumes that all firms are profitmaximizers and thus respond in essentially the same way to the imposition of standards. Because of this
C-l 

## APPENDIX C. LBL MANUFACTURER IMPACT MODEL

C.I OVERVIEW OF MANUFACTURER IMPACT MODEL 

### C.I.I Purpose TheLawrence Berkeley Laboratory Manufacturer Impact Model (LBL-MIM) collects into one spreadsheet all the calculations necessary to determine the impact ofa change in appliance efficiency standards on an industry'sprofitability and scale ofoperation. The spreadsheetdesign makes itpossible to quickly analyze the effects ofuncertainties in input values andto efficiently incorporate new data into the calculations. The model has a control panel, which makes the analysis of uncertainty particularly straightforward for nine of the model's crucial inputs. Through this panel, the model provides the user not only with the best estimates offinancial impacts, but also with the uncertainty ofthese estimates and a sensitivity analysis attributing that uncertainty to certain input variables
. 

### C.I.2 The Economic Approach The impact of efficiency standards on manufacturers depend on the interaction of three factors: (1) the costs imposed by the change in standards, (2) the price elasticity of a single typical firm, and (3) the industry'S price and operating cost elasticities of demand. The LBL-MIM integrates and analyzes these four factors as they apply to a single typical firm
. A change in standards will generally create three types ofcosts for a firm
. First, variable costs of goods sold (yeaS) will·be affected by new parts and labor requirements. Second, some engineering will be required to design, test, and plan the manufacturing ofnew products. And

__._ third, new products willrequirere-tooling and new equipment (capital) costs. Theincorporation ofYCGS is straightforward, butengineering and capital costs mustbe amortized to be converted from one-time costs to annual costs for a typical-year model. Markupsfrom manufacturingcoststo ex-factory pricesdependon the single-firm priceelasticity. Because it is difficult to find data on single-firm price elasticities, the LBL-MIM infers them from the 

### fmn's fixed costs and return on equity (ROE). The markup is over variable cost and not fixed; hence itmust be greaterthan oneifafirm with fixed costs is to recoverits fixed costs and turn a profit. The industry has two elasticities ofinterest: its price elasticity and its operating cost elasticity. Although individual firms must calculate according to their own elasticities of demand, it is ultimately the industry elasticities that determine the level ofdemandexperiencedby each firm. This apparentparadox arises because the model assumes that all firms are profitmaximizers and thus respond in essentially the same way to the imposition of standards. Because of this
C-I

### assumption, all firms change the prices and operating costs of their appliances by the same amount (Le., the industry acts in unison). When this happens, sales for each fIrm are determined by the industry elasticities. 

### C.I.3 Measures ofImpact The LBL-MIM computes two primary and several secondary measures ofimpact. The two primary measures, ROE and net income, are both necessary because they can move in 

### opposite directions. For instance, ifnet income increases, but assets and thus equity increase by a greater percentage, ROE will decline. Ifthe decline is large, the net effect will be viewed as negative, but if a small decrease in a healthy ROE is accompanied by a large increase in net income, the net effect might be viewed as positive. In addition to ROE, return on assets and gross margin are also computed. Other outputs are total assets, shipments, revenues, and averageprice; these help explain the origins ofchanges in the primary inputs. 

### C.IA Methodology How stanikzrds are implemented in the model Generally, a change in standards affects the model in three distinct ways. Standards require additional investment, raise production costs, and affect revenue. .I 

### \. The most obvious investment induced by standards is the purchase of new plants and equipment. This cost is fIrst evaluated from engineering data, and then amortizedby taking into accountthe life ofthe investment, the timing ofthe expenditures, tax laws, and the costoffunds. An additional (and sometimes larger) investment**i~** 

### made as the old inventory is replaced with more expensive new units. The model assumes that previous inventory ratios are maintained
..-.- A third form ofinvestment tracked by LBL-MIM is the change in the transactions demand for cash that accompanies a change in revenues. Increasedcosts ofproduction are modeledby coupling changes in unitcosts with changes in product shipments. Changes in unit costs come from engineering data and shipments data come from LBL-REM. Revenue is affected both by price and shipments. Price is determined by computing the markup over long-run marginal costs, and then using the markup to determine an optimal price. Shipments (demand) is determined by price and operating cost elasticities, coupled with the changes in price and operating costs resulting from the standards.
. .

C-2 

### assumption, all finns change the prices and operating costs of their appliances by the same amount (Le., the industry acts in unison). When this happens, sales for each fInn are determined by the industry elasticities. 

### C.l.3 Measures ofImpact 

### The LBL-MIM computes two primary and several secondary measures ofimpact. The two primary measures, ROE and net income, are both necessary because they can move in opposite directions. Forinstance, ifnet income increases, but assets and thus equity increase by a greater percentage, ROE will decline. Ifthe decline is large, the net effect will be viewed as negative, but if a small decrease in a healthy ROE is accompanied by a large increase in 

### net income, the net effect might be viewed as positive. In addition to ROE, return on assets and gross margin are also computed. Other outputs are total assets, shipments, revenues, and averageprice; these help explain the origins ofchanges in the primary inputs. 

### C.l.4 Methodology 

### How stanilllrds are implemented in the model Generally, a change in standards affects the model in three distinct ways. Standards require additional investment, raise production costs, and affect revenue. f 

### \. The most obvious investment induced by standards is the purchase of new plants and equipment. This cost is fIrst evaluated from engineering data, andthen amortizedby takinginto accountthe life ofthe investment, the timing ofthe expenditures, tax laws, andthe costoffunds. An additional (and sometimes larger) investment**i~** 

### made as the old inventory is replaced with more expensive new units. The model assumes that previous inventory ratios are maintained.

-- . A third fonn ofinvestment tracked by LBL-MIM is the change in the transactions demand for cash that accompanies a change in revenues. Increasedcosts ofproduction are modeledby couplingchanges in unitcosts with changes in product shipments. Changes in unit costs come from engineering data and 'Shipments data come from LBL-REM. Revenue is affected both by price and shipments. Price is detennined by computing the markup over long-run marginal costs, and then using the markup to detennine an optimal price. Shipments (demand) is detennined by price and operating cost elasticities, coupled with the changes in price and operating costs resulting from the standards. ' ,

C-2

## R / / /.
/ / / / / /



### Figure C.I 

## Q* 

## Output (Q) Profit Maximization and Fixed Costs
C-3 / /



## R

Q* 

## Output (Q) Figure C.I Profit Maximization and Fixed Costs
C-3

### Typical-yearapproach The LBL-MIM uses a typical-year approach rather than a dynamic approach. This approach models a "typical year" for the industry both in 

### the base case and in the new standards case. The year chosen for the model is the fourth year after the imposition ofstandards, and is selectedbecause it

### is believedto be long enough to capture any majorimpacts from the standard, such as profitability changes or finn entry into or exit·from the industry. 

### Use of prototypicalfinns Because the engineering and financial data for most finns are proprietary, LBL-MIM models a prototypical finn. In many cases, this firm must be thought of as representing an autonomous division of a larger finn. A prototypical finn is a hypothetical finn that is representative ofthe industry. Prototypical finns are defined by parameters that are important for detennining the impacts of standards and are consistent with data for the portion of the industry theyrepresent. Importantparameters used in the model include the cost structure ofthe finns, profitability ratios, relative costs of complying with the new standard, and marketing strategies. 

### Product segmentation The LBL-MIM assumes that the product market is segmented. Thus, differentproducts have different markups. In general, it is found that appliance manufacturers are able to charge different markups for products that have different characteristics. They can charge higher markups on products that have desirable characteristics, such as water heaters with heatpumps orcolorTVs with remote control. Products without such desirable features are generally bought in larger quantities at lower prices by consumers who are more price-conscious, and thus the markups for these products are lower. The per-unit profits made by manufacturers for these different products may differ significantly. 

### C.2 STRUCTURE OF THE LBL-MIM 

### C.2.! Economic Theory and Assumptions The basic question that LBL-MIM must answer is: how much of the cost increases caused by standards can be passed through to 

### consumers? If

### less than the nonnal pass-through occurs, profits will suffer, while an above normal pass-through will increase profits. To answer this question precisely, it is necessary to compute a markup; but, before that, a more intuitive understanding ofthe relevant economics will be useful. Economists often divide cost into fixed and marginal components; fixed costs are just what their name suggests, and marginal costs are the costs ofproducing an additional unit of output. Three facts are true in general ofthese cost distinctions:

C-4 

### Typical-year approach 

### The LBL-MIM uses a typical-year approach rather than a dynamic approach. This approach models a "typical year" for the industry both in the base case and in the new standards case. The year chosen for the model is the fourth year after the imposition ofstandards, and is selectedbecause itis believed to be long enough to capture any majorimpactsfrom the standard, such as profitability changes or finn entry into or exit· from the industry
. Use of prototypicalfirms 

### Because the engineering and financial data for most finns are proprietary, LBL-MJM models a prototypical finn. In many cases, this finn must be thought of as representing an autonomous division of a larger finn. A prototypical finn is a hypothetical finn that is representative ofthe industry. Prototypical finns are defined by parameters that are important for detennining the impacts of standards and are consistent with data for the portion of the industry theyrepresent. Importantparameters used in the model include the cost structure ofthe finns, profitability ratios, relative costs of complying with the new standard, and marketing strategies. Product segmentation 

### TheLBL-MIM assumes that the product market is segmented. Thus, different products have different markups. In 

### general, it is found that appliance manufacturers are able to charge different markups for products that have different characteristics
. They can charge higher markups on products that have desirable characteristics, such as water heaters with heat pumps orcolorTVs with remote control. Products without such desirable features are generally bought in larger quantities at lower prices by consumers who are more price-conscious, and thus the markups for these products are lower
. The per-unit profits made by manufacturers for these different products may differ significantly. 

### C.2 STRUCTURE OF THE LBL-MIM 

### C.2.! Economic Theory and Assumptions The basic question that LBL-MIM must answer is: how much of the cost increases caused by standards can be passed through to

### consumers? If

### less than the nonnalpass-through occurs, profits will suffer, while an above nonnal pass-through will increase profits
. To answer this question precisely, it is necessary to compute a markup; but, before that, a more intuitive understanding ofthe relevant economics will be useful. Economists often divide cost into fixed and marginal components; fixed costs arejust what their name suggests, and marginal costs are the costs ofproducing an additional unit of output. 

### Three facts are true in general ofthese cost distinctions:

C-41. 

### Under perfect competition, price equals marginal cost. 2. Under imperfect competition, i.e., with adownward-sloping demand curve, price equals marginal cost times a markup. 3. In neither case do fixed costs enter the calculation ofpriceP The firstpointmay be understood by assuming for amomentthat it

### is false and thatprice is higher. In this case, each firm would think it profitable to produce more (since in perfect competition each knows that as an individual finn it 

### can have no effect on price), which would certainly drive prices down. The reverse argument holds with price below marginal cost, so, in either case, price will inevitably change to equal marginal cost. The second fact is derived in the next section. The third point is most easily understood by examining Figure C.I, which shows a revenue curve and two total-cost curves that differ only in their fixed components. How will a firm respond to an increase in fixed cost (represented by the upward shift displayed in the costcurve)? Since the firm wishes to maximize the difference between costand revenue, it chooses the optimal output Q*. 

### This choice is clearly independent ofthe shift in fixed cost, but once output is determined, the demand curve will dictate the price at which that output can be 

### sold. A change in fixed costhas no effecton the demand curve and thus no effect on price. The separation ofcosts into fixed and variable must be done with an eye to the time horizon under consideration, because many (if not most) costs that are fixed in the short run become variable in the long run. Three distinct time horizons are useful: the short run, the long run, and the very long run. In 

### the shortrun, the productive capacity ofthe firms in the industry remains fixed. In the long run, capacity may be adjusted, but the number of firms remains constant. In the very long run, firms may enter or leave the industry, with the result that an individual firm's market power, as measured by its price elasticity of demand, may change. Thus, in the very long run, markup may change. In the long run, price is a markup over long-run marginal cost, while in the short run, price is a markup over short-run marginal cost. Since in the long run, but not in the short run, capital is considered a marginal cost, how can the two pricing rules be reconciled? If 

### a finn finds thatits long-run plans have beenrealized (in otherwords, ifthe marketconditions are those that it

### foresaw atthe time itplannedits presentcapacity), then long- and short-runmarginalcosts 13oywo interpretations may legitimately be given to this mathematical result First, one may say that given the demand function, marginal cost determines price and fixed costs are not passed on. Alternatively, one may argue that fixed costs indirectly determine the elasticity ofdemand, and thereby the markup over marginal cost. Thus (indirectly) fixed costs are passed on through the markup ofmarginal cost. The mechanism providing this indirect linkis the foUowing: iffixed costs are notcovered, theneconomic profits will be negativeand some rums will leave theindustry; this willreducecompetition and thusreduce eachrum's elasticityofdemand. Conversely, iffixedcosts. are more than covered, positiveeconomic profits will induce entry and elasticity and markup over marginal costwill be reduced. C-5 1. 

### Under perfect competition, price equals marginal cost. 2. Under imperfect competition, i.e
., with adownward-sloping demand curve, price equals marginal cost times a markup. 3. In neither case do fixed costs enter the calculation ofprice. 13 

### The firstpointmay be understood by assumingfor amomentthat it

### is false andthatprice is higher. In this case, each firm would think it

### profitable to produce more (since in perfect competition each knows that as an individual firm itcan have no effect on price), which would certainly drive prices down
. The reverse argument holds with price below marginal cost, so, in either case, price will inevitably change to equal marginal cost. The second fact is derived in the next section. The third point is most easily understood by examining Figure C.I, which shows a revenue curve and two total-cost curves that differ only in their fixed components. How will a firm respond to an increase in fixed cost (represented by the upward shift displayedin the costcurve)? Since the firm wishes to maximize the difference betweencostand revenue, it 

### chooses the optimal output Q*
. 

### This choice is clearly independent ofthe shift in fixed cost, but once output is determined, the demand curve will dictate the price at which that output can be 

### sold
. A change in fixed costhas no effecton the demandcurve andthus no effect on price. The separation ofcosts into fixed and variable must be done with an eye to the time horizon under consideration, because many (if 

### not most) costs that are fixed in the short run become variable in the longrun. Three distinct time horizons are useful: the shortrun, the long run, andthe very long run. In

### the shortrun, the productive capacity ofthe finns in the industry remains fixed. In the long run, capacity may be adjusted, but the number of firms remains constant. In the very long run, firms may enter or leave the industry, with the result that an individual firm's market power, as measured by its price elasticity of demand, may change
. Thus, in the very long run, markup may change. In the long run, price is a markup over long-run marginal cost, while in the short run, price is a markup over short-run marginal cost. Since in the long run, but not in the short run, capital is considered a marginal cost, how can the two pricing rules be reconciled? If

### a finn finds thatits long

-run plans have beenrealized (in otherwords, ifthe marketconditions are those thatitforesaw atthe time itplanneditspresentcapacity),then long- and short

-runmarginalcosts 13<ywo interpretations may legitimatelybe given to this mathematicalresult First, onemay say that given the demand function, marginal cost detennines price and fixed costs are not passed on. Alternatively, one may argue that fixed costs indirectly detennine the elasticity ofdemand, and thereby the markup over marginal cost. Thus (indirectly) fixed costs are passed on through the markup ofmarginal cost. The mechanism providing this indirect linkis the following: iffixedcostsarenotcovered,theneconomicprofitswillbe negativeandsomefirms willleave theindustry; thiswillreducecompetition andthusreduceeachlInn'selasticityofdemand. Conversely,iffixedcosts
. aremorethan covered,positiveeconomicprofitswill induceentryandelasticity andmarkup over marginal costwill bereduced. C-5

### will be equal. This is possible because although capital is notincluded as a short-run cost, other costs are short-run but not long-run costs. These are the costs associated with an increase in output beyond the planned long-run capacity level. If 

### a plant is running optimally, output has been pushed to the point that short-run marginal costs equal long-run marginal costs; otherwise a different size plant would have been more profitable.
. The next section gives an overview ofthe LBL-MIM 

### from a theoreticalperspective, after which some results ofcentral importance are derived and their application to the long-run model is reviewed. Finally, the short-run part ofthe model is discussed. 

### Theory andthe LBL-MIM From ourdiscussions with the industries involved andindustry consultants, we know that there are fixed costs (Le., unit cost decreases as quantity decreases) in the production of the appliances analyzed. Consequently, each producing finn must not face an infinite elasticity of demand because a small increase in price charged will notresult in 

### a 100% loss ofsales due to induced entry ofcompetitors. As a result, we know that the appliance-manufacturing industry is not perfectly competitive and thus, the LBL-MIM. 

### models typical firms as having a combination offixed and variable costs, and as facing a demand curve with finite elasticity. The next section derives the equations relating markup, fixed costs, variable cost, and economic profit. These relationships have been a standard part ofeconomics for many years, hence these derivations are included for the convenience ofthe reader and not as an explanation of theoretical results new to this study. One exception to this disclaimer is the discussion on oligopsony power and the "effective" number offinns. There is no generally accepted theory on how to model a market characterized by significantmarket concentration on both the seller's and the buyer's side. As a result, we discuss our approach in greater detail below. 

### Derivation ofmarkup To begin the derivation ofthe markup equation, assume that finns maximize economic profit, where economic profit is revenue minus economic cost, and economic cost includes the cost ofequity and all taxes. Economic costs (C) 

### are assumed to be linearfunctions ofoutput (Q) 

### and revenues (R):
C 

## =btO + btlQ

C-6 (C.I) will be equal. This is possible because although capital is notincluded as a short-run cost, other costs are short-run but not long-run costs. These are the costs associated with an increase in output beyond the planned long-run capacity level. Ifa plant is running optimally, output has been pushed to the point that short-run marginal costs equal long-run marginal costs; otherwise a different size plant would have been more profitable.
. Thenext section gives an overview ofthe LBL-MIMfrom a theoreticalperspective, after which someresults ofcentral importance are derived andtheir application to the long-run model is reviewed. Finally, the short-run part ofthe model is discussed
. 

### Theory andthe LBL-MIM Fromourdiscussions with the industries involved andindustryconsultants, we know that there are fixed costs (i.e., unit cost decreases as quantity decreases) in the production of the appliances analyzed. Consequently, each producing firm must not face an infinite elasticity of demand because a small increase in price charged will not result in a 100% loss ofsales due to induced entry ofcompetitors. As a result, we know that the appliance-manufacturing industry is not perfectly competitive and thus, the LBL-MIM
.models typical firms as having a combination offixed and variable costs, and as facing a demand curve with finite elasticity. The next section derives the equations relating markup, fixed costs, variable cost, and economic profit. These relationships have been a standard part ofeconomics for many years, hence these derivations are included for the convenience ofthereader andnot as an explanation of theoretical results new to this study. One exception to this disclaimer is the discussion on oligopsony power and the "effective" number of firms. There is no generally accepted theory on how tomodel a market characterized by significantmarket concentration on both the seller's and the buyer's side. As a result, we discuss our approach in greater detail below. 

### Derivation 

### 0/markup To begin the derivation ofthe markup equation, assume that firms maximize economic profit, where economic profit is revenue minus economic cost, and economic cost includes the cost ofequity and all taxes. Economic costs (C) are assumedtobelinearfunctions ofoutput (Q) and revenues (R):


### C 

## =btO + btlQ (C.I)

C-6Although cost is written as a linear function of output, this is not essential; a non-linear cost function would give a mathematically equivalent, but more difficult to express, result. 14 Next, make the standard economics assumption that firms maximize economic profit (R-C). Since the cost ofcapital is included in economic cost, a f"mn with an ROE equal to the cost ofcapital will find its economic profit to be zero. If, however, a fIrm can earn a higher ROE, it is assumed that it will, in which case R-C will be positive. The firm can maximize profit by picking either a quantity to produce (Q) or, equivalently, a price to sell it at, but it cannot choose both. The following computations assume that profit is maximized with respect to Q. This gives the foilowing first-order condition:

d -(R-C)=O dQ (C2)

This equation completes the description ofthe supply-side assumptions. The demand side is described by an elasticity ofdemand which may vary, but which at the markl;t equilibrium takes the value 

### e. Elasticity of demand is the percentage change in demand caused by a 1

### % change in price P, and consequently is negative. Mathematically, this is expressed as follows:
dQ dP p Q =e (C.3)

All necessary assumptions have now been made, so all that remains are some**~~-~** mathematical manipulations. 

### From the two supply assumptions, and denoting dR I dQ by R'and dP I dQ by p', we have:
d - [ R - btO

- btlQ ] 

## =0 dQ (C.4)

14 Ifwe assume that cost is an arbitrary function of Q, C(Q.R), our fmal result, will differ only in that the two types ofcost ( btO and btl) will be replaced by C - Q (dC / dQ ) and dC / dR. respectively.
C-7 Although cost is written as a linear function of output, this is not essential; a non-linear cost function would give a mathematically equivalent, but more difficult to express, result. 14 Next, make the standard economics assumption that firms maximize economic profit (R-C). Since the cost ofcapital is included in economic cost, a fInn with an ROE equal to the cost ofcapital will find its economic profit to be zero. If, however, a flrm can earn a higher ROE, it is assumed that it will, in which case R-C will be positive. The firm can maximize profit by picking either a quantity to produce (Q) or, equivalently, a price to sell it at, but it cannot choose both. The following computations assume that profit is maximized with respect to Q. This gives the foilowing first-order condition:

## ~(R-C)=O dQ (C.2)

Thisequationcompletes thedescriptionofthe supply-side assumptions. Thedemandside is described by an elasticity ofdemand which may vary, but which at the**mark~t** equilibrium takes the value 

### e. Elasticity of demand is the percentage change in demand caused by a 1

### % change in price P, and consequently is negative
. Mathematically, this is expressed as follows:
dQ dP p Q =e (C.3)

All necessary assumptions have now been made, so all that remains are some**~-­** mathematicalmanipulations. From the two supply assumptions, and denoting dR IdQ byR' and dP IdQ by P', we have:
d - [ R - btO

- btlQ ] = 

### 0 dQ (C.4)

14 Ifwe assume that cost is an arbitrary function of Q, C(Q.R), our fmal result, will differ only in that the two types ofcost ( btO and btl) will bereplaced by C

- Q (dC / dQ ) and dC / dR, respectively.
C-7

### By definition: 1 _A 

### R -IJI



### R=P·Q

R 1- P +P dQ P



# -- dP Q

### R/=~+p e

p=_e_btl l+e (C.5)

(C.6)

(C.?)

(C.8)

(C.9)

(C.lO)



### (C.II)

In 

### this last equation, price is a simple markup over marginal cost and may be found in 

### any standard text on industrial organization. Now we define the markup:



### e p=- 1

### +e (C.12) 

### By definition:

R=P'Q

R '- P +P dQ P



# -- dP Q

### ~ 

### R' =P' oQ+P

R'=P+P 

### e

p=_e_btl l+e (C.S)

(C.6)

(C.7)

(C.8)

(C.9)

(C.lO)



### (C.II)

In 

### this last equation, price is a simple markup over marginal cost and may be found in 

### any standard text on industrial organization. Now we define the markup:



### e p=- 

### 1+e 

### (C.12)Note that because the own-price elasticity (e) is negative, J.l is greater than 1. Of course if 

### e = -1, then J.l is undefined, since the firm makes more the less it sells. A small rearrangement ofEq. C.12 provides a simpler look at the markup equation:
P = pbtl (C.13)

Price formation can be explained as follows: marginal costs are marked up by bothJ.l and fixed costs are not passed on at all. Itis now easy to derive anotherrepresentation ofJ.l which is used in the calibration stage ofthe LBL-MIM to estimate J.l from Eq. C.l:

Using Eq. C.5:

so

and

Finally, (R-C) =R-btO-bt1 'Q

R=P-Q =pbtlQ

(R-C) =pbtlQ-btO-bt1Q

(R-C) =(p-l)btlQ-btO



## =1+(R-C)+btO p bt1Q

C-9 (C. 14)

(C.15)



### (C. 16)

(C. 17)

(C.18) Note that because the own-price elasticity (e) is negative, J.l is greater than 1. Of course if 

### e**~** = -1, thenJ.l is undefined, since the firm makes more the less it sells
. A small rearrangement ofEq. C.12 provides a simpler look at the markup equation:
P = pbtl (C.13)

Priceformation canbe explained as follows: marginal costs are markedup by bothJ.l andfixed costs are notpassed on at all. Itis now easy to derive anotherrepresentation ofJ.l which is usedinthe calibration stage ofthe LBL-MIM to estimateJ.l from Eq. C.l: (R-C)=R-btO-btl'Q (C.14)
UsingEq. C.S:
R=P-Q=pbtlQ (C.lS)

so (R-C)=pbtlQ-btO-btlQ 

### (C.16)

and (R-C)=(p-l)btlQ-btO (C.17)
Finally,



## = 1 +(R-C)+btO p btlQ (C.18)

C-9This equation shows that markup is economic income (R-C) plus fixed costs, all over total variable cost. The calibration calculations ofthe long-run module.uses a simple variation ofthis equation in which btlQ is replaced by C-btO, and the relation, R-C

## =EP, where EP is economic profit, is used twice. The new form ofthe equation is:

1 EP+btO )1= 

### +---- R-EP-btO (C.19)
Markup is detennined by revenue, economic profit, and fixed costs. Generally, it has been found that fixed costs are large relative to economic profit, so the numerator is controlled largely by the fixed cost. Thus, markup is primarily determined by the size of fixed cost relative to revenue. 

### Oligopsony Power andthe "Effective Number ofFirms From an evaluation ofthe analyzed industries (see Chapter 4, Industry ProfIle), we see that the market structures of the industries are oligopolies and model them accordingly. However, the situation is further complicated by the fact that the demand side ofthe market is also heavily concentrated due to the presence oflarge retailers who have a great deal ofmarket power.IS In otherwords, we have an oligopoly facing an oligopsony. Consequently, the standard oligopoly model needs to be modified. Unfortunately, there is no standard theory on how to model an industry with such a market structure. After examining the evidence ofoligopsony, an outline will be made to show how we incorporate this into the model. The rise of large retailers in the home appliance industry is a relatively recent phenomenon. They are generally defined as multi-market large-volume retailers, which includes ,"- firms such as Sears, Montgomery Ward, Circuit City, and Highland. For example, the home appliance retail share ofSears and Montgomery Ward alone has been estimated to be about 30% forvarious products. One indication ofthis oligopsonypoweris that iftheoretical markups based on the actual number offirms in the industry are estimated, we find that the markups are much too large relative to what we observe in the market. We address both the problems of oligopsony and of "over-estimated" markups by assuming that the low observed markup is the result ofoligopsony power, and is an indication ofthe strength ofthat power. Since a firm's markup is directly related to the elasticity ofthe
15nte increasing concentration on the demand side ofthe market also agrees with comments by the home appliance industry during the comment for these and other appliances. Forinstance, see AHAM, "State ofthe AHAM to the Department ofEnergy on Notice ofProposed Rulemaking on Energy Efficiency Standards for Clothes Dishwashers, Clothes Washers and Clothes Dryers," CE-RM88-101, October 

### to, 1989. C-lO This equation shows that markup is economic income (R-C) plus fixed costs, all over total variable cost. The calibrationcalculations ofthe long-run module
. uses a simple variation ofthis equation inwhichbtlQis replacedby C-btO, and therelation,R-C

## =EP, whereEPis economic profit, is used twice. The new form ofthe equation is:

1 EP+btO ).1= 

### +---- R-EP-btO (C.19)
Markupisdeterminedbyrevenue, economicprofit, and fixed costs. Generally,ithasbeenfound that fixed costs are large relative to economic profit, so the numerator is controlled largely by the fixed cost. Thus, markup is primarily determined by the size of fixed cost relative to revenue. 

### Oligopsony Power andthe "Effective Number ofFirms From an evaluation ofthe analyzed industries (see Chapter 4, Industry ProfIle), we see that the market structures of the industries are oligopolies and model them accordingly. However, the situation is further complicated by the fact that the demand side ofthe market is also heavily concentrateddue to the presence oflarge retailers who have a greatdeal ofmarket power.IS Inotherwords,we have an oligopoly facing an oligopsony. Consequently,the standard oligopoly model needs to be modified. Unfortunately, there is no standard theory on how to model an industry with such a market structure. After examining the evidence ofoligopsony, an outline will be made to show how we incorporate this into the model. The rise of large retailers in the home appliance industry is a relatively recent phenomenon. Theyare generallydefinedas multi-marketlarge-volumeretailers, which inc1udes '-- firms such as Sears, Montgomery Ward, Circuit City, and Highland
. For example, the home applianceretail share ofSears andMontgomeryWard alone has been estimatedtobeabout 30% forvariousproducts. Oneindicationofthis oligopsonypoweristhatiftheoreticalmarkups based on the actual number offirms in the industry are estimated, we find that the markups are much too large relative to what we observe in the market
. We address both the problems of oligopsony and of "over-estimated" markups by assuming that the low observed markup is the result ofoligopsony power, and is an indication ofthe strength ofthat power. Since a firm's markup is directly related to the elasticity ofthe
15nteincreasing concentration on the demand side ofthe market also agrees with comments by the home appliance industry during the comment for these and other appliances. Forinstance, see AHAM, "State ofthe AHAM to the Department ofEnergy on Notice ofProposed Rulemaking on Energy Efficiency Standards for Oothes Dishwashers, Clothes Washers and Oothes Dryers," CE-RM88-101, October 10, 1989. C-lO

### demand it faces, we can find the single-fInn demand elasticity that corresponds to the observed markup. This same elasticity is also implied by the industry elasticity and a Cournot oligopoly with some hypothetical number offIrms. This allows us to compute the hypothetical orwhatwe refer to as the "effective" number of firms from the observed markup. We then model the industry as ifit were a Coumot oligopoly facing a competitive, not oligopolistic. demand for its product. but we use the "effective" number offirms rather than the actual number offInns to reflect the impact ofthe oligopsony. The effect of this is to reduce the market power ofthe individual firm to agree with our observation ofmarkup. Although the reduced market power is a consequence ofspecifying too many firms, and not ofmodeling oligopsony, we believe that it provides a good first-order approximation to the actual market structure and provides a way around the intractable problem ofmodeling a market with imperfectly competitive demand and supply sides. We now show how markup is computed from economic profIt and fixed costs, and how the "effective" number offinns is computed from markup and industry demand elasticity. A well-known result for a Cournot oligopoly is:

where: e=NF*e· 1



### the firm elasticity ofdemand, the industry elasticity ofdemand, and number offirms. 

### (C.20)

We can solve for NF 

### if the two**el~sticities** 

### are known. We have an estimate of ei' and can estimate 

### e as follows. From Eq. C.5, we know that:

### e=~ 1-p (C.21)
·Thus. we can find the markup, pt, 

### in Eq. C.21 from Eq. CA. 

### if we know marginal cost c. Marginal cost can be found from the firm's return on equity, its fixed costs, and the following price identity: 

### rc/Q=P-c+FC/Q, where 

### FC is fixed cost. This is just a restatement of the definition ofeconomic profit, which is revenue minus costs. Noting thatrevenue. R. 

### equalsP *Q. 

### we have:

C-ll 

### demand itfaces, we can find the single-flnn demand elasticity that corresponds to the observed markup. This same elasticity is also implied by the industry elasticity and a Cournot oligopoly with some hypotheticalnumber offlrms. This allows us to compute the hypothetical orwhatwe refer to as the "effective" number of firms from the observed markup. We then model the industry as ifit were a Coumot oligopoly facing a competitive, not oligopolistic, demand for its product, but we use the "effective" number offirms rather than the actual number offlnns to reflect the impact ofthe oligopsony. The effect of this is to reduce the market power ofthe individual firm to agree with our observation ofmarkup. Although the reduced market power is a consequence ofspecifying too many firms, andnot ofmodeling oligopsony, we believe that itprovides a good first-order approximation to the actual market structure and provides a way around the intractable problem ofmodeling a market with imperfectly competitive demand and supply sides. We now show how markup is computed from economic proflt and flxed costs, and how the "effective" number offlnns is computed from markup and industry demand elasticity. A well-known result for a Coumot oligopoly is:

where:


### e = 

### e=NF*e· 1



### the flrmelasticity ofdemand, the industry elasticity ofdemand, and number offlrms. 

### (C.20)

We can solve for NF 

### if the two**el~sticities** 

### are known. We have an estimate of ei' and can estimate 

### e as follows. From Eq. C.5, we know that:

### e=~ 1-p (C.21) ·Thus, we can flnd the markup, pt, 

### in Eq. C.21 from Eq. CA, 

### if we know marginal cost c. Marginal cost can be found from the firm's return on equity, its flxed costs, and the following price identity: 

### rc/Q=P-c+FC/Q, where 

### FC is fixed cost. This is just a restatement of the deflnitionofeconomicprofit, which is revenue minus costs. Notingthatrevenue,R, 

### equalsP *Q, 

### we have:

C-ll

### p p p=------- c p_FC_"::' 

## Q Q R R-FC-TI (C.22)

This gives a fonnula for markup JA- based on our most easily observed variables. Solving Eqs. C.9 and C.lO for 

### e, we can now fmd the number offirms, NF: 

### NP- P ei '(l-p) (C.23)

We find that given two elasticities estimated from actual data, NF is higher than the actual number offinns observed. To summarize, we frrst look at a typical firm's profit and fixed costs. In order to recover fixed costs, a finn must mark up over variable costs, and ifit can mark up enough, it will make above "normal" ROE. From these considerations, we find its markup. The markup indicates a finn's industry demand elasticity and the effective number offrrms in the industry. We use it in subsequent calculations. The base case: 1990 NAECA standards In the long-run modelmodule, the calibration case is immediately followed by long-run calculations for the state ofthe industry in 1996 provided that the 1990 NAECA standards are unmodified. These calculations use the markup that is based on the one just calculated and modified in keeping with the assumption that demand elasticity with respect to life-cycle cost is constant. The modified markup is calculated as in Eq. C.5 but from a modified price elasticity. The new price elasticity is obtained as follows. First, the original price and operating cost
...- elasticities are used to detennine a life-cycle cost and an elasticity with respect to that cost. A demand curve with constant life-cycle cost elasticity is then computed. It takes this form: 

### Q 

## = a'(P+ff'F)B (C.24)

where 

### P is price, 

### F is operating (fuel) cost, 

### ffis the fuel-cost factor, and

### B is the elasticity with respect to life-cycle cost. Thenprice andfuel cost are changed from the calibration case to either the base case ora long run case. These changes shift the division ofdemand elasticity between the price and operating cost. In particular, the price elasticity is given by:

C-12 P P R p=-------- c 

### p_FC_..:: R-FC-TI (C.22) 

## Q Q
This gives a fonnula for markup }l 

### based on our most easily observed· variables
. Solving Eqs. C.9 and C.lO for 

### e, we can now fmd the number offirms, NF:


### NF- P ei'(1-p) (C.23)



### We find that given two elasticities estimated from actual data, NF 

### is higher than the actual number offinns observed. To summarize, we first look at atypical finn's profit and fixed costs. In ordertorecover fixed costs, a finn must mark up over variable costs, and ifit

### can mark up enough, it

### will make above "nonnal" ROE. From these considerations, we find its markUp. The markup indicates a finn's industry demand elasticity and the effective number offinns in the industry
.We use it in subsequent calculations. The base case: 1990 NAECA standards 

### In the long-run modelmodule, 

### the calibration case is immediately followed by long-run calculations for the state ofthe industry in 1996 provided that the 1990 NAECA standards are unmodified. These calculations use the markup that is based on the one just calculated and modified inkeeping with the assumption that demand elasticity with respect to life-cycle cost is constant. The modified markup is calculated as in Eq. C.S but from a modified price elasticity. The new price elasticity is obtained as follows. First, the original price and operating cost
---- elasticities are used to detennine a life-cycle cost and an elasticity with respect to that cost. A demand curve with constant life-cycle cost elasticity is then computed. It takes this fonn: 

### Q 

## =a'(P+ff'F)B (C.24)



### where 

### P is price, 

### F is operating (fuel) cost,

### ffis the fuel-cost factor, and

### B is the elasticity with respect to life-cycle cost. Thenprice andfuel costarechangedfrom the calibrationcase toeither the base case ora longrun case. These changes shift the division ofdemand elasticity between the price and operating cost. In particular, the price elasticity is given by:

C-12

### B·P e 

## ="':::P:-"+-:::'ff:::-·=F
As price increases as a fraction oflife-cycle cost, the price elasticity increases. (C.25)

The added costs to meet standards are dividedinto two categories: capital, andparts and labor. Capital costs come in two varieties, tooling and equipment. These are not currently separated by the model because we do not have separate data from the manufacturers. Since the chief economic difference between these two is their expected life, the LBL-MIM, in its amortization procedure, uses an intermediate value for the lifetime of new capital. This procedure takes a one-time capital expense and converts it to an annual expense, taking into account the cost ofdebt and equity, the tax laws, the lifetime ofthe capital, and the age ofany capital being replaced. Once the cost ofcapital has been annualized, it must be divided into fixed and variable parts. The fixed partis then added to btO, while the remainderis divided by 

### Qand added to btl. Since there are undoubtedly some economies of scale when parts are purchased, the LBL-MIM provides for a division ofthe cost ofparts and labor between the fixed and variable categories. However, the savings generated by buying an additional 100,000 units above an initial million, for example, will generally be so small that the cost of parts and labor will be referred to as a variable cost and thus can be thought of as being added directly to 

### btl. Once base case costs have been assigned, price is easily computedfrom Eq. CA, by using the price elasticity calculated as described above. With the new price in hand, sales (Q) can be computedfrom price and operating-costelasticities. Revenue is nowjust

### R=P*Q, andeconomic income is justR-C. Assets are those ofthe calibration case plus the new capital resulting from 1990 NAECA standards, and equity is computed from the debt-to-equity ratio. This calculation ofequity is the last essential calculation for the base case. 

### The long-run analysis: changedstandards The analysis ofthe long-run effectofa change in standards from their 1990NAECA level is essentially the same as the analysis ofthe base case. New costs are dividedbetweenfixed and variable categories in the same way and added to the base case costs. From there, the calculation proceeds in exactly the same way as above. 

### C.2.2 Structural Assumptions The cost structure. Costs are assumed to have two components: one fixed and one proportional to quantity.

C-13 

### e 

## = B·P P+ff·F As price increases as a fraction oflife-cycle cost, the price elasticity increases. (C.25)

Theaddedcosts to meet standards are dividedintotwocategories: capital, andparts and labor. Capital costs come in two varieties, tooling and equipment. These are not currently separatedby the model because we donot have separate data from the manufacturers. Since the chief economic difference between these two is their expected life, the LBL-MIM, in its amortization procedure, uses an intermediate value for the lifetime of new capital. This procedure takes a one-time capital expense and converts it to an annual expense, taking into account the cost ofdebt and equity, the tax laws, the lifetime ofthe capital, and the age ofany capital being replaced. Once the cost ofcapital has been annualized, it must be divided into fixed and variable parts. The fixedpartis then added to btO, while theremainderis dividedby 

### Qand addedto btl. Since there are undoubtedly some economies of scale when parts are purchased, the LBL-MIM provides for a division ofthe cost ofparts and labor between the fixed and variable categories. However, the savings generated by buying an additional 100,000 units above an initial million, for example, will generally be so small that the cost of parts and labor will be referred to as a variable cost and thus can be thought of as being added directly to 

### btl. Oncebase casecosts have beenassigned, priceis easilycomputedfrom Eq. CA,byusing the price elasticity calculated as described above. With the new price in hand, sales (Q) can be computedfrom price andoperating-costelasticities. Revenue is nowjust

### R=P*Q, andeconomic income isjustR-C. Assets are those ofthe calibration case plus the new capitalresulting from 1990 NAECA standards, and equity is computed from the debt-to-equity ratio. This calculation ofequity is the last essential calculation for the base case. 

### The long-run analysis: changedstandards Theanalysisofthelong-runeffectofa changein standards from their 1990NAECAlevel is essentially the same as the analysis ofthe base case. New costs are dividedbetweenfixed and variable categoriesin the same way andadded to the basecasecosts. Fromthere, the calculation proceeds in exactly the same way as above. 

### C.2.2 Structural Assumptions The cost structure. Costs are assumed to have two components: one fixed and one proportional to quantity.

C-13

### EC=FC +

### MCq

### 'Q The assetstructure. Assets are assumed to have the same two components.
A=AO+Aq'Q 

### Demandelasticity. The demand elasticity with respect to life-cycle cost experienced by a single fInn is assumed to be independent ofprice and operating cost over the relevant region. This means that ifthe industry raises price, the percentage change in demand caused by a 1% 

### change in price remains the same as before the price increase. 

### Optimizing behavior. A finn is assumed to maximize revenue minus economic cost (R-EC). 

### Debt-to-equity ratio. The debt-to-equity ratio is assumed to remain constant while the fInn finances any investment necessary to meet standards. It is generally assumed that finns have some DIE 

### ratio that they consider optimal, and there is no reason for standards to change this fIgure. 

### Amortization ofcapital costs. It is assumed that the one-time capital cost is to be amortized over its life (typically seven years), that its cost is the weighted average after-tax cost ofcapital, and that depreciation is straight-line over the tax life. 

### Structure ofcost resultingfrom changes in standards. Engineering costs, if 

### they are separated from capital costs, are assumed to be entirely fixed. The cost ofcapital is assumed to be partly fixed and partly variable (proportional to shipments). Parts and labor are assumed to be variable costs. 

### C.2.3 Description ofModules The LBL-MIM is aLotus 1-2-3 spreadsheet that is organized into 12 modules orpanels. A flowchart depicting the main connections among these appears in Figure C.2, and a brief description ofeach is given below. Starting with subsection C.2.3.1, each panel is given a full description.

C-14 EC=FC+MCq

### 'Q The assetstructure. Assets are assumed to have the same two components.
A=AO+Aq-Q 

### Demandelasticity. The demand elasticity withrespect to life-cycle cost experienced by a single ftnn is assumed to be independent ofprice and operating cost overthe relevantregion. This means that ifthe industry raises price, the percentage change in demand caused by a 1% change in price remains the same as before the price increase
. 

### Optimizing behavior
. A firm is assumed to maximize revenue minus economic cost (R-EC). 

### Debt-to-equity ratio. The debt-to-equity ratio is assumed to remain constant while the ftrm ftnances any investment necessary to meet standards. It is generally assumed that fums have some DIE ratio that they consider optimal, and there is noreason for standards to change this ftgure. 

### Amortization ofcapital costs. It is assumed that the one-time capital cost is to be amortized overits life (typically seven years), that its costis the weighted average after-tax cost ofcapital, and that depreciation is straight-line over the tax life. 

### Structure ofcost resultingfrom changes in standards. Engineering costs, if they are separatedfrom capital costs, are assumed to be entirely fixed. Thecost ofcapital is assumed to be partly ftxed and partly variable (proportional to shipments). Parts and labor are assumed to be variable costs
. 

### C.2.3 Description ofModules TheLBL-MIM is aLotus 1-2-3 spreadsheet that is organizedinto 12 modules orpanels. A flowchart depicting the main connections among these appears in Figure C.2, and a brief description ofeach is given below. Starting with subsection C.2.3.1, each panel is given a full description.

C-14

## MIM Modules and Primary Routes of Data Flow

Engineering Inputs 1-----, 4

Standards Cost One-Time Conversion Financial )0 CostArnorLevels 7 tization 5 12 8 

# 1 I'
Costs, Sales

Revenues6



## ., Long-Run

Model 9 

# 1 r •..



## ,**,~** Accounting Control Short-Run Charts Panel Model 3 1 10 11 

# 1

Monte Carlo 2 Figure C.2 MIM Modules and Primary Routes ofData Flow C-15 

# MIM Modules and Primary Routes of Data Flow

Engineering Inputs -----, 4

Standards**~**

Levels 5 

# 1
Costs, Sales

Revenues6



## , Accounting3 Cost Conversion 12



# ., Long-Run

Model 

# 1r
Control

Panel 

# 1

Monte Carlo 9

1

2 Financial 7

Short-Run Model 10



### Figure C.2 MIM Modules 

### and Primary Routes ofDataFlow C-15 One-Time )0 CostArnortization 8 I'



### ...**. ~**  Charts111. 

### ControlPanel. This module displays nine key variables and their control factors, plus a summary of crucial results. The control factors are normally set to one, but can be changedto test the model's reaction to any ofthe control variables. Thechange inresults is immediately displayed in the lower part ofthe control panel. 

### 2. Monte Carlo. The nine control variables can be selected randomly from probability distributions determined by the variables' standarderrors and median (estimated) values. The Monte Carlo module is designed to record many such runs and display the mean value and standard deviation ofsix outputs: percentage change in price, sales, revenue, and net income, plus the change in ROE in the long and short runs. 

### 3. Accounting. The Accounting module gives a summary, in the form of a simplified income statement, of the financial impact of standards on the fIrm. In 

### addition, a summary ofthe financial impacts ofstandards from an economic point ofview is shown at the bottom ofthe page. 

### 4. Engineering Inputs. The Engineering Inputs module accepts as input raw engineering data that are used elsewhere in the model for the impacts analysis. These data include incremental variable costs, capital costs, engineering costs, and changes in unit energy consumption for the products being analyzed. 

### 5. Standards Levels. The Standards Levels module provides the same data as the Engineering Inputs module, except that the data have been processed by the LBL-REM and/orthe "CostConversion" module (12) and arepresentedfor the standards levels being analyzed. 

### 6. Costs, Sales, and Revenues. This module uses prices, markups, market shares, and industry shipmentlevels to compute variable costofgoods soldand revenues for a single prototypical firm. Changes in ,operating cost, capital cost, and engineering expense are· ....- also computed here. 

### 7. Financial. The Financial module computes the weighted average cost of capital and a factor describing how costs depend on revenues. 

### 8. One-Time CostAmortization. The One-Time module derives amortized costs from the one-time capital cost and one-time engineering expense associated with the imposition of new standards. 

### 9. Long-Run Model. This module does most ofthe economic calculations. Its calibration phase estimates the markup over variable cost, which is used to estimate new prices once standards have been imposed. These prices are then used to compute sales, and, from sales, all other descriptive variables.

C-16 1. 

### ControlPanel. This module displays nine key variables and their control factors, plus a summary of crucial results. The control factors are normally set to one, but can be changedto test the model'sreaction to any ofthe controlvariables. Thechange in

### results is immediately displayed in the lower part ofthe control panel. 

### 2
. Monte Carlo
. The nine control variables can be selected randomly from probability distributions detenninedby the variables' standarderrors and median (estimated) values. The Monte Carlo module is designed to record many such runs and display the mean value and standard deviation ofsix outputs: percentage change in price, sales, revenue, and net income, plus the change in ROE in the long and short runs. 

### 3
. Accounting. The Accounting module gives a summary, in the form of a simplified income statement, of the financial impact of standards on the fInn. In 

### addition, a summary ofthe financial impacts ofstandards from an economic point ofview is shown at the bottom ofthe page. 

### 4. Engineering Inputs. The Engineering Inputs module accepts as input raw engineering data that are used elsewhere in the model for the impacts analysis. These data include incremental variable costs, capital costs, engineering costs, and changes in unit energy consumption for the products being analyzed. 

### 5
. Standards Levels. The Standards Levels module provides the same data as the Engineering Inputs module, except that the data have been processed by the LBL-REM and/orthe "CostConversion" module (12) and arepresentedfor the standardslevels being analyzed. 

### 6. Costs, Sales, and Revenues. This module uses prices, markups, market shares, and industry shipmentlevels to compute variable costofgoods sold and revenues for a single prototypical firm. Changes in , operat ing cost, capital cost, and engineering expense are -_.- , also computed here. 

### 7. Financial. The Financial module computes the weighted average cost of capital and a factor describing how costs depend on revenues. 

### 8
. One-Time CostAmortization. The One-Time module derives amortized costs from the one-time capital cost and one-time engineering expense associated with the imposition of new standards. 

### 9. Long-Run Model. This module does most ofthe economic calculations. Its calibration phase estimates the markup over variable cost, which is usedto estimate new prices once standards have been imposed. These prices are then used to compute sales, and, from sales, all other descriptive variables
.

C-16

### 10. Short-Run Model. This module modifies the price computed by the long-run module in order to take into account the possible short-run effects ofexcess capacity. An estimate of the responsiveness of price to changes in demand during the business cycle is combined with the change in demand predicted by the long-run model. 11. 

### Sensitivity and Profit Charts. This module produces two charts. The first shows how profit is affected by a one-standard-deviation change in each of the control variables. These changes are all made with the standards level set to its middle value. The second chart shows how profits change as the standards levelis changed, but all control variables are kept at their base value. 

### 12. Engineering-to-Standards Conversion. This module weights the costs given by the engineering model in proportion to the number ofproducts ofaparticular design that will be produced at any given standards level and thereby generates the costs for that level. These costs appear in the Standards Level module. 

### C.2.3.t Control Panel The control panel displays the nine most important input variables of the model and allows the user to easily adjust them. These variables are described below. The 

### Input Values are the best available estimates of the relevant variables. The control panel has one control for each input variable. The controls, found in the column labeled 

### entrl, allow the

### Input Values to be modified to become the 

### Model Values before they are used by the model. The controls work as follows: when a control is set to zero, its 

### Input Value becomes a 

### Model Value without any adjustment. When a control is set to 1, its 

### Input Value is increased (in absolute value) by approximately one standard deviation to become a

### Model Value. Because the error distributions associated with the

### Input Values are not normal, they are not changed by exactly one standard deviation when the control is set to plus or minus'

--- I. A control value of 

### ± 1 actually changes an 

### Input Value by an amount sufficient to make it just as unlikely as a point that is one standard deviation from the mean using a normal distribution. Consider an 

### Input Value that is known to be strictly positive and have a mean of 5 and a standarddeviation of3. Setting its control to -2, which wouldotherwise change its value to -1 (an impossibility) actually only changes it to some small but positive value. This value would bejust as unlikely as a point -2 in a normal distribution. The 

### Variation column shows either the standard deviation of an input variable if the variable is normally distributed, or the coefficient ofvariation 

### (S.D.lmean) ifthe input variable is non-normal. At the bottom ofthe control panel is a short output summary, which shows the five key output variables: 

### shipments, price, revenue, net income, and 

### ROE. The first column gives the output values in the base case-that is, the case in which the 1996 (or which everfuture year is relevant for the product under consideration) standards are the same as the 1990 standards. The C-17 

### 10. Short-Run Model. This module modifies the price computed by the long-run module in order to take into account the possible short-run effects ofexcess capacity. An estimate of the responsiveness of price to changes in demand during the business cycle is combined with the change in demand predicted by the long-run model. 11. 

### Sensitivity and Profit Charts. This module produces two charts. The first shows how profit is affected by a one-standard-deviation change in each of the control variables. These changes are all made with the standards level set to its middle value. The second chart showshowprofits change as the standards levelis changed, butallcontrol variables are kept at their base value. 

### 12. Engineering-to-Standards Conversion. This module weights the costs given by the engineering modelin proportion to the numberofproducts ofaparticulardesignthat will be produced at any given standards level and thereby generates the costs for that level. These costs appear in the Standards Level module. 

### C.2.3.t Control Panel The control panel displays the nine most important input variables of the model and allows the user to easily adjust them. These variables are described below. The 

### Input Values are the best available estimates of the relevant variables. The control panel has one control for each input variable. The controls, found in the column labeled

### entrl, allow the

### Input Values to be modifiedto become the

### Model Values before they are used by the model. The controls work as follows: when a control is set to zero, its 

### Input Value becomes a 

### Model Value without any adjustment. When a control is set to 1, its 

### Input Value is increased (in absolute value) by approximately one standard deviation to become a

### Model Value. Because the errordistributions associated with the

### Input Values are not normal, they are not changed by exactly one standard deviation when the control is settoplus orminus '

---- . 1. A control value of 

### ± 1 actually changes an 

### Input Value by an amount sufficient to make it just as unlikely as a point that is one standard deviation from the mean using a normal distribution. Consider an 

### Input Value that is known to be strictly positive and have a mean of 5 and a standarddeviationof3. Settingits control to -2, which wouldotherwisechangeits value to -1 (an impossibility) actually only changes it to some small but positive value. This value would bejust as unlikely as a point -2 in a normal distribution. The 

### Variation column shows either the standard deviation of an input variable if the variable is normally distributed, or the coefficient ofvariation 

### (S.D.lmean) ifthe input variable is non-normal. At the bottom ofthe control panel is a short output summary, which shows the five key output variables: 

### shipments, price, revenue, net income, and 

### ROE. The first column gives the output values in the base case-thatis, the case in which the 1996 (or which everfuture year is relevant for the productunderconsideration) standards are the same as the 1990 standards. The C-17second column assumes some particular change in standards and gives long-run values-thatis, values that would hold after capacity adjustments (by about 1999). The third column gives the percentage change from the base case to the new standards case for each ofthe five key output variables. The fourth column, labeledPrvious Change, simply lists the results ofthe previous run to use as acomparison with the results ofthe currentrun. The fifth column lists the results from the short-run part ofthe model (Le., the estimated output values for the period immediately after the imposition ofthe new standards). The following is a list ofthe nine key input variables that appear as control factors in the Control Panel module: 1. The industry's price elasticity ofdemand (IPE), 2. The consumer discount rate (RD), 3. The market cost ofcapital financed through equity (ECC), 4. Economic profit: the industry's ROE minus normal ROE (EP), 5. The long-run fixed part ofcosts and assets (FCA), 6. The long-run fixed part ofone-time costs and assets (FIX), 7. One-time capital costs resulting from the change in standards (CC.L), 8. Variable cost ofgoods sold after the change in standards (dVC.L), and 9. The short-run price response to demand (SRPR). 

### C.2.3.2 The Monte Carlo Module This module estimates the standard errors ofthe six most important output variables by randomly choosing the nine control variables in accordance with estimates of their standard errors, running the model, and recording the six output variables. This procedure is repeated up to 400 times. Afterwards, the mean and standard deviations ofthe 400 computed values ofeach output variable are computed and displayed near the top ofthe Monte Carlo module. The top row, labeled Value, is the most recently computed random set ofoutputs. The first four output variables are the percentchange resulting from standards, in output (Q), price (P), revenue (R), and net income (NI). The last two variables are changes in long- and short-run return on equity (ROE). Because these lasttwo variables are percentages, the changes presentedhere are simple differences between their values before and after achangein standards. 

### C.2.3.3 The Accounting Module The Accounting Summary consists ofa simplified income statement that is presented in standard form, along with several key financial ratios and figures that highlight the frrm's financial position. The income statement and financial ratios are presented for the 1989 calibration case, a 1996 base case that includes the impact ofthe 1990 NAECA standards, and a 1996 new standards case that lists the firm's financial results under the new standards being analyzed. Thus, the financial impacts ofthe new standards are easily seen by comparing the two' 1996 scenarios. To highlight the results, a final column in the accounting summary indicates the percentage change from the 1996 base case to the 1996 new standards case.
C-18 second column assumes some particular change in standards and gives long-run values-thatis, values that would hold after capacity adjustments (by about 1999). The third column gives the percentage change from the base case to the new standards case for each ofthe five key output variables. The fourth column, labeledPrvious Change, simplylists theresults oftheprevious run to use as acomparison with theresults ofthe currentrun. The fifth columnlists theresults from the short-runpartofthe model (Le., the estimated output values for the periodimmediately after the imposition ofthe new standards). The following is a list ofthe nine key inputvariables that appearas controlfactors in the Control Panel module: 1. The industry'S price elasticity ofdemand (IPE), 2. The consumer discount rate (RD), 3. The market cost ofcapital financed through equity (BCC), 4. Economic profit: the industry's ROE minus normal ROE (BP), 5. The long-run fixed part ofcosts and assets (FCA), 6. The long-run fixed part ofone-time costs and assets (FIX), 7. One-time capital costs resulting from the change in standards (CC.L), 8. Variable cost ofgoods sold after the change in standards (dVC.L), and 9. The short-run price response to demand (SRPR)
. 

### C.2.3.2 The Monte Carlo Module This module estimates the standard errors ofthe six most important output variables by randomly choosing the nine control variables in accordance with estimates of their standard errors, running the model, andrecording the six output variables. This procedure is repeated up to 400times. Afterwards, the mean and standarddeviations ofthe 400 computedvalues ofeach output variable are computed and displayed near the top ofthe Monte Carlo module. The top row, labeled Value, is the mostrecently computed random set ofoutputs. The first four output variables are the percent change resulting from standards, in output (Q),price (P),revenue (R), andnetincome (NI). Thelast two variables are changesinlong- and short-runreturn on equity (ROE). Because these last two variables are percentages, the changes presentedhere are simpledifferences between their values before andafter achangein standards. 

### C.2.3.3 The Accounting Module The Accounting Summary consists ofa simplifiedincome statement that is presented in standard form, along with several key financial ratios and figures that highlight the frrm's financial position. The income statement and financial ratios are presented for the 1989 calibration case, a 1996 base case that includes the impact ofthe 1990 NAECA standards, and a 1996 new standards case that lists the firm's financial results under the new standards being analyzed. Thus, the financial impacts ofthe new standards are easily seen by comparingthe two' 1996 scenarios. To highlight theresults, a final columnin the accounting summary indicates the

.percentage change from the 1996 base case to the 1996 new standards case.
C-18Table C.I Accounting Module Item Fonnula Earnings Before Interest and Taxes Total Expenses Interest Taxes Net Income Gross Margin Return on Sales Return on Assets (ROA) Return on Equity (ROE) EBIT

## =Revenue

- Total Expenses Does not include interest ortaxes (Interest rate mb Debt ratio mb Assets)

- Interest earned on "Cash" assets (EBIT

- Interest) mb Tax rate EBIT

- Interest

- Taxes. 1 - (Variable Cost ofGoods Sold/Revenue) Net IncomelRevenue (EBIT

- Taxes

- (Tax rate mb Interest))/Assets This calculation eliminates interest tax shield Net IncomelEquity A second table atthe bottom ofthe Accounting Summary page lists financial datafor the 

### fmn using figures that are necessary for an economic analysis. Note thatthe assets andexpenses are broken down into two categories: fixed and proportional to quantity produced. These proportionalities have economic significance when the price markup is computed, and for that reason they are reported separately in the economic analysis. Note that "one-time" (i-X) in the Accounting module refers to expenses incurred because ofthe change in standards. 

### C.2.3.4 The Engineering Inputs Module The Engineering Inputs module receives all the engineering costing data and energy consumption data for the product classes being analyzed. The data include the incremental variable cost and change in unit energy consumption for each design option. They also include the one-time engineering expense and capital costs incurred in meeting the new standards; the module assigns these costs to design options and product classes where appropriate ornecessary. The various components ofthe module are described below: 1. 

### Increment in additional 

## uve (Unit Variable Cost): Engineering input data. Each "increment" is measured relative to the previous engineering design. The numbers refer to 1989 dollars per unit produced and are displayed only to the nearest whole dollar. 

### 2. Additional 

## uveabove Level 0 cost (VCS.E): "Additional" UVC is measured relative to the 1989 calibration case, which is shown as level 

### O. In other words, this table is a cumulative version ofthe above "incremental" table.
C-19 Table C.I Accounting Module

Item Fonnula Earnings Before Interest and Taxes Total Expenses Interest Taxes Net Income Gross Margin Return on Sales Return on Assets (ROA) Return on Equity (ROE) EBIT

## =Revenue

- Total Expenses Does not include interest ortaxes (Interest rate mb Debt ratio mb Assets)

- Interest earned on "Cash" assets (EBIT

- Interest) mb Tax rate EBIT

- Interest

- Taxes
. 1 - (Variable Cost ofGoods Sold/Revenue) Net Income/Revenue (EBIT

- Taxes

- (Tax rate mb Interest))/Assets This calculation eliminates interest tax shield Net Income!Equity A second table at the bottom ofthe Accounting Summarypage lists financial datafor the firm usingfigures that are necessary foran economic analysis. Note thatthe assets andexpenses are broken down into two categories: fixed and proportional to quantity produced
. These proportionalities have economic significance when the price markup is computed, and for that reason they are reported separately in the economic analysis. Note that "one-time" (i-X) in the Accountingmodule refers to expenses incurredbecause ofthe change in standards. 

### C.2.3.4 The Engineering Inputs Module The Engineering Inputs module receives all the engineering costing data and energy consumption data for the product classes being analyzed. The data include the incremental variable cost and change in unit energy consumption for each design option. They also include the one-time engineering expense and capital costs incurred in meeting the new standards; the module assigns these costs to design options andproduct classes where appropriate ornecessary. The various components ofthe module are described below: 1. 

### Increment in additional 

## uve (Unit Variable Cost): Engineering input data. Each "increment" is measuredrelative to the previous engineering design. The numbers refer to 

### 1989 dollars perunit produced and are displayed only to the nearest whole dollar. 

### 2. Additional 

## uveabove Level 0 cost (VCS.E): "Additional" DYCis measuredrelative to the 

### 1989 calibration case, which is shown as level 

### O. In other words, this table is a cumulative version ofthe above "incremental" table.
C-193. Annualunitenergy consumptionperyear: Forelectric appliances, theseengineeringinput data are the energy use per year measured in kilowatt hours. For natural gas appliances, the energy use is measured in Btus (generally millions of Btus but sometimes in thousands. Specifics are in the relevant Appendix C for the product concerned.) 4. Capital costs: These engineering input data are given on aper-unitbasis amortized (with zero interest) over seven years. 

### 5. Additional capital cost (above Level 

### 0 Cost): This row translates the amortized capital costs into total one-time capital costs. The numbers refer to 1990 dollars per unit produced. 6. Total capitaland engineering costs (CCEE.E): The top row ofthis matrix is the sum of the columns in the "additional capital cost" matrix. Thus the CCEE.E matrix gives the combined added capital costfor all classes ofan appliance. The maximum number (from the highest level) in this row is calledMxCC and is used to assign engineering expenses to the levels as follows:
EEi =EE:CC'MxCC'CCi where EE:CC is the ratio oftotal engineering expense to total capital cost, and EEi and CCi are expenses for level "i." The CCEE.E matrix contains the capital and engineering costs that are used as inputs to the rest ofthe model. The Long-Run module calculates the average prices for all classes and for each level. 

### C.2.3.5 The Standards Levels Module The Standards Levels module provides the same information as the Engineering Inputs module except that instead oflisting the cost and energy data for various engineering design options, 

### it lists the data for the various standards levels being analyzed. The detail for each product is supplied in the relevant Appendix C for that product's volume. The data that appear in the three REM panel tables for each product-VCS.R, KWS.R and CCEE.R-may correspond exactly to some specific engineering levels or, if it has been determined that the manufacturer, when faced with certain standard levels, would use a mixture ofengineering designs, then the "Engineering-to-Standards Module" appropriately averages the engineering data and places the results in the three REM tables. The exact mixtures of engineering options are determined by the LBL-REM. The top section of this module allows the user to determine what standards will be analyzed in a particular run. The module is used interactively by the user, who can choose "levels" LEV.B andLEV.N, which correspond to the Basecase andLong-Run case, respectively.

C-20 3. Annualunitenergyconsumptionperyear: Forelectricappliances,theseengineeringinput data are the energy use peryearmeasured inkilowatt hours. Fornatural gas appliances, the energy use is measured in Btus (generally millions of Btus but sometimes in thousands. Specifics are in the relevant Appendix C for the product concerned.) 4. Capitalcosts: Theseengineering inputdata are given on aper-unitbasis amortized (with zero interest) over seven years. 

### 5. Additional capital cost (above Level 

### 0 Cost): This row translates the amortized capital costs into total one-time capital costs. The numbers refer to 1990 dollars per unit produced. 6. Total capitalandengineering costs (CCEE.E): The top row ofthis matrix is the sum of the columns in the "additional capital cost" matrix. Thus the CCEE.E matrix gives the combinedaddedcapital costfor all classes ofan appliance. Themaximum number (from the highest level) in this row is calledMxCC and is used to assign engineering expenses to the levels as follows:
EEi=EE:CC'MxCC'CCi where EE:CC is the ratio oftotal engineering expense to total capital cost, and EEi and CCi are expenses for level 

### "i." The CCEE.E matrix contains the capital and engineering costs that are used as inputs to the rest ofthe model. The Long-Run module calculates the average prices for all classes and for each level. 

### C.2.3.S The Standards Levels Module The Standards Levels module provides the same information as the Engineering Inputs module except that instead oflisting the cost and energy data for various engineering design options, 

### it lists the data for the various standards levels being analyzed. The detail for each product is supplied in the relevant Appendix C for that product's volume. The data that appear in the three REM panel tables for each product-VCS.R, KWS.R and CCEE.R-may correspond exactly to some specific engineering levels or, if it has been determined that the manufacturer, when faced with certain standardlevels, would use a mixture ofengineering designs, then the "Engineering-to-Standards Module" appropriately averages the engineering data and places the results in the three REM tables. The exact mixtures of engineering options are determined by the LBL-REM. The top section of this module allows the user to determine what standards will be analyzed in a particular run. The module is used interactively by the user, who can choose "levels"LEV.B andLEV.N, whichcorrespondto the Basecase andLong-Runcase,respectively.

C-20Thebottom numbers in the "LevIn" matrix determine whetherthese levels willreferto "standards levels" or "engineering designs." The top numbers specify the exact level or design. The values in the "LevIn" matrix determine what values are placed in the bottom two matrices when the model is run. For example ifin "LevIn," LEV.B = 4 and SIE.B = 0, then the columns of VCB, KWB, CCB, and EEB will contain data for the fourth engineering design option. Changing SIE.B from 0 to 1 would cause all ofthese data to be replaced with data for the fourth standards level. 

### C.2.3.6 The Costs, Sales, and Revenue Module This module has two functions. First, it provides the Long-Run Model module with several inputs; these are averages. Second, it uses many of the data from the input pages and processes these datainto prices, quantities sold, and revenue. These intermediate output data are then used in the financial and economic summaries to give outputresults, which are used for the analysis of the impact of standards. Table C.2 lists the equations that are used in the Costs, Sales, and Revenue module. In the
.new standards case, the variables are named with a ".N" suffix instead of a ".B" suffix. The "Totals" row gives the weighted average change in DVC (dVeB), the weighted average change in price, the new weighted average price (PB), the firm's total revenues 

## C£ (Ri), RB), the weighted average change in operating costs (OC%B), and the total finn shipments 

### (QB). PB, QB and

### RB are actually computed in the Long-Run module. The first stage in this module is setting up the 1990 calibration case, which uses known information about industry shipments, product class market shares, prototypical firm market shares, markups for each ofthe product classes, and baseline unit variable costs to calculate the prototypicalfirm's shipments for eachproductclass, its cost ofgoods sold, andits revenue. Also calculated are a weighted-average unit variable cost, selling price, and markup. Markup is calculatedfrom data on the ratio between the highest and lowest markups (ratio) and the typical markup (mid).

C-21 Thebottomnumbers inthe "LevIn" matrixdeterminewhetherthese levelswillreferto "standards levels" or "engineering designs." The top numbers specify the exact level ordesign. The values in the "LevIn" matrix determine what values are placed in the bottom two matrices when the modelis run. Forexample ifin "LevIn," LEV.B = 4 and SIE.B = 0, then the columns of VCB, KWB, CCB, and EEB will contain data for the fourth engineering design option. Changing SIE.B from 0 to 1 would cause all ofthese data to be replaced with data for the fourth standards level. 

### C.2.3.6 The Costs, Sales, andRevenue Module This module has two functions. First, it provides the Long-Run Model module with several inputs; these are averages. Second, it uses many of the data from the input pages and processes thesedataintoprices, quantities sold, andrevenue. These intermediateoutputdata are thenusedin the financial and economic summaries to give outputresults, which are usedfor the analysis of the impact of standards. Table C.2 lists the equations that are used in the Costs, Sales, and Revenue module. In the
. new standards case, the variables are named with a ".N" suffix instead of a ".B" suffix. The "Totals" row gives the weighted average change in UVC (dVeB), the weighted average change in price, the new weighted average price (PB), the firm's total revenues 

### (£ (Ri), RB), the weighted average change in operating costs (OC%B), and the total finn shipments 

### (QB). PB, QB and

### RB are actually computed in the Long-Run module. The first stage in this module is setting up the 1990 calibration case, which uses known information about industry shipments, product class market shares, prototypical firm market shares, markups for each ofthe product classes, and baseline unit variable costs tocalculate the prototypicalfirm's shipments foreachproductclass,its costofgoods sold, anditsrevenue. Also calculated are a weighted-average unit variable cost, selling price, and markup. Markup is calculatedfrom data on the ratiobetween the highest and lowestmarkups (ratio) and the typical markup (mid).

C-21

### Table C.2 Equations Used in the Costs, Sales, and Revenue Module Item Size Industry Shipments Relative Shipments (Q%) Shipments (Q.I) Price (p.I) Revenue (R.I) Markup (M.I) Unit Variable Cost (UVC.I) Weighted UVC Weighted Change in UVC Weighted Change in Price Weighted Change in Price Mark.B New Price (pi.B) Operating Cost Ratio New Shipments (Qi.B) New Revenue (Ri.B) Weighted Operating Cost Ratio Calibmtion Case Formula or Definition Prototypical firm market share Annual industry shipments for a product class Product class market share (product class units sold/total units sold) Product class shipments for a protypical firm Retail price/retail market Price mb Shipments 1 + (P.I
- BB)/AA P.I/M.I UVC mb Relative Shipments (Q%) Change in UVC mb Relative shipments (Q%) Change in Price mb Relative shipments (Q%) Wghtd dp=q%*M.I mb Change in UVC (p.B-P)/IWghtd dP P.I+Mark.B*M. 1mb Change in UVC 1996 VEC/1990 VEC Opeating Cost Ratio mb Relative Shipments (Q%) Q%*Q.B Pi.B 

### * Qi.B


### The next stage in the Cost, Sales, and Revenue Module calculates the changes necessary to produce the 1996 base case (1990 NAECA standards) and the 1996 new standards case. The calibration case is used as a base, and the changes in unit variable cost and unit energy consumption are fed in from the REM panel. From this input, new unit variable costs and prices are calculated for each ofthe product classes. The model then uses known price and operating cost elasticities to calculate changes in shipments for each ofthe 1996 cases. For each ofthe 1996 cases, the model calculates the firm's new shipments, revenue, and the weighted- average change in 

### unit variable cost and price.

C-22

Table 

### C.2 Equations Used in the Costs, Sales, and Revenue Module Item Size Industry Shipments Relative Shipments (Q%) Shipments (Q.l) Price (p.I) Revenue (R.I) Markup (M.I) Unit Variable Cost (UVC.l) Weighted UVC Weighted Change in UVC Weighted Change in Price Weighted Change in Price Mark.B New Price (pi.B) Operating CostRatio New Shipments (Qi.B) New Revenue (Ri.B) Weighted Operating CostRatio Calibration Case Formula or Definition Prototypical firm market share Annual industry shipments for a product class Product class market share (productclass units sold/total units sold) Product class shipments for a protypical firm Retail price/retail market Price mb Shipments 1 + (P.I

- BB)/AA P.l/M.1 UVC mb Relative Shipments (Q%) Change in UVC mb Relative shipments (Q%) Change in Price mb Relative shipments (Q%) Wghtd dp=q%*M.l mb Change in UVC (p.B-P)/IWghld dP P.l+Mark.B*M. 1mb Change in UVC 1996 VEC/1990 VEC Opeating CostRatio mb Relative Shipments (Q%) Q%*Q.B Pi.B 

### *Qi.B


### The next stage in the Cost, Sales, and Revenue Module calculates the changes necessary to produce the 1996 base case (1990 NAECA standards) and the 1996 new standards case. The calibration case is used as a base, and the changes in unit variable cost and unit energy consumption are fed infrom the REM panel. From this input, new unit variable costs and prices are calculated for each ofthe product classes. The model then uses known price and operating cost elasticities to calculate changes in shipments for each ofthe 1996 cases. For each of the 1996 cases, the model calculates the firm's new shipments, revenue, and the weighted- average change in 

### unit variable cost and price.

C-22

### The manufacturermarkup listedin thepanel is usedtocalculate thebaseline modelprices, but it 

### is not 

### used in calculating the manufacturer prices for the 1996 base case or the standards levels. In these cases, LBL-MIM uses an economic markup to determine prices. 16 

### This markup is on 

### economic variable costs, which are a broader category than the typical business definition of variable costs. The markup varies depending on elasticities and changes in prices and operating costs at different standards levels. Thus, prices at different standards levels do not exactly reflect the manufacturer cost times the manufacturer markup. 

### C.2.3.7 The Financial Module The Financial module lists the financial characteristics of the prototypical firm being analyzed. Some of the inputs are used to calculate intermediate outputs of financial characteristics ofthe firm. The financial inputs and intermediate outputs are then usedelsewhere in the model when the impacts ofstandards are calculated. A number of variables in the Financial module are non-standard and thus require explanation. First, in order to make the model adjustable to a particularpoint in time when the size ofthe firm may be different from what it was in the year in which the data were collected, some ofthe inputs are listed as ratios proportional to 

### revenue. For instance, the historical ratio between depreciable assets and revenues for the room air conditioner manufacturing industry (calledDA:R) 

### is 0.224. This is used by the model to compute a value for depreciable assets at some typical yearin the future after standards have changedby multiplyingDA:R 

### by revenue in that year. 

### Table C.3 Financial Module·
Item Depreciation (DR:R

## =Depreciable assets as a percentage ofrevenues Pre-Tax Equity Cost ofCapital (pECC) Weighted Average Cost ofCapital (WACC) Mter-Tax WACC Return on Equity (ROE) (BCC

## =Equity Cost ofCapital) (This ROE figure is the expected ROE for a prototypical firm.) Formula Average depreciation rate x DR:R Equity Cost ofCapital/(l

- Tax Rate) (Debt ratio mb Interest rate) + (Equity ratio mb PECC) WACC mb (1

- Tax rate) After-Tax ECC + Economic Profit



### A complication may occur ifa value changes in proportion to revenue between now and the typical year, but under standards it is expected that a portion of the value will remain proportional to revenue while a portion ofthe value will not change with revenues because of

16For a thorough description ofhow markups are derived and used in LBL-:MIM, see Appendix C.2.I. 

### C-23 Themanufacturermarkuplistedin thepanelis usedtocalculatethebaselinemodelprices, but it

### is not

### used in calculating the manufacturer prices for the 1996 base case orthe standards levels. In these cases, LBL-MIM uses an economic markup to determineprices. 16 

### This markup is on 

### economic variable costs, which are a broader category than the typical business definition of variable costs. The markup varies depending on elasticities and changes in prices and operating costs at different standards levels. Thus, prices at different standards levels do not exactly reflect the manufacturer cost times the manufacturer markup. C.2.3.7 The Financial Module The Financial module lists the financial characteristics of the prototypical firm being analyzed. Some of the inputs are used to calculate intermediate outputs of financial characteristics ofthe firm. The financial inputs and intermediate outputs are then usedelsewhere in the model when the impacts ofstandards are calculated. A number of variables in the Financial module are non-standard and thus require explanation. First, in orderto make the model adjustable to a particularpoint in time when the size ofthe firm may be different from what it was in the year in which the data were collected, some ofthe inputs are listed as ratios proportional to

### revenue. For instance, the historicalratio between depreciable assets and revenues for the room air conditioner manufacturing industry (calledDA:R) 

### is 0.224. This is used by the model to compute a value for depreciable assets at some typical yearin the future after standards have changedby multiplyingDA:R 

### by revenue in that year. Table C.3 Financial Module·
Item Depreciation (DR:R

## =Depreciable assets as a percentage ofrevenues Pre-Tax Equity Cost ofCapital (PECC) Weighted Average Cost ofCapital (W ACC) Mter-Tax WACC Return on Equity (ROE) (BCC

## =Equity CostofCapital) (This ROE figure is the expected ROE for a prototypical firm.) Formula Average depreciation rate x DR:R Equity Cost ofCapital/(l

- Tax Rate) (Debt ratio mb Interest rate) + (Equity ratio mb PECC) WACC mb (1

- Tax rate) After-Tax ECC +Economic Profit



### A complication may occur ifa value changes in proportion to revenue between now and the typical year, but under standards it is expected that a portion of the value will remain proportional to revenue while a portion ofthe value will not change with revenues because of

16For a thorough description ofhow markups are derived and used inLBL-:MIM, see Appendix C.2.I. 

### C-23the change in standards (Le., it is "fixed"). In this case, that value is analyzed as having a "variable" and a "fixed" component, where "variable" and "fixed" are used with respect to a change in standards. 

### C.2.3.8 The One-Time Cost Amortization Module Crucial to this analysis are the capital cost and engineering expense that the industry will incur because of the change in standards. This module takes as input from the Engineering Inputs Module the estimate ofthe total value ofthese two one-time expenses and reduces these one-time expenses to annual expenses. To annualize (or "amortize") these costs, the module takes into account: the expected life of the new capital, the tax life of the investment, the weighted average cost ofcapital, and the timing ofthe investment relative to any capital that is being replaced. The amortized engineering expense and capital cost are calculated for the change from the 1990 calibration case to the 1996 base case that includes the 1990 standards, and for the change from the 1990 calibration case to whatever standards case is under consideration. Also, the average value (over the amortization period) ofthe new capital is computed. The calculations are basedon the following model ofinvestment: new capital will bepaid for according to the CC SCHEDULE, and engineering costs will be expended according to the EE SCHEDULE. These schedules give expenditures for the four years previous to 1993; these expenditures are assumed to occur at the middle ofeach year. They are assumed to be financed by debt and equity in the firm's normal debt-to-equity ratio. Tax benefits will accrue over the tax life ofthe capital. Some part (0%-10%) ofthe capital expenditure is assumed to be for new plant andequipmentthatdo not take theplace ofanything in existence; theremainderis assumed to replace inadequate existing equipment. The replaced equipment is assumed to have had the same potential lifetime (L) as the replacing equipment and is assigned a certain age. Ifthe new equipment replaces old equipment that would soon have needed replacing anyway, savings are included at the time the natural replacement would have occurred. The computed annualized cost tells what constant real dollar payment would need to be made each year in order that at the end ofthe capital's useful life all ofthe issued debt could have been paid off, and all ofthe issued equity could have been repurchased.I? Since standards are assumed to last indefinitely, new capital will need to be purchased at the end ofthis cycle; but ofcourse the next cycle starts with a clean slate and repeats the same process we havejust described. This process can repeat indefinitely with no change in amortized payments. This procedure overestimates the burden to the firm in two ways. First, engineering costs should actually be lower, possibly zero, in the second cycle. Second, because industry is constantly improving the efficiency ofproductlines, lines would undoubtedly be much nearerto

17 Ofcourse tax benefits are counted toward eliminating debt and equity. C-24 the change in standards (Le., it is "fixed"). In this case, that value is analyzed as having a "variable" and a "fixed" component, where "variable" and "fixed" are used with respect to a change in standards. 

### C.2.3.S The One-Time Cost Amortization Module Crucial to this analysis are the capital cost andengineering expense that the industry will incur because of the change in standards. This module takes as input from the Engineering Inputs Module the estimate ofthe total value ofthese two one-time expenses andreduces these one-time expenses to annual expenses. To annualize (or "amortize") these costs, the module takes into account: the expected life of the new capital, the tax life of the investment, the weighted average cost ofcapital, and the timing ofthe investmentrelative to any capital that is being replaced. The amortized engineering expense and capital cost are calculated for the change from the 1990 calibration case to the 1996 base case that includes the 1990 standards, and for the change from the 1990 calibration case to whatever standards case is under consideration. Also, the average value (over the amortization period) ofthe new capital is computed. Thecalculations are basedon the following model ofinvestment: new capital willbepaid for according to the CC SCHEDULE, and engineering costs will be expended according to the EE SCHEDULE. These schedules give expenditures for the four years previous to 1993; these expenditures are assumed to occur at the middle ofeach year. They are assumed to be financed by debt and equity in the firm's normal debt-to-equity ratio. Tax benefits will accrue over the tax life ofthe capital. Somepart (0%-10%) ofthe capital expenditure is assumedto be for new plantandequipmentthatdo not take theplace ofanything in existence; theremainderis assumed to replace inadequate existing equipment. The replaced equipment is assumed to have had the same potential lifetime (L) as the replacing equipment andis assigned a certain age. Ifthe new equipment replaces old equipment that would soon have needed replacing anyway, savings are included at the time the natural replacement would have occurred. The computed annualized cost tells what constant real dollar payment would need to be made each year in order that at the end ofthe capital's useful life all ofthe issued debt could have beenpaid off, and all ofthe issued equity could have been repurchased.I7 Since standards are assumed to last indefinitely, new capital will need to be purchased at the end ofthis cycle; but ofcourse the next cycle starts with a clean slate and repeats the same process we havejust described. This process can repeat indefinitely with no change in amortized payments. Thisprocedure overestimates the burden to the firm in two ways. First, engineeringcosts should actually be lower, possibly zero, in the second cycle. Second, because industry is constantlyimproving the efficiency ofproductlines, lines would undoubtedly be muchnearerto

17 Ofcourse tax benefits are counted toward eliminating debt and equity. C-24

### meeting the standards by the beginning ofthe secondcycle. Thus, much ofthe capitalequipment re-installed at the end ofthe first capital's lifetime would have been a normal expense by this date (around 2001). For the interestedreader, the details ofthe calculation method are given in Table CA.


### Table C.4 One-Time Cost Amortization Module
Item DR [ER] T ECC PECC WACC ATWACC ATR CCi EEi WCCi WEEi LTC LTE EMRT EMRL BN RTL PVD OCS OCTL TBS PVCC APVCC CCLF EELF AFB

APC

B:T AFF Definition
Tax rate (34%) Equity cost ofcapital Pre-tax ECC Weighted average CC After-tax WACC Continuous ATWACC Capital cost i years before standards Engineering expense i years before standards (see I-X Cost module) (see I-X Cost module) (see I-X Cost module) (see I-X Cost module)

"Tax benefit rate" Remaining tax life Present value ofdepreciation Old capital savings Old capital tax loss Tax benefit from scrapping Present value ofCC Adjusted for lead timer CC amortization factor EE amortization factor (see One-Time Cost module)
(see One-Time Cost module)
(see One-Time Cost module) Average asset factor
C-25 Formula Debt [Equity] / (Debt + Equity)
ECC/(l

- T) DR*/+ ER 

### * PECC WACC* (l -1) In(l +ATW ACe)

CCi 

### * exp (ATR * i) EEl * exp (ATR 

### * i) Sum ofWCCi over 4 years Sum ofWEEi over 4 years exp (-ATR*TL) exp (-ATR*L) T/I'L max (TL

- AGE, 0)

- BN 

### * (I -EMR1)/ATR

- (I

- %NC) * exp (-ATR * (L
- AGE» (l

- %NC) 

### * BN 

### * (1

- EMR1)/ATR -RTL 

### * BN 

### * (I

- %NC) 1+ PVD + OCS + OCTL +TBS PVCC * LTC ATR 

### * APVCCC/(1

- EMRL) LTE * (I -1) 

### * ATR/(l

- EMRL) I - ATR 

### * L 

### * EMRL/(I

- EMRL) 1- EMRL I - ATR 

### * TL

### * EMRT/(I

- EMRT) 1- EMRL (I

- EMRT)/(ATR/BN + I - EMRT) B:T 

### * AFB + (l

- B:T) * APC 

### meeting the standards by the beginningofthe secondcycle. Thus, much ofthe capitalequipment re-installed at the end ofthe first capital's lifetime would have been a normal expense by this date (around 2001). For the interestedreader, the details ofthe calculation method are given in 

### Table C.4
. 

### Table C.4 One-Time Cost Amortization Module
Item DR [ER] T ECC PECC WACC ATWACC ATR CCi EEi WCCi WEEi LTC LTE EMRT EMRL BN RTL PVD OCS OCTL 

### TBS PVCC APVCC CCLF EELF AFB

APC

B:T AFF Definition
Tax rate (34%) Equity cost ofcapital Pre-tax ECC Weighted average CC After-tax WACC Continuous ATWACC Capital cost i years before standards Engineering expense i years before standards (see I-X Cost module) (see I-X Cost module) (see I-X Cost module) (see I-X Cost module)

"Tax benefit rate" Remaining tax life Present value ofdepreciation Old capital savings Old capital tax loss Tax benefit from scrapping Present value ofCC Adjusted for lead timer CC amortization factor EE amortization factor (see One-Time Cost module)
(see One-Time Cost module)
(see One-Time Cost module) Average asset factor
C-25 Formula Debt [Equity] / (Debt + Equity)
ECC/(l

- T) DR*I+ER 

### * PECC WACC* (1 -1) In(1 +ATW ACC)

CCi 

### * exp (ATR 

### *i) EEl

### * exp (ATR * i) Sum ofWCCi over 4 years Sum ofWEEi over 4 years exp (-ATR*TL) exp (-ATR*L) T/TL max (TL

- AGE, 0)

- BN

### * (1 -E:MR1)/ATR

- (1

- %NC) * exp (-ATR * (L
- AGE» (1
- %NC) 

### * BN

### * (1
- EMR1)/ATR -RTL 

### * BN

### * (1

- %NC) 1 + PVD + OCS + OClL +TBS PVCC 

### * LTC ATR

### *APVCCC/(1

- EMRL) LTE 

### * (1 -1) * ATR/(I

- EMRL) 1 - ATR * L * EMRL/(I

- EMRL) 1 - EMRL 1 - ATR 

### * lL * EMRT/(l

- EMRT) 1 - EMRL (1

- EMRT)/(ATR/BN + 1 - EMRT) B:T

### * AFB + (1

- B:T) 

### * APC

### Table C.4 should be read in conjunction with the One-Time Cpst Amortization module. Note that the calculations are all done with continuous interest and discount rates, which makes it necessary to convert A1WACC

### to a continuous equivalent cost ofcapital (ATR) 

### by taking the natural log of (l 

### + ATWACC). 

### The continuous rate is equivalent to, and for small rates it is numerically close to, the annual rate. The continuous time formula for the present value of$Y received t 

### years in the future is Y 'exp(-rt), 

### where r 

### is the discount rate and exp denotes exponentiation. For a constant stream ofincome at $Y per year for L 

### years, the present value is 

### y. (l-exp(-rL)Yr. 

### The most important output of this module is the amortized cost factor CCLF, 

### which is constructed by summing the following five present values: (1) 

### The initial cost, which is normalized to 1 

### in orderto produce a cost factor instead ofan actual cost. (2) 

### The present value ofthe tax 

### benefit from straight-line depreciation ofthe initial cost. (3) 

### Present value ofsavings from not replacing old capital at the end ofits normal life ifit has already been replaced with new capital to meet standards. (4) 

### The tax benefit from the one-time depreciation of the old capital when it is scrapped. (5) 

### The loss of tax benefit from the old capital after it has been scrapped. The sum is then adjusted for the fact that investment must start before the implementation ofstandards. All that is left to do now is to convert from a present value to an equivalentconstant streamofpayments thatcontinues for the lifetime ofthe capital. The formula for this equation is shown in Table CA. 

### Because capital depreciates, its average value is lower than its initial value. In 

### order to derive the factor AAF, 

### by which the initial value should be multiplied to arrive at the average value, it is necessary to account for the exact path by which the capital depreciates. Since the value ofa piece ofcapital is, at any time, simply the present value ofits future income stream, the value ofthe depreciated capital as afunction oftime can be found from the stream ofincome that the capital will generate. Ifthat stream has a constantflow for aperiod of T

### years, then the

»

__ 

### depreciated value ofa unit ofcapital is 1 -r(T-t) 

### DF(t)

## =_-e_-=_ l-e -rT


### Now ifthat capital is held for aperiod ofL 

### years, it makes sense to ask what the average value ofthe assetis overthe L 

### years. Because ofdiscounting, aunit ofcapital now shouldweigh more heavily than aunitofcapital later. This is properly taken into accountbyrequiring the following equivalence:

C-26 Table C.4 should be read in conjunction with the One-Time Cpst Amortization module. Note that the calculations are all done with continuous interest and discount rates, which makes it necessary to convertA1WACCto a continuous equivalent cost ofcapital (ATR) by taking the natural log of (1 

### + ATWACC). The continuous rate is equivalent to, and for small rates it is numerically close to, the annual rate. The continuous time formula for the present value of$Y received t years in the future is Y -exp(-rt), where r is the discount rate and exp denotes exponentiation. For a constant stream ofincome at $Y per year for L years, the present value is Y
- (J-exp(-rL)Yr. The most important output of this module is the amortized cost factor eClF', which is constructed by summing the following five present values: (1) The initial cost, which is normalizedto 1in orderto produce a costfactor instead ofan actual cost. (2) Thepresent value ofthe tax benefit from straight-line depreciation ofthe initial cost. (3) Present value ofsavings from not replacing old capital at the end ofits normal life ifit has already been replaced with new capital to meet standards. (4) The tax benefit from the one-time depreciation of the old capital when it is scrapped. (5) The loss of tax benefit from the old capital after it has been scrapped. The sum is then adjusted for the fact that investment must start before the implementation ofstandards. All that is left to do now is to convert from a present value to an equivalentconstant streamofpayments thatcontinues for the lifetime ofthe capital. The formula for this equation is shown in Table C.4. Because capital depreciates, its average value is lower than its initial value. Inorder to derive the factor AAF, by which the initial value should be multiplied to arrive at the average value, itis necessary to account for the exact path by which the capital depreciates. Since the value ofa piece ofcapital is, at any time, simply the present value ofits future income stream, the valueofthe depreciated capital as afunction oftime can be found from the stream ofincome that the capital will generate. Ifthat stream has a constantflow for a period of Tyears, then the
..-

_ _ depreciated value ofa unit ofcapital is 1 -r(T-t) DF(t)

## =_-e_-=_ l-e -rT
Now ifthatcapital is held for aperiod ofL years, itmakes sense to ask what the average value ofthe assetis overthe L years. Because ofdiscounting, aunit ofcapitalnow shouldweigh more heavily than aunitofcapitallater. This is properly taken into accountbyrequiringthe following equivalence:

C-26

### PV(DF(t)) =PV(AAV) where 

### PV() is the 

### L-year present value operator, and 

### AAV is the average asset value corresponding to DF(t). 

### This equation can be solved for AAV, 

### to find the following:
-rT 

### AAV= 1 [I_rT e ] 

### 1

### -e -rL 

### I

### -e -rT


### This would be the end of the problem-AAV 

### would be the sought-for average asset factor-except for the fact thatthe assetofinterestdoes not generate aconstant stream ofincome for 

### T years before falling to zero for the remaining 

### L-T years. Instead the income stream of interest is made up of T 

### years of constant tax benefit and L 

### years of (roughly) constant production by the capital. Each ofthese streams separately fits the description ofthe problem that has been solved. The solution to this new problem isjust the average ofthe solutions for the two separate problems. Forexample, if20% ofthe total stream is tax benefit that comes to an end after T 

### years, while the remaining 80% continues for the full L 

### years, then the AAF 

### of this combined stream should be 20% of the way from the tax-benefit asset factor to the production asset factor. The variable R:T 

### gives the fraction of the total stream made up of tax benefits. The factors APR

### andAFC 

### are the average asset factors for tax benefits and the productive income of capitalrespectively, andAAF

### is the weighted average ofthese two using R:T 

### as the weight for AFR. 

### At the bottom of the One-Time Cost Amortization module are found the capital and engineering costforthebase andlong-run levels, andthe coefficients ofvariation associatedwith each. The three factors just derived, CCLF, EELF, 

### and AAF, 

### can be applied to CC, EE, 

### and again CC, 

### respectively (foreither the basecase or long-run case), to derive the three "amortized" outputs. 

### C.2.3.9 The Long-Run Model Module The Long-Run Model module is the heart of the LBL-MIM. It does all of the computations concerning a typical firm's behavior. It proceeds in 

### three stages. The calibration stage uses data on present costs and profit to estimate the firm's markup. The second stage estimates the industry's situation in 1990, after it has metcurrent NAECA standards. The third estimates a typical firm's situation with more stringent standards than the current NAECA standards. A detailed discussion ofthe economic assumptions and results used by this section can be found in C.2.I.

C-27 PV(DF(t)) =PV(AAV) where 

### PV() is the 

### L-year present value operator, and 

### AAV is the average asset value corresponding to DF(t). 

### This equation can be solved for AAV, 

### to find the following:
-rT 

### AAV= 1 [I_rT e ] 

### 1

### -e -rL 

### 1

### -e -rT


### This would be the end of the problem-AAV 

### would be the sought-for average asset factor-exceptfor the fact thatthe assetofinterestdoes not generate aconstant stream ofincome for 

### T years before falling to zero for the remaining 

### L-T years. Instead the income stream of interest is made up of T 

### years of constant tax benefit and L 

### years of (roughly) constant production by the capital. Each ofthese streams separately fits the description ofthe problem that has been solved. The solution to this new problem isjust the average ofthe solutions for the two separate problems. Forexample, if20% ofthe total stream is tax benefit that comes to an end after T 

### years, while the remaining 80% continues for the full L 

### years, then the AAF 

### of this combined stream should be 20% of the way from the tax-benefit asset factor to the production asset factor. The variable R:T 

### gives the fraction of the total stream made up of tax benefits. The factors APR

### andAFC 

### are the average asset factors for tax benefits andthe productive income of capitalrespectively, andAAF

### is the weighted average ofthese two using R:T 

### as the weight for AFR. 

### At the bottom of the One-Time Cost Amortization module are found the capital and engineeringcostforthebase andlong-run levels, andthe coefficients ofvariation associatedwith each. The three factors just derived, CCLF, EELF, 

### and AAF, 

### can be applied to CC, EE, 

### and again CC, 

### respectively (foreither the basecase or long-run case), to derive the three "amortized" outputs. 

### C.2.3.9 The Long-Run Model Module The Long-Run Model module is the heart of the LBL-MIM. It does all of the computations concerning a typical firm's behavior. It proceeds in 

### three stages. The calibration stage uses data on present costs and profit to estimate the firm's markup. The second stage estimates the industry'S situation in 1990, after it 

### has metcurrent NAECA standards. The third estimates a typical firm's situation with more stringent standards than the current NAECA standards. A detailed discussion ofthe economic assumptions and results used by this section can be found in C.2.1.

C-27Notation. The following naming conventions have been followed with the variables in the long-run module and generally throughout the LBL-MIM. The suffixes .F and .Q, are used with variables that measure an asset or a cost that is fixed or proportional to sales, respectively. The suffixes .B and .N refer to the 1990 NAECA standards and the 1996 NAECA standards, respectively. A combined suffix such as .BF has the natural interpretation (Le., a fixed variable in the 1990NAECA case). The most importantvariable names in the long-run module are A for assets, TC for total costexcluding taxes and the cost ofequity, EC for economic cost (including taxes and equity), 

### EI for economic income, and,ul for markup above marginal cost,,u - 1 . Calibration. The calibration stage estimates a typical finn's markup over unit variable costs using the following basic economic equation: Markup 

## = (Fixed Cost + Economic Profit) VaI-iable Cost (In this equation, fixed and variable cost are components oftotal cost and are notper-unitcosts.) Economic Profit is the difference between ROE and the equity cost ofcapital, both ofwhich are obtained by observation. Fixed and Variable Costs must be deduced from accounting data and from information on economies of scale in the industry. Assets and costs in the calibration stage are both represented as proportions ofrevenue, so fInn size is irrelevant. This means, for instance, that fixed and marginal costs are not given as dollars and dollars per unit, but instead, total fixed and total marginal costs are described as a fraction ofrevenue. Depreciable assets are divided between fixed and marginal according to the input parameterFCA, the "fixedpartofcosts and assets." Inventories are classified asmarginal assets, while cash and receivables are classified as proportional to revenue. Now consider total costs, Le., those that are tax-deductible. FCA is also used to determine the proportion of fixed (total) costs in revenues. Interest loss resulting from cash holdings and a part of general and administrative expenses given by RGA account for revenue-related costs. All othercosts are marginal, and their amountis deduced from the fInn'S rate ofeconomic profit. Because the costs so far are tax-deductible, but revenue is taxable, economic fixed costs are given by the following formula:
EC.F

## = (1-T) ·TC.F+A.F
.ER
.ECC where 

### T is the tax rate, 

### ER is the ratio ofequity to assets, and 

### ECC is the cost ofassets. In principle, we could now estimate marginal economic cost (EC.Q) directly and use this value to C-28 Notation. The following naming conventions have been followed with the variables in the long-run module and generally throughout the LBL-MIM. The suffixes .F and .Q, are used with variables that measure an asset or a cost that is fixed orproportional to sales, respectively. The suffixes .B and .N refer to the 1990NAECA standards and the 1996 NAECA standards, respectively. A combined suffix such as .BF has the natural interpretation (Le., a fixed variable in the 1990NAECA case). The most importantvariable names in the long-run module are A for assets, TC for total costexcluding taxes and the cost ofequity, ECfor economic cost (including taxes and equity), 

### EI for economic income, and,ul for markup above marginal cost,,u - 1 . Calibration. The calibration stage estimates a typical finn's markup over unit variable costs using the following basic economic equation: Markup 

## = (Fixed Cost + Economic Profit) Variable Cost (In this equation, fixed and variable cost are components oftotal cost and are notper-unitcosts.) EconomicProfit is the difference between ROE and the equity costofcapital, both ofwhich are obtained by observation. Fixed and Variable Costs must be deduced from accounting data and from information on economies of scale in the industry. Assets and costs in the calibration stage are both represented as proportions ofrevenue, so fInn size is irrelevant. This means, for instance, that fixed and marginal costs are not given as dollars and dollars per unit, but instead, total fixed and total marginal costs are described as a fraction ofrevenue. Depreciable assets are divided between fixed and marginal according to the input parameterFCA, the "fixedpartofcosts and assets." Inventories are classifiedasmarginal assets, while cash and receivables are classified as proportional to revenue. Now consider total costs, i.e., those that are tax-deductible. FCA is also used to determine the proportion of fixed (total) costs in revenues. Interest loss resulting from cash holdings and a part of general and administrative expenses given by RGA account for revenue-relatedcosts. All othercosts are marginal, and their amountis deduced from the fInn'S rate ofeconomic profit. Because the costs so far are tax-deductible, but revenue is taxable, economic fixed costs are given by the following formula:
EC.F

## = (1-T) ·TC.F+A.F
.ER
.ECC where 

### T is the tax rate, 

### ER is the ratio ofequity to assets, and 

### ECC is the cost ofassets. In principle, we could now estimate marginal economic cost (EC.Q) directly and use this value to C-28finish our analysis. However, because costs cannot be measured accurately**enough~** there could be considerable error in the implied value ofeconomic profit and thus in markup. (Typically a 5% error in total estimated cost will cause a 10% error in implied economic profit.) It is much better to deduce marginal economic cost from economic profit, because economic profit is relatively well-known. This approach gives us the equation for economic marginal cost:
EC.Q=l-EC.F-EC.R-EP'ER-A:R where EP is the rate ofeconomic profit, and A:R is the ratio oftotal assets to revenue. Now the markup is easily computed using the formula given at the beginning of this section on calibration and the model is ready to analyze the 1996 NAECA level of standards. The base case (1990NAECA). The base case is labeled in the model as applying to 1996 in order to remind the reader that this is a long-run calculation and not an analysis ofwhat may happen in the year 1990. The variables in both the base case and the new standards case give fixed costs and assets in dollars and variable costs in dollars per unit. Other than this, no adjustments are made to the three components of assets. However, the total asset ratio A.B contains the new assets resulting from NAECA, LAB. This apparent discrepancy is necessary because the costs associated with the new assets are computed separately in the One-Time Cost Amortization module and added to the economic cost. Total costs are the same as during calibration, except that new variable costs are added into 

### TC.Q. Economic costs are computed from 

### A and 

### TC as before, except for the addition of amortized capital costs (LCC), which are divided between fixed and variable according to the input parameter FlX, and amortized engineering expenses (LEE), which are counted as entirely fixed. The new standards case (1996 NAECA). The case of changes in standards is handled exactly as the base case, except that new values ofDVC (the change in variable cost), LA, 

### LCC, and LEE are used. The remaining descriptive variables. For the calibration case, most of the remaining descriptive variables, 

### Q, P, R, UVC, TC, A, and

### ROE are known because they are inputs orhave been computed. The others are obtained as follows:
Variable Cost of Goods Sold=VCGS=UVC-Q Interest, Not IX=IC=1 ·DR-A-C:R'R-(I-ICash) Pre-interestCost=PIC=TC-IC
C-29 finish our analysis. However, because costs cannot be measured accurately enough, there could be considerable errorin the implied value ofeconomic profit and thus in markup. (Typically a 5% error in total estimated cost will cause a 10% error in implied economic profit.) Itis much better to deduce marginal economic cost from economic profit, because economic profit is relatively well-known. This approach gives us the equation for economic marginal cost:

EC
.Q =1-EC.F-EC.R-EP·ER'A:R where EP is the rate ofeconomic profit, andA:R is the ratio oftqtal assets to revenue. Now the markup is easily computed using the formula given at the beginning of this section on calibration and the model is ready to analyze the 1996 NAECA level of standards. The base case (1990NAECA). The base case is labeledin the model as applyingto 1996 in order to remind the reader that this is a long-run calculation and not an analysis ofwhat may happen in the year 1990. The variables in both the base case and the new standards case give fixed costs and assets in dollars and variable costs in dollars per unit. Other than this, no adjustments are made to the three components of assets
. However, the total asset ratio A.B contains the new assets resulting from NAECA, LA.B. This apparent discrepancy is necessary because the costs associated with the new assets are computed separately in the One-Time Cost Amortization module and added to the economic cost. Total costs are the same as during calibration, except that new variable costs are added into 

### TC.Q. Economic costs are computed from 

### A and 

### TC as before, except for the addition of amortized capital costs (LCC), which are divided between fixed and variable according to the input parameterFlX, and amortized engineering expenses (LEE), which are counted as entirely fixed. The new standards case (1996 NAECA). The case of changes in standards is handled exactly as the base case, except that new values ofDVC (the change in variable cost), LA,

### LCC, and LEE are used. The remaining descriptive variables. For the calibration case, most of the remaining descriptive variables, 

### Q, P, R, UVC, TC, A, and

### ROE are known because they are inputs orhave been computed
. The others are obtained as follows: Variable Cost of Goods Sold=VCGS=UVC·Q Interest, Not IX=IC=1 ·DR'A-C:R'R'(1-ICash) Pre-interestCost=PIC=TC-IC
C

-29Pre-tax Cost= PTC =TC Taxes = TAX=(R-TC) °T

Equity = EQ=ERoA Economic Income=EJ=NI

- EQ° ECC and
Net Income =NI =R-TC-TAX
Return on Equity = ROE =ECC+ EI EQ For the base case the above formulas hold with the exception ofthe one forNI, but several other descriptive variables must be computed. Their formulas follow: Q.B=Q 'LCC(PE+ICE) P.B =(pl +1) °EC.BQ R.B=Q.B oP.B UVC.B=UVC+DVC.B VCGS.B=UVC.B oQ.B IX depreciation =XID.B =CC.BjL IX interest=XII.B=LA.B ·DRor°LTC IXequity cost=XlI.B=LA.B °ER°ECC IXtax benefit=XIT.B=LCC.B +LEE.B-XID.B-XlI.B-XIE.B IC.B=I°DRo(A.B -LA.B)-C:R°R.B o(I-ICASH) PIC.B=TC.B +XID.B -IC.B

PTC=TC.B +XID.B +XII.B
C-30 Pre-taxCost= PTC=TC Taxes= TAX=(R-TC)·T

Equity= EQ=ER-A Economic Income=EI

## = NI

- EQ

-ECC and
Net Income=NI=R-TC-TAX
Return on Equity= ROE=ECC+ EI EQ Forthe base case the above fonnulas holdwith the exception ofthe oneforNI, but several other descriptive variables must be computed. Their formulas follow: Q.B=Q_LCC(PE+ICE) P.B =(pl+1) -EC.BQ R.B=Q.B -P.B UVC.B=UVC+DVC.B VCGS.B=UVC.B -Q.B IXdepreciation =XID.B=CC.B/L IXinterest=XlI.B=LA.B-DR-I-LTC IXequitycost=XlI.B=LA.B-ER-ECC IXtax benefit=XIT.B=LCC.B+LEE.B-XID.B-XlI.B-XIE.B IC.B=I-DR-(A.B-LA.B)-C:R-R.B -(l-ICASH) PIC.B=TC.B+XID.B-IC.B

PTC=TC.B+XID.B+XII.B
C-30

### NI=R-TC-TAX-LCC.B-LEE.B ROE.B=NI.B/EQ.B

In 

### the fonnula for Q.B 

### above, LCC 

### stands for life-cycle cost. This fonnula is equivalent to the more general one used in the model when the shape parameter (ro) 

### for the demand function is set to one. Setting ro 

### to one simply implements the assumption ofconstant demand elasticity with respect to life-cycle cost. All of the descriptive variables for the changed standards (long-run) case are computed in exactly the same way as the corresponding variables in the base case. 

### C.2.3.10 The Short-Run Model Module During a recession an appliance manufacturing industry will typically face a substantial decline in demand for its product, which will usually cause increased price competition within the industry, with a consequent fall in price. By regressing price on quantity sold and on a time trend, it is possible to estimate the industry's "short-runprice response to demand" (SRPR). 

### The LBL-MIM requires this as an input to the short-run module. From SRPR, 

### the calculation proceeds as follows. Assume for explanatory purposes that demand has fallen because ofthe change in standards. From SRPR

### compute the impliedfall inpriceresulting from this unexpected drop in demand. At this new price level, compute a new level ofdemand that will necessarily be a little higher at the new lower price than at the previous higher price. Now take the new estimate of the level of (and change in) demand resulting from standards and repeat the calculations. Afterfive orfewer repetitions ofthese calculations, oui' estimates will converge to a stable answer. At this point, the price correction exactly agrees with the estimated fall in demand, and the estimated demand agrees with the price. At no other values of price and quantity is this true. Once price and quantity are known, all other variables are easily computed using procedures that have already been described. 

### C.2.4 Model Inputs C.2.4.1 Input Generating Assumptions Many ofthe necessary input data are not publicly available and so have been estimated from available data or estimated from averages of subjective estimates made by industry representatives. This section describes the various origins ofLBL-MIM's data. 

### Cost data. All cost data are provided by the Engineering Analysis.

C-31 

### NI=R-TC-TAX-LCC.B-LEE.B ROE.B=NI.BIEQ
.B

In the fonnula for Q.B 

### above, LCC 

### stands for life-cycle cost. This fonnula is equivalent to the more general one used in the model when the shape parameter (ro) 

### for the demand function is set to one. Setting ro 

### to one simply implements the assumption ofconstant demand elasticity with respect to life-cycle cost. All of the descriptive variables for the changed standards (long-run) case are computed in exactly the same way as the corresponding variables in the base case. 

### C.2.3.10 The Short-Run Model Module During a recession an appliance manufacturing industry will typically face a substantial decline in demand for its product, which will usually cause increased price competition within the industry, with a consequent fall inprice. By regressing price on quantity sold andon a time trend, itis possible to estimate the industry's "short-runpriceresponse to demand" (SRPR). 

### The LBL-MIM requires this as an input to the short-run module. From SRPR, 

### the calculation proceeds as follows. Assume for explanatory purposes that demand has fallen because ofthe changein standards. From SRPR

### computethe impliedfall inpriceresultingfrom this unexpected drop in demand. At this new price level, compute a new level ofdemand that will necessarily be a little higher at the new lower price than at the previous higher price. Now take the new estimate of the level of (and change in) demand resulting from standards and repeat the calculations. Afterfive orfewer repetitions ofthese calculations, ourestimates will converge to a stable answer. At this point, the price correction exactly agrees with the estimated fall in demand, and the estimated demand agrees with the price. At no other values of price and quantity is this true
. Once price and quantity are known, all other variables are easily computed using procedures that have already been described. 

### C.2.4 Model Inputs C.2.4.1 Input Generating Assumptions Many ofthe necessary , input data are not publicly available and so have been estimated from available data or estimated from averages of subjective estimates made by industry representatives. This section describes the various origins ofLBL-MIM's data. 

### Cost data. All cost data are provided by the Engineering Analysis
.

C-31Elasticities andoperating costs. Industry price elasticity and operating cost elasticity are both supplied by the LBL-REM, as is the change in operating cost that occurs as a result ofthe change in standards. Financialdata. Financialdata arecollectedfrom publicly available sources such as Value Line, Standard and Poor's, Moody's, and company reports. The data are often for fInns whose operations intheindustry underconsideration are onlypartofthe fIrm's totalbusiness operations. It is assumed that the relevant division is similar to the finn as a whole. Amortization data. In orderto amortize the one-timecosts associatedwith new standards, the life of new capital is assumed to be seven years, which has been designated by several manufacturers as the period used in their own amortization calculations. Other values relating to such issues as taxation are displayed in the spreadsheet and are generally derived from discussions with industry representatives. The timing ofengineering and capital costs come from the Engineering Analysis. Fixedandmarginalcosts andassets. Some costs increase in proportion to shipments, and some are fIxed. Industryrepresentatives indicate thatthe bulkofcosts and assets areproportional to shipments. The specifIc values are shown in the Control Panel. The following sections list and describe the inputs used by the model. 

### C.2.4.2 Control Factors in the Control Panel Industry price elasticity. Industry price elasticity measures the way a change in the average industry price for a product will affect the industry's shipments. For example, if the price of a product increases 4% and shipments drop 2% as a result, the price elasticityis -0.5. Because standards will cause the prices ofproducts to increase, this input is obviously crucial in detennining the impact ofstandards on manufacturers. Industry operating cost elasticity. Industry operating cost elasticity measures the effect of a change in operating costs for a product on the industry's shipments of that product. For example, ifoperating costs ofaproduct decrease 

### 10% and shipments increase 

### 1% as aresult, the operating cost elasticity is -0.1. Because standards mandate more effIcient products that will have lower operating costs, this input is important in detennining the impact of standards on manufacturers. The operating cost elasticity is calculated from the discount rate supplied by the LBL-REM. Equity cost ofcapital. This is the rate ofreturn expected by equity shareholders. This fIgure is in real tenns (Le., with inflation subtracted). It is an after-tax rate, which means that it is the rate of return the company must earn for its shareholders after its taxes have been accounted for. Data values come from calculations using standard fInance equations and public fInancial data.

C-32 Elasticities andoperating costs. Industry price elasticity andoperatingcostelasticity are both suppliedby the LBL-REM, as is the change in operating cost that occurs as a result ofthe change in standards. Financialdata. Financialdataarecollectedfrom publicly available sources such as Value Line, Standard and Poor's, Moody's, and company reports. The data are often for firms whose operationsintheindustry underconsideration are onlypartofthe fIrm's totalbusinessoperations. It is assumed that the relevant division is similar to the firm as a whole
. Amortizationdata. Inorderto amortize the one-timecosts associatedwith new standards, the life of new capital is assumed to be seven years, which has been designated by several manufacturers as the period used in their own amortization calculations. Other values relating to such issues as taxation are displayed in the spreadsheet and are generally derived from discussionswith industryrepresentatives
. Thetiming ofengineering andcapitalcostscomefrom the Engineering Analysis. Fixedandmarginalcostsandassets. Somecosts increasein proportionto shipments, and some arefIxed. Industryrepresentatives indicate thatthe bulkofcosts and assets areproportional to shipments. The specific values are shown in the Control Panel. The following sections list and describe the inputs used by the model. 

### C.2.4.2 Control Factors in the Control Panel Industry price elasticity. Industry price elasticity measures the way a change in the average industry price for a product will affect the industry's shipments. For example, ifthe price of a product increases 4% and shipments drop 2% as a result, the price elasticity is -0.5. Because standards will cause the prices ofproducts to increase, this input is obviously crucial in determining the impact ofstandards on manufacturers. Industry operating cost elasticity. Industry operating cost elasticity measures the effect of a change in operating costs for a product on the industry's shipments of that product. For example,ifoperatingcosts ofaproductdecrease 

### 10% and shipments increase 

### 1 % as aresult, the operating cost elasticity is -0.1. Because standards mandate more efficient products that will have lower operating costs, this input is important in determining the impact of standards on manufacturers
. The operating cost elasticity is calculatedfrom the discount rate supplied by the LBL-REM. Equity cost ofcapital. This is the rate ofreturn expected by equity shareholders. This fIgure is in real terms (Le., with inflation subtracted). It is an after-tax rate, which means that it is the rate of return the company must earn for its shareholders after its taxes have been accounted for. Data values come from calculations using standard fInance equations and public fInancial data
.
C-32Economicprofit. Economic profitis the profit earnedby a finn above its expectedreturn on equity. In other words, it is the profit earned after all costs are taken into account, including the cost ofequity (the market rate ofreturn). A firm makes economic profit only when it has some degree ofmarket power. Long-runfixedpartof costs andassets. This figure is the portion ofall costs and assets that is fixed over time, that is, which does not vary with quantity sold or with revenue. It is expressed as a percent of the base case costs and assets. This figure is important, from an economic standpoint, for determining the impact of standards on manufacturers. Long-runfixedpartof one-time capitalcost. This figure is the portion ofone-time capital costs that is fixed in the long run. Although all capital costs are normally considered fixed in the short run, this portion ofthe capital costs is spent regardless of the output capacity of the tooling purchased. The other portion of one-time capital costs varies proportionally with the output capacity of the new equipment. One-time capital costs. Some of the design options used to meet the increased efficiencies mandated by the new standards require additional capital investment in the form of retooling, new equipment, or other capital expenditures. The "one-time capital costs" figure represents the capital costs expended to meet a specific standard being analyzed; this number covers all products in a product type and is an input from the Engineering Inputs module. Unit variable cost increase. This number is the shipment-weighted average increase in unit variable costs incurred by the manufacturer in meeting the new standards. The incremental variable cost includes raw materials, direct labor, purchased parts, and increased transportation costs. This number is an input from the Costs, Sales, and Revenues module. Short-runprice response to demand (SRPR). When industry demand falls by Y%, price will, in the shortrun, fall by SRPR mb Y%. This response is expected to die out within afew
.. ·· years, and Y% should be less than 20% when SRPR is used. 

### C.2.4.3 Engineering Data Incremental unitvariable cost. The incremental unit variable costs are the variable costs associated with design options that increase a product's energy efficiency. This input differs from the variable cost increase input for the Control Panel (Section C.2.3.1) in that it lists incremental variable costs for each design option foreach productclass ofthe producttype being analyzed, rather than a single shipments-weighted average number. The increased variable cost includes raw materials, directlabor, purchasedparts, andincreasedtransportation costs. The data also come in the form ofa table that lists the incremental variable costfor each productclass for each ofthe standards levels being analyzed.

C-33 Economicprofit. 

### Economicprofitis the profit earnedby a finn 

### above its expectedreturn on equity. In other words, it is the profit earned after all costs are taken into account, including the cost ofequity (the market rate ofreturn). A firm makes economic profit only when it 

### has some degree ofmarket power. Long-runfixedpartof costs andassets. 

### This figure is the portion ofall costs and assets that is fixed over time, that is, which does not vary with quantity sold or with revenue. It is expressed as a percent of the base case costs and assets. This figure is important, from an economic standpoint, for determining the impact of standards on manufacturers. Long-runfixedpartof one-time capitalcost. 

### This figure is the portion ofone-time capital costs that is fixed in the long run. Although all capital costs are normally considered fixed in the short run, this portion ofthe capital costs is spent regardless of the output capacity ofthe tooling purchased. The other portion of one-time capital costs varies proportionally with the output capacity ofthe new equipment. One-time capital costs. 

### Some of the design options used to meet the increased efficiencies mandated by the new standards require additional capital investment in the form of retooling, new equipment, or other capital expenditures. The "one-time capital costs" figure represents the capital costs expended to meet a specific standard being analyzed; this number covers all products in a product type and is an input from the Engineering Inputs module. Unit variable cost increase. 

### This number is the shipment-weighted average increase in unit variable costs incurred by the manufacturer in meeting the new standards. The incremental variable cost includes raw materials, direct labor, purchased parts, and increased transportation costs. This number is an input from the Costs, Sales, and Revenues module. Short-runprice response to demand (SRPR). 

### When industry demand falls by Y%, price will, in the shortrun, fall by SRPR mb 

### Y%. This response is expected to die out within afew
.. - . years, and Y% should be less than 20% when SRPR 

### is used. C.2.4.3 Engineering Data Incremental unitvariable cost. 

### The incremental unit variable costs are the variable costs associated with design options that increase a product's energy efficiency. This input differs from the variable cost increase input for the Control Panel (Section C.2.3.1) in that it lists· incremental variable costs for each design option foreachproductclass oftheproducttype being analyzed, rather than a single shipments-weighted average number. Theincreased variable cost includesrawmaterials, directlabor,purchasedparts, andincreasedtransportation costs. The data also comein the form ofatable that lists the incremental variable costfor eachproductclass for each ofthe standards levels being analyzed.

C-33Unit energy consumption (UEe)

. 

### Unit energy consumption is the annual energy cost (in dollars) of a product. The input data come in the form of a table that lists the VEC for each productclass foreachdesign option (and standards level) being analyzed. Electricityis measured in kilowatt hours per year (kWh/y). Gas and oil are generally measured in millions of BTUs (MBTU) though BTUs for some products are measured in thousands (kBTUs). Engineering expense. 

### This is the total engineering expense associated with producing more energy-efficient products within a product type. The input is given as a table listing the engineering expenses to reach each of the various design options and standards levels being analyzed. The engineering expense also varies depending on the size ofthe manufacturer. If more than one size ofmanufacturer is being analyzed for a product type, then the data for all fIrm sizes are included. Capital costs. 

### Some of the design options used to meet the increased efficiencies mandatedby the new standards require additional capitalinvestmentin the form ofretooling, new equipment, orothercapital expenditures. This inputis given as atable listing the cost associated with each design option that requires a capital expenditure. The input is also given in a table listing the total capital expenditure for the design options used.to reach each of the various standards levels being analyzed. The capital cost input covers all product classes within a product type, unless noted otherwise. The one-time capital costs resulting from standards vary with the size ofthe manufacturer. If 

### more than one size ofmanufacturer is being analyzed for a product type, then input data for all sizes being analyzed are included. C.2.4.4 

### Costs, Sales, and 

### Revenues Industryshipments. 

### Industry shipments are annual shipments forall classes oftheproduct type being analyzed. These inputs are actual datafor a 1990 calibration case, an estimate for the 1996base case using the 1990 standards, and 1996estimates foreachofthe new standards levels being analyzed. The 1996 shipments estimates are based on projected product price increases and product price and operating cost elasticities, from forecasts provided by the LBL-REM. Product class market shares. 

### Each of the classes within a product type has a "market share" ofthe shipments for thatproducttype. Forexample, electric storage waterheaters account for about45% ofall waterheater shipments. The product class market share combined with the total industry shipments give a breakdown ofannual shipments by product class. Manufacturer marketshare. 

### Each ofthe prototypical flrms in the analysis is assigned a market share appropriate to that segment ofthe industry. For example, flve large water heater manufacturers may each be assigned a 20% market share. The manufacturer market share, combined with the total industry shipments, gives a firm's annual shipments. This figure, combined with the product class market shares, gives aprototypical firm's annual shipments for each product class.

C-34 Unit energy consumption (UEe)

.

### Unit energy consumption is the annual energycost (in dollars) of a product. The input data come in the form of a table that lists the VEC for each productclass foreachdesign option (and standards level) beinganalyzed. Electricityis measured in kilowatt hours per year (kWh/y). Gas and oil are generally measured in millions of BTUs (MBTU) though BTUs for some products are measured in thousands (kBTUs). Engineering expense. 

### This is the total engineering expense associated with producing more energy-efficient products within a product type. The input is given as a table listing the engineering expenses to reach each of the various design options and standards levels being analyzed. The engineering expense also varies depending on the size ofthe manufacturer. If more than one size ofmanufacturer is being analyzed for a product type, then the data for all fIrm sizes are included. Capital costs. 

### Some of the design options used to meet the increased efficiencies mandatedby the new standardsrequireadditional capitalinvestmentin the form ofretooling, new equipment, orothercapital expenditures. This inputis given as atable listing the costassociated with each design option that requires a capital expenditure. The input is also given in a table listing the total capital expenditure for the design options used.to reach each of the various standards levels being analyzed. The capital cost input covers all product classes within a product type, unless noted otherwise. The one-time capital costs resulting from standards vary with the size ofthe manufacturer. If

### more than one size ofmanufacturer is being analyzed for a product type, then input data for all sizes being analyzed are included. 

### C.2.4.4 Costs, Sales, andRevenues Industryshipments. 

### Industry shipments are annual shipmentsforall classesoftheproduct type being analyzed. These inputs are actual datafor a 1990 calibration case, an estimateforthe 1996basecase using the 1990 standards, and 1996estimates foreachofthe new standards levels being analyzed. The 1996 shipments estimates are based on projected product price increases and product price and operating cost elasticities, from forecasts provided by the LBL-REM. Product class market shares. 

### Each of the classes within a product type has a "market share" ofthe shipments for thatproducttype. Forexample, electric storagewaterheaters account for about45% ofall waterheater shipments. The product class market share combined with the total industry shipments give a breakdown ofannual shipments by product class. Manufacturer marketshare. 

### Each ofthe prototypical firms in the analysis is assigned a market share appropriate to that segment ofthe industry. For example, five large water heater manufacturers may each be assigned a 20% market share. The manufacturer market share, combined with the total industry shipments, gives a firm's annual shipments. This figure, combinedwith the product class market shares, gives aprototypical firm's annual shipments for each product class.

C-34Markups. The markup is the figure used by the manufacturer to determine the finn's selling price for aparticularproduct. It is an increase over the unit variable cost ofthatproduct, and it covers overhead, capital costs, and profit. In some industries, manufacturers use different markups on different product classes. The markup input lists the markup used for each class in the product type being analyzed. Initialprices. This input is the 1990 baseline manufacturer's selling price per unit for each product class. Added to the baseline unit price are the incremental costs ofreaching the higher efficiency levels required by standards. Energyprice. This figure is the price ratio ofa 1990kWh to a 1996kWh taken from the LBL-REM; energy price is used to calculate the change in operating cost from the calibration case to the base case (OC%B) and to the new standards case (OC%.N). C.2.4.5 Financial Inputs Rates of financial costs: After-taxequity costof capital. This inputis explainedin Section C.2.3.1, ControlPaneL Interest rate on debt. This is the interest rate paid by a corporation on its debt. This figure is in real tenns (Le., with inflation subtracted), and is the yield ofcorporate bonds with a Moody's rating comparable to ratings in the industry being analyzed. Interest loston cash. Companies hold aportion oftheircurrent assets in the fonn ofcash in bank accounts, marketable securities, and other liquid holdings, all lumped under the tenn "cash." This category ofassets has a cost associated with it because ofthe cost ofthe debt and equity associated with all assets. However, companies manage theircash accounts very well and are able to earn interest each day on the cash they are not using that day. The interest earned ,-- on the cash accounts tends to be low, since the investment is only overnight and varies from day to day. The interest earned is lower than the costofthe capital used to fonn the asset. Thus, the cash account costs the company money but the cost is lower than for any ofthe company's other assets. Rate ofdepreciation. This figure is the rate ofdepreciation on fixed assets for a typical finn in the industry. Itis obtained by assuming straight-line depreciation and an average tax life ofeight years for the f1IlJl's fixed assets. It is then revised by analyzing actual depreciation data from the industry under consideration. Tax rate. This rate is the average tax rate faced by an industry in 1996. It is based on past data and recent revisions to the tax law.

C-35 Markups. The markup is the figure used by the manufacturer to determine the finn's sellingprice for aparticularproduct. It is an increase overthe unit variable cost ofthatproduct, anditcovers overhead, capital costs, andprofit. In some industries, manufacturers use different markups on different product classes. The markup input lists the markup usedfor each class in the product type being analyzed. Initialprices. This input is the 1990 baseline manufacturer's selling price per unit for each product class. Added to the baseline unit price are the incremental costs ofreaching the higher efficiency levels required by standards. Energyprice. This figure is thepriceratioofa 1990kWh to a 1996kWh taken from the LBL-REM; energy price is used to calculate the change in operating cost from the calibration case to the base case (OC%B) and to the new standards case (OC%.N). 

### C.2.4.S Financial Inputs Rates of financial costs: After-taxequity costof capital. This inputis explainedin Section C.2.3
.1, ControlPanel. Interest rate on debt. This is the interest rate paid by a corporation on its debt. This figure is in real tenns (Le., with inflation subtracted), and is the yield ofcorporate bonds with a Moody's rating comparable to ratings in the industry being analyzed. Interest loston cash. Companies hold aportionoftheircurrent assets in the fonn ofcash in bank accounts, marketable securities, and other liquid holdings, all lumped under the tenn "cash." This category ofassets has a cost associated with it because ofthe cost ofthe debt and equity associatedwith all assets. However, companies manage theircash accounts verywell and are able to earn interest each day on the cash they are not using that day. The interest earned .- - on the cash accounts tends to be low, since the investmentis only overnightandvaries from day to day. The interest earned is lower than the cost.ofthe capital used to fonn the asset. Thus, the cash account costs the company money but the cost is lower than for any ofthe company's other assets. Rate ofdepreciation. This figure is the rate ofdepreciation on fixed assets for a typical finn in the industry. Itis obtainedby assuming straight-line depreciation and an average taxlife ofeight years for the finn's fixed assets. Itis then revised by analyzing actual depreciation data from the industry under consideration. Tax rate. This rate is the average tax rate faced by an industry in 1996. It is based on past data and recentrevisions to the tax law.

C-35Assets and costs as apercent ofrevenue: Cash. Companies hold a portion of their current assets in the fonn of cash in bank accounts, marketable securities, and other liquid holdings, all lumped under the term "cash." In the model, this input is expressed as a percent oftotal revenue. Inventory and receivables. This figure represents the inventory and receivables assets, expressedas apercent oftotalrevenue. Inventory includes raw materials ownedby the company, work in process, and finished goods that have not yet been shipped. Receivables refers to goods that have already been sold and shipped by the firm for which the firm has not yet been paid, but is instead owed money. Depreciable assets. This figure represents the netdepreciable, orfixed, assets ofthe firm, expressed as a percent of revenue. Depreciable assets are tooling, equipment, production facilities, and other goods that have an original lifetime of longer than one year and thus are depreciated over several years rather than expensed in one year. General and administrative expenses (G&A). This figure represents the general and administrative and selling expenses for the firm, expressed as a percent ofrevenue. It is the "overhead" of the company. In the model, this figure does not include engineering, since engineering expense is treated separately because of its relevance for analyzing the financial impact of standards on manufacturers. Engineering. This figure represents the annual engineering expenses for a firm, apart from the engineering expenses induced by standards, expressed as a percent ofrevenue. The engineering expense associated with meeting the standards appears on the Costs and Assets of the One-Time Investment page ofthe model. Fixed and variable costs: Fixedpartof allcosts anddepreciable assets. This inputis explainedin the ControlPanel section ofC.2.3.1. Fixedpartof one-time capital cost. This input is explained in the Control Panel section ofC.2.3.1. Otherfinancial inputs: Economicprofit. This input is explained in the Control Panel section ofC.2.3.1. Debt-to-equity ratio. All of a firm's assets are paid for either by debt or by equity. The debt-to-equity ratio is used in calculating a firm's weighted average cost ofcapital. It is also associated with the risk factor ofthe firm and affects how much money a firm is able to borrow and at what interest rate, so it is important in the analysis ofthe impacts ofstandards. C-36 Assets and costs as apercent ofrevenue: Cash. Companies hold a portion of their current assets in the fonn of cash in bank accounts, marketable securities, and other liquid holdings, all lumped under the tenn "cash." In the model, this input is expressed as a percent oftotal revenue. Inventory and receivables. This figure represents the inventory and receivables assets, expressedas apercent oftotalrevenue. Inventoryincludesrawmaterials ownedbythecompany, workin process, andfinished goods that have not yet been shipped. Receivables refers to goods that have already been sold and shipped by the firm for which the firm has not yet been paid, but is instead owed money. Depreciable assets. This figure represents the netdepreciable, orfixed, assets ofthe firm, expressed as a percent of revenue. Depreciable assets are tooling, equipment, production facilities, and other goods that have an original lifetime of longer than one year and thus are depreciated over several years rather than expensed in one year. General and administrative expenses (G&A). This figure represents the general and administrative and selling expenses for the firm, expressed as a percent ofrevenue. It is the "overhead" of the company. In the model, this figure does not include engineering, since engineering expense is treated separately because of its relevance for analyzing the financial impact of standards on manufacturers. Engineering. This figure represents the annual engineering expenses for a firm, apart from the engineenng expenses induced by standards, expressed as a percent ofrevenue. The engineering expense associated with meeting the standards appears on the Costs and Assets of the One-Time Investment page ofthe model. Fixed andvariable costs: Fixedpartof allcostsanddepreciable assets. This inputis explainedinthe ControlPanel section ofC.2.3.1. Fixedpartofone-time capital cost. This inputis explained in the Control Panel section of C.2.3
.1. Otherfinancial inputs: Economicprofit. This input is explained in the Control Panel section ofC.2.3.1. Debt-to-equity ratio. All
.ofa firm's assets are paid for either by debt or by equity. The debt-to-equity ratio is used in calculating a firm's weighted average cost ofcapital. It is also associated with the risk factor ofthe firm and affects how much money a firm is able to borrow and at what interest rate, so itis important in the analysis ofthe impacts ofstandards. C-36Markup on a typical model. This figure is the markup used by a fIrm in determining the manufacturer's selling price. It is the markup used on the unit variable cost ofa typical product in the industry being analyzed. Ratio ofhighest to lowest markup. In some industries, manufacturers use different markups on different product classes. Ifsuch a situation exists in the industry being analyzed, this ratio indicates the range ofmarkups used. Costs and assets ofone-time investment: One-time capital cost's life. This inputis the productive life ofthe equipmentpurchased to meet the standards, expressed in years. The number is also used as the period of time over which the one-time costs induced by standards are amortized. One-time capital cost's tax life. This input is the tax life ofthe equipment purchased to meet the standards, expressed in years. Percentadditionalone-time capital. Meeting standards may require both the addition of new capital thatreplaces nothing, and the replacementofold capital thathas become inadequate. This variable tells what percent ofnew capital is additional rather than fpr replacement. Age ofold(replaced) capital. This input gives the age, in years, ofthe old capital that gets replaced because of the new standards. Under' standards, some old equipment will get scrapped before the end ofits productive life andreplaced by new equipment. Depending on the age ofthe replaced capital, tax and/or economic consequences are factored into the calculations ofthe cost ofthe new equipment.
. One-time capital costand engineering expense schedules. The one-time capital cost and engineering expense schedules are inputs that list the costs incurred over time for preparations .-..- to meet the new standards. The capital costs and the engineering expenses are listed togetherin a table. A percentage of the total cost of each of these categories is attributed to each year before the standards go into effect, because that is when these expenses will occur. These cost schedules allow the model to be more accurate in calculating the present value ofthe amortized costs, rather than assuming that the costs all occur when the standards go into effect. 

### C.2.S Model Outputs C.2.S.1 Control Panel Outputs The Control Panel outputs are the summary outputs of the model. This output section enables the reader or model user to see at a glance the main short-run and long-run impacts of standards on a few key variables: shipments, price, revenue, net income, and return on equity.

C-37 Markup ona typical model. Thisfigure is the markup used by a fIrm in determining the manufacturer's selling price. Itis the markup used on the unit variable costofa typical product in the industry being analyzed. Ratio ofhighest to lowest markup. In some industries, manufacturers use different markups on different product classes. Ifsuch a situation exists in the industry being analyzed, this ratio indicates the range ofmarkups used. Costs and assets ofone-time investment: One-time capital cost's life. This inputis the productive life ofthe equipmentpurchased to meet the standards, expressed in years. The number is also used as the period of time over which the one-time costs induced by standards are amortized. One-time capital cost's tax life. This input is the tax life ofthe equipmentpurchasedto meet the standards, expressed in years. Percentadditionalone-time capital. Meeting standards mayrequire both the addition of new capital thatreplaces nothing, and the replacementofoldcapital thathas becomeinadequate. This variable tells what percent ofnew capital is additional rather than fpr replacement. Age ofold(replaced) capital. This input gives the age, in years, ofthe old capital that gets replaced because of the new standards. Under' standards, some old equipment will get scrappedbefore the endofits productive life andreplacedby new equipment. Dependingonthe age ofthe replaced capital, tax and/or economic consequences are factored into the calculations ofthe cost ofthe new equipment.
. One-time capital costand engineering expense schedules. The one-time capitalcost and engineering expense schedules are inputs that list the costs incurred over time for preparations
.- ..- . to meet the new standards. The capital costs and the engineering expenses are listed togetherin a table. A percentage of the total cost of each of these categories is attributed to each year before the standards go into effect, because that is when these expenses will occur. These cost schedules allow the model to be more accurate in calculating the present value ofthe amortized costs, rather than assuming that the costs all occur when the standards go into effect. 

### C.2.S Model Outputs C.2.S.1 Control Panel Outputs The Control Panel outputs are the summary outputs of the model. This output section enables the reader ormodel user to see at a glance the main short-run and long-run impacts of standards on a few key variables: shipments, price, revenue, net income, and return on equity.

C-37

### For each ofthe five variables listed above, the Control Panel output table lists the base case number, the new long-run number, and the percentage change between the two. The table also lists the percentage changes for these variables from the most recent previous run for comparison purposes. The last column in the output table lists the short-run results for each of the variables. The base case numbers refer to 1999 values ifthe 1990 standards are in effect but ifno new 1996 standards are implemented. The long-run numbers are the model's estimates for these variables when new 1996 standards are implemented and after manufacturers have had time to adjust to the changes and find new optimal selling prices and production quantities. The short-run numbers are the model's estimates ofimpacts on manufacturers in the shortrun, before they have time to find a new optimum. Shipments. 

### Shipments are the total units sold by a prototypical firm in a given year, expressed in millions ofunits. The shipments variable is the sum of shipments for all product classes within aproduct type. Shipments data are calculated on the Costs, Sales, and Revenues page. Price. 

### Price is the shipments-weighted average ofthe manufacturer's selling prices for the different classes within the product type being analyzed. Price data are calculated on the

. Costs, Sales, and Revenues page. Revenue. 

### Revenue is the amountofmoneyreceivedby the manufacturerfor the products it has sold. It is the sum of the revenue for each of the product classes being sold by the manufacturer and it is expressedin millions ofdollars. It can also be calculated by multiplying the annual shipments times the shipments-weighted average price. Revenue data are calculated on the Costs, Sales, and Revenues page. Net income. 

### Net income is the after-tax profit made by the company, expressed in ,..- millions ofdollars. Net income and the associated return-on-equity are primary measures ofa company's [mancial performance. In LBL-MIM, net income is calculated in the Accounting Summary. Return on equity (ROE). 

### Netincome is adollar amount that varies inpart simply because of a firm's size, and ROE adjusts for different-sized companies by giving net income as a proportion of stockholders' equity. It is the return an investor makes on his investment in the stock ofa particular company. ROE is calculated in the Accounting Summary. 

### C.2.5.2 Accounting Summary Outputs Net income. 

### See the Control Panel section above for a description ofnet income. In the Accounting Summary, net income is expressed in thousands ofdollars rather than millions.



### C-38 Foreach ofthe five variables listed above, the Control Panel output table lists the base case number, the new long-run number, and the percentage change between the two. The table also lists the percentage changes for these variables from the most recent previous run for comparison purposes. The last column in the output table lists the short-run results for each of the variables. The base case numbers refer to 1999 values ifthe 1990 standards are in effect but ifno new 1996 standards areimplemented. The long-run numbers are the model'sestimates for these variables when new 1996 standards are implemented and after manufacturers have had time to adjust to the changes and find new optimal selling prices and production quantities. The short-run numbers are the model's estimates ofimpacts on manufacturers in the shortrun, before they have time to find a new optimum. Shipments. 

### Shipments are the total units sold by a prototypical firm in 

### a given year, expressed in millions ofunits. The shipments variable is the sum of shipments for all product classes within aproduct type. Shipments data are calculated on the Costs, Sales, and Revenues page. Price. 

### Price is the shipments-weighted average ofthe manufacturer's selling prices for the different classes within the product type being analyzed. Price data are calculated on the

.Costs, Sales, and Revenues page. Revenue. 

### Revenue is the amountofmoneyreceivedby the manufacturerfor the products it has sold. It is the sum of the revenue for each of the product classes being sold by the manufacturer and itis expressed in millions ofdollars. It can also be calculated by multiplying the annual shipments times the shipments-weighted average price. Revenue data 

### are calculated on the Costs, Sales, and Revenues page. Net income. 

### Net income is the after-tax profit made by the company, expressed in , ..... millions ofdollars. Net income and the associated return-on-equity are 

### primary measures ofa company's fmancial performance. In LBL-MIM, 

### net income is calculated in the Accounting Summary. Return onequity(ROE). 

### Netincomeis adollar amount thatvaries inpart simplybecause of a firm's size, and ROE adjusts for different-sized companies by giving net income as a proportion of stockholders' equity. It is the return an investor makes on his investment in the stock ofa particular company. ROE is calculated in the Accounting Summary. C.2.S.2 Accounting Summary Outputs Net income. 

### See the Control Panel section above for a description ofnet income. In the Accounting Summary, net income is expressed in thousands ofdollars rather than millions.



### C-38Gross margin. Gross margin shows, on a percentage basis, how much revenue remains after the variable cost ofgoods sold is accounted for: GM

## = (R- VCGS)/R. Return on sales. Return on sales is net income as a percentage ofrevenue. Total assets. Total assets is the sum ofall ofthe prototypical fIrm's assets. This includes cash, inventory, and receivables; depreciable assets; and in the case of new standards, the one-time assets acquired to meet the standards. Return on assets. Return on assets is the total return on the debt and equity portions of a fIrm's assets. Itis a measure ofhow productively a fIrm's assets are being used. The interest paid on debt instruments and the net income that belongs to equity shareholders are the returns on the fIrm's assets. The returns are divided by the total assets ofthe firm to calculate return on assets. Equity. Equity is the portion of a firm's assets owned by the stockholders ofthe fIrm. It is the difference between a firm's assets and its liabilities. In this model it is calculated by multiplying the equity percentage obtained from the debt-to-equity ratio times the total assets fIgure. Return on equity (ROE). See the Control Panel section above for a description ofROE. 

### C.2.5.3 Economic Analysis Outputs The economic analysis outputs give aclear and concisepicture ofwhat is going on in the company from an economic standpoint. Most ofthe outputs show up in the other two outputs sections. Shipments, price, and revenue are discussed in the Control Panel section. The total assets numberis calculatedinthe Accounting Summary section, andthe economic analysis output breaks down total assets into three categories: cash, inventories, anddepreciable assets. Expenses are looked at from an economic point ofview rather than an accounting one. Thus expenses are broken down into fixed expenses and expenses that vary with the quantity produced. 

### C.2.5.4 Monte Carlo Outputs The Monte Carlo outputs section lists the results ofsix key measures ofimpact when the Monte Carlo option is used. Forkey inputvariables, the Monte Carlo optionrandomly generates values around the best available estimates oftheir true values. As aresult, the outputs vary from run to run, and the model can calculate means and standard deviations for the key measures of impact. It is then possible to bound the estimates ofimpact and attribute a degree ofcertainty to them. The Monte Carlo Outputs section is a table that lists for each variable the value ofthe currentiteration, the mean ofall the iterations done in the currentrun, and the standarddeviation

C-39 Gross margin. Gross margin shows, on a percentage basis, how much revenue remains after the variable cost ofgoods sold is accounted for: GM

## = (R-VCGS)/R. Return on sales. Return on sales is net income as a percentage ofrevenue. Totalassets. Total assets is the sum ofall oftheprototypicalfIrm's assets. This includes cash, inventory, and receivables; depreciable assets; and in the case of new standards, the one-time assets acquired to meet the standards. Return on assets. Return on assets is the total return on the debt and equity portions of a fIrm's assets. Itis a measure ofhow productively a fIrm's assets are being used. The interest paid on debt instruments and the net income that belongs to equity shareholders are the returns on the fIrm's assets. The returns are divided by the total assets ofthe firm to calculate return on assets. Equity. Equity is the portion of a firm's assets owned by the stockholders ofthe fIrm. Itis the difference between a firm's assets and its liabilities. In this model it is calculated by multiplying the equity percentage obtained from the debt-to-equity ratio times the total assets fIgure. Return on equity (ROE). See the Control Panel section above for a description ofROE. 

### C.2.S.3 Economic Analysis Outputs Theeconomic analysis outputs give aclearandconcisepicture ofwhatis goingonin the company from an economic standpoint. Most ofthe outputs show up in the other two outputs sections. Shipments, price, and revenue are discussed in the Control Panel section. The total assets numberis calculatedintheAccountingSummarysection, andthe economic analysisoutput breaksdowntotal assetsintothreecategories:cash,inventories, anddepreciable assets. Expenses are looked at from an economicpointofview rather than an accounting one. Thus expenses are broken down into fixed expenses and expenses that vary with the quantity produced. 

### C.2.S.4 Monte Carlo Outputs TheMonte Carlooutputs section lists the results ofsix key measures ofimpact when the Monte Carlooptionis used. Forkeyinputvariables, the Monte Carlo optionrandomlygenerates values around the bestavailableestimates oftheirtrue values. As aresult, the outputs vary from run to run, and the model can calculate means and standard deviations for the key measures of impact. It is then possible to bound the estimates ofimpact and attribute a degree ofcertainty to them. The Monte Carlo Outputs section is a table that lists for each variable the value ofthe currentiteration, the mean ofall the iterations done in the currentrun, and the standarddeviation

C-39of the results. It then lists in chronological order the results of all the iterations done in the current run. The measures ofimpacts are percentage change in shipments, percentage changeinprice, percentage change in revenue, percentage change in net income, and percentage point change in ROE for both the long-run analysis and the short-run analysis. See the Control Panel section (Section C.2.3.1) for a more complete description ofeach ofthese outputs. C.2.5.6 

### Retail Prices One important role ofLBL-MIM is to provide estimates ofretail prices ofappliances at different standards levels for the LBL-REM andthe Life-Cycle Costanalysis. Theprimary input for the retail price calculation in LBL-MIM is MIM's estimates ofmanufacturer prices. The manufacturerprices are derived from base manufacturer costs, engineering production costs, and appropriate manufacturer markups for each product. The base manufacturercost is derivedfrom current retail price data for appropriate product classes (e.g., electric and gas storage water heaters). These prices are divided by both the retail markup for the product and the wholesale (manufacturer) markup. This calculation is described in more detail in the description of the Costs, Sales, and Revenue module (C.2.3.6). The model generates a matrix ofmanufacturers' selling prices for each product class at each standards level. These manufacturers prices are then multiplied by an estimate of the manufacturer-to-retail markup appropriate for the product class, which produces the retail price estimate (this markup varies depending on the product analyzed. See Appendix C ofthe relevant products volume for specific details). Retail prices are given in 1990 dollars. C.3 INPUT DATA AND DATA DEVELOPMENT FOR THE EIGHT PRODUCTS TheLBL-MIM inputdata for the fluorescent lamp ballast, TV, water heater, pool heater, direct heating equipment, mobile home furnaces, room air conditioners, and kitchen ranges and ovens (including microwave ovens) model runs are described in the Appendix C in their respective volumes ofthe TSD.

C-40 of the results. It then lists in chronological order the results of all the iterations done in the current run. Themeasuresofimpacts are percentagechangein shipments, percentagechangeinprice, percentagechange inrevenue, percentage changeinnetincome, andpercentagepoint change in ROE for both the long-run analysis and the short-run analysis. See the Control Panel section (Section C.2.3.1) for a more complete description ofeach ofthese outputs. C.2.S.6 

### Retail Prices One important role ofLBL-MIM is to provide estimates ofretail prices ofappliances at different standards levels fortheLBL-REMandthe Life-CycleCostanalysis. Theprimaryinput for the retail price calculation in LBL-MIM is MIM's estimates ofmanufacturer prices. The manufacturerprices arederivedfrom base manufacturer costs, engineeringproduction costs, and appropriatemanufacturer markups for each product. Thebase manufacturercostis derivedfrom current retail price data for appropriate product classes (e.g., electric and gas storage water heaters). These prices are divided by both the retail markup for the product and the wholesale (manufacturer) markup. This calculation is described in more detail in the description of the Costs, Sales, and Revenue module (C.2.3.6). The model generates a matrix ofmanufacturers' selling prices for each product class at each standards level. These manufacturers prices are then multiplied by an estimate of the manufacturer-to-retail markup appropriate for the product class, which produces the retail price estimate (this markupvaries dependingonthe product analyzed. See Appendix C oftherelevant products volume for specific details). Retail prices are given in 1990 dollars. C.3 INPUT DATA AND DATA DEVELOPMENT FORTHE EIGHT PRODUCTS TheLBL-MIM inputdata for the fluorescent lamp ballast, TV, waterheater, pool heater, direct heating equipment, mobile home furnaces, room air conditioners, and kitchen ranges and ovens (including microwave ovens) model runs are described in the Appendix C in their respective volumes ofthe TSD.

C-40

## APPENDIX D. MAINTENANCE COSTS FOR COMMON APPLIANCE COMPONENTS

D.I INTRODUCTION This appendix describes the analyses used to characterize the maintenance costs for several components that are common to more than one appliance. Maintenance costs for components that are unique to a particular appliance are described in the volumes of this document that treat that appliance. 

### AnalysisApproach andResults Maintenance cost has been characterized for this analysis as a present value cost incrementto be added to the purchase price ofan appliance. Thus, the maintenance cost can be described as Cmaint 

## =f-Co ' where Co is the cost ofthe maintenance procedure in the year that it occurs ("n" years after the appliance purchase), and f

## = (1 

### + dfD is the present value factor thatdiscounts the Co value to thepurchase time using discountfactord, takenhere as 6%. Using this approach, the maintenance cost (C maint ) for any component can be obtained from two values: a cost Co and a lifetime n. The discount factor is a real discount factor since the analysis assumes constant dollars. COST DATA. National average values ofCo were calculatedby fIrst obtaining regional cost average values, Co', for the San Francisco Bay Area. These Co' values, in 1990 dollars, were obtained from a survey consisting ofphone interviews with principal service department personnel at four heating and airconditioning installation companies. After averaging the cost values from the different service organizations, alocationmultiplierof(1/1.367 =) .732 was used to adjust the costs to a national average value from the San Francisco regional value. The 1.367 value was obtained from a listing oflocation multipliers for mechanical construction work cost estimation data by the R.S. Means Company (23). LIFETIMEDATA. The service organizationpersonnelmentioned above were also asked in the same survey to estimate the lifetimes ("n" in the above equation) of the subject components, and their responses were averaged in order to obtain values of n for analysis. Values obtained in this way were used withcare, since itcan be argued that service organizations donothave a goodperspective oncomponentlifetimes. Wherepossible, otherlifetimedatawere used, as will be described below. RESULTS. Table D.1 presents the values ofCo' and n used for each component. Also presented are the calculated Co and Cmaint values. The following subsections describe the data and assumptions used in obtaining the values shown.

D-1 

## APPENDIX D. MAINTENANCE COSTS FOR COMMON APPLIANCE COMPONENTS

D.I INTRODUCTION This appendix describes the analyses used to characterize the maintenance costs for several components that are common to more than one appliance. Maintenance costs for components that are unique to a particular appliance are described in the volumes of this document that treat that appliance. 

### AnalysisApproach andResults Maintenance cost has been characterized for this analysis as a present value cost incrementtobe added to the purchaseprice ofan appliance. Thus, the maintenance cost can be described as Cmaint

## =f-Co ' where Co is the cost ofthe maintenance procedure in the year that itoccurs ("n" years after the appliance purchase), and f

## =(1 

### + dfn is the present value factor thatdiscounts the Co value tothepurchase time usingdiscountfactord, takenhere as 6%. Using this approach, the maintenance cost (C maint ) for any component can be obtained from two values: a cost Co and a lifetime n. The discount factor is a real discount factor since the analysis assumes constant dollars. COSTDATA. National average values ofCo were calculatedby fIrst obtainingregional cost average values, Co', for the San Francisco Bay Area. These Co' values, in 1990 dollars, were obtained from a survey consisting ofphone interviews with principal service department personnel at four heating and airconditioning installation companies. After averaging the cost values from thedifferent serviceorganizations, alocationmultiplierof(1/1.367 =) .732was used to adjustthe costs to a national average value from the SanFranciscoregional value. The 1.367 value was obtained from a listing oflocation multipliers for mechanical construction work cost estimation data by the R.S. Means Company (23). LIFETIMEDATA. The serviceorganizationpersonnelmentioned above were also asked in the same survey to estimate the lifetimes ("n" in the above equation) of the subject components, and their responses were averaged in order to obtain values of n for analysis. Values obtainedinthis way wereused withcare, since itcanbe arguedthat service organizations donothave a goodperspectiveoncomponentlifetimes. Wherepossible, otherlifetimedatawere used, as will be described below. RESULTS. Table D.1 presents the values ofCo' and n usedfor each component. Also presented are the calculated Co and Cmaint values. The following subsections describe the data and assumptions used in obtaining the values shown.

D-1

### Table D.I Data and Calculations Used to Obtain Maintenance Costs (1990 $) (Co') 

### (cJ (n) (Cmaint) San Francisco National Maintenance PresentValue of ComponentName Regional Cost Average Cost Lifetime Maintenance Cost Data Source Standing Pilot $73.1 $53.5 10.2 $30 survey Intennittent Ignition $249.0 $182.0 na $81 or $55 survey + analysis Combustion Fan $149.2 $109.2 11.1 $57 survey BurnerBox Damper $87.0 $63.6 10.0 $36 estimate Condensing Design $105.0 $76.8 7.7 $49 survey Two-Stage Operation na na na $32 or $22 estimate Continuous Modulation na na na $64 or $44 estimate


### D.2 STANDING PILOT IGNITION SYSTEMS Standingpilotsystems typicallyrequiremaintenanceforreplacementofpilotthermocouples. The values of Co' service call cost) and n (thermocouple lifetime) shown in Table D.1 were obtained from survey averages. The average thermocouple lifetime value in the Table also includes lifetime estimates obtained from the mobile home furnace manufacturers. 

### D.3 INTERMITTENT IGNITION DEVICES (lIDs) lIDs are common to mobile home furnaces, waterheaters, direct heating equipment, ovens, clothes dryers, and cooktops. The lID devices for ovens are considerably different (with more simple controls) than those for the space-andwater-heating appliances, so the maintenance costs for ovens are not treated in this appendix. The cost analysis here was generated. specifically for direct spark and spark-to-pilot lIDs, and does not necessarily apply to hot surface ignition (HSI) devices. However, the main difference between HSI and spark devices is the actual ignitor
... -. element itself, since control modules, gas valves, and ignition sensors happen to be similar in function and maintenance costs no matter what the system. Therefore, the assumption is made here that HSI and spark ignitors have the same maintenance costs. While HSI ignitor breakage rates are probably substantially higher than spark ignitor failure rates at the present, the assumption was made forthis analysis thatHSIignitorbreakagerates will havebeen significantly reduced by the time the subject standards take effect (24). Regardless ofthe system type, lID systems were considered for this analysis to consist of three main subcomponents: ignitor device, control module, and ignition sensor. After discussions with furnace maintenance organizations, ignitor devices were considered to have negligible maintenance requirements (apart from current HSI breakage replacements). Sensors and control modules were considered separately in terms ofcost (Co) values.

D-2 

### Table D.I Data and Calculations Used to Obtain Maintenance Costs (1990 $) (Co') (CO> (n) (Cmaint) San Francisco National Maintenance PresentValue of ComponentName Regional Cost Average Cost Lifetime Maintenance Cost Data Source Standing Pilot $73.1 $53.5 10.2 $30 survey Intennittent Ignition $249.0 $182.0 na $81 or $55 survey + analysis Combustion Fan $149.2 $109

.2 11.1 $57 survey BurnerBox Damper $87.0 $63.6 10.0 $36 estimate Condensing Design $105.0 $76.8 7.7 $49 ' survey Two-Stage Operation na na na $32or $22 estimate Continuous Modulation na na na $64 or $44 estimate


### D.2 STANDING PILOT IGNITION SYSTEMS Standingpilotsystemstypicallyrequiremaintenanceforreplacementofpilotthermocouples. The values of Co' service call cost) and n (thermocouple lifetime) shown in Table D.1 were obtained from survey averages. The average thermocouple lifetime value in the Table also includes lifetime estimates obtained from the mobile home furnace manufacturers
. 

### D.3 INTERMITTENT IGNITION DEVICES (lIDs) lIDs are commonto mobile homefurnaces, waterheaters, direct heating equipment, ovens, clothes dryers, and cooktops
. The lID devices for ovens are considerably different (with more simple controls) than those for the space-andwater-heating appliances, sothe maintenance costs for ovens are not treated in this appendix. The cost analysis here was generated specifically for direct spark and spark-to-pilot lIDs, and does not necessarily apply to hot surface ignition (HSI) devices. However, the main difference between HSI and spark devices is the actual ignitor .-. - ' element itself, since control modules, gas valves. and ignition sensors happen to be similar in function and maintenance costs no matter what the system. Therefore, the assumption is made here that HSI and spark ignitors have the same maintenance costs. While HSI ignitor breakage rates are probably substantially higher than spark ignitor failure rates at the present, the assumptionwas madeforthis analysisthatHSIignitorbreakagerates willhavebeen significantly reduced by the time the subject standards take effect (24). Regardless ofthe system type, lID systems were considered for this analysis to consist of three main subcomponents: ignitor device, control module, and ignition sensor. After discussions with furnace maintenance organizations, ignitor devices were considered to have negligible maintenance requirements (apart from currentHSI breakage replacements). Sensors and control modules were considered separately in terms ofcost (Co) values.

D-2

### The Co' values for sensors and control modules were obtained directly from the furnace maintenance organization survey: Sensor replacement service call: Control module replacement service call: Total Cost: $79 $170 $249 These values representthetotal amounts thatan appliance ownerin the SanFrancisco areawould have to pay for replacement ofa defective part. The sensor and control module costs have been added above due to the unavailability ofseparate lifetime information for sensors. Both sensor and control module were assigned the same lifetime as the control module. MAINTENANCELIFETIME (n). The value ofnwas estimatedfrom information obtained from lID manufacturers and not from the survey results. There were several reasons for using alternative lifetime infonnation in this case. The fIrst reason has been mentioned above. Secondly, it appeared that TID systems in particular are still developing rapidly, and that the estimates for lifetimes obtained from services organizations would be 

### faulty if based on experience from the past. The lifetime ofan TID component must be characterized by the average present value for all possible failure times. In order to achieve such an average, a probability density function (PDF) of failures over time was generated based on information obtained from telephone interviews with manufacturers oflID control modules. A separate lifetime was not available for sensors, so the lifetime forcontrol modules was chosen for sensors as well. Conversations with service organizations indicate that sensor lifetimes are probably shorter than control module lifetimes. Thefollowing datawerederivedfrom conversations withlIDcontrolmodulemanufacturers: Item 1: Control module average lifetime is on the order of 14 years. Item 2: The averagecontrol module failure rate during the ftrst yearofoperationis below 2.5%. The value 2.5 was used for this analysis. Failures in the ftrst year are covered under warranty and do not represent a cost to the consumer. Item 3: After 10 years ofproduction, yearly TID sales for the aftennarket (repairs) were on the orderof5% oftotal yearly sales. Table D.2 presents a tabular summary ofthe probability density function (Columns A and B) developed using some ofthe above information. The values are calculated on the basis of unitary maintenance cost ($1), assuming appliance installation in year 0, and maintenance occurring due to lID failure at some time during the 18 years oflID life. Results (in Column E) are calculated for appliances with a 15-year life (speciftcally, direct heating equipment) and for appliances with lifetimes greater than or equal to 18 years (speciftcally, mobile home D-3 The Co' values for sensors and control modules were obtained directly from the furnace maintenance organization survey: Sensor replacement service call: Control module replacement service call: Total Cost: $79 $170 $249 These valuesrepresentthetotalamounts thatan applianceownerinthe SanFrancisco areawould have to payforreplacement ofadefectivepart
. The sensor and control modulecosts have been added above due to the unavailability ofseparate lifetime information for sensors. Both sensor and control module were assigned the same lifetime as the control module. MAINTENANCELIFETIME (n). The valueofnwasestimatedfrom informationobtained from lID manufacturers and not from the survey results. There were several reasons for using alternative lifetime information in this case. The flrst reason has been mentioned above. Secondly, it appeared that TID systems in particular are still developing rapidly, and that the estimates for lifetimes obtained from services organizations would be 

### faulty if based on experience from the past. The lifetime ofan TID component must be characterized by the average present value for all possible failure times. In order to achieve such an average, a probability density function (PDF) of failures over time was generated based on information obtained from telephone interviews with manufacturers oflID control modules
. A separate lifetimewas not available for sensors, so the lifetime forcontrol modules was chosen for sensors as well. Conversations with service organizations indicate that sensor lifetimes are probably shorter than control module lifetimes
. Thefollowingdatawerederivedfrom conversationswithlIDcontrolmodulemanufacturers: Item 1: Control module average lifetime is on the order of 14 years. Item 2: The averagecontrol modulefailure rate during the ftrst yearofoperationis below 2.5%. The value 2.5 was used for this analysis. Failures in the ftrst year are covered under warranty and do not represent a cost to the consumer. Item 3: After 10 years ofproduction, yearly TID sales for the aftermarket (repairs) were on the orderof5% oftotal yearly sales. Table D.2 presents a tabular summary ofthe probability density function (Columns A and B) developed using some ofthe above information. The values are calculated on the basis of unitary maintenance cost ($1), assuming appliance installation in year 0, and maintenance occurring due to lID failure at some time during the 18 years oflID life. Results (in Column E) are calculatedfor appliances with a 15-year life (speciflcally, direct heating equipment) and for appliances with lifetimes greater than or equal to 18 years (speciftcally, mobile home D-3furnaces). The assumption is made that the cost of all failures (as accumulated in Column E) does notinclude the cost offailures occurring in the first year. Using the total cost, Co = $182 (given above), the present values for lID maintenance were calculated as follows: Appliances with 15 years avg. life: Appliances with 18 or more years oflife: $182 

### x ($.30/$1.0) =$55 $182 

### x ($.44/$1.0) = $81 Table D.2 Calculations for Mean Lifetime oflID units

Year Mter Yearly Fraction of Present Value Average Present Value of Summation ofPresent Purchase Units Failing (F) DiscountFactor (t) Maintenance ($lxFxt) Value ofMaintenance Column A Column B Column C Column D Column E 1 0.025 na na 2 0.01 0.890 $0.009 3 0.01 0.840 $0.008 4 0.01 0.792 $0.008 5 0.01 0:747 $0.007 6 0.01 0.705 $0.007 7 0.01 0.665 $0.007 8 0.0175 0.627 $0.011 9 0.0275 0.592 $0.016 10 0.045 0.558 $0.025 11 0.0575 0.527 $0.030 12 0.075 0.497 $0.037 13 0.0875 0.469 $0.041 14 0.105 0.442 $0.046 15 0.115 0.417 $0.048 $0.30 16 0.1225 0.394 $0.048 17 0.1275 0.371 $0.047 18 0.135 0.350 $0.047 $0.44


### D.4 FORCED COMBUSTIONIDRAFf FANS The lifetime and cost values shown in Table D.I for "CombustionFan" are taken directly from the survey described above. The primary failure mode for fans was assumed to be bearing failure. One surveyed maintenance contractor stated that fan motors, fan capacitors, and fan relays also fail, but that these account for less than about 5% offan failures. Engineering personnel at some fan-manufacturing companies were contacted to obtain alternative lifetime values to the survey value. However, the fan manufacturers contacteddidnot provide lifetime measurements specifically for furnaces. They were able to provide curves of bearing lifetime versus temperature, but usage of this data would have required testing to
D-4 furnaces). The assumption is made that the cost of all failures (as accumulated in Column E) does notinclude the cost offailures occurringin the fIrst year. Using the total cost, Co = $182 (given above), the present values for lID maintenance were calculated as follows: Appliances with 15 years avg. life: $182 

### x ($.30/$1.0) <==$55 Appliances with 18 ormore years oflife: $182 

### x ($.44/$1.0) 

## =$81 Table D.2 Calculations for Mean Lifetime oflID units

Year Mter Yearly Fraction of Present Value Average Present Value of Summation ofPresent Purchase Units Failing (F) Discount Factor (t) Maintenance ($lxFxt) Value ofMaintenance Column A Column B Column C Column D Column E 1 0
.025 na na 2 0.01 0.890 $0.009 3 0.01 0.840 $0.008 4 0.01 0.792 $0.008 5 0.01 0:747 $0.007 6 0.01 0.705 $0.007 7 0.01 0.665 $0.007 8 0.0175 0.627 $0.011 9 0.0275 0.592 $0.016 10 0.045 0.558 $0.025 11 0.0575 0.527 $0.030 12 0.075 0.497 $0.037 13 0.0875 0.469 $0.041 14 0.105 0.442 $0.046 15 0.115 0.417 $0.048 $0.30 16 0.1225 0.394 $0.048 17 0.1275 0.371 $0.047 18 0.135 0.350 $0.047 $0.44


### D.4 FORCED COMBUSTIONIDRAFf FANS Thelifetime and cost values shown in TableD.I for "CombustionFan" are taken directly from the survey described above. The primary failure mode for fans was assumedto be bearing failure. One surveyed maintenance contractor stated that fan motors, fan capacitors, and fan relays also fail, but that these account for less than about 5% offan failures. Engineering personnel at some fan-manufacturing companies were contacted to obtain alternativelifetime values to the surveyvalue. However, the fan manufacturers contacteddidnot provide lifetime measurements specifically for furnaces. They were able to provide curves of bearing lifetime versus temperature, but usage of this data would have required testing to
D-4establish both bearing temperature and actual hours of operation per year (or per furnace lifetime). D.S CONDENSING FURNACE MAINTENANCE The maintenance contractors that responded to questions about condensing furnace maintenance cited a number ofcauses for maintenance calls, including sensor orifice clogging, circuit board failure, pressure switch failure, and dirt fouling ofthe secondary heat exchangers. The cleaning of coils and sensors were the most common causes of maintenance calls. The average of cost and lifetime values for all causes reported are shown in Table D.l under "Condensing Design." D.6 COMBUSTION BOX DAMPERS Among the survey respondents, there was no maintenance experience with combustion box airdampers atthe time that this investigation was performed. Therefore, a maintenance costwas estimated. The main components assumed for the damper were the mechanical damper itself, the damper motor, and a limit switch. The most likely components to fail were presumed to be the motor and the limit switch. Service lifetime was estimated at 10 years, and a maintenance call (based on San Francisco area costs) was estimated to have a $75 labor charge (which was the average minimum labor cost cited by the surveyed contractors) and a $12 parts cost. D.7 TWO-STAGE OPERATION AND FULL MODULATION No datawere available on maintenance costs for these two options. Given the lackofdata, the values shown in Table D.l were estimated. For two-stage operations, incremental maintenance requirements were assumed to arise solely from control components necessary to the implementation of two-stage operations, and the maintenance costs for these control components were estimated in Table D.I as 40% ofthe lID costs. Thus, two costs are shown for two-stage maintenance costs, one for l5-year lifetime equipment (e.g., direct heating equipment) and one for 18-yearor longer lifetime equipment (e.g., mobile home furnaces). For full or continuous modulation, incremental maintenance costs were also attributed to control components. The costs in Table D.I were estimated at two times the incremental cost ofmaintenance for two-stage furnaces.

D-5 

### establish both bearing ternperature and actual hours of operation per year (or per furnace lifetime). D.S CONDENSING FURNACE MAINTENANCE The rnaintenance contractors that responded to questions about condensing furnace maintenance cited a number ofcauses for maintenance calls, including sensor orifice clogging, circuit board failure, pressure switch failure, and dirt

### fouling ofthe secondary heat exchangers. The cleaning ofcoils and sensors were the most common causes of maintenance calls. The average of cost and lifetime values for all causes reported are shown in Table D.l under "CondensingDesign." D.6 COMBUSTION BOX DAMPERS Amongthe surveyrespondents, there was nornaintenance experience with cornbustion box air

### dampers atthe time thatthis investigation was performed. Therefore, amaintenance costwas estimated. The main components assumed for the damper were the mechanical damper itself, the damper motor, and alirnit switch. The rnost likely components to fail were presumed to be 

### the motor and the limit switch. Service lifetirne was estimated at 10 years, and a maintenance call (based on San Francisco area costs) was estimated to have a $75 labor charge (which was the average minimum labor costcited by the surveyed contractors) and a $12 parts cost. D.7 TWO-STAGE OPERATION AND FULL MODULATION Nodata

### were available on maintenance costs for these two options. Giventhe lackofdata, the values shown in Table D.I were estimated. For two-stage operations, incremental maintenance requirements were assurned to arise solely from control components necessary to the implementation of two-stage operations, and the maintenance costs for these control components were estirnated in Table D.I as 40% ofthe lID costs. Thus, two costs are shown for two-stage maintenance costs, one for l5-year lifetime equipment (e.g., direct heating equipment) and one for 18-yearor longer lifetime equiprnent (e.g., mobile home furnaces). For full or continuous rnodulation, incrernental maintenance costs were also attributed to control components. The costs in Table D.I were estimated at 

### two times the incrernental cost ofmaintenance for two-stage furnaces
.

D-5

### APPENDIX E. ELECTRIC UTILITY IMPACT MODELING

E.! INTRODUCTION This appendix summarizes the methodology used to calculate utility avoided costs for energy savings that result from the imposition of appliance efficiency standards. The second section explains the conventions and background information used in the analysis, and the third and fourth sections explain the basis for valuing the energy and reliability savings of the standards. The goal of this analysis is to calculate avoided costs, in dollars per million Btu (MMBtu) of electricity saved, that can be used to value electricity savings from the societal perspective. These avoidedcosts contain both the marginal energy andmarginalreliability value ofone MMBtu saved by the proposed standards in each year ofthe period analyzed. E.2 BACKGROUND AND CONVENTIONS The analysis adopts the standardutility convention thatthe valueofelectricity savings can be separated into energy (or marginal cost) savings, reliability (or capacity) savings, and transmission distribution capital cost savings. The energy impact measures the production costs avoided by reduced electrical demands, valued at the marginal energy costs ofthe utility (plus variable operation and maintenance costs). The reliability impact measures the reliability value ofreduced load during system peak periods, which is, by convention, valued at the capital cost ofa combustion turbine. This analysis relies on disaggregation by National Electric Reliability Council (NERC) region of capacity savings and energy savings, and on re-aggregation to the national level.· Disaggregation is necessary because different regions need capacity at different times, and the relative energy savings attributable to each region for heating, cooling, and all other appliances vary substantially. For the purposes ofcalculating utility avoided costs, heating appliances are defined as electricheatpumps andelectricresistance heat, coolingappliances are definedasroom and central air-conditioning plus heat pumps, and baseload appliances are derIDed as all 

### other appliances affected by standards. Regional variation in energy consumption and peak demand is accounted for by use of energy savings fractions, which are arelative measure ofheating, cooling, and "baseload" energy consumption (and hence energy savings) attributable to each region. Calculating these fractions requires regional information on appliance saturations, relative appliance efficiencies, numberof households, and heating and cooling load hours (25,26,27). This approach assumes that energy savings accrue in proportion to current levels ofenergy consumption.



### E-l 

### APPENDIX E. ELECTRIC UTILITY IMPACT MODELING

E.1 INTRODUCTION This appendix summarizes the methodology used to calculate utility avoided costs for energy savings that result from the imposition of appliance efficiency standards. The second section explains the conventions and background infonnationusedin the analysis, and the third and fourth sections explain the basis for valuing the energy and reliability savings of the standards. The goal of this analysis is to calculate avoided costs, in dollars per million Btu (MMBtu) of electricity saved, that can be used to value electricity savings from the societal perspective. These avoidedcostscontain both the marginal energy andmarginalreliabilityvalue ofone MMBtu saved by the proposed standards in each yearofthe period analyzed. E.2 BACKGROUND AND CONVENTIONS Theanalysis adopts the standardutilityconventionthatthe valueofelectricity savingscan be separated into energy (or marginal cost) savings, reliability (or capacity) savings, and transmission distribution capital cost savings. The energy impact measures the production costs avoided by reduced electrical demands, valued at the marginal energy costs ofthe utility (plus variable operation and maintenance costs). The reliability impact measures the reliability value ofreduced load during system peak periods, which is, by convention, valued atthe capital cost ofa combustion turbine. This analysis relies on disaggregation by National Electric Reliability Council (NERC) region of capacity savings and energy savings, and on re-aggregation to the national level. Disaggregation is necessary because different regions need capacity at different times, and the relative energy savings attributable to each region for heating, cooling, and all other appliances vary substantially. For the purposes ofcalculating utility avoided costs, heating appliances are definedas electricheatpumps andelectricresistance heat,coolingappliances are definedasroom and central air-conditioning plus heat pumps, and baseload appliances are defmed as all 

### other appliances affected by standards. Regional variation in energy consumption and peak demand is accounted for by use of energy savings fractions, which are arelativemeasure ofheating, cooling, and "baseload" energy consumption (and hence energy savings) attributable to each region. Calculating these fractions requires regionalinfonnation onappliance saturations, relative appliance efficiencies, numberof households, and heating and cooling load hours (25,26,27). 

### This approach assumes that energy savings accrue in proportion to current levels ofenergy consumption.

E-l

### Mostofthe infonnation used to calculate the energy savings fractions (ESFs) is contained in Tables E.l and E.2. The Heating Load Hours (HLH) and Cooling Load Hours (CLH) are calculated as population-weighted averages of state-level data. 

### Alaska and Hawaii are not includedin this analysis because these states are such a small fraction ofthe country andbecause they are not connected to the NERC regions in any significant way. Room CLH and Central CLH are weighted by 1980 regional saturations to derive an average CLH for each region.


### Table E.! Regional Appliance Saturations (in Fraction ofTotal) Central Heat Primp

Aitt Room Heating Electric NERCRegion Households Air Resistance
NPCC 11,328,877 0.008 0.045 0.001 0.009 MAAC 9,259,883 0.021 0.040 0.002 0.008 ECAR 11,686,342 0.028 0.033 0.004 0.012 MAIN 7,042,024 0.020 0.025 0.001 0.005 spp 6,039,959 0.027 0.024 0.002 0.009 SERC 15,379,811 0.078 0.055 0.017 0.044 ERCOT 5,485,273 0.031 0.020 0.002 0.013 MAPP 3,792,833 0.011 0.012 0.001 0.003 WSCC 16,357,454 0.043 0.025 0.006 0.030 Alaska & 475,103 0.000 0.000 0.000 0.000 Hawaii National
. 86,847,558 0.267 0.281 0.036 0.132 Total (a) Includes heat pumps

B-2 

### Mostofthe infonnationused to calculate theenergy savingsfractions (ESFs) is contained in Tables E.l and E.2. The Heating Load Hours (HLH) and Cooling Load Hours (CLH) are calculated as population-weighted averages of state-level data. 

### Alaska and Hawaii are not includedin this analysis because these states are such a small fraction ofthe country andbecause they are not connected to the NERC regions in any significant way. Room CLH and Central CLH are weighted by 1980 regional saturations to derive an average CLH for each region.


### Table E.1 Regional Appliance Saturations (in Fraction ofTotal) Central Heat Primp

A.itt Room Heating Electric NERCRegion Households Air Resistance
NPCC 11,328,877 0.008 0.045 0.001 0.009 MAAC 9,259,883 0.021 0.040 0.002 0.008 ECAR 11,686,342 0.028 0.033 0.004 0.012 MAIN 7,042,024 0.020 0.025 0.001 0.005 spp 6,039,959 0.027 0.024 0.002 0.009 SERC 15,379,811 0.078 0.055 0.017 0.044 ERCOT 5,485,273 0.031 0.020 0.002 0.013 MAPP 3,792,833 0.011 0.012 0.001 0.003 WSCC 16,357,454 0.043 0.025 0.006 0.030 Alaska & 475,103 0.000 0.000 0.000 0.000 Hawaii National
. 86,847,558 0.267 0.281 0.036 0.132 Total (a) Includes heat pumps

E-2

### Table E.2 Regional Population-Weighted Heating and Cooling Load Hours Saturation Central Air Room Air weifhted NERC Region HLH CLH CLH CH NPCC 2506 437 324 342 MAAC 2358 600 448 499 ECAR 2404 573 479 522 MAIN 2322 583 467 519 SPP 1646 1061 984 1024 SERC 1378 1276 964 1147 ERCOT 1250 1500 1250 1403 MAPP 2604 509 449 477 WSCC 2408 501 408 466 Alaska and 1126 1170 1170 1170 Hawaii National Average 2101 763 613 686

B-3 

### Table E.2 Regional Population-Weighted Heating and Cooling LoadHours Saturation Central Air Room Air weifhted NERC Region HLH CLH CLH CH NPCC 2506 437 324 342 MAAC 2358 600 448 499 ECAR 2404 573 479 522 MAIN 2322 583 467 519 SPP 1646 1061 984 1024 SERC 1378 1276 964 1147 ERCOT 1250 1500 1250 1403 MAPP 2604 509 449 477 WSCC 2408 501 408 466 Alaska and 1126 1170 1170 1170 Hawaii National Average 2101 763 613 686

B-3

### Table E.3 Regional Energy Savings Fractions

NERC Region Heating Cooling Baseload NPCC 0.073 0.049 0.131 MAAC 0.055 0.064 0.107 ECAR 0.110 0.083 0.135 MAIN 0.026 0.037 0.082 spp 0.027 0.069 0.070 SERC 0.314 0.498 0.178 ERCOT 0.026 0.083 0.064 MAPP 0.009 0.009 0.044 WSCC 0.359 0.109 0.189 Total 1.000 1.000 1.000



### The equations used to derive the energy savings fractions (ESFs) in Table E.3 are: SatHPi**SatE~** l-n-l. 

### x + X 

### HLJ-l·

........'1 EftHP EffER"'1 ESF Htg 

## =----->--,..------""'"'""':---- N 

### [satHPi

# SatE~ 

# J .**[~x** EftHP + 

### EffER x

## HL~ ]

Sat (HPi = 

### CAq) x 

### CAC CLIft + 

### SatRACi x 

### RAC**CL~** HR· 

### x 1 

### EffCAC EffRAC ESF Clg =**---~--r--------------------~** N 

### Sat (HPi + 

### CAq) x CAC**C~** 

### SatRAq x RAC**CL~** 

# tr**[~x** " 

### EffCAC + 

### EffRAC

E-4 Table E.3 Regional Energy Savings Fractions

NERC Region Heating Cooling Baseload NPCC 0.073 0.049 0.131 MAAC 0.055 0.064 0.107 ECAR 0.110 0.083 0.135 MAIN 0.026 0.037 0.082 spp 0.027 0.069 0.070 SERC 0.314 0.498 0.178 ERCOT 0.026 0.083 0.064 MAPP 0.009 0.009 0.044 WSCC 0.359 0.109 0.189 Total 1.000 1.000 1.000



### The equations used to derive the energy savings fractions (ESFs) in Table E.3 are: SatHPi**SatE~** l-n-l. 

### x + X HLl-l.

........'1 

### EftHP EffER'" ESF Htg 

## =----->--,...------""""-.:---- N 

### [satHPi

# SatE~

# J .**~ [~x**  EftHP + 

### EfiER x

## HL~ ]



### Sat (HPi = 

### CAq) x 

### CAC CLIft + 

### SatRACi x 

### RAC**CL~** HH· 

### x 1 

### EffCAC EffRAC ESF Clg =**---~--r--------------------~** N 

### Sat (HPi + 

### CAq) x CAC**C~** 

### SatRAq x RAC**CL~** 

# tr**[~x** " 

### EffCAC + 

### EffRAC

E-4where: 

## =**SatE~** 

## = SatHP· 

## = 1 Sat(HPi + CAq) 

## = SatRAq 

## =**HL~** 

## =**CACCL~** 

## =**RACCL~** 

## = N 

## = EftER 

## = EffHP 

## = EffCAC 

## = EffRAC 

## = number ofhouseholds in region i (1980), saturation ofelectric resistance (ER) heat in region i (1980), saturation ofheat pumps (HP) in region i (1980),

saturation ofHPs plus central air conditioners (CAC) in region i**(l~** saturation ofroom air conditioners (RAC) in region i (1980), heating load hours in region 

### i, cooling load hours for CACs in region i, cooling load hours for RACs in region i, number ofNERC regions (9), efficiency ofelectric resistance heat (1.0), efficiency ofaverage heat pump in 1980 for heating (1.75), efficiency ofaverage CAC and HP cooling in 1980 (7.79 SEER), and efficiency ofaverage RAC in 1980 (6.05 EER). 

### E.3 ENERGY SAVINGS The energy value of load shape modifications is measured by the marginal cost of electricity, which depends on the variable cost ofoperating the power plants that will change their output in response to a change in load. This variable cost consists primarily ofthe cost of fuel for these marginal units. U.S. utilities are characterizedby a substantial difference among the prices offuels used to generate electricity on the margin. Marginal resources ofa utility can beeithermore expensive oil and gas orless expensive non-oil-and-gas resources (typically coal). Very low variable-cost nuclear or hydro resources are usually base loaded and thus only rarely contribute to marginal energy costs. The relevantmarginal cost is the cost ofeach marginal fuel weighted by the fraction of time each fuel is on the margin plus any variable Operation and Maintenance (0 & M). Formally, this relationship can be simplified as follows: Marginal Cost = OGF mX Natural Gas Price + NOGFmX Coal Price +Variable O&M To calculate the oil-and-gas fraction onthe margin OGF m, the total amountofoil andgas generated electricity is divided by the total forecasted amount of electricity generation from NERC data (28) to obtain the "total oil and gas fraction" OGFt" OGFt can then be related to OGF mwith a simplifiedloadduration curve (LDC) foreachregion. Figure G.l shows this LDC. where B' is the OGFm. The non-oil-and-gas fraction NOGFm is simply 1 - OGF m. The purpose ofthe following derivation is to calculate the OGF m (B'). First, note that A tanQ =_ B

E-5 where:

### ~**SatE~** SatHP· 1 Sat(HPi + CAq) SatRAq**HL~ CACCL~** **RACCL~** N EftER EffHP EffCAC EffRAC 

## = = =

= = = = = = = = = = number ofhouseholds in region i (1980), saturation ofelectric resistance (ER) heat in region i (1980), saturation ofheatpumps (HP) in region i (1980),

saturation ofHPs plus central air conditioners (CAC) in region i**(l~** saturation ofroom air conditioners (RAC) in region i (1980), heating load hours inregion 

### i, cooling load hours for CACs in region i, cooling load hours for RACs in region i, numberofNERC regions (9), efficiency ofelectric resistance heat (1.0), efficiency ofaverage heatpump in 1980 for heating (1.75), efficiency ofaverage CAC and HP cooling in 1980 (7.79 SEER), and efficiency ofaverage RAC in 1980 (6.05 EER). 

### E.3 ENERGY SAVINGS The energy value of load shape modifications is measured by the marginal cost of electricity, which depends on the variable cost ofoperating the power plants that will change their output in response to a change in load. This variable cost consists primarily ofthe cost of fuel for these marginal units. U.S. utilities are characterizedby a substantial difference among the prices offuels used to generate electricity on the margin. Marginalresources ofa utility can beeithermoreexpensive oilandgas orlessexpensivenon-oil-and-gas resources (typicallycoal). Very low variable-cost nuclear orhydro resources are usually base loaded and thus only rarely contribute to marginal energy costs. The relevantmarginal costis the costofeach marginal fuel weighted by the fraction of time each fuel is on the margin plus any variable Operation and Maintenance (0 & M). Formally, this relationship can be simplified as follows: Marginal Cost = OGF mX Natural Gas Price + NOGFmX Coal Price +Variable O&M Tocalculate theoil-and-gas fraction onthe margin OGF m,the totalamountofoilandgas generated electricity is divided by the total forecasted amount of electricity generation from NERC data (28) to obtain the "total oil and gas fraction" OGFe OGFt can then be related to OGF mwith a simplifiedloadduration curve (LDC) foreachregion. FigureG.l shows thisLDC. where B' is the OGFm. The non-oil-and-gas fraction NOGFm is simply 1 - OGF m. The purpose ofthe following derivation is to calculate the OGF m (B'). First, note that A tan!} =_ B

E-5

### and B = 

### 1.0, which implies that A 

## =B tanQ

## =tanQ The nonnalized area ofthe triangle at the top ofthe LDC represents the fraction oftotal generation from oil and gas (OGFt). 

### Calculating the area ofthis triangle yields OGFt 

## =0.5A

### 'B' 

## =O.5(BQ)B' B' can thus be expressed as follows:

### 2OGF~ B' 

## =OGFill 

## =......,....----",.....,t,....., 

### (tanQ)

E-6 

### and B = 

### 1.0, which implies that A 

## =B tanQ

## =tanQ The nonnalized area ofthe triangle at the top ofthe LDC represents the fraction oftotal generation from oil and gas (OGFt). 

### Calculating the area ofthis triangle yields OGFt 

## =O.SA

### 'B' 

## =O.5(BQ)B' B' can thus be expressed as follows:

### 2OGF~ B' 

## =OGF m 

## =--,-----",.....,t,....., 

### (tanQ)

E-6

# Simplified Load Duration Curve

1.0 "0 t':l 0 

# a

0 

# ... "0 <l) N 

# .- 

# - t':l 

# e 0 

# zo 0 0 

# t A 

# !

1.0 Normalized Duration (in fraction ofa year)

FigureE.1 SimplifiedLoad-Duration Curve

E-7 

# Simplified Load Duration Curve 

# t 

# t
1.0 A "0 t':$ 0**~** 

# a**~** 

# !

0 

# ... "0 <I) N 

# .- 

# - t':$ 

# e 0 

# zo 0 0 

### 1.0 Normalized Duration (in fraction ofa year)

FigureE.1 SimplifiedLoad-Duration Curve

E-7The appropriate value for A = tan

## n is detennined empirically: when OOF t 0.4, OGF m = 1.0. The value ofA must therefore be greater than or equal to 0.8. Note that when A = 0.8, Nonnalized peak demand = 1.4 X mean load and Nonnalized minimum load = 0.6 X mean load. A weighted-average marginal energy cost is detennined for each ofthe NERC regions usingthe above fonnulas, andanational weighted-average marginalcostforheating andbaseload energy savings is calculatedusing the regional fuel price multipliers and energy savings fractions described above. This approach assumes that the marginal oil-and-gas fractions remain constant over the analysis period. Forcooling energy savings, the appropriate MarginalEnergy Cost (MEC) willexceedthe annual average MEC because the OGF m is typically higher in summer than in winter. The annual average OGF m is approximately 52% when calculated using the energy savings fractions for heating, cooling, and baseload energy. Cooling MECs are calculated using a national OGF m of60 percent to accountfor the seasonal differences in marginal fuel usage. Table E.4 shows the annual average OaF m for each NERC region.

E-8 The appropriate value for A

## =tan

## n is detennined empirically: when OGF t**~** 0.4, OGF m

## =1.0. The value ofA must therefore be greater than orequal to 

### O
.S
. Note that when A = 0.8, Nonnalized peak demand = 1.4 X mean load and Nonnalized minimum load = 0.6 X mean load. A weighted-average marginal energy cost is detennined for each ofthe NERC regions usingtheabove fonnulas, andanational weighted-averagemarginalcostforheatingandbaseload energy savings is calculatedusing theregionalfuel price multipliers andenergy savings fractions described above. This approach assumes that the marginal oil-and-gas fractions remain constant over the analysis period. Forcoolingenergy savings, the appropriate MarginalEnergy Cost(MEC) willexceedthe annual average MEC because the OGF m is typically higher in summer than in winter. The annual average OGF mis approximately 52% when calculatedusing the energy savings fractions for heating, cooling, and baseloadenergy. Cooling MECs are calculatedusing a national OGF m of60percent to accountfor the seasonal differences in marginalfuel usage. TableE.4 showsthe annual average OaF mfor each NERCregion
.

E

-STable 

### E.4 Utility Marginal ooFs, and Year ofCapacity Need by NERC Region

NERC Marginal Year Capacity Region 

### OGF is Needed
NPCC 0.88 1992 MAAC 0.61 1992 ECAR 0.16 1992 MAIN 0.19 1992 spp 0.77 1999 SERC 0.47 1992 ERCOT 0.92 1999 MAPP 0.15 1997 WSCC 0.55 1999 National Avg. 0.52 

### The calculation ofMECs in yearj can be summarized as follows: N i 

### ooF m = L.J 

### ESFi 

### x ooF m i=1


### NooF 

## = 1 - OGF m m 

### MEC j = 

### (OGFm 

### x gas pricej 

### + NooFm 

### x coal price 

### + Variable 0& M) x 1.06 where: i 

### denotes a particular NERC region, 

### ESFi 

### represents the Energy Savings Fraction for region i, 

### and 1.06 adjusts for transmission and distribution losses.

E-9 Table E.4 Utility Marginal OGFs, and Year ofCapacity Need by NERC Region

NERC Marginal Year Capacity Region 

### OGF is Needed
NPCC 0.88 1992 MAAC 0.61 1992 ECAR 0.16 1992 MAIN 0.19 1992 spp 0.77 1999 SERC 0.47 1992 ERCOT 0.92 1999 MAPP 0.15 1997 WSCC 0.55 1999 National Avg. 0.52 The calculation ofMECs in yearj can be summarized as follows: N " i OGFm = L.J ESFi x OGF m i=1
NOGF 

## = 1 - OGF m m MEC j = (OGFm x gas pricej 

### + NOGFm x coal price 

### + Variable 0& M) x 1.06 where: i denotes a particular NERC region, ESFi represents the Energy Savings Fraction for region i, and 1.06 adjusts for transmission and distribution losses.

E-9Variable 0 & M is calculated using references (27) and (28). Average 0 & M (fixedand variable) for all fossil-fuel frred steam-electric powerplants for investor-owned utilities in 1990 is reported in (30) as 0.5¢/kWh (in 1990 dollars). This can be split into fixed and variable components using (27). The analysis uses the fraction of 0 &M that is variable for a supercritical coal steam unit (50%) and for a natural gas Combustion Turbine (20%), weights these by the appropriate marginal oil and gas fractions, and applies the resulting percentage (variable 0 & M as a percent oftotal 0 & M) to the total 0 & M (0.5¢/kWh). This calculation results in a variable 0 & M cost of0.33¢/kWh or 0.29 $/MMBtu ofresource energy (in 1990 dollars). 

### E.4 Reliability Savings and Avoided TransmissionlDistribution System Capital Costs E.4.! Value ofReliability Savings
The value ofreliability is the value associated with having additional generating capacity available to meet the load in case ofunexpected generator outages or load increases. For utility systems with excess capacity, this value may be equal to zero. When capacity does have value, this value is commonly measured by the cost ofthe marginal investment a utility would make to enhance system reliability, such as the purchase ofa combustion turbine (29). The intuitivejustification for this convention is that the lowest capital cost investment a utility can make to improve system reliability is a combustion turbine. This investment adds reliability to the system even 

### ifitnever operates, since it is ready to prevent outages if another generating unit fails. Ifthe utility chooses to build another type ofplant with highercapital costs (such as a baseload coal unit), it must be basing this choice on other considerations than reliability. Baseload coalplants, for example, have much loweroperating costs than oil and gas frred generation, and can be usedto displace generators with higher variable cost perkWh. The additional capital cost ofa coal plant is thus not related to reliability but to energy benefits or fuel savings (29). The additional coal plant capital cost (sometimes called energy-related capital) is contained implicitly in the marginal energy cost described above. Consider a utility with a large number ofexpensive oil and gas units on the margin and dwindling adjusted reserves. Such a utility would need capacity to preserve the system's reliability at an acceptable level. It would also benefit from installing a baseload unit that would reduce oil and gas consumption at the margin. This reduction infuel costs is the incentive for expending capital in addition to the costs ofa combustion turbine. A utility with coal on the margin a large fraction ofthe time wouldnot have the same incentive, and wouldprobably install a combustion turbine to preserve reliability. The utility will choose to have a baseload plant come on line when the present value of the energy-related capital cost ofthe plant is just equal to the PV ofthe reduction in fuel costs from the operation ofthe plant. The reduction in fuel costs and hence the incentive to build a
E-IO 

### Variable 

### 0 & 

### Mis calculated usingreferences (27) and (28). Average 

### 0 & 

### M (fixedand variable) for all fossil-fuel fired steam-electric powerplants for investor-owned utilities in 1990 is reported in (30) as 0.5¢/kWh (in 1990 dollars). This .can be 

### split into fixed and variable components using (27). The analysis uses the fraction of 

### 0 &M that is variable for a supercritical coal steam unit (50%) and for a natural gas Combustion Turbine (20%), weights these by the appropriate marginal oil and gas fractions, and applies the resulting percentage (variable 

### 0 & 

### M as apercent oftotal 

### 0 & 

### M) to the total 

### 0 & 

### M (0.5¢/kWh). This calculation results in a variable 

### 0 & M cost of0.33¢/kWh or 0.29 $/MMBtu 

### ofresource energy (in 1990 dollars). 

### E.4 Reliability Savings and Avoided TransmissionlDistribution System Capital Costs E.4.1 Value ofReliability Savings
The value ofreliability is the value associatedwith having additional generatingcapacity available to meet the loadin case ofunexpected generatoroutages or load increases. For utility systems with excess capacity, this value may be equal to zero. When capacity does have value, this value is commonly measured by the cost ofthe marginal investment a utility would make to enhance system reliability, such as the purchase ofa combustion turbine (29). The intuitivejustification for this convention is that the lowest capital cost investment a utility can make to improve system reliability is a combustion turbine. This investment adds reliability to the system even 

### ifitnever operates, 

### since it is ready to prevent outages if

### another generating unitfails. Ifthe utility chooses to build another type ofplantwithhighercapitalcosts (such as a baseload coal unit), it must be basing this choice on other considerations than reliability. Baseloadcoalplants, for example, have much loweroperatingcosts than oil and gas fired generation, and can be usedto displace generators with higher variable costperkWh. The additional capital cost ofa coal plant is thus not related to reliability but to energy benefits or fuel savings (29). The additional coal plant capital cost (sometimes called energy-related capital) is containedimplicitly in the marginal energy costdescribed above. Consider autility with a large number ofexpensive oil and gas units on the margin and dwindling adjusted reserves. Such a utility would need capacity to preserve the system's reliability at an acceptable level. It would also benefit from installing a baseload unit that would reduce oil and gas consumption at the margin. Thisreductioninfuel costs isthe incentive for expendingcapitalin addition tothe costs ofa combustionturbine. Autility withcoalon the margin a large fraction ofthe time wouldnot have the same incentive, and wouldprobably install acombustion turbine topreservereliability. The utility will choose to have a baseload plant come on line when the present value of the energy-related capital cost ofthe plant is justequal to the PV ofthe reduction in 

### fuel costs from the operation ofthe plant. The reduction in 

### fuel costs and hence the incentive to build a
E-IObaseload plant depend on the marginal oil and gas fraction. Thus, the marginal energy cost contains an avoided capital component in addition to an avoided fuel component. The following two rules are applied to detennine the value ofcapacity for the years 1996 to 1998 based on the DOE's interpretation ofNERC data (32): 1. If the NERC region is forecast to have an adjusted reserve margin larger than 5% of forecasted peak demand, there is no reliability value for capacity.18 2 Ifthe NERC region is forecast to have a capacity deficiency (operating adjusted 

### reserves ofless than 5 %), the capacity value for the combustion turbine is applied.
. After 1998,DOEhas notcalculated adjustedreserve margins, anditis assumed thatevery region will need capacity. Table 0.4 shows the year that each region will need capacity based on these rules. The calculations in this analysis follow standard industry practice developed for detennining the value ofreductions in peak load caused by cogenerators and other small power producers (5). First, the present value ofrevenue requirements is calculated for a combustion turbine basedonits capitalcost ($400perkWin 1987 dollars), a discountrate equal to the utility rate of disadvantage or ROD (10.0%), a fixed charge rate (15.2%),19 a turbine lifetime of 25 years, and the assumption ofstraight-line depreciation. The calculation is repeatedfor each year of the analysis assuming no real capital cost escalation for the combustion turbine. Next, the present values are spread over time using an economic carrying charge (ECC). The BCC technique yields a current dollar stream ofcosts (escalating at an inflation rate of5%) that has the same present value (when discounted at the ROD) as the original stream. A stream ofcosts that escalates at the inflation rate is also constant in real terms, so this technique 

### "1evelizes" the costs to some constant dollar value ($47.07 per kW-yr in 1990 dollars). This cost is then converted to dollars perMMBtu ofelectricity savings by the method detailed in the next section.

18An equivalentrule ofthumb for adequatereliability is that the overallreserve margin should equal about20% offorecasted peakdemand. Adjustedreserves are definedby DOEas [plannedCapacity+NetPowerTransfers(in)

- (Forced Outages + Scheduled Maintenance + Other Outages)

- Forecasted Peak Demand]. Overall reserves are defined as [planned Capacity + Net Power Transfers(in)

- Forecasted Peak Demand] (6). This analysis does not account for the possibility that the standards could cause a significant change in a region's adjusted reserves, and hence change the date when capacity is needed.**l~e** estimates ofROD and fixed charge rate have been calculated based on current interest rates and the corporate tax rate (34%) adopted in the 1986 tax revisions. The standard utility capital structure from EPRI (28) is used (45% debt, 10% preferred stock, and 10% common stock). Nominal costs ofcapital are 10% for debt and preferred stock and 13.4% for common stock.
E-ll

baseload plant depend on the marginal oil and gas fraction. Thus, the marginal energy cost contains an avoided capital component in addition to an avoided fuel component. Thefollowing tworules are applied to determine the value ofcapacityforthe years 1996 to 1998 based on the DOE's interpretation ofNERC data (32): 1. Ifthe NERC region is forecast to have an adjusted reserve margin larger than 5% of forecasted peak demand, there is no reliability value for capacity.18 2 Ifthe NERC region is forecast to have a capacity deficiency (operating adjusted 

### reserves ofless than 5 %), the capacity value for the combustion turbine is applied.
. After 1998,DOEhasnotcalculatedadjustedreservemargins, anditis assumedthatevery region will need capacity. Table 0.4 shows the year that each region will need capacity based on these rules. The calculations in this analysis follow standard industry practice developed for determining the value ofreductions in peakload caused by cogenerators and other small power producers (5). First, the present value ofrevenue requirements is calculated for a combustion turbine basedonits capitalcost($400perkWin 1987 dollars), a discountrateequal to the utility rate of disadvantage or ROD (10.0%), a fixed charge rate (15.2%),19 a turbine lifetime of 25 years, and the assumptionofstraight-line depreciation. Thecalculation is repeatedfor eachyear of the analysis assuming no real capital cost escalation for the combustion turbine. Next, the present values are spread over time using an economic carrying charge (ECC). The BCC technique yields a current dollar stream ofcosts (escalating at an inflation rate of5%) that has the samepresent value (when discounted at the ROD) as the original stream. A stream ofcosts that escalates at the inflationrate is also constant in real tenns, so this technique "levelizes" the costs to some constant dollar value ($47.07 per kW-yr in 1990 dollars). This cost is then convertedto dollars perMMBtu ofelectricity savings bythe methoddetailedin the next section.

18An equivalentrule ofthumb for adequatereliability is that theoverallreserve margin shouldequal about20% offorecastedpeakdemand. Adjustedreserves aredefinedbyDOEas [plannedCapacity+NetPowerTransfers(in)

- (Forced Outages + Scheduled Maintenance + Other Outages)

- Forecasted Peak Demand]. Overall reserves are defined as [planned Capacity +Net Power Transfers(in)

- Forecasted Peak Demand] (6). This analysis does not account for the possibility that the standards could cause a significant change in a region's adjusted reserves, and hence change the date when capacity is needed
.**l~e** estimates ofROD and fixed charge rate have been calculated based on current interest rates and the corporate tax rate (34%) adopted in the 1986 tax revisions. The standard utility capital structure from EPRI (28) is used (45% debt, 10% preferred stock, and 10% common stock). Nominal costs ofcapital are 10% for debtand preferred stock and 13.4% for common stock.

.E-ll

### E.4.2 Amount ofReliability Savings An analysis ofthe load shape impacts ofimprovedefficiencies for residential appliances must consider both the magnitude and timing ofthe impacts relative to periods ofhigh system loads becauseresidentialclass load shape changes only havereliability valuefor the system when they coincide with times ofhigh system loads. This relationship is approximated from modeling runs ofthe LBL Hourly and Peak Demand Model and two LBL utility case studies (33,34). There are large but varying degrees ofcoincidence between residential peak loads and system peak loads. The case studies show that the average change in the 250 highestresidential hourly loads is areasonable measure ofthe system-level reliability value ofappliance standards.
The results of these studies also indicated that load shape impacts, measured as a conservation load factor (CLF), vary in a predictable fashion across utility service territories. A conservation load factor is the annual average load savings divided by the peak savings ofthe conservation measure. Peaksavings are defined as the forecasted differences between the average of the highest 250 hourly loads before and after the appliance efficiency standards are implemented. The peak: savings for residential appliances are calculated using the LBL Residential Hourly and Peak Demand Model (35). Peak savings for commercial ballasts are calculated using the COMMEND model's output. Conservation load factors can be used to characterize the peak load impacts ofheating, cooling, and baseload energy savings. More formally, a conservation load factor can be expressed as:
CLF = Annual Load Savings Peak Load Savings Multiplying both numerator and denominator by 8760hours gives:
CLF 

## = Annual Energy Savings Peak Load Savings x 8760 The peakload savings can be expressed as a function ofthe annual energy savings and the CLF: Peak Load Savings 

## = Annual Energy Savings CLF x 8760
Another useful form ofthis equation is as follows:

E-12 

### E.4.2 Amount ofReliability Savings An analysis ofthe load shape impacts ofimprovedefficiencies forresidential appliances must consider both the magnitude and timing ofthe impacts relative to periods ofhigh system loads becauseresidentialclassloadshapechangesonlyhavereliabilityvalueforthe systemwhen they coincide with times ofhigh system loads. Thisrelationship is approximated from modeling runs ofthe LBLHourly and PeakDemand Model and two LBL utility case studies (33,34). There are large but varying degrees ofcoincidence between residential peak loads and systempeak: loads. The case studies show that the average change in the 250 highestresidential hourlyloads is a reasonable measure ofthe system-levelreliabilityvalueofappliance standards.
The results of these studies also indicated that load shape impacts, measured as a conservation load factor (CLF), vary in a predictable fashion across utility service territories. A conservation load factor is the annual average load savings divided by the peak: savings ofthe conservationmeasure. Peak: savings aredefinedas theforecasteddifferencesbetween theaverage of the highest 250 hourly loads before and after the appliance efficiency standards are implemented. The peak: savings for residential appliances are calculated using the LBL Residential Hourly and Peak: Demand Model (35)
. Peak: savings for commercial ballasts are calculated using the COMMEND model's output. Conservation load factors can be used to characterize the peak: load impacts ofheating, cooling, and baseload energy savings. More formally, a conservation load factor can be expressed as:
eLF = Annual Load Savings Peak: Load Savings Multiplying both numerator and denominatorby 8760hours gives:
CLF 

## = Annual Energy Savings Peak: Load Savings x 8760 Thepeak: load savings can be expressedas a function ofthe annual energy savings andthe CLF: Peak: Load Savings 

## = Annual Energy Savings CLF x 8760
Another useful form ofthis equation is as follows:

E-12CLF x 8760 

## = Annual Energy Savings Peak Load Savings CLF x 8760 = kWh ofenergy savings needed to yeild 1 kW ofpeak load savings. Conservation loadfactors for the electrical appliances in this analysis are shown in Table 

### G.5. CLFs for cooking, RAC and waterheating are taken from runs ofLBL's Hourly and Peak Demand Model. CLFs for fluorescent ballasts 

### are taken from runs ofEPRI's COMMEND model. The CLF for TVs for a given policy case is calculated as a weighted average over the percentage ofenergy savings that is from reducing standby losses (which has a CLF of 1.0) and the percentage ofenergy savings that is from increasing the efficiency ofthe TV when itis being used (which has a CLF of0.86, from reference (36». The load impacts of the heating energy savings are excluded because NERC data and forecasts suggest thatall NERCregions will have loweradjustedreserve margins in summerthan in winterthrough 1998; therefore, to arough approximation, energy savings only have reliability value in summer.20 As a consequence ofthis assumption, the indirect effects on electricity use of standards on gas-fired mobile home furnaces and gas-fired direct heating equipment are valued using avoided costs that include only the avoided energy and O&M costs. There is no capacity component to the avoided costs for these end uses.

20rhisapproachimplicitlyassumes that transmissioncapacitywithineachregionallows mostefficientutilization ofthe region's generating capacity. The analysis does not account for the possibility that the efficiency standards could cause regions to shift from summer to winter peaking.
E-13 CLF x 8760 

## = Annual Energy Savings Peak Load Savings CLF x 8760 = kWh ofenergy savings needed to yeild 1 kW ofpeak load savings. Conservationloadfactors for the electrical appliances in this analysis are showninTable G.S. CLFs forcooking, RAC and waterheating are taken from runs ofLBL's Hourly andPeak Demand Model. CLFs for fluorescent ballasts 

### are taken from runs of EPRI's COMMEND model. The CLF for TVs for a given policy case is calculated as a weighted average over the percentage ofenergy savings that is from reducing standby losses (which has a CLFof 1.0) and thepercentageofenergy savings that is from increasingthe efficiency oftheTVwhen itisbeing used (which has a CLF of0.86, from reference (36». The load impacts of the heating energy savings are excluded because NERC data and forecasts suggest thatall NERCregions willhave loweradjustedreservemarginsinsummerthan inwinterthrough 1998; therefore, to arough approximation, energy savingsonlyhavereliability value in summer.20 As a consequence ofthis assumption, the indirect effects on electricity use of standards on gas-fired mobile home furnaces and gas-fired direct heating equipment are valued using avoided costs that include only the avoided energy and O&M costs. There is no capacity component to the avoided costs for these enduses.

2O-:rhisapproachimplicitlyassumesthattransmissioncapacitywithineachregionallowsmostefficientutilization ofthe region's generating capacity. The analysis does not account for the possibility that the efficiency standards could causeregions to shift from summer to winter peaking.
E-13Table E.5 Conservation Load Factors for Electrical Products Appliance/Product RAC Water heating Cooktops/ovens/microwaves TVs policy 1 (standby energy use) Other TV policies (not standby) Fluorescentballasts CLF 0.14 0.89 0.46 1.00 0.86 057 The calculation of avoided reliability costs can be summarized in the following equations: ARC
. 

## = Capacity Value 

### 1 kWh 106 clgJ CLFcooling x 8760 x 11,500 

### Btu x MMBtu x 1.2 x L ESF C1gj

ARCBaseloadj

where: 

### ARC Clg,Baseloadj Capacity Value 11,500Btu/kWh CLF Baseload 

### LESF Clgj


### LESF Baseloadj 1.2 Capacity Value 

### 1 kWh 106


## = CLFBaseload x 8760 x 11,500 Btu x MMBtu x 1.2 x ESFBaseloadj



## =Avoided reliability costs in 

### 1990 $/MMBtu in year

### j,

= value of 

### 1 kW ofpeak load savings in 

### (1990 $/kW-yr),

= conversion factor that includes generation and transmission losses,

= conservation load factor for baseload appliances,

= sum of cooling ESFs for regions that need capacity ill year j,

= sum ofbaseloadESFs for regions that need capacity in yearj, and



## =reserve margin adjustment needed for adequate reliability.

E-14 Table E.S Conservation LoadFactors for Electrical Products Appliance/Product 

### eLF RAC Water heating Cooktops!ovens!microwaves 0
.14 0.89 0.46 TVspolicy 1 (standby energy use) OtherTVpolicies (not standby) Fluorescentballasts 1.00 0.86 057 The calculation of avoided reliability costs can be summarized in the following equations: ARC
. 

## = Capacity Value 

### 1 kWh 106 clgJ CLFcooling x 8760 x 

### 11,500 Btu x MMBtu x 1.2 x 

### L ESF C1gj

ARCBaseloadj 

## =

where: 

### ARCClg,Baseloadj Capacity Value 11,500Btu/kWh

,eLF Base/oad 

### LESF C

/ gj


### LESF Base/oadj 1.2 Capacity Value 1 

### kWh 106 CLFBaseload x 8760 

### x 11,500 Btu 

### x MMBtu 

### x 1.2 

### x k ESFBaseloadj



## =Avoidedreliability costs in 1990 $/MMBtu in year

### j,

= value of 1kW ofpeak load savings in (1990 $/kW-yr),

= conversion factor that includes generation and transmission losses,

= conservation load factor for baseload appliances,

= sum of cooling ESFs for regions that need capacity ill year j,

= sum ofbaseloadESFs for regions that need capacity in yearj, and



## =reserve margin adjustment needed for adequate reliability.

E-14

### Capacity savings are peak load savings for only those regions that need capacity in a given year. Load savings in regions that do not need capacity contribute to deferring capacity additions in future years, but this analysis does not account for the value of such deferrals. Table E.6 Sums ofAC andBaseloadEnergy Savings Fractions for NERC Regions ThatNeed Capacity

Year

1996 1997 1998 1999 2000 Regions Cooling Baseload Needing

ESF ESF Capacity
0.693 0.552 5 1.703 0.552 6 0.703 0.596 6 1.000 1.000 9 1.000 1.000 9


### E.4.3 Avoided Transmission and Distribution Capital Costs Recently, utility planners have begun to accountfor capitalcosts ofthe Transmission and Distribution (T & 

### D) System that can be avoided by reductions in electricity use. There are 

### a wide range ofestimates of such costs, depending on the particular characteristics ofthe utility system and ofthe conservation programs and policies that lead to such reductions in electricity use. Krause et a!. (37) review the most recent studies on this topic, and settle on a range for T & 

### D savings of from $10 to $40/kW-yr. Krause et a!. also cite reasons why this range is probably too low, and suggest that further work will probably raise these values. For example, they cite marginal T & D cost estimates ofnine different utilities that (on average) totaled more than $l00/kW-yr. We adoptthe lowerendofKrause et al.'s range ($10/kW-yr) as ourestimate for avoided T & D capital costs. We choose this lower bound estimate because of the substantial uncertainties in applying such estimates (which are derived at the level ofindividual utilities) in national analyses. E.5 RESULTS Tables E.7 and E.8 show base case avoided energy and reliability costs (respectively) for electricity savings from the analyzed end-uses. Tables E.9 through E.ll show the sum of avoided costs for the base-, high-, and low-energy-price cases.

E-15 Capacity savings are peak load savings for only those regions that need capacity in a given year. Load savings in regions that do not need capacity contribute to deferring capacity additions in future years, but this analysis does not account for the value of such deferrals. Table E.6 Sums ofAC andBaseloadEnergy Savings Fractions for NERCRegionsThatNeed Capacity

Year

1996 1997 1998 1999 2000 Cooling ESF
0.693 l.703 0.703 l.000 1.000 Baseload ESF
0.552 5 0.552 6 0.596 6 l.000 9 1.000 9


### E.4.3 Avoided Transmission and Distribution Capital CostsRegions Needing Capacity



### Recently,utility planners havebegun to accountforcapitalcosts ofthe Transmission and Distribution (T & 

### D) System that can be avoided by reductions in electricity use. There are 

### a wide range ofestimates of such costs, depending on the particular characteristics ofthe utility system and ofthe conservation programs and policies that lead to such reductions in electricity use. Krause et al. (37) review the most recent studies on this topic, and settle on arange for T & 

### D savings of from $10 to $40/kW-yr. Krause et al. also cite reasons why this range is probably too low, and suggest that further work will probably raise these values. Forexample, ,, - they cite marginal T & 

### D cost estimates ofnine different utilities that (on average) totaled more than $l00/kW-yr

. We adoptthe lowerendofKrause etal.'s range ($10/kW-yr) as ourestimatefor avoided T & 

### D capital costs. We choose this lower bound estimate because of the substantial uncertainties in applying such estimates (which are derived at the level ofindividual utilities) in national analyses
. 

### E.5 RESULTS Tables E.7 and E.8 show base case avoided energy and reliability costs (respectively) for electricity savings from the analyzed end-uses. Tables E.9 through E.ll show the sum of avoided costs for the base-, high-, and low-energy-price cases.

E-15Table E.7 Electric Utility Avoided Energy Costs: Base Case (Dollars per MMBtu of Resource Energy Saved)

Year Avoided Energy Avoided Energy Avoided Energy Cost: Heating Cost: Cooling Cost: Baseload
1990 2.488 2.650 2.486 1991 2.654 2.842 2.655 1992 2.836 3.052 2.838 1993 3.035 3.281 3.039 1994 3.252 3.533 3.259 1995 3.490 3.808 3.498 1996 3.622 3.962 3.632 1997 3.761 4.124 3.773 1998 3.907 4.294 3.920 1999 4.060 4.473 4.075 2000 4.221 4.661 4.238 2001 4.389 4.858 4.409 2002 4.567 5.066 4.588 2003 4.753 5.284 4.776 2004 4.949 5.512 4.974 2005 5.154 5.753 5.182 2006 5.370 6.006 5.400 2007 5.596 6.271 5.630 2008 5.834 6.550 5.871 2009 6.085 6.843 6.124 2010 6.347 7.152 6.390 2011 6.624 7.476 6.669 2012 6.914 7.816 6.963 2013 7.219 8.174 7.272 2014 7.539 8.550 7.596 2015 7.876 8.946 7.937

E-16 Table E.7 Electric Utility Avoided Energy Costs: Base Case (Dollars per MMBtu of Resource Energy Saved)

Year Avoided Energy Avoided Energy AvoidedEnergy Cost: Heating Cost: Cooling Cost: Baseload
1990 2.488 2.650 2.486 1991 2.654 2.842 2.655 1992 2.836 3.052 2.838 1993 3.035 3.281 3.039 1994 3.252 3.533 3.259 1995 3.490 3.808 3.498 1996 3.622 3.962 3.632 1997 3.761 4.124 3.773 1998 3.907 4.294 3.920 1999 4.060 4.473 4.075 2000 4.221 4.661 4.238 2001 4.389 4.858 4.409 2002 4.567 5.066 4.588 2003 4.753 5.284 4.776 2004 4.949 5.512 4.974 2005 5.154 5.753 5.182 2006 5.370 6.006 5.400 2007 5.596 6.271 5.630 2008 5.834 6.550 5.871 2009 6.085 6.843 6.124 2010 6.347 7.152 6.390 2011 6.624 7.476 6.669 2012 6.914 7.816 6.963 2013 7.219 8.174 7.272 2014 7.539 8.550 7.596 2015 7.876 8.946 7.937

E-16Table E.8 Electric Utility AvoidedReliability Costs (Dollars perMMBtu ofResource Energy Saved
Year

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Avoided Reliability Cost: Heating
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Avoided Reliability Cost: Cooling
0.270 0.421 0.421 1.516 1.793 1.793 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436

E-17 Avoided Reliability Cost: Baseload
0.049 0.086 0.086 0.247 0.309 0.308 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 Table E.8 Electric Utility AvoidedReliability Costs (Dollars perMMBtu ofResourceEnergy Saved
Year

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Avoided Reliability Cost: Heating
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Avoided Reliability Cost: Cooling

E-17 0.270 0.421 0.421 1.516 1.793 1.793 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 2.436 Avoided Reliability Cost: Baseload
0.049 0.086 0.086 0.247 0.309 0.308 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 0.457 00457Table E.9 Total Utility Avoided Costs: Base Case (Dollars per MMBtu ofResource Energy Saved)

Year Total Avoided Cost Total Avoided Cost Total Avoided Cost Heating Cooling Base10ad
1990 2.488 2.920 2.535 1991 2.654 3.263 2.741 1992 2.836 3.473 2.925 1993 3.035 4.797 3.287 1994 3.252 5.326 3.568 1995 3.490 5.601 3.806 1996 3.622 6.398 4.089 1997 3.761 6.560 4.229 1998 3.907 6.730 4.377 1999 4.060 6.909 4.532 2000 4.221 7.097 4.694 2001 4.389 7.294 4.865 2002 4.567 7.502 5.045 2003 4.753 7.720 5.233 2004 4.949 7.948 5.431 2005 5.154 8.189 5.639 2006 5.370 8.442 5.857 2007 5.596 8.707 6.086 2008 5.834 8.986 6.327 2009 6.085 9.279 6.580 2010 6.347 9.588 6.846 2011 6.624 9.912 7.126 2012 6.914 10.252 7.420 2013 7.219 10.610 7.728 2014 7.539 10.986 8.053 2015 7.876 11.382 8.394

E-18 

### Table E.9 Total Utility Avoided Costs: Base Case (Dollars perMMBtu ofResource Energy Saved)

Year Total Avoided Cost Total Avoided Cost Total Avoided Cost: Heating Cooling Baseload
1990 2.488 2
.920 2.535 1991 2.654 3.263 2.741 1992 2.836 3.473 2.925 1993 3.035 4.797 3.287 1994 3.252 5.326 3.568 1995 3.490 5.601 3.806 1996 3
.622 6.398 4.089 1997 3.761 6.560 4.229 1998 3.907 6.730 4.377 1999 4.060 6.909 4.532 2000 4.221 7.097 4.694 2001 4.389 7.294 4.865 2002 4.567 7.502 5.045 2003 4.753 7.720 5
.233 2004 4.949 7.948 5.431 2005 5.154 8.189 5.639 2006 5.370 8
.442 5.857 2007 5.596 8.707 6.086 2008 5.834 8.986 6.327 2009 6.085 9
.279 6.580 2010 6.347 9.588 6.846 2011 6.624 9
.912 7.126 2012 6.914 10.252 7.420 2013 7.219 10.610 7.728 2014 7.539 10.986 8.053 2015 7.876 11.382 8.394

E-18

### Table E.I0 Total Utility A voided Costs: High Price Case (Dollars per MMBtu of Resource Energy Saved)

Year Total Avoided Cost: Total Avoided Cost: Total Avoided Cost: Heating Cooling Baseload 

### 1990 2.531 2.972 2.579 1991 2.708 3.327 2.795 1992 2.902 3.551 2.991 1993 3.115 4.891 3.367 1994 3.348 5.439 3.665 1995 3.604 5.736 3.921 1996 3.825 6.638 4.295 1997 4.065 6.920 4.538 1998 4.325 7.224 4.800 1999 4.606 7.554 5.085 2000 4.911 7.912 5.394 200l 5.241 8.300 5.728 2002 5.598 8.720 6.089 2003 5.985 9.176 6.482 2004 6.405 9.669 6.907 2005 6.860 10.204 7.367 2006 7.353 10.784 7.866 2007 7.887 11.413 8.407 2008 8.467 12.095 8.994 2009 9.094 12.835 9.630 2010 9.775 13.637 10.319 2011 10.513 14.506 11.066 2012 11.313 15.449 11.877 2013 12.181 16.472 12.756 2014 13.122 17.581 13.709 2015 14.142 18.784 14.742

E-19 

### Table E.I0 Total Utility A voided Costs: High Price Case (Dollars per MMBtu of Resource Energy Saved)

Year Total Avoided Cost: Total Avoided Cost: Total Avoided Cost: Heating Cooling Baseload 

### 1990 2.531 2.972 2.579 1991 2.708 3.327 2.795 1992 2.902 3.551 2.991 1993 3.115 4.891 3.367 1994 3.348 5.439 3.665 1995 3.604 5.736 3.921 1996 3.825 6.638 4.295 1997 4.065 6.920 4.538 1998 4.325 7.224 4.800 1999 4.606 7.554 5.085 2000 4.911 7.912 5.394 2001 5.241 8.300 5.728 2002 5.598 8.720 6.089 2003 5.985 9.176 6.482 2004 6.405 9.669 6.907 2005 6.860 10.204 7.367 2006 7.353 10.784 7.866 2007 7.887 11.413 8.407 2008 8.467 12.095 8.994 2009 9.094 12.835 9.630 2010 9.775 13.637 10.319 2011 10.513 14.506 11.066 2012 11.313 15.449 11.877 2013 12.181 16.472 12.756 2014 13.122 17.581 13.709 2015 14.142 18.784 14.742

E-19Table 

### E.11 Total Utility Avoided Costs: Low Price Case (Dollars per MMBtu of Resource Energy Cost:

Year Total Avoided Cost Total Avoided Cost Total Avoided Cost Heating Cooling Baseload 

### 1990 2.314 2.715 2.359 1991 2.451 3.023 2.534 1992 2.599 3.193 2.684 1993 2.759 4.471 3.007 1994 2.932 4.948 3.244 1995 3.120 5.164 3.432 1996 3.264 5.975 3.727 1997 3.418 6.154 3.882 1998 3.581 6.344 4.046 1999 3.754 6.547 4.222 2000 3.939 6.762 4.409 2001 4.135 6.992 4.607 2002 4.344 7.236 4.819 2003 4.566 7.496 5.044 2004 4.803 7.773 5.283 2005 5.055 8.069 5.538 2006 5.323 8.383 5.810 2007 5.609 8.718 6.099 2008 5.913 9.075 6.407 2009 6.237 9.455 6.735 2010 6.582 9.860 7.084 2011 6.950 10.291 7.456 2012 7.342 10.751 7.852 2013 7.759 11.241 8.275 2014 8.203 11.764 8.725 2015 8.677 12.320 9.204

E-20 

### Table E.11 Total Utility Avoided Costs: Low Price Case (Dollars perMMBtu of Resource Energy Cost:

Year Total Avoided Cost Total Avoided Cost TotalAvoided Cost Heating Cooling Baseload 

### 1990 2.314 2.715 2.359 1991 2.451 3.023 2.534 1992 2.599 3.193 2.684 1993 2.759 4.471 3.007 1994 2.932 4.948 3.244 1995 3.120 5.164 3.432 1996 3.264 5.975 3.727 1997 3.418 6.154 3.882 1998 3.581 6.344 4.046 1999 3.754 6.547 4.222 2000 3.939 6.762 4.409 2001 4.135 6.992 4.607 2002 4.344 7.236 4.819 2003 4.566 7.496 5.044 2004 4.803 7.773 5.283 2005 5.055 8.069 5.538 2006 5.323 8
.383 5.810 2007 5.609 8.718 6.099 2008 5.913 9.075 6.407 2009 6.237 9.455 6.735 2010 6.582 9.860 7.084 2011 6.950 10.291 7.456 2012 7.342 10.751 7
.852 2013 7.759 11.241 8.275 2014 8.203 11.764 8.725 2015 8.677 12.320 9.204

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