### 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 determinants of the current residential appliance market, as well as parameters characterizing market decisions that will affect the energy consumption of future appliances. This appendix describes the LBL-REM, the types of data it uses, the calculations it performs, and the results the LBL-REM provides. The LBL-REM projects numbers of households by house type, energy-related building shell characteristics, average energy efficiency of appliances, fraction of households 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 formats, for convenience, including: annual energy consumption and expenditures, annual equipment shipments, cumulative energy consumption and expenditures over a period (e.g., from policy implementation date 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 of fuel i for end use k in housing type m during year t is:

Qt'" = STOKCtm x Ct'k` x EUtikm x Ut'k x TI, ikm (B.1)

where STOKC is the stock of occupied housing units, C is the saturation (percent of households with this equipment): EU is the unit energy consumption, U is the usage behavior factor, and TI is the thermal integrity factor (for space conditioning only). Households respond to changes in operating expenses in three different 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
J(3-a 

## 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 determinants 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 itperforms, and the results the LBL-REM provides. The LBL-REM projects numbers ofhouseholds by house type, energy-related building shellcharacteristics, average energyefficiency 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 enduse, and monetary expenditures for equipment (both replacement and new installations) and for fuels. The results are reported in several formats, 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 TI

### is the thermal integrity factor (for space conditioning only). Households respondto 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 ofthe important characteristics of the residential appliance market. The LBL-REM utilizes a database of significant determinants 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 itperforms, and the results the LBL-REM provides. The LBL-REM projects numbers ofhouseholds by house type, energy-related building shellcharacteristics, average energyefficiency 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 enduse, and monetary expenditures for equipment (both replacement and new installations) and for fuels. The results are reported in several formats, 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 TI

### is the thermal integrity factor (for space conditioning only). Households respondto 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-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 of that fuel can be thought of as being separated into three elements-a usage elasticity (Es), a technical efficiency elasticity (Ee), and an equipment/fuel choice (market share) elasticity (Ems):

E=Eu+Ee+Ems 

### (B.2)

In fact, the LBL-REM also includes "cross-price" effects in the elasticity of market share Ems, i.e., the effect of changes in the operating expense of one fuel on the market shares of competing fuels and technologies. B.1.2 Housing The housing submodel uses: (1) population projections, which are more stable than projections of housing completions because of the comparatively larger influence of economics on housing completions. In the long run, the number of housing units available is expected to agree with the need for housing units, which is determined by the size and composition of the population; (2) population projections for states by house type; and (3) uses projections of the number of people in each age group in future years to estimate changes in the distribution of house types. 

### B.1.2.1 Number of Households The method for determining the number of households of each type in any future year has several steps. First, the 0.1% sample of the 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 or more units per building, and mobile homes). Second, a projection of future population by state and by age cohort is multiplied by the fraction of those people who live in each house type to derive a projection of population in each house type in future years. Third, the number of persons per household by house type and by state is determined from the 1980 Census 0.1% sample. Fourth, the number of houses required of each type is obtained by dividing the number of persons in each house type by the persons per household appropriate for that state and house type. The number of persons 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 of households required by the projected population and the projected number of new housing units. Accepting the projection of new construction requires rejection of either the population projection, the household projection, or the projected decline in persons per household.)

B-2
)b 

## i 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 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-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 of the 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 Themethodfordetermining the number ofhouseholds 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 ofhousesrequired ofeach type is obtainedby dividing the numberofpersons ineachhouse type by the persons perhousehold 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 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-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 of the 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 Themethodfordetermining the number ofhouseholds 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 ofhousesrequired ofeach type is obtainedby dividing the numberofpersons ineachhouse type by the persons perhousehold 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

### B.1.2.2 Housing Starts

The long-term projection of households, described above, is based on demographic trends, including changes in the age distribution of the 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 of voluntary turnover or replacement of houses, 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 constructed in future years. To account for economic factors, the LBL estimate of housing 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 tradeoff for structures is:

TIN TINF TBETA 1TALFA TINO TINS [TBETA+C] 1 TINF 

### 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 of the structure due to the change in TIN; and TINF, TALFA, and TBETA are parameters. 

### Table B. 1 shows values of the parameters TINF/TINO, TALFA, and TBETA. Table B.1 Parameters of Thermal Integrity/Cost Curves by House Type TINF/TINQ 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

165 B.l.2.2 Housing Starts Thelong-termprojection ofhouseholds, describedabove,is basedondemographic 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 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 account for economic factors, the LBLestimate ofhousing starts by state is scaled to agree with a recent projection of regional housing starts that includes economic factors. Bol.3 Building Shell Thermal Integrity The equation used in the Residential Energy Model to represent the thermal performance/cost tradeoff for 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 of the structure due to the change in TIN; and TINF, TALFA, and TBETA are parameters. Table B.l shows values ofthe parameters TINPITIN O

' TALPA, and TBETA. Table Bol 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.l.2.2 Housing Starts Thelong-termprojection ofhouseholds, describedabove,is basedondemographic 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 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 account for economic factors, the LBLestimate ofhousing starts by state is scaled to agree with a recent projection of regional housing starts that includes economic factors. Bol.3 Building Shell Thermal Integrity The equation used in the Residential Energy Model to represent the thermal performance/cost tradeoff for 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 of the structure due to the change in TIN; and TINF, TALFA, and TBETA are parameters. Table B.l shows values ofthe parameters TINPITIN O

' TALPA, and TBETA. Table Bol 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-3Thermal integrity is largely irrelevant in the analysis of televisions, 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 The LBL-REM projects the average efficiency or unit energy consumption for a new unit purchased in each future year. The method involves characterization of historical market behavior, in the form of an 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), current residential 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 are reported by industry trade associations. Taking the average efficiency of units purchased as the minimum life-cycle cost defines the implicit market discount rate. The average efficiency in future years is projected based on the range of engineering designs available (characterized by unit price and efficiency) and projected residential energy prices, given the implicit market discount rate. 

### B.1.5 Market Shares The number of potential purchasers is calculated for two markets: housing starts and potential replacement/retrofit in existing housing. Total product sales for each year are determined by applying market share elasticities to projected equipment price, operating expense, and income to determine the fraction of potential purchasers who will actually purchase the product. The market share elasticities include cross-price variables, where appropriate, to capture inter-fuel competition. (An example of inter-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 of Volume B through I. 

### B.1.6 Usage Behavior The usage elasticities for televisions, water heaters, and pool heaters are assumed to be zero. Projected usage is assumed constant at the current values. The usage elasticities for room air conditioners, direct heating equipment, mobile home furnaces, and ranges and ovens are given in Appendix B of Volumes B through I.



### B-4
jL

### 1 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-REM projects the average efficiency orunitenergyconsumption for a new unit purchased in each future year. The method involves characterization of historical market behavior, in the form of an 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 are reportedby 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. 

### B.1.S Market Shares The number of potential purchasers is calculated for two markets: housing starts and potentialreplacement!retrofitinexistinghousing. Totalproduct sales foreachyeararedetermined by applyingmarket share elasticities 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. 

### 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 current values. 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-REM projects the average efficiency orunitenergyconsumption for a new unit purchased in each future year. The method involves characterization of historical market behavior, in the form of an 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 are reportedby 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. 

### B.1.S Market Shares The number of potential purchasers is calculated for two markets: housing starts and potentialreplacement!retrofitinexistinghousing. Totalproduct sales foreachyeararedetermined by applyingmarket share elasticities 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. 

### 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 current values. The usage elasticities forroom airconditioners,directheating equipment, mobile homefurnaces, andranges andovens are given in Appendix B ofVolumes B through 1.

B-4

### B.1.7 Equipment Turnover

Tables 2.1 of Volumes B through I show average equipment lifetimes. These lifetimes are assumed to be constant over the 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 of houses in stock by house type; • housing starts by house type; • thermal integrity factors for stock and new houses by house type; • average unit energy consumption of new units sold (by class and by end use/fuel); • average unit equipment price of new units sold (by class and by end use/fuel); • fraction of existing 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. 

### 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
)a7 

### 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. 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.I.S 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 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. 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 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.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.I.S 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 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. 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 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-5B.1.9 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 of products being analyzed has changed. (2) The set of design 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) Energy price projections have been updated (toAnnual Energy 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, based upon 1987 Residential Energy Consumption Survey, industry shipments data, and (where applicable) revised equipment lifetimes (see Volumes H and I, Appendix B). 

### B.1.10 Input Data The complete input listing is available on electronic medium in ASCII format. 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 for power system planning, demand side management planning, and market planning. 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-1. The detailed focus is driven naturally by emerging market issues and analysis needs.



### B-6

1c^J 

### 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 been updated (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 1987ResidentialEnergyConsumptionSurvey, 

### 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 format. 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 system planning, demand side managementplanning, and marketplanning. 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-6 

### 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 been updated (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 1987ResidentialEnergyConsumptionSurvey, 

### 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 format. 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 system planning, demand side managementplanning, and marketplanning. 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 of the COMMEND framework is long-term forecasting. However, the

market databases that result from model implementation are vital inputs to a wide variety of planning and analysis activities. 

### Figure B.1 COMMEND Framework

BUILDINGS Small Office Large Office Restaurant Retail Grocery Warehouse Schools Colleges Health Lodging Miscellaneous Uses for Market Data n Forecasting End v n Demand-Side Planning

n Integrated Planning n Marketing

END USES FUELS Space Heat Electricity Cooling Natural Gas Ventilation Fuel Oil Rater Heat Cooking Refrigeration lighting Miscellaneous



### The COMMEND Models. The COMMEND 2.0 model was a mainframe model, which has 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 of the COMMEND Market Framework The COMMEND model provides a conceptual framework for organizing 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

16c, 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 Colleen

. Health

Lod:in,**~Uscellaneous** END USES Space Heat Cootinc Ventilation Water Heat CookillC Refriceration UchUne Miscellaneous Uses for Market Data
• Forecasting
• Demand-Side Planning
• Integrated Planning
• Marketing

FUELS Electricity Natural Gas Fuel Oil

TheCOMMENDModels. TheCOMMEND 2.0modelwas 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 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 Colleen

. Health

Lod:in,**~Uscellaneous** END USES Space Heat Cootinc Ventilation Water Heat CookillC Refriceration UchUne Miscellaneous Uses for Market Data
• Forecasting
• Demand-Side Planning
• Integrated Planning
• Marketing

FUELS Electricity Natural Gas Fuel Oil

TheCOMMENDModels. TheCOMMEND 2.0modelwas 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-7Market Segments

The purpose of segmenting a market is to group customers into segments with common properties. Across groups, the customers should have different product requirements or different market attitudes and preferences. Within groups, these requirements and attitudes should be more 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 of energy-using activities, - Different types of energy-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, and we 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 of energy-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 of the internal space as well as the characteristics of the 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 

### I>o Market Segments The purpose of segmenting a market is to group customers into segments with common properties. Across groups, the customers shouldhave differentproductrequirements ordifferent market attitudes 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, and we 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 of segmenting a market is to group customers into segments with common properties. Across groups, the customers shouldhave differentproductrequirements ordifferent market attitudes 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, and we 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-8These segments are defined in terms of the final service being provided by energy inputs.

Within each end-use segment, these classes of decisions will impact the type of fuel and the level of energy use: - Fuel choice refers to decisions among alternative equipment that provide the same service but use different types of fuel. The main competitive uses are heating and cooling and the main fuels are electricity, natural gas, and fuel oil. - Efficiency decisions refer to decisions about equipment features and structure features that determine how much energy is required to deliver a given level of end-use service. - Utilization refers to the frequency and duration of equipment usage. This is impacted by occupant behavior and equipment operating controls. Typically, all of the end-use segments appear in each of the 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. From the perspective of the equipment producers 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 of the 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 of this is the separate treatment of large 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 of HVAC technologies than do smaller buildings. 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 of building space), - Energy intensity (energy per square foot), 

### - Fuel share (percent of area served by an end use and fuel type), - Energy-use index (energy per square foot for an end use), - Peak-day fractions (share of annual energy), and - End-use load profiles.

B-9 

### III 

### 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 different types offuel. The main competitive uses are heating andcooling andthe main fuels are electricity, natural gas, and fuel oil. 

### Efficiency decisions refer to decisions about equipmentfeatures 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. Fromthe perspective 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 of this 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 smaller buildings. 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. 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 different types offuel. The main competitive uses are heating andcooling andthe main fuels are electricity, natural gas, and fuel oil. 

### Efficiency decisions refer to decisions about equipmentfeatures 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. Fromthe perspective 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 of this 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 smaller buildings. 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-9These are the key concepts used in commercial-sector energy analysis. By developing data

for these concepts, a complete profile of the commercial sector can be produced. For each market segment, the central energy equation in COMMEND defines current energy 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 of floor stock, S is average share of space 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 equipment efficiencies and average usage levels across the customer base in the segment. 

### As an example of this equation: - Fifty million square feet of office 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). The central energy equation is a definition of energy use. Other definitions are possible 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 equipmentdecisions 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-10 111, 

### 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 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). Thecentral energyequation is 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 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 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). Thecentral energyequation is 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-10A 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 a point in time, the efficiency and usage factors are captured by the average EUI value. 

### Fuel Shares

. Fuel shares indicate the share of building space that is served by a particular end use and fuel type. The term is used to indicate both stock and flow concepts. - The stock concept refers to the share of all buildings existing at a point in time. This is 

### sometimes referred to as a penetration or market saturation. We call 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 of current shipments. We call this the marginal share. The share concept used in COMMEND is applied to total floor stock rather than the penetrated portion of the stock. For example, if 90% of floor 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 of space in buildings with an end use regardless of the -- portion of each building .that is served or conditioned by the end use. The second is the "conditioned-space" approach, which accounts for the fraction of each building that is conditioned by the end use. 

### Energy Use Index (EUI). The term energy use intensity refers to a measure of average annual energy use per square foot of floor space in buildings that are served by an end use. In the residential sector, a similar concept is used, called unit energy consumption, or UEC. 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 of building sizes and equipment types that are used in these buildings. By focusing on a typical square foot, the EUI is a standardized concept. EUI values embody an average level of service and average equipment efficiency. There are several options for units of measurement. The standard approach is to develop electric values in kWh/foot and fossil fuel values in kBtu/foot.

B-11 2/3 

### 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 particular end use and fuel type. The term is used to indicate both stock and flow concepts. The stock concept refers to the share of all buildings existing at a point in time. This is sometimes referredto 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 enduse regardless ofthe portion of each building that is served or conditioned by the end use. The second is the "conditioned-space" approach, which accounts for the 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 the residential 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 EUI 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 particular end use and fuel type. The term is used to indicate both stock and flow concepts. The stock concept refers to the share of all buildings existing at a point in time. This is sometimes referredto 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 enduse regardless ofthe portion of each building that is served or conditioned by the end use. The second is the "conditioned-space" approach, which accounts for the 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 the residential 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 EUI 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-l1For 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 50 kBtu/foot. This difference in EUI values across fuels reflects differences in equipment efficiencies, in the thermal features of buildings 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 of energy 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. Load Shapes 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 of fractions indicates the share of annual energy use that occurs on the winter and summer peak days. These are referred to as peak-day fractions. The second set of fractions contains load profiles for each electric 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 Market Profiles The market data described above represent a COMMEND data base. Figure B.2 provides an illustration of the types of results that can be obtained from a COMMEND implementation. The data presented here reflect actual results that have been developed from the COMMEND 3.1 national data base. These data have been scaled down to reflect about 1% of the U.S. total.

B-12 

### Ii 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 50 kBtu/foot. This difference inEUI values across fuels reflects differences inequipment 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 Heatremoved 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 arereferred to aspeak-dayfractions. The second setoffractions contains loadprofiles for eachelectric enduse. 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 Market Profiles 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 % of the 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 50 kBtu/foot. This difference inEUI values across fuels reflects differences inequipment 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 Heatremoved 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 arereferred to aspeak-dayfractions. The second setoffractions contains loadprofiles for eachelectric enduse. 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 Market Profiles 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 % of the U.S. total.

B-12

### Figure B.2 1986 Commercial Energy Sales by End Use (1% of U.S. Total)

Space Heat

Space Heat

Misc.

Cool Light ELECTRICITY (Total = 7.6 TWH) Space Heat NATURAL GAS (Total = 22 .4 TBTU)

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



### 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 of the 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 of economic growth, changes in fuel shares, changes in efficiencies, and changes in usage levels. This approach allows consideration of key issues in future markets, such as fuel competition, technology competition, building standards, and customer behavior. 

### Central Energy Equation As discussed above, end uses within building types are referred to as market segments. The COMMEND forecast framework applies separately to each segment. As a result, it is appropriate to think of COMMEND as a matrix of models, as depicted in Figure B.3.

B-13 

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

Cool

Light ELECTRICITY (Total 

## =7.6 TWll) Space Hea.t

Misc.

Spl\ce Hent Space Heat

Cool l'f'.He1\t

NATURAL GAS (Total 

## =22.4 TBTU)

)'CHeat. 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 in efficiencies, andchanges in usage levels. This approach allows consideration of key 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 of COMMEND 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 Hea.t

Misc.

Spl\ce Hent Space Heat

Cool l'f'.He1\t

NATURAL GAS (Total 

## =22.4 TBTU)

)'CHeat. 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 in efficiencies, andchanges in usage levels. This approach allows consideration of key 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 of COMMEND as a matrix ofmodels, as depicted in Figure B.3.

B-13

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

Market Framework End-Use Segment Market Decisions



### 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:
Salest = R Ufv * Efv * Sfv * Fv (B.5) This equation defines annual energy sales for each fuel (f) as the sum across vintages (v) of the product of four factors. Starting from the right-hand end, these factors are: Floor stock of vintage v (Fd, The share of vintage v space using fuelfequipment (Sfr), EUI for fuel f equipment in vintage v space (Efr), and Utilization rate for fuelfequipment in vintage space (Ufr). 

### This definition holds in each forecast year for each fuel. All end-use models use this type of definition as a starting point. The definition is not a static one, since each of the 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 //6 

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

Market Framework End-Use· Segment Market Decisions



# o o o 

# @ sage 

### /

..·....·..1
If It 

## 1 "

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 

### (FvJ, The share 

### ofvintage v space using fuel

## f equipment (Sfv)' 

### EUI for fuel 

## f equipment in vintage v space (Efv), 

### and Utilization rate for fuel 

## f equipment in vintage space (U fv

)' 

### 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-Tenn Forecasting

Market Framework End-Use· Segment Market Decisions



# o o o 

# @ sage 

### /

..·....·..1
If It 

## 1 "

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 

### (FvJ, The share 

### ofvintage v space using fuel

## f equipment (Sfv)' 

### EUI for fuel 

## f equipment in vintage v space (Efv), 

### and Utilization rate for fuel 

## f equipment in vintage space (U fv

)' 

### 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 of this 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 B.4 COMMEND Forecast Framework

Employment. Population....

Energy Prices Technology Data Discount Rates Elasticities

Energy Prices Elasticities


### Floor Stock floor Stock

Fuel Shares

EUI Values Central Energy Equation

Usage Level

. The floor stock component of COMMEND is used to organize information 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 year floor stock (e.g., 1987). A historical

floorstock series from a distant year to 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. Survival functions describing building survival and decay over time.


### B-15 

### ill 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 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 B.4 COMMEND Forecast Framework

Employment. Popululion•...

Energy Prices Technology Dala Discount Rales EIo.sticities

Energy Prices Ehislicities


### Floor Stock

### ~

# ~ Floor Stock

Fuel Shares

BUl Values
Usage Level CenlrOoI Energy EquaLion

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 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 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 B.4 COMMEND Forecast Framework

Employment. Popululion•...

Energy Prices Technology Dala Discount Rales EIo.sticities

Energy Prices Ehislicities


### Floor Stock

### ~

# ~ Floor Stock

Fuel Shares

BUl Values
Usage Level CenlrOoI Energy EquaLion

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 or population), or a combination. 

### Survivalfunctions describing building survival and decay over time.

B-15A 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 of construction required to produce the projected stock value. With either approach, the user provides forecasting equations , including estimated coefficients and exogenous 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 interest rates . 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, BUT 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 of construction or equipment replacement. Decisions involved include: - The decision to use a generic technology (such as an electric heat pump 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 of building managers and occupants. A variety of approaches have been used to model these decisions. The focus of these approaches is on the impact of fuel 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 )i6 

### 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 and exogenous 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 interest rates. 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 and exogenous 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 interest rates. 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- Usage Behavior. An increase in a fuel price may cause a reduction in the usage level

through changes in the behavior of building occupants. Examples are reduced lighting level and more conservative thermostat settings. COMMEND 3.2 uses a probabilistic choice approach for fuel and efficiency choice. In this 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 of all system options, and 

### - Characteristics of the 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, it is not possible to predict these decisions perfectly. This philosophy contrasts with the life-cycle cost (LCC) minimization approach, which posits that each choice is known precisely, based on a complete set of cost information and pure economic optimization. The proability approach does not have the knife's edge property associated with LCC minimization. For example, a change in fuel prices alters operating costs, which in turn reorients the probabilities. These shifts will be sudden and dramatic only if estimated 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 of the segment are EUI range percentages and a tradeoff elasticity between outlay and energy use. The implied cost range is computed internally. 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 equipment efficiencies and installed costs may be specified. These impacts can be used to evaluate the impacts of naturally-occurring technology improvements.

B-17 

### Iicc Usage Behavior. An increase in a fuel price may cause a reduction in the usage level throughchanges inthe behaviorofbuilding occupants. Examples are reducedlightinglevel and more conservative thermostat settings. COMMEND 3.2 uses a probabilistic 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 of the building and other relevant factors. The probabilistic approach is appealing because itis not possible to observe all the factors that affect equipment decisions. Therefore, itis not possible to predict these decisions perfectly. Thisphilosophycontrasts withthe 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, a change in fuel prices alters operatingcosts, which in turn 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 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 rangepercentages and a tradeoffelasticitybetween outlay andenergyuse. Theimplied costrange is computedinternally. Thisis 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 throughchanges inthe behaviorofbuilding occupants. Examples are reducedlightinglevel and more conservative thermostat settings. COMMEND 3.2 uses a probabilistic 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 of the building and other relevant factors. The probabilistic approach is appealing because itis not possible to observe all the factors that affect equipment decisions. Therefore, itis not possible to predict these decisions perfectly. Thisphilosophycontrasts withthe 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, a change in fuel prices alters operatingcosts, which in turn 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 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 rangepercentages and a tradeoffelasticitybetween outlay andenergyuse. Theimplied costrange is computedinternally. Thisis 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- 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 of changes in building thermal characteristics on HVAC energy use. The equipment cost data determine the relationship between capital costs and operating costs, which is important in determining the importance of energy 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 of discount rates. These distributions may differ across building types. The decision-makers have price expectations which are based on a single distributed lag adjustment mechanism. This implies that price expectations are formed on the basis of past price events. - Efficiency Option Elasticities. These parameters give the sensitivity of market shares to life-cycle cost, where life-cycle cost includes both initial equipment cost and the present value of operating costs. These sensitivities are used to model efficiency choice for all end uses. Share Option Elasticities. Like the efficiency option elasticities, these parameters give the sensitivity of market shares to life-cycle cost, where life-cycle cost includes both initial equipment cost and the present value of operating costs. These sensitivities are to model market shares of competing 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 of equipment 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 appliance replacement. They reflect the presence of barriers to fuel conversion when equipment is replaced. EUI inertial factors apply to efficiency changes at the time of equipment replacement. 

### Office Equipment and Miscellaneous Equipment EUI Growth. These parameters allow office equipment and miscellaneous equipment EUIs 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 

### 1 q_0 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, which is importantin determining the importance ofenergy prices inequipment 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 of

### discount 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 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 choice for all end uses. ShareOptionElasticities. Likethe efficiencyoption 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 of operating costs. These sensitivities are to model market shares ofcompeting fuels and technologies. Automatic Calibration. The technology data and decision data are combinedto 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**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 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, which is importantin determining the importance ofenergy prices inequipment 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 of

### discount 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 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 choice for all end uses. ShareOptionElasticities. Likethe efficiencyoption 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 of operating costs. These sensitivities are to model market shares ofcompeting fuels and technologies. Automatic Calibration. The technology data and decision data are combinedto 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**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 and DSM program impacts. These inputs are described briefly below.

B-18- Efficiency Standards. This section contains data that identify the timing of efficiency

standards and that describe the impact of these standards on (1) equipment efficiency ranges and (2) the level of thermal efficiency in new construction. 

### Efficiency Incentives. This section allows introduction of incentive or rebate payments for equipment that meets specific efficiency requirements. Specific DSM Program Impacts. This section allows imposition of DSM program impacts by building type, and end use and fuel. General DSM Program Impacts . This section allows imposition of impacts 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.
Forecast Results 

### 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 of forecast results using the COMMEND 3.1 national data base.

B-19 

### ryl Efficiency Standards. This section contains data that identify the timing of efficiency standards and thatdescribe 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. SpecificDSMProgramImpacts. 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. 

### Forecast Results 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. 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 and thatdescribe 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. SpecificDSMProgramImpacts. 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. 

### Forecast Results 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. Figure B.5 presents an example offorecast results using the COMMEND 3.1 national data base.

B-19

### Figure B

.5 Forecast of Electricity Sales by End Use (gWh) (1% of U.S. Total)

GWYH 3500

3000

2500

2000

1500

1000

500 200
199

1986.

S.Heat Vent Cool w. Heat Cook Refrig Light Mize



### B-20 

### Figure B.S Forecast ofElectricity Sales by End Use (gWh) (1% 

### ofU.S. Total)

GWH 3500**r---------------------------------------------~** 200 3000 199
2500 1986 

# ;

::::t,]i .•.. 1[.·:!:.':,,!

.. :::.:., ... :.: ::.:=: :**o~-w~~::~~u·**__**~~~~L-~~**__**~ll_~·~·~,L_~~_J** 2000**S.He:a~** Vent Cool lI'.Heal Cook Rc!rig LiCht. Millc

B-20 Figure B.S 

### Forecast ofElectricity Sales by End Use (gWh) (1% 

### ofU.S. Total)

GWH 3500

# r----------------------------------------------

3000
2500
2000 

# ;**S.He:a~** Vent Cool lI'.Heal Cook Rc!rig LiCht. Millc

B-20

### APPENDIX C. L,BL. MANUFACTURER IMPACT MODEL.

C.1 OVERVIEW OF MANUFACTURER IMPACT MODEL C.1.1 Purpose The Lawrence Berkeley Laboratory Manufacturer Impact Model (LBL-MIM) collects into one spreadsheet all the calculations necessary to determine the impact of a change in appliance efficiency standards on an industry's profitability and scale of operation. The spreadsheet design makes it possible to quickly analyze the effects of uncertainties 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 of financial impacts, but also with the uncertainty of these estimates and a sensitivity analysis attributing that uncertainty to certain input variables. 

### C.1.2 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 of costs for a firm
. First, variable costs of goods sold (VCGS) will be affected by new parts and labor requirements

. Second, some engineering will be required to design , test, and plan the manufacturing of new products. And third, new products will require re-tooling and new equipment (capital) costs

. The incorporation of VCGS is straightforward, but engineering and capital costs must be amortized to be converted from one-time costs to annual costs for a typical

-year model. Markups from manufacturing costs to ex-factory prices depend on 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 not fixed; hence it must be greater than one if a firm with fixed costs is to recover its fixed costs and turn a profit. The industry has two elasticities of interest: 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 of demand experienced by each firm. This apparent paradox arises because the model assumes that all firms are profit maximizers and thus respond in essentially the same way to the imposition of standards. Because of this


### C-1
/'2 

## 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 of a change in appliance efficiency standards on an industry's profitability and scale ofoperation. The spreadsheetdesign makes it

### possible 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 of financial 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 willrequire re-tooling andnew equipment (capital) costs. Theincorporation ofyeaS is straightforward, butengineering and capital costs must be amortized to be converted from one-time costs to annual costs for a typical-year model. Markupsfrom manufacturingcosts to ex-factory pricesdependon the single-fum price elasticity. Because it is difficult to find data on single-firm price elasticities, the LBL-MIM infers them from the fum's fixed costs and return on equity (ROE). The markup is over variable cost and not fixed; hence itmust be greater than one ifa firm with fixed costs is to recoverits fixed costs and tum 

### a profit. The industry has two elasticities of interest: its price elasticity and its operating cost elasticity. Although individual fums must calculate according to their own elasticities of demand, it is ultimately the industry elasticities that determine the level ofdemand experienced by each fum. This apparentparadox arises because the model assumes that all fums are profit maximizers 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 The Lawrence Berkeley Laboratory Manufacturer Impact Model (LBL-MIM) collects into one spreadsheet all the calculations necessary to determine the impact of a change in appliance efficiency standards on an industry's profitability and scale ofoperation. The spreadsheetdesign makes it

### possible 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 of financial 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 willrequire re-tooling andnew equipment (capital) costs. Theincorporation ofyeaS is straightforward, butengineering and capital costs must be amortized to be converted from one-time costs to annual costs for a typical-year model. Markupsfrom manufacturingcosts to ex-factory pricesdependon the single-fum price elasticity. Because it is difficult to find data on single-firm price elasticities, the LBL-MIM infers them from the fum's fixed costs and return on equity (ROE). The markup is over variable cost and not fixed; hence itmust be greater than one ifa firm with fixed costs is to recoverits fixed costs and tum 

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

C-lassumption, all firms change the prices and operating costs of their appliances by the same

amount (i.e., the industry acts in unison). When this happens, sales for each firm are determined by the industry elasticities. 

### C.1.3 Measures of Impact The LBL-MIM computes two primary and several secondary measures of impact. The two primary measures, ROE and net income, are both necessary because they can move in opposite directions. For instance, if net income increases, but assets and thus equity increase by a greater percentage, ROE will decline. If the 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 average price; these help explain the origins of changes in the primary inputs. C.1.4 Methodology How standards 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. 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 amortized by taking into account the life of the investment, the timing of the expenditures, tax laws, and the cost of funds. An additional (and sometimes larger) investment is made as the old inventory is replaced with more expensive new units. The model assumes that previous inventory ratios are maintained. A third form of investment tracked by LBL-MIM is the change in the transactions demand for cash that accompanies a change in revenues. Increased costs of production are modeled by coupling changes in unit costs 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 

### lI 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 detennined 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. 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 average price; these help explain the origins ofchanges in the primary inputs. 

### C.I.4 Methodology How standards 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 takinginto 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 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 unit costs 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 

### 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 detennined 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. 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 average price; these help explain the origins ofchanges in the primary inputs. 

### C.I.4 Methodology How standards 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 takinginto 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 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 unit costs 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

# Q* Output (Q) Figure C.1 Profit Maximization and Fixed Costs
C-3

) a fJ) 

### 1;) 

# o o "'C 

## c: 

# as 

# ft

....... fJ) (I) :::J 

## c: 

# g! Q) 

# a: / /



## Q* 

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

# R U) (I) :::J C

# ~ a: / / / / /



## Q* 

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

### Change 

### inC



# RTypical 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 of standards, and is selected because it is believed to be long enough to capture any major impacts from the standard, such as profitability changes or firm entry into or exit-from the industry. Use of prototypical firms Because the engineering and financial data for most firms are proprietary, LBL-MIM models a prototypical firm. In many cases, this firm must be thought of as representing an autonomous division of a larger firm. A prototypical firm is a hypothetical firm that is representative of the industry. Prototypical firms are defined by parameters that are important for determining the impacts of standards and are consistent with data for the portion of the industry they represent. Important parameters used in the model include the cost structure of the firms, 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, 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 or color TVs 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.2.1 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 normal 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 of the 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 of producing an additional unit of output. Three facts are true in general of these 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 majorimpacts from the standard, such as profitability changes or firm 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 firm. In many cases, this firm must be thought of as representing an autonomous division of a larger firm. A prototypical finn is a hypothetical firm that is representative of the industry. Prototypical firms are defined by parameters that are important for determining 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 firms, 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, 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 or colorTVs 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? Ifless than the normal 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 majorimpacts from the standard, such as profitability changes or firm 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 firm. In many cases, this firm must be thought of as representing an autonomous division of a larger firm. A prototypical finn is a hypothetical firm that is representative of the industry. Prototypical firms are defined by parameters that are important for determining 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 firms, 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, 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 or colorTVs 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? Ifless than the normal 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

### 1. Under perfect competition, price equals marginal cost.

2. Under imperfect competition, i.e., with a downward-sloping demand curve, price equals marginal cost times a markup. 

### 3. In neither case do fixed costs enter the calculation of price.13 The first point may be understood by assuming for a moment that it is false and that price 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 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.1, 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 cost curve)? Since the firm wishes to maximize the difference between cost and revenue, it chooses the optimal output Q*. This choice is clearly independent of the 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 cost has no effect on the demand curve and thus no effect on price. The separation of costs 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 short run, the productive capacity of the 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 firm finds that its long-run plans have been realized (in other words, if the market conditions are those that it foresaw at the time it planned its present capacity), then long- and short-run marginal costs
13Two 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 of demand, and thereby the markup over marginal cost. Thus (indirectly) fixed costs are passed on through the markup of marginal cost. The mechanism providing this indirect link is the following: if fixed costs are not covered, then economic profits will be negative and some firms will leave the industry; this will reduce competition and thus reduce each firm's elasticity of demand. Conversely, if fixed costs, are more than covered, positive economic profits will induce entry and elasticity and markup over marginal cost will be reduced. 

### C-5 

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

### The first pointmay be understood by assuming for amomentthatitis 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 firm 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.1, 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 between costand revenue, it 

### chooses the optimal output Q*. 

### This choice is clearly independent of the 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 cost has no effect on the demand curve and thus no effect on price. The separation of costs 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 that its long-run plans have beenrealized (in otherwords, ifthe market conditions are those thatitforesaw atthe time itplannedits presentcapacity), then long- and short-runmarginal costs

13Two 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 of demand, and thereby the markup over marginal cost. Thus (indirectly) fixed costs are passed on through the markup ofmarginal cost. The mechanism providing this indirect link is the following: iffixedcosts arenotcovered, theneconomicprofits willbenegativeand somefirms will leave theindustry; this willreducecompetition and thusreduce eachfirm's elasticityofdemand. Conversely,iffixedcosts. aremore than covered, positiveeconomicprofits will induceentryandelasticity and markup over marginal costwill be reduced. C-5 

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

### The first pointmay be understood by assuming for amomentthatitis 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 firm 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.1, 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 between costand revenue, it 

### chooses the optimal output Q*. 

### This choice is clearly independent of the 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 cost has no effect on the demand curve and thus no effect on price. The separation of costs 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 that its long-run plans have beenrealized (in otherwords, ifthe market conditions are those thatitforesaw atthe time itplannedits presentcapacity), then long- and short-runmarginal costs

13Two 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 of demand, and thereby the markup over marginal cost. Thus (indirectly) fixed costs are passed on through the markup ofmarginal cost. The mechanism providing this indirect link is the following: iffixedcosts arenotcovered, theneconomicprofits willbenegativeand somefirms will leave theindustry; this willreducecompetition and thusreduce eachfirm's elasticityofdemand. Conversely,iffixedcosts. aremore than covered, positiveeconomicprofits will induceentryandelasticity and markup over marginal costwill be reduced. C-5will be equal. This is possible because although capital is not included 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 of the LBL-MIM from a theoretical perspective, after which some results of central importance are derived and their application to the long-run model is reviewed. Finally, the short-run part of the model is discussed. Theory and the LBL-MIM From our discussions with the industries involved and industry consultants, 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 of sales due to induced entry of competitors. 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 of fixed 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 of economics for many years, hence these derivations are included for the convenience of the 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 of firms. There is no generally accepted theory on how to model a market characterized by significant market concentration on both the seller's and the buyer's side. As a result, we discuss our approach in greater detail below. Derivation of markup To begin the derivation of the markup equation, assume that firms maximize economic profit, where economic profit is revenue minus economic cost, and economic cost includes the cost of equity and all taxes. Economic costs (C) are assumed to be linear functions of output (Q) and revenues (R): 

### C = bt0 + btlQ

C-6 (C.1) will be equal. This is possible because although capital is not included 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.

. Thenext section gives an overview ofthe LBL-MIMfrom a theoreticalperspective, after which someresults 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 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 of economics 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 of firms. There is no generally accepted theory on how to model a market characterized by significant market 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 of the 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 assumed to be linear functions ofoutput (Q) and revenues (R):


### C 

## =btO + btlQ (C.I)

C-6 will be equal. This is possible because although capital is not included 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.

. Thenext section gives an overview ofthe LBL-MIMfrom a theoreticalperspective, after which someresults 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 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 of economics 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 of firms. There is no generally accepted theory on how to model a market characterized by significant market 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 of the 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 assumed to be linear functions 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 of capital is included in economic cost, a firm with an ROE equal to the cost of capital 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 following first-order condition:



### dQ (R -C) =0 (C.2)

This equation completes the description of the supply-side assumptions. The demand side is described by an elasticity of demand which may vary, but which at the market 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.P=e dP Q (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 / dQ by R' and dP / dQ by P' , we have:
aQ [ R - bt0 - btlQ ] = 0 

### (C.4)

14 If we assume that cost is an arbitrary function of Q , C(Q,R), our final result, will differ only in that the two types of cost ( btO and btl ) will be replaced by C - Q (dC / dQ ) and dC / dR, respectively.


### C-7
/ -LI 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 includedin economic cost, a firm with an ROE equal to the cost of capital 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 -CR-C)=O dQ (C.2)

Thisequation completes the description ofthe 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 mathematical manipulations. From the two supply assumptions, and denoting dR I dQ byR' 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 [mal result, will differ only in that the two types ofcost ( btO and btl) will be replaced by C - Q (dC I dQ ) and dC I 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 includedin economic cost, a firm with an ROE equal to the cost of capital 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 -CR-C)=O dQ (C.2)

Thisequation completes the description ofthe 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 mathematical manipulations. From the two supply assumptions, and denoting dR I dQ byR' 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 [mal result, will differ only in that the two types ofcost ( btO and btl) will be replaced by C - Q (dC I dQ ) and dC I dR, respectively.

C-7By definition: R" =R1

R =P •Q

R"= P +P dQ P 

### dP Q

-> R / =P / •Q +P

R"=P+P e

R"=P+P e 

### (C.5)

(C.6)

(C.7)

(C.8)

(C.9)

(C.10)

P= e btl (C.11) 1 +e 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 u 1 +e



### C-8 (C.12)

30 

### By definition:

R=P'Q

R '- P +P dQ P



# -- dP 

### Q

R'

### =~+P e



### p=_e_bt1 l+e (C.5)

(C.6)

(C.?)

(C.8)

(C.9)

(C.lO)

(C.11)

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=- l+e

C-8 

### (C.12) By definition:

R=P'Q

R '- P +P dQ P



# -- dP 

### Q

R'

### =~+P e



### p=_e_bt1 l+e (C.5)

(C.6)

(C.?)

(C.8)

(C.9)

(C.lO)

(C.11)

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=- l+e

C-8 

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

e 5 = -1, then u is undefined, since the firm makes more the less it sells. A small rearrangement of Eq. 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 both u and fixed costs are not passed on at all. It is now easy to derive another representation of u which is used in the calibration stage of the LBL-MIM to estimate Iu from Eq. C. 1: 

### (R-C) =R-btO-btl •Q (C.14)

Using Eq. C.5: R =PQ =pbtlQ 

### (C.15)

so 

### (R-C) =pbt1Q-bt0-btlQ (C. 16)

and (R-C) =(p-1)btlQ

### -bt0 (C.17)

Finally, 

### p =1 + (R-C)+bt0 (C.18) btlQ

C-9 

### /-3r 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.l2 provides a simpler look at the markup equation:

P = pbtl 

### (C.l3)

Price formation can be explained as follows: marginal costs are markedup by bothJ.l and fixed costs are not passed on at all. 

### It is now easy to derive anotherrepresentation ofJ.l which is used in the calibration stage of the LBL-MIM to estimate J.l from Eq. C.l: (R-C)=R-btO-btl 'Q (C.l4)

Using Eq. C.S:

R =P-Q =pbtlQ (C.lS)

so (R-C)=pbtlQ-btO-btlQ (C.l6)

and (R-C)=(p-l)btlQ-btO (C.l7)

Finally,



## = 1 +(R-C)+btO p btlQ (C.l8)

C-9 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.l2 provides a simpler look at the markup equation:

P = pbtl 

### (C.l3)

Price formation can be explained as follows: marginal costs are markedup by bothJ.l and fixed costs are not passed on at all. 

### It is now easy to derive anotherrepresentation ofJ.l which is used in the calibration stage of the LBL-MIM to estimate J.l from Eq. C.l: (R-C)=R-btO-btl 'Q (C.l4)

Using Eq. C.S:

R =P-Q =pbtlQ (C.lS)

so (R-C)=pbtlQ-btO-btlQ (C.l6)

and (R-C)=(p-l)btlQ-btO (C.l7)

Finally,



## = 1 +(R-C)+btO p btlQ (C.l8)

C-9This equation shows that markup is economic income (R-C) plus fixed costs, all over total

variable cost. The calibration calculations of the long-run module uses a simple variation of this equation in which btl Q is replaced by C-btO, and the relation, R-C = EP, where EPis economic profit, is used twice. The new form of the equation is:

u -1 + EP+btO R-EP-btO 

### (C.19)

Markup is determined 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 and the "Effective Number of Firms From an evaluation of the 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 of the market is also heavily concentrated due to the presence of large retailers who have a great deal of market power.15 In other words, 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 of oligopsony, 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 fir ms such as Sears, Montgomery Ward, Circuit City, and Highland. For example, the home appliance retail share of Sears and Montgomery Ward alone has been estimated to be about 30% for various products. One indication of this oligopsony power is that if theoretical markups based on the actual number of firms 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 of oligopsony power, and is an indication of the strength of that power. Since a firm's markup is directly related to the elasticity of the
15The increasing concentration on the demand side of the market also agrees with comments by the home appliance industry during the comment for these and other appliances. For instance, see AHAM, "State of the AHAM to the Department of Energy on Notice of Proposed Rulemaking onEnergy Efficiency Standards for Clothes Dishwashers, Clothes Washers and Clothes Dryers," CE-RM88-101, October 10, 1989. 

### C-10

/3V This equation shows that markup is economic income (R-C) plus fixed costs, all over total variable cost. Thecalibration calculations ofthe long-run module.uses a simple variation ofthis equation inwhichbtlQis replaced by C-btO, and therelation, R-C

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

1 EP+btO p= +._--- R-EP-btO (C.19)

Markup is determined byrevenue, economic profit, and fixed costs. Generally, 

### ithas beenfound 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 and the "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-volumeretailers, which includes firms such as Sears, Montgomery Ward, Circuit City, and Highland. For example, the home applianceretail share ofSears and MontgomeryWard alone has been estimatedto be about 30% forvarious products. Oneindication ofthis oligopsonypoweris thatiftheoretical 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. For instance, 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 This equation shows that markup is economic income (R-C) plus fixed costs, all over total variable cost. Thecalibration calculations ofthe long-run module.uses a simple variation ofthis equation inwhichbtlQis replaced by C-btO, and therelation, R-C

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

1 EP+btO Jl

## = +**"""'R--=E--P--b""-t~O** (C.19)

Markupis determinedbyrevenue, economic profit, and fixed costs. Generally, 

### ithas beenfound 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 and the "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-volumeretailers, which includes firms such as Sears, Montgomery Ward, Circuit City, and Highland. For example, the home applianceretail share ofSears and MontgomeryWard alone has been estimatedto be about 30% forvarious products. Oneindication ofthis oligopsonypoweris thatiftheoretical 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 of oligopsony 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 Oothes Dishwashers, Clothes Washers and Oothes Dryers," CE-RM88-101, October 10, 1989. C-lOdemand it faces, we can find the single-firm 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 of firms. This allows us to compute the hypothetical or what we refer to as the "effective" number of firms from the observed markup. We then model the industry as if it were a Cournot oligopoly facing a competitive, not oligopolistic, demand for its product, but we use the "effective" number of firms rather than the actual number of firms to reflect the impact of the oligopsony. The effect of this is to reduce the market power of the individual firm to agree with our observation of markup. Although the reduced market power is a consequence of specifying too many firms, and not of modeling oligopsony, we believe that it provides a good first-order approximation to the actual market structure and provides a way around the intractable problem of modeling 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 of firms is computed from markup and industry demand elasticity. A well-known result for a Cournot oligopoly is: e =NF * e i (C.20)


### where: e = the firm elasticity of demand, et = the industry elasticity of demand, and NF = number of firms. We can solve for NF if the two elasticities are known. We have an estimate of ei, and can estimate e as follows. From Eq. C.5, we know that:
e= P (C.21) 1-p Thus, we can find the markup, u, in Eq. C.21 from Eq. C.4, 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: n/Q=P-c+FC/Q, where FC is fixed cost. This is just a restatement of the definition of economic profit, which is revenue minus costs. Noting that revenue, R, equals P *Q, we have:

C-11 

### /33 

### 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 hypothetical number offlrms. This allows us to compute the hypothetical orwhat we 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 of the oligopsony. The effect of this is to reduce the market power of the individual firm to agree with our observation of markup. 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 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 deflnition 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 hypothetical number offlrms. This allows us to compute the hypothetical orwhat we 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 of the oligopsony. The effect of this is to reduce the market power of the individual firm to agree with our observation of markup. 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 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 deflnition ofeconomic profit, which is revenue minus costs. Noting thatrevenue,R, 

### equalsP *Q, 

### we have:

C-llP P _ R c P- FC n R-FC41

--- Q Q 

### (C.22)

This gives a formula for markup u based on our most easily observed variables
. Solving Eqs. C.9 and C. 10 for e, we can now find the number of firms, NF:
NF= P (C.23) et•(1-p)

We find that given two elasticities estimated from actual data, NF is higher than the actual number of firms observed. To summarize, we first look at a typical firm's profit and fixed costs. In order to recover fixed costs, a firm must mark up over variable costs, and if it can mark up enough, it will make above "normal" ROE. From these considerations, we find its markup. The markup indicates a firm's industry demand elasticity and the effective number of firms in the industry. We use it in subsequent calculations. 

### The base case: 1990 NAECA standards
In the long

-run model module, the calibration case is immediately followed by long-run calculations for the state of the 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 determine 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, ifis the fuel-cost factor, and B is the elasticity with respect to life-cycle cost. Then price and fuel cost are changed from the calibration case to either the base case or a long run case. These changes shift the division of demand 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- J.l 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 first look at a typical firm's profit and fixed costs. In orderto recover fixed costs, a finn must mark up over variable costs, and ifit

### can mark up enough, itwill make above "normal" ROE. From these considerations, we find its markUp. The markup indicates a finn's industry demand elasticity and the effective number offirms in the industry. We use itin subsequent calculations. The base case: 1990 NAECA standards In the long-run model module, the calibration case is immediately followed by long-run calculations for the state of the 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 changedfrom the calibration case toeither 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 offinns, NF:



### NF- J.l 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 first look at a typical firm's profit and fixed costs. In orderto recover fixed costs, a finn must mark up over variable costs, and ifit

### can mark up enough, itwill make above "normal" ROE. From these considerations, we find its markUp. The markup indicates a finn's industry demand elasticity and the effective number offirms in the industry. We use itin subsequent calculations. The base case: 1990 NAECA standards In the long-run model module, the calibration case is immediately followed by long-run calculations for the state of the 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 changedfrom the calibration case toeither 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-12B -P e= P +ff -F As price increases as a fraction of life-cycle cost, the price elasticity increases. 

### (C.25)

The added costs to meet standards are divided into two categories: capital, and parts 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 of debt and equity, the tax laws, the lifetime of the capital, and the age of any capital being replaced. 

### Once the cost of capital has been annualized, it must be divided into fixed and variable parts. The fixed part is then added to bt0, while the remainder is divided by Q and added to btl. Since there are undoubtedly some economies of scale when parts are purchased, the LBL-MIM provides for a division of the cost of parts 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 computed from Eq. C.4, by using the price elasticity calculated as described above. With the new price in hand , sales (Q) can be computed from price and operating-cost elasticities. Revenue is now just R=P *Q, and economic income is just R-C. Assets are those of the calibration case plus the new capital resulting from 1990 NAECA standards, and equity is computed from the debt-to-equity ratio. This calculation of equity is the last essential calculation for the base case. The long-run analysis: changed standards The analysis of the long-run effect of a change in standards from their 1990 NAECA level is essentially the same as the analysis of the base case. New costs are divided between fixed 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
M 

### e 

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

The addedcosts to meet standards are dividedinto two categories: capital, andparts and labor. Capital costs come in two varieties, tooling and equipment. These are not currently separatedby 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 of any 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 dividedby 

### Qand added to btl. Since there are undoubtedly some economies of scale when parts are purchased, the LBL-MIM provides for a division of the 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 been assigned, price is easily computedfrom 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 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: changed standards Theanalysis ofthelong-runeffectofa changein standards from their 1990NAECAlevel is essentially the same as the analysis ofthe base case. New costs are dividedbetweenfixed and variable categories in the same way andadded to the base casecosts. 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 

### e 

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

The addedcosts to meet standards are dividedinto two categories: capital, andparts and labor. Capital costs come in two varieties, tooling and equipment. These are not currently separatedby 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 of any 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 dividedby 

### Qand added to btl. Since there are undoubtedly some economies of scale when parts are purchased, the LBL-MIM provides for a division of the 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 been assigned, price is easily computedfrom 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 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: changed standards Theanalysis ofthelong-runeffectofa changein standards from their 1990NAECAlevel is essentially the same as the analysis ofthe base case. New costs are dividedbetweenfixed and variable categories in the same way andadded to the base casecosts. 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 asset structure. Assets are assumed to have the same two components.

A =AO +Aq •Q Demand elasticity. The demand elasticity with respect to life-cycle cost experienced by a single firm is assumed to be independent of price and operating cost over the relevant region. This means that if the 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 firm finances any investment necessary to meet standards. It is generally assumed that firms have some DIE ratio that they consider optimal, and there is no reason for standards to change this figure. Amortization of capital 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 of capital, and that depreciation is straight-line over the tax life. Structure of cost resulting from changes in standards. Engineering costs, if they are separated from capital costs, are assumed to be entirely fixed. The cost of capital 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 of Modules The LBL-MIM is a Lotus 1-2-3 spreadsheet that is organized into 12 modules or panels. A flowchart depicting the main connections among these appears in Figure C.2, and a brief description of each is given below. Starting with subsection C.2.3.1, each panel is given a full description.

C-14



### 134 

### EC=FC+MCq'Q 

### The asset structure. Assets are assumed to have the same two components.
A=AO+Aq·Q


### Demand elasticity. The demand elasticity with respect to life-cycle cost experienced by a single ftnn is assumed to be independent ofprice and operating cost over the relevant region. This means that if the 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 firm ftnances any investment necessary to meet standards. It 

### is generally assumed that ftnns have some DIE 

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

### 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 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 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 asset structure. Assets are assumed to have the same two components.
A=AO+Aq·Q


### Demand elasticity. The demand elasticity with respect to life-cycle cost experienced by a single ftnn is assumed to be independent ofprice and operating cost over the relevant region. This means that if the 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 firm ftnances any investment necessary to meet standards. It 

### is generally assumed that ftnns have some DIE 

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

### 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 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 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

# MIM Modules and Primary Routes of Data Flow

Engineering Inputs 4

V 

### Standards Levels 5 

# 1
Costs, Sales Revenues6

Accounting 3 

### Cost

Conversion

W W 12

W 

### Long-Run Model 9

Control Panel 1

Monte Carlo 2 Financial



### Short-Run Model 10 7

Figure C.2 MIM Modules and Primary Routes of Data Flow 

### C-15 One-TimeCost Amortization 8



### Charts

137 

# 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 ofData Flow C-15 One-Time )0 CostArnortization 8**~** Charts 11 

# 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 

# 1

# I

Control

Panel 

# 1

Monte Carlo 9

1

2 Financial 7



## " Short-Run Model 10



### Figure C.2 MIM Modules and Primary 

### Routes ofData Flow C-15 One-Time )0 CostArnortization 8

'If Charts 11Control Panel. 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 changed to test the model's reaction to any of the control variables. The change in results is immediately displayed in the lower part of the control panel. 2. Monte Carlo. The nine control variables can be selected randomly from probability distributions determined by the variables' standard errors and median (estimated) values. The Monte Carlo module is designed to record many such runs and display the mean value and standard deviation of six 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 of the financial impacts of standards from an economic point of view is shown at the bottom of the 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/or the "Cost Conversion" module (12) and are presented for the standards levels being analyzed. 

### 6. Costs, Sales, and Revenues. This module uses prices, markups, market shares, and industry shipment levels to compute variable cost of goods sold and 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 Cost Amortization. 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 of the 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

/3e 1. 

### Control Panel. 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 control variables. Thechangeinresults is immediately displayed in the lower part ofthe control panel. 

### 2. Monte Carlo. The nine control variables can be selected randomly from probability distributions determinedby the variables' standard errors andmedian (estimated) values. The Monte Carlo module is designed to record many such runs and display the mean value and standard deviation of six 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) andarepresentedfor the standards levels being analyzed. 6. 

### Costs, Sales, and Revenues. This module uses prices, markups, market shares, and industry shipmentlevels to compute variable cost ofgoods 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 overvariable 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. 

### Control Panel. 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 control variables. Thechangeinresults is immediately displayed in the lower part ofthe control panel. 

### 2. Monte Carlo. The nine control variables can be selected randomly from probability distributions determinedby the variables' standard errors andmedian (estimated) values. The Monte Carlo module is designed to record many such runs and display the mean value and standard deviation of six 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) andarepresentedfor the standards levels being analyzed. 6. 

### Costs, Sales, and Revenues. This module uses prices, markups, market shares, and industry shipmentlevels to compute variable cost ofgoods 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 overvariable 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-1610. 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 of excess 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 level is 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 of products of a particular 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.2.3.1 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 Cntrl, 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 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 standard deviation of 3. Setting its control to -2, which would otherwise change its value to -1 (an impossibility) actually only changes it to some small but positive value. This value would be just 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 of variation (S.D.Imean) if the input variable is non-normal. At the bottom of the 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 ever future year is relevant for the product under consideration) standards are the same as the 1990 standards. The


### C-17
13(1 

### 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 howprofits 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 Cntrl, 

### 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 aModel 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 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 standarddeviation of3. Settingits control to -2, which wouldotherwisechange 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-thatis, 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 shows howprofits 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 Cntrl, 

### 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 aModel 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 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 standarddeviation of3. Settingits control to -2, which wouldotherwisechange 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-thatis, 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-17second column assumes some particular change in standards and gives long-run values-that is,

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 of the five key output variables. The fourth column, labeled Prvious Change, simply lists the results of the previous run to use as a comparison with the results of the current run. The fifth column lists the results from the short-run part of the model (i.e., the estimated output values for the period immediately after the imposition of the new standards). The following is a list of the nine key input variables that appear as control factors in the Control Panel module: 1. The industry's price elasticity of demand (IPE), 2. The consumer discount rate (RD), 3. The market cost of capital financed through equity (ECC), 4. Economic profit: the industry's ROE minus normal ROE (EP), 5. The long-run fixed part of costs and assets (FCA), 6. The long-run fixed part of one-time costs and assets (FiX), 7. One-time capital costs resulting from the change in standards (CC.L), 8. Variable cost of goods 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 of the 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 of the 400 computed values of each output variable are computed and displayed near the top of the Monte Carlo module. The top row, labeled Value, is the most recently computed random set of outputs. The first four output variables are the percent change 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 last two variables are percentages, the changes presented here are simple differences between their values before and after a change in standards. 

### C.2.3.3 The Accounting Module The Accounting Summary consists of a simplified income statement that is presented in standard form, along with several key financial ratios and figures that highlight the firm's financial position. The income statement and financial ratios are presented for the 1989 calibration case, a 1996 base case that includes the impact of the 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 of the 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 /y a 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, labeled

### Prvious Change, simply lists the results oftheprevious run to use as a comparison with the results ofthe currentrun. The fifth column lists the results 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 ninekey inputvariables 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 of goods 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 of the 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 standard deviations ofthe 400 computedvalues ofeach output variable are computed and displayed near the top of the 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 inlong- and short-runreturn on equity (ROE). Because these lasttwo variables are percentages, the changes presentedhere are simpledifferences between their values before and after achangein standards. 

### C.2.3.3 The Accounting Module The Accounting Summary consists of a simplified income statement that is presented in standard form, along with several key financial ratios and figures that highlight the firm'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 bycomparingthe two' 1996 scenarios. To highlight theresults, 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, labeled

### Prvious Change, simply lists the results oftheprevious run to use as a comparison with the results ofthe currentrun. The fifth column lists the results 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 ninekey inputvariables 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 of goods 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 of the 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 standard deviations ofthe 400 computedvalues ofeach output variable are computed and displayed near the top of the 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 inlong- and short-runreturn on equity (ROE). Because these lasttwo variables are percentages, the changes presentedhere are simpledifferences between their values before and after achangein standards. 

### C.2.3.3 The Accounting Module The Accounting Summary consists of a simplified income statement that is presented in standard form, along with several key financial ratios and figures that highlight the firm'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 bycomparingthe two' 1996 scenarios. To highlight theresults, a final column in the accounting summary indicates the percentage change from the 1996 base case to the 1996 new standards case.
C-18Table C.1 Accounting Module

Item Earnings Before Interest and Taxes Total Expenses Interest

Taxes Net Income Gross Margin Return on Sales Return on Assets (ROA) Return on Equity (ROE) Formula EBIT = Revenue - Total Expenses Does not include interest or taxes (Interest rate mb Debt ratio mb Assets) - Interest earned on "Cash" assets (EBIT - Interest) mb Tax rate EBIT - Interest - Taxes. 1 - (Variable Cost of Goods 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 of the Accounting Summary page lists financial data for the firm using figures that are necessary for an economic analysis

. Note that the assets and expenses 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" (1-X) in the Accounting module refers to expenses incurred because of the 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 or necessary. The various components of the module are described below: 1. Increment in additional UVC (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 UVC above Level 0 cost (VCS.E): "Additional" UVC is measured relative to the 1989 calibration case, which is shown as level 0. In other words, this table is a cumulative version of the above "incremental" table.



### C-19 I q/ Table C.I Accounting Module

Item Fonnula Earnings Before Interest and Taxes Total Expenses Interest
Taxes**~et** 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)**~et** Income/Revenue (EBIT

- Taxes

- (Tax rate mb Interest))/Assets This calculation eliminates interest tax shield**~et** Income!Equity A second table atthe bottom ofthe Accounting Summarypage lists financial datafor the firm usingfigures 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 UVC (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 UVC above Level 0 cost (VeS.E): "Additional" UVCis 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-19 Table C.I Accounting Module

Item Fonnula Earnings Before Interest and Taxes Total Expenses Interest
Taxes**~et** 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)**~et** Income/Revenue (EBIT

- Taxes

- (Tax rate mb Interest))/Assets This calculation eliminates interest tax shield**~et** Income!Equity A second table atthe bottom ofthe Accounting Summarypage lists financial datafor the firm usingfigures 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 UVC (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 UVC above Level 0 cost (VeS.E): "Additional" UVCis 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. Annual unit energy consumption per year: For electric appliances, these engineering input

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 a per-unit basis 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 capital and engineering costs (CCEE.E): The top row of this matrix is the sum of the columns in the "additional capital cost" matrix. Thus the CCEE.E matrix gives the combined added capital cost for all classes of an appliance. The maximum number (from the highest level) in this row is called MxCC and is used to assign engineering expenses to the levels as follows:


### EEi =EE:CC -MXCC •CCi
where EE:CC is the ratio of total engineering expense to total capital cost, and EEC 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 of the 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 of listing 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 of engineering 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 and LEV.N, which correspond to the Base case and Long-Run case, respectively.



### C-20
j! 1- 

### 3. Annualunitenergy consumptionperyear: Forelectric appliances, theseengineeringinput data are the energy use peryear measured 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. 

### 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 capital and 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 combinedaddedcapital cost for all classes ofan appliance. Themaximum number (from the highest level) in this row is called

### MxCC 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 of the 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 of listing 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, whichcorrespond to the Basecase andLong-Run case,respectively.

C-20 

### 3. Annualunitenergy consumptionperyear: Forelectric appliances, theseengineeringinput data are the energy use peryear measured 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. 

### 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 capital and 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 combinedaddedcapital cost for all classes ofan appliance. Themaximum number (from the highest level) in this row is called

### MxCC 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 of the 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 of listing 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, whichcorrespond to the Basecase andLong-Run case,respectively.

C-20The bottom numbers in the "Levin" matrix determine whether these levels will refer to "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 if in "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 of these 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 data into prices, quantities sold, and revenue. These intermediate output data are then used in the financial and economic summaries to give output results, 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 UVC (dVC.B), the weighted average change in price, the new weighted average price (PB), the firm's total revenues (Y, (Ri), RB), the weighted average change in operating costs (OC%B), and the total firm shipments (Q.B). PB, Q.B and R.B 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 of the product classes, and baseline unit variable costs to calculate the prototypical firm's shipments for each product class, its cost of goods sold, and its revenue. Also calculated are a weighted-average unit variable cost, selling price, and markup. Markup is calculated from data on the ratio between the highest and lowest markups (ratio) and the typical markup (mid).

C-21

X13 Thebottom numbers in the "LevIn" matrixdetermine 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. 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, 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 dataintoprices, quantities sold, and revenue. These intermediate outputdata are then usedin 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 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 to calculate the prototypicalfirm's shipments for eachproductclass, its cost ofgoods sold, anditsrevenue. 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 Thebottom numbers in the "LevIn" matrixdetermine 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. 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, 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 dataintoprices, quantities sold, and revenue. These intermediate outputdata are then usedin 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 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 to calculate the prototypicalfirm's shipments for eachproductclass, its cost ofgoods sold, anditsrevenue. 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-21Table C.2 Equations Used in the Costs, Sales, and Revenue Module

Item Size Industry Shipments Relative Shipments (Q%)
Shipments (Q.1) Price (P.1) Revenue (R.1) Markup (M.1) Unit Variable Cost (UVC.1) 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 Calibration 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.1 - BB)/AA P.1/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.1 mb Change in UVC (P.B-P)/EWghtd dP P.1 +MarkB*M. 1 mb Change in UVC 1996 UEC/1990 UEC 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 of the product classes. The model then uses known price and operating cost elasticities to calculate changes in shipments for each of the 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



### lci

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.l) Markup (M.I) Unit Variable Cost (UVC.l) Weighted UVC Weighted Change in 

### uve 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 CostRatio 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 I + (P.I
- BB)/AA P.I/M.l 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)/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 PLB 

### * QLB


### 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 of the 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



### 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.l) Markup (M.I) Unit Variable Cost (UVC.l) Weighted UVC Weighted Change in 

### uve 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 CostRatio 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 I + (P.I
- BB)/AA P.I/M.l 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)/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 PLB 

### * QLB


### 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 of the 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-22The manufacturer markup listed in the panel is used to calculate the baseline model prices,

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 of the firm. The financial inputs and intermediate outputs are then used elsewhere in the model when the impacts of standards 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 particular point in time when the size of the firm may be different from what it was in the year in which the data were collected, some of the 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 (called DA:R) is 0.224. This is used by the model to compute a value for depreciable assets at some typical year in the future after standards have changed by multiplying DA:R by revenue in that year. Table C.3 Financial Module
Item Formula Depreciation Average depreciation rate x DR:R (DR:R = Depreciable assets as a percentage of revenues Pre-Tax Equity Cost of Capital (PECC) Equity Cost of Capital/(1 - Tax Rate) Weighted Average Cost of Capital (WACC) (Debt ratio mb Interest rate) + (Equity ratio mb PECC) After-Tax WACC W ACC mb (1 - Tax rate) Return on Equity (ROE) After-Tax ECC + Economic Profit (ECC = Equity Cost of Capital) (This ROE figure is the expected ROE for a prototypical firm.) A complication may occur if a 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 of the value will not change with revenues because of

16For a thorough description of how markups are derived and used in LBL-MIM, see Appendix C.2.1. 

### C-23 

### Themanufacturermarkup listedin thepanel is usedto calculatethe baseline 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 used elsewhere in the model when the impacts of standards 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 changed by 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 in LBL-:MIM, see Appendix C.2.I. 

### C-23 Themanufacturermarkup listedin thepanel is usedto calculatethe baseline 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 used elsewhere in the model when the impacts of standards 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 changed by 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 in LBL-:MIM, see Appendix C.2.I. 

### C-23the change in standards (i.e., 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 of the total value of these 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 of capital, and the timing of the 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) of the new capital is computed. The calculations are based on the following model of investment: new capital will be paid 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 of each 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 of the capital. Some part (0%-10%) of the capital expenditure is assumed to be for new plant and equipment that do not take the place of anything in existence; the remainder is 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. If the 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 of the capital's useful life all of the issued debt could have been paid off, and all of the issued equity could have been repurchased.17 Since standards are assumed to last indefinitely, new capital will need to be purchased at the end of this cycle; but of course the next cycle starts with a clean slate and repeats the same process we have just 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 of product lines, lines would undoubtedly be much nearer to

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


### /V6 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 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. Thecalculations are basedon the following model ofinvestment: new capital will be paid 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 assumed to be for new plant andequipmentthatdo not take the place ofanything in existence; the remainderis 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. If

### the 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 of the 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 of this 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, engineering costs should actually be lower, possibly zero, in the second cycle. Second, because industry is constantly improving the efficiency ofproduct lines, 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 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. Thecalculations are basedon the following model ofinvestment: new capital will be paid 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 assumed to be for new plant andequipmentthatdo not take the place ofanything in existence; the remainderis 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. If

### the 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 of the 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 of this 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, engineering costs should actually be lower, possibly zero, in the second cycle. Second, because industry is constantly improving the efficiency ofproduct lines, lines would undoubtedly be much nearerto

17 Ofcourse tax benefits are counted toward eliminating debt and equity. C-24meeting the standards by the beginning of the second cycle. Thus, much of the capital equipment

re-installed at the end of the first capital's lifetime would have been a normal expense by this date (around 2001). For the interested reader, the details of the calculation method are given in Table C.4.

Table C.4 One-Time Cost Amortization Module

Item Definition Formula DR [ER] T ECC PECC WACC ATWACC ATR Tax rate (34 %)

Equity cost of capital Pre-tax ECC Weighted average CC After-tax WACC Continuous ATWACC Debt [Equity] / (Debt + Equity)

ECC/(1 - T) DR *I + ER * PECC WACC* (1 - T) ln(1 + ATWACC) CCi EEi WCCi WEEi LTC LTE EMRT E BN RTL Capital cost i years before standards

Engineering expense i years before standards (see 1-X Cost module) (see 1-X Cost module) (see 1-X Cost module) (see 1-X Cost module)

"Tax benefit rate" Remaining tax life CCi * exp (ATR * i) EEI * exp (ATR * i) Sum of WCCi over 4 years Sum of WEEi over 4 years exp (-ATR*TL) exp (-ATR*L) T/TL max (TL - AGE, 0) PVD OCS OCTL TBS PVCC APVCC CCLF EELF Present value of depreciation

Old capital savings Old capital tax loss Tax benefit from scrapping Present value of CC Adjusted for lead timer CC amortization factor EE amortization factor - BN * (1 -EMRT)/ATR - (1 - %NC) * exp (-ATR * (L - AGE)) (1 - %NC) * BN * (1 - EMRT)/ATR -RTL * BN * (1 - %NC) 1 + PVD + OCS + OCTL +TBS PVCC * LTC ATR * APVCCC/(1 - EMRL) LTE*(1-T)*ATR/(1-EMRL) AFB (see One-Time Cost module) 1 -ATR*L*EMRL/(1-EMRL)

AFC (see One-Time Cost module) 1 - EMRL 1 - ATR * TL * EMRT/(1 - EMRT)

B:T AFF (see One-Time Cost module)

Average asset factor 1 - EMRL (1 - EMRT)/(ATR/BN + 1 - EMRT) B:T * AFB + (1 - B:T) * AFC


### C-25 meeting the standards by the beginning ofthe secondcycle. Thus, much ofthe capitalequipment re-installed at the end of the first capital's lifetime would have been a normal expense by this date (around 2001). For the interested reader, 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 ATW ACC 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* (1 -1) In(l +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/I'L max (TL

- AGE, 0)

- BN * (1 -EMR1)/ATR

- (1

- %NC) * exp (-ATR 

### * (L
- AGE» (I

- %NC) 

### * BN 

### * (1

- EMR1)/ATR -RTL 

### * BN 

### * (1

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

- EMRL) LTE * (1 -1) 

### * ATR/(1

- EMRL) 1 - ATR 

### *L 

### *EMRL/(I

- EMRL) 1 - EMRL 1 - ATR 

### * TL

### *EMRT/(1

- EMRT) 1 - EMRL (1

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

### * APB + (1

- B:T) 

### * APC 

### meeting the standards by the beginning ofthe secondcycle. Thus, much ofthe capitalequipment re-installed at the end of the first capital's lifetime would have been a normal expense by this date (around 2001). For the interested reader, 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 ATW ACC 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* (1 -1) In(l +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/I'L max (TL

- AGE, 0)

- BN * (1 -EMR1)/ATR

- (1

- %NC) * exp (-ATR 

### * (L
- AGE» (I

- %NC) 

### * BN 

### * (1

- EMR1)/ATR -RTL 

### * BN 

### * (1

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

- EMRL) LTE * (1 -1) 

### * ATR/(1

- EMRL) 1 - ATR 

### *L 

### *EMRL/(I

- EMRL) 1 - EMRL 1 - ATR 

### * TL

### *EMRT/(1

- EMRT) 1 - EMRL (1

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

### * APB + (1

- B:T) 

### * APCTable C.4 should be read in conjunction with the One-Time Cost Amortization module.

Note that the calculations are all done with continuous interest and discount rates, which makes it necessary to convert ATWACC to a continuous equivalent cost of capital ( 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 of income at $Y per year for L years, the present value is Y- (1

-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 order to produce a cost factor instead of an actual cost . (2) The present value of the tax benefit from straight

-line depreciation of the initial cost. (3) Present value of savings from not replacing old capital at the end of its normal life if it 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 of standards. All that is left to do now is to convert from a present value to an equivalent constant stream of payments that continues for the lifetime of the 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 of a piece of capital is, at any time, simply the present value of its future income stream, the value of the depreciated capital as a function of time can be found from the stream of income that the capital will generate. If that stream has a constant flow for a period of T years, then the depreciated value of a unit of capital is DF(t) = 1-e -r(T-t)

1-e -rT Now if that capital is held for a period of L years, it makes sense to ask what the average value of the asset is over the L years. Because of discounting, a unit of capital now should weigh more heavily than a unit of capital later. This is properly taken into account by requiring the following equivalence:



### C-26


### six 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 convertATWACC 

### 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 oexp(-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 

### Yo (J-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 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 that continues 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 constant flow for a period ofT 

### years, then the depreciated value of a 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 aunit ofcapitallater. This is properly taken into accountbyrequiringthe 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 convertATWACC 

### 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 oexp(-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 

### Yo (J-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 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 that continues 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 constant flow for a period ofT 

### years, then the depreciated value of a 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 aunit ofcapitallater. This is properly taken into accountbyrequiringthe following equivalence:

C-26PV(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 [1-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-except for the fact that the asset of interest does not generate a constant stream of income 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 of these streams separately fits the description of the problem that has been solved. The solution to this new problem is just the average of the solutions for the two separate problems. For example, if 20% of the 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 B:T gives the fraction of the total stream made up of tax benefits. The factors AFB andAFC are the average asset factors for tax benefits and the productive income of capital respectively, and AAF is the weighted average of these two using B:T as the weight for AFB. At the bottom of the One-Time Cost Amortization module are found the capital and engineering cost for the base and long-run levels, and the coefficients of variation associated with each. The three factors just derived, CCLF, EELF, and AAF, can be applied to CC, EE, and again CC, respectively (for either 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 met current NAECA standards. The third estimates a typical firm's situation with more stringent standards than the current NAECA standards. A detailed discussion of the economic assumptions and results used by this section can be found in C.2.1.

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 asset ofinterest does not generate a constant 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 is just 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 engineeringcostfor the base 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 met current NAECA standards. The third estimates a typical firm's situation with more stringent standards than the current NAECA standards. A detailed discussion of the economic assumptions and results used by this section can be found in C.2.1.

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 asset ofinterest does not generate a constant 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 is just 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 engineeringcostfor the base 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 met current NAECA standards. The third estimates a typical firm's situation with more stringent standards than the current NAECA standards. A detailed discussion of the 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 (i.e., a fixed variable in the 1990 NAECA case). The most important variable names in the long

-run module are A for assets, TC for total cost excluding taxes and the cost of equity, EC for economic cost (including taxes and equity), El for economic income, and ul for markup above marginal cost, u - 1 . Calibration

. The calibration stage estimates a typical firm '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 of total cost and are not per-unit costs.) Economic Profit is the difference between ROE and the equity cost of capital, both of which 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 of revenue, so firm 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 of revenue. Depreciable assets are divided between fixed and marginal according to the input parameter FCA, the "fixed part of costs and assets." Inventories are classified as marginal 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-related costs. All other costs are marginal, and their amount is deduced from the fum's rate of economic profit. Because the costs so far are tax-deductible, but revenue is taxable, economic fixed costs are given by the following formula:
EC.F =(l -T) •TC.F+A.F °ER •ECC
where T is the tax rate, ER is the ratio of equity to assets, and EGG is the cost of assets. 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 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 important variable 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) Variable Cost (In this equation, fixed and variable cost are components oftotal cost and are notper-unit costs.) 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, 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-related costs. All other costs are marginal, and their amount is 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 of assets. 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 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 important variable 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) Variable Cost (In this equation, fixed and variable cost are components oftotal cost and are notper-unit costs.) 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, 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-related costs. All other costs are marginal, and their amount is 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 of assets. 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 of economic 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 = 1 -EC.F-EC.R-EP -ER •A:R
where EP is the rate of economic profit, and A:R is the ratio of total 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 (1990 NAECA. 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 of what 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 FIX, 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 or have been computed

. The others are obtained as follows:

Variable Cost of Goods Sold =VCGS =UVC •Q Interest, Not lX =IC =I •DR •A-C:R •R •(I-ICash)

Pre-interest Cost= PIC =TC-IC



### C-29 

### finish 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, andA: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 (1990 NAECA). 

### 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'(1-ICash) Pre-interestCost=PIC=TC-IC

C-29 

### finish 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, andA: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 (1990 NAECA). 

### 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'(1-ICash) Pre-interestCost=PIC=TC-IC

C-29Pre-tax Cost = PTC =TC Taxes= TAX = (R-TC) •T
Equity = EQ =ER •A Economic Income =EI =NI - EQ •ECC and

Net Income =NI =R-TC-TAX

xeturn on hquity = Kvrr =t3t;u T EQ For the base case the above formulas hold with the exception of the one for NI, but several other descriptive variables must be computed. Their formulas follow:
Q.B =Q •LCC (PE+ICE) P.B =(p1+1) •EC.BQ R.B =Q.B °P.B UVC.B =UVC+DVC.B VCGS.B =UVC.B •Q.B 1X depreciation =X1D.B =CC.B/L 1X interest=XlI.B =LA.B -DR -1 -LTC 1X equity cost=Xll.B=LA.B -ER •ECC 1X tax benefit=X1T.B =LCC.B +LEE.B -X 1D.B -XlI.B -X1E.B IC.B=I -DR - (A.B -LA.B) -C:R •R.B •(1-ICASH)

PIC.B =TC.B +X1D.B -IC.B

PTC=TC.B +X1D.B +X1I.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 one for

### NI, 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 -(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 one for

### NI, 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 -(I-ICASH) PIC.B=TC.B+XID.B-IC.B

PTC=TC.B+XID.B+XII.B

C-30NI =R -TC -TAX -LCC.B -LEE.B ROE.B =NI.B/EQ.B

In the formula for Q.B above, LCC stands for life-cycle cost. This formula 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 of constant 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-run price 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 of the change in standards. From SRPR compute the implied fall in price resulting from this unexpected drop in demand. At this new price level, compute a new level of demand 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. After five or fewer repetitions of these calculations, our 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 of the 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 of LBL-MIM's data. Cost data. All cost data are provided by the Engineering Analysis.



### C-31 /j3 

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

In the formula for Q.B 

### above, LCC 

### stands for life-cycle cost. This formula 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, itis possible to estimate the industry'S "short-run price 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 in price resulting 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, oUfestimates 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.B/EQ.B

In the formula for Q.B 

### above, LCC 

### stands for life-cycle cost. This formula 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, itis possible to estimate the industry'S "short-run price 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 in price resulting 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, oUfestimates 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 and operating 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 of the change in standards. Financialdata. Financial data are collected from publicly available sources such as Value Line, Standard and Poor's, Moody's, and company reports. The data are often for firms whose operations in the industry under consideration are only part of the firm's total business operations. It is assumed that the relevant division is similar to the firm as a whole. Amortization data. In order to amortize the one-time costs associated with 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 of engineering and capital costs come from the Engineering Analysis. Fixed and marginal costs and assets. Some costs increase in proportion to shipments, and some are fixed. Industry representatives indicate that the bulk of costs and assets are proportional 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 elasticity is -0.5. Because standards will cause the prices of products to increase, this input is obviously crucial in determining the impact of standards 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, if operating costs of a product decrease 10% and shipments increase 1% as a result, 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 calculated from the discount rate supplied by the LBL-REM. Equity cost of capital. This is the rate of return expected by equity shareholders. This figure is in real terms (i.e., 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 

## )sv Elasticities andoperating costs. Industry price elasticity and operatingcost 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 operationsintheindustry underconsideration are onlypartofthe firm's totalbusiness operations. It is assumed that the relevant division is similar to the finn 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 discussions with industryrepresentatives. The timing ofengineering andcapital costs comefrom the Engineering Analysis. 

### Fixedandmarginalcostsandassets. Some costs increasein proportionto shipments, and some arefIxed. Industryrepresentatives indicate thatthe bulk: ofcosts 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 elasticity is -0.5. Because standards will cause the prices ofproducts to increase, this input is obviously crucial in determining the impact of standards 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 ofa product decrease 10% and shipments increase 1% as a result, 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 (i.e., 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 and operatingcost 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 operationsintheindustry underconsideration are onlypartofthe firm's totalbusiness operations. It is assumed that the relevant division is similar to the finn 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 discussions with industryrepresentatives. The timing ofengineering andcapital costs comefrom the Engineering Analysis. 

### Fixedandmarginalcostsandassets. Some costs increasein proportionto shipments, and some arefIxed. Industryrepresentatives indicate thatthe bulk: ofcosts 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 elasticity is -0.5. Because standards will cause the prices ofproducts to increase, this input is obviously crucial in determining the impact of standards 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 ofa product decrease 10% and shipments increase 1% as a result, 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 (i.e., 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-32Economic profit. Economic profit is the profit earned by a firm above its expected return

on equity. In other words, it is the profit earned after all costs are taken into account, including the cost of equity (the market rate of return). A firm makes economic profit only when it has some degree of market power. Long-run fixed part of costs and assets. This figure is the portion of all 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-run fixed part of one-time capital cost. This figure is the portion of one-time capital costs that is fixed in the long run. Although all capital costs are normally considered fixed in the short run, this portion of the 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-run price response to demand (SRPR). When industry demand falls by Y%, price will, in the short run, fall by SRPR mb Y%. This response is expected to die out within a few years, and Y% should be less than 20% when SRPR is used. C.2.4.3 Engineering Data Incremental unit variable 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 for each product class of the product type being analyzed, rather than a single shipments-weighted average number. The increased variable cost includes raw materials, direct labor, purchased parts, and increased transportation costs. The data also come in the form of a table that lists the incremental variable cost for each product class for each of the standards levels being analyzed.



### C-33

is5 

### Economicprofit. Economic profitis the profit earnedby a firm 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 portionofone-time capital costs that is fixed in the long run. Although all capital costs are normally considered fixed in the short run, this portion of the 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 a few years, and Y% should be less than 20% when 

### SRPR is used. C.2.4.3 Engineering Data 

### Incremental unitvariable cost. The incremental unitvariable 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 ofthe producttype being analyzed, rather than a single shipments-weighted average number. The increased variable cost includesrawmaterials,directlabor, purchasedparts, andincreasedtransportation costs. The data also come in the form ofa table that lists the incremental variable costfor each product class for each ofthe standards levels being analyzed.

C-33 

### Economicprofit. Economic profitis the profit earnedby a firm 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 portionofone-time capital costs that is fixed in the long run. Although all capital costs are normally considered fixed in the short run, this portion of the 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 a few years, and Y% should be less than 20% when 

### SRPR is used. C.2.4.3 Engineering Data 

### Incremental unitvariable cost. The incremental unitvariable 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 ofthe producttype being analyzed, rather than a single shipments-weighted average number. The increased variable cost includesrawmaterials,directlabor, purchasedparts, andincreasedtransportation costs. The data also come in the form ofa table that lists the incremental variable costfor each product class for each ofthe standards levels being analyzed.

C-33Unit energy consumption (UEC) . 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 UEC for each product class for each design option (and standards level) being analyzed

. Electricity is 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 of the manufacturer. If more than one size of manufacturer 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 mandated by the new standards require additional capital investment in the form of retooling, new equipment, or other capital expenditures. This input is given as a table 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 of the manufacturer. If more than one size of manufacturer 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 Industry shipments. Industry shipments are annual shipments for all classes of the product type being analyzed. These inputs are actual data for a 1990 calibration case, an estimate for the 1996 base case using the 1990 standards, and 1996 estimates for each of the 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" of the shipments for that product type. For example, electric storage water heaters account for about 45% of all water heater shipments. The product class market share combined with the total industry shipments give a breakdown of annual shipments by product class. Manufacturer market share. Each of the prototypical firms in the analysis is assigned a market share appropriate to that segment of the 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, combined with the product class market shares, gives a prototypical firm's annual shipments for each product class.



### C-34
%6^ Unit 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 product class for eachdesign 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 of the 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 standardsrequire 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 shipmentsfor all 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 1996 estimates for eachofthe 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 thatproduct type. Forexample, electric storage waterheaters account for about45% ofall water heater 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 of the 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, 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 energy cost (in dollars) of a product. The input data come in the form of a table that lists the VEC for each product class for eachdesign 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 of the 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 standardsrequire 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 shipmentsfor all 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 1996 estimates for eachofthe 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 thatproduct type. Forexample, electric storage waterheaters account for about45% ofall water heater 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 of the 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, combined with 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 firm's

selling price for a particular product. It is an increase over the unit variable cost of that product, 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 of reaching the higher efficiency levels required by standards. Energy price. This figure is the price ratio of a 1990 kWh to a 1996 kWh 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%.1V). 

### 0.2.4.5 Financial Inputs Rates of financial costs: After-tax equity cost of capital. This input is explained in Section C.2.3.1, Control Panel. Interest rate on debt. This is the interest rate paid by a corporation on its debt. This figure is in real terms (i.e., with inflation subtracted), and is the yield of corporate bonds with a Moody's rating comparable to ratings in the industry being analyzed. Interest lost on cash. Companies hold a portion of their current assets in the form of cash in bank accounts, marketable securities, and other liquid holdings, all lumped under the term "cash." This category of assets has a cost associated with it because of the cost of the debt and equity associated with all assets. However, companies manage their cash 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 cost of the capital used to form the asset. Thus, the cash account costs the company money but the cost is lower than for any of the company's other assets. Rate of depreciation. This figure is the rate of depreciation on fixed assets for a typical firm in the industry. It is obtained by assuming straight-line depreciation and an average tax life of eight years for the firm'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

157 

### Markups. 

### The markup is the figure used by the manufacturer to determine the firm's selling price for aparticularproduct. It is an increase over the unit variable cost ofthatproduct, and it

### covers overhead, capital costs, andprofit. 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.S Financial Inputs Rates of financial costs: After-taxequity costof capital. 

### This input is 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 terms (Le., with inflation subtracted), and is the yield of corporate bonds with a Moody's rating comparable to ratings in the industry being analyzed. Interest loston cash. 

### Companies hold aportion oftheircurrent assets in the form ofcash in bank accounts, marketable securities, and other liquid holdings, all lumped under the term "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 form 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 firm in the industry. Itis obtained by assuming straight-line depreciation and an average tax life ofeight years for the firm'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 firm's selling price for aparticularproduct. It is an increase over the unit variable cost ofthatproduct, and it

### covers overhead, capital costs, andprofit. 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.S Financial Inputs Rates of financial costs: After-taxequity costof capital. 

### This input is 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 terms (Le., with inflation subtracted), and is the yield of corporate bonds with a Moody's rating comparable to ratings in the industry being analyzed. Interest loston cash. 

### Companies hold aportion oftheircurrent assets in the form ofcash in bank accounts, marketable securities, and other liquid holdings, all lumped under the term "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 form 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 firm in the industry. Itis obtained by assuming straight-line depreciation and an average tax life ofeight years for the firm'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

### Assets and costs as a percent of revenue:

Cash. Companies hold a portion of their current assets in the form 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 of total revenue. Inventory and receivables

. This figure represents the inventory and receivables assets, expressed as a percent of total revenue. Inventory includes raw materials owned by 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 net depreciable, or fixed, assets of the 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 of revenue. 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 of revenue. The engineering expense associated with meeting the standards appears on the Costs and Assets of the One

-Time Investment page of the model. 

### Fixed and variable costs: Fixed part of all costs and depreciable assets
. This input is explained in the Control Panel section of C.2.3.1. Fixed part of one

-time capital cost

. This input is explained in the Control Panel section of C.2.3.1. 

### Other financial inputs: Economic profit. This input is explained in the Control Panel section of C.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 of capital

. It is also associated with the risk factor of the firm and affects how much money a firm is able to borrow and at what interest rate, so it is important in the analysis of the impacts of standards. 

### C-36 Assets and costs as apercent ofrevenue: Cash. 

### Companies hold a portion of their current assets in the form 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 of total revenue. Inventory and receivables. 

### This figure represents the inventory and receivables assets, expressed as apercent oftotalrevenue. Inventoryincludes 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 andvariable costs: Fixedpartof allcostsanddepreciable 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 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 of capital. 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 of standards.
C-36 Assets and costs as apercent ofrevenue: Cash. 

### Companies hold a portion of their current assets in the form 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 of total revenue. Inventory and receivables. 

### This figure represents the inventory and receivables assets, expressed as apercent oftotalrevenue. Inventoryincludes 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 andvariable costs: Fixedpartof allcostsanddepreciable 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 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 of capital. 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 of standards.
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 of a typical product in the industry being analyzed. Ratio of highest to lowest markup. In some industries, manufacturers use different markups on different product classes. If such a situation exists in the industry being analyzed, this ratio indicates the range of markups used. 

### Costs and assets of one-time investment: One-time capital cost's life. This input is the productive life of the equipment purchased 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 of the equipment purchased to meet the standards, expressed in years. Percent additional one-time capital. Meeting standards may require both the addition of new capital that replaces nothing, and the replacement of old capital that has become inadequate. This variable tells what percent of new capital is additional rather than for replacement. Age of old (replaced) capital. This input gives the age, in years, of the old capital that gets replaced because of the new standards. Under standards, some old equipment will get scrapped before the end of its productive life and replaced by new equipment. Depending on the age of the replaced capital, tax and/or economic consequences are factored into the calculations of the cost of the new equipment. One-time capital cost and 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 together in 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 of the amortized costs, rather than assuming that the costs all occur when the standards go into effect. 

### C.2.5 Model Outputs C.2.5.1 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 

### shy Markup on a typical model. 

### This figure is the markup used by a fIrm in determining the manufacturer's selling price. Itis 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. If such 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 oftime 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 replacement ofold capital thathas become inadequate. This variable tells what percent of new 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.l 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 on a typical model. 

### This figure is the markup used by a fIrm in determining the manufacturer's selling price. Itis 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. If such 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 oftime 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 replacement ofold capital thathas become inadequate. This variable tells what percent of new 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.l 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-37For each of the 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 if the 1990 standards are in effect but if no 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 of impacts on manufacturers in the short run, 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 of units. The shipments variable is the sum of shipments for all product classes within a product type. Shipments data are calculated on the Costs, Sales, and Revenues page. Price. Price is the shipments-weighted average of the 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 amount of money received by the manufacturer for 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 expressed in millions of dollars. 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 of dollars. Net income and the associated return-on-equity are primary measures of a company's financial performance. In LBL-MIM, net income is calculated in the Accounting Summary. Return on equity (ROE). Net income is a dollar amount that varies in part 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 of a 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 of net income. In the Accounting Summary, net income is expressed in thousands of dollars rather than millions.



### C-38

/AO 

### 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 amount ofmoney received by 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 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 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.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-38 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 amount ofmoney received by 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 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 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.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 of goods sold is accounted for: GM=(R-VCGS)1R. Return on sales. Return on sales is net income as a percentage of revenue. Total assets. Total assets is the sum of all of the 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. It is a measure of how 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 of the firm to calculate return on assets. Equity. Equity is the portion of a firm's assets owned by the stockholders of the 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 of ROE. 

### C.2.5.3 Economic Analysis Outputs The economic analysis outputs give a clear and concise picture of what is going on in the company from an economic standpoint. Most of the outputs show up in the other two outputs sections. Shipments, price, and revenue are discussed in the Control Panel section. The total assets number is calculated in the Accounting Summary section, and the economic analysis output breaks down total assets into three categories: cash, inventories, and depreciable assets. Expenses are looked at from an economic point of view 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 of six key measures of impact when the Monte Carlo option is used. For key input variables, the Monte Carlo option randomly generates values around the best available estimates of their true values. As a result, 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 of impact and attribute a degree of certainty to them. The Monte Carlo Outputs section is a table that lists for each variable the value of the current iteration, the mean of all the iterations done in the current run, and the standard deviation



### C-39
/W 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. 

### 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 of the firm to calculate return on assets. 

### Equity. Equity is the portion of a firm's assets owned by the stockholders of the 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.S.3 Economic Analysis Outputs Theeconomic analysis outputs give a clear and concisepicture ofwhat is going onin the company from an economic standpoint. Most of the 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 breaksdowntotal assetsinto three categories: cash, inventories, anddepreciable assets. Expenses are looked at from an economicpoint ofview 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 The Monte Carlo outputs section lists the results of six key measures ofimpact when the Monte Carlooptionis used. Forkey inputvariables, the Monte Carlo optionrandomly generates values around the best availableestimates oftheir true values. As a result, 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 current run, 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. 

### 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 of the firm to calculate return on assets. 

### Equity. Equity is the portion of a firm's assets owned by the stockholders of the 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.S.3 Economic Analysis Outputs Theeconomic analysis outputs give a clear and concisepicture ofwhat is going onin the company from an economic standpoint. Most of the 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 breaksdowntotal assetsinto three categories: cash, inventories, anddepreciable assets. Expenses are looked at from an economicpoint ofview 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 The Monte Carlo outputs section lists the results of six key measures ofimpact when the Monte Carlooptionis used. Forkey inputvariables, the Monte Carlo optionrandomly generates values around the best availableestimates oftheir true values. As a result, 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 current run, 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 of impacts are percentage change in shipments, percentage change in price, 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 of each of these outputs. 

### C.2.5.6 Retail Prices One important role of LBL-MIM is to provide estimates of retail prices of appliances at different standards levels for the LBL-REM and the Life-Cycle Cost analysis. The primary input for the retail price calculation in LBL-MIM is MIM's estimates of manufacturer prices. The manufacturer prices are derived from base manufacturer costs, engineering production costs, and appropriate manufacturer markups for each product. The base manufacturer cost is derived from 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 of manufacturers' 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 of the relevant products volume for specific details). Retail prices are given in 1990 dollars. 

### C.3 INPUT DATA AND DATA DEVELOPMENT FOR THE EIGHT PRODUCTS The LBL-MIM input data 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 of the TSD.



### C-40 

## r(,.I of the results. It then lists in chronological order the results of all the iterations done in the current run. Themeasures ofimpacts are percentage change in shipments, percentage changeinprice, percentagechange inrevenue, percentage change in net income, andpercentage 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 of each of these outputs. C.2.S.6 

### Retail Prices One important role ofLBL-MIM is to provide estimates ofretail prices ofappliances at different standards levels for the LBL-REMandthe Life-Cycle Costanalysis. Theprimaryinput for the retail price calculation in LBL-MIM is MIM's estimates of manufacturer prices. The manufacturerprices are derived from base manufacturer costs, engineeringproduction costs, and appropriatemanufacturer 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 dependingon the 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 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 of the TSD.

C-40 of the results. It then lists in chronological order the results of all the iterations done in the current run. Themeasures ofimpacts are percentage change in shipments, percentage changeinprice, percentagechange inrevenue, percentage change in net income, andpercentage 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 of each of these outputs. C.2.S.6 

### Retail Prices One important role ofLBL-MIM is to provide estimates ofretail prices ofappliances at different standards levels for the LBL-REMandthe Life-Cycle Costanalysis. Theprimaryinput for the retail price calculation in LBL-MIM is MIM's estimates of manufacturer prices. The manufacturerprices are derived from base manufacturer costs, engineeringproduction costs, and appropriatemanufacturer 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 dependingon the 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 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 of the TSD.

C-40

### APPENDIX D. MAINTENANCE COSTS FOR COMMON APPLIANCE COMPONENTS

DA 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. Analysis Approach and Results Maintenance cost has been characterized for this analysis as a present value cost increment to be added to the purchase price of an appliance. Thus, the maintenance cost can be described as Cmaint = f Co , where Co is the cost of the maintenance procedure in the year that it occurs ("n" years after the appliance purchase), and f = (1 + d) 'n is the present value factor that discounts the Co value to the purchase time using discount factor d, taken here as 6%. Using this approach, the maintenance cost (Cma;nt ) 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 of Co were calculated by 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 of phone interviews with principal service department personnel at four heating and air conditioning installation companies. After averaging the cost values from the different service organizations, a location multiplier of (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 of location multipliers for mechanical construction work cost estimation data by the R.S. Means Company (23). LIFETIME DATA. The service organization personnel mentioned 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 with care, since it can be argued that service organizations do not have a good perspective on component lifetimes. Where possible, other lifetime data were used, as will be described below. RESULTS. Table D.1 presents the values of Co' 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



## 63 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 increment to 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 

### + dfn is the present value factor that discounts the Co value to the purchase 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 air

### conditioning installation companies. After averaging the cost values from the different service organizations, a locationmultiplierof(1/1.367 =) 

### .732 was used to adjust the 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 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 with care, since it

### canbe 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 increment to 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 

### + dfn is the present value factor that discounts the Co value to the purchase 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 air

### conditioning installation companies. After averaging the cost values from the different service organizations, a locationmultiplierof(1/1.367 =) 

### .732 was used to adjust the 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 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 with care, since it

### canbe 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

### Table DA Data and Calculations Used to Obtain Maintenance Costs (1990 $)

Component Name (C")

San Francisco Regional Cost (Co)

National Average Cost (n)

Maintenance Lifetime (Cmaind

Present Value of Maintenance Cost Data Source Standing Pilot $73.1 $53.5 10.2 $30 survey Intermittent Ignition $249.0 $182.0 na $81 or $55 survey + analysis Combustion Fan $149.2 $109.2 11.1 $57 survey Burner Box 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 Standing pilot systems typically require maintenance for replacement of pilot thermocouples. 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 (IIDs) IIDs are common to mobile home furnaces, water heaters, direct heating equipment, ovens, clothes dryers, and cooktops. The TID devices for ovens are considerably different (with more simple controls) than those for the space-and water-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 IIDs, 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 for this analysis that HSI ignitor breakage rates will have been significantly reduced by the time the subject standards take effect (24). Regardless of the system type, IIIID 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 of cost (Co) values.

D-2

) ^J 

### Table D.I Data and Calculations Used to Obtain Maintenance Costs (1990 $) (Co') (CO> (n) (Cmaint) San Francisco National Maintenance PresentValue of Component Name Regional Cost Average Cost Lifetime Maintenance Cost Data Source S

tanding Pilot $73.1 $53.5 10.2 $30 survey Intermittent Ignition $249.0 $182.0 na $81 or $55 survey + analysis Combustion Fan $149.2 $109.2 11.1 $57 survey Burner Box 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 home furnaces, water heaters, 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 for this analysis thatHSIignitorbreakagerates 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 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 Component Name Regional Cost Average Cost Lifetime Maintenance Cost Data Source S

tanding Pilot $73.1 $53.5 10.2 $30 survey Intermittent Ignition $249.0 $182.0 na $81 or $55 survey + analysis Combustion Fan $149.2 $109.2 11.1 $57 survey Burner Box 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 home furnaces, water heaters, 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 for this analysis thatHSIignitorbreakagerates 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 current HSI breakage replacements). Sensors and control modules were considered separately in terms ofcost (Co) values.



### D-2The Co' values for sensors and control modules were obtained directly from the furnace

maintenance organization survey: Sensor replacement service call: $79 Control module replacement service call: $170 Total Cost: $249 These values represent the total amounts that an appliance owner in the San Francisco area would have to pay for replacement of a defective part. The sensor and control module costs have been added above due to the unavailability of separate lifetime information for sensors. Both sensor and control module were assigned the same lifetime as the control module. MAINTENANCE LIFETIME W. The value of n was estimated from information obtained from III) manufacturers and not from the survey results. There were several reasons for using alternative lifetime information in this case. The first reason has been mentioned above. Secondly, it appeared that IDD 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 of an IID 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 of IID control modules. A separate lifetime was not available for sensors, so the lifetime for control modules was chosen for sensors as well. Conversations with service organizations indicate that sensor lifetimes are probably shorter than control module lifetimes. 

### The following data were derived from conversations with IID control module manufacturers: Item 1: Control module average lifetime is on the order of 14 years. Item 2: The average control module failure rate during the first year of operation is below

2.5%. The value 2.5 was used for this analysis. Failures in the first year are covered under warranty and do not represent a cost to the consumer. Item 3: After 10 years of production, yearly III) sales for the aftermarket (repairs) were

on the order of 5% of total yearly sales. Table D.2 presents a tabular summary of the probability density function (Columns A and B) developed using some of the 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 IID failure at some time during the 18 years of IID life. Results (in Column E) are calculated for appliances with a 15-year life (specifically, direct heating equipment) and for appliances with lifetimes greater than or equal to 18 years (specifically, mobile home


### D-3 jb5 

### 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 representthe total amounts that an 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 of separate lifetime information for sensors. Both sensor and control module were assigned the same lifetime as the control module. MAINTENANCELIFETIME (n). The value ofnwasestimatedfrom informationobtained from lID 

### manufacturers and not from the survey results. There were several reasons for using alternative lifetime information in this case. The ftrst 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 of an 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 for control modules was chosen for sensors as well. Conversations with service organizations indicate that sensor lifetimes are probably shorter than control module lifetimes. Thefollowing datawerederivedfrom conversationswithlID

### controlmodulemanufacturers: Item 1: Control module average lifetime is on the order of 14 years. Item 2: The average control module failure rate during the fIrst yearofoperation is below 2.5%. The value 2.5 was used for this analysis. Failures in the fIrst 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 order of5% 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 TID failure at some time during the 18 years oflID 

### life. Results (in Column E) are calculated for appliances with a 15-year life (specifIcally, direct heating equipment) and for appliances with lifetimes greater than or equal to 18 years (specifIcally, 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 values representthe total amounts that an 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 of separate lifetime information for sensors. Both sensor and control module were assigned the same lifetime as the control module. MAINTENANCELIFETIME (n). The value ofnwasestimatedfrom informationobtained from lID 

### manufacturers and not from the survey results. There were several reasons for using alternative lifetime information in this case. The ftrst 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 of an 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 for control modules was chosen for sensors as well. Conversations with service organizations indicate that sensor lifetimes are probably shorter than control module lifetimes. Thefollowing datawerederivedfrom conversationswithlID

### controlmodulemanufacturers: Item 1: Control module average lifetime is on the order of 14 years. Item 2: The average control module failure rate during the fIrst yearofoperation is below 2.5%. The value 2.5 was used for this analysis. Failures in the fIrst 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 order of5% 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 TID failure at some time during the 18 years oflID 

### life. Results (in Column E) are calculated for appliances with a 15-year life (specifIcally, direct heating equipment) and for appliances with lifetimes greater than or equal to 18 years (specifIcally, mobile home
D-3furnaces). The assumption is made that the cost of all failures (as accumulated in Column E)

does not include the cost of failures occurring in the first year

. Using the total cost, Co = $182 (given above), the present values for IID maintenance were calculated as follows: 

### Appliances with 15 years avg. life: $182 x ($.30/$1.0) = $55 Appliances with 18 or more years of life: $182 x ($.44/$1.0) = $81 Table D.2 Calculations for Mean Lifetime of IID units
Year After Yearly Fraction of Present Value Average Present Value of Summation of Present Purchase Units Failing (F) Discount Factor (f) Maintenance ($1xFxf) 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



### DA FORCED COMBUSTION/ DRAFT FANS The lifetime and cost values shown in Table D.1 for "Combustion Fan" 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% of fan failures. Engineering personnel at some fan-manufacturing companies were contacted to obtain alternative lifetime values to the survey value. However, the fan manufacturers contacted did not 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 occurring in the first year. Using the total cost, Co = 

### $182 (given above), the present values for TID maintenance were calculated as follows: Appliances with 15 years avg. life: $182 

### x ($.30/$1.0) =$55 Appliances with 18 or more 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 COMBUSTION/DRAFf FANS Thelifetime 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 occurring in the first year. Using the total cost, Co = 

### $182 (given above), the present values for TID maintenance were calculated as follows: Appliances with 15 years avg. life: $182 

### x ($.30/$1.0) =$55 Appliances with 18 or more 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 COMBUSTION/DRAFf FANS Thelifetime 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-4establish both bearing temperature and actual hours of operation per year (or per furnace

lifetime). 

### D.5 CONDENSING FURNACE MAINTENANCE The maintenance contractors that responded to questions about condensing furnace maintenance cited a number of causes for maintenance calls, including sensor orifice clogging, circuit board failure, pressure switch failure, and dirt fouling of the 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.1 under "Condensing Design." 

### D.b COMBUSTION BOX DAMPERS Among the survey respondents , there was no maintenance experience with combustion box air dampers at the time that this investigation was performed

. Therefore, a maintenance cost was 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 data were available on maintenance costs for these two options

. Given the lack of data, 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.l as 40 % of the IID costs. Thus, two costs are shown for two

-stage maintenance costs, one for 15-year lifetime equipment (e.g., direct heating equipment) and one for 18

-year or 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.1 were estimated at two times the incremental cost of maintenance for two-stage furnaces.



### D-5

l^^ 11 G' 

### establish 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 "CondensingDesign." D.6 COMBUSTION BOX DAMPERS Among the surveyrespondents, there was no maintenance experience with combustion box air

### dampers at the time that this investigation was performed. Therefore, amaintenance 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 forthese 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.l 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 l8-year or longer lifetime equipment (e.g., mobile home furnaces). Forfull or continuous modulation, incremental maintenance costs were also attributed to control components. The costs in Table D.l were estimated at two times the incremental cost ofmaintenance for two-stage furnaces.

D-5 establish 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 "CondensingDesign." D.6 COMBUSTION BOX DAMPERS Among the surveyrespondents, there was no maintenance experience with combustion box air

### dampers at the time that this investigation was performed. Therefore, amaintenance 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 forthese 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.l 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 l8-year or longer lifetime equipment (e.g., mobile home furnaces). Forfull or continuous modulation, incremental maintenance costs were also attributed to control components. The costs in Table D.l were estimated at two times the incremental cost ofmaintenance for two-stage furnaces.

D-5

### APPENDIX E. ELECTRIC UTILITY IMPACT MODELING

EA 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 avoided costs contain both the marginal energy and marginal reliability value of one MMBtu saved by the proposed standards in each year of the period analyzed. 

### E.2 BACKGROUND AND CONVENTIONS The analysis adopts the standard utility convention that the value of electricity 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 of the utility (plus variable operation and maintenance costs). The reliability impact measures the reliability value of reduced load during system peak periods, which is, by convention, valued at the capital cost of a combustion turbine. This analysis relies on disaggregation by National Electric Reliability Council (NERQ 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 of calculating utility avoided costs, heating appliances are defined as electric heat pumps and electric resistance heat, cooling appliances are defined as room and central air-conditioning plus heat pumps, and baseload appliances are defined 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 a relative measure of heating, 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, number of households, and heating and cooling load hours (25,26,27). This approach assumes that energy savings accrue in proportion to current levels of energy consumption. 

## 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 avoided costs contain both the marginal energy and marginalreliability value ofone MMBtu saved by the proposed standards in each year ofthe period analyzed. E.2 BACKGROUND AND CONVENTIONS Theanalysis adopts the standardutilityconventionthatthe value ofelectricity 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 definedas room 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 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, number of 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.! 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 avoided costs contain both the marginal energy and marginalreliability value ofone MMBtu saved by the proposed standards in each year ofthe period analyzed. E.2 BACKGROUND AND CONVENTIONS Theanalysis adopts the standardutilityconventionthatthe value ofelectricity 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 definedas room 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 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, number of 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-lMost of the information used to calculate the energy savings fractions (ESFs) is contained

in Tables E.l and E.2

. The Heating Load Hours (T-II,H) and Cooling Load Hours (CLH) aze calculated as population

-weighted averages of state

-level data. Alaska and Hawaii aze not included in this analysis because these states are such a small fraction of the country and because 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 of Total) entr ^ eat mg NERC Region Households Aid` Room ^ Heating Electric Resistance
NPCC 11,328,877 0.008 0.045 0

.001 0.009 MAAC 9,259,883 0.021 0.(?40 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


### ^^b 

### Mostofthe infonnationused 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.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 

### Mostofthe infonnationused 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.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-2Table E.2 Regional Population-Weighted Heating and Cooling Load Hours

NERC Region HLH Central Air CLH Room Air

CLH Saturation

Weighted CLH 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



### E-3 

## /-) 1 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 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-3Table E.3 Regional Energy Savings Fractions

NERC Region Heating Cooling Baseload
-NPCC U.U73- -0049 0.M
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: 1 

### ESFHtg =

ESFClg = SatHPi SatERi ' x + x BLH, EMP EffER N SatHPi SatERi [' x EffHP + EffER i=1 x HLHi ]



### HH x Sat (HPi = CACi) x CAC CLHi + SatRACi x RAC CLHi ' EffCAC EffRAC N Sat (HPi + CAC) x CAC CLHi SatRACi x RAC CLHi

x EffCAC + EffRAC i=1



### E-4
) J2-, 

### 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~**

## ~ x 

### EftHP + 

### EffER x

## HL~ ESF Htg 

## =

# ----...........,::--------..1........",---- N 

### [satHPi

# SatE~

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

### EfiER x

## HL~ ]

/**~**



### Sat (HPi = 

### CAq) x 

### CAC CLIft SatRACi 

### x 

### RAC**CL~** 

### FUH· x _____**~~~~---_** + 1 

### EffCAC EffRAC ESF Clg =**-----""---:o--------------------~**

### ~ 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~**

## ~ x 

### EftHP + 

### EffER x

## HL~ ESF Htg 

## =

# ----...........,::--------..1........",---- N 

### [satHPi

# SatE~

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

### EfiER x

## HL~ ]

/**~**



### Sat (HPi = 

### CAq) x 

### CAC CLIft SatRACi 

### x 

### RAC**CL~** 

### FUH· x _____**~~~~---_** + 1 

### EffCAC EffRAC ESF Clg =**-----""---:o--------------------~**

### ~ Sat (HPi + 

### CAq) x CAC

### C~ SatRAq x RAC

### CL~ 

# tr**[~x** EffCAC + 

### EffRAC 

## , /



### E-4where:

HFIi. SatERi SatHPi SatWj + CAQ SatRACi IRA. CAC CLHj RAC CLHi N EffER EffHP EffCAC EffRAC = number of households in region i (1980),

= saturation of electric resistance (ER) heat in region i (1980), = saturation of heat pumps (HP) in region i (1980), = saturation of HPs plus central air conditioners (CAC) in region i (1980, saturation of room 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 of NERC regions (9), 

### efficiency of electric resistance heat (1.0), efficiency of average heat pump in 1980 for heating (1.75), 

### efficiency of average CAC and HP cooling in 1980 (7.79 SEER), and efficiency of average 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 of operating the power plants that will change their output in response to a change in load. This variable cost consists primarily of the cost of fuel for these marginal units. U.S. utilities are characterized by a substantial difference among the prices of fuels used to generate electricity on the margin. Marginal resources of a utility can be either more expensive oil and gas or less 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 relevant marginal cost is the cost of each marginal fuel weighted by the fraction of time each fuel is on the margin plus any variable Operation and Maintenance (O & M). Formally, this relationship can be simplified as follows: 

### Marginal Cost = OGFm X Natural Gas Price + NOGFm X Coal Price +Variable O&M To calculate the oil-and-gas fraction on the margin OGFm, the total amount of oil and gas 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 OGFm with a simplified load duration curve (LDC) for each region. Figure G.1 shows this LDC, where B' is the OGFm. The non-oil-and-gas fraction NOGFm is simply 1 - OGFm. The purpose of the following derivation is to calculate the OGFm (B'). First, note that

tann = A B



### E-5 

## lq3 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 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 of average CAC and HP cooling in 1980 (7.79 SEER), and efficiency of average 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 oiland 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 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 + NOGFm X Coal Price +Variable O&M Tocalculate theoil-and-gas fraction onthe margin OGF m, the totalamountofoil 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" OGFe 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 tan!} =_ 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 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 of average CAC and HP cooling in 1980 (7.79 SEER), and efficiency of average 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 oiland 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 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 + NOGFm X Coal Price +Variable O&M Tocalculate theoil-and-gas fraction onthe margin OGF m, the totalamountofoil 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" OGFe 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 tan!} =_ B

E-5and B = 1.0, which implies that 

### A = B tanS2 = tanS2

The normalized area of the triangle at the top of the LDC represents the fraction of total generation from oil and gas (OGFt). Calculating the area of this triangle yields 

### OGFt = 0.5A'B' = 0.5(BSZ )B' B' can thus be expressed as follows:

24GF ^ B' = OGFm = t



### (tanS2 )

E-6 

### 7g 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 

### 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

Figure E.1 Simplified Load-Duration Curve



### E-7

175 

# 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-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 Q is determined empirically: when OGFt > 0.4, OGFm = 1.0.

The value of A must therefore be greater than or equal to 0.8. Note that when A = 0.8, Normalized peak demand = 1.4 X mean load

and Normalized minimum load = 0.6 X mean load. A weighted-average marginal energy cost is determined for each of the NERC regions using the above formulas, and a national weighted-average marginal cost for heating and baseload energy savings is calculated using 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. For cooling energy savings, the appropriate Marginal Energy Cost (MEC) will exceed the annual average MEC because the OGFm is typically higher in summer than in winter. The annual average OGFm is approximately 52% when calculated using the energy savings fractions for heating, cooling, and baseload energy. Cooling MECs are calculated using a national OGFm of 60 percent to account for the seasonal differences in marginal fuel usage. Table EA shows the annual average OGFm for each NERC region. 

### The appropriate value for A

## =tan

## n is determined 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 = 

### O.S, Normalized peak demand = 

### 1.4 X mean load and Normalized minimum load = 

### 0.6 X mean load. A weighted-average marginal energy cost is determined for each of the NERC regions usingthe above formulas, andanational weighted-averagemarginalcostforheatingandbaseload energy savings is calculatedusing theregional fuel 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 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 

## oap m for each NERC region.

E-S

..... 1. 

### The appropriate value for A

## =tan

## n is determined 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 = 

### O.S, Normalized peak demand = 

### 1.4 X mean load and Normalized minimum load = 

### 0.6 X mean load. A weighted-average marginal energy cost is determined for each of the NERC regions usingthe above formulas, andanational weighted-averagemarginalcostforheatingandbaseload energy savings is calculatedusing theregional fuel 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 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 

## oap m for each NERC region.

E-S

..... 1.Table EA Utility Marginal OGFs, and Year of Capacity 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 of MECs in year j can be summarized as follows: N OGFm = ESFi x OGFm

i=1



### NOGFm = 1 - OGFm MECj = (OGFm x gas price + NOGFm x coal price + Variable O& 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 

### ^'7I Table 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 ESFi 

### x OOF m i=l



### NOOF 

## = 1 - OOF m m 

### MEC j = 

### (OOF m x gas pricej 

### + NOOF m 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 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 ESFi 

### x OOF m i=l



### NOOF 

## = 1 - OOF m m 

### MEC j = 

### (OOF m x gas pricej 

### + NOOF m 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 O & M is calculated using references (27) and (28). Average O & M (fixed and

variable) for all fossil-fuel fired steam-electric powerplants for investor-owned utilities in 1990 is reported in (30) as 0.50/kWh (in 1990 dollars). This .can be split into fixed and variable components using (27). The analysis uses the fraction of O &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 O & M as a percent of total O & M) to the total O & M (0.5¢/kWh). This calculation results in a variable O & M cost of 0.330/kWh or 0.29 $/MMBtu of resource energy (in 1990 dollars). 

### EA Reliability Savings and Avoided Transmission/ Distribution System Capital Costs E.4.1 Value of Reliability Savings
The value of reliability is the value associated with having additional generating capacity available to meet the load in case of unexpected 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 of the marginal investment a utility would make to enhance system reliability, such as the purchase of a combustion turbine (29). The intuitive justification 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 if it never operates, since it is ready to prevent outages if another generating unit fails. If the utility chooses to build another type of plant with higher capital costs (such as a baseload coal unit), it must be basing this choice on other considerations than reliability. Baseload coal plants, for example, have much lower operating costs than oil and gas fired generation, and can be used to displace generators with higher variable cost per kWh. The additional capital cost of a 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 of expensive 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 in fuel costs is the incentive for expending capital in addition to the costs of a combustion turbine. A utility with coal on the margin a large fraction of the time would not have the same incentive, and would probably 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 of the plant is just equal to the PV of the reduction in fuel costs from the operation of the plant. The reduction in fuel costs and hence the incentive to build a

E-10


### icy Variable 0 & M is calculated using references (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 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 TransmissioniDistribution System Capital Costs E.4.1 Value ofReliability Savings

The value ofreliability is the value associated with having additional generating capacity available to meet the loadin case ofunexpected generator outages orload increases. Forutility systems with excess capacity, this value may be equal to zero. When capacity does have value, this value is commonly measured by the cost of the 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 ifanother generating unit fails. Ifthe utility chooses to build another type ofplant withhighercapital costs (such as a baseload coal unit), it must be basing this choice on other considerations than reliability. Baseloadcoalplants, for example, have much loweroperating costs than oil and gas fired generation, and can be usedto displace generators with higher variable costperkWh. The additional capital cost of a 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. Thisreductioninfuel costs isthe incentive for expendingcapitalin addition tothe costs ofa combustionturbine. A utility withcoalon the margin a largefraction ofthe time wouldnot have the same incentive, and wouldprobably install a combustion 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 just equal to the PV ofthe reduction in fuel costs from the operation of the plant. The reduction in fuel costs and hence the incentive to build a

E-lO Variable 0 & M is calculated using references (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 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 TransmissioniDistribution System Capital Costs E.4.1 Value ofReliability Savings

The value ofreliability is the value associated with having additional generating capacity available to meet the loadin case ofunexpected generator outages orload increases. Forutility systems with excess capacity, this value may be equal to zero. When capacity does have value, this value is commonly measured by the cost of the 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 ifanother generating unit fails. Ifthe utility chooses to build another type ofplant withhighercapital costs (such as a baseload coal unit), it must be basing this choice on other considerations than reliability. Baseloadcoalplants, for example, have much loweroperating costs than oil and gas fired generation, and can be usedto displace generators with higher variable costperkWh. The additional capital cost of a 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. Thisreductioninfuel costs isthe incentive for expendingcapitalin addition tothe costs ofa combustionturbine. A utility withcoalon the margin a largefraction ofthe time wouldnot have the same incentive, and wouldprobably install a combustion 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 just equal to the PV ofthe reduction in fuel costs from the operation of the plant. The reduction in fuel costs and hence the incentive to build a

E-lObaseload 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 determine the value of capacity for the years 1996 to 1998 based on the DOE's interpretation of NERC 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 If the NERC region is forecast to have a capacity deficiency (operating adjusted reser^m of less than 5 %), the capacity value for the combustion turbine is applied. After 1998, DOE has not calculated adjusted reserve margins, and it is assumed that every region will need capacity. Table GA 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 of reductions in peak load caused by cogenerators and other small power producers (5). First, the present value of revenue requirements is calculated for a combustion turbine based on its capital cost ($400 per kW in 1987 dollars), a discount rate 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 of straight-line depreciation. The calculation is repeated for 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 ECC technique yields a current dollar stream of costs (escalating at an inflation rate of 5%) that has the same present value (when discounted at the ROD) as the original stream. A stream of costs that escalates at the inflation rate is also constant in real terms, so this technique "levelizes" the costs to some constant dollar value ($47.07 per kW-yr in 1990 dollars). This cost is then converted to dollars per MMBtu of electricity savings by the method detailed in the next section.

18An equivalent rule of thumb for adequate reliability is that the overall reserve margin should equal about 20% of forecasted peak demand

. Adjusted reserves are defined by DOE as [Planned Capacity + Net Power Transfers(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. 19The estimates of ROD 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 of capital are 10% for debt and preferred stock and 13.4% for common stock.

E-11

)A 

### 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 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.lS 2 If

### the 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,DOEhasnotcalculated adjustedreservemargins, 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 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 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 assumption ofstraight-line depreciation. The calculation 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 (BCC). 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 "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 by the method detailed inthe next section.

ISAn equivalentrule ofthumb for adequatereliability isthat theoverallreserve margin should equal 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 of ROD 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 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.lS 2 If

### the 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,DOEhasnotcalculated adjustedreservemargins, 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 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 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 assumption ofstraight-line depreciation. The calculation 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 (BCC). 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 "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 by the method detailed inthe next section.

ISAn equivalentrule ofthumb for adequatereliability isthat theoverallreserve margin should equal 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 of ROD 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-llE.4.2 Amount of Reliability Savings

An analysis of the load shape impacts of improved efficiencies for residential appliances must consider both the magnitude and timing of the impacts relative to periods of high system loads because residential class load shape changes only have reliability value for the system when they coincide with times of high system loads

. This relationship is approximated from modeling runs of the LBL Hourly and Peak Demand Model and two LBL utility case studies (33,34). There are large but varying degrees of coincidence between residential peak loads and system peak loads. The case studies show that the average change in the 250 highest residential hourly loads is a reasonable measure of the system-level reliability value of appliance 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 of the conservation measure. Peak savings 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 of heating, 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 8760 hours gives: CLF = Annual Energy Savings Peak Load Savings x 8760 

### The peak load savings can be expressed as a function of the annual energy savings and the CLF: Peak Load Savings = Annual Energy Savings CLF x 8760 Another useful form of this equation is as follows:

E-12 

### 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 becauseresidential classload shapechangesonly havereliabilityvaluefor the systemwhen 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 of coincidence between residential peak loads and system peak: loads. The case studies show that the average change in the 250 highest residential 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 of the conservation measure. Peaksavings are definedas 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 for residential appliances must consider both the magnitude and timing ofthe impacts relative to periods ofhigh system loads becauseresidential classload shapechangesonly havereliabilityvaluefor the systemwhen 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 of coincidence between residential peak loads and system peak: loads. The case studies show that the average change in the 250 highest residential 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 of the conservation measure. Peaksavings are definedas 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-12CLF x 876(} .Annual Energy Savings Peak Load Savings 

### CLF x 8760 =kWh of energy savings needed to yeild 1 kW of peak load savings. Conservation load factors for the electrical appliances in this analysis are shown in Table G.5. CLFs for cooking, RAC and water heating are taken from runs of LBL's Hourly and Peak 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 of energy savings that is from reducing standby losses (which has a CLF of 1.0) and the percentage of energy savings that is from increasing the efficiency of the TV when it is being used (which has a CLF of 0.86, from reference (36)). The load impacts of the heating energy savings are excluded because NERC data and forecasts suggest that all NERC regions will have lower adjusted reserve margins in summer than in winter through 1998; therefore, to a rough approximation, energy savings only have reliability value in summer.20 As a consequence of this assumption, the indirect effects on electricity use of standazds on gas-fired mobile home furnaces and gas-fired direct heating equipment aze 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.

20This approach implicitly assumes that transmission capacity within each region allows mostefficient utilization of the 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 

## 1 ^1 CLF 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 inTable G.S. CLFs forcooking, RAC and waterheating are taken from runs ofLBL's Hourly and Peak 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 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 loweradjustedreservemarginsin summerthan in winterthrough 1998; therefore, to arough approximation, energy savings onlyhavereliability 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-:rhisapproachimplicitlyassumes thattransmissioncapacitywithineachregionallows 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. Conservation loadfactors for the electrical appliances in this analysis are shown inTable G.S. CLFs forcooking, RAC and waterheating are taken from runs ofLBL's Hourly and Peak 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 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 loweradjustedreservemarginsin summerthan in winterthrough 1998; therefore, to arough approximation, energy savings onlyhavereliability 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-:rhisapproachimplicitlyassumes thattransmissioncapacitywithineachregionallows 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-13Table E.5 Conservation Load Factors for Electrical Products

Appliance/Product CLF RAC 0.14 Water heating 0.89 Cooktops/ovens

/ microwaves 0.46 TVs policy 1 (standby energy use) 1.00 Other TV policies (not standby) 0.86 Fluorescent ballasts 0M 

### The calculation of avoided reliability costs can be summarized in the following equations: ARC - Capacity Value x 1 kWh x 106 x 1.2 x E ESF clgj - CLFcoo^g x 8760 11,500 Btu MMBtu Clgj



### Capacity Value 1 kWh 106 ARCBaseloadj x x x 1.2 x E ESFBaseload,j

CLFBaseload x 8760 11,500 Btu MMBtu

where: ARCCIg,Baseload j Capacity Value 11,500 BtulkWh

CLFBaseload EESFCIgj

YLESFBaseloadj

1.2 

### = Avoided reliability costs in 1990 $/MMBtu in year j,

= value of 1 kW of peak 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 in year j, 

### = sum of baseload ESFs for regions that need capacity in year j, and = reserve margin adjustment needed for adequate reliability.



### E-14 Table E.S Conservation Load Factors for Electrical Products Appliance/Product CLF RAC Water heating Cooktops!ovens!microwaves 0.14 0.89 0.46 TVs policy 1 (standby energy use) Other TV policies (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 12 1: ESF c1gJ eLF lin x 8760 x 11,500 Btu x MMBtu x . x Clgj

ARCBaseload,j

where: 

### ARC Clg,Baseloadj 

### Capacity Value 11,500Btu/kWh 

## eLF Baseload 

### LESF Clgj


### LESF Baseloadj
1.2 coo g
Capacity Value 1 kWh 106 

## = CLFBaseload x 8760 x 11,500 Btu x MMBtu x 1.2 x 1: 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 in year 

### j,

= sum of baseload ESFs for regions that need capacity in yearj, and

= reserve margin adjustment needed for adequate reliability.

E-14**l/l~** Table E.S Conservation Load Factors for Electrical Products Appliance/Product CLF RAC Water heating Cooktops!ovens!microwaves 0.14 0.89 0.46 TVs policy 1 (standby energy use) Other TV policies (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 12 1: ESF c1gJ eLF lin x 8760 x 11,500 Btu x MMBtu x . x Clgj

ARCBaseload,j

where: 

### ARC Clg,Baseloadj 

### Capacity Value 11,500Btu/kWh 

## eLF Baseload 

### LESF Clgj


### LESF Baseloadj
1.2 coo g
Capacity Value 1 kWh 106 

## = CLFBaseload x 8760 x 11,500 Btu x MMBtu x 1.2 x 1: 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 in year 

### j,

= sum of baseload ESFs for regions that need capacity in yearj, and

= reserve margin adjustment needed for adequate reliability.

E-14Capacity 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 of AC and Baseload Energy Savings Fractions for NERC Regions That Need

Capacity

Year Cooling Baseload Regions Needing ESF ESF Capacity
1996 0.693 0.552 5 1997 1.703 0.552 6 1998 0.703 0.596 6 1999 1.000 1.000 9 2000 1.000 1.000 9


### E.4.3 Avoided Transmission and Distribution Capital Costs Recently, utility planners have begun to account for capital costs of the Transmission and Distribution (T & D) System that can be avoided by reductions in electricity use. There are a wide range of estimates of such costs, depending on the particular characteristics of the utility system and of the 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 a range 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. For example, they cite marginal T & D cost estimates of nine different utilities that (on average) totaled more than $100

/kW-yr. We adopt the lower end of Krause et al.'s range ($10/kW-yr) as our estimate 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 of individual 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.11 show the sum of avoided costs for the base-, high-, and low-energy-price cases.

E-15 

### /W1, 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 ofACand BaseloadEnergy Savings Fractions for NERC Regions ThatNeed Capacity

Year

1996 1997 1998 1999 2000 Cooling ESF
0.693 1.703 0.703 1.000 1.000 Baseload ESF
0.552 5 0.552 6 0.596 6 1.000 9 1.000 9


### E.4.3 Avoided Transmission and Distribution Capital CostsRegions Needing Capacity



### Recently, utility planners have begun to accountforcapital costs 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 of the 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 a range 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 $IOO/kW-yr. We adopt the lowerendofKrause et al.'s range ($IO/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.S 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.II 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 ofACand BaseloadEnergy Savings Fractions for NERC Regions ThatNeed Capacity

Year

1996 1997 1998 1999 2000 Cooling ESF
0.693 1.703 0.703 1.000 1.000 Baseload ESF
0.552 5 0.552 6 0.596 6 1.000 9 1.000 9


### E.4.3 Avoided Transmission and Distribution Capital CostsRegions Needing Capacity



### Recently, utility planners have begun to accountforcapital costs 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 of the 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 a range 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 $IOO/kW-yr. We adopt the lowerendofKrause et al.'s range ($IO/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.S 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.II 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

Cost: Heating Avoided Energy

Cost: Cooling Avoided Energy

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 

### 15// Table 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 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-16Table E.8 Electric Utility Avoided Reliability Costs (Dollars per MMBtu of Resource Energy Saved

Year Avoided Reliability

Cost: Heating Avoided Reliability Cost: Cooling Avoided Reliability

Cost: Baseload
0.000 0.270 0.049 1990 1991 0.000 0.421 0.086 1992 0.000 0.421 0.086 1993 0.000 1.516 0.247 1994 0.000 1.793 0.309 1995 0.000 1.793 0.308 1996 0.000 2.436 0.457 1997 0.000 2.436 0.457 1998 0.000 2.436 0.457 1999 0.000 2.436 0.457 2000 0.000 2.436 0.457 2001 0.000 2.436 0.457 2002 0.000 2.436 0.457 2003 0.000 2.436 0.457 2004 0.000 2.436 0.457 2005 0.000 2.436 0.457 2006 0.000 2.436 0.457 2007 0.000 2.436 0.457 2008 0.000 2.436 0.457 2009 0.000 2.436 0.457 2010 0.000 2.436 0.457 2011 0.000 2.436 0.457 2012 0.000 2.436 0.457 2013 0.000 2.436 0.457 2014 0.000 2.436 0.457 2015 0.000 2.436 0.457

E-17

/^/) 

### 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 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 0.457

### Table E.9 Total Utility Avoided Costs: Base Case (Dollars per MMBtu of Resource Energy Saved)

Year Total Avoided Cost: Heating Total Avoided Cost Cooling Total Avoided Cost 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

1^- 

### Table E.9 Total Utility Avoided Costs: Base Case (Dollars per MMBtu of Resource 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 Total Avoided Cost: Heating

2.488 2.654 2.836 3.035 3.252 3.490 3.622 3.761 3.907 4.060 4.221 4.389 4.567 4.753 4.949 5.154 5.370 5.596 5.834 6.085 6.347 6.624 6.914 7.219 7.539 7.876 Total Avoided Cost:

E-18 Cooling
2.920 3.263 3.473 4.797 5.326 5.601 6.398 6.560 6.730 6.909 7.097 7.294 7.502 7.720 7.948 8.189 8.442 8.707 8.986 9.279 9.588 9.912 10.252 10.610 10.986 11.382 Total Avoided Cost: Baseload

2.535 2.741 2.925 3.287 3.568 3.806 4.089 4.229 4.377 4.532 4.694 4.865 5.045 5.233 5.431 5.639 5.857 6.086 6.327 6.580 6.846 7.126 7.420 7.728 8.053 8.394 

### Table E.9 Total Utility Avoided Costs: Base Case (Dollars per MMBtu of Resource 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 Total Avoided Cost: Heating

2.488 2.654 2.836 3.035 3.252 3.490 3.622 3.761 3.907 4.060 4.221 4.389 4.567 4.753 4.949 5.154 5.370 5.596 5.834 6.085 6.347 6.624 6.914 7.219 7.539 7.876 Total Avoided Cost:

E-18 Cooling
2.920 3.263 3.473 4.797 5.326 5.601 6.398 6.560 6.730 6.909 7.097 7.294 7.502 7.720 7.948 8.189 8.442 8.707 8.986 9.279 9.588 9.912 10.252 10.610 10.986 11.382 Total Avoided Cost: Baseload

2.535 2.741 2.925 3.287 3.568 3.806 4.089 4.229 4.377 4.532 4.694 4.865 5.045 5.233 5.431 5.639 5.857 6.086 6.327 6.580 6.846 7.126 7.420 7.728 8.053 8.394Table E.10 Total Utility Avoided Costs: High Price Case (Dollars per MMBtu of Resource Energy Saved)

Year Total Avoided Cost: Heating Total Avoided Cost: Cooling Total Avoided Cost

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-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-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 Heating Total Avoided Cost

Cooling Total Avoided Cost 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 

### I ^Ij Table 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 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

### DOE/EA-0819

ENVIRONMENTAL ASSESSMENT FOR PROPOSED ENERGY CONSERVATION STANDARDS FOR EIGHT TYPES OF CONSUMER PRODUCTS: ROOM AIR CONDITIONERS, WATER HEATERS, DIRECT HEATING EQUIPMENT, MOBILE HOME FURNACES, RANGES AND OVENS, POOL HEATERS, FLUORESCENT BALLASTS, AND TELEVISIONS

1 INTRODUCTION AND NEED FOR PROPOSED ACTION This Environmental Assessment (EA) on the candidate energy conservation standards for eight appliances (room air conditioners, water heaters, direct heating equipment, mobile home furnaces, kitchen ranges and ovens, pool heaters, fluorescent lamp ballasts, and television sets) was prepared pursuant to the National Environmental Policy Act of 1969 (NEPA), regulations of the Council on Environmental Quality, Title 40, Code of Federal Regulations, Parts 1500 through 1508. The candidate conservation standards are being proposed pursuant to the Energy Policy and Conservation Act, as amended by the National Energy Conservation Policy Act, the National Appliance Energy Conservation Act and the National Appliance Energy Conservation Amendments. The EA presents the results of the associated environmental impacts from a range of new candidate energy conservation standards for eight types of household appliances

. The results are presented for each potential standard level for each of the eight appliance types. Each measure of possible environmental change is an alternative action, and it is compared to what is expected to happen if no new standards for each product were finalized, i.e., the "no action" alternative. The main environmental concern addressed is emissions from fossil-fueled electricity generation. Most of the design options for the eight appliances result in decreased electricity use and, therefore, a reduction of power plant emissions. The proposed efficiency standards will generally decrease air pollution by decreasing future energy demand. The greatest decreases in air pollution will be for sulfur oxides, listed in equivalent weight of sulfur dioxide, or S02. Reductions of nitrogen oxides and carbon dioxide also occur and are listed by weight of NOX and C02, respectively. Carbon dioxide (C02) emissions from fossil-fuel burning is considered an environmental hazard because it contributes to the "greenhouse effect" by trapping heat energy from the earth that is emitted as infrared radiation. The "greenhouse effect" is expected to gradually raise the mean global temperature. Design options for many of the appliances (i.e. gas and oil water heaters, gas pool heaters, direct heating equipment, mobile home furnaces, and gas ranges and ovens) also reduce in-home fuel consumption, resulting in lower in-home emissions from fuel-burning appliances.

1 

### DOEIEA-0819



## ENVIRONMENTAL ASSESSMENT FOR PROPOSED ENERGY CONSERVATION STANDARDS FOR EIGHT TYPES OF CONSUMER PRODUCTS: ROOMAIRCONDITIONERS,WATERHEATERS,DIRECT HEATING EQUIPMENT, MOBILE HOME FURNACES, RANGES AND OVENS, POOL HEATERS, FLUORESCENT BALLASTS, AND TELEVISIONS

1 INTRODUCTION AND NEED FOR PROPOSED ACTION This Environmental Assessment (EA) on the candidate energy conservation standards for eight appliances (room air conditioners, water heaters, direct heating equipment, mobile home furnaces, kitchen ranges and ovens, pool heaters, fluorescent lamp ballasts, and television sets) was prepared pursuant to the National Environmental Policy Act of 1969 (NEPA), regulations of the Council on Environmental Quality, Title 40, Code of Federal Regulations, Parts 1500 through 1508. The candidate conservation standards are being proposedpursuant to the Energy Policy and Conservation Act, as amended by the National Energy Conservation Policy Act, the National Appliance Energy Conservation Act and the National Appliance Energy Conservation Amendments. TheEApresents the results ofthe associated environmental impacts from a range ofnew candidateenergy conservation standards for eight types ofhousehold appliances. Theresults are presented for each potential standard level for each ofthe eight appliance types. Each measure ofpossible environmental change is an alternative action, and itis comparedto whatis expected to happen ifno new standards for each product were finalized, Le., the "no action" alternative. The main environmental concern addressed is emissions from fossil-fueled electricity generation. Most ofthe design options for the eight appliances resultin decreased electricity use and, therefore, a reduction of power plant emissions. The proposed efficiency standards will generally decrease airpollution by decreasing future energy demand. The greatest decreases in air pollution will be for sulfur oxides, listed in equivalent weight of sulfur dioxide, or S02. Reductions ofnitrogen oxides andcarbon dioxide also occurand are listedbyweightofNOx and CO2, respectively. Carbon dioxide (C02) emissions from fossil-fuel burning is considered an environmental hazard because it contributes to the "greenhouse effect" by trapping heat energy from the earth that is emitted as infrared radiation. The "greenhouse effect" is expected to gradually raise the mean global temperature. Design options for many ofthe appliances (Le. gas and oil waterheaters, gas poolheaters, direct heating equipment, mobile home furnaces, and gas ranges and ovens) also reduce in-home fuel consumption, resulting in lower in-home emissions from fuel-burning appliances.

1 

### DOEIEA-0819



## ENVIRONMENTAL ASSESSMENT FOR PROPOSED ENERGY CONSERVATION STANDARDS FOR EIGHT TYPES OF CONSUMER PRODUCTS: ROOMAIRCONDITIONERS,WATERHEATERS,DIRECT HEATING EQUIPMENT, MOBILE HOME FURNACES, RANGES AND OVENS, POOL HEATERS, FLUORESCENT BALLASTS, AND TELEVISIONS

1 INTRODUCTION AND NEED FOR PROPOSED ACTION This Environmental Assessment (EA) on the candidate energy conservation standards for eight appliances (room air conditioners, water heaters, direct heating equipment, mobile home furnaces, kitchen ranges and ovens, pool heaters, fluorescent lamp ballasts, and television sets) was prepared pursuant to the National Environmental Policy Act of 1969 (NEPA), regulations of the Council on Environmental Quality, Title 40, Code of Federal Regulations, Parts 1500 through 1508. The candidate conservation standards are being proposedpursuant to the Energy Policy and Conservation Act, as amended by the National Energy Conservation Policy Act, the National Appliance Energy Conservation Act and the National Appliance Energy Conservation Amendments. TheEApresents the results ofthe associated environmental impacts from a range ofnew candidateenergy conservation standards for eight types ofhousehold appliances. Theresults are presented for each potential standard level for each ofthe eight appliance types. Each measure ofpossible environmental change is an alternative action, and itis comparedto whatis expected to happen ifno new standards for each product were finalized, Le., the "no action" alternative. The main environmental concern addressed is emissions from fossil-fueled electricity generation. Most ofthe design options for the eight appliances resultin decreased electricity use and, therefore, a reduction of power plant emissions. The proposed efficiency standards will generally decrease airpollution by decreasing future energy demand. The greatest decreases in air pollution will be for sulfur oxides, listed in equivalent weight of sulfur dioxide, or S02. Reductions ofnitrogen oxides andcarbon dioxide also occurand are listedbyweightofNOx and CO2, respectively. Carbon dioxide (C02) emissions from fossil-fuel burning is considered an environmental hazard because it contributes to the "greenhouse effect" by trapping heat energy from the earth that is emitted as infrared radiation. The "greenhouse effect" is expected to gradually raise the mean global temperature. Design options for many ofthe appliances (Le. gas and oil waterheaters, gas poolheaters, direct heating equipment, mobile home furnaces, and gas ranges and ovens) also reduce in-home fuel consumption, resulting in lower in-home emissions from fuel-burning appliances.

1Although the quantity of raw materials used per appliance will remain relatively constant,

in most scenarios increased initial cost is expected to decrease slightly the number of appliances sold, resulting in small decreases in raw materials used. The main effect of decreased appliance production is the S02 emitted in steel production. The decrease is small, however, in comparison to the S02 decreases from fuel burning avoided at power plants. The contribution from steel production is not included in the estimates for net S02 decreases resulting from design changes in these products. The effects on particulate emissions related to a standazd-induced decrease in electricity generation would be minor compazed to effects on decreases in S02, N02, and C02. For example, in 1984, power plants contributed only 7% of U.S. total particulate emissions as compared to contributions of 83% and 34% to total S02 and N02 emissions, respectively Though the reduction in particulate emissions would be relatively small, any reduction would possibly be beneficial to improving the quality of surfacewater. Since the amount of particulates emitted would be decreased, it is very likely that less particulates would reach the surfacewater. Reductions in particulate emissions accompanied by decreases in 502 and N02 would have other beneficial effects on the environment. The resultant improvement to air quality and the decreased potential of acid rain formation would help improve the quality of wetlands and fish & wildlife as well as aid in the preservation of historical and azchaeological sites. Reduced in-home fuel consumption will decrease the amount of gas or oil burned within some homes, thereby decreasing the impact of combustion on indoor air quality. Indoor air problems are usually due to a combination of factors, including a tight house envelope, insufficient ventilation for cooking appliances, presence of sources such as cigarette smokers or formaldehyde containing products, and radon diffusion from soil. In comparison to the above factors, and because fuel-burning appliances are normally vented to the outside, the projected changes in in-home fuel-burning appliance use is expected to have little effect on indoor air quality. 

### 2 METIIODS OF ESTIMATING ENVIRONMENTAL IMPACTS The greatest impacts of the proposed standards would be a reduction in electricity demand growth. The main environmental effects of power plants on air and water quality result from emissions of sulfur dioxide (S02), nitrogen oxides (NOX), and cazbon dioxide (C02). With the proposed standards lessening the need for electricity generation, power plant emissions would be reduced. A second source of these pollutants is fuel-burning household appliances. Pollutants from fuel-burning household aplliances will be termed "in-house" emissions. The proposed standards would also reduce these emissions. Since fuel-burning household appliances aze normally vented to the outside, both the power plant and "in-house" emissions are reported.

2 

### Although the quantity ofraw materials usedper appliance willremainrelativelyconstant, in most scenarios increased initial costis expected to decrease slightly the number ofappliances sold, resulting in small decreases in raw materials used. The main effect ofdecreased appliance productionis the S02emittedin steelproduction. Thedecrease is small, however, incomparison to the S02 decreases from fuel burning avoided at power plants. The contribution from steel production is not included in 

### the estimates for net S02 decreases resulting from design changes in these products. The effects on particulate emissions related to a standard-induced decrease in electricity generation would be minor compared to effects on decreases in S02' N02, and CO2, For example, in 1984, power plants contributed only 7% of U.S. total particulate emissions as compared to contributions of 83% and 34% to total S02 and N02 emissions, respectively 1. 

### Though the reduction in particulate emissions would be relatively small, any reduction would possibly be beneficial to improving the quality ofsurfacewater. Since the amount ofparticulates emitted would be decreased, it is very likely that less particulates would reach the surfacewater. Reductions in particulate emissions accompanied by decreases in S02 and N02 would have other beneficial effects on the environment. The resultant improvement to air quality and the decreased potential of acid rain formation would help improve the quality of wetlands and fish & 

### wildlife as well as aid in the preservation ofhistorical and archaeological sites. Reduced in-horne fuel consumption will decrease the amount ofgas oroil burned within some homes, thereby decreasing the impact of combustion on indoor air 

### quality. Indoor air problems are usually due to a combination of factors, including a tight house envelope, insufficient ventilation for cooking appliances, presence ofsources such as cigarette smokers or formaldehyde containing products, and radon diffusion from soiL In comparison to the above factors, and because fuel-burning appliances are normally vented to the outside, the projected changes in in-home fuel-burning appliance use is expected to have little effect on indoor air quality. 

### 2 METHODS OF ESTIMATING ENVIRONMENTAL IMPACTS The greatestimpacts ofthe proposedstandards wouldbe

### areductioninelectricity demand growth. The main environmental effects ofpower plants on air and water quality result from emissions of sulfur dioxide (S02)' nitrogen oxides (NOx)' 

### and carbon dioxide (C02 ), With the proposed standards lessening the need for electricity generation, powerplantemissions would be reduced. A second source ofthese pollutants is fuel-burning household appliances. Pollutants from fuel-burning household aplliances will be termed "in-house" emissions. The proposed standards would also reduce these emissions. Since' fuel-burning household appliances are normally vented to the outside, both the power plant and "in-house" emissions are reported.

2 

### Although the quantity ofraw materials usedper appliance willremainrelativelyconstant, in most scenarios increased initial costis expected to decrease slightly the number ofappliances sold, resulting in small decreases in raw materials used. The main effect ofdecreased appliance productionis the S02emittedin steelproduction. Thedecrease is small, however, incomparison to the S02 decreases from fuel burning avoided at power plants. The contribution from steel production is not included in 

### the estimates for net S02 decreases resulting from design changes in these products. The effects on particulate emissions related to a standard-induced decrease in electricity generation would be minor compared to effects on decreases in S02' N02, and CO2, For example, in 1984, power plants contributed only 7% of U.S. total particulate emissions as compared to contributions of 83% and 34% to total S02 and N02 emissions, respectively 1. 

### Though the reduction in particulate emissions would be relatively small, any reduction would possibly be beneficial to improving the quality ofsurfacewater. Since the amount ofparticulates emitted would be decreased, it is very likely that less particulates would reach the surfacewater. Reductions in particulate emissions accompanied by decreases in S02 and N02 would have other beneficial effects on the environment. The resultant improvement to air quality and the decreased potential of acid rain formation would help improve the quality of wetlands and fish & 

### wildlife as well as aid in the preservation ofhistorical and archaeological sites. Reduced in-horne fuel consumption will decrease the amount ofgas oroil burned within some homes, thereby decreasing the impact of combustion on indoor air 

### quality. Indoor air problems are usually due to a combination of factors, including a tight house envelope, insufficient ventilation for cooking appliances, presence ofsources such as cigarette smokers or formaldehyde containing products, and radon diffusion from soiL In comparison to the above factors, and because fuel-burning appliances are normally vented to the outside, the projected changes in in-home fuel-burning appliance use is expected to have little effect on indoor air quality. 

### 2 METHODS OF ESTIMATING ENVIRONMENTAL IMPACTS The greatestimpacts ofthe proposedstandards wouldbe

### areductioninelectricity demand growth. The main environmental effects ofpower plants on air and water quality result from emissions of sulfur dioxide (S02)' nitrogen oxides (NOx)' 

### and carbon dioxide (C02 ), With the proposed standards lessening the need for electricity generation, powerplantemissions would be reduced. A second source ofthese pollutants is fuel-burning household appliances. Pollutants from fuel-burning household aplliances will be termed "in-house" emissions. The proposed standards would also reduce these emissions. Since' fuel-burning household appliances are normally vented to the outside, both the power plant and "in-house" emissions are reported.

22.1 Baseline Emissions

The estimates of emissions of SO2, NOX, and CO2 are also expressed as a percentage of U.S. power plant emissions for the year under consideration. In a report that accompanies the 1991 National Energy Strategy (NES), the impact on power plant emissions as a result of revisions to Title V of the 1990 Clean Air Act (i.e., H.R. 3030) are estimated 2. To reduce NOX emissions, the Clean Air Act will require utilities to add low NOX burners to coal plants greater than 75 megawatts (nameplate). NOX emissions are required to be reduced by 2 million tons by the year 2000 under the legislation. No emissions cap exists for NOX, therefore, as new units are added after the year 2000, NOX emissions will increase. The legislation calls for SO2 emissions reductions in two phases. In the first phase of the planned reductions (beginning December 31, 1995 and carrying through the year 2000), electric utilities will have several options for reducing their SO2 emissions to comply with the allowance constraints imposed by the provisions of the Clean Air Act. The major options are 1) to decrease their use of high emission units and increase the use of their clean coal, gas, or oil units, 2) to switch units using high sulfur coal to low sulfur coal, 3) to retrofit plants emitting at a high rate with emissions reduction technologies (e.g., scrubbers), and 4) to purchase allowances from other utilities who reduce their emissions below their permitted levels, in order to sell their allowances for a profit. Most utilities will make use of a combination of these options to minimize the cost of complying with the allowance constraints. Total SO2 emissions by utilities cannot exceed 8.9 million tons after December 31, 2000. In the second phase of the planned reductions (beginning December 31, 2000), the options available to electric utilities for maintaining the 8.9 million-ton cap will broaden with the expected introduction of new, advanced generating technologies. However, during this period utilities are less able to reduce emissions by changing the way they utilize their plants. Since most plants will be fully utilized, there will be few opportunities for reducing emissions by decreasing the use of a high emission plant. In the report accompanying the 1991 NES, two possible outcomes are presented, a flexible case and a restricted case, so that the effect of different levels of permitted trading of emission allowances can be evaluated. (The report does not go beyond this explanation in defining the differences between the two cases.) As presented in the report, the results for the two cases are virtually identical. Because the two cases are so similar, the U.S. power plant emission projections for the three effluents under the assumptions made in the flexible case are presented only. Table 1 summarizes the results.



### 3

10) 

### 2.1 Baseline Emissions The estimates ofemissions ofS02' NOx' 

### and CO2 are also expressed as a percentage of U.S. power plant emissions for the year under consideration. In a report that accompanies the 1991 National Energy Strategy (NES), the impact on power plant emissions as a result of revisions to Title V of the 1990 Clean Air Act (Le., H.R. 3030) are estimated 2. Toreduce NOx 

### emissions, the Clean AirActwillrequireutilities to addlow NOx

### burners to coal plants greater than 75 megawatts (nameplate). NOx 

### emissions are required to bereduced by 2 million tons by the year 2000 under the legislation. No emissions cap exists for NOx' 

### therefore, as new units are added after the year 2000, NOx 

### emissions will increase. Thelegislation calls for S02emissionsreductions in two phases. In the fIrst phase ofthe plannedreductions (beginning December 31, 1995 and carrying through the year 2000), electric utilities will have several options for reducing their S02emissions to comply with the allowance constraints imposedby the provisions ofthe Clean Air Act. The majoroptions are 1) to decrease their use ofhigh emission units and increase the use oftheir clean coal, gas, or oil units, 2) 

### to switch units using high sulfur coal to low sulfur coal, 3) to retrofIt plants emitting at a high rate with emissions reduction technologies (e.g., scrubbers), and4) to purchase allowances from other utilities whoreduce their emissions below theirpermitted levels, in order to sell their allowances for a profit. Most utilities will make use ofa combination ofthese options to minimize the cost ofcomplying with the allowanceconstraints. Total S02emissions by utilities cannotexceed 8.9 million tons after December 31, 2000. Inthe secondphaseofthe plannedreductions (beginningDecember31, 2000), the options available to electric utilities for maintaining the 8.9 million-ton cap will broaden with the expected introduction ofnew, advanced generating technologies. However, during this period utilities are less able to reduce emissions by changing the way they utilize their plants. Since most plants will be fully utilized, there will be few opportunities for reducing emissions by decreasing the use of a high emission plant. Inthereportaccompanyingthe 1991 NES, twopossible outcomes arepresented, aflexible case and a restricted case, so that the effect ofdifferent levels ofpermitted trading ofemission allowances can be evaluated. (The report does not go beyond this explanation in defIning the differences between the two cases.) As presented in the report, the results for the two cases are virtually identical. Because the two cases are so similar, the U.S. power plant emission projections for the three effluents under the assumptions made in the flexible case are presented only. Table 1 summarizes the results.



### 3 2.1 Baseline Emissions The estimates ofemissions ofS02' NOx' 

### and CO2 are also expressed as a percentage of U.S. power plant emissions for the year under consideration. In a report that accompanies the 1991 National Energy Strategy (NES), the impact on power plant emissions as a result of revisions to Title V of the 1990 Clean Air Act (Le., H.R. 3030) are estimated 2. Toreduce NOx 

### emissions, the Clean AirActwillrequireutilities to addlow NOx

### burners to coal plants greater than 75 megawatts (nameplate). NOx 

### emissions are required to bereduced by 2 million tons by the year 2000 under the legislation. No emissions cap exists for NOx' 

### therefore, as new units are added after the year 2000, NOx 

### emissions will increase. Thelegislation calls for S02emissionsreductions in two phases. In the fIrst phase ofthe plannedreductions (beginning December 31, 1995 and carrying through the year 2000), electric utilities will have several options for reducing their S02emissions to comply with the allowance constraints imposedby the provisions ofthe Clean Air Act. The majoroptions are 1) to decrease their use ofhigh emission units and increase the use oftheir clean coal, gas, or oil units, 2) 

### to switch units using high sulfur coal to low sulfur coal, 3) to retrofIt plants emitting at a high rate with emissions reduction technologies (e.g., scrubbers), and4) to purchase allowances from other utilities whoreduce their emissions below theirpermitted levels, in order to sell their allowances for a profit. Most utilities will make use ofa combination ofthese options to minimize the cost ofcomplying with the allowanceconstraints. Total S02emissions by utilities cannotexceed 8.9 million tons after December 31, 2000. Inthe secondphaseofthe plannedreductions (beginningDecember31, 2000), the options available to electric utilities for maintaining the 8.9 million-ton cap will broaden with the expected introduction ofnew, advanced generating technologies. However, during this period utilities are less able to reduce emissions by changing the way they utilize their plants. Since most plants will be fully utilized, there will be few opportunities for reducing emissions by decreasing the use of a high emission plant. Inthereportaccompanyingthe 1991 NES, twopossible outcomes arepresented, aflexible case and a restricted case, so that the effect ofdifferent levels ofpermitted trading ofemission allowances can be evaluated. (The report does not go beyond this explanation in defIning the differences between the two cases.) As presented in the report, the results for the two cases are virtually identical. Because the two cases are so similar, the U.S. power plant emission projections for the three effluents under the assumptions made in the flexible case are presented only. Table 1 summarizes the results.



### 3Table 1 U.S. C02, S02, NOX Power Plant Emissions C02 S02 NOX Year 106 tons 106 tons 106 tons 1995 2232.5 13.8 8.4 2000 2506.2 9.0 6.7 2010 3219.3 8.4 7.3 2020 3964.2 6.7 6.7 2030 4804.4 4.8 5.9 

### 2.2 Sulfur and Nitrogen Oxide Emissions For each of the scenarios analyzed, emissions abated from fossil fuel-burning power plants are estimated. In analyzing the impacts of design changes to each of the appliances, lower sulfur emissions resulting from decreased steel production are not considered. No changes in the amount of steel used per unit are expected. Analysis of the impacts resulting from several design options includes the consideration of S02 and NOX from in-home combustion of gas and oil. In order to capture the effects of cleaner-burning power plants in future years, emission rates (tons/Quad) for power plant fuel-burning are calculated from projected emissions and electrical generation data. The electrical generation data is translated into energy use (Quads) by multiplying the generation data by 11,500 Btu/kWh. The source of these projected emissions and electrical generation data is the report that accompanies the 1991 National Energy Strategy (data for Table 1 was extracted from the same Service Report). (2) Table 2 presents these data and the calculated emission rates for S02 and NOX. Table 2 Electrical Generation and Emissions Data and Emission Rates for S02 and NOX at

Fossil Fuel-Burning Power Plants Electrical Generation Energy Use Emissions Emissions Rates Coal Oil Gas Total S02 NOX S02 NOX Year 109 kWh 109 kWh 109 kWh Quads , 106 106 tons 103 tonslQuad 103 tons/Quad tons 1995 1602.2 193.7 442.0 25.74 13.8 8.4 552.5 336.3 2000 1814.0 179.8 605.0 29.89 9.0 6.7 310.3 231.0 2010 2660.6 149.9 482.5 37.88 8.4 7.3 228.6 198.6 2020 3727.8 67.2 292.3 47.01 6.7 6.7 146.9 146.9 2030 4837.3 29.0 179.2 58.03 4.8 5.9 85.2 104.8
The calculated emissions rate data listed in Table 2 represent the average S02 and NOX emissions rates for all fossil fuel-burning power plants in the United States. Emissions rates were not calculated for each fuel-burning source as the emissions data supplied by the Service Report

4

117- 

### Table 1 U.S. CO2, S02' NOx Power Plant Emissions CO2 S02 NO x Year 

### uP tons 

## 106 tons 

## 106 tons 1995 2232.5 13.8 8.4 2000 2506.2 9.0 6.7 2010 3219.3 8.4 7.3 2020 3964.2 6.7 6.7 2030 4804.4 4.8 5.9

2.2 

### Sulfur and Nitrogen Oxide Emissions Foreachofthe scenarios analyzed, emissions abatedfrom fossil fuel-burningpowerplants are estimated. In analyzing the impacts ofdesign changes to each ofthe appliances, lower sulfur emissions resulting from decreased steel production are not considered. No changes in the amountofsteel usedperunit are expected. Analysis oftheimpacts resultingfrom several design options includes the consideration of S02 and NOx from in-home combustion of gas and oil. In order to capture the effects ofcleaner-burning power plants in future years, emission rates (tons/Quad) for power plant fuel-burning are calculated from projected emissions and electrical generation data. The electrical generation data is translated into energy use (Quads) by multiplying the generation data by 11,500 Btu/kWh. The source ofthese projected emissions and electrical generation data is the report that accompanies the 1991 National Energy Strategy (data for Table 

### 1 was extracted from the same Service Report). 

### (2) Table 

### 2 presents these data and the calculated emission rates for S02 and NOx' 

### Table 2 Electrical Generation and Emissions Data and Emission Rates for S02 and NOx at Fossil Fuel-Burning Power Plants

Electrical Generation Energy Use Emissions Emissions Rates Coal Oil Gas Total S02 NOx S02 NOx 

## 109 kWh 

## 109 kWh 

## 109 kWh , 

## 106 106 tons 

## 103 tons/Quad 

## 103 tons/Quad Year Quads tons 1995 1602.2 193.7 442.0 25.74 13.8 8.4 552.5 336.3 2000 1814.0 179.8 605.0 29.89 9.0 6.7 310.3 231.0 2010 2660.6 149.9 482.5 37.88 8.4 7.3 228.6 198.6 2020 3727.8 67.2 292.3 47.01 6.7 6.7 146.9 146.9 2030 4837.3 29.0 179.2 58.03 4.8 5.9 85.2 104.8

The calculated emissions rate data listed in Table 2 represent the average S02 and NOx emissionsratesfor all fossil fuel-burning powerplants intheUnitedStates. Emissionsrates were not calculated for each fuel-burning source as the emissions data supplied by the Service Report



### 4 Table 1 U.S. CO2, S02' NOx Power Plant Emissions CO2 S02 NO x Year 

### uP tons 

## 106 tons 

## 106 tons 1995 2232.5 13.8 8.4 2000 2506.2 9.0 6.7 2010 3219.3 8.4 7.3 2020 3964.2 6.7 6.7 2030 4804.4 4.8 5.9

2.2 

### Sulfur and Nitrogen Oxide Emissions Foreachofthe scenarios analyzed, emissions abatedfrom fossil fuel-burningpowerplants are estimated. In analyzing the impacts ofdesign changes to each ofthe appliances, lower sulfur emissions resulting from decreased steel production are not considered. No changes in the amountofsteel usedperunit are expected. Analysis oftheimpacts resultingfrom several design options includes the consideration of S02 and NOx from in-home combustion of gas and oil. In order to capture the effects ofcleaner-burning power plants in future years, emission rates (tons/Quad) for power plant fuel-burning are calculated from projected emissions and electrical generation data. The electrical generation data is translated into energy use (Quads) by multiplying the generation data by 11,500 Btu/kWh. The source ofthese projected emissions and electrical generation data is the report that accompanies the 1991 National Energy Strategy (data for Table 

### 1 was extracted from the same Service Report). 

### (2) Table 

### 2 presents these data and the calculated emission rates for S02 and NOx' 

### Table 2 Electrical Generation and Emissions Data and Emission Rates for S02 and NOx at Fossil Fuel-Burning Power Plants

Electrical Generation Energy Use Emissions Emissions Rates Coal Oil Gas Total S02 NOx S02 NOx 

## 109 kWh 

## 109 kWh 

## 109 kWh , 

## 106 106 tons 

## 103 tons/Quad 

## 103 tons/Quad Year Quads tons 1995 1602.2 193.7 442.0 25.74 13.8 8.4 552.5 336.3 2000 1814.0 179.8 605.0 29.89 9.0 6.7 310.3 231.0 2010 2660.6 149.9 482.5 37.88 8.4 7.3 228.6 198.6 2020 3727.8 67.2 292.3 47.01 6.7 6.7 146.9 146.9 2030 4837.3 29.0 179.2 58.03 4.8 5.9 85.2 104.8

The calculated emissions rate data listed in Table 2 represent the average S02 and NOx emissionsratesfor all fossil fuel-burning powerplants intheUnitedStates. Emissionsrates were not calculated for each fuel-burning source as the emissions data supplied by the Service Report



### 4were not disaggregated according to power plant type (i.e. coal, oil, gas). To obtain emission rate

values, the amount of emissions were divided by the total energy use of fossil fuel-burning power plants. The total energy use by fossil fuel-burning power plants was calculated from the electrical generation data supplied by the report accompanying the 1991 NES. The electrical generation data was disaggregated by fuel source. To obtain the total energy use (input), the electrical generation data from each fossil fuel source was summed and then divided by the assumed efficiency of fossil fuel-burning power plants (30.6%). This fossil fuel-burning power plant efficiency is consistent with that used by the LBL Residential Energy Model (REM). For further information on the assumed efficiency of fossil fuel-burning power plants, refer to Appendix B of volume A of the Technical Support Document (TSD). The amount of S02 and NOX emissions abated for any particular year is determined by multiplying the estimates of energy saved through reduced electricity generation in that year by the emission rate for that particular year. For years not covered in the Service Report, linear interpolation was used to derive emission rates and, in turn, the corresponding abated emissions. Table 3 presents the emission factors (rates) that were used for S02 and NO, for in-home gas and oil combustion. The values for reduction of S02 and NO, emissions from in-home gas and oil combustion are produced by multiplying in-home fuel savings for gas and oil by the corresponding emission rates. Emission factors that appear in Table 3 are from a Lawrence Berkeley Laboratory report. (3) Emission factors for gas in-home combustion were assumed to equal the average of those for residential gas space heaters and water heaters. Emission factors for oil in-home combustion were assumed to equal those associated with a residential #2 oil boiler. Table 3 Emission Rates for S02 and NO, for In-Home Combustion SO Gas Emission SO Oil Emission NOX Gas Emission NOx Oil Emission W tons/Quad 1 tons/Quad 103 tonslQuad 103 tons/Quad
0.0 156.5



### 2.3 Carbon Dioxide Emissions 52.5 65.0

Emission rates for carbon dioxide were derived in the same manner as those derived for S02 and NOX. As presented in Table 1, the report accompanyinf the 1991 National Energy Strategy (NES) also provided emissions data with regard to CO2. Table 4 presents the C02 emission rate data as derived from the electrical generation data and emissions data supplied by the 1991 NES report.

1Upon phone conversations with David Streets at Argonne National Laboratory (February 1992), it was determined that the carbon emissions data provided in the report accompanying the 1991 NES were mistakenly reported as tons of carbon emitted. David Streets was one of authors at Argonne who contributed to the 1991 NES report.


### 5 were notdisaggregated according topowerplanttype (Le. coal, oil, gas). Toobtainemissionrate values, the amountofemissions were dividedby the total energyuseoffossil fuel-burning power plants. The total energy use by fossil fuel-burning power plants was calculated from the electrical generation data supplied by the report accompanying the 1991 NBS. The electrical generation data was disaggregated by fuel source. To obtain the total energy use (input), the electrical generation data from each fossil fuel source was summed and then divided by the assumed efficiency offossil fuel-burning powerplants (30.6%). This fossil fuel-burning power plant efficiency is consistent with that used by the LBL Residential Energy Model (REM). For further information on the assumed efficiency of fossil fuel-burning power plants, refer to Appendix B ofvolume A ofthe Technical Support Document (TSD). The amount of S02 and NOx emissions abated for any particular year is determined by multiplying the estimates ofenergy saved through reduced electricity generation in that year by the emission rate for that particular year. For years not covered in the Service Report, linear interpolation was used to derive emissionrates and, in turn, the corresponding abated emissions. Table 3presents the emission factors (rates) that were usedfor S02 andNOx for in-home gas and oil combustion. The values for reduction ofS02 and NOx emissions from in-home gas and oil combustion are produced by multiplying in-home fuel savings for gas and oil by the corresponding emission rates. Emission factors that appear in Table 3 are from a Lawrence Berkeley Laboratory report. (3) Emission factors for gas in-home combustion were assumed to equal the average ofthose for residential gas space heaters and water heaters. Emission factors for oil in-home combustion were assumed to equal those associated with a residential #2 oil boiler. Table 3 Emission Rates for S02 and NOx for In-Home Combustion

SO;z Gas Emission 1(Y tons/Quad
0.0 SO;z Oil Emission 1(Y tons/Quad
156.5



### 2.3 Carbon Dioxide Emissions NOx Gas Emission 1rl tons/Quad
52.5 NOx Oil Emission lrl tons/Quad
65.0

Emission rates for carbon dioxide were derived in the same manner as those derived for S02 and NOx. As presented in Table 1, the report accompanyin

# rthe 1991 National Energy Strategy (NES) also provided emissions data with regard to CO2. Table 4 presents the CO2 emission rate data as derived from the electrical generation data and emissions data supplied by the 1991 NES report.

luponphone col\versations with ])avid S1Jee1S III Arg\lI\ne National Laboratory (February 1992), itwas determined lItat lite carbon emissiollli data provided in thereport accompanying the 1991 NBS were mistakenly reported as tons ofcarbon emitted. David Streets was one ofauthors at Argonne who contributed to the 1991 NBS report
5 were notdisaggregated according topowerplanttype (Le. coal, oil, gas). Toobtainemissionrate values, the amountofemissions were dividedby the total energyuseoffossil fuel-burning power plants. The total energy use by fossil fuel-burning power plants was calculated from the electrical generation data supplied by the report accompanying the 1991 NBS. The electrical generation data was disaggregated by fuel source. To obtain the total energy use (input), the electrical generation data from each fossil fuel source was summed and then divided by the assumed efficiency offossil fuel-burning powerplants (30.6%). This fossil fuel-burning power plant efficiency is consistent with that used by the LBL Residential Energy Model (REM). For further information on the assumed efficiency of fossil fuel-burning power plants, refer to Appendix B ofvolume A ofthe Technical Support Document (TSD). The amount of S02 and NOx emissions abated for any particular year is determined by multiplying the estimates ofenergy saved through reduced electricity generation in that year by the emission rate for that particular year. For years not covered in the Service Report, linear interpolation was used to derive emissionrates and, in turn, the corresponding abated emissions. Table 3presents the emission factors (rates) that were usedfor S02 andNOx for in-home gas and oil combustion. The values for reduction ofS02 and NOx emissions from in-home gas and oil combustion are produced by multiplying in-home fuel savings for gas and oil by the corresponding emission rates. Emission factors that appear in Table 3 are from a Lawrence Berkeley Laboratory report. (3) Emission factors for gas in-home combustion were assumed to equal the average ofthose for residential gas space heaters and water heaters. Emission factors for oil in-home combustion were assumed to equal those associated with a residential #2 oil boiler. Table 3 Emission Rates for S02 and NOx for In-Home Combustion

SO;z Gas Emission 1(Y tons/Quad
0.0 SO;z Oil Emission 1(Y tons/Quad
156.5



### 2.3 Carbon Dioxide Emissions NOx Gas Emission 1rl tons/Quad
52.5 NOx Oil Emission lrl tons/Quad
65.0

Emission rates for carbon dioxide were derived in the same manner as those derived for S02 and NOx. As presented in Table 1, the report accompanyin

# rthe 1991 National Energy Strategy (NES) also provided emissions data with regard to CO2. Table 4 presents the CO2 emission rate data as derived from the electrical generation data and emissions data supplied by the 1991 NES report.

luponphone col\versations with ])avid S1Jee1S III Arg\lI\ne National Laboratory (February 1992), itwas determined lItat lite carbon emissiollli data provided in thereport accompanying the 1991 NBS were mistakenly reported as tons ofcarbon emitted. David Streets was one ofauthors at Argonne who contributed to the 1991 NBS report
5Table 4 Electrical Generation Data, Emissions Data and Emissions Rates for C02 at Fossil Fuel-Burning Power Plants

Electrical Generation
Year Coal Oil Gas

109 kWh 109 kWh 109 kWh Energy Use Total Emission C02 Emission Rate CO,) Quads 106 tons 106 tons/Quad 1995 1602.2 193.7 442.0 25.74 2232.5 89.39 2000 1814.0 179.8 605.0 29.89 2506.2 86.41 2010 2660.6 149.9 482.5 37.88 3219.3 87.60 2020 3727.8 67.2 292.3 47.01 3964.2 86.90 2030 4837.3 29.0 179.2 58.03 4804.4 85.32
As with the S02 and NOx emissions, the amount of C02 emissions abated for any particular year is determined by multiplying the estimates of energy saved through reduced electricity generation by the emission rate for that particular year. For years not covered in the Service Report, linear interpolation was used to derive emission rates and, in turn, the corresponding abated emissions. Table 5 presents the emission factors (rates) that were used for C02 for in-home gas and oil combustion. The values for the reduction of C02 emissions from in-home gas and oil combustion are produced by multiplying in-home fuel savings for gas and oil by the corresponding emission rates. Emission factors that appear in Table 5 are from a Lawrence Berkeley Laboratory report3. The emission factor for gas in-home combustion was assumed to equal the average of those for residential gas space heaters and water heaters. The emission factor for oil in-home combustion was assumed to equal the one associated with a residential #2 oil boiler. Table 5 Emission Rates for C02 for In-Home Combustion

C02 Gas Emission C02 Oil Emission 103 tonslQuad 103 tons/Quad 55,000 84,300



### 3 RESULTS The following tabular results indicate what changes can be brought about in the amounts of emitted C02, S02, and NO, by imposing efficiency standards for each of the eight appliances considered in this analysis. A table is presented for each of the appliances' trial standard levels. Each appliance has been analyzed separately in order to determine the emission changes resulting from each of their prospective trial standards levels. Each table details the changes that occur to each of the three emissions (i.e. C02, S02, and NOx) through the imposition of an appliance's specific trial standard level. Each table includes the following information for a specific year between 1996 and 2030: the amount of emission abated from power plant generation, the amount


### 6 j9c) Table 4 

### Electrical Generation Data, Emissions Data and Emissions Rates for CO2 at Fossil Fuel-Burning Power Plants Electrical Generation Coal Oil Gas Energy Use Total Emission CO2 Emission Rate CO2 Year 

## 109 kWh 

## 109 kWh 

## 109 kWh Quads 

## 106 tons 

## 106 tons/Quad 1995 1602.2 193.7 442.0 25.74 2232.5 89.39 2000 1814.0 179.8 605.0 29.89 2506.2 86.41 2010 2660.6 149.9 482.5 37.88 3219.3 87.60 2020 3727.8 67.2 292.3 47.01 3964.2 86.90 2030 4837.3 29.0 179.2 58.03 4804.4 85.32



### As with the S02 and NOx 

### emissions, the amount of CO2 emissions abated for any particular year is determined by multiplying the estimates of energy saved through reduced electricity generation by the emission rate for that particular year. For years not covered in the Service Report, linear interpolation was used to derive emission rates and, in turn, the corresponding abated emissions. Table 5presents the emission factors (rates) that were used for CO2for in-home gas and oil combustion. The values for the reduction of CO2 emissions from in-home gas and oil

. combustion are produced by multiplying in-home fuel savings for gas and oil by the corresponding emission rates. Emission factors that appear in Table 5 are from a Lawrence Berkeley Laboratory report3

. The emission factor for gas in-home combustion was assumed to equal the average of those for residential gas space heaters and water heaters. The emission factor for oil in-home combustion was assumed to equal the one associated with a residential #2 oil boiler. Table 5 

### Emission Rates for CO2 for In-Home Combustion

3 RESULTS CO2 Gas Emission 1()3 tons/Quad 55,000 CO2 Oil Emission 1jj3 tons/Quad 84,300



### The following tabularresults indicate what changes can be brought about in the amounts ofemitted CO2, S02'and NOx 

### by imposing efficiency standards for each ofthe eight appliances considered in this analysis. A table is presented for each ofthe appliances' trial standard levels. Each appliance has been analyzed separatelyin orderto determine the emissionchanges resulting from each of their prospective trial standards levels. Each table details the changes that occur to each ofthe three emissions (i.e. CO2, S02'andNOx ) through the imposition ofan appliance's specific trial standard level. Each table includes the following information for a specific year between 1996 and 2030: 

### the amount ofemission abatedfrom powerplant generation, the amount


### 6 Table 4 

### Electrical Generation Data, Emissions Data and Emissions Rates for CO2 at Fossil Fuel-Burning Power Plants Electrical Generation Coal Oil Gas Energy Use Total Emission CO2 Emission Rate CO2 Year 

## 109 kWh 

## 109 kWh 

## 109 kWh Quads 

## 106 tons 

## 106 tons/Quad 1995 1602.2 193.7 442.0 25.74 2232.5 89.39 2000 1814.0 179.8 605.0 29.89 2506.2 86.41 2010 2660.6 149.9 482.5 37.88 3219.3 87.60 2020 3727.8 67.2 292.3 47.01 3964.2 86.90 2030 4837.3 29.0 179.2 58.03 4804.4 85.32



### As with the S02 and NOx 

### emissions, the amount of CO2 emissions abated for any particular year is determined by multiplying the estimates of energy saved through reduced electricity generation by the emission rate for that particular year. For years not covered in the Service Report, linear interpolation was used to derive emission rates and, in turn, the corresponding abated emissions. Table 5presents the emission factors (rates) that were used for CO2for in-home gas and oil combustion. The values for the reduction of CO2 emissions from in-home gas and oil

. combustion are produced by multiplying in-home fuel savings for gas and oil by the corresponding emission rates. Emission factors that appear in Table 5 are from a Lawrence Berkeley Laboratory report3

. The emission factor for gas in-home combustion was assumed to equal the average of those for residential gas space heaters and water heaters. The emission factor for oil in-home combustion was assumed to equal the one associated with a residential #2 oil boiler. Table 5 

### Emission Rates for CO2 for In-Home Combustion

3 RESULTS CO2 Gas Emission 1()3 tons/Quad 55,000 CO2 Oil Emission 1jj3 tons/Quad 84,300



### The following tabularresults indicate what changes can be brought about in the amounts ofemitted CO2, S02'and NOx 

### by imposing efficiency standards for each ofthe eight appliances considered in this analysis. A table is presented for each ofthe appliances' trial standard levels. Each appliance has been analyzed separatelyin orderto determine the emissionchanges resulting from each of their prospective trial standards levels. Each table details the changes that occur to each ofthe three emissions (i.e. CO2, S02'andNOx ) through the imposition ofan appliance's specific trial standard level. Each table includes the following information for a specific year between 1996 and 2030: 

### the amount ofemission abatedfrom powerplant generation, the amount


### 6abated from in-home generation, the net change in the emission, and the percent the net change

comprises of total U.S. power plant emissions. Also included are the cumulative changes of each pollutant (between the years 1996 and 2030). For each section that follows, only the results from the last standard level are discussed for each appliance. In order to view the results for each standard level, tables for each level are provided after the discussion. It should be noted that the standard levels studied for each appliance are not consistent between the eight appliances. The number of standard levels that are analyzed for a particular appliance depends on the number and type of technologies that were considered for it. For a detailed explanation of the specific technologies considered for each appliance, please refer to the appropriate sections and appendices of the Technical Support Document. 

### 3.1 ROOM AIR CONDITIONERS Decreases in the amounts of C02, S02, and NOX emitted at trial standards levels 1 through 7 are summarized in Tables 6 through 12. 3.1.1 Sulfur and Nitrogen Oxide Emissions Sulfur dioxide emissions would be decreased by a cumulative total of up to 0.78 million tons between 1996 and 2030 in the level 7 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.11% of the S02 emissions estimated to come from power plants in that year. In the year 2030, decreases in S02 emissions will represent about 0.26% of the S02 emissions estimated to come from power plants in that year. Because of provisions in the Clean Air Act Amendments (Pub. L. 101-549, November 15, 1990), the possible reductions of S02 that are caused by standards can be earned as credits by the utility realizing the reductions. To the extent S02 credits are used for future emissions, the net effect on S02 emissions from standards would be only a postponement of those S02 emissions. Level 7 design changes to room air conditioners would result in an estimated decrease in NOX emissions of 0.70 million tons between 1996 and 2030. NOX decreases would represent 0.11% and 0.26% of the NOX emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### 3.1.2 Carbon Dioxide Emissions The cumulative reduction in C02 emissions from level 7 design changes is 360 million tons of C02. For the year 1996, the estimated C02 reduction is 2496 thousand tons of C02 or about 0.11% of estimated U.S. power plant C02 emissions in 1996. For the year 2030, the estimated C02 reduction is 12,253 thousand tons of C02 or about 0.26% of estimated U.S.power plant C02 emissions in 2030.

7 abated from in-home generation, the net change in the emission, and the percent the net change comprises oftotal U.S. powerplantemissions. Alsoincludedare the cumulativechanges ofeach pollutant (between the years 1996 and 2030). For each section that follows, only the results from the last standard level are discussed for each appliance. In order to view the results for each standard level, tables for each level are provided after the discussion. It should be noted that the standard levels studied for each appliance are not consistent between the eight appliances. The number of standard levels that are analyzed for a particular appliance depends on the number and type of technologies that were considered for 

### it. For a detailed explanation of the specific technologies considered for each appliance, please refer to the appropriate sections and appendices ofthe Technical Support Document. 

### 3.1 ROOM AIR CONDITIONERS Decreases in the amounts of CO2, S02' and NOx emitted at trial standards levels 1 through 7 are summarized in Tables 6 through 12. 

### 3.1.1 Sulfur and Nitrogen Oxide Emissions Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.78 million tons between 1996 and 2030 in the level 7 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.11% ofthe S02 emissions estimated to come from power plants in that year. Inthe year2030, decreases in S02emissions willrepresent about0.26% ofthe S02 emissions estimatedto come from powerplants in that year. Because ofprovisions in the Clean Air Act Amendments (Pub. 

### L. 101-549, November 15, 1990), the possiblereductions ofS02that are caused by standards can be earned as credits by the utility realizing the reductions. To the extent S02credits are used for future emissions, the net effect on S02emissions from standards would be only a postponement ofthose S02 emissions. Level 7 design changes to room air conditioners wouldresult in an estimateddecrease in NOx emissions of 0.70 million tons between 1996 and 2030. NOx decreases would represent 0.11% and 0.26% ofthe NOxemissions estimated to come from powerplants in the years 1996 and 2030, respectively. 

### 3.1.2 Carbon Dioxide Emissions The cumulative reduction in CO2emissions from level 7 design changes is 360 million tons ofCO2, For the year 1996, the estimated CO2reduction is 2496 thousand tons of CO2or about 0.11% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimatedCO2reduction is 12,253 thousand tons ofCO2orabout0.26% ofestimatedU.S.power plant CO2 emissions in 2030.

7 abated from in-home generation, the net change in the emission, and the percent the net change comprises oftotal U.S. powerplantemissions. Alsoincludedare the cumulativechanges ofeach pollutant (between the years 1996 and 2030). For each section that follows, only the results from the last standard level are discussed for each appliance. In order to view the results for each standard level, tables for each level are provided after the discussion. It should be noted that the standard levels studied for each appliance are not consistent between the eight appliances. The number of standard levels that are analyzed for a particular appliance depends on the number and type of technologies that were considered for 

### it. For a detailed explanation of the specific technologies considered for each appliance, please refer to the appropriate sections and appendices ofthe Technical Support Document. 

### 3.1 ROOM AIR CONDITIONERS Decreases in the amounts of CO2, S02' and NOx emitted at trial standards levels 1 through 7 are summarized in Tables 6 through 12. 

### 3.1.1 Sulfur and Nitrogen Oxide Emissions Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.78 million tons between 1996 and 2030 in the level 7 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.11% ofthe S02 emissions estimated to come from power plants in that year. Inthe year2030, decreases in S02emissions willrepresent about0.26% ofthe S02 emissions estimatedto come from powerplants in that year. Because ofprovisions in the Clean Air Act Amendments (Pub. 

### L. 101-549, November 15, 1990), the possiblereductions ofS02that are caused by standards can be earned as credits by the utility realizing the reductions. To the extent S02credits are used for future emissions, the net effect on S02emissions from standards would be only a postponement ofthose S02 emissions. Level 7 design changes to room air conditioners wouldresult in an estimateddecrease in NOx emissions of 0.70 million tons between 1996 and 2030. NOx decreases would represent 0.11% and 0.26% ofthe NOxemissions estimated to come from powerplants in the years 1996 and 2030, respectively. 

### 3.1.2 Carbon Dioxide Emissions The cumulative reduction in CO2emissions from level 7 design changes is 360 million tons ofCO2, For the year 1996, the estimated CO2reduction is 2496 thousand tons of CO2or about 0.11% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimatedCO2reduction is 12,253 thousand tons ofCO2orabout0.26% ofestimatedU.S.power plant CO2 emissions in 2030.

7Table 6 Reduction of Pollutants for Room Air Conditioners - Level One

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 172 0 172 0.01% 2000 419 0 419 0.02% 2005 676 0 676 0.02% 2010 765 0 765 0.02% 2015 931 0 931 0.03% 2020 928 0 928 0.02% 2025 835 0 835 0.02% 2030 580 0 580 0.01% Cumulative CO2 reduction, 1996-2030 = 24 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 966 0 966 0.01% 2000 1,505 0 1,505 0.02% 2005 2,054 0 2,054 0.02% 2010 1,996 0 1,996 0.02% 2015 1,957 0 1,957 0.03% 2020 1,568 0 1,568 0.02% 2025 1,095 0 1,095 0.02% 2030 579 0 579 0.01%
Cumulative SO2 reduction, 1996-2030 = 52,274 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 607 0 607 0.01% 2000 1,121 0 1,121 0.02% 2005 1,653 0 1,653 0.02% 2010 1,735 0 1,735 0.02% 2015 1,814 0 1,814 0.03% 2020 1,568 0 1,568 0.02% 2025 1,200 0 1,200 0.02% 2030 712 0 712 0.01% Cumulative NOx reduction, 1996-2030 = 46,839 tons


### 8 /q& Table 6 ReductionofPollutants for RoomAir Conditioners

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 172 0 172 0.01% 2000 419 0 419 0.02% 2005 676 0 676 0.02% 2010 765 0 765 0.02% 2015 931 0 931 0.03% 2020 928 0 928 0.02% 2025 835 0 835 0.02% 2030 580 0 580 0.01% Cumulative C02reduction, 1996-2030

### = 24 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 966 0 966 0.01% 2000 1,505 0 1,505 0.02% 2005 2,054 0 2,054 0.02% 2010 1,996 0 1,996 0.02% 2015 1,957 0 1,957 0.03% 2020 1,568 0 1,568 0.02% 2025 1,095 0 1,095 0.02% 2030 579 0 579 0.01% Cumulative S02reduction, 1996-2030

### = 52,274 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 607 0 607 0.01% 2000 1,121 0 1,121 0.02% 2005 1,653 0 1,653 0.02% 2010 1,735 0 1,735 0.02% 2015 1,814 0 1,814 0.03% 2020 1,568 0 1,568 0.02% 2025 1,200 0 1,200 0.02% 2030 712 0 712 0.01% Cumulative NOx reduction, 1996-2030 = 46,839 tons



### 8 Table 6 ReductionofPollutants for RoomAir Conditioners

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 172 0 172 0.01% 2000 419 0 419 0.02% 2005 676 0 676 0.02% 2010 765 0 765 0.02% 2015 931 0 931 0.03% 2020 928 0 928 0.02% 2025 835 0 835 0.02% 2030 580 0 580 0.01% Cumulative C02reduction, 1996-2030

### = 24 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 966 0 966 0.01% 2000 1,505 0 1,505 0.02% 2005 2,054 0 2,054 0.02% 2010 1,996 0 1,996 0.02% 2015 1,957 0 1,957 0.03% 2020 1,568 0 1,568 0.02% 2025 1,095 0 1,095 0.02% 2030 579 0 579 0.01% Cumulative S02reduction, 1996-2030

### = 52,274 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 607 0 607 0.01% 2000 1,121 0 1,121 0.02% 2005 1,653 0 1,653 0.02% 2010 1,735 0 1,735 0.02% 2015 1,814 0 1,814 0.03% 2020 1,568 0 1,568 0.02% 2025 1,200 0 1,200 0.02% 2030 712 0 712 0.01% Cumulative NOx reduction, 1996-2030 = 46,839 tons



### 8Table 7 Reduction of Pollutants for Room Air Conditioners - Level Two

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 344 0 344 0.02% 2000 838 0 838 0.03% 2005 1,436 0 1,436 0.05% 2010 1,785 0 1,785 0.06% 2015 1,946 0 1,946 0.05% 2020 1,939 0 1,939 0.05% 2025 1,670 0 1,670 0.04% 2030 1,325 0 1,325 0.03% Cumulative CO2 reduction, 1996-2030 = 53 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,933 0 1,933 0.02% 2000 3,011 0 3,011 0.03% 2005 4,365 0 4,365 0.05% 2010 4,657 0 4,657 0.06% 2015 4,091 0 4,091 0.05% 2020 3,278 0 3,278 0.05% 2025 2,190 0 2,190 0.04% 2030 1,323 0 1,323 0.03% Cumulative SO2 reduction, 1996-2030 = 115,744 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,213 0 1,213 0.01% 2000 2,241 0 2,241 0.03% 2005 3,512 0 3,512 0.05% 2010 4,047 0 4,047 0.06% 2015 3,793 0 3,793 0.05% 2020 3,278 0 3,278 0.05% 2025 2,399 0 2,399 0.04% 2030 1,627 0 1,627 0.03% Cumulative NOx reduction, 1996-2030 = 103,365 tons


### 9 

## i7V Table 7 Reduction ofPollutants for Room Air Conditioners

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 344 0 344 0.02% 2000 838 0 838 0.03% 2005 1,436 0 1,436 0.05% 2010 1,785 0 1,785 0.06% 2015 1,946 0 1,946 0.05% 2020 1,939 0 1,939 0.05% 2025 1,670 0 1,670 0.04% 2030 1,325 0 1,325 0.03% CumulativeC02reduction, 1996-2030= 53 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,933 0 1,933 0.02% 2000 3,011 0 3,011 0.03% 2005 4,365 0 4,365 0.05% 2010 4,657 0 4,657 0.06% 2015 4,091 0 4,091 0.05% 2020 3,278 0 3,278 0.05% 2025 2,190 0 2,190 0.04% 2030 1,323 0 1,323 0.03% Cumulative S02reduction, 1996-2030 = 115,744 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,213 0 1,213 0.01% 2000 2,241 0 2,241 0.03% 2005 3,512 0 3,512 0.05% 2010 4,047 0 4,047 0.06% 2015 3,793 0 3,793 0.05% 2020 3,278 0 3,278 0.05% 2025 2,399 0 2,399 0.04% 2030 1,627 0 1,627 0.03% CumulativeNOx reduction, 1996-2030 = 103,365 tons


### 9 Table 7 Reduction ofPollutants for Room Air Conditioners

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 344 0 344 0.02% 2000 838 0 838 0.03% 2005 1,436 0 1,436 0.05% 2010 1,785 0 1,785 0.06% 2015 1,946 0 1,946 0.05% 2020 1,939 0 1,939 0.05% 2025 1,670 0 1,670 0.04% 2030 1,325 0 1,325 0.03% CumulativeC02reduction, 1996-2030= 53 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,933 0 1,933 0.02% 2000 3,011 0 3,011 0.03% 2005 4,365 0 4,365 0.05% 2010 4,657 0 4,657 0.06% 2015 4,091 0 4,091 0.05% 2020 3,278 0 3,278 0.05% 2025 2,190 0 2,190 0.04% 2030 1,323 0 1,323 0.03% Cumulative S02reduction, 1996-2030 = 115,744 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,213 0 1,213 0.01% 2000 2,241 0 2,241 0.03% 2005 3,512 0 3,512 0.05% 2010 4,047 0 4,047 0.06% 2015 3,793 0 3,793 0.05% 2020 3,278 0 3,278 0.05% 2025 2,399 0 2,399 0.04% 2030 1,627 0 1,627 0.03% CumulativeNOx reduction, 1996-2030 = 103,365 tons


### 9Table 8 Reduction of Pollutants for Room Air Conditioners - Level Three

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 689 0 689 0.03% 2000 1,677 0 1,677 0.07% 2005 2,788 0 2,788 0.10% 2010 3,485 0 3,485 0.11% 2015 3,808 0 3,808 0.11% 2020 3,795 0 3,795 0.10% 2025 3,506 0 3,506 0.08% 2030 2,980 0 2,980 0.06% Cumulative CO2 reduction, 1996-2030 = 105 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 3,866 0 3,866 0.03% 2000 6,022 0 6,022 0.07% 2005 8,473 0 8,473 0.10% 2010 9,093 0 9,093 0.11% 2015 8,005 0 8,005 0.11% 2020 6,413 0 6,413 0.10% 2025 4,598 0 4,598 0.08% 2030 2,978 0 2,978 0.06% Cumulative SO2 reduction, 1996-2030 = 229,357 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 2,427 0 2,427 0.03% 2000 4,483 0 4,483 0.07% 2005 6,818 0 6,818 0.10% 2010 7,902 0 7,902 0.11% 2015 7,422 0 7,422 0.11% 2020 6,413 0 6,413 0.10% 2025 5,038 0 5,038 0.08% 2030 3,660 0 3,660 0.06% Cumulative NOx reduction, 1996-2030 = 205,243 tons



### 10


## v Table 8 ReductionofPollutants for RoomAir Conditioners

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 689 0 689 0.03% 2000 1,677 0 1,677 0.07% 2005 2,788 0 2,788 0.10% 2010 3,485 0 3,485 0.11% 2015 3,808 0 3,808 0.11% 2020 3,795 0 3,795 0.10% 2025 3,506 0 3,506 0.08% 2030 2,980 0 2,980 0.06% CumulativeC02reduction, 1996-2030= 105 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,866 0 3,866 0.03% 2000 6,022 0 6,022 0.07% 2005 8,473 0 8,473 0.10% 2010 9,093 0 9,093 0.11% 2015 8,005 0 8,005 0.11% 2020 6,413 0 6,413 0.10% 2025 4,598 0 4,598 0.08% 2030 2,978 0 2,978 0.06% Cumulative S02reduction, 1996-2030 = 229,357 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,427 0 2,427 0.03% 2000 4,483 0 4,483 0.07% 2005 6,818 0 6,818 0.10% 2010 7,902 0 7,902 0.11% 2015 7,422 0 7,422 0.11% 2020 6,413 0 6,413 0.10% 2025 5,038 0 5,038 0.08% 2030 3,660 0 3,660 0.06% CumulativeNOx reduction, 1996-2030 = 205,243 tons


### 10 Table 8 ReductionofPollutants for RoomAir Conditioners

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 689 0 689 0.03% 2000 1,677 0 1,677 0.07% 2005 2,788 0 2,788 0.10% 2010 3,485 0 3,485 0.11% 2015 3,808 0 3,808 0.11% 2020 3,795 0 3,795 0.10% 2025 3,506 0 3,506 0.08% 2030 2,980 0 2,980 0.06% CumulativeC02reduction, 1996-2030= 105 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,866 0 3,866 0.03% 2000 6,022 0 6,022 0.07% 2005 8,473 0 8,473 0.10% 2010 9,093 0 9,093 0.11% 2015 8,005 0 8,005 0.11% 2020 6,413 0 6,413 0.10% 2025 4,598 0 4,598 0.08% 2030 2,978 0 2,978 0.06% Cumulative S02reduction, 1996-2030 = 229,357 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,427 0 2,427 0.03% 2000 4,483 0 4,483 0.07% 2005 6,818 0 6,818 0.10% 2010 7,902 0 7,902 0.11% 2015 7,422 0 7,422 0.11% 2020 6,413 0 6,413 0.10% 2025 5,038 0 5,038 0.08% 2030 3,660 0 3,660 0.06% CumulativeNOx reduction, 1996-2030 = 205,243 tons


### 10Table 9 Reduction of Pollutants for Room Air Conditioners - Level Four

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 861 0 861 0.04% 2000 1,928 0 1,928 0.08% 2005 3,126 0 3,126 0.11% 2010 3,995 0 3,995 0.12% 2015 4,231 0 4,231 0.12% 2020 4,301 0 4,301 0.11% 2025 4,007 0 4,007 0.09% 2030 3,312 0 3,312 0.07% Cumulative CO2 reduction, 1996-2030 = 119 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 4,832 0 4,832 0.04% 2000 6,925 0 6,925 0.08% 2005 9,500 0 9,500 0.11% 2010 10,424 0 10,424 0.12% 2015 8,894 0 8,894 0.12% 2020 7,268 0 7,268 0.11% 2025 5,255 0 5,255 0.09% 2030 3,308 0 3,308 0.07% Cumulative SO2 reduction, 1996-2030 = 261,109 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 3,033 0 3,033 0.04% 2000 5,155 0 5,155 0.08% 2005 7,644 0 7,644 0.11% 2010 9,059 0 9,059 0.12% 2015 8,246 0 8,246 0.12% 2020 7,268 0 7,268 0.11% 2025 5,758 0 5,758 0.09% 2030 4,067 0 4,067 0.07% Cumulative NOx reduction, 1996-2030 = 233,507 tons



### 11 Jai Table 9 Reduction ofPollutants forRoomAir Conditioners

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 861 0 861 0.04% 2000 1,928 0 1,928 0.08% 2005 3,126 0 3,126 0.11% 2010 3,995 0 3,995 0.12% 2015 4,231 0 4,231 0.12% 2020 4,301 0 4,301 0.11% 2025 4,007 0 4,007 0.09% 2030 3,312 0 3,312 0.07% CumulativeC02reduction, 1996-2030= 119 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 4,832 0 4,832 0.04% 2000 6,925 0 6,925 0.08% 2005 9,500 0 9,500 0.11% 2010 10,424 0 10,424 0.12% 2015 8,894 0 8,894 0.12% 2020 7,268 0 7,268 0.11% 2025 5,255 0 5,255 0.09% 2030 3,308 0 3,308 0.07% Cumulative S02reduction, 1996-2030 = 261,109 tons

NOxReduction (inTons) Abated from Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,033 0 3,033 0.04% 2000 5,155 0 5,155 0.08% 2005 7,644 0 7,644 0.11% 2010 9,059 0 9,059 0.12% 2015 8,246 0 8,246 0.12% 2020 7,268 0 7,268 0.11% 2025 5,758 0 5,758 0.09% 2030 4,067 0 4,067 0.07% Cumulative NOx reduction, 1996-2030 = 233,507 tons


### 11 Table 9 Reduction ofPollutants forRoomAir Conditioners

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 861 0 861 0.04% 2000 1,928 0 1,928 0.08% 2005 3,126 0 3,126 0.11% 2010 3,995 0 3,995 0.12% 2015 4,231 0 4,231 0.12% 2020 4,301 0 4,301 0.11% 2025 4,007 0 4,007 0.09% 2030 3,312 0 3,312 0.07% CumulativeC02reduction, 1996-2030= 119 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 4,832 0 4,832 0.04% 2000 6,925 0 6,925 0.08% 2005 9,500 0 9,500 0.11% 2010 10,424 0 10,424 0.12% 2015 8,894 0 8,894 0.12% 2020 7,268 0 7,268 0.11% 2025 5,255 0 5,255 0.09% 2030 3,308 0 3,308 0.07% Cumulative S02reduction, 1996-2030 = 261,109 tons

NOxReduction (inTons) Abated from Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,033 0 3,033 0.04% 2000 5,155 0 5,155 0.08% 2005 7,644 0 7,644 0.11% 2010 9,059 0 9,059 0.12% 2015 8,246 0 8,246 0.12% 2020 7,268 0 7,268 0.11% 2025 5,758 0 5,758 0.09% 2030 4,067 0 4,067 0.07% Cumulative NOx reduction, 1996-2030 = 233,507 tons


### 11Table 10 Reduction of Pollutants for Room Air Conditioners - Level Five

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,291 0 1,291 0.06% 2000 2,935 0 2,935 0.12% 2005 4,731 0 4,731 0.17% 2010 5,950 0 5,950 0.18% 2015 6,262 0 6,262 0.17% 2020 6,409 0 6,409 0.16% 2025 6,261 0 6,261 0.14% 2030 5,630 0 5,630 0.12% Cumulative CO2 reduction, 1996-2030 = 181 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 7,249 0 7,249 0.06% 2000 10,538 0 10,538 0.12% 2005 14,379 0 14,379 0.17% 2010 15,525 0 15,525 0.18% 2015 13,163 0 13,163 0.17% 2020 10,831 0 10,831 0.16% 2025 8,211 0 8,211 0.14% 2030 5,624 0 5,624 0.12% Cumulative SO2 reduction, 1996-2030 = 394,176 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 4,550 0 4,550 0.06% 2000 7,845 0 7,845 0.12% 2005 11,569 0 11,569 0.17% 2010 13,492 0 13,492 0.18% 2015 12,204 0 12,204 0.17% 2020 10,831 0 10,831 0.16% 2025 8,996 0 8,996 0.14% 2030 6,913 0 6,913 0.12% Cumulative NOx reduction, 1996-2030 = 353,124 tons



### 12

4^^11 Table 10 Reduction ofPollutants for RoomAir Conditioners

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,291 0 1,291 0.06% 2000 2,935 0 2,935 0.12% 2005 4,731 0 4,731 0.17% 2010 5,950 0 5,950 0.18% 2015 6,262 0 6,262 0.17% 2020 6,409 0 6,409 0.16% 2025 6,261 0 6,261 0.14% 2030 5,630 0 5,630 0.12% CumulativeC02reduction, 1996-2030

### = 181 milliontons ofCO2

S02Reduction (inTons) Abated from Abatedfrom Net % U.S. S02 Year Power Plant In-Home Change PowerPlant Generation Generation Emissions 1996 7,249 0 7,249 0.06% 2000 10,538 0 10,538 0.12% 2005 14,379 0 14,379 0.17% 2010 15,525 0 15,525 0.18% 2015 13,163 0 13,163 0.17% 2020 10,831 0 10,831 0.16% 2025 8,211 0 8,211 0.14% 2030 5,624 0 5,624 0.12% Cumulative S02reduction, 1996-2030 = 394,176 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 4,550 0 4,550 0.06% 2000 7,845 0 7,845 0.12% 2005 11,569 0 11,569 0.17% 2010 13,492 0 13,492 0.18% 2015 12,204 0 12,204 0.17% 2020 10,831 0 10,831 0.16% 2025 8,996 0 8,996 0.14% 2030 6,913 0 6,913 0.12% CumulativeNOx reduction, 1996-2030 = 353,124 tons


### 12 Table 10 Reduction ofPollutants for RoomAir Conditioners

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,291 0 1,291 0.06% 2000 2,935 0 2,935 0.12% 2005 4,731 0 4,731 0.17% 2010 5,950 0 5,950 0.18% 2015 6,262 0 6,262 0.17% 2020 6,409 0 6,409 0.16% 2025 6,261 0 6,261 0.14% 2030 5,630 0 5,630 0.12% CumulativeC02reduction, 1996-2030

### = 181 milliontons ofCO2

S02Reduction (inTons) Abated from Abatedfrom Net % U.S. S02 Year Power Plant In-Home Change PowerPlant Generation Generation Emissions 1996 7,249 0 7,249 0.06% 2000 10,538 0 10,538 0.12% 2005 14,379 0 14,379 0.17% 2010 15,525 0 15,525 0.18% 2015 13,163 0 13,163 0.17% 2020 10,831 0 10,831 0.16% 2025 8,211 0 8,211 0.14% 2030 5,624 0 5,624 0.12% Cumulative S02reduction, 1996-2030 = 394,176 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 4,550 0 4,550 0.06% 2000 7,845 0 7,845 0.12% 2005 11,569 0 11,569 0.17% 2010 13,492 0 13,492 0.18% 2015 12,204 0 12,204 0.17% 2020 10,831 0 10,831 0.16% 2025 8,996 0 8,996 0.14% 2030 6,913 0 6,913 0.12% CumulativeNOx reduction, 1996-2030 = 353,124 tons


### 12Table 11 Reduction of Pollutants for Room Air Conditioners - Level Six

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,636 0 1,636 0.07% 2000 3,689 0 3,689 0.15% 2005 6,168 0 6,168 0.22% 2010 7,820 0 7,820 0.24% 2015 8,293 0 8,293 0.23% 2020 8,432 0 8,432 0.21% 2025 8,348 0 8,348 0.19% 2030 7,865 0 7,865 0.16% Cumulative CO2 reduction, 1996-2030 = 240 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 9,182 0 9,182 0.07% 2000 13,248 0 13,248 0.15% 2005 18,744 0 18,744 0.22% 2010 20,404 0 20,404 0.24% 2015 17,433 0 17,433 0.23% 2020 14,252 0 14,252 0.21% 2025 10,948 0 10,948 0.19% 2030 7,858 0 7,858 0.16% Cumulative SO2 reduction, 1996-2030 = 520,335 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 5,764 0 5,764 0.07% 2000 9,863 0 9,863 0.15% 2005 15,081 0 15,081 0.22% 2010 17,732 0 17,732 0.24% 2015 16,163 0 16,163 0.23% 2020 14,252 0 14,252 0.21% 2025 11,995 0 11,995 0.19% 2030 9,658 0 9,658 0.16% Cumulative NOx reduction, 1996-2030 = 466,729 tons


### 3
2oi Table 11 Reduction ofPollutants for Room Air Conditioners

- Level Six C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,636 0 1,636 0.07% 2000 3,689 0 3,689 0.15% 2005 6,168 0 6,168 0.22% 2010 7,820 0 7,820 0.24% 2015 8,293 0 8,293 0.23% 2020 8,432 0 8,432 0.21% 2025 8,348 0 8,348 0.19% 2030 7,865 0 7,865 0.16% CumulativeC02reduction, 1996-2030= 240 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 9,182 0 9,182 0.07% 2000 13,248 0 13,248 0.15% 2005 18,744 0 18,744 0.22% 2010 20,404 0 20,404 0.24% 2015 17,433 0 17,433 0.23% 2020 14,252 0 14,252 0.21% 2025 10,948 0 10,948 0.19% 2030 7,858 0 7,858 0.16% Cumulative S02reduction, 1996-2030

### = 520,335 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 5,764 0 5,764 0.07% 2000 9,863 0 9,863 0.15% 2005 15,081 0 15,081 0.22% 2010 17,732 0 17,732 0.24% 2015 16,163 0 16,163 0.23% 2020 14,252 0 14,252 0.21% 2025 11,995 0 11,995 0.19% 2030 9,658 0 9,658 0.16% CumulativeNOx reduction, 1996-2030

### = 466,729 tons


### 13 Table 11 Reduction ofPollutants for Room Air Conditioners

- Level Six C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,636 0 1,636 0.07% 2000 3,689 0 3,689 0.15% 2005 6,168 0 6,168 0.22% 2010 7,820 0 7,820 0.24% 2015 8,293 0 8,293 0.23% 2020 8,432 0 8,432 0.21% 2025 8,348 0 8,348 0.19% 2030 7,865 0 7,865 0.16% CumulativeC02reduction, 1996-2030= 240 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 9,182 0 9,182 0.07% 2000 13,248 0 13,248 0.15% 2005 18,744 0 18,744 0.22% 2010 20,404 0 20,404 0.24% 2015 17,433 0 17,433 0.23% 2020 14,252 0 14,252 0.21% 2025 10,948 0 10,948 0.19% 2030 7,858 0 7,858 0.16% Cumulative S02reduction, 1996-2030

### = 520,335 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 5,764 0 5,764 0.07% 2000 9,863 0 9,863 0.15% 2005 15,081 0 15,081 0.22% 2010 17,732 0 17,732 0.24% 2015 16,163 0 16,163 0.23% 2020 14,252 0 14,252 0.21% 2025 11,995 0 11,995 0.19% 2030 9,658 0 9,658 0.16% CumulativeNOx reduction, 1996-2030

### = 466,729 tons


### 13Table 12 Reduction of Pollutants for Room Air Conditioners - Level Seven

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 2,496 0 2,496 0.11% 2000 5,785 0 5,785 0.23% 2005 9,125 0 9,125 0.32% 2010 11,390 0 11,390 0.35% 2015 12,101 0 12,101 0.34% 2020 12,564 0 12,564 0.32% 2025 12,688 0 12,688 0.29% 2030 12,253 0 12,253 0.26% Cumulative CO2 reduction, 1996-2030 = 360 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In

-Home

Generation Change Power Plant Emissions 1996 14,014 0 14,014 0.11% 2000 20,776 0 20,776 0.23% 2005 27,731 0 27,731 0.32% 2010 29,718 0 29,718 0.35% 2015 25,437 0 25,437 0.33% 2020 21,235 0 21,235 0.32% 2025 16,641 0 16,641 0.29% 2030 12,241 0 12,241 0.26% Cumulative SO2 reduction, 1996-2030 = 777,408 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 8,797 0 8,797 0.11% 2000 15,466 0 15,466 0.23% 2005 22,312 0 22,312 0.32% 2010 25,827 0 25,827 0.35% 2015 23,584 0 23,584 0.34% 2020 21,235 0 21,235 0.32% 2025 18,232 0 18,232 0.29% 2030 15,047 0 15,047 0.26% Cumulative NOx reduction, 1996-2030 = 697,544 tons



### 14 Table 12 ReductionofPollutants for RoomAir Conditioners

- Level Seven C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,496 0 2,496 0.11% 2000 5,785 0 5,785 0.23% 2005 9,125 0 9,125 0.32% 2010 11,390 0 11,390 0.35% 2015 12,101 0 12,101 0.34% 2020 12,564 0 12,564 0.32% 2025 12,688 0 12,688 0.29% 2030 12,253 0 12,253 0.26% Cumulative C02reduction, 1996-2030= 360 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 14,014 0 14,014 0.11% 2000 20,776 0 20,776 0.23% 2005 27,731 0 27,731 0.32% 2010 29,718 0 29,718 0.35% 2015 25,437 0 25,437 0.33% 2020 21,235 0 21,235 0.32% 2025 16,641 0 16,641 0.29% 2030 12,241 0 12,241 0.26% Cumulative S02reduction, 1996-2030

### = 777,408 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 8,797 0 8,797 0.11% 2000 15,466 0 15,466 0.23% 2005 22,312 0 22,312 0.32% 2010 25,827 0 25,827 0.35% 2015 23,584 0 23,584 0.34% 2020 21,235 0 21,235 0.32% 2025 18,232 0 18,232 0.29% 2030 15,047 0 15,047 0.26% CumulativeNOxreduction, 1996-2030

### = 697,544 tons


### 14 Table 12 ReductionofPollutants for RoomAir Conditioners

- Level Seven C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,496 0 2,496 0.11% 2000 5,785 0 5,785 0.23% 2005 9,125 0 9,125 0.32% 2010 11,390 0 11,390 0.35% 2015 12,101 0 12,101 0.34% 2020 12,564 0 12,564 0.32% 2025 12,688 0 12,688 0.29% 2030 12,253 0 12,253 0.26% Cumulative C02reduction, 1996-2030= 360 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 14,014 0 14,014 0.11% 2000 20,776 0 20,776 0.23% 2005 27,731 0 27,731 0.32% 2010 29,718 0 29,718 0.35% 2015 25,437 0 25,437 0.33% 2020 21,235 0 21,235 0.32% 2025 16,641 0 16,641 0.29% 2030 12,241 0 12,241 0.26% Cumulative S02reduction, 1996-2030

### = 777,408 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 8,797 0 8,797 0.11% 2000 15,466 0 15,466 0.23% 2005 22,312 0 22,312 0.32% 2010 25,827 0 25,827 0.35% 2015 23,584 0 23,584 0.34% 2020 21,235 0 21,235 0.32% 2025 18,232 0 18,232 0.29% 2030 15,047 0 15,047 0.26% CumulativeNOxreduction, 1996-2030

### = 697,544 tons


### 14

### 3.2 WATER HEATERS

Decreases in the amounts of CO2, SO2, and NOX emitted at trial standards levels 1 through 8 are summarized in Tables 13 through 20. 3.2.1 Sulfur and Nitrogen Oxide Emissions Sulfur dioxide emissions would be decreased by a cumulative total of up to 7.12 million tons between 1996 and 2030 in the level 8 scenario. In the year 1996, decreases in sulfur dioxide will represent about 1.14% of the SO2 emissions estimated to come from power plants in that year. In the year 2030, decreases in SO2 emissions will represent about 2.44% of the SO2 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions of SO2 emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those SO2 emissions would be only a postponement. Level 8 design changes to water heaters would result in an estimated decrease in NOX emissions of 7.11 million tons between 1996 and 2030. NOX decreases would represent 1.21% and 2.81% of the NOX emissions estimated to come from power plants in the years 1996 and 2030, respectively. 3.2.2 Carbon Dioxide Emissions The cumulative reduction in CO2 emissions from level 8 design changes is 4113 million tons of CO2. For the year 1996, the estimated CO2 reduction is 33,450 thousand tons of CO2 or about 1.46% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2 reduction is 142,630 thousand tons of CO2 or about 2.97% of estimated U.S. power plant CO2 emissions in 2030.



### 15


### 0^ 3.2 WATER HEATERS Decreases in the amounts of CO2, S02' and NOx emitted at trial standards levels 1 through 8 are summarized in Tables 13 through 20. 

### 3.2.1 Sulfur and Nitrogen Oxide Emissions Sulfurdioxide emissions would be decreased by a cumulative total ofup to 7.12 million tons between 1996 and 2030 in the level 8 scenario. In the year 1996, decreases in sulfur dioxide will represent about 1.14% ofthe S02 emissions estimated to come from power plants in thatyear. In the year2030, decreases in S02emissions willrepresent about2.44% ofthe S02 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions ofS02emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 8 design changes to water heaters would result in an estimated decrease in NOx emissions of7.11 million tons between 1996 and 2030. NOx decreases would represent 1.21% and 2.81% of the NOx emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### 3.2.2 Carbon Dioxide Emissions The cumulative reduction in CO2emissions from level 8 design changes is 4113 million tons ofCO2, For the year 1996, the estimated CO2reduction is 33,450 thousand tons ofCO2 or about 1.46% ofestimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2 reduction is 142,630 thousand tons of CO2 or about 2.97% of estimated U.S. power plant CO2 emissions in 2030.

15 

### 3.2 WATER HEATERS Decreases in the amounts of CO2, S02' and NOx emitted at trial standards levels 1 through 8 are summarized in Tables 13 through 20. 

### 3.2.1 Sulfur and Nitrogen Oxide Emissions Sulfurdioxide emissions would be decreased by a cumulative total ofup to 7.12 million tons between 1996 and 2030 in the level 8 scenario. In the year 1996, decreases in sulfur dioxide will represent about 1.14% ofthe S02 emissions estimated to come from power plants in thatyear. In the year2030, decreases in S02emissions willrepresent about2.44% ofthe S02 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions ofS02emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 8 design changes to water heaters would result in an estimated decrease in NOx emissions of7.11 million tons between 1996 and 2030. NOx decreases would represent 1.21% and 2.81% of the NOx emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### 3.2.2 Carbon Dioxide Emissions The cumulative reduction in CO2emissions from level 8 design changes is 4113 million tons ofCO2, For the year 1996, the estimated CO2reduction is 33,450 thousand tons ofCO2 or about 1.46% ofestimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2 reduction is 142,630 thousand tons of CO2 or about 2.97% of estimated U.S. power plant CO2 emissions in 2030.

15Table 13 Reduction of Pollutants for Water Heaters - Level One C02 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. C02 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 86 0 86 0.00% 2000 252 0 252 0.01% 2005 338 139 477 0.02% 2010 170 0 170 0.01% 2015 85 0 85 0.00% 2020 169 55 224 0.01% 2025 83 55 138 0.00% 2030 166 0 166 0.00% Cumulative C02 reduction, 1996-2030 = 7 million tons of C02
S02 Reduction (in Tons) Abated from Abated from Net % U.S. S02 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 483 0 483 0.00% 2000 903 0 903 0.01% 2005 1,027 157 1,184 0.01% 2010 444 0 444 0.01% 2015 178 0 178 0.00% 2020 285 0 285 0.00% 2025 109 0 109 0.00% 2030 165 0 165 0.00% Cumulative S02 reduction, 1996-2030 = 16,662 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 303 0 303 0.00% 2000 672 0 672 0.01% 2005 826 117 944 0.01% 2010 385 0 385 0.01% 2015 165 0 165 0.00% 2020 285 52 338 0.01% 2025 120 53 172 0.00% 2030 203 0 203 0.00% Cumulative NOx reduction, 1996-2030 = 14,405 tons



### 16 Table 13 ReductionofPollutants forWaterHeaters

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 86 0 86 0.00% 2000 252 0 252 0.01% 2005 338 139 477 0.02% 2010 170 0 170 0.01% 2015 85 0 85 0.00% 2020 169 55 224 0.01% 2025 83 55 138 0.00% 2030 166 0 166 0.00% CumulativeC02reduction, 1996-2030 = 7 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 483 0 483 0.00% 2000 903 0 903 0.01% 2005 1,027 157 1,184 0.01% 2010 444 0 444 0.01% 2015 178 0 178 0.00% 2020 285 0 285 0.00% 2025 109 0 109 0.00% 2030 165 0 165 0.00% Cumulative S02reduction, 1996-2030 = 16,662 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 303 0 303 0.00% 2000 672 0 672 0.01% 2005 826 117 944 0.01% 2010 385 0 385 0.01% 2015 165 0 165 0.00% 2020 285 52 338 0.01% 2025 120 53 172 0.00% 2030 203 0 203 0.00% CumulativeNOxreduction, 1996-2030 = 14,405 tons


### 16 Table 13 ReductionofPollutants forWaterHeaters

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 86 0 86 0.00% 2000 252 0 252 0.01% 2005 338 139 477 0.02% 2010 170 0 170 0.01% 2015 85 0 85 0.00% 2020 169 55 224 0.01% 2025 83 55 138 0.00% 2030 166 0 166 0.00% CumulativeC02reduction, 1996-2030 = 7 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 483 0 483 0.00% 2000 903 0 903 0.01% 2005 1,027 157 1,184 0.01% 2010 444 0 444 0.01% 2015 178 0 178 0.00% 2020 285 0 285 0.00% 2025 109 0 109 0.00% 2030 165 0 165 0.00% Cumulative S02reduction, 1996-2030 = 16,662 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 303 0 303 0.00% 2000 672 0 672 0.01% 2005 826 117 944 0.01% 2010 385 0 385 0.01% 2015 165 0 165 0.00% 2020 285 52 338 0.01% 2025 120 53 172 0.00% 2030 203 0 203 0.00% CumulativeNOxreduction, 1996-2030 = 14,405 tons


### 16Table 14 Reduction of Pollutants for Water Heaters - Level Two CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 689 84 773 0.03% 2000 1,845 224 2,068 0.08% 2005 2,619 418 3,037 0.11% 2010 1,955 334 2,289 0.07% 2015 1,269 418 1,687 0.05% 2020 928 334 1,261 0.03% 2025 584 279 863 0.02% 2030 497 363 860 0.02% Cumulative CO2 reduction, 1996-2030 = 59 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 3,866 157 4,022 0.03% 2000 6,624 313 6,937 0.08% 2005 7,960 470 8,429 0.10% 2010 5,101 313 5,414 0.06% 2015 2,668 470 3,138 0.04% 2020 1,568 313 1,881 0.03% 2025 766 313 1,079 0.02% 2030 496 470 966 0.02% Cumulative SO2 reduction, 1996-2030 = 145,477 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 2,427 65 2,492 0.03% 2000 4,931 182 5,114 0.08% 2005 6,404 352 6,757 0.10% 2010 4,433 287 4,720 0.06% 2015 2,474 352 2,826 0.04% 2020 1,568 287 1,855 0.03% 2025 840 235 1,075 0.02% 2030 610 300 910 0.02% Cumulative NOx reduction, 1996-2030 = 118,434 tons



### 17 LLO

## S Table 14 ReductionofPollutants for Water Heaters

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 689 84 773 0.03% 2000 1,845 224 2,068 0.08% 2005 2,619 418 3,037 0.11% 2010 1,955 334 2,289 0.07% 2015 1,269 418 1,687 0.05% 2020 928 334 1,261 0.03% 2025 584 279 863 0.02% 2030 497 363 860 0.02% CumulativeC02reduction, 1996-2030= 59 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,866 157 4,022 0.03% 2000 6,624 313 6,937 0.08% 2005 7,960 470 8,429 0.10% 2010 5,101 313 5,414 0.06% 2015 2,668 470 3,138 0.04% 2020 1,568 313 1,881 0.03% 2025 766 313 1,079 0.02% 2030 496 470 966 0.02% Cumulative S02reduction, 1996-2030 = 145,477 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,427 65 2,492 0.03% 2000 4,931 182 5,114 0.08% 2005 6,404 352 6,757 0.10% 2010 4,433 287 4,720 0.06% 2015 2,474 352 2,826 0.04% 2020 1,568 287 1,855 0.03% 2025 840 235 1,075 0.02% 2030 610 300 910 0.02% CumulativeNOx reduction, 1996-2030

## = 118,434 tons


### 17 Table 14 ReductionofPollutants for Water Heaters

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 689 84 773 0.03% 2000 1,845 224 2,068 0.08% 2005 2,619 418 3,037 0.11% 2010 1,955 334 2,289 0.07% 2015 1,269 418 1,687 0.05% 2020 928 334 1,261 0.03% 2025 584 279 863 0.02% 2030 497 363 860 0.02% CumulativeC02reduction, 1996-2030= 59 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,866 157 4,022 0.03% 2000 6,624 313 6,937 0.08% 2005 7,960 470 8,429 0.10% 2010 5,101 313 5,414 0.06% 2015 2,668 470 3,138 0.04% 2020 1,568 313 1,881 0.03% 2025 766 313 1,079 0.02% 2030 496 470 966 0.02% Cumulative S02reduction, 1996-2030 = 145,477 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,427 65 2,492 0.03% 2000 4,931 182 5,114 0.08% 2005 6,404 352 6,757 0.10% 2010 4,433 287 4,720 0.06% 2015 2,474 352 2,826 0.04% 2020 1,568 287 1,855 0.03% 2025 840 235 1,075 0.02% 2030 610 300 910 0.02% CumulativeNOx reduction, 1996-2030

## = 118,434 tons


### 17Table 15 Reduction of Pollutants for Water Heaters - Level Three C02 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. C02 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 18,077 1,991 20,068 0.88% 2000 44,857 4,286 49,143 1.96% 2005 70,802 6,500 77,302 2.70% 2010 73,607 6,911 80,518 2.50% 2015 74,385 7,520 81,904 2.28% 2020 76,651 7,824 84,475 2.13% 2025 78,634 8,128 86,762 1.98% 2030 81,381 8,517 89,898 1.87% Cumulative C02 reduction, 1996-2030 = 2,607 million tons of C02
S02 Reduction (in Tons) Abated from Abated from Net % U.S. S02 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 101,482 939 102,421 0.80% 2000 161,086 2,035 163,120 1.81% 2005 215,169 3,287 218,455 2.51% 2010 192,061 3,130 195,191 2.32% 2015 156,360 3,443 159,803 2.10% 2020 129,550 3,600 133,149 1.99% 2025 103,128 3,756 106,884 1.84% 2030 81,306 4,069 85,375 1.78% Cumulative S02 reduction, 1996-2030 = 5,381,123 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 63,703 1,808 65,511 0.81% 2000 119,919 3,890 123,809 1.85% 2005 173,124 5,880 179,004 2.56% 2010 166,910 6,288 173,198 2.37% 2015 144,970 6,838 151,807 2.17% 2020 129,550 7,113 136,662 2.04% 2025 112,993 7,388 120,380 1.91% 2030 99,939 7,728 107,667 1.82% Cumulative NOx reduction, 1996-2030 = 4,890,115 tons



### 18

5,6 Table 15 ReductionofPollutants for Water Heaters

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 18,077 1,991 20,068 0.88% 2000 44,857 4,286 49,143 1.96% 2005 70,802 6,500 77,302 2.70% 2010 73,607 6,911 80,518 2.50% 2015 74,385 7,520 81,904 2.28% 2020 76,651 7,824 84,475 2.13% 2025 78,634 8,128 86,762 1.98% 2030 81,381 8,517 89,898 1.87% CumulativeC02reduction, 1996-2030= 2,607 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 101,482 939 102,421 0.80% 2000 161,086 2,035 163,120 1.81% 2005 215,169 3,287 218,455 2.51% 2010 192,061 3,130 195,191 2.32% 2015 156,360 3,443 159,803 2.10% 2020 129,550 3,600 133,149 1.99% 2025 103,128 3,756 106,884 1.84% 2030 81,306 4,069 85,375 1.78% Cumulative S02reduction, 1996-2030

### = 5,381,123 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 63,703 1,808 65,511 0.81% 2000 119,919 3,890 123,809 1.85% 2005 173,124 5,880 179,004 2.56% 2010 166,910 6,288 173,198 2.37% 2015 144,970 6,838 151,807 2.17% 2020 129,550 7,113 136,662 2.04% 2025 112,993 7,388 120,380 1.91% 2030 99,939 7,728 107,667 1.82% CumulativeNOxreduction, 1996-2030

### = 4,890,115 tons


### 18 Table 15 ReductionofPollutants for Water Heaters

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 18,077 1,991 20,068 0.88% 2000 44,857 4,286 49,143 1.96% 2005 70,802 6,500 77,302 2.70% 2010 73,607 6,911 80,518 2.50% 2015 74,385 7,520 81,904 2.28% 2020 76,651 7,824 84,475 2.13% 2025 78,634 8,128 86,762 1.98% 2030 81,381 8,517 89,898 1.87% CumulativeC02reduction, 1996-2030= 2,607 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 101,482 939 102,421 0.80% 2000 161,086 2,035 163,120 1.81% 2005 215,169 3,287 218,455 2.51% 2010 192,061 3,130 195,191 2.32% 2015 156,360 3,443 159,803 2.10% 2020 129,550 3,600 133,149 1.99% 2025 103,128 3,756 106,884 1.84% 2030 81,306 4,069 85,375 1.78% Cumulative S02reduction, 1996-2030

### = 5,381,123 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 63,703 1,808 65,511 0.81% 2000 119,919 3,890 123,809 1.85% 2005 173,124 5,880 179,004 2.56% 2010 166,910 6,288 173,198 2.37% 2015 144,970 6,838 151,807 2.17% 2020 129,550 7,113 136,662 2.04% 2025 112,993 7,388 120,380 1.91% 2030 99,939 7,728 107,667 1.82% CumulativeNOxreduction, 1996-2030

### = 4,890,115 tons


### 18Table 16 Reduction of Pollutants for Water Heaters - Level Four CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 21,263 2,156 23,418 1.02% 2000 50,139 4,645 54,784 2.19% 2005 76,885 6,775 83,660 2.92% 2010 79,727 7,270 86,997 2.70% 2015 80,732 7,850 88,581 2.47% 2020 83,312 8,154 91,466 2.31% 2025 85,563 8,598 94,160 2.15% 2030 88,666 8,986 97,652 2.03% Cumulative CO2 reduction, 1996-2030 = 2,837 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 119,363 939 120,302 0.94% 2000 180,055 2,191 182,246 2.02% 2005 233,656 3,287 236,942 2.72% 2010 208,029 3,287 211,316 2.52% 2015 169,701 3,443 173,144 2.28% 2020 140,809 3,600 144,408 2.16% 2025 112,215 3,913 116,128 2.00% 2030 88,585 4,226 92,811 1.93% Cumulative SO2 reduction, 1996-2030 = 5,885,240 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 74,927 1,965 76,892 0.95% 2000 134,041 4,218 138,258 2.06% 2005 187,999 6,143 194,141 2.77% 2010 180,787 6,615 187,402 2.57% 2015 157,339 7,153 164,491 2.35% 2020 140,809 7,428 148,236 2.21% 2025 122,949 7,820 130,769 2.08% 2030 108,886 8,160 117,046 1.98% Cumulative NOx reduction, 1996-2030 = 5,337,547 tons



### 19 `,a"7 Table 16 ReductionofPollutants for WaterHeaters

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 21,263 2,156 23,418 1.02% 2000 50,139 4,645 54,784 2.19% 2005 76,885 6,775 83,660 2.92% 2010 79,727 7,270 86,997 2.70% 2015 80,732 7,850 88,581 2.47% 2020 83,312 8,154 91,466 2.31% 2025 85,563 8,598 94,160 2.15% 2030 88,666 8,986 97,652 2.03% Cumulative C02reduction, 1996-2030 = 2,837 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 119,363 939 120,302 0.94% 2000 180,055 2,191 182,246 2.02% 2005 233,656 3,287 236,942 2.72% 2010 208,029 3,287 211,316 2.52% 2015 169,701 3,443 173,144 2.28% 2020 140,809 3,600 144,408 2.16% 2025 112,215 3,913 116,128 2.00% 2030 88,585 4,226 92,811 1.93% Cumulative S02reduction, 1996-2030 = 5,885,240 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 74,927 1,965 76,892 0.95% 2000 134,041 4,218 138,258 2.06% 2005 187,999 6,143 194,141 2.77% 2010 180,787 6,615 187,402 2.57% 2015 157,339 7,153 164,491 2.35% 2020 140,809 7,428 148,236 2.21% 2025 122,949 7,820 130,769 2.08% 2030 108,886 8,160 117,046 1.98% CumulativeNOx reduction, 1996-2030 = 5,337,547 tons


### 19 Table 16 ReductionofPollutants for WaterHeaters

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 21,263 2,156 23,418 1.02% 2000 50,139 4,645 54,784 2.19% 2005 76,885 6,775 83,660 2.92% 2010 79,727 7,270 86,997 2.70% 2015 80,732 7,850 88,581 2.47% 2020 83,312 8,154 91,466 2.31% 2025 85,563 8,598 94,160 2.15% 2030 88,666 8,986 97,652 2.03% Cumulative C02reduction, 1996-2030 = 2,837 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 119,363 939 120,302 0.94% 2000 180,055 2,191 182,246 2.02% 2005 233,656 3,287 236,942 2.72% 2010 208,029 3,287 211,316 2.52% 2015 169,701 3,443 173,144 2.28% 2020 140,809 3,600 144,408 2.16% 2025 112,215 3,913 116,128 2.00% 2030 88,585 4,226 92,811 1.93% Cumulative S02reduction, 1996-2030 = 5,885,240 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 74,927 1,965 76,892 0.95% 2000 134,041 4,218 138,258 2.06% 2005 187,999 6,143 194,141 2.77% 2010 180,787 6,615 187,402 2.57% 2015 157,339 7,153 164,491 2.35% 2020 140,809 7,428 148,236 2.21% 2025 122,949 7,820 130,769 2.08% 2030 108,886 8,160 117,046 1.98% CumulativeNOx reduction, 1996-2030 = 5,337,547 tons


### 19Table 17 Reduction of Pollutants for Water Heaters - Level Five CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 21,176 2,350 23,527 1.03% 2000 49,972 5,034 55,005 2.19% 2005 76,631 7,278 83,909 2.93% 2010 79,472 7,717 87,190 2.71% 2015 80,562 8,271 88,834 2.47% 2020 83,144 8,575 91,719 2.31% 2025 85,479 8,964 94,443 2.15% 2030 88,583 9,408 97,991 2.04% Cumulative CO2 reduction, 1996-2030 = 2,845 million tons of CO2

SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 118,879 1,096 119,975 0.94% 2000 179,453 2,504 181,957 2.02% 2005 232,885 3,913 236,798 2.72% 2010 207,364 3,913 211,276 2.52% 2015 169,346 4,226 173,571 2.28% 2020 140,524 4,382 144,906 2.16% 2025 112,106 4,695 116,801 2.01% 2030 88,502 5,008 93,510 1.95% Cumulative SO2 reduction, 1996-2030 = 5,890,278 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 74,624 2,135 76,759 0.95% 2000 133,592 4,558 138,150 2.06% 2005 187,379 6,560 193,939 2.77% 2010 180,209 6,980 187,189 2.56% 2015 157,009 7,478 164,487 2.35% 2020 140,524 7,753 148,276 2.21% 2025 122,829 8,093 130,921 2.08% 2030 108,784 8,485 117,269 1.99% Cumulative NOx reduction, 1996-2030 = 5,335,580 tons



### 20 Table 17 ReductionofPollutants for Water Heaters

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 21,176 2,350 23,527 1.03% 2000 49,972 5,034 55,005 2.19% 2005 76,631 7,278 83,909 2.93% 2010 79,472 7,717 87,190 2.71% 2015 80,562 8,271 88,834 2.47% 2020 83,144 8,575 91,719 2.31% 2025 85,479 8,964 94,443 2.15% 2030 88,583 9,408 97,991 2.04% Cumulative C02reduction, 1996-2030= 2,845 million tons ofC02

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 118,879 1,096 119,975 0.94% 2000 179,453 2,504 181,957 2.02% 2005 232,885 3,913 236,798 2.72% 2010 207,364 3,913 211,276 2.52% 2015 169,346 4,226 173,571 2.28% 2020 140,524 4,382 144,906 2.16% 2025 112,106 4,695 116,801 2.01% 2030 88,502 5,008 93,510 1.95% Cumulative S02reduction, 1996-2030

### = 5,890,278 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year Power Plant In-Home Change Power Plant Generation Generation Emissions 1996 74,624 2,135 76,759 0.95% 2000 133,592 4,558 138,150 2.06% 2005 187,379 6,560 193,939 2.77% 2010 180,209 6,980 187,189 2.56% 2015 157,009 7,478 164,487 2.35% 2020 140,524 7,753 148,276 2.21% 2025 122,829 8,093 130,921 2.08% 2030 108,784 8,485 117,269 1.99% CumulativeNOx reduction, 1996-2030

### = 5,335,580 tons


### 20 Table 17 ReductionofPollutants for Water Heaters

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 21,176 2,350 23,527 1.03% 2000 49,972 5,034 55,005 2.19% 2005 76,631 7,278 83,909 2.93% 2010 79,472 7,717 87,190 2.71% 2015 80,562 8,271 88,834 2.47% 2020 83,144 8,575 91,719 2.31% 2025 85,479 8,964 94,443 2.15% 2030 88,583 9,408 97,991 2.04% Cumulative C02reduction, 1996-2030= 2,845 million tons ofC02

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 118,879 1,096 119,975 0.94% 2000 179,453 2,504 181,957 2.02% 2005 232,885 3,913 236,798 2.72% 2010 207,364 3,913 211,276 2.52% 2015 169,346 4,226 173,571 2.28% 2020 140,524 4,382 144,906 2.16% 2025 112,106 4,695 116,801 2.01% 2030 88,502 5,008 93,510 1.95% Cumulative S02reduction, 1996-2030

### = 5,890,278 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year Power Plant In-Home Change Power Plant Generation Generation Emissions 1996 74,624 2,135 76,759 0.95% 2000 133,592 4,558 138,150 2.06% 2005 187,379 6,560 193,939 2.77% 2010 180,209 6,980 187,189 2.56% 2015 157,009 7,478 164,487 2.35% 2020 140,524 7,753 148,276 2.21% 2025 122,829 8,093 130,921 2.08% 2030 108,784 8,485 117,269 1.99% CumulativeNOx reduction, 1996-2030

### = 5,335,580 tons


### 20Table 18 Reduction of Pollutants for Water Heaters - Level Six CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 28,063 2,211 30,274 1.32% 2000 65,986 4,205 70,191 2.80% 2005 101,218 5,620 106,838 3.73% 2010 105,906 5,785 111,692 3.47% 2015 108,235 6,284 114,519 3.19% 2020 112,067 6,588 118,655 2.99% 2025 115,530 6,977 122,507 2.79% 2030 119,960 7,475 127,436 2.65% Cumulative CO2 reduction, 1996-2030 = 3,662 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 157,539 939 158,478 1.24% 2000 236,962 2,191 239,153 2.66% 2005 307,604 3,287 310,890 3.57% 2010 276,337 3,287 279,624 3.33% 2015 227,514 3,600 231,113 3.04% 2020 189,407 3,756 193,163 2.88% 2025 151,518 4,069 155,587 2.68% 2030 119,850 4,382 124,232 2.59% Cumulative SO2 reduction, 1996-2030 = 7,795,782 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 98,892 2,018 100,909 1.25% 2000 176,405 3,798 180,202 2.69% 2005 247,497 5,040 252,537 3.61% 2010 240,150 5,198 245,348 3.36% 2015 210,940 5,643 216,582 3.09% 2020 189,407 5,918 195,325 2.92% 2025 166,011 6,258 172,268 2.73% 2030 147,316 6,703 154,019 2.61 % Cumulative NOx reduction, 1996-2030 = 6,995,324 tons



### 21 Table 18 Reduction ofPollutants for Water Heaters

- Level Six C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 28,063 2,211 30,274 1.32% 2000 65,986 4,205 70,191 2.80% 2005 101,218 5,620 106,838 3.73% 2010 105,906 5,785 111,692 3.47% 2015 108,235 6,284 114,519 3.19% 2020 112,067 6,588 118,655 2.99% 2025 115,530 6,977 122,507 2.79% 2030 119,960 7,475 127,436 2.65% Cumulative C02reduction, 1996-2030= 3,662 milliontons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 157,539 939 158,478 1.24% 2000 236,962 2,191 239,153 2.66% 2005 307,604 3,287 310,890 3.57% 2010 276,337 3,287 279,624 3.33% 2015 227,514 3,600 231,113 3.04% 2020 189,407 3,756 193,163 2.88% 2025 151,518 4,069 155,587 2.68% 2030 119,850 4,382 124,232 2.59% Cumulative S02reduction, 1996-2030

### = 7,795,782 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 98,892 2,018 100,909 1.25% 2000 176,405 3,798 180,202 2.69% 2005 247,497 5,040 252,537 3.61% 2010 240,150 5,198 245,348 3.36% 2015 210,940 5,643 216,582 3.09% 2020 189,407 5,918 195,325 2.92% 2025 166,011 6,258 172,268 2.73% 2030 147,316 6,703 154,019 2.61% CumulativeNOx reduction, 1996-2030

### = 6,995,324 tons



### 21 Table 18 Reduction ofPollutants for Water Heaters

- Level Six C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 28,063 2,211 30,274 1.32% 2000 65,986 4,205 70,191 2.80% 2005 101,218 5,620 106,838 3.73% 2010 105,906 5,785 111,692 3.47% 2015 108,235 6,284 114,519 3.19% 2020 112,067 6,588 118,655 2.99% 2025 115,530 6,977 122,507 2.79% 2030 119,960 7,475 127,436 2.65% Cumulative C02reduction, 1996-2030= 3,662 milliontons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 157,539 939 158,478 1.24% 2000 236,962 2,191 239,153 2.66% 2005 307,604 3,287 310,890 3.57% 2010 276,337 3,287 279,624 3.33% 2015 227,514 3,600 231,113 3.04% 2020 189,407 3,756 193,163 2.88% 2025 151,518 4,069 155,587 2.68% 2030 119,850 4,382 124,232 2.59% Cumulative S02reduction, 1996-2030

### = 7,795,782 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 98,892 2,018 100,909 1.25% 2000 176,405 3,798 180,202 2.69% 2005 247,497 5,040 252,537 3.61% 2010 240,150 5,198 245,348 3.36% 2015 210,940 5,643 216,582 3.09% 2020 189,407 5,918 195,325 2.92% 2025 166,011 6,258 172,268 2.73% 2030 147,316 6,703 154,019 2.61% CumulativeNOx reduction, 1996-2030

### = 6,995,324 tons



### 21Table 19 Reduction of Pollutants for Water Heaters - Level Seven CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 28,149 3,175 31,324 1.37% 2000 66,154 6,215 72,368 2.89% 2005 101,471 8,400 109,871 3.84% 2010 106,246 8,620 114,866 3.57% 2015 108,573 8,788 117,361 3.27% 2020 112,404 8,597 121,002 3.05% 2025 115,864 8,546 124,410 2.84% 2030 120,291 8,605 128,896 2.68% Cumulative CO2 reduction, 1996-2030 = 3,747 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 158,023 1,096 159,118 1.24% 2000 237,564 2,348 239,911 2.67% 2005 308,374 3,443 311,817 3.58% 2010 277,224 3,443 280,667 3.34% 2015 228,225 3,756 231,981 3.05% 2020 189,978 3,913 193,890 2.89% 2025 151,956 4,226 156,181 2.69% 2030 120,181 4,539 124,720 2.60% Cumulative SO2 reduction, 1996-2030 = 7,823,000 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 99,195 2,923 102,117 1.26% 2000 176,853 5,700 182,553 2.72% 2005 248,117 7,678 255,795 3.65% 2010 240,921 7,888 248,809 3.41% 2015 211,599 8,018 219,617 3.14% 2020 189,978 7,820 197,798 2.95% 2025 166,491 7,740 174,231 2.77% 2030 147,723 7,765 155,488 2.64% Cumulative NOx reduction, 1996-2030 = 7,085,801 tons



### 22
11 P Table 19 Reduction ofPollutants for Water Heaters

- Level Seven C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 28,149 3,175 31,324 1.37% 2000 66,154 6,215 72,368 2.89% 2005 101,471 8,400 109,871 3.84% 2010 106,246 8,620 114,866 3.57% 2015 108,573 8,788 117,361 3.27% 2020 112,404 8,597 121,002 3.05% 2025 115,864 8,546 124,410 2.84% 2030 120,291 8,605 128,896 2.68% Cumulative C02reduction, 1996-2030= 3,747 milliontons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 158,023 1,096 159,118 1.24% 2000 237,564 2,348 239,911 2.67% 2005 308,374 3,443 311,817 3.58% 2010 277,224 3,443 280,667 3.34% 2015 228,225 3,756 231,981 3.05% 2020 189,978 3,913 193,890 2.89% 2025 151,956 4,226 156,181 2.69% 2030 120,181 4,539 124,720 2.60% Cumulative S02reduction, 1996-2030 = 7,823,000 tons

NOxReduction (inTons) Abated from Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 99,195 2,923 102,117 1.26% 2000 176,853 5,700 182,553 2.72% 2005 248,117 7,678 255,795 3.65% 2010 240,921 7,888 248,809 3.41% 2015 211,599 8,018 219,617 3.14% 2020 189,978 7,820 197,798 2.95% 2025 166,491 7,740 174,231 2.77% 2030 147,723 7,765 155,488 2.64% CumulativeNOx reduction, 1996-2030 = 7,085,801 tons


### 22 Table 19 Reduction ofPollutants for Water Heaters

- Level Seven C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 28,149 3,175 31,324 1.37% 2000 66,154 6,215 72,368 2.89% 2005 101,471 8,400 109,871 3.84% 2010 106,246 8,620 114,866 3.57% 2015 108,573 8,788 117,361 3.27% 2020 112,404 8,597 121,002 3.05% 2025 115,864 8,546 124,410 2.84% 2030 120,291 8,605 128,896 2.68% Cumulative C02reduction, 1996-2030= 3,747 milliontons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 158,023 1,096 159,118 1.24% 2000 237,564 2,348 239,911 2.67% 2005 308,374 3,443 311,817 3.58% 2010 277,224 3,443 280,667 3.34% 2015 228,225 3,756 231,981 3.05% 2020 189,978 3,913 193,890 2.89% 2025 151,956 4,226 156,181 2.69% 2030 120,181 4,539 124,720 2.60% Cumulative S02reduction, 1996-2030 = 7,823,000 tons

NOxReduction (inTons) Abated from Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 99,195 2,923 102,117 1.26% 2000 176,853 5,700 182,553 2.72% 2005 248,117 7,678 255,795 3.65% 2010 240,921 7,888 248,809 3.41% 2015 211,599 8,018 219,617 3.14% 2020 189,978 7,820 197,798 2.95% 2025 166,491 7,740 174,231 2.77% 2030 147,723 7,765 155,488 2.64% CumulativeNOx reduction, 1996-2030 = 7,085,801 tons


### 22Table 20 Reduction of Pollutants for Water Heaters - Level Eight CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 25,739 7,711 33,450 1.46% 2000 60,117 17,651 77,768 3.10% 2005 92,008 26,630 118,639 4.14% 2010 95,877 30,070 125,946 3.91% 2015 97,910 31,723 129,634 3.61% 2020 102,201 31,866 134,067 3.38% 2025 106,265 31,573 137,837 3.14% 2030 111,516 31,114 142,630 2.97% Cumulative CO2 reduction, 1996-2030 = 4,113 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 144,492 939 145,431 1.14% 2000 215,885 2,035 217,920 2.42% 2005 279,617 3,287 282,903 3.25% 2010 250,167 3,443 253,610 3.02% 2015 205,812 3,756 209,568 2.76% 2020 172,733 4,226 176,958 2.64% 2025 139,366 5,008 144,374 2.49% 2030 111,414 5,791 117,204 2.44% Cumulative SO2 reduction, 1996-2030 = 7,124,332 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 90,701 7,268 97,969 1.21% 2000 160,714 16,648 177,362 2.65% 2005 224,979 25,095 250,074 3.57% 2010 217,407 28,363 245,770 3.37% 2015 190,819 29,910 220,729 3.15% 2020 172,733 30,000 202,733 3.03% 2025 152,696 29,643 182,339 2.89% 2030 136,946 29,128 166,073 2.81% Cumulative NOx reduction, 1996-2030 = 7,109,299 tons



### 23

all Table 20 Reduction ofPollutants for Water Heaters

- Level Eight C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 25,739 7,711 33,450 1.46% 2000 60,117 17,651 77,768 3.10% 2005 92,008 26,630 118,639 4.14% 2010 95,877 30,070 125,946 3.91% 2015 97,910 31,723 129,634 3.61% 2020 102,201 31,866 134,067 3.38% 2025 106,265 31,573 137,837 3.14% 2030 111,516 31,114 142,630 2.97% CumulativeC02reduction, 1996-2030 = 4,113 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 144,492 939 145,431 1.14% 2000 215,885 2,035 217,920 2.42% 2005 279,617 3,287 282,903 3.25% 2010 250,167 3,443 253,610 3.02% 2015 205,812 3,756 209,568 2.76% 2020 172,733 4,226 176,958 2.64% 2025 139,366 5,008 144,374 2.49% 2030 111,414 5,791 117,204 2.44% Cumulative S02reduction, 1996-2030 = 7,124,332 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 90,701 7,268 97,969 1.21% 2000 160,714 16,648 177,362 2.65% 2005 224,979 25,095 250,074 3.57% 2010 217,407 28,363 245,770 3.37% 2015 190,819 29,910 220,729 3.15% 2020 172,733 30,000 202,733 3.03% 2025 152,696 29,643 182,339 2.89% 2030 136,946 29,128 166,073 2.81% CumulativeNOx reduction, 1996-2030 = 7,109,299 tons


### 23 Table 20 Reduction ofPollutants for Water Heaters

- Level Eight C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 25,739 7,711 33,450 1.46% 2000 60,117 17,651 77,768 3.10% 2005 92,008 26,630 118,639 4.14% 2010 95,877 30,070 125,946 3.91% 2015 97,910 31,723 129,634 3.61% 2020 102,201 31,866 134,067 3.38% 2025 106,265 31,573 137,837 3.14% 2030 111,516 31,114 142,630 2.97% CumulativeC02reduction, 1996-2030 = 4,113 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 144,492 939 145,431 1.14% 2000 215,885 2,035 217,920 2.42% 2005 279,617 3,287 282,903 3.25% 2010 250,167 3,443 253,610 3.02% 2015 205,812 3,756 209,568 2.76% 2020 172,733 4,226 176,958 2.64% 2025 139,366 5,008 144,374 2.49% 2030 111,414 5,791 117,204 2.44% Cumulative S02reduction, 1996-2030 = 7,124,332 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 90,701 7,268 97,969 1.21% 2000 160,714 16,648 177,362 2.65% 2005 224,979 25,095 250,074 3.57% 2010 217,407 28,363 245,770 3.37% 2015 190,819 29,910 220,729 3.15% 2020 172,733 30,000 202,733 3.03% 2025 152,696 29,643 182,339 2.89% 2030 136,946 29,128 166,073 2.81% CumulativeNOx reduction, 1996-2030 = 7,109,299 tons


### 23

### 3.3 DIRECT HEATING EQUIPMENT

Changes in the amount of C02, SO2, and NOX emitted at trial standards levels 1 through 5 are summarized in Tables 21 through 25. 

### 3.3.1 Sulfur and Nitrogen Oxide Emissions Sulfur dioxide emissions would be increased by a cumulative total of up to 0.32 million tons between 1996 and 2030 in the level 5 scenario. In the year 1996, increases in sulfur dioxide will represent about 0.04% of the SO2 emissions estimated to come from power plants in that year. In the year 2030, increases in SO2 emissions will represent about 0.04% of the SO2 emissions estimated to come from power plants in that year. Although increased in the level 5 scenario, SO2 emissions would be decreased between 1996 and 2030 in the level 1, 2, and 3 scenarios. However, as discussed earlier, the possible reductions of SO2 emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those SO2 emissions would be only a postponement. Level 5 design changes to direct heating equipment would result in an estimated increase in NOX emissions of 0.18 million tons between 1996 and 2030. NOX increases would represent 0.03% and 0.01% of the NOx emissions estimated to come from power plants in the years 1996 and 2030, respectively. It should be noted that, although direct heating equipment consists entirely of fuel-burning space conditioning appliances, design options for direct heaters have the effect of impacting the sales of electric space heating equipment. In the trial standard level cases where SO2 and NOX emissions have increased, design options for direct heating equipment have actually increased the sales of electric space heating equipment. This ultimately results in increased electricity use and, therefore, increases in the amount of power plant emissions. 

### 3.3.2 Carbon Dioxide Emissions The cumulative increase in CO2 emissions from level 5 design changes is 60 million tons of CO2. For the year 1996, the estimated CO2 increase is 173 thousand tons of CO2 or about 0.01% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2 increase is 285 thousand tons of CO2 or about 0.01% of estimated U.S. power plant CO2 emissions in 2030. The reasons that were given for increased SO2 and NOX emissions (namely, increased sales of electric space heating equipment) also serve to explain why CO2 emissions have increased in some trial standard level cases.

24

i 

### 3.3 DIRECT HEATING EQUIPMENT Changes in the amount ofCO2, S02' and NOxemitted at trial standards levels 1through 5 are summarized in Tables 21 through 25. 

### 3.3.1 Sulfur and Nitrogen Oxide Emissions Sulfur dioxide emissions would be increased by a cumulative total ofup to 0.32 million tons between 1996 and 2030in the level 5 scenario. In the year 1996, increases in sulfurdioxide will represent about 0.04% of the S02 emissions estimated to come from power plants in that year. In the year 2030, increases in S02 emissions will represent about 0.04% of the S02 emissions estimated to come from powerplants in that year. Although increased in the level 5 scenario, S02 emissions would be decreased between 1996 and 2030 in the levell, 2, and 3 scenarios. However, as discussed earlier, the possible reductions of S02 emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 5 design changes to direct heating equipment wouldresult in an estimatedincrease in NOx emissions of0.18 million tons between 1996 and 2030. NOx increases wouldrepresent 0.03% and 0.01% ofthe NOx emissions estimated to come from powerplants in the years 1996 and 2030, respectively. Itshouldbenotedthat, although directheatingequipmentconsists entirelyoffuel-burning space conditioning appliances, design options for direct heaters have the effect ofimpacting the sales ofelectric space heating equipment. In the trial standard level cases where S02 and NOx emissions have increased, design options for direct heating equipment have actually increased the sales ofelectric space heating equipment. This ultimately results in increased electricity use and, therefore, increases in the amount ofpower plant emissions. 

### 3.3.2 Carbon Dioxide Emissions Thecumulative increase in CO2emissions from level 5 design changes is 60million tons of CO2. For the year 1996, the estimated CO2 increase is 173 thousand tons ofCO2or about 0.01% ofestimated U.S. powerplant CO2emissions in 1996. For the year 2030, the estimated CO2increase is 285 thousand tons ofCO2 or about 0.01% ofestimated U.S. power plant CO2 emissions in 2030. The reasons that were given for increased 

### S02 and NOx emissions (namely, increased sales of electric space heating equipment) also serve to explain why CO2 emissions have increased in some trial standard level cases.

24 

### 3.3 DIRECT HEATING EQUIPMENT Changes in the amount ofCO2, S02' and NOxemitted at trial standards levels 1through 5 are summarized in Tables 21 through 25. 

### 3.3.1 Sulfur and Nitrogen Oxide Emissions Sulfur dioxide emissions would be increased by a cumulative total ofup to 0.32 million tons between 1996 and 2030in the level 5 scenario. In the year 1996, increases in sulfurdioxide will represent about 0.04% of the S02 emissions estimated to come from power plants in that year. In the year 2030, increases in S02 emissions will represent about 0.04% of the S02 emissions estimated to come from powerplants in that year. Although increased in the level 5 scenario, S02 emissions would be decreased between 1996 and 2030 in the levell, 2, and 3 scenarios. However, as discussed earlier, the possible reductions of S02 emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 5 design changes to direct heating equipment wouldresult in an estimatedincrease in NOx emissions of0.18 million tons between 1996 and 2030. NOx increases wouldrepresent 0.03% and 0.01% ofthe NOx emissions estimated to come from powerplants in the years 1996 and 2030, respectively. Itshouldbenotedthat, although directheatingequipmentconsists entirelyoffuel-burning space conditioning appliances, design options for direct heaters have the effect ofimpacting the sales ofelectric space heating equipment. In the trial standard level cases where S02 and NOx emissions have increased, design options for direct heating equipment have actually increased the sales ofelectric space heating equipment. This ultimately results in increased electricity use and, therefore, increases in the amount ofpower plant emissions. 

### 3.3.2 Carbon Dioxide Emissions Thecumulative increase in CO2emissions from level 5 design changes is 60million tons of CO2. For the year 1996, the estimated CO2 increase is 173 thousand tons ofCO2or about 0.01% ofestimated U.S. powerplant CO2emissions in 1996. For the year 2030, the estimated CO2increase is 285 thousand tons ofCO2 or about 0.01% ofestimated U.S. power plant CO2 emissions in 2030. The reasons that were given for increased 

### S02 and NOx emissions (namely, increased sales of electric space heating equipment) also serve to explain why CO2 emissions have increased in some trial standard level cases.

24Table 21 Reduction of Pollutants for Direct Heating Equipment - Level One

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 84 84 0.00% 2000 84 55 139 0.01% 2005 84 0 84 0.00% 2010 0 0 0 0.00% 2015 85 0 85 0.00% 2020 0 0 0 0.00% 2025 0 84 84 0.00% 2030 83 -55 28 0.00% Cumulative CO2 reduction, 1996-2030 = 2 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 157 157 0.00% 2000 301 0 301 0.00% 2005 257 0 257 0.00% 2010 0 0 0 0.00% 2015 178 0 178 0.00% 2020 0 0 0 0.00% 2025 0 157 157 0.00% 2030 83 0 83 0.00% Cumulative SO2 reduction, 1996-2030 = 5,474 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 65 65 0.00% 2000 224 53 277 0.00% 2005 207 0 207 0.00% 2010 0 0 0 0.00% 2015 165 0 165 0.00% 2020 0 0 0 0.00% 2025 0 65 65 0.00% 2030 102 -53 49 0.00% Cumulative NOx reduction, 1996-2030 = 4,477 tons



### 25 

## X13 Table 21 Reduction ofPollutants for DirectHeating Equipment

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 84 84 0.00% 2000 84 55 139 0.01% 2005 84 0 84 0.00% 2010 0 0 0 0.00% 2015 85 0 85 0.00% 2020 0 0 0 0.00% 2025 0 84 84 0.00% 2030 83 -55 28 0.00% Cumulative C02reduction, 1996-2030= 2 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 157 157 0.00% 2000 301 0 301 0.00% 2005 257 0 257 0.00% 2010 0 0 0 0.00% 2015 178 0 178 0.00% 2020 0 0 0 0.00% 2025 0 157 157 0.00% 2030 83 0 83 0.00% Cumulative S02reduction, 1996-2030 = 5,474 tons

NOx Reduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 0 65 65 0.00% 2000 224 53 277 0.00% 2005 207 0 207 0.00% 2010 0 0 0 0.00% 2015 165 0 165 0.00% 2020 0 0 0 0.00% 2025 0 65 65 0.00% 2030 102 -53 49 0.00% CumulativeNOx reduction, 1996-2030 = 4,477 tons


### 25

,/) Table 21 Reduction ofPollutants for DirectHeating Equipment

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 84 84 0.00% 2000 84 55 139 0.01% 2005 84 0 84 0.00% 2010 0 0 0 0.00% 2015 85 0 85 0.00% 2020 0 0 0 0.00% 2025 0 84 84 0.00% 2030 83 -55 28 0.00% Cumulative C02reduction, 1996-2030= 2 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 157 157 0.00% 2000 301 0 301 0.00% 2005 257 0 257 0.00% 2010 0 0 0 0.00% 2015 178 0 178 0.00% 2020 0 0 0 0.00% 2025 0 157 157 0.00% 2030 83 0 83 0.00% Cumulative S02reduction, 1996-2030 = 5,474 tons

NOx Reduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 0 65 65 0.00% 2000 224 53 277 0.00% 2005 207 0 207 0.00% 2010 0 0 0 0.00% 2015 165 0 165 0.00% 2020 0 0 0 0.00% 2025 0 65 65 0.00% 2030 102 -53 49 0.00% CumulativeNOx reduction, 1996-2030 = 4,477 tons


### 25Table 22 Reduction of Pollutants for Direct Heating Equipment - Level Two

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 86 194 280 0.01% 2000 84 220 304 0.01% 2005 169 220 389 0.01% 2010 170 220 390 0.01% 2015 338 194 533 0.01% 2020 337 55 392 0.01 % 2025 334 84 418 0.01% 2030 331 -55 276 0.01% Cumulative CO2 reduction, 1996-2030 = 14 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 483 157 640 0.00% 2000 301 0 301 0.00% 2005 514 0 514 0.01% 2010 444 0 444 0.01% 2015 712 157 868 0.01% 2020 570 0 570 0.01% 2025 438 157 594 0.01% 2030 331 0 331 0.01% Cumulative SO2 reduction, 1996-2030 = 19,780 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 303 170 473 0.01% 2000 224 210 434 0.01% 2005 413 210 623 0.01% 2010 385 210 595 0.01% 2015 660 170 830 0.01% 2020 570 53 623 0.01% 2025 480 65 545 0.01% 2030 407 -53 354 0.01% Cumulative NOx reduction, 1996-2030 = 21,442 tons



### 26 

# 9.►y Table 22 Reduction ofPollutants for DirectHeating Equipment

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 86 194 280 0.01% 2000 84 220 304 0.01% 2005 169 220 389 0.01% 2010 170 220 390 0.01% 2015 338 194 533 0.01% 2020 337 55 392 0.01% 2025 334 84 418 0.01% 2030 331 -55 276 0.Q1% Cumulative C02reduction, 1996-2030

## = 14 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 483 157 640 0.00% 2000 301 0 301 0.00% 2005 514 0 514 0.01% 2010 444 0 444 0.01% 2015 712 157 868 0.01% 2020 570 0 570 0.01% 2025 438 157 594 0.01% 2030 331 0 331 0.01% Cumulative S02reduction, 1996-2030 = 19,780 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 303 170 473 0.01% 2000 224 210 434 0.01% 2005 413 210 623 0.Q1% 2010 385 210 595 0.01% 2015 660 170 830 0.01% 2020 570 53 623 0.Q1% 2025 480 65 545 0.01% 2030 407 -53 354 0.01% CumulativeNOx reduction, 1996-2030 = 21,442 tons


### 26 Table 22 Reduction ofPollutants for DirectHeating Equipment

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 86 194 280 0.01% 2000 84 220 304 0.01% 2005 169 220 389 0.01% 2010 170 220 390 0.01% 2015 338 194 533 0.01% 2020 337 55 392 0.01% 2025 334 84 418 0.01% 2030 331 -55 276 0.Q1% Cumulative C02reduction, 1996-2030

## = 14 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 483 157 640 0.00% 2000 301 0 301 0.00% 2005 514 0 514 0.01% 2010 444 0 444 0.01% 2015 712 157 868 0.01% 2020 570 0 570 0.01% 2025 438 157 594 0.01% 2030 331 0 331 0.01% Cumulative S02reduction, 1996-2030 = 19,780 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 303 170 473 0.01% 2000 224 210 434 0.01% 2005 413 210 623 0.Q1% 2010 385 210 595 0.01% 2015 660 170 830 0.01% 2020 570 53 623 0.Q1% 2025 480 65 545 0.01% 2030 407 -53 354 0.01% CumulativeNOx reduction, 1996-2030 = 21,442 tons


### 26Table 23 Reduction of Pollutants for Direct Heating Equipment - Level Three

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 86 165 251 0.01% 2000 84 385 469 0.02% 2005 253 495 748 0.03% 2010 340 440 780 0.02% 2015 508 359 867 0.02% 2020 675 249 924 0.02% 2025 668 224 891 0.02% 2030 662 84 747 0.02% Cumulative CO2 reduction, 1996-2030 = 26 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In

-Home

Generation Change Power Plant Emissions 1996 483 0 483 0.00% 2000 301 0 301 0.00% 2005 770 0 770 0.01% 2010 887 0 887 0.01% 2015 1,067 157 1,224 0.02% 2020 1,140 157 1,297 0.02% 2025 876 313 1,189 0.02% 2030 662 157 818 0.02% Cumulative SO2 reduction, 1996-2030 = 31,644 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 303 158 461 0.01% 2000 224 368 592 0.01% 2005 620 473 1,092 0.02% 2010 771 420 1,191 0.02% 2015 990 327 1,317 0.02% 2020 1,140 222 1,363 0.02% 2025 960 183 1,142 0.02% 2030 813 65 878 0.01% Cumulative NOx reduction, 1996-2030 = 37,241 tons



### 27 Table 23 Reduction ofPollutants for Direct HeatingEquipment

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 86 165 251 0.01% 2000 84 385 469 0.02% 2005 253 495 748 0.03% 2010 340 440 780 0.02% 2015 508 359 867 0.02% 2020 675 249 924 0.02% 2025 668 224 891 0.02% 2030 662 84 747 0.02% Cumulative C02reduction, 1996-2030

### = 26 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 483 0 483 0.00% 2000 301 0 301 0.00% 2005 770 0 770 0.01% 2010 887 0 887 0.01% 2015 1,067 157 1,224 0.02% 2020 1,140 157 1,297 0.02% 2025 876 313 1,189 0.02% 2030 662 157 818 0.02% Cumulative S02reduction, 1996-2030

### = 31,644 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 303 158 461 0.01% 2000 224 368 592 0.01% 2005 620 473 1,092 0.02% 2010 771 420 1,191 0.02% 2015 990 327 1,317 0.02% 2020 1,140 222 1,363 0.02% 2025 960 183 1,142 0.02% 2030 813 65 878 0.01% CumulativeNOx reduction, 1996-2030 = 37,241 tons



### 27 Table 23 Reduction ofPollutants for Direct HeatingEquipment

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 86 165 251 0.01% 2000 84 385 469 0.02% 2005 253 495 748 0.03% 2010 340 440 780 0.02% 2015 508 359 867 0.02% 2020 675 249 924 0.02% 2025 668 224 891 0.02% 2030 662 84 747 0.02% Cumulative C02reduction, 1996-2030

### = 26 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 483 0 483 0.00% 2000 301 0 301 0.00% 2005 770 0 770 0.01% 2010 887 0 887 0.01% 2015 1,067 157 1,224 0.02% 2020 1,140 157 1,297 0.02% 2025 876 313 1,189 0.02% 2030 662 157 818 0.02% Cumulative S02reduction, 1996-2030

### = 31,644 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 303 158 461 0.01% 2000 224 368 592 0.01% 2005 620 473 1,092 0.02% 2010 771 420 1,191 0.02% 2015 990 327 1,317 0.02% 2020 1,140 222 1,363 0.02% 2025 960 183 1,142 0.02% 2030 813 65 878 0.01% CumulativeNOx reduction, 1996-2030 = 37,241 tons



### 27Table 24 Reduction of Pollutants for Direct Heating Equipment - Level Four

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -172 385 213 0.01% 2000 -671 766 96 0.00% 2005 -1,183 1,012 -171 -0.01% 2010 -1,275 928 -347 -0.01% 2015 -846 821 -25 0.00% 2020 -422 686 264 0.01% 2025 -167 550 383 0.01% 2030 0 301 301 0.01% Cumulative CO2 reduction, 1996-2030 = 2 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -966 0 -966 -0.01% 2000 -2,409 -313 -2,722 -0.03% 2005 -3,595 -469 -4,064 -0.05% 2010 -3,327 -626 -3,953 -0.05% 2015 -1,779 -313 -2,092 -0.03% 2020 -713 -157 -869 -0.01% 2025 -219 0 -219 0.00% 2030 0 -157 -157 0.00% Cumulative SO2 reduction, 1996-2030 = -72,413 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -607 368 -239 0.00% 2000 -1,793 763 -1,031 -0.02% 2005 -2,892 1,013 -1,880 -0.03% 2010 -2,891 948 -1,944 -0.03% 2015 -1,649 815 -834 -0.01% 2020 -713 670 -43 0.00% 2025 -240 525 285 0.00% 2030 0 303 303 0.01% Cumulative NOx reduction, 1996-2030 = -26,942 tons



### 28 ^_j Table 24 Reduction ofPollutants for Direct HeatingEquipment

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -172 385 213 0.01% 2000 -671 766 96 0.00% 2005 -1,183 1,012 -171 -0.01% 2010 -1,275 928 -347 -0.01% 2015 -846 821 -25 0.00% 2020 -422 686 264 0.01% 2025 -167 550 383 0.01% 2030 0 301 301 0.01% Cumulative C02reduction, 1996-2030

### = 2 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -966 0 -966 -0.01% 2000 -2,409 -313 -2,722 -0.03% 2005 -3,595 -469 -4,064 -0.05% 2010 -3,327 -626 -3,953 -0.05% 2015 -1,779 -313 -2,092 -0.03% 2020 -713 -157 -869 -0.01% 2025 -219 0 -219 0.00% 2030 0 -157 -157 0.00% Cumulative S02reduction, 1996-2030

### = -72,413 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -607 368 -239 0.00% 2000 -1,793 763 -1,031 -0.02% 2005 -2,892 1,013 -1,880 -0.03% 2010 -2,891 948 -1,944 -0.03% 2015 -1,649 815 -834 -0.01% 2020 -713 670 -43 0.00% 2025 -240 525 285 0.00% 2030 0 303 303 0.01% CumulativeNOxreduction, 1996-2030 = -26,942 tons


### 28 Table 24 Reduction ofPollutants for Direct HeatingEquipment

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -172 385 213 0.01% 2000 -671 766 96 0.00% 2005 -1,183 1,012 -171 -0.01% 2010 -1,275 928 -347 -0.01% 2015 -846 821 -25 0.00% 2020 -422 686 264 0.01% 2025 -167 550 383 0.01% 2030 0 301 301 0.01% Cumulative C02reduction, 1996-2030

### = 2 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -966 0 -966 -0.01% 2000 -2,409 -313 -2,722 -0.03% 2005 -3,595 -469 -4,064 -0.05% 2010 -3,327 -626 -3,953 -0.05% 2015 -1,779 -313 -2,092 -0.03% 2020 -713 -157 -869 -0.01% 2025 -219 0 -219 0.00% 2030 0 -157 -157 0.00% Cumulative S02reduction, 1996-2030

### = -72,413 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -607 368 -239 0.00% 2000 -1,793 763 -1,031 -0.02% 2005 -2,892 1,013 -1,880 -0.03% 2010 -2,891 948 -1,944 -0.03% 2015 -1,649 815 -834 -0.01% 2020 -713 670 -43 0.00% 2025 -240 525 285 0.00% 2030 0 303 303 0.01% CumulativeNOxreduction, 1996-2030 = -26,942 tons


### 28

### 3.4 MOBILE HOME FURNACES

Changes in the amount of CO2, SO2, and NOX emitted at trial standards levels 1 through 6 are summarized in Tables 26 through 31. 

### 3.4.1 Sulfur and Nitrogen Oxide Emissions Sulfur dioxide emissions would be increased by a cumulative total of up to 0.27 million tons between 1996 and 2030 in the level 6 scenario. In the year 1996, increases in sulfur dioxide will represent about 0.01% of the SO2 emissions estimated to come from power plants in that year. In the year 2030, increases in SOZ emissions will represent about 0.18% of the SO2 emissions estimated to come from power plants in that year. Although increased in the level 6 scenario, SOZ emissions would be decreased between 1996 and 2030 in the level 1 scenario. However, as discussed earlier, the possible reductions of SO2 emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those SO2 emissions would be only a postponement. Level 6 design changes to mobile home furnaces would result in an estimated increase in NOx emissions of 0.087 million tons between 1996 and 2030. NOX increases would represent 0.005% and 0.04% of the NOX emissions estimated to come from power plants in the years 1996 and 2030, respectively. It should be noted that, although mobile home furnaces consist entirely of fuel-burning space conditioning appliances, design options for mobile home furnaces have the effect of impacting the sales of electric space heating equipment. In the trial standard level cases where SO2 and NOX emissions have increased, design options for mobile home furnaces have actually increased the sales of electric space heating equipment. This ultimately results in increased electricity use and, therefore, increases in the amount of power plant emissions. 

### 3.4.2 Carbon Dioxide Emissions The cumulative increase in CO2 emissions from level 6 design changes is 49 million tons of CO2. For the year 1996, the estimated CO2 reduction is 5 thousand tons of CO2 or about 0.001% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated COZ increase is 2464 thousand tons of COZ or about 0.05% of estimated U.S. power plant CO2 emissions in 2030. The reasons that were given for increased SOZ and NOX emissions (namely, increased sales of electric space heating equipment) also serve to explain why CO2 emissions have increased in some trial standard level cases.



### 30

^7^ 

### 3.4 MOBILE HOME FURNACES Changes in the amount ofCO2, S02'andNOx emitted at trial standards levels 1through 6 are summarized in Tables 26 through 31. 

### 3.4.1 Sulfur and Nitrogen Oxide Emissions Sulfurdioxide emissions wouldbe increased by a cumulative total ofup to 0.27 million tons between 1996 and 2030in the level 6 scenario. In the year 1996, increases in sulfurdioxide will represent about 0.01% of the S02 emissions estimated to come from power plants in that year. In the year 2030, increases in S02 emissions will represent about 0.18% of the S02 emissions estimated to come from power plants in that year. Although increased in the level 6 scenario, S02 emissions would be decreased between 1996 and 2030 in the level 1 scenario. However, as discussed earlier, the possiblereductions ofS02emissions caused by standards can be earnedas credits. Tothe extentcredits are used for future emissions, the standards' neteffect on those S02 emissions would be only a postponement. Level 6 design changes to mobile home furnaces would result in an estimated increase in NOxemissions of0.087 million tons between 1996 and2030. NOxincreases wouldrepresent 0.005% and 0.04% ofthe NOxemissions estimatedto come from powerplants in the years 1996 and 2030, respectively. It should be noted that, although mobile home furnaces consist entirely offuel-burning space conditioning appliances, design options for mobile home furnaces have the effect of impacting the sales ofelectric space heating equipment. In the trial standard level cases where S02and NOx emissions have increased, design options for mobile home furnaces have actually increased the sales of electric space heating equipment. This ultimately results in increased electricity use and, therefore, increases in the amount ofpower plant emissions. 

### 3.4.2 Carbon Dioxide Emissions Thecumulative increase in CO2emissions from level 6 design changes is 49 million tons of CO2, For the year 1996, the estimated CO2reduction is 5 thousand tons of CO2 or about 0.001% ofestimated U.S. powerplant CO2emissions in 1996. Forthe year 2030, the estimated CO2increase is 2464 thousand tons ofCO2or about 0.05% ofestimated U.S. powerplant CO2 emissions in 2030. The reasons that were given for increased S02 and NOx emissions (namely, increased sales of electric space heating equipment) also serve to explain why CO2 emissions have increased in some trial standard level cases.

30 

### 3.4 MOBILE HOME FURNACES Changes in the amount ofCO2, S02'andNOx emitted at trial standards levels 1through 6 are summarized in Tables 26 through 31. 

### 3.4.1 Sulfur and Nitrogen Oxide Emissions Sulfurdioxide emissions wouldbe increased by a cumulative total ofup to 0.27 million tons between 1996 and 2030in the level 6 scenario. In the year 1996, increases in sulfurdioxide will represent about 0.01% of the S02 emissions estimated to come from power plants in that year. In the year 2030, increases in S02 emissions will represent about 0.18% of the S02 emissions estimated to come from power plants in that year. Although increased in the level 6 scenario, S02 emissions would be decreased between 1996 and 2030 in the level 1 scenario. However, as discussed earlier, the possiblereductions ofS02emissions caused by standards can be earnedas credits. Tothe extentcredits are used for future emissions, the standards' neteffect on those S02 emissions would be only a postponement. Level 6 design changes to mobile home furnaces would result in an estimated increase in NOxemissions of0.087 million tons between 1996 and2030. NOxincreases wouldrepresent 0.005% and 0.04% ofthe NOxemissions estimatedto come from powerplants in the years 1996 and 2030, respectively. It should be noted that, although mobile home furnaces consist entirely offuel-burning space conditioning appliances, design options for mobile home furnaces have the effect of impacting the sales ofelectric space heating equipment. In the trial standard level cases where S02and NOx emissions have increased, design options for mobile home furnaces have actually increased the sales of electric space heating equipment. This ultimately results in increased electricity use and, therefore, increases in the amount ofpower plant emissions. 

### 3.4.2 Carbon Dioxide Emissions Thecumulative increase in CO2emissions from level 6 design changes is 49 million tons of CO2, For the year 1996, the estimated CO2reduction is 5 thousand tons of CO2 or about 0.001% ofestimated U.S. powerplant CO2emissions in 1996. Forthe year 2030, the estimated CO2increase is 2464 thousand tons ofCO2or about 0.05% ofestimated U.S. powerplant CO2 emissions in 2030. The reasons that were given for increased S02 and NOx emissions (namely, increased sales of electric space heating equipment) also serve to explain why CO2 emissions have increased in some trial standard level cases.

30Table 25 Reduction of Pollutants for Direct Heating Equipment - Level Five

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -775 601 -173 -0.01% 2000 -2,599 1,390 -1,209 -0.05% 2005 -4,478 1,933 -2,545 -0.09% 2010 -5,100 2,013 -3,086 -0.10% 2015 -4,147 1,687 -2,460 -0.07% 2020 -3,036 1,445 -1,591 -0.04% 2025 -2,087 1,258 -829 -0.02% 2030 -1,242 957 -285 -0.01% Cumulative CO2 reduction, 1996-2030 = -60 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -4,349 -313 -4,662 -0.04% 2000 -9,334 -1,096 -10,429 -0.12% 2005 -13,609 -1,722 -15,330 -0.18% 2010 -13,307 -1,878 -15,185 -0.18% 2015 -8,716 -1,565 -10,281 -0.14% 2020 -5,131 -1,096 -6,226 -0.09% 2025 -2,737 -626 -3,363 -0.06% 2030 -1,241 -470 -1,710 -0.04% Cumulative SO2 reduction, 1996-2030 = -319,957 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -2,730 605 -2,125 -0.03% 2000 -6,949 1,435 -5,514 -0.08% 2005 -10,949 2,015 -8,934 -0.13% 2010 -11,564 2,108 -9,457 -0.13% 2015 -8,081 1,765 -6,316 -0.09% 2020 -5,131 1,488 -3,643 -0.05% 2025 -2,999 1,263 -1,736 -0.03% 2030 -1,525 960 -565 -0.01% Cumulative NOx reduction, 1996-2030 = -183,878 tons



### 29 ^J^ Table 25 Reduction ofPollutants for Direct Heating Equipment

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -775 601 -173 -0.01% 2000 -2,599 1,390 -1,209 -0.05% 2005 -4,478 1,933 -2,545 -0.09% 2010 -5,100 2,013 -3,086 -0.10% 2015 -4,147 1,687 -2,460 -0.07% 2020 -3,036 1,445 -1,591 -0.04% 2025 -2,087 1,258 -829 -0.02% 2030 -1,242 957 -285 -0.01% CumulativeC02reduction, 1996-2030= -60 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -4,349 -313 -4,662 -0.04% 2000 -9,334 -1,096 -10,429 -0.12% 2005 -13,609 -1,722 -15,330 -0.18% 2010 -13,307 -1,878 -15,185 -0.18% 2015 -8,716 -1,565 -10,281 -0.14% 2020 -5,131 -1,096 -6,226 -0.09% 2025 -2,737 -626 -3,363 -0.06% 2030 -1,241 -470 -1,710 -0.04% Cumulative S02reduction, 1996-2030 = -319,957 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -2,730 605 -2,125 -0.03% 2000 -6,949 1,435 -5,514 -0.08% 2005 -10,949 2,015 -8,934 -0.13% 2010 -11,564 2,108 -9,457 -0.13% 2015 -8,081 1,765 -6,316 -0.09% 2020 -5,131 1,488 -3,643 -0.05% 2025 -2,999 1,263 -1,736 -0.03% 2030 -1,525 960 -565 -0.01% CumulativeNOx reduction, 1996-2030 = -183,878 tons


### 29 Table 25 Reduction ofPollutants for Direct Heating Equipment

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -775 601 -173 -0.01% 2000 -2,599 1,390 -1,209 -0.05% 2005 -4,478 1,933 -2,545 -0.09% 2010 -5,100 2,013 -3,086 -0.10% 2015 -4,147 1,687 -2,460 -0.07% 2020 -3,036 1,445 -1,591 -0.04% 2025 -2,087 1,258 -829 -0.02% 2030 -1,242 957 -285 -0.01% CumulativeC02reduction, 1996-2030= -60 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -4,349 -313 -4,662 -0.04% 2000 -9,334 -1,096 -10,429 -0.12% 2005 -13,609 -1,722 -15,330 -0.18% 2010 -13,307 -1,878 -15,185 -0.18% 2015 -8,716 -1,565 -10,281 -0.14% 2020 -5,131 -1,096 -6,226 -0.09% 2025 -2,737 -626 -3,363 -0.06% 2030 -1,241 -470 -1,710 -0.04% Cumulative S02reduction, 1996-2030 = -319,957 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -2,730 605 -2,125 -0.03% 2000 -6,949 1,435 -5,514 -0.08% 2005 -10,949 2,015 -8,934 -0.13% 2010 -11,564 2,108 -9,457 -0.13% 2015 -8,081 1,765 -6,316 -0.09% 2020 -5,131 1,488 -3,643 -0.05% 2025 -2,999 1,263 -1,736 -0.03% 2030 -1,525 960 -565 -0.01% CumulativeNOx reduction, 1996-2030 = -183,878 tons


### 29Table 26 Reduction of Pollutants for Mobile Home Furnaces -Level One

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 0 0 0.00% 2000 8 11 19 0.00% 2005 8 25 33 0.00% 2010 8 30 39 0.00% 2015 8 39 47 0.00% 2020 0 44 44 0.00% 2025 0 30 30 0.00% 2030 0 25 25 0.00% Cumulative CO2 reduction, 1996-2030 = 1 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 0 0 0.00% 2000 30 0 30 0.00% 2005 26 16 41 0.00% 2010 22 16 38 0.00% 2015 18 31 49 0.00% 2020 0 31 31 0.00% 2025 0 16 16 0.00% 2030 0 16 16 0.00% Cumulative SO2 reduction, 1996-2030 = 992 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 0 0 0.00% 2000 22 11 33 0.00% 2005 21 22 43 0.00% 2010 19 28 47 0.00% 2015 16 34 50 0.00% 2020 0 39 39 0.00% 2025 0 28 28 0.00% 2030 0 22 22 0.00% Cumulative NOx reduction, 1996-2030 = 1,151 tons



### 31

^^ Table 26 ReductionofPollutants for Mobile HomeFurnaces

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 8 11 19 0.00% 2005 8 25 33 0.00% 2010 8 30 39 0.00% 2015 8 39 47 0.00% 2020 0 44 44 0.00% 2025 0 30 30 0.00% 2030 0 25 25 0.00% CumulativeC02reduction, 1996-2030

### = 1 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 30 0 30 0.00% 2005 26 16 41 0.00% 2010 22 16 38 0.00% 2015 18 31 49 0.00% 2020 0 31 31 0.00% 2025 0 16 16 0.00% 2030 0 16 16 0.00% Cumulative S02reduction, 1996-2030

### = 992 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 22 11 33 0.00% 2005 21 22 43 0.00% 2010 19 28 47 0.00% 2015 16 34 50 0.00% 2020 0 39 39 0.00% 2025 0 28 28 0.00% 2030 0 22 22 0.00% CumulativeNOxreduction, 1996-2030 = 1,151 tons


### 31 Table 26 ReductionofPollutants for Mobile HomeFurnaces

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 8 11 19 0.00% 2005 8 25 33 0.00% 2010 8 30 39 0.00% 2015 8 39 47 0.00% 2020 0 44 44 0.00% 2025 0 30 30 0.00% 2030 0 25 25 0.00% CumulativeC02reduction, 1996-2030

### = 1 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 30 0 30 0.00% 2005 26 16 41 0.00% 2010 22 16 38 0.00% 2015 18 31 49 0.00% 2020 0 31 31 0.00% 2025 0 16 16 0.00% 2030 0 16 16 0.00% Cumulative S02reduction, 1996-2030

### = 992 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 22 11 33 0.00% 2005 21 22 43 0.00% 2010 19 28 47 0.00% 2015 16 34 50 0.00% 2020 0 39 39 0.00% 2025 0 28 28 0.00% 2030 0 22 22 0.00% CumulativeNOxreduction, 1996-2030 = 1,151 tons


### 31Table 27 Reduction of Pollutants for Mobile Home Furnaces - Level Two

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 11 11 0.00% 2000 -8 47 38 0.00% 2005 -17 68 51 0.00% 2010 -34 87 53 0.00% 2015 -42 101 59 0.00% 2020 -51 98 47 0.00% 2025 -50 78 28 0.00% 2030 -58 67 10 0.00% Cumulative CO2 reduction, 1996-2030 = 1 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 0 0 0.00% 2000 -30 -16 -46 0.00% 2005 -51 -47 -98 0.00% 2010 -89 -63 -151 0.00% 2015 -89 -78 -167 0.00% 2020 -86 -94 -179 0.00% 2025 -66 -110 -175 0.00% 2030 -58 -110 -167 0.00% Cumulative SO2 reduction, 1996-2030 = -4,696 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 10 10 0.00% 2000 -22 46 24 0.00% 2005 -41 70 28 0.00% 2010 -77 90 12 0.00% 2015 -82 104 22 0.00% 2020 -86 103 17 0.00% 2025 -72 86 14 0.00% 2030 -71 75 4 0.00% Cumulative NOx reduction, 1996-2030 = 541 tons



### 32
.J,'A,u Table 27 Reduction ofPollutants for Mobile HomeFurnaces

- LevelTwo C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S.C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 11 11 0.00% 2000 -8 47 38 0.00% 2005 -17 68 51 0.00% 2010 -34 87 53 0.00% 2015 -42 101 59 0.00% 2020 -51 98 47 0.00% 2025 -50 78 28 0.00% 2030 -58 67 10 0.00% CumulativeC02reduction, 1996-2030= 1 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 -30 -16 -46 0.00% 2005 -51 -47 -98 0.00% 2010 -89 -63 -151 0.00% 2015 -89 -78 -167 0.00% 2020 -86 -94 -179 0.00% 2025 -66 -110 -175 0.00% 2030 -58 -110 -167 0.00% Cumulative S02reduction, 1996-2030 = -4,696 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 10 10 0.00% 2000 -22 46 24 0.00% 2005 -41 70 28 0.00% 2010 -77 90 12 0.00% 2015 -82 104 22 0.00% 2020 -86 103 17 0.00% 2025 -72 86 14 0.00% 2030 -71 75 4 0.00% CumulativeNOx reduction, 1996-2030 = 541 tons


### 32 Table 27 Reduction ofPollutants for Mobile HomeFurnaces

- LevelTwo C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S.C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 11 11 0.00% 2000 -8 47 38 0.00% 2005 -17 68 51 0.00% 2010 -34 87 53 0.00% 2015 -42 101 59 0.00% 2020 -51 98 47 0.00% 2025 -50 78 28 0.00% 2030 -58 67 10 0.00% CumulativeC02reduction, 1996-2030= 1 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 -30 -16 -46 0.00% 2005 -51 -47 -98 0.00% 2010 -89 -63 -151 0.00% 2015 -89 -78 -167 0.00% 2020 -86 -94 -179 0.00% 2025 -66 -110 -175 0.00% 2030 -58 -110 -167 0.00% Cumulative S02reduction, 1996-2030 = -4,696 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 10 10 0.00% 2000 -22 46 24 0.00% 2005 -41 70 28 0.00% 2010 -77 90 12 0.00% 2015 -82 104 22 0.00% 2020 -86 103 17 0.00% 2025 -72 86 14 0.00% 2030 -71 75 4 0.00% CumulativeNOx reduction, 1996-2030 = 541 tons


### 32Table 28 Reduction of Pollutants for Mobile Home Furnaces -Level Three

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -17 30 12 0.00% 2000 -59 92 33 0.00% 2005 -127 137 10 0.00% 2010 -204 174 -30 0.00% 2015 -254 183 -70 0.00% 2020 -278 169 -109 0.00% 2025 -267 125 -142 0.00% 2030 -240 90 -150 0.00% Cumulative CO2 reduction, 1996-2030 = -2 million tons of CO2

SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -97 -47 -144 0.00% 2000 -211 -157 -367 0.00% 2005 -385 -297 -682 -0.01% 2010 -532 -423 -955 -0.01% 2015 -534 -548 -1,081 -0.01 % 2020 -470 -626 -1,096 -0.02% 2025 -350 -626 -976 -0.02% 2030 -240 -579 -819 -0.02% Cumulative SO2 reduction, 1996-2030 = -28,747 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -61 33 -28 0.00% 2000 -157 103 -54 0.00% 2005 -310 160 -150 0.00% 2010 -463 208 -255 0.00% 2015 -495 229 -266 0.00% 2020 -470 223 -247 0.00% 2025 -384 181 -203 0.00% 2030 -295 143 -152 0.00% Cumulative NOx reduction, 1996-2030 = -6,507 tons



### 33 ^^`^ i Table 28 ReductionofPollutants for Mobile HomeFurnaces

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -17 30 12 0.00% 2000 -59 92 33 0.00% 2005 -127 137 10 0.00% 2010 -204 174 -30 0.00% 2015 -254 183 -70 0.00% 2020 -278 169 -109 0.00% 2025 -267 125 -142 0.00% 2030 -240 90 -150 0.00% CumulativeC02reduction, 1996-2030 = -2 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -97 -47 -144 0.00% 2000 -211 -157 -367 0.00% 2005 -385 -297 -682 -0.01% 2010 -532 -423 -955 -0.01% 2015 -534 -548 -1,081 -0.01% 2020 -470 -626 -1,096 -0.02% 2025 -350 -626 -976 -0.02% 2030 -240 -579 -819 -0.02% Cumulative S02reduction, 1996-2030 = -28,747 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -61 33 -28 0.00% 2000 -157 103 -54 0.00% 2005 -310 160 -150 0.00% 2010 -463 208 -255 0.00% 2015 -495 229 -266 0.00% 2020 -470 223 -247 0.00% 2025 -384 181 -203 0.00% 2030 -295 143 -152 0.00% CumulativeNOxreduction, 1996-2030 = -6,507 tons


### 33 Table 28 ReductionofPollutants for Mobile HomeFurnaces

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -17 30 12 0.00% 2000 -59 92 33 0.00% 2005 -127 137 10 0.00% 2010 -204 174 -30 0.00% 2015 -254 183 -70 0.00% 2020 -278 169 -109 0.00% 2025 -267 125 -142 0.00% 2030 -240 90 -150 0.00% CumulativeC02reduction, 1996-2030 = -2 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -97 -47 -144 0.00% 2000 -211 -157 -367 0.00% 2005 -385 -297 -682 -0.01% 2010 -532 -423 -955 -0.01% 2015 -534 -548 -1,081 -0.01% 2020 -470 -626 -1,096 -0.02% 2025 -350 -626 -976 -0.02% 2030 -240 -579 -819 -0.02% Cumulative S02reduction, 1996-2030 = -28,747 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -61 33 -28 0.00% 2000 -157 103 -54 0.00% 2005 -310 160 -150 0.00% 2010 -463 208 -255 0.00% 2015 -495 229 -266 0.00% 2020 -470 223 -247 0.00% 2025 -384 181 -203 0.00% 2030 -295 143 -152 0.00% CumulativeNOxreduction, 1996-2030 = -6,507 tons


### 33Table 29 Reduction of Pollutants for Mobile Home Furnaces - Level Four

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -34 37 3 0.00% 2000 -117 95 -22 0.00% 2005 -245 153 -92 0.00% 2010 -365 172 -194 -0.01% 2015 -465 172 -294 -0.01% 2020 -506 140 -366 -0.01% 2025 -476 82 -393 -0.01% 2030 -414 37 -377 -0.01% Cumulative CO2 reduction, 1996-2030 = -8 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -193 -94 -287 0.00% 2000 -422 -313 -735 -0.01% 2005 -745 -563 -1,308 -0.02% 2010 -954 -845 -1,799 -0.02% 2015 -978 -1,080 -2,058 -0.03%a 2020 -855 -1,221 -2,076 -0.03% 2025 -624 -1,205 -1,829 -0.03% 2030 -414 -1,096 -1,509 -0.03% Cumulative SO2 reduction, 1996-2030 = -54,358 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -121 45 -76 0.00% 2000 -314 122 -192 0.00% 2005 -599 202 -397 -0.01% 2010 -829 248 -581 -0.01 %a 2015 -907 271 -636 -0.01% 2020 -855 254 -601 -0.01% 2025 -684 198 -486 -0.01% 2030 -508 144 -365 -0.01% Cumulative NOx reduction, 1996-2030 = -15,781 tons



### 34 ,^ ^, 2 Table 29 ReductionofPollutants for Mobile HomeFurnaces

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -34 37 3 0.00% 2000 -117 95 -22 0.00% 2005 -245 153 -92 0.00% 2010 -365 172 -194 -0.01% 2015 -465 172 -294 -0.01% 2020 -506 140 -366 -0.01% 2025 -476 82 -393 -0.01% 2030 -414 37 -377 -0.01% Cumulative C02reduction, 1996-2030 = -8 million tons ofCO2

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -193 -94 -287 0.00% 2000 -422 -313 -735 -0.01% 2005 -745 -563 -1,308 -0.02% 2010 -954 -845 -1,799 -0.02% 2015 -978 -1,080 -2,058 -0.03% 2020 -855 -1,221 -2,076 -0.03% 2025 -624 -1,205 -1,829 -0.03% 2030 -414 -1,096 -1,509 -0.03% Cumulative S02reduction, 1996-2030 = -54,358 tons

NOxReduction (inTons) Abated from Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -121 45 -76 0.00% 2000 -314 122 -192 0.00% 2005 -599 202 -397 -0.01% 2010 -829 248 -581 -0.01% 2015 -907 271 -636 -0.01% 2020 -855 254 -601 -0.01% 2025 -684 198 -486 -0.01% 2030 -508 144 -365 -0.01% CumulativeNOx reduction, 1996-2030 = -15,781 tons


### 34 Table 29 ReductionofPollutants for Mobile HomeFurnaces

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -34 37 3 0.00% 2000 -117 95 -22 0.00% 2005 -245 153 -92 0.00% 2010 -365 172 -194 -0.01% 2015 -465 172 -294 -0.01% 2020 -506 140 -366 -0.01% 2025 -476 82 -393 -0.01% 2030 -414 37 -377 -0.01% Cumulative C02reduction, 1996-2030 = -8 million tons ofCO2

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -193 -94 -287 0.00% 2000 -422 -313 -735 -0.01% 2005 -745 -563 -1,308 -0.02% 2010 -954 -845 -1,799 -0.02% 2015 -978 -1,080 -2,058 -0.03% 2020 -855 -1,221 -2,076 -0.03% 2025 -624 -1,205 -1,829 -0.03% 2030 -414 -1,096 -1,509 -0.03% Cumulative S02reduction, 1996-2030 = -54,358 tons

NOxReduction (inTons) Abated from Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -121 45 -76 0.00% 2000 -314 122 -192 0.00% 2005 -599 202 -397 -0.01% 2010 -829 248 -581 -0.01% 2015 -907 271 -636 -0.01% 2020 -855 254 -601 -0.01% 2025 -684 198 -486 -0.01% 2030 -508 144 -365 -0.01% CumulativeNOx reduction, 1996-2030 = -15,781 tons


### 34Table 30 Reduction of Pollutants for Mobile Home Furnaces - Level Five

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -77 111 34 0.00% 2000 -268 293 25 0.00% 2005 -558 449 -109 0.00% 2010 -841 527 -314 -0.01% 2015 -1,075 537 -537 -0.01% 2020 -1,164 420 -744 -0.02% 2025 -1,119 264 -854 -0.02% 2030 -1,035 147 -888 -0.02% Cumulative CO2 reduction, 1996-2030 = -15 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -435 -141 -576 0.00% 2000 -964 -579 -1,543 -0.02% 2005 -1,695 -1,158 -2,853 -0.03% 2010 -2,196 -1,737 -3,933 -0.05% 2015 -2,259 -2,301 -4,560 -0.06% 2020 -1,967 -2,723 -4,690 -0.07% 2025 -1,467 -2,848 -4,315 -0.07% 2030 -1,034 -2,801 -3,835 -0.08% Cumulative SO2 reduction, 1996-2030 = -122,401 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In

-Home

Generation Change Power Plant Emissions 1996 -273 120 -153 0.00% 2000 -717 337 -380 -0.01% 2005 -1,364 543 -821 -0.01% 2010 -1 ,908 675 -1,233 -0.02% 2015 -2,095 740 -1,354 -0.02% 2020 -1,967 670 -1,297 -0.02% 2025 -1,607 534 -1,074 -0.02% 2030 -1,271 417 -854 -0.01% Cumulative NOx reduction, 1996-2030 = -33,753 tons



### 35 Table 30 ReductionofPollutants for Mobile Home Furnaces

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -77 111 34 0.00% 2000 -268 293 25 0.00% 2005 -558 449 -109 0.00% 2010 -841 527 -314 -0.01% 2015 -1,075 537 -537 -0.01% 2020 -1,164 420 -744 -0.02% 2025 -1,119 264 -854 -0.02% 2030 -1,035 147 -888 -0.02% CumulativeC02reduction, 1996-2030 = -15 million tons ofCO2

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -435 -141 -576 0.00% 2000 -964 -579 -1,543 -0.02% 2005 -1,695 -1,158 -2,853 -0.03% 2010 -2,196 -1,737 -3,933 -0.05% 2015 -2,259 -2,301 -4,560 -0.06% 2020 -1,967 -2,723 -4,690 -0.07% 2025 -1,467 -2,848 -4,315 -0.07% 2030 -1,034 -2,801 -3,835 -0.08% Cumulative S02reduction, 1996-2030 = -122,401 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -273 120 -153 0.00% 2000 -717 337 -380 -0.01% 2005 -1,364 543 -821 -0.01% 2010 -1,908 675 -1,233 -0.02% 2015 -2,095 740 -1,354 -0.02% 2020 -1,967 670 -1,297 -0.02% 2025 -1,607 534 -1,074 -0.02% 2030 -1,271 417 -854 -0.01% Cumulative NOx reduction, 1996-2030 = -33,753 tons



### 35 Table 30 ReductionofPollutants for Mobile Home Furnaces

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -77 111 34 0.00% 2000 -268 293 25 0.00% 2005 -558 449 -109 0.00% 2010 -841 527 -314 -0.01% 2015 -1,075 537 -537 -0.01% 2020 -1,164 420 -744 -0.02% 2025 -1,119 264 -854 -0.02% 2030 -1,035 147 -888 -0.02% CumulativeC02reduction, 1996-2030 = -15 million tons ofCO2

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -435 -141 -576 0.00% 2000 -964 -579 -1,543 -0.02% 2005 -1,695 -1,158 -2,853 -0.03% 2010 -2,196 -1,737 -3,933 -0.05% 2015 -2,259 -2,301 -4,560 -0.06% 2020 -1,967 -2,723 -4,690 -0.07% 2025 -1,467 -2,848 -4,315 -0.07% 2030 -1,034 -2,801 -3,835 -0.08% Cumulative S02reduction, 1996-2030 = -122,401 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -273 120 -153 0.00% 2000 -717 337 -380 -0.01% 2005 -1,364 543 -821 -0.01% 2010 -1,908 675 -1,233 -0.02% 2015 -2,095 740 -1,354 -0.02% 2020 -1,967 670 -1,297 -0.02% 2025 -1,607 534 -1,074 -0.02% 2030 -1,271 417 -854 -0.01% Cumulative NOx reduction, 1996-2030 = -33,753 tons



### 35Table 31 Reduction of Pollutants for Mobile Home Furnaces - Level Six

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -164 159 -5 0.00% 2000 -595 372 -223 -0.01% 2005 -1,217 544 -673 -0.02% 2010 -1,827 637 -1,190 -0.04% 2015 -2,310 607 -1,703 -0.05% 2020 -2,462 335 -2,127 -0.05% 2025 -2,379 45 -2,334 -0.05% 2030 -2,268 -196 -2,464 -0.05% Cumulative CO2 reduction, 1996-2030 = -49 million tons of CO2

SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -918 -297 -1,216 -0.01% 2000 -2,138 -1,330 -3,468 -0.04% 2005 -3,697 -2,676 -6,374 -0.07% 2010 -4,768 -3,881 -8,649 -0.10% 2015 -4,856 -5,071 -9,927 -0.13% 2020 -4,162 -6,025 -10,187 -0.15% 2025 -3,120 -6,370 -9,490 -0.16% 2030 -2,266 -6,479 -8,745 -0.18% Cumulative SO2 reduction, 1996-2030 = -269,787 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 -576 181 -395 0.00% 2000 -1,591 487 -1,104 -0.02% 2005 -2,975 784 -2,191 -0.03% 2010 -4,144 992 -3,152 -0.04% 2015 -4,502 1,081 -3,422 -0.05% 2020 -4,162 915 -3,246 -0.05% 2025 -3,419 673 -2,746 -0.04% 2030 -2,786 454 -2,332 -0.04% Cumulative NOx reduction, 1996-2030 = -87,340 tons



### 36 a2ej Table 31 ReductionofPollutants for Mobile HomeFurnaces

- Level Six C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -164 159 -5 0.00% 2000 -595 372 -223 -0.01% 2005 -1,217 544 -673 -0.02% 2010 -1,827 637 -1,190 -0.04% 2015 -2,3lO 607 -1,703 -0.05% 2020 -2,462 335 -2,127 -0.05% 2025 -2,379 45 -2,334 -0.05% 2030 -2,268 -196 -2,464 -0.05% Cumulative C02reduction, 1996-2030

### = -49 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abated from Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -918 -297 -1,216 -0.01% 2000 -2,138 -1,330 -3,468 -0.04% 2005 -3,697 -2,676 -6,374 -0.07% 2010 -4,768 -3,881 -8,649 -0.10% 2015 -4,856 -5,071 -9,927 -0.13% 2020 -4,162 -6,025 -lO,187 -0.15% 2025 -3,120 -6,370 -9,490 -0.16% 2030 -2,266 -6,479 -8,745 -0.18% Cumulative S02reduction, 1996-2030

### = -269,787 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -576 181 -395 0.00% 2000 -1,591 487 -1,lO4 -0.02% 2005 -2,975 784 -2,191 -0.03% 2010 -4,144 992 -3,152 -0.04% 2015 -4,502 1,081 -3,422 -0.05% 2020 -4,162 915 -3,246 -0.05% 2025 -3,419 673 -2,746 -0.04% 2030 -2,786 454 -2,332 -0.04% CumulativeNOx reduction, 1996-2030 = -87,340 tons


### 36 Table 31 ReductionofPollutants for Mobile HomeFurnaces

- Level Six C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -164 159 -5 0.00% 2000 -595 372 -223 -0.01% 2005 -1,217 544 -673 -0.02% 2010 -1,827 637 -1,190 -0.04% 2015 -2,3lO 607 -1,703 -0.05% 2020 -2,462 335 -2,127 -0.05% 2025 -2,379 45 -2,334 -0.05% 2030 -2,268 -196 -2,464 -0.05% Cumulative C02reduction, 1996-2030

### = -49 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abated from Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 -918 -297 -1,216 -0.01% 2000 -2,138 -1,330 -3,468 -0.04% 2005 -3,697 -2,676 -6,374 -0.07% 2010 -4,768 -3,881 -8,649 -0.10% 2015 -4,856 -5,071 -9,927 -0.13% 2020 -4,162 -6,025 -lO,187 -0.15% 2025 -3,120 -6,370 -9,490 -0.16% 2030 -2,266 -6,479 -8,745 -0.18% Cumulative S02reduction, 1996-2030

### = -269,787 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 -576 181 -395 0.00% 2000 -1,591 487 -1,lO4 -0.02% 2005 -2,975 784 -2,191 -0.03% 2010 -4,144 992 -3,152 -0.04% 2015 -4,502 1,081 -3,422 -0.05% 2020 -4,162 915 -3,246 -0.05% 2025 -3,419 673 -2,746 -0.04% 2030 -2,786 454 -2,332 -0.04% CumulativeNOx reduction, 1996-2030 = -87,340 tons


### 36

### 3.5 RANGES AND OVENS

Decreases in the amounts of CO,), S02, and NOX are presented for cooktops, conventional ovens and microwave ovens. Trial standards levels 1 through 5 for cooktops are summarized in Tables 32 through 36, trial standards levels 1 through 5 for conventional ovens are summarized in Tables 37 through 41, and trial standards levels 1 through 5 for microwave ovens are summarized in Tables 42 through 46. 3.5.1 Sulfur and Nitrogen Oxide Emissions Cooktops Sulfur dioxide emissions would be decreased by a cumulative total of up to 0.09 million tons between 1996 and 2030 in the level 5 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.01% of the S02 emissions estimated to come from power plants in that year. In the year 2030, decreases in S02 emissions will represent about 0.04% of the S02 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions of S02 emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 5 design changes to cooktops would result in an estimated decrease in NOX emissions of 0.21 million tons between 1996 and 2030. NOX decreases would represent 0.02% and 0.11% of the NOX emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### Conventional Ovens Sulfur dioxide emissions would be decreased by a cumulative total of up to 0.40 million tons between 1996 and 2030 in the level 5 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.04% of the S02 emissions estimated to come from power plants in that year. In the year 2030, decreases in S02 emissions will represent about 0.15% of the S02 emissions estimated to come from power plants in that year. As discussed above, the possible reductions of S02 emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 5 design changes to conventional ovens would result in an estimated decrease in NOX emissions of 0.45 million tons between 1996 and 2030. NOX decreases would represent 0.05% and 0.19% of the NOX emissions estimated to come from power plants in the years 1996 and 2030, respectively.



### 37

P'1 

### 3.5 RANGES AND OVENS

Decreases in the amounts ofCO2,S02'andNOxare presentedforcooktops, conventional ovens andmicrowave ovens. Trial standards levels 1through 5 for cooktops are summarizedin Tables 32 through 36, trial standards levels 1 through 5 for conventional ovens are summarized in Tables 37 through 41, and trial standards levels 1 through 5 for microwave ovens are summarized in Tables 42 through 46. 

### 3.5.1 Sulfur and Nitrogen Oxide Emissions Cooktops Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.09 million tons between 1996 and 2030 in the level 5 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.01% ofthe S02 emissions estimated to come from power plants in that year. In the year2030, decreases in S02emissions willrepresent about0.04% ofthe S02 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions ofS02emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 5 design changes to cooktops would result in an estimated decrease in NOx emissions of0.21 million tons between 1996 and 2030. NOxdecreases would represent 0.02% and 0.11% of the NOx emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### Conventional Ovens Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.40 million tons between 1996 and 2030 in the level 5 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.04% ofthe S02 emissions estimated to come from power plants in that year. In the year2030, decreases in S02emissions willrepresent about0.15% ofthe S02 emissions estimated to come from power plants in that year. As discussed above, the possible reductions ofS02emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 5 design changes to conventional ovens would result in an estimated decrease in NOx emissions of 0.45 million tons between 1996 and 2030. NOx decreases would represent 0.05% and0.19% ofthe NOxemissions estimated to come from powerplants in the years 1996 and 2030, respectively.

37 

### 3.5 RANGES AND OVENS

Decreases in the amounts ofCO2,S02'andNOxare presentedforcooktops, conventional ovens andmicrowave ovens. Trial standards levels 1through 5 for cooktops are summarizedin Tables 32 through 36, trial standards levels 1 through 5 for conventional ovens are summarized in Tables 37 through 41, and trial standards levels 1 through 5 for microwave ovens are summarized in Tables 42 through 46. 

### 3.5.1 Sulfur and Nitrogen Oxide Emissions Cooktops Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.09 million tons between 1996 and 2030 in the level 5 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.01% ofthe S02 emissions estimated to come from power plants in that year. In the year2030, decreases in S02emissions willrepresent about0.04% ofthe S02 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions ofS02emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 5 design changes to cooktops would result in an estimated decrease in NOx emissions of0.21 million tons between 1996 and 2030. NOxdecreases would represent 0.02% and 0.11% of the NOx emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### Conventional Ovens Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.40 million tons between 1996 and 2030 in the level 5 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.04% ofthe S02 emissions estimated to come from power plants in that year. In the year2030, decreases in S02emissions willrepresent about0.15% ofthe S02 emissions estimated to come from power plants in that year. As discussed above, the possible reductions ofS02emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 5 design changes to conventional ovens would result in an estimated decrease in NOx emissions of 0.45 million tons between 1996 and 2030. NOx decreases would represent 0.05% and0.19% ofthe NOxemissions estimated to come from powerplants in the years 1996 and 2030, respectively.

37

### Microwave Ovens

Sulfur dioxide emissions would be decreased by a cumulative total of up to 0.19 million tons between 1996 and 2030 in the level 5 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.05% of the SO2 emissions estimated to come from power plants in that year. In the year 2030, decreases in SO2 emissions will represent about 0.02% of the SO2 emissions estimated to come from power plants in that year. As discussed above, the possible reductions of SO2 emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those SO2 emissions would be only a postponement. Level 5 design changes to microwave ovens would result in an estimated decrease in NOX emissions of 0.16 million tons between 1996 and 2030. NOX decreases would represent 0.05% and 0.02% of the NO, emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### 3.5.2 Carbon Dioxide Emissions Cooktops The cumulative reduction in CO2 emissions from level 5 design changes is 172 million tons of CO2. For the year 1996, the estimated CO2 reduction is 1028 thousand tons of CO2 or about 0.04% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2 reduction is 6139 thousand tons of CO2 or about 0.13% of estimated U.S. power plant CO2 emissions in 2030. Conventional Ovens The cumulative reduction in CO2 emissions from level 5 design changes is 280 million tons of CO2. For the year 1996, the estimated CO2 reduction is 1442 thousand tons of CO2 or about 0.06% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2 reduction is 9897 thousand tons of CO2 or about 0.21% of estimated U.S. power plant CO2 emissions in 2030. Microwave Ovens The cumulative reduction in CO2 emissions from level 5 design changes is 71 million tons of CO2. For the year 1996, the estimated CO2 reduction is 1085 thousand tons of CO2 or about 0.05% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2 reduction is 1076 thousand tons of CO2 or about 0.02% of estimated U.S. power plant CO2 emissions in 2030.



### 38 

### Microwave Ovens Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.19 million tons between 1996 and 2030 in the level 5 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.05% ofthe S02emissions estimated to come from powerplants inthat year. In the year2030, decreases in S02emissions willrepresent about0.02% ofthe S02 emissions estimated to come from power plants in that year. As discussed above, the possible reductions ofS02emissions caused by standards canbe earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level5 design changes to microwave ovens wouldresultin an estimateddecreasein NOx emissions of0.16 million tons between 1996 and 2030. NOx decreases would represent 0.05% and 0.02% of the NOx emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### 3.5.2 Carbon Dioxide Emissions Cooktops The cumulative reduction in CO2emissions from level 5 design changes is 172 million tons of CO2, For the year 1996, the estimated CO2reduction is 1028 thousand tons of CO2or about 0.04% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2reduction is 6139 thousand tons ofCO2or about 0.13% ofestimatedU.S. power plant CO2 emissions in 2030. 

### Conventional Ovens The cumulative reduction in CO2emissions from level 5 design changes is 280 million tons of CO2, For the year 1996, the estimated CO2reduction is 1442 thousand tons of CO2or about 0.06% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2reduction is 9897 thousand tons ofCO2or about 0.21% ofestimatedU.S. power plant CO2emissions in 2030. 

### Microwave Ovens Thecumulativereductionin CO2emissions from level 5 designchangesis71 milliontons ofCO2, Forthe year 1996, the estimated CO2reduction is 1085 thousand tons ofCO2or about 0.05% ofestimated U.S. powerplant CO2emissions in 1996. For the year 2030, the estimated CO2reduction is 1076 thousand tons ofCO2or about 0.02% ofestimatedU.S. powerplant CO2 emissions in 2030.

38 

### Microwave Ovens Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.19 million tons between 1996 and 2030 in the level 5 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.05% ofthe S02emissions estimated to come from powerplants inthat year. In the year2030, decreases in S02emissions willrepresent about0.02% ofthe S02 emissions estimated to come from power plants in that year. As discussed above, the possible reductions ofS02emissions caused by standards canbe earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level5 design changes to microwave ovens wouldresultin an estimateddecreasein NOx emissions of0.16 million tons between 1996 and 2030. NOx decreases would represent 0.05% and 0.02% of the NOx emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### 3.5.2 Carbon Dioxide Emissions Cooktops The cumulative reduction in CO2emissions from level 5 design changes is 172 million tons of CO2, For the year 1996, the estimated CO2reduction is 1028 thousand tons of CO2or about 0.04% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2reduction is 6139 thousand tons ofCO2or about 0.13% ofestimatedU.S. power plant CO2 emissions in 2030. 

### Conventional Ovens The cumulative reduction in CO2emissions from level 5 design changes is 280 million tons of CO2, For the year 1996, the estimated CO2reduction is 1442 thousand tons of CO2or about 0.06% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2reduction is 9897 thousand tons ofCO2or about 0.21% ofestimatedU.S. power plant CO2emissions in 2030. 

### Microwave Ovens Thecumulativereductionin CO2emissions from level 5 designchangesis71 milliontons ofCO2, Forthe year 1996, the estimated CO2reduction is 1085 thousand tons ofCO2or about 0.05% ofestimated U.S. powerplant CO2emissions in 1996. For the year 2030, the estimated CO2reduction is 1076 thousand tons ofCO2or about 0.02% ofestimatedU.S. powerplant CO2 emissions in 2030.

38Table 32 Reduction of Pollutants for Cooktops - Level One CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 55 55 0.00% 2000 0 55 55 0.00% 2005 0 55 55 0.00% 2010 0 55 55 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative CO2 reduction, 1996-2030 = 1 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative SO2 reduction, 1996-2030 = 265 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 53 53 0.00% 2000 0 53 53 0.00% 2005 0 53 53 0.00% 2010 0 53 53 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative NOx reduction, 1996-2030 = 997 tons



### 39 Table 32 Reduction ofPollutants for Cooktops

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 0 55 55 0.00% 2000 0 55 55 0.00% 2005 0 55 55 0.00% 2010 0 55 55 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% CumulativeC02reduction, 1996-2030 = 1 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030 = 265 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 0 53 53 0.00% 2000 0 53 53 0.00% 2005 0 53 53 0.00% 2010 0 53 53 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative NOx reduction, 1996-2030 = 997 tons


### 39 Table 32 Reduction ofPollutants for Cooktops

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 0 55 55 0.00% 2000 0 55 55 0.00% 2005 0 55 55 0.00% 2010 0 55 55 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% CumulativeC02reduction, 1996-2030 = 1 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030 = 265 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 0 53 53 0.00% 2000 0 53 53 0.00% 2005 0 53 53 0.00% 2010 0 53 53 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative NOx reduction, 1996-2030 = 997 tons


### 39Table 33 Reduction of Pollutants for Cooktops - Level Two CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 86 715 801 0.04% 2000 168 1,760 1,928 0.08% 2005 338 3,080 3,418 0.12% 2010 510 4,290 4,800 0.15% 2015 508 4,675 5,183 0.14% 2020 422 4,675 5,097 0.13% 2025 417 4,675 5,092 0.12% 2030 580 4,565 5,145 0.11% Cumulative CO2 reduction, 1996-2030 = 144 million tons of CO2

SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 483 0 483 0.00% 2000 602 0 602 0.01% 2005 1,027 0 1,027 0.01% 2010 1,331 0 1,331 0.02% 2015 1,067 0 1,067 0.01% 2020 713 0 713 0.01% 2025 547 0 547 0.01% 2030 579 0 579 0.01% Cumulative SO2 reduction, 1996-2030 = 29,529 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 303 682 986 0.01% 2000 448 1,680 2,128 0.03% 2005 826 2,940 3,766 0.05% 2010 1,156 4,095 5,251 0.07% 2015 990 4,463 5,452 0.08% 2020 713 4,463 5,175 0.08% 2025 600 4,463 5,062 0.08% 2030 712 4,358 5,069 0.09% Cumulative NOx reduction, 1996-2030 = 151,231 tons



### 40 5, a-^ Table 33 Reduction ofPollutants for Cooktops

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 86 715 801 0.04% 2000 168 1,760 1,928 0.08% 2005 338 3,080 3,418 0.12% 2010 510 4,290 4,800 0.15% 2015 508 4,675 5,183 0.14% 2020 422 4,675 5,097 0.13% 2025 417 4,675 5,092 0.12% 2030 580 4,565 5,145 0.11% Cumulative C02reduction, 1996-2030

## = 144 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 483 0 483 0.00% 2000 602 0 602 0.01% 2005 1,027 0 1,027 0.01% 2010 1,331 0 1,331 0.02% 2015 1,067 0 1,067 0.01% 2020 713 0 713 0.01% 2025 547 0 547 0.01% 2030 579 0 579 0.01% Cumulative S02reduction, 1996-2030 = 29,529 tons

NOxReduction (inTons) Abated from Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 303 682 986 0.01% 2000 448 1,680 2,128 0.03% 2005 826 2,940 3,766 0.05% 2010 1,156 4,095 5,251 0.07% 2015 990 4,463 5,452 0.08% 2020 713 4,463 5,175 0.08% 2025 600 4,463 5,062 0.08% 2030 712 4,358 5,069 0.09% Cumulative NOx reduction, 1996-2030 = 151,231 tons


### 40 Table 33 Reduction ofPollutants for Cooktops

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 86 715 801 0.04% 2000 168 1,760 1,928 0.08% 2005 338 3,080 3,418 0.12% 2010 510 4,290 4,800 0.15% 2015 508 4,675 5,183 0.14% 2020 422 4,675 5,097 0.13% 2025 417 4,675 5,092 0.12% 2030 580 4,565 5,145 0.11% Cumulative C02reduction, 1996-2030

## = 144 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 483 0 483 0.00% 2000 602 0 602 0.01% 2005 1,027 0 1,027 0.01% 2010 1,331 0 1,331 0.02% 2015 1,067 0 1,067 0.01% 2020 713 0 713 0.01% 2025 547 0 547 0.01% 2030 579 0 579 0.01% Cumulative S02reduction, 1996-2030 = 29,529 tons

NOxReduction (inTons) Abated from Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 303 682 986 0.01% 2000 448 1,680 2,128 0.03% 2005 826 2,940 3,766 0.05% 2010 1,156 4,095 5,251 0.07% 2015 990 4,463 5,452 0.08% 2020 713 4,463 5,175 0.08% 2025 600 4,463 5,062 0.08% 2030 712 4,358 5,069 0.09% Cumulative NOx reduction, 1996-2030 = 151,231 tons


### 40Table 34 Reduction of Pollutants for Cooktops - Level Three CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 880 880 0.04% 2000 84 2,035 2,119 0.08% 2005 253 3,410 3,663 0.13% 2010 255 4,675 4,930 0.15% 2015 338 5,005 5,343 0.15% 2020 253 5,005 5,258 0.13% 2025 250 5,005 5,255 0.12% 2030 331 4,895 5,226 0.11% Cumulative CO2 reduction, 1996-2030 = 150 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 0 0 0.00% 2000 301 0 301 0.00% 2005 770 0 770 0.01% 2010 665 0 665 0.01% 2015 712 0 712 0.01% 2020 428 0 428 0.01% 2025 328 0 328 0.01% 2030 331 0 331 0.01% Cumulative SO2 reduction, 1996-2030 = 17,264 tons
NOx Reduction (in Tons) Abated from Abated from Net M.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 840 840 0.01% 2000 224 1,943 2,167 0.03% 2005 620 3,255 3,875 0.06% 2010 578 4,463 5,041 0.07% 2015 660 4,778 5,437 0.08% 2020 428 4,778 5,205 0.08% 2025 360 4,778 5,137 0.08% 2030 407 4,673 5,079 0.09% Cumulative NOx reduction, 1996-2030 = 150,971 tons



### 41 

### j_-^' 1 Table 34 Reduction ofPollutants for Cooktops

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 880 880 0.04% 2000 84 2,035 2,119 0.08% 2005 253 3,410 3,663 0.13% 2010 255 4,675 4,930 0.15% 2015 338 5,005 5,343 0.l5% 2020 253 5,005 5,258 0.l3% 2025 250 5,005 5,255 0.l2% 2030 331 4,895 5,226 0.l1% Cumulative C02reduction, 1996-2030= 150 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 301 0 301 0.00% 2005 770 0 770 0.01% 2010 665 0 665 0.01% 2015 712 0 712 0.01% 2020 428 0 428 0.01% 2025 328 0 328 0.01% 2030 331 0 331 0.01% Cumulative S02reduction, 1996-2030 = 17,264 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 0 840 840 0.01% 2000 224 1,943 2,167 0.03% 2005 620 3,255 3,875 0.06% 2010 578 4,463 5,041 0.07% 2015 660 4,778 5,437 0.08% 2020 428 4,778 5,205 0.08% 2025 360 4,778 5,137 0.08% 2030 407 4,673 5,079 0.09% CumulativeNOx reduction, 1996-2030 = 150,971 tons



### 41 Table 34 Reduction ofPollutants for Cooktops

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 880 880 0.04% 2000 84 2,035 2,119 0.08% 2005 253 3,410 3,663 0.13% 2010 255 4,675 4,930 0.15% 2015 338 5,005 5,343 0.l5% 2020 253 5,005 5,258 0.l3% 2025 250 5,005 5,255 0.l2% 2030 331 4,895 5,226 0.l1% Cumulative C02reduction, 1996-2030= 150 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 301 0 301 0.00% 2005 770 0 770 0.01% 2010 665 0 665 0.01% 2015 712 0 712 0.01% 2020 428 0 428 0.01% 2025 328 0 328 0.01% 2030 331 0 331 0.01% Cumulative S02reduction, 1996-2030 = 17,264 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 0 840 840 0.01% 2000 224 1,943 2,167 0.03% 2005 620 3,255 3,875 0.06% 2010 578 4,463 5,041 0.07% 2015 660 4,778 5,437 0.08% 2020 428 4,778 5,205 0.08% 2025 360 4,778 5,137 0.08% 2030 407 4,673 5,079 0.09% CumulativeNOx reduction, 1996-2030 = 150,971 tons



### 41Table 35 Reduction of Pollutants for Cooktops - Level Four CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 430 715 1,145 0.05% 2000 922 1,705 2,627 0.10% 2005 1,690 2,860 4,550 0.16% 2010 2,380 3,905 6,285 0.20% 2015 2,708 4,180 6,888 0.19% 2020 2,698 4,180 6,878 0.17% 2025 2,671 4,125 6,796 0.16% 2030 2,898 4,070 6,968 0.15% Cumulative CO2 reduction, 1996-2030 = 193 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 2,416 0 2,416 0.02% 2000 3,312 0 3,312 0.04% 2005 5,135 0 5,135 0.06% 2010 6,210 0 6,210 0.07% 2015 5,692 0 5,692 0.07% 2020 4,561 0 4,561 0.07% 2025 3,503 0 3,503 0.06% 2030 2,895 0 2,895 0.06% Cumulative SO2 reduction, 1996-2030 = 156,128 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,517 682 2,199 0.03% 2000 2,466 1,628 4,093 0.06% 2005 4,132 2,730 6,862 0.10% 2010 5,397 3,728 9,124 0.12% 2015 5,278 3,990 9,268 0.13% 2020 4,561 3,990 8,551 0.13% 2025 3,838 3,938 7,776 0.12% 2030 3,558 3,885 7,443 0.13% Cumulative NOx reduction, 1996-2030 = 254,836 tons



### 42

u Table 35 Reduction ofPollutants for Cooktops

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 430 715 1,145 0.05% 2000 922 1,705 2,627 0.10% 2005 1,690 2,860 4,550 0.16% 2010 2,380 3,905 6,285 0.20% 2015 2,708 4,180 6,888 0.19% 2020 2,698 4,180 6,878 0.17% 2025 2,671 4,125 6,796 0.16% 2030 2,898 4,070 6,968 0.15% CumulativeC02reduction, 1996-2030 = 193 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 2,416 0 2,416 0.02% 2000 3,312 0 3,312 0.04% 2005 5,135 0 5,135 0.06% 2010 6,210 0 6,210 0.07% 2015 5,692 0 5,692 0.07% 2020 4,561 0 4,561 0.07% 2025 3,503 0 3,503 0.06% 2030 2,895 0 2,895 0.06% Cumulative S02reduction, 1996-2030 = 156,128 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,517 682 2,199 0.03% 2000 2,466 1,628 4,093 0.06% 2005 4,132 2,730 6,862 0.10% 2010 5,397 3,728 9,124 0.12% 2015 5,278 3,990 9,268 0.13% 2020 4,561 3,990 8,551 0.13% 2025 3,838 3,938 7,776 0.12% 2030 3,558 3,885 7,443 0.13% CumulativeNOxreduction, 1996-2030 = 254,836 tons


### 42 Table 35 Reduction ofPollutants for Cooktops

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 430 715 1,145 0.05% 2000 922 1,705 2,627 0.10% 2005 1,690 2,860 4,550 0.16% 2010 2,380 3,905 6,285 0.20% 2015 2,708 4,180 6,888 0.19% 2020 2,698 4,180 6,878 0.17% 2025 2,671 4,125 6,796 0.16% 2030 2,898 4,070 6,968 0.15% CumulativeC02reduction, 1996-2030 = 193 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 2,416 0 2,416 0.02% 2000 3,312 0 3,312 0.04% 2005 5,135 0 5,135 0.06% 2010 6,210 0 6,210 0.07% 2015 5,692 0 5,692 0.07% 2020 4,561 0 4,561 0.07% 2025 3,503 0 3,503 0.06% 2030 2,895 0 2,895 0.06% Cumulative S02reduction, 1996-2030 = 156,128 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,517 682 2,199 0.03% 2000 2,466 1,628 4,093 0.06% 2005 4,132 2,730 6,862 0.10% 2010 5,397 3,728 9,124 0.12% 2015 5,278 3,990 9,268 0.13% 2020 4,561 3,990 8,551 0.13% 2025 3,838 3,938 7,776 0.12% 2030 3,558 3,885 7,443 0.13% CumulativeNOxreduction, 1996-2030 = 254,836 tons


### 42Table 36 Reduction of Pollutants for Cooktops - Level Five CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 258 770 1,028 0.04% 2000 503 1,815 2,318 0.09% 2005 1,014 3,080 4,094 0.14% 2010 1,445 4,235 5,680 0.18% 2015 1,608 4,510 6,118 0.17% 2020 1,602 4,510 6,112 0.15% 2025 1,586 4,510 6,096 0.14% 2030 1,739 4,400 6,139 0.13% Cumulative CO2 reduction, 1996-2030 = 172 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,450 0 1,450 0.01% 2000 1,807 0 1,807 0.02% 2005 3,081 0 3,081 0.04% 2010 3,770 0 3,770 0.04% 2015 3,380 0 3,380 0.04% 2020 2,708 0 2,708 0.04% 2025 2,080 0 2,080 0.04% 2030 1,737 0 1,737 0.04% Cumulative SO2 reduction, 1996-2030 = 92,421 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 910 735 1,645 0.02% 2000 1,345 1,733 3,077 0.05% 2005 2,479 2,940 5,419 0.08% 2010 3,277 4,043 7,319 0.10% 2015 3,134 4,305 7,439 0.11% 2020 2,708 4,305 7,013 0.10% 2025 2,279 4,305 6,584 0.10% 2030 2,135 4,200 6,335 0.11% Cumulative NOx reduction, 1996-2030 = 206,040 tons



### 43 

## q' ^ G Table 36 ReductionofPollutants for Cooktops

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 258 770 1,028 0.04% 2000 503 1,815 2,318 0.09% 2005 1,014 3,080 4,094 0.14% 2010 1,445 4,235 5,680 0.18% 2015 1,608 4,510 6,118 0.17% 2020 1,602 4,510 6,112 0.15% 2025 1,586 4,510 6,096 0.14% 2030 1,739 4,400 6,139 0.13% CumulativeC02reduction, 1996-2030 = 172 milliontons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,450 0 1,450 0.01% 2000 1,807 0 1,807 0.02% 2005 3,081 0 3,081 0.04% 2010 3,770 0 3,770 0.04% 2015 3,380 0 3,380 0.04% 2020 2,708 0 2,708 0.04% 2025 2,080 0 2,080 0.04% 2030 1,737 0 1,737 0.04% Cumulative S02reduction, 1996-2030

### = 92,421 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 910 735 1,645 0.02% 2000 1,345 1,733 3,077 0.05% 2005 2,479 2,940 5,419 0.08% 2010 3,277 4,043 7,319 0.10% 2015 3,134 4,305 7,439 0.11% 2020 2,708 4,305 7,013 0.10% 2025 2,279 4,305 6,584 0.10% 2030 2,135 4,200 6,335 0.11% CumulativeNOx reduction, 1996-2030

### = 206,040 tons


### 43 Table 36 ReductionofPollutants for Cooktops

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 258 770 1,028 0.04% 2000 503 1,815 2,318 0.09% 2005 1,014 3,080 4,094 0.14% 2010 1,445 4,235 5,680 0.18% 2015 1,608 4,510 6,118 0.17% 2020 1,602 4,510 6,112 0.15% 2025 1,586 4,510 6,096 0.14% 2030 1,739 4,400 6,139 0.13% CumulativeC02reduction, 1996-2030 = 172 milliontons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,450 0 1,450 0.01% 2000 1,807 0 1,807 0.02% 2005 3,081 0 3,081 0.04% 2010 3,770 0 3,770 0.04% 2015 3,380 0 3,380 0.04% 2020 2,708 0 2,708 0.04% 2025 2,080 0 2,080 0.04% 2030 1,737 0 1,737 0.04% Cumulative S02reduction, 1996-2030

### = 92,421 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 910 735 1,645 0.02% 2000 1,345 1,733 3,077 0.05% 2005 2,479 2,940 5,419 0.08% 2010 3,277 4,043 7,319 0.10% 2015 3,134 4,305 7,439 0.11% 2020 2,708 4,305 7,013 0.10% 2025 2,279 4,305 6,584 0.10% 2030 2,135 4,200 6,335 0.11% CumulativeNOx reduction, 1996-2030

### = 206,040 tons


### 43Table 37 Reduction of Pollutants for Ovens - Level One CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 86 110 196 0.01% 2000 335 275 610 0.02% 2005 676 550 1,226 0.04% 2010 850 770 1,620 0.05% 2015 846 880 1,726 0.05% 2020 843 825 1,668 0.04% 2025 668 770 1,438 0.03% 2030 745 715 1,460 0.03% Cumulative CO2 reduction, 1996-2030 = 46 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 483 0 483 0.00% 2000 1,204 0 1,204 0.01% 2005 2,054 0 2,054 0.02% 2010 2,218 0 2,218 0.03% 2015 1,779 0 1,779 0.02% 2020 1,425 0 1,425 0.02% 2025 876 0 876 0.02% 2030 744 0 744 0.02% Cumulative SO2 reduction, 1996-2030 = 51,111 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 303 105 408 0.01% 2000 897 262 1,159 0.02% 2005 1,653 525 2,178 0.03% 2010 1,927 735 2,662 0.04% 2015 1,649 840 2,489 0.04% 2020 1,425 788 2,213 0.03% 2025 960 735 1,695 0.03% 2030 915 683 1,598 0.03% Cumulative NOx reduction, 1996-2030 = 67,149 tons



### 44 Table 37 Reduction ofPollutants for Ovens

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 86 110 196 0.01% 2000 335 275 610 0.02% 2005 676 550 1,226 0.04% 2010 850 770 1,620 0.05% 2015 846 880 1,726 0.05% 2020 843 825 1,668 0.04% 2025 668 770 1,438 0.03% 2030 745 715 1,460 0.03% Cumulative C02reduction, 1996-2030= 46 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 483 0 483 0.00% 2000 1,204 0 1,204 0.01% 2005 2,054 0 2,054 0.02% 2010 2,218 0 2,218 0.03% 2015 1,779 0 1,779 0.02% 2020 1,425 0 1,425 0.02% 2025 876 0 876 0.02% 2030 744 0 744 0.02% Cumulative S02reduction, 1996-2030

### = 51,111 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 303 105 408 0.01% 2000 897 262 1,159 0.02% 2005 1,653 525 2,178 0.03% 2010 1,927 735 2,662 0.04% 2015 1,649 840 2,489 0.04% 2020 1,425 788 2,213 0.03% 2025 960 735 1,695 0.03% 2030 915 683 1,598 0.03% CumulativeNOxreduction, 1996-2030 = 67,149 tons


### 44 Table 37 Reduction ofPollutants for Ovens

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 86 110 196 0.01% 2000 335 275 610 0.02% 2005 676 550 1,226 0.04% 2010 850 770 1,620 0.05% 2015 846 880 1,726 0.05% 2020 843 825 1,668 0.04% 2025 668 770 1,438 0.03% 2030 745 715 1,460 0.03% Cumulative C02reduction, 1996-2030= 46 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 483 0 483 0.00% 2000 1,204 0 1,204 0.01% 2005 2,054 0 2,054 0.02% 2010 2,218 0 2,218 0.03% 2015 1,779 0 1,779 0.02% 2020 1,425 0 1,425 0.02% 2025 876 0 876 0.02% 2030 744 0 744 0.02% Cumulative S02reduction, 1996-2030

### = 51,111 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 303 105 408 0.01% 2000 897 262 1,159 0.02% 2005 1,653 525 2,178 0.03% 2010 1,927 735 2,662 0.04% 2015 1,649 840 2,489 0.04% 2020 1,425 788 2,213 0.03% 2025 960 735 1,695 0.03% 2030 915 683 1,598 0.03% CumulativeNOxreduction, 1996-2030 = 67,149 tons


### 44Table 38 Reduction of Pollutants for Ovens - Level Two CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 172 275 447 0.02% 2000 587 660 1,247 0.05% 2005 1,098 1,265 2,363 0.08% 2010 1,530 1,705 3,235 0.10% 2015 1,608 1,925 3,533 0.10% 2020 1,518 1,815 3,333 0.08% 2025 1,336 1,705 3,041 0.07% 2030 1,407 1,485 2,892 0.06% Cumulative CO2 reduction, 1996-2030 = 93 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 966 0 966 0.01% 2000 2,108 0 2,108 0.02% 2005 3,338 0 3,338 0.04% 2010 3,992 0 3,992 0.05% 2015 3,380 0 3,380 0.04% 2020 2,565 0 2,565 0.04% 2025 1,752 0 1,752 0.03% 2030 1,406 0 1,406 0.03% Cumulative SO2 reduction, 1996-2030 = 91,860 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 607 263 869 0.01% 2000 1,569 630 2,199 0.03% 2005 2,686 1,208 3,893 0.06% 2010 3,469 1,628 5,097 0.07% 2015 3,134 1,838 4,971 0.07% 2020 2,565 1,733 4,298 0.06% 2025 1,919 1,628 3,547 0.06% 2030 1,728 1,418 3,146 0.05% Cumulative NOx reduction, 1996-2030 = 130,408 tons



### 45 ^133 Table 38 ReductionofPollutants for Ovens

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 172 275 447 0.02% 2000 587 660 1,247 0.05% 2005 1,098 1,265 2,363 0.08% 2010 1,530 1,705 3,235 0.10% 2015 1,608 1,925 3,533 0.10% 2020 1,518 1,815 3,333 0.08% 2025 1,336 1,705 3,041 0.07% 2030 1,407 1,485 2,892 0.06% CumulativeC02reduction, 1996-2030 = 93 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 966 0 966 0.01% 2000 2,108 0 2,108 0.02% 2005 3,338 0 3,338 0.04% 2010 3,992 0 3,992 0.05% 2015 3,380 0 3,380 0.04% 2020 2,565 0 2,565 0.04% 2025 1,752 0 1,752 0.03% 2030 1,406 0 1,406 0.03% Cumulative S02reduction, 1996-2030 = 91,860 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 607 263 869 0.01% 2000 1,569 630 2,199 0.03% 2005 2,686 1,208 3,893 0.06% 2010 3,469 1,628 5,097 0.07% 2015 3,134 1,838 4,971 0.07% 2020 2,565 1,733 4,298 0.06% 2025 1,919 1,628 3,547 0.06% 2030 1,728 1,418 3,146 0.05% CumulativeNOx reduction, 1996-2030 = 130,408 tons


### 45 Table 38 ReductionofPollutants for Ovens

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 172 275 447 0.02% 2000 587 660 1,247 0.05% 2005 1,098 1,265 2,363 0.08% 2010 1,530 1,705 3,235 0.10% 2015 1,608 1,925 3,533 0.10% 2020 1,518 1,815 3,333 0.08% 2025 1,336 1,705 3,041 0.07% 2030 1,407 1,485 2,892 0.06% CumulativeC02reduction, 1996-2030 = 93 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 966 0 966 0.01% 2000 2,108 0 2,108 0.02% 2005 3,338 0 3,338 0.04% 2010 3,992 0 3,992 0.05% 2015 3,380 0 3,380 0.04% 2020 2,565 0 2,565 0.04% 2025 1,752 0 1,752 0.03% 2030 1,406 0 1,406 0.03% Cumulative S02reduction, 1996-2030 = 91,860 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 607 263 869 0.01% 2000 1,569 630 2,199 0.03% 2005 2,686 1,208 3,893 0.06% 2010 3,469 1,628 5,097 0.07% 2015 3,134 1,838 4,971 0.07% 2020 2,565 1,733 4,298 0.06% 2025 1,919 1,628 3,547 0.06% 2030 1,728 1,418 3,146 0.05% CumulativeNOx reduction, 1996-2030 = 130,408 tons


### 45Table 39 Reduction of Pollutants for Ovens - Level Three CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 430 385 815 0.04% 2000 1,174 935 2,109 0.08% 2005 2,281 1,705 3,986 0.14% 2010 3,230 2,365 5,595 0.17% 2015 3,470 2,585 6,055 0.17% 2020 3,457 2,475 5,932 0.15% 2025 3,339 2,365 5,704 0.13% 2030 3,394 2,200 5,594 0.12% Cumulative CO2 reduction, 1996-2030 = 165 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 2,416 0 2,416 0.02% 2000 4,215 0 4,215 0.05% 2005 6,933 0 6,933 0.08% 2010 8,428 0 8,428 0.10% 2015 7,293 0 7,293 0.10% 2020 5,843 0 5,843 0.09% 2025 4,379 0 4,379 0.08% 2030 3,391 0 3,391 0.07% Cumulative SO2 reduction, 1996-2030 = 201,949 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,517 368 1,884 0.02% 2000 3,138 893 4,031 0.06% 2005 5,578 1,628 7,205 0.10% 2010 7,324 2,258 9,582 0.13% 2015 6,762 2,468 9,229 0.13% 2020 5,843 2,363 8,206 0.12% 2025 4,798 2,258 7,056 0.11% 2030 4,168 2,100 6,268 0.11% Cumulative NOx reduction, 1996-2030 = 249,364 tons



### 46 Table 39 Reduction ofPollutants for Ovens

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 430 385 815 0.04% 2000 1,174 935 2,109 0.08% 2005 2,281 1,705 3,986 0.14% 2010 3,230 2,365 5,595 0.17% 2015 3,470 2,585 6,055 0.17% 2020 3,457 2,475 5,932 0.15% 2025 3,339 2,365 5,704 0.13% 2030 3,394 2,200 5,594 0.12% Cumulative C02reduction, 1996-2030 = 165 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,416 0 2,416 0.02% 2000 4,215 0 4,215 0.05% 2005 6,933 0 6,933 0.08% 2010 8,428 0 8,428 0.10% 2015 7,293 0 7,293 0.10% 2020 5,843 0 5,843 0.09% 2025 4,379 0 4,379 0.08% 2030 3,391 0 3,391 0.07% Cumulative S02reduction, 1996-2030 = 201,949 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,517 368 1,884 0.02% 2000 3,138 893 4,031 0.06% 2005 5,578 1,628 7,205 0.10% 2010 7,324 2,258 9,582 0.13% 2015 6,762 2,468 9,229 0.13% 2020 5,843 2,363 8,206 0.12% 2025 4,798 2,258 7,056 0.11% 2030 4,168 2,100 6,268 0.11% CumulativeNOx reduction, 1996-2030 = 249,364 tons

46 Table 39 Reduction ofPollutants for Ovens

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 430 385 815 0.04% 2000 1,174 935 2,109 0.08% 2005 2,281 1,705 3,986 0.14% 2010 3,230 2,365 5,595 0.17% 2015 3,470 2,585 6,055 0.17% 2020 3,457 2,475 5,932 0.15% 2025 3,339 2,365 5,704 0.13% 2030 3,394 2,200 5,594 0.12% Cumulative C02reduction, 1996-2030 = 165 million tons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,416 0 2,416 0.02% 2000 4,215 0 4,215 0.05% 2005 6,933 0 6,933 0.08% 2010 8,428 0 8,428 0.10% 2015 7,293 0 7,293 0.10% 2020 5,843 0 5,843 0.09% 2025 4,379 0 4,379 0.08% 2030 3,391 0 3,391 0.07% Cumulative S02reduction, 1996-2030 = 201,949 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,517 368 1,884 0.02% 2000 3,138 893 4,031 0.06% 2005 5,578 1,628 7,205 0.10% 2010 7,324 2,258 9,582 0.13% 2015 6,762 2,468 9,229 0.13% 2020 5,843 2,363 8,206 0.12% 2025 4,798 2,258 7,056 0.11% 2030 4,168 2,100 6,268 0.11% CumulativeNOx reduction, 1996-2030 = 249,364 tons

46Table 40 Reduction of Pollutants for Ovens -Level Four CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 516 440 956 0.04% 2000 1,425 990 2,415 0.10% 2005 2,704 1,815 4,519 0.16% 2010 3,825 2,475 6,300 0.20% 2015 4,147 2,750 6,897 0.19% 2020 4,048 2,640 6,688 0.17% 2025 4,007 2,530 6,537 0.15% 2030 4,057 2,365 6,422 0.13% Cumulative CO2 reduction, 1996-2030 = 187 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. 802 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 2,899 0 2,899 0.02% 2000 5,119 0 5,119 0.06% 2005 8,216 0 8,216 0.09% 2010 9,980 0 9,980 0.12% 2015 8,716 0 8,716 0.11% 2020 6,841 0 6,841 0.10% 2025 5,255 0 5,255 0.09% 2030 4,053 0 4,053 0.08% Cumulative SO2 reduction, 1996-2030 = 238,528 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,820 420 2,240 0.03% 2000 3,811 945 4,756 0.07% 2005 6,611 1,733 8,343 0.12% 2010 8,673 2,363 11,036 0.15% 2015 8,081 2,625 10,706 0.15% 2020 6,841 2,520 9,361 0.14% 2025 5,758 2,415 8,173 0.13% 2030 4,982 2,258 7,239 0.12% Cumulative NOx reduction, 1996-2030 = 287,223 tons



### 47

^^^ Table 40 ReductionofPollutants for Ovens

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 516 440 956 0.04% 2000 1,425 990 2,415 0.10% 2005 2,704 1,815 4,519 0.16% 2010 3,825 2,475 6,300 0.20% 2015 4,147 2,750 6,897 0.19% 2020 4,048 2,640 6,688 0.17% 2025 4,007 2,530 6,537 0.15% 2030 4,057 2,365 6,422 0.13% CumulativeC02reduction, 1996-2030= 187 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,899 0 2,899 0.02% 2000 5,119 0 5,119 0.06% 2005 8,216 0 8,216 0.09% 2010 9,980 0 9,980 0.12% 2015 8,716 0 8,716 0.11% 2020 6,841 0 6,841 0.10% 2025 5,255 0 5,255 0.09% 2030 4,053 0 4,053 0.08% Cumulative S02reduction, 1996-2030 = 238,528 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,820 420 2,240 0.03% 2000 3,811 945 4,756 0.07% 2005 6,611 1,733 8,343 0.12% 2010 8,673 2,363 11,036 0.15% 2015 8,081 2,625 10,706 0.15% 2020 6,841 2,520 9,361 0.14% 2025 5,758 2,415 8,173 0.13% 2030 4,982 2,258 7,239 0.12% CumulativeNOx reduction, 1996-2030 = 287,223 tons


### 47 Table 40 ReductionofPollutants for Ovens

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 516 440 956 0.04% 2000 1,425 990 2,415 0.10% 2005 2,704 1,815 4,519 0.16% 2010 3,825 2,475 6,300 0.20% 2015 4,147 2,750 6,897 0.19% 2020 4,048 2,640 6,688 0.17% 2025 4,007 2,530 6,537 0.15% 2030 4,057 2,365 6,422 0.13% CumulativeC02reduction, 1996-2030= 187 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,899 0 2,899 0.02% 2000 5,119 0 5,119 0.06% 2005 8,216 0 8,216 0.09% 2010 9,980 0 9,980 0.12% 2015 8,716 0 8,716 0.11% 2020 6,841 0 6,841 0.10% 2025 5,255 0 5,255 0.09% 2030 4,053 0 4,053 0.08% Cumulative S02reduction, 1996-2030 = 238,528 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,820 420 2,240 0.03% 2000 3,811 945 4,756 0.07% 2005 6,611 1,733 8,343 0.12% 2010 8,673 2,363 11,036 0.15% 2015 8,081 2,625 10,706 0.15% 2020 6,841 2,520 9,361 0.14% 2025 5,758 2,415 8,173 0.13% 2030 4,982 2,258 7,239 0.12% CumulativeNOx reduction, 1996-2030 = 287,223 tons


### 47Table 41 Reduction of Pollutants for Ovens - Level Five C02 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. C02 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 947 495 1,442 0.06% 2000 2,432 1,210 3,642 0.15% 2005 4,478 2,090 6,568 0.23% 2010 6,375 2,915 9,290 0.29% 2015 6,939 3,190 10,129 0.28% 2020 6,999 3,135 10,134 0.26% 2025 6,928 3,025 9,953 0.23% 2030 7,037 2,860 9,897 0.21% Cumulative C02 reduction, 1996-2030 = 280 million tons of C02
S02 Reduction (in Tons) Abated from Abated from Net % U.S. S02 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 5,316 0 5,316 0.04% 2000 8,732 0 8,732 0.10% 2005 13,609 0 13,609 0.16% 2010 16,633 0 16,633 0.20% 2015 14,586 0 14,586 0.19% 2020 11,829 0 11,829 0.18% 2025 9,087 0 9,087 0.16% 2030 7,031 0 7,031 0.15% Cumulative S02 reduction, 1996-2030 = 404,878 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 3,337 473 3,809 0.05% 2000 6,500 1,155 7,655 0.11% 2005 10,949 1,995 12,944 0.18% 2010 14,455 2,783 17,238 0.24% 2015 13,524 3,045 16,569 0.24% 2020 11,829 2,993 14,822 0.22% 2025 9,956 2,888 12,843 0.20% 2030 8,642 2,730 11,372 0.19% Cumulative NOx reduction, 1996-2030 = 451,360 tons



### 48 Table 41 Reduction ofPollutants for Ovens

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 947 495 1,442 0.06% 2000 2,432 1,210 3,642 0.15% 2005 4,478 2,090 6,568 0.23% 2010 6,375 2,915 9,290 0.29% 2015 6,939 3,190 10,129 0.28% 2020 6,999 3,135 10,134 0.26% 2025 6,928 3,025 9,953 0.23% 2030 7,037 2,860 9,897 0.21% CumulativeC02reduction, 1996-2030 = 280 milliontons ofC02

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 5,316 0 5,316 0.04% 2000 8,732 0 8,732 0.10% 2005 13,609 0 13,609 0.16% 2010 16,633 0 16,633 0.20% 2015 14,586 0 14,586 0.19% 2020 11,829 0 11,829 0.18% 2025 9,087 0 9,087 0.16% 2030 7,031 0 7,031 0.15% Cumulative S02reduction, 1996-2030

### = 404,878 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 3,337 473 3,809 0.05% 2000 6,500 1,155 7,655 0.11% 2005 10,949 1,995 12,944 0.18% 2010 14,455 2,783 17,238 0.24% 2015 13,524 3,045 16,569 0.24% 2020 11,829 2,993 14,822 0.22% 2025 9,956 2,888 12,843 0.20% 2030 8,642 2,730 11,372 0.19% CumulativeNOx reduction, 1996-2030

### = 451,360 tons


### 48 Table 41 Reduction ofPollutants for Ovens

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 947 495 1,442 0.06% 2000 2,432 1,210 3,642 0.15% 2005 4,478 2,090 6,568 0.23% 2010 6,375 2,915 9,290 0.29% 2015 6,939 3,190 10,129 0.28% 2020 6,999 3,135 10,134 0.26% 2025 6,928 3,025 9,953 0.23% 2030 7,037 2,860 9,897 0.21% CumulativeC02reduction, 1996-2030 = 280 milliontons ofC02

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 5,316 0 5,316 0.04% 2000 8,732 0 8,732 0.10% 2005 13,609 0 13,609 0.16% 2010 16,633 0 16,633 0.20% 2015 14,586 0 14,586 0.19% 2020 11,829 0 11,829 0.18% 2025 9,087 0 9,087 0.16% 2030 7,031 0 7,031 0.15% Cumulative S02reduction, 1996-2030

### = 404,878 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 3,337 473 3,809 0.05% 2000 6,500 1,155 7,655 0.11% 2005 10,949 1,995 12,944 0.18% 2010 14,455 2,783 17,238 0.24% 2015 13,524 3,045 16,569 0.24% 2020 11,829 2,993 14,822 0.22% 2025 9,956 2,888 12,843 0.20% 2030 8,642 2,730 11,372 0.19% CumulativeNOx reduction, 1996-2030

### = 451,360 tons


### 48Table 42 Reduction of Pollutants for Microwave Ovens - Level One

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 723 0 723 0.03% 2000 1,643 0 1,643 0.07% 2005 2,492 0 2,492 0.09% 2010 2,380 0 2,380 0.07% 2015 982 0 982 0.03% 2020 17 0 17 0.00% 2025 -8 0 -8 0.00% 2030 -8 0 -8 0.00% Cumulative CO2 reduction, 1996-2030 = 39 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 4,059 0 4,059 0.03% 2000 5,901 0 5,901 0.07% 2005 7,575 0 7,575 0.09% 2010 6,210 0 6,210 0.07% 2015 2,063 0 2,063 0.03% 2020 29 0 29 0.00% 2025 -11 0 -11 0.00% 2030 -8 0 -8 0.00% Cumulative SO2 reduction, 1996-2030 = 118,044 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 2,548 0 2,548 0.03% 2000 4,393 0 4,393 0.07% 2005 6,094 0 6,094 0.09% 2010 5,397 0 5,397 0.07% 2015 1,913 0 1,913 0.03% 2020 29 0 29 0.00% 2025 -12 0 -12 0.00% 2030 -10 0 -10 0.00% Cumulative NOx reduction, 1996-2030 = 94,479 tons



### 49 ^3^ Table 42 Reduction ofPollutants for Microwave Ovens

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 723 0 723 0.03% 2000 1,643 0 1,643 0.07% 2005 2,492 0 2,492 0.09% 2010 2,380 0 2,380 0.07% 2015 982 0 982 0.03% 2020 17 0 17 0.00% 2025 -8 0 -8 0.00% 2030 -8 0 -8 0.00% CumulativeC02reduction, 1996-2030

### = 39 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 4,059 0 4,059 0.03% 2000 5,901 0 5,901 0.07% 2005 7,575 0 7,575 0.09% 2010 6,210 0 6,210 0.07% 2015 2,063 0 2,063 0.03% 2020 29 0 29 0.00% 2025 -11 0 -11 0.00% 2030 -8 0 -8 0.00% Cumulative S02reduction, 1996-2030

### = 118,044 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 2,548 0 2,548 0.03% 2000 4,393 0 4,393 0.07% 2005 6,094 0 6,094 0.09% 2010 5,397 0 5,397 0.07% 2015 1,913 0 1,913 0.03% 2020 29 0 29 0.00% 2025 -12 0 -12 0.00% 2030 -10 0 -10 0.00% CumulativeNOx reduction, 1996-2030 = 94,479 tons


### 49 Table 42 Reduction ofPollutants for Microwave Ovens

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 723 0 723 0.03% 2000 1,643 0 1,643 0.07% 2005 2,492 0 2,492 0.09% 2010 2,380 0 2,380 0.07% 2015 982 0 982 0.03% 2020 17 0 17 0.00% 2025 -8 0 -8 0.00% 2030 -8 0 -8 0.00% CumulativeC02reduction, 1996-2030

### = 39 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 4,059 0 4,059 0.03% 2000 5,901 0 5,901 0.07% 2005 7,575 0 7,575 0.09% 2010 6,210 0 6,210 0.07% 2015 2,063 0 2,063 0.03% 2020 29 0 29 0.00% 2025 -11 0 -11 0.00% 2030 -8 0 -8 0.00% Cumulative S02reduction, 1996-2030

### = 118,044 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 2,548 0 2,548 0.03% 2000 4,393 0 4,393 0.07% 2005 6,094 0 6,094 0.09% 2010 5,397 0 5,397 0.07% 2015 1,913 0 1,913 0.03% 2020 29 0 29 0.00% 2025 -12 0 -12 0.00% 2030 -10 0 -10 0.00% CumulativeNOx reduction, 1996-2030 = 94,479 tons


### 49Table 43 Reduction of Pollutants for Microwave Ovens - Level Two

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 844 0 844 0.04% 2000 1,845 0 1,845 0.07% 2005 2,737 0 2,737 0.10% 2010 2,643 0 2,643 0.08% 2015 1,252 0 1,252 0.03% 2020 295 0 295 0.01% 2025 284 0 284 0.01% 2030 290 0 290 0.01% Cumulative CO2 reduction, 1996-2030 = 48 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 4,736 0 4,736 0.04% 2000 6,624 0 6,624 0.07% 2005 8,319 0 8,319 0.10% 2010 6,897 0 6,897 0.08% 2015 2,633 0 2,633 0.03% 2020 499 0 499 0.01% 2025 372 0 372 0.01% 2030 289 0 289 0.01% Cumulative SO2 reduction, 1996-2030 = 138,308 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 2,973 0 2,973 0.04% 2000 4,931 0 4,931 0.07% 2005 6,694 0 6,694 0.10% 2010 5,994 0 5,994 0.08% 2015 2,441 0 2,441 0.03% 2020 499 0 499 0.01% 2025 408 0 408 0.01% 2030 356 0 356 0.01% Cumulative NOx reduction, 1996-2030 = 112,216 tons



### 50 

### _). 5 Table 43 ReductionofPollutants for Microwave Ovens

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 844 0 844 0.04% 2000 1,845 0 1,845 0.07% 2005 2,737 0 2,737 0.10% 2010 2,643 0 2,643 0.08% 2015 1,252 0 1,252 0.03% 2020 295 0 295 0.01% 2025 284 0 284 0.01% 2030 290 0 290 0.01% CumulativeC02reduction, 1996-2030= 48 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 4,736 0 4,736 0.04% 2000 6,624 0 6,624 0.07% 2005 8,319 0 8,319 0.10% 2010 6,897 0 6,897 0.08% 2015 2,633 0 2,633 0.03% 2020 499 0 499 0.01% 2025 372 0 372 0.Q1% 2030 289 0 289 0.01% Cumulative S02reduction, 1996-2030 = 138,308 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,973 0 2,973 0.04% 2000 4,931 0 4,931 0.07% 2005 6,694 0 6,694 0.10% 2010 5,994 0 5,994 0.08% 2015 2,441 0 2,441 0.03% 2020 499 0 499 0.01% 2025 408 0 408 0.01% 2030 356 0 356 0.01% CumulativeNOx reduction, 1996-2030 = 112,216 tons


### 50 Table 43 ReductionofPollutants for Microwave Ovens

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 844 0 844 0.04% 2000 1,845 0 1,845 0.07% 2005 2,737 0 2,737 0.10% 2010 2,643 0 2,643 0.08% 2015 1,252 0 1,252 0.03% 2020 295 0 295 0.01% 2025 284 0 284 0.01% 2030 290 0 290 0.01% CumulativeC02reduction, 1996-2030= 48 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 4,736 0 4,736 0.04% 2000 6,624 0 6,624 0.07% 2005 8,319 0 8,319 0.10% 2010 6,897 0 6,897 0.08% 2015 2,633 0 2,633 0.03% 2020 499 0 499 0.01% 2025 372 0 372 0.Q1% 2030 289 0 289 0.01% Cumulative S02reduction, 1996-2030 = 138,308 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 2,973 0 2,973 0.04% 2000 4,931 0 4,931 0.07% 2005 6,694 0 6,694 0.10% 2010 5,994 0 5,994 0.08% 2015 2,441 0 2,441 0.03% 2020 499 0 499 0.01% 2025 408 0 408 0.01% 2030 356 0 356 0.01% CumulativeNOx reduction, 1996-2030 = 112,216 tons


### 50Table 44 Reduction of Pollutants for Microwave Ovens - Level Three

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 938 0 938 0.04% 2000 2,012 0 2,012 0.08% 2005 2,957 0 2,957 0.10% 2010 2,873 0 2,873 0.09% 2015 1,498 0 1,498 0.04% 2020 548 0 548 0.01% 2025 543 0 543 0.01% 2030 563 0 563 0.01% Cumulative CO2 reduction, 1996-2030 = 56 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 5,267 0 5,267 0.04% 2000 7,226 0 7,226 0.08% 2005 8,987 0 8,987 0.10% 2010 7,496 0 7,496 0.09% 2015 3,149 0 3,149 0.04% 2020 926 0 926 0.01% 2025 712 0 712 0.01% 2030 562 0 562 0.01% Cumulative SO2 reduction, 1996-2030 = 156,181 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 3,306 0 3,306 0.04% 2000 5,380 0 5,380 0.08% 2005 7,231 0 7,231 0.10% 2010 6,515 0 6,515 0.09% 2015 2,919 0 2,919 0.04% 2020 926 0 926 0.01% 2025 780 0 780 0.01% 2030 691 0 691 0.01% Cumulative NOx reduction, 1996-2030 = 127,936 tons



### 51 

# )3 Table 44 Reduction ofPollutants for Microwave Ovens

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 938 0 938 0.04% 2000 2,012 0 2,012 0.08% 2005 2,957 0 2,957 0.10% 2010 2,873 0 2,873 0.09% 2015 1,498 0 1,498 0.04% 2020 548 0 548 0.01% 2025 543 0 543 0.01% 2030 563 0 563 0.01% Cumulative C02reduction, 1996-2030= 56 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 5,267 0 5,267 0.04% 2000 7,226 0 7,226 0.08% 2005 8,987 0 8,987 0.10% 2010 7,496 0 7,496 0.09% 2015 3,149 0 3,149 0.04% 2020 926 0 926 0.01% 2025 712 0 712 0.01% 2030 562 0 562 0.01% Cumulative S02reduction, 1996-2030 = 156,181 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,306 0 3,306 0.04% 2000 5,380 0 5,380 0.08% 2005 7,231 0 7,231 0.10% 2010 6,515 0 6,515 0.09% 2015 2,919 0 2,919 0.04% 2020 926 0 926 0.01% 2025 780 0 780 0.01% 2030 691 0 691 0.01% CumulativeNOx reduction, 1996-2030 = 127,936 tons


### 51 Table 44 Reduction ofPollutants for Microwave Ovens

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 938 0 938 0.04% 2000 2,012 0 2,012 0.08% 2005 2,957 0 2,957 0.10% 2010 2,873 0 2,873 0.09% 2015 1,498 0 1,498 0.04% 2020 548 0 548 0.01% 2025 543 0 543 0.01% 2030 563 0 563 0.01% Cumulative C02reduction, 1996-2030= 56 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 5,267 0 5,267 0.04% 2000 7,226 0 7,226 0.08% 2005 8,987 0 8,987 0.10% 2010 7,496 0 7,496 0.09% 2015 3,149 0 3,149 0.04% 2020 926 0 926 0.01% 2025 712 0 712 0.01% 2030 562 0 562 0.01% Cumulative S02reduction, 1996-2030 = 156,181 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,306 0 3,306 0.04% 2000 5,380 0 5,380 0.08% 2005 7,231 0 7,231 0.10% 2010 6,515 0 6,515 0.09% 2015 2,919 0 2,919 0.04% 2020 926 0 926 0.01% 2025 780 0 780 0.01% 2030 691 0 691 0.01% CumulativeNOx reduction, 1996-2030 = 127,936 tons


### 51Table 45 Reduction of Pollutants for Microwave Ovens - Level Four

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,024 0 1,024 0.04% 2000 2,205 0 2,205 0.09% 2005 3,236 0 3,236 0.11% 2010 3,170 0 3,170 0.10% 2015 1,803 0 1,803 0.05% 2020 869 0 869 0.02% 2025 868 0 868 0.02% 2030 894 0 894 0.02% Cumulative CO2 reduction, 1996-2030 = 65 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 5,751 0 5,751 0.04% 2000 7,919 0 7,919 0.09% 2005 9,834 0 9,834 0.11% 2010 8,272 0 8,272 0.10% 2015 3,789 0 3,789 0.05% 2020 1,468 0 1,468 0.02% 2025 1,139 0 1,139 0.02% 2030 893 0 893 0.02% Cumulative SO2 reduction, 1996-2030 = 177,609 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 3,610 0 3,610 0.04% 2000 5,895 0 5,895 0.09% 2005 7,912 0 7,912 0.11% 2010 7,189 0 7,189 0.10% 2015 3,513 0 3,513 0.05% 2020 1,468 0 1,468 0.02% 2025 1,247 0 1,247 0.02% 2030 1,098 0 1,098 0.02% Cumulative NOx reduction, 1996-2030 = 146,928 tons



### 52 Bulb Table 45 Reduction ofPollutants for Microwave Ovens

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,024 0 1,024 0.04% 2000 2,205 0 2,205 0.09% 2005 3,236 0 3,236 0.11% 2010 3,170 0 3,170 0.10% 2015 1,803 0 1,803 0.05% 2020 869 0 869 0.02% 2025 868 0 868 0.02% 2030 894 0 894 0.02% CumulativeC02reduction, 1996-2030= 65 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 5,751 0 5,751 0.04% 2000 7,919 0 7,919 0.09% 2005 9,834 0 9,834 0.11% 2010 8,272 0 8,272 0.10% 2015 3,789 0 3,789 0.05% 2020 1,468 0 1,468 0.02% 2025 1,139 0 1,139 0.02% 2030 893 0 893 0.02% Cumulative S02reduction, 1996-2030 = 177,609 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 3,610 0 3,610 0.04% 2000 5,895 0 5,895 0.09% 2005 7,912 0 7,912 0.11% 2010 7,189 0 7,189 0.10% 2015 3,513 0 3,513 0.05% 2020 1,468 0 1,468 0.02% 2025 1,247 0 1,247 0.02% 2030 1,098 0 1,098 0.02% CumulativeNOx reduction, 1996-2030 = 146,928 tons


### 52 Table 45 Reduction ofPollutants for Microwave Ovens

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,024 0 1,024 0.04% 2000 2,205 0 2,205 0.09% 2005 3,236 0 3,236 0.11% 2010 3,170 0 3,170 0.10% 2015 1,803 0 1,803 0.05% 2020 869 0 869 0.02% 2025 868 0 868 0.02% 2030 894 0 894 0.02% CumulativeC02reduction, 1996-2030= 65 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 5,751 0 5,751 0.04% 2000 7,919 0 7,919 0.09% 2005 9,834 0 9,834 0.11% 2010 8,272 0 8,272 0.10% 2015 3,789 0 3,789 0.05% 2020 1,468 0 1,468 0.02% 2025 1,139 0 1,139 0.02% 2030 893 0 893 0.02% Cumulative S02reduction, 1996-2030 = 177,609 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 3,610 0 3,610 0.04% 2000 5,895 0 5,895 0.09% 2005 7,912 0 7,912 0.11% 2010 7,189 0 7,189 0.10% 2015 3,513 0 3,513 0.05% 2020 1,468 0 1,468 0.02% 2025 1,247 0 1,247 0.02% 2030 1,098 0 1,098 0.02% CumulativeNOx reduction, 1996-2030 = 146,928 tons


### 52Table 46 Reduction of Pollutants for Microwave Ovens - Level Five

CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,085 0 1,085 0.05% 2000 2,323 0 2,323 0.09% 2005 3,388 0 3,388 0.12% 2010 3,332 0 3,332 0.10% 2015 1,972 0 1,972 0.05% 2020 1,046 0 1,046 0.03% 2025 1,052 0 1,052 0.02% 2030 1,076 0 1,076 0.02% Cumulative CO2 reduction, 1996-2030 = 71 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 6,089 0 6,089 0.05% 2000 8,340 0 8,340 0.09% 2005 10,296 0 10,296 0.12% 2010 8,694 0 8,694 0.10% 2015 4,145 0 4,145 0.05% 2020 1,767 0 1,767 0.03% 2025 1,379 0 1,379 0.02% 2030 1,075 0 1,075 0.02% Cumulative SO2 reduction, 1996-2030 = 189,945 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 3,822 0 3,822 0.05% 2000 6,209 0 6,209 0.09% 2005 8,284 0 8,284 0.12% 2010 7,555 0 7,555 0.10% 2015 3,843 0 3,843 0.05% 2020 1,767 0 1,767 0.03% 2025 1,511 0 1,511 0.02% 2030 1,322 0 1,322 0.02% Cumulative NOx reduction, 1996-2030 = 157,793 tons



### 53

^'Uk Table 46 ReductionofPollutants for Microwave Ovens

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,085 0 1,085 0.05% 2000 2,323 0 2,323 0.09% 2005 3,388 0 3,388 0.12% 2010 3,332 0 3,332 0.10% 2015 1,972 0 1,972 0.05% 2020 1,046 0 1,046 0.03% 2025 1,052 0 1,052 0.02% 2030 1,076 0 1,076 0.02% CumulativeC02reduction, 1996-2030

## = 71 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 6,089 0 6,089 0.05% 2000 8,340 0 8,340 0.09% 2005 10,296 0 10,296 0.12% 2010 8,694 0 8,694 0.10% 2015 4,145 0 4,145 0.05% 2020 1,767 0 1,767 0.03% 2025 1,379 0 1,379 0.02% 2030 1,075 0 1,075 0.02% Cumulative S02reduction, 1996-2030

## = 189,945 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,822 0 3,822 0.05% 2000 6,209 0 6,209 0.09% 2005 8,284 0 8,284 0.12% 2010 7,555 0 7,555 0.10% 2015 3,843 0 3,843 0.05% 2020 1,767 0 1,767 0.03% 2025 1,511 0 1,511 0.02% 2030 1,322 0 1,322 0.02% CumulativeNOxreduction, 1996-2030 = 157,793 tons


### 53 Table 46 ReductionofPollutants for Microwave Ovens

- Level Five C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,085 0 1,085 0.05% 2000 2,323 0 2,323 0.09% 2005 3,388 0 3,388 0.12% 2010 3,332 0 3,332 0.10% 2015 1,972 0 1,972 0.05% 2020 1,046 0 1,046 0.03% 2025 1,052 0 1,052 0.02% 2030 1,076 0 1,076 0.02% CumulativeC02reduction, 1996-2030

## = 71 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 6,089 0 6,089 0.05% 2000 8,340 0 8,340 0.09% 2005 10,296 0 10,296 0.12% 2010 8,694 0 8,694 0.10% 2015 4,145 0 4,145 0.05% 2020 1,767 0 1,767 0.03% 2025 1,379 0 1,379 0.02% 2030 1,075 0 1,075 0.02% Cumulative S02reduction, 1996-2030

## = 189,945 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,822 0 3,822 0.05% 2000 6,209 0 6,209 0.09% 2005 8,284 0 8,284 0.12% 2010 7,555 0 7,555 0.10% 2015 3,843 0 3,843 0.05% 2020 1,767 0 1,767 0.03% 2025 1,511 0 1,511 0.02% 2030 1,322 0 1,322 0.02% CumulativeNOxreduction, 1996-2030 = 157,793 tons


### 53

### 3.6 POOL HEATERS

Decreases in the amounts of CO2, SO2, and NOX emitted at .trial standards levels 1 through 4 for gas pool heaters are summarized in Tables 47 through 50. 

### 3.6.1 Sulfur and Nitrogen Oxide Emissions Sulfur dioxide emissions would be decreased by a cumulative total of up to 0.00 million tons between 1996 and 2030 in the level 4 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.00% of the SO2 emissions estimated to come from power plants in that year. In the year 2030, decreases in SO2 emissions will represent about 0.00% of the SOZ emissions estimated to come from power plants in that year. Level 4 design changes to pool heaters would result in an estimated decrease in NOX emissions of 0.04 million tons between 1996 and 2030. NOX decreases would represent 0.003% and 0.03% of the NOX emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### 3.6.2 Carbon Dioxide Emissions The cumulative reduction in CO2 emissions from level 4 design changes is 43 million tons of CO2. For the year 1996, the estimated CO2 reduction is 270 thousand tons of CO2 or about 0.01% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2 reduction is 1744 thousand tons of CO2 or about 0.04% of estimated U.S. power plant CO2 emissions in 2030.



### 54 3.6 POOL HEATERS Decreases in the amounts of CO2, S02' and NOx emitted at.trial standards levels 1 through 4 for gas pool heaters are summarized in Tables 47 through 50. 3.6.1 Sulfur and Nitrogen Oxide Emissions Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.00 million tons between 1996 and 2030 in the level 4 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.00% ofthe S02 emissions estimated to come from power plants in that year. In the year2030, decreases in S02emissions willrepresent about0.00% ofthe S02 emissions estimated to come from powerplants in that year. Level 4 design changes to pool heaters would result in an estimated decrease in NOx emissions of0.04 million tons between 1996 and 2030. NOxdecreases wouldrepresent 0.003% and 0.03% of the NOx emissions estimated to come from power plants in the years 1996 and 2030, respectively. 3.6.2 Carbon Dioxide Emissions Thecumulativereduction in CO2emissions from level4 designchanges is 43 million tons ofCO2. For the year 1996, the estimated CO2reduction is 270 thousand tons ofCO2 or about 0.01% ofestimated U.S. powerplant CO2emissions in 1996. For the year 2030, the estimated CO2reduction is 1744 thousand tons ofCO2or about 0.04% ofestimatedU.S. powerplant CO2 emissions in 2030.

54 3.6 POOL HEATERS Decreases in the amounts of CO2, S02' and NOx emitted at.trial standards levels 1 through 4 for gas pool heaters are summarized in Tables 47 through 50. 3.6.1 Sulfur and Nitrogen Oxide Emissions Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.00 million tons between 1996 and 2030 in the level 4 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.00% ofthe S02 emissions estimated to come from power plants in that year. In the year2030, decreases in S02emissions willrepresent about0.00% ofthe S02 emissions estimated to come from powerplants in that year. Level 4 design changes to pool heaters would result in an estimated decrease in NOx emissions of0.04 million tons between 1996 and 2030. NOxdecreases wouldrepresent 0.003% and 0.03% of the NOx emissions estimated to come from power plants in the years 1996 and 2030, respectively. 3.6.2 Carbon Dioxide Emissions Thecumulativereduction in CO2emissions from level4 designchanges is 43 million tons ofCO2. For the year 1996, the estimated CO2reduction is 270 thousand tons ofCO2 or about 0.01% ofestimated U.S. powerplant CO2emissions in 1996. For the year 2030, the estimated CO2reduction is 1744 thousand tons ofCO2or about 0.04% ofestimatedU.S. powerplant CO2 emissions in 2030.

54Table 47 Reduction of Pollutants for Pool Heaters - Level One CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 110 110 0.00% 2000 0 292 292 0.01% 2005 0 292 292 0.01% 2010 0 198 198 0.01% 2015 0 44 44 0.00% 2020 0 -11 -11 0.00% 2025 0 -11 -11 0.00% 2030 0 -17 -17 0.00% Cumulative CO2 reduction, 1996-2030 = 4 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative SO2 reduction, 1996-2030 = 0 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 105 105 0.00% 2000 0 278 278 0.00% 2005 0 278 278 0.00% 2010 0 189 189 0.00% 2015 0 42 42 0.00% 2020 0 -10 -10 0.00% 2025 0 -11 -11 0.00% 2030 0 -16 -16 0.00% Cumulative NOx reduction, 1996-2030 = 3,864 tons



### 55
1) d1% Table 47 Reduction ofPollutants for Pool Heaters

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 110 110 0.00% 2000 0 292 292 0.01% 2005 0 292 292 0.01% 2010 0 198 198 0.01% 2015 0 44 44 0.00% 2020 0 -11 -11 0.00% 2025 0 -11 -11 0.00% 2030 0 -17 -17 0.00% CumulativeC02reduction, 1996-2030

### = 4 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030 = 

## otons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 105 105 0.00% 2000 0 278 278 0.00% 2005 0 278 278 0.00% 2010 0 189 189 0.00% 2015 0 42 42 0.00% 2020 0 -10 -10 0.00% 2025 0 -11 -11 0.00% 2030 0 -16 -16 0.00% Cumulative NOx reduction, 1996-2030 = 3,864 tons


### 55 Table 47 Reduction ofPollutants for Pool Heaters

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 110 110 0.00% 2000 0 292 292 0.01% 2005 0 292 292 0.01% 2010 0 198 198 0.01% 2015 0 44 44 0.00% 2020 0 -11 -11 0.00% 2025 0 -11 -11 0.00% 2030 0 -17 -17 0.00% CumulativeC02reduction, 1996-2030

### = 4 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030 = 

## otons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 105 105 0.00% 2000 0 278 278 0.00% 2005 0 278 278 0.00% 2010 0 189 189 0.00% 2015 0 42 42 0.00% 2020 0 -10 -10 0.00% 2025 0 -11 -11 0.00% 2030 0 -16 -16 0.00% Cumulative NOx reduction, 1996-2030 = 3,864 tons


### 55Table 48 Reduction of Pollutants for Pool Heaters - Level Two CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 149 149 0.01% 2000 0 396 396 0.02% 2005 0 479 479 0.02% 2010 0 440 440 0.01% 2015 0 319 319 0.01% 2020 0 292 292 0.01% 2025 0 319 319 0.01% 2030 0 347 347 0.01% Cumulative CO2 reduction, 1996-2030 = 12 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative SO2 reduction, 1996-2030 = 0 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 142 142 0.00% 2000 0 378 378 0.01% 2005 0 457 457 0.01% 2010 0 420 420 0.01% 2015 0 305 305 0.00% 2020 0 278 278 0.00% 2025 0 305 305 0.00% 2030 0 331 331 0.01% Cumulative NOx reduction, 1996-2030 = 11,870 tons



### 56

I1 31 Table 48 ReductionofPollutants for Pool Heaters

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 149 149 0.01% 2000 0 396 396 0.02% 2005 0 479 479 0.02% 2010 0 440 440 0.01% 2015 0 319 319 0.01% 2020 0 292 292 0.01% 2025 0 319 319 0.01% 2030 0 347 347 0.01% Cumulative C02reduction, 1996-2030

### = 12 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030

### = 

## otons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 142 142 0.00% 2000 0 378 378 0.01% 2005 0 457 457 0.01% 2010 0 420 420 0.01% 2015 0 305 305 0.00% 2020 0 278 278 0.00% 2025 0 305 305 0.00% 2030 0 331 331 0.01% CumulativeNOx reduction, 1996-2030 = 11,870 tons

56 Table 48 ReductionofPollutants for Pool Heaters

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 149 149 0.01% 2000 0 396 396 0.02% 2005 0 479 479 0.02% 2010 0 440 440 0.01% 2015 0 319 319 0.01% 2020 0 292 292 0.01% 2025 0 319 319 0.01% 2030 0 347 347 0.01% Cumulative C02reduction, 1996-2030

### = 12 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030

### = 

## otons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 142 142 0.00% 2000 0 378 378 0.01% 2005 0 457 457 0.01% 2010 0 420 420 0.01% 2015 0 305 305 0.00% 2020 0 278 278 0.00% 2025 0 305 305 0.00% 2030 0 331 331 0.01% CumulativeNOx reduction, 1996-2030 = 11,870 tons

56Table 49 Reduction of Pollutants for Pool Heaters - Level Three CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 226 226 0.01% 2000 0 616 616 0.02% 2005 0 891 891 0.03% 2010 0 990 990 0.03% 2015 0 963 963 0.03% 2020 0 1,018 1,018 0.03% 2025 0 1,122 1,122 0.03% 2030 0 1,232 1,232 0.03% Cumulative CO2 reduction, 1996-2030 = 32 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative SO2 reduction, 1996-2030 = 0 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 215 215 0.00% 2000 0 588 588 0.01% 2005 0 851 851 0.01% 2010 0 945 945 0.01% 2015 0 919 919 0.01% 2020 0 971 971 0.01% 2025 0 1,071 1,071 0.02% 2030 0 1,176 1,176 0.02% Cumulative NOx reduction, 1996-2030 = 30,508 tons



### 57 '^_rl Table 49 ReductionofPollutants for Pool Heaters

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 226 226 0.01% 2000 0 616 616 0.02% 2005 0 891 891 0.03% 2010 0 990 990 0.03% 2015 0 963 963 0.03% 2020 0 1,018 1,018 0.03% 2025 0 1,122 1,122 0.03% 2030 0 1,232 1,232 0.03% Cumulative C02reduction, 1996-2030= 32 million tons ofCO2

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030 = 

## otons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 215 215 0.00% 2000 0 588 588 0.01% 2005 0 851 851 0.01% 2010 0 945 945 0,01% 2015 0 919 919 0.01% 2020 0 971 971 0.01% 2025 0 1,071 1,071 0.02% 2030 0 1,176 1,176 0.02% CumulativeNOx reduction, 1996-2030 = 30,508 tons


### 57 Table 49 ReductionofPollutants for Pool Heaters

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 226 226 0.01% 2000 0 616 616 0.02% 2005 0 891 891 0.03% 2010 0 990 990 0.03% 2015 0 963 963 0.03% 2020 0 1,018 1,018 0.03% 2025 0 1,122 1,122 0.03% 2030 0 1,232 1,232 0.03% Cumulative C02reduction, 1996-2030= 32 million tons ofCO2

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030 = 

## otons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 215 215 0.00% 2000 0 588 588 0.01% 2005 0 851 851 0.01% 2010 0 945 945 0,01% 2015 0 919 919 0.01% 2020 0 971 971 0.01% 2025 0 1,071 1,071 0.02% 2030 0 1,176 1,176 0.02% CumulativeNOx reduction, 1996-2030 = 30,508 tons


### 57Table 50 Reduction of Pollutants for Pool Heaters - Level Four CO2 Reduction (in Thousand Tons)
Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 270 270 0.01% 2000 0 748 748 0.03% 2005 0 1,128 1,128 0.04% 2010 0 1,304 1,304 0.04% 2015 0 1,326 1,326 0.04% 2020 0 1,430 1,430 0.04% 2025 0 1,584 1,584 0.04% 2030 0 1,744 1,744 0.04% Cumulative CO2 reduction, 1996-2030 = 43 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative SO2 reduction, 1996-2030 = 0 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 0 257 257 0.00% 2000 0 714 714 0.01% 2005 0 1,076 1,076 0.02% 2010 0 1,244 1,244 0.02% 2015 0 1,265 1,265 0.02% 2020 0 1,365 1,365 0.02% 2025 0 1,512 1,512 0.02% 2030 0 1,664 1,664 0.03% Cumulative NOx reduction, 1996-2030 = 41,176 tons



### 58 

### Ily Table 50 Reduction ofPollutants for Pool Heaters

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 270 270 0.01% 2000 0 748 748 0.03% 2005 0 1,128 1,128 0.04% 2010 0 1,304 1,304 0.04% 2015 0 1,326 1,326 0.04% 2020 0 1,430 1,430 0.04% 2025 0 1,584 1,584 0.04% 2030 0 1,744 1,744 0.04% CumulativeC02reduction, 1996-2030= 43 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030 = 

## otons

NOxReduction (inTons) Abated from Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 257 257 0.00% 2000 0 714 714 0.01% 2005 0 1,076 1,076 0.02% 2010 0 1,244 1,244 0.02% 2015 0 1,265 1,265 0.02% 2020 0 1,365 1,365 0.02% 2025 0 1,512 1,512 0.02% 2030 0 1,664 1,664 0.03% Cumulative NOx reduction, 1996-2030 = 41,176 tons


### 58

Table 50 Reduction ofPollutants for Pool Heaters

- Level Four C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 270 270 0.01% 2000 0 748 748 0.03% 2005 0 1,128 1,128 0.04% 2010 0 1,304 1,304 0.04% 2015 0 1,326 1,326 0.04% 2020 0 1,430 1,430 0.04% 2025 0 1,584 1,584 0.04% 2030 0 1,744 1,744 0.04% CumulativeC02reduction, 1996-2030= 43 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 0 0 0.00% 2000 0 0 0 0.00% 2005 0 0 0 0.00% 2010 0 0 0 0.00% 2015 0 0 0 0.00% 2020 0 0 0 0.00% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030 = 

## otons

NOxReduction (inTons) Abated from Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 0 257 257 0.00% 2000 0 714 714 0.01% 2005 0 1,076 1,076 0.02% 2010 0 1,244 1,244 0.02% 2015 0 1,265 1,265 0.02% 2020 0 1,365 1,365 0.02% 2025 0 1,512 1,512 0.02% 2030 0 1,664 1,664 0.03% Cumulative NOx reduction, 1996-2030 = 41,176 tons


### 58

### 3.7 FLUORESCENT BALLASTS

Decreases in the amounts of CO2, SO2, and NOX are presented for two baseline cases: a reference case and a sensitivity (high efficiency base) case. Trial standards levels 1 through 3 for the reference case are summarized in Tables 51 through 53 while trial standards levels 1 through 3 for the sensitivity (high efficiency base) case are summarized in Tables 54 through 56. For an explanation of the differences in the two cases , please refer to Volume B, Chapter 2 of this Technical Support Document. 

### 3.7.1 Sulfur and Nitrogen Oxide Emissions Reference Case Sulfur dioxide emissions would be decreased by a cumulative total of up to 2.99 million tons between 1996 and 2030 in the level 3 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.06% of the SO2 emissions estimated to come from power plants in that year. In the year 2030, decreases in SO2 emissions will represent about 1.18% of the SO2 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions of SO2 emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those SO2 emissions would be only a postponement. Level 3 design changes to fluorescent ballasts would result in an estimated decrease in NOX emissions of 2.74 million tons between 1996 and 2030. NOX decreases would represent 0.06% and 1.18% of the NOX emissions estimated to come from power plants in the years 1996 and 2030, respectively. Sensitivity Case: High Efficiency Base Case Sulfur dioxide emissions would be decreased by a cumulative total of up to 0.72 million tons between 1996 and 2030 in the level 3 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.06% of the SO2 emissions estimated to come from power plants in that year. In the year 2030, decreases in SO2 emissions will represent about 0.28% of the SO2 emissions estimated to come from power plants in that year. As discussed above, the possible reductions of SO2 emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those SO2 emissions would be only a postponement. Level 3 design changes to fluorescent ballasts would result in an estimated decrease in NOX emissions of 0.65 million tons between 1996 and 2030. NOX decreases would represent 0.06% and 0.27% of the NOX emissions estimated to come from power plants in the years 1996 and 2030, respectively.



### 59 

### 3.7 FLUORESCENT BALLASTS Decreases in the amounts ofCO2, S02' and NOx are presented for two baseline cases: a reference case and a sensitivity (high efficiency base) case. Trial standards levels 1 through 3 for the reference case are summarized in Tables 51 through 53 while trial standards levels 1 through 3 for the sensitivity (high efficiencybase) case are summarizedin Tables 54 through 56. For an explanation ofthe differences in the two cases, please refer to Volume B, Chapter 

### 2 of this Technical Support Document. 

### 3.7.1 Sulfur and 

### Nitrogen Oxide Emissions 

### Reference Case Sulfurdioxide emissions would be decreased by a cumulative total ofup to 2.99 million tons between 1996 and 2030 in the level 3 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.06% ofthe S02 emissions estimated to come from powerplants in thatyear. In the year2030, decreases in S02emissions willrepresent about 1.18% ofthe S02 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions ofS02emissions caused by standards canbe earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 3 design changes to fluorescent ballasts would result in an estimated decrease in NOx emissions of 2.74 million tons between 1996 and 2030. NOx decreases would represent 0.06% and 1.18% ofthe NOxemissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### Sensitivity Case: High Efficiency Base Case Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.72 million tons between 1996 and 2030 in the level 3 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.06% ofthe S02 emissions estimated to come from power plants in thatyear. In the year2030, decreases in S02emissions will represent about0.28% ofthe S02 emissions estimated to come from power plants in that year. As discussed above, the possible reductions ofS02emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 3 design changes to fluorescent ballasts would result in an estimated decrease in NOx emissions of 0.65 million tons between 1996 and 2030. NOx decreases would represent 0.06% and 0.27% ofthe NOxemissions estimated to come from powerplants in the years 1996 and 2030, respectively.

59 

### 3.7 FLUORESCENT BALLASTS Decreases in the amounts ofCO2, S02' and NOx are presented for two baseline cases: a reference case and a sensitivity (high efficiency base) case. Trial standards levels 1 through 3 for the reference case are summarized in Tables 51 through 53 while trial standards levels 1 through 3 for the sensitivity (high efficiencybase) case are summarizedin Tables 54 through 56. For an explanation ofthe differences in the two cases, please refer to Volume B, Chapter 

### 2 of this Technical Support Document. 

### 3.7.1 Sulfur and 

### Nitrogen Oxide Emissions 

### Reference Case Sulfurdioxide emissions would be decreased by a cumulative total ofup to 2.99 million tons between 1996 and 2030 in the level 3 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.06% ofthe S02 emissions estimated to come from powerplants in thatyear. In the year2030, decreases in S02emissions willrepresent about 1.18% ofthe S02 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions ofS02emissions caused by standards canbe earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 3 design changes to fluorescent ballasts would result in an estimated decrease in NOx emissions of 2.74 million tons between 1996 and 2030. NOx decreases would represent 0.06% and 1.18% ofthe NOxemissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### Sensitivity Case: High Efficiency Base Case Sulfurdioxide emissions would be decreased by a cumulative total ofup to 0.72 million tons between 1996 and 2030 in the level 3 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.06% ofthe S02 emissions estimated to come from power plants in thatyear. In the year2030, decreases in S02emissions will represent about0.28% ofthe S02 emissions estimated to come from power plants in that year. As discussed above, the possible reductions ofS02emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 3 design changes to fluorescent ballasts would result in an estimated decrease in NOx emissions of 0.65 million tons between 1996 and 2030. NOx decreases would represent 0.06% and 0.27% ofthe NOxemissions estimated to come from powerplants in the years 1996 and 2030, respectively.

593.7.2 Carbon Dioxide Emissions



### Reference Case The cumulative reduction in CO2 emissions from level 3 design changes is 1450 million tons of CO2. For the year 1996, the estimated CO2 reduction is 1325 thousand tons of CO2 or about 0.06% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2 reduction is 56,050 thousand tons of CO2 or about 1.17% of estimated U.S. power plant CO2 emissions in 2030. 

### Sensitivity Case: High Efficiency Base Case The cumulative reduction in CO2 emissions from level 3 design changes is 338 million tons of CO2. For the year 1996, the estimated CO2 reduction is 1419 thousand tons of CO2 or about 0.06% of estimated U.S. power' plant CO2 emissions in 1996. For the year 2030, the estimated CO2 reduction is 12,957 thousand tons of CO2 or about 0.27% of estimated U.S.power plant CO2 emissions in 2030.



### 60

U^ 

### 3.7.2 Carbon Dioxide Emissions 

### Reference Case The cumulative reduction in CO2

### emissions from level 3 design changes is 1450 million tons ofCO2. 

### For the year 1996, the estimated CO2

### reduction is 1325 thousand tons ofCO2 

### or about 0.06% of estimated U.S. power plant CO2 

### emissions in 1996. For the year 2030, the estimatedCO2

### reduction is 56,050thousandtons ofCO2

### orabout 1.17% ofestimatedU.S. power plant CO2 

### emissions in 2030. 

### Sensitivity Case: High Efficiency Base Case The cumulative reduction in 

### CO2

### emissions from level 3 design changes is 338 million tons ofCO2• 

### For the year 1996, the estimated CO2 

### reduction is 1419 thousand tons ofCO2 

### or about 0.06% of estimated U.S. power" plant CO2 

### emissions in 1996. For the year 2030, the estimated CO2

### reduction is 12,957 thousand tons ofCO2

### orabout 0.27% ofestimatedU.S.power plant CO2 

### emissions in 2030.

60 

### 3.7.2 Carbon Dioxide Emissions 

### Reference Case The cumulative reduction in CO2

### emissions from level 3 design changes is 1450 million tons ofCO2. 

### For the year 1996, the estimated CO2

### reduction is 1325 thousand tons ofCO2 

### or about 0.06% of estimated U.S. power plant CO2 

### emissions in 1996. For the year 2030, the estimatedCO2

### reduction is 56,050thousandtons ofCO2

### orabout 1.17% ofestimatedU.S. power plant CO2 

### emissions in 2030. 

### Sensitivity Case: High Efficiency Base Case The cumulative reduction in 

### CO2

### emissions from level 3 design changes is 338 million tons ofCO2• 

### For the year 1996, the estimated CO2 

### reduction is 1419 thousand tons ofCO2 

### or about 0.06% of estimated U.S. power" plant CO2 

### emissions in 1996. For the year 2030, the estimated CO2

### reduction is 12,957 thousand tons ofCO2

### orabout 0.27% ofestimatedU.S.power plant CO2 

### emissions in 2030.

60Table 51 Reduction of Pollutants for Fluorescent Ballasts (Low DSM Case) - Level One CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 990 -41 949 0.04% 2000 11,763 -503 11,260 0.45% 2005 25,359 -1,014 24,345 0.85% 2010 38,414 -1,470 36,945 1.15% 2015 43,599 -1,583 42,016 1.17% 2020 46,741 -1,596 45,145 1.14% 2025 49,630 -1,629 48,001 1.09% 2030 45,414 -1,270 44,144 0.92% Cumulative CO2 reduction, 1996-2030 = 1,174 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 5,557 -16 5,542 0.04% 2000 42,244 -219 42,025 0.47% 2005 77,068 -423 76,645 0.88% 2010 100,233 -595 99,639 1.19% 2015 91,646 -610 91,035 1.20% 2020 78,998 -595 78,403 1.17% 2025 65,090 -595 64,496 1.11% 2030 45,372 -673 44,699 0.93% Cumulative SO2 reduction, 1996-2030 = 2,417,491 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 3,489 -38 3,451 0.04% 2000 31,448 -459 30,990 0.46% 2005 62,009 -926 61,082 0.87% 2010 87,107 -1,344 85,763 1.17% 2015 84,970 -1,451 83,519 1.19% 2020 78,998 -1,465 77,533 1.16% 2025 71,316 -1,497 69,820 1.11% 2030 55,770 -1,146 54,624 0.93% Cumulative NOx reduction, 1996-2030 = 2,218,305 tons



### 61 "I ? Table 51 ReductionofPollutants for FluorescentBallasts (Low DSM Case)

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 990 -41 949 0.04% 2000 11,763 -503 11,260 0.45% 2005 25,359 -1,014 24,345 0.85% 2010 38,414 -1,470 36,945 1.15% 2015 43,599 -1,583 42,016 1.17% 2020 46,741 -1,596 45,145 1.14% 2025 49,630 -1,629 48,001 l.09% 2030 45,414 -1,270 44,144 0.92% Cumulative C02reduction, 1996-2030= 1,174 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 5,557 -16 5,542 0.04% 2000 42,244 -219 42,025 0.47% 2005 77,068 -423 76,645 0.88% 2010 100,233 -595 99,639 1.19% 2015 91,646 -610 91,035 1.20% 2020 78,998 -595 78,403 1.17% 2025 65,090 -595 64,496 1.11% 2030 45,372 -673 44,699 0.93% Cumulative S02reduction, 1996-2030 = 2,417,491 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 3,489 -38 3,451 0.04% 2000 31,448 -459 30,990 0.46% 2005 62,009 -926 61,082 0.87% 2010 87,107 -1,344 85,763 1.17% 2015 84,970 -1,451 83,519 1.19% 2020 78,998 -1,465 77,533 1.16% 2025 71,316 -1,497 69,820 1.11% 2030 55,770 -1,146 54,624 0.93% CumulativeNOx reduction, 1996-2030 = 2,218,305 tons


### 61 Table 51 ReductionofPollutants for FluorescentBallasts (Low DSM Case)

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 990 -41 949 0.04% 2000 11,763 -503 11,260 0.45% 2005 25,359 -1,014 24,345 0.85% 2010 38,414 -1,470 36,945 1.15% 2015 43,599 -1,583 42,016 1.17% 2020 46,741 -1,596 45,145 1.14% 2025 49,630 -1,629 48,001 l.09% 2030 45,414 -1,270 44,144 0.92% Cumulative C02reduction, 1996-2030= 1,174 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 5,557 -16 5,542 0.04% 2000 42,244 -219 42,025 0.47% 2005 77,068 -423 76,645 0.88% 2010 100,233 -595 99,639 1.19% 2015 91,646 -610 91,035 1.20% 2020 78,998 -595 78,403 1.17% 2025 65,090 -595 64,496 1.11% 2030 45,372 -673 44,699 0.93% Cumulative S02reduction, 1996-2030 = 2,417,491 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 3,489 -38 3,451 0.04% 2000 31,448 -459 30,990 0.46% 2005 62,009 -926 61,082 0.87% 2010 87,107 -1,344 85,763 1.17% 2015 84,970 -1,451 83,519 1.19% 2020 78,998 -1,465 77,533 1.16% 2025 71,316 -1,497 69,820 1.11% 2030 55,770 -1,146 54,624 0.93% CumulativeNOx reduction, 1996-2030 = 2,218,305 tons


### 61Table 52 Reduction of Pollutants for Fluorescent Ballasts (Low DSM Case) - Level Two CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,188 -61 1,127 0.05% 2000 13,595 -580 13,015 0.52% 2005 29,343 -1,174 28,169 0.98% 2010 44,377 -1,693 42,683 1.33% 2015 50,313 -1,826 48,488 1.35% 2020 53,917 -1,845 52,072 1.31% 2025 57,310 -1,875 55,435 1.26% 2030 53,506 -1,516 51,990 1.08% Cumulative CO2 reduction, 1996-2030 = 1,358 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 6,669 -31 6,638 0.05% 2000 48,823 -250 48,572 0.54% 2005 89,174 -485 88,689 1.02% 2010 115,791 -673 115,118 1.37% 2015 105,761 -704 105,057 1.38% 2020 91,127 -689 90,438 1.35% 2025 75,162 -673 74,489 1.28% 2030 53,457 -751 52,706 1.10% Cumulative SO2 reduction, 1996-2030 = 2,797,165 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 4,186 -55 4,131 0.05% 2000 36,346 -529 35,816 0.53% 2005 71,749 -1,073 70,676 1.01% 2010 100,628 -1,550 99,078 1.36% 2015 98,056 -1,673 96,383 1.38% 2020 91,127 -1,693 89,434 1.33% 2025 82,352 -1,723 80,628 1.28% 2030 65,708 -1,373 64,335 1.09% Cumulative NOx reduction, 1996-2030 = 2,566,401 tons



### 62 Table 52 Reduction ofPollutants for FluorescentBallasts (Low DSM Case)

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,188 -61 1,127 0.05% 2000 13,595 -580 13,015 0.52% 2005 29,343 -1,174 28,169 0.98% 2010 44,377 -1,693 42,683 1.33% 2015 50,313 -1,826 48,488 1.35% 2020 53,917 -1,845 52,072 1.31% 2025 57,310 -1,875 55,435 1.26% 2030 53,506 -1,516 51,990 1.08% Cumulative C02reduction, 1996-2030= 1,358 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 6,669 -31 6,638 0.05% 2000 48,823 -250 48,572 0.54% 2005 89,174 -485 88,689 1.02% 2010 115,791 -673 115,118 1.37% 2015 105,761 -704 105,057 1.38% 2020 91,127 -689 90,438 1.35% 2025 75,162 -673 74,489 1.28% 2030 53,457 -751 52,706 1.10% Cumulative S02reduction, 1996-2030 = 2,797,165 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 4,186 -55 4,131 0.05% 2000 36,346 -529 35,816 0.53% 2005 71,749 -1,073 70,676 1.01% 2010 100,628 -1,550 99,078 1.36% 2015 98,056 -1,673 96,383 1.38% 2020 91,127 -1,693 89,434 1.33% 2025 82,352 -1,723 80,628 1.28% 2030 65,708 -1,373 64,335 1.09% CumulativeNOx reduction, 1996-2030 = 2,566,401 tons


### 62 Table 52 Reduction ofPollutants for FluorescentBallasts (Low DSM Case)

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,188 -61 1,127 0.05% 2000 13,595 -580 13,015 0.52% 2005 29,343 -1,174 28,169 0.98% 2010 44,377 -1,693 42,683 1.33% 2015 50,313 -1,826 48,488 1.35% 2020 53,917 -1,845 52,072 1.31% 2025 57,310 -1,875 55,435 1.26% 2030 53,506 -1,516 51,990 1.08% Cumulative C02reduction, 1996-2030= 1,358 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 6,669 -31 6,638 0.05% 2000 48,823 -250 48,572 0.54% 2005 89,174 -485 88,689 1.02% 2010 115,791 -673 115,118 1.37% 2015 105,761 -704 105,057 1.38% 2020 91,127 -689 90,438 1.35% 2025 75,162 -673 74,489 1.28% 2030 53,457 -751 52,706 1.10% Cumulative S02reduction, 1996-2030 = 2,797,165 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S. NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 4,186 -55 4,131 0.05% 2000 36,346 -529 35,816 0.53% 2005 71,749 -1,073 70,676 1.01% 2010 100,628 -1,550 99,078 1.36% 2015 98,056 -1,673 96,383 1.38% 2020 91,127 -1,693 89,434 1.33% 2025 82,352 -1,723 80,628 1.28% 2030 65,708 -1,373 64,335 1.09% CumulativeNOx reduction, 1996-2030 = 2,566,401 tons


### 62Table 53

Reduction of Pollutants for Fluorescent Ballasts (Low DSM Case) - Level Three CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,386 -61 1,325 0.06% 2000 14,560 -622 13,938 0.56% 2005 31,286 -1,252 30,035 1.05% 2010 47,309 -1,807 45,503 1.41% 2015 53,622 -1,950 51,672 1.44% 2020 57,505 -1,975 55,530 1.40% 2025 61,054 -1,999 59,055 1.35% 2030 57,695 -1,645 56,050 1.17% Cumulative CO2 reduction, 1996-2030= 1,450 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 7,780 -31 7,749 0.06% 2000 52,285 -266 52,019 0.58% 2005 95,080 -516 94,563 1.09% 2010 123,442 -720 122,723 1.46% 2015 112,716 -751 111,965 1.47% 2020 97,191 -736 96,455 1.44% 2025 80,072 -720 79,352 1.37% 2030 57,642 -798 56,844 1.18% Cumulative SO2 reduction, 1996-2030 = 2,985,814 tons

NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 4,884 -55 4,829 0.06% 2000 38,923 -567 38,356 0.57% 2005 76,501 -1,144 75,357 1.08% 2010 107,277 -1,653 105,624 1.45% 2015 104,505 -1,787 102,718 1.47% 2020 97,191 -1,812 95,378 1.42% 2025 87,731 -1,837 85,894 1.36% 2030 70,852 -1,492 69,360 1.18% Cumulative NOx reduction, 1996-2030 = 2,739,383 tons



### 63 Table 53 ReductionofPollutantsfor FluorescentBallasts (Low DSM Case)

- LevelThree C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,386 -61 1,325 0.06% 2000 14,560 -622 13,938 0.56% 2005 31,286 -1,252 30,035 1.05% 2010 47,309 -1,807 45,503 1.41% 2015 53,622 -1,950 51,672 1.44% 2020 57,505 -1,975 55,530 1.40% 2025 61,054 -1,999 59,055 1.35% 2030 57,695 -1,645 56,050 1.17% Cumulative C02reduction, 1996-2030= 1,450 milliontons ofCO2

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 7,780 -31 7,749 0.06% 2000 52,285 -266 52,019 0.58% 2005 95,080 -516 94,563 1.09% 2010 123,442 -720 122,723 1.46% 2015 112,716 -751 111,965 1.47% 2020 97,191 -736 96,455 1.44% 2025 80,072 -720 79,352 1.37% 2030 57,642 -798 56,844 1.18% Cumulative S02reduction, 1996-2030 = 2,985,814 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 4,884 -55 4,829 0.06% 2000 38,923 -567 38,356 0.57% 2005 76,501 -1,144 75,357 1.08% 2010 107,277 -1,653 105,624 1.45% 2015 104,505 -1,787 102,718 1.47% 2020 97,191 -1,812 95,378 1.42% 2025 87,731 -1,837 85,894 1.36% 2030 70,852 -1,492 69,360 1.18% CumulativeNOx reduction, 1996-2030 = 2,739,383 tons


### 63 Table 53 ReductionofPollutantsfor FluorescentBallasts (Low DSM Case)

- LevelThree C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,386 -61 1,325 0.06% 2000 14,560 -622 13,938 0.56% 2005 31,286 -1,252 30,035 1.05% 2010 47,309 -1,807 45,503 1.41% 2015 53,622 -1,950 51,672 1.44% 2020 57,505 -1,975 55,530 1.40% 2025 61,054 -1,999 59,055 1.35% 2030 57,695 -1,645 56,050 1.17% Cumulative C02reduction, 1996-2030= 1,450 milliontons ofCO2

S02Reduction (in Tons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 7,780 -31 7,749 0.06% 2000 52,285 -266 52,019 0.58% 2005 95,080 -516 94,563 1.09% 2010 123,442 -720 122,723 1.46% 2015 112,716 -751 111,965 1.47% 2020 97,191 -736 96,455 1.44% 2025 80,072 -720 79,352 1.37% 2030 57,642 -798 56,844 1.18% Cumulative S02reduction, 1996-2030 = 2,985,814 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 4,884 -55 4,829 0.06% 2000 38,923 -567 38,356 0.57% 2005 76,501 -1,144 75,357 1.08% 2010 107,277 -1,653 105,624 1.45% 2015 104,505 -1,787 102,718 1.47% 2020 97,191 -1,812 95,378 1.42% 2025 87,731 -1,837 85,894 1.36% 2030 70,852 -1,492 69,360 1.18% CumulativeNOx reduction, 1996-2030 = 2,739,383 tons


### 63Table 54 Reduction of Pollutants for Fluorescent Ballasts (High DSM Case) - Level One CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,089 -47 1,042 0.05% 2000 3,182 -138 3,044 0.12% 2005 6,024 -232 5,792 0.20% 2010 8,406 -312 8,094 0.25% 2015 8,953 -315 8,638 0.24% 2020 9,309 -315 8,994 0.23% 2025 9,504 -296 9,208 0.21% 2030 9,901 -253 9,648 0.20% Cumulative CO2 reduction, 1996-2030 = 250 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 6,113 -16 6,097 0.05% 2000 11,427 -63 11,364 0.13% 2005 18,307 -94 18,213 0.21% 2010 21,934 -141 21,793 0.26% 2015 18,820 -125 18,695 0.25% 2020 15,734 -125 15,609 0.23% 2025 12,464 -110 12,355 0.21% 2030 9,892 -31 9,861 0.21% Cumulative SO2 reduction, 1996-2030 = 536,129 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change

(tons) Power Plant

Emissions 1996 3,837 -43 3,794 0.05% 2000 8,506 -126 8,381 0.13% 2005 14,730 -212 14,518 0.21% 2010 19,062 -284 18,777 0.26% 2015 17,449 -288 17,161 0.25% 2020 15,734 -288 15,446 0.23% 2025 13,656 -271 13,385 0.21% 2030 12,159 -239 11,921 0.20% Cumulative NOx reduction, 1996-2030 = 483,517 tons



### 64 Table 54 Reduction ofPollutants for FluorescentBallasts (HighDSMCase)

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,089 -47 1,042 0.05% 2000 3,182 -138 3,044 0.12% 2005 6,024 -232 5,792 0.20% 2010 8,406 -312 8,094 0.25% 2015 8,953 -315 8,638 0.24% 2020 9,309 -315 8,994 0.23% 2025 9,504 -296 9,208 0.21% 2030 9,901 -253 9,648 0.20% Cumulative C02reduction, 1996-2030

## = 250 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 6,113 -16 6,097 0.05% 2000 11,427 -63 11,364 0.13% 2005 18,307 -94 18,213 0.21% 2010 21,934 -141 21,793 0.26% 2015 18,820 -125 18,695 0.25% 2020 15,734 -125 15,609 0.23% 2025 12,464 -110 12,355 0.21% 2030 9,892 -31 9,861 0.21% Cumulative S02reduction, 1996-2030 = 536,129 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation (tons) Emissions 1996 3,837 -43 3,794 0.05% 2000 8,506 -126 8,381 0.13% 2005 14,730 -212 14,518 0.21% 2010 19,062 -284 18,777 0.26% 2015 17,449 -288 17,161 0.25% 2020 15,734 -288 15,446 0.23% 2025 13,656 -271 13,385 0.21% 2030 12,159 -239 11,921 0.20% CumulativeNOxreduction, 1996-2030 = 483,517 tons


### 64 Table 54 Reduction ofPollutants for FluorescentBallasts (HighDSMCase)

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,089 -47 1,042 0.05% 2000 3,182 -138 3,044 0.12% 2005 6,024 -232 5,792 0.20% 2010 8,406 -312 8,094 0.25% 2015 8,953 -315 8,638 0.24% 2020 9,309 -315 8,994 0.23% 2025 9,504 -296 9,208 0.21% 2030 9,901 -253 9,648 0.20% Cumulative C02reduction, 1996-2030

## = 250 milliontons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 6,113 -16 6,097 0.05% 2000 11,427 -63 11,364 0.13% 2005 18,307 -94 18,213 0.21% 2010 21,934 -141 21,793 0.26% 2015 18,820 -125 18,695 0.25% 2020 15,734 -125 15,609 0.23% 2025 12,464 -110 12,355 0.21% 2030 9,892 -31 9,861 0.21% Cumulative S02reduction, 1996-2030 = 536,129 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation (tons) Emissions 1996 3,837 -43 3,794 0.05% 2000 8,506 -126 8,381 0.13% 2005 14,730 -212 14,518 0.21% 2010 19,062 -284 18,777 0.26% 2015 17,449 -288 17,161 0.25% 2020 15,734 -288 15,446 0.23% 2025 13,656 -271 13,385 0.21% 2030 12,159 -239 11,921 0.20% CumulativeNOxreduction, 1996-2030 = 483,517 tons


### 64Table 55 Reduction of Pollutants for Fluorescent Ballasts (High DSM Case) - Level Two C02 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. C02 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,287 -66 1,221 0.05% 2000 3,953 -169 3,785 0.15% 2005 7,384 -293 7,091 0.25% 2010 10,263 -390 9,874 0.31% 2015 10,997 -392 10,605 0.30% 2020 11,443 -378 11,064 0.28% 2025 11,712 -373 11,339 0.26% 2030 12,091 -331 11,761 0.24% Cumulative C02 reduction, 1996-2030 = 307 million tons of C02

S02 Reduction (in Tons) Abated from Abated from Net % U.S. S02 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 7,225 -31 7,193 0.06% 2000 14,197 -78 14,118 0.16% 2005 22,441 -125 22,316 0.26% 2010 26,780 -172 26,608 0.32% 2015 23,116 -157 22,960 0.30% 2020 19,340 -141 19,199 0.29% 2025 15,360 -141 15,219 0.26% 2030 12,080 -63 12,018 0.25% Cumulative S02 reduction, 1996-2030 = 657,463 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change

(tons) Power Plant

Emissions 1996 4,535 -60 4,475 0.06% 2000 10,569 -153 10,415 0.16% 2005 18,056 -267 17,789 0.25% 2010 23,273 -355 22,918 0.31% 2015 21,432 -359 21,073 0.30% 2020 19,340 -347 18,993 0.28% 2025 16,829 -342 16,487 0.26% 2030 14,849 -309 14,539 0.25% Cumulative NOx reduction, 1996-2030 = 593,047 tons



### 65 Table 55 ReductionofPollutants for FluorescentBallasts (HighDSM Case)

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,287 -66 1,221 0.05% 2000 3,953 -169 3,785 0.15% 2005 7,384 -293 7,091 0.25% 2010 10,263 -390 9,874 0.31% 2015 10,997 -392 10,605 0.30% 2020 11,443 -378 11,064 0.28% 2025 11,712 -373 11,339 0.26% 2030 12,091 -331 11,761 0.24% CumulativeC02reduction, 1996-2030= 307 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 7,225 -31 7,193 0.06% 2000 14,197 -78 14,118 0.16% 2005 22,441 -125 22,316 0.26% 2010 26,780 -172 26,608 0.32% 2015 23,116 -157 22,960 0.30% 2020 19,340 -141 19,199 0.29% 2025 15,360 -141 15,219 0.26% 2030 12,080 -63 12,018 0.25% Cumulative S02reduction, 1996-2030 = 657,463 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation (tons) Emissions 1996 4,535 -60 4,475 0.06% 2000 10,569 -153 10,415 0.16% 2005 18,056 -267 17,789 0.25% 2010 23,273 -355 22,918 0.31% 2015 21,432 -359 21,073 0.30% 2020 19,340 -347 18,993 0.28% 2025 16,829 -342 16,487 0.26% 2030 14,849 -309 14,539 0.25% CumulativeNOx reduction, 1996-2030 = 593,047 tons


### 65 Table 55 ReductionofPollutants for FluorescentBallasts (HighDSM Case)

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,287 -66 1,221 0.05% 2000 3,953 -169 3,785 0.15% 2005 7,384 -293 7,091 0.25% 2010 10,263 -390 9,874 0.31% 2015 10,997 -392 10,605 0.30% 2020 11,443 -378 11,064 0.28% 2025 11,712 -373 11,339 0.26% 2030 12,091 -331 11,761 0.24% CumulativeC02reduction, 1996-2030= 307 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 7,225 -31 7,193 0.06% 2000 14,197 -78 14,118 0.16% 2005 22,441 -125 22,316 0.26% 2010 26,780 -172 26,608 0.32% 2015 23,116 -157 22,960 0.30% 2020 19,340 -141 19,199 0.29% 2025 15,360 -141 15,219 0.26% 2030 12,080 -63 12,018 0.25% Cumulative S02reduction, 1996-2030 = 657,463 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change Power Plant Generation Generation (tons) Emissions 1996 4,535 -60 4,475 0.06% 2000 10,569 -153 10,415 0.16% 2005 18,056 -267 17,789 0.25% 2010 23,273 -355 22,918 0.31% 2015 21,432 -359 21,073 0.30% 2020 19,340 -347 18,993 0.28% 2025 16,829 -342 16,487 0.26% 2030 14,849 -309 14,539 0.25% CumulativeNOx reduction, 1996-2030 = 593,047 tons


### 65Table 56

Reduction of Pollutants for Fluorescent Ballasts (High DSM Case) - Level Three CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,485 -66 1,419 0.06% 2000 4,339 -185 4,154 0.17% 2005 8,162 -323 7,838 0.27% 2010 11,241 -426 10,815 0.34% 2015 12,067 -434 11,634 0.32% 2020 12,606 -420 12,187 0.31% 2025 12,960 -414 12,545 0.29% 2030 13,329 -372 12,957 0.27% Cumulative CO2 reduction, 1996-2030 = 338 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 8,336 -31 8,305 0.06% 2000 15,582 -78 15,503 0.17% 2005 24,803 -141 24,663 0.28% 2010 29,330 -188 29,143 0.35% 2015 25,366 -172 25,194 0.33% 2020 21,307 -157 21,150 0.32% 2025 16,996 -157 16,840 0.29% 2030 13,317 -78 13,238 0.28% Cumulative SO2 reduction, 1996-2030 = 723,969 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change

(tons) Power Plant

Emissions 1996 5,233 -60 5,173 0.06% 2000 11,600 -169 11,431 0.17% 2005 19,957 -295 19,662 0.28% 2010 25,489 -388 25,102 0.34% 2015 23,518 -397 23,121 0.33% 2020 21,307 -385 20,921 0.31% 2025 18,622 -380 18,242 0.29% 2030 16,368 -347 16,021 0.27% Cumulative NOx reduction, 1996-2030 = 652,995 tons



### 66 21^y Table 56 ReductionofPollutants for FluorescentBallasts (HighDSMCase)

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,485 -66 1,419 0.06% 2000 4,339 -185 4,154 0.17% 2005 8,162 -323 7,838 0.27% 2010 11,241 -426 10,815 0.34% 2015 12,067 -434 11,634 0.32% 2020 12,606 -420 12,187 0.31% 2025 12,960 -414 12,545 0.29% 2030 13,329 -372 12,957 0.27% Cumulative C02reduction, 1996-2030= 338 milliontons ofCO2

S02Reduction (inTons) Abated from Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 8,336 -31 8,305 0.06% 2000 15,582 -78 15,503 0.17% 2005 24,803 -141 24,663 0.28% 2010 29,330 -188 29,143 0.35% 2015 25,366 -172 25,194 0.33% 2020 21,307 -157 21,150 0.32% 2025 16,996 -157 16,840 0.29% 2030 13,317 -78 13,238 0.28% Cumulative S02reduction, 1996-2030

### = 723,969 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation (tons) Emissions 1996 5,233 -60 5,173 0.06% 2000 11,600 -169 11,431 0.17% 2005 19,957 -295 19,662 0.28% 2010 25,489 -388 25,102 0.34% 2015 23,518 -397 23,121 0.33% 2020 21,307 -385 20,921 0.31% 2025 18,622 -380 18,242 0.29% 2030 16,368 -347 16,021 0.27% CumulativeNOx reduction, 1996-2030

### = 652,995 tons


### 66 Table 56 ReductionofPollutants for FluorescentBallasts (HighDSMCase)

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,485 -66 1,419 0.06% 2000 4,339 -185 4,154 0.17% 2005 8,162 -323 7,838 0.27% 2010 11,241 -426 10,815 0.34% 2015 12,067 -434 11,634 0.32% 2020 12,606 -420 12,187 0.31% 2025 12,960 -414 12,545 0.29% 2030 13,329 -372 12,957 0.27% Cumulative C02reduction, 1996-2030= 338 milliontons ofCO2

S02Reduction (inTons) Abated from Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 8,336 -31 8,305 0.06% 2000 15,582 -78 15,503 0.17% 2005 24,803 -141 24,663 0.28% 2010 29,330 -188 29,143 0.35% 2015 25,366 -172 25,194 0.33% 2020 21,307 -157 21,150 0.32% 2025 16,996 -157 16,840 0.29% 2030 13,317 -78 13,238 0.28% Cumulative S02reduction, 1996-2030

### = 723,969 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation (tons) Emissions 1996 5,233 -60 5,173 0.06% 2000 11,600 -169 11,431 0.17% 2005 19,957 -295 19,662 0.28% 2010 25,489 -388 25,102 0.34% 2015 23,518 -397 23,121 0.33% 2020 21,307 -385 20,921 0.31% 2025 18,622 -380 18,242 0.29% 2030 16,368 -347 16,021 0.27% CumulativeNOx reduction, 1996-2030

### = 652,995 tons


### 66

### 3.8 TELEVISIONS

Decreases in the amounts of CO2, SO2, and NOx emitted at trial standards levels 1 through 3 are summarized in Tables 57 through 59. 3.8.1 Sulfur and Nitrogen Oxide Emissions Sulfur dioxide emissions would be decreased by a cumulative total of up to 0.60 million tons between 1996 and 2030 in the level 3 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.08% of the SO2 emissions estimated to come from power plants in that year. In the year 2030, decreases in SO2 emissions will represent about 0.16% of the SO2 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions of SO2 emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those SO2 emissions would be only a postponement. Level 3 design changes to televisions would result in an estimated decrease in NOX emissions of 0.53 million tons between 1996 and 2030. NOX decreases would represent 0.08% and 0.16% of the NOX emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### 3.8.2 Carbon Dioxide Emissions The cumulative reduction in CO2 emissions from level 3 design changes is 263 million tons of CO2. For the year 1996, the estimated CO2 reduction is 1902 thousand tons of CO2 or about 0.08% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2 reduction is 7774 thousand tons of CO2 or about 0.16% of estimated U.S. power plant CO2 emissions in 2030.



### 67

S `^' 

### 3.8 TELEVISIONS Decreases in the amounts of CO2, S02' and NOx emitted at trial standards levels 1 through 3 are summarized in Tables 57 through 59. 

### 3.8.1 Sulfur and Nitrogen Oxide Emissions Sulfurdioxide emissions would be decreasedby a cumulative total ofup to 

### 0.60 million tons between 1996 and 2030 in the level 3 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.08% ofthe S02 emissions estimated to come from power plants in thatyear. In the year2030, decreases in S02emissions willrepresent about0.16% ofthe S02 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions ofS02emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 3 design changes to televisions would result in an estimated decrease in NOx emissions of0.53 million tons between 1996 and 2030. NOx decreases would represent 0.08% and 0.16% of the NOx emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### 3.8.2 Carbon Dioxide Emissions The cumulative reduction in CO2 emissions from level 3 design changes is 263 million tons ofCO2, For the year 1996, the estimated CO2reduction is 1902 thousand tons ofCO2or about 0.08% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2reduction is 7774 thousand tons ofCO2 or about 0.16% ofestimatedU.S. power plant CO2 emissions in 2030.

67 

### 3.8 TELEVISIONS Decreases in the amounts of CO2, S02' and NOx emitted at trial standards levels 1 through 3 are summarized in Tables 57 through 59. 

### 3.8.1 Sulfur and Nitrogen Oxide Emissions Sulfurdioxide emissions would be decreasedby a cumulative total ofup to 

### 0.60 million tons between 1996 and 2030 in the level 3 scenario. In the year 1996, decreases in sulfur dioxide will represent about 0.08% ofthe S02 emissions estimated to come from power plants in thatyear. In the year2030, decreases in S02emissions willrepresent about0.16% ofthe S02 emissions estimated to come from power plants in that year. As discussed earlier, the possible reductions ofS02emissions caused by standards can be earned as credits. To the extent credits are used for future emissions, the standards' net effect on those S02 emissions would be only a postponement. Level 3 design changes to televisions would result in an estimated decrease in NOx emissions of0.53 million tons between 1996 and 2030. NOx decreases would represent 0.08% and 0.16% of the NOx emissions estimated to come from power plants in the years 1996 and 2030, respectively. 

### 3.8.2 Carbon Dioxide Emissions The cumulative reduction in CO2 emissions from level 3 design changes is 263 million tons ofCO2, For the year 1996, the estimated CO2reduction is 1902 thousand tons ofCO2or about 0.08% of estimated U.S. power plant CO2 emissions in 1996. For the year 2030, the estimated CO2reduction is 7774 thousand tons ofCO2 or about 0.16% ofestimatedU.S. power plant CO2 emissions in 2030.

67Table 57 Reduction of Pollutants for Televisions - Level One CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 542 0 542 0.02% 2000 1,342 0 1,342 0.05% 2005 2,112 0 2,112 0.07% 2010 2,269 0 2,269 0.07% 2015 1,913 0 1,913 0.05% 2020 801 0 801 0.02% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative CO2 reduction, 1996-2030 = 43 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant In-Home Change Power Plant

Generation Generation Emissions 1996 3,044 0 3,044 0.02% 2000 4,818 0 4,818 0.05% 2005 6,419 0 6,419 0.07% 2010 5,922 0 5,922 0.07% 2015 4,020 0 4,020 0.05% 2020 1,354 0 1,354 0.02% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative SO2 reduction, 1996-2030 = 120,177 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change

(tons) Power Plant

Emissions 1996 1,911 0 1,911 0.02% 2000 3,586 0 3,586 0.05% 2005 5,165 0 5,165 0.07% 2010 5,146 0 5,146 0.07% 2015 3,727 0 3,727 0.05% 2020 1,354 0 1,354 0.02% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative NOx reduction, 1996-2030 = 99,428 tons



### 68 Table 57 ReductionofPollutants for Televisions

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 542 0 542 0.02% 2000 1,342 0 1,342 0.05% 2005 2,112 0 2,112 0.07% 2010 2,269 0 2,269 0.07% 2015 1,913 0 1,913 0.05% 2020 801 0 801 0.02% 2025 0 0 0 0.00% 2030 0 0 0 0.00% CumulativeC02reduction, 1996-2030= 43 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,044 0 3,044 0.02% 2000 4,818 0 4,818 0.05% 2005 6,419 0 6,419 0.07% 2010 5,922 0 5,922 0.07% 2015 4,020 0 4,020 0.05% 2020 1,354 0 1,354 0.02% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030 = 120,177 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation (tons) Emissions 1996 1,911 0 1,911 0.02% 2000 3,586 0 3,586 0.05% 2005 5,165 0 5,165 0.07% 2010 5,146 0 5,146 0.07% 2015 3,727 0 3,727 0.05% 2020 1,354 0 1,354 0.02% 2025 0 0 0 0.00% 2030 0 0 0 0.00% CumulativeNOx reduction, 1996-2030 = 99,428 tons


### 68 Table 57 ReductionofPollutants for Televisions

- Level One C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. CO2 Year PowerPlant In-Home Change Power Plant Generation Generation Emissions 1996 542 0 542 0.02% 2000 1,342 0 1,342 0.05% 2005 2,112 0 2,112 0.07% 2010 2,269 0 2,269 0.07% 2015 1,913 0 1,913 0.05% 2020 801 0 801 0.02% 2025 0 0 0 0.00% 2030 0 0 0 0.00% CumulativeC02reduction, 1996-2030= 43 million tons ofCO2

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 3,044 0 3,044 0.02% 2000 4,818 0 4,818 0.05% 2005 6,419 0 6,419 0.07% 2010 5,922 0 5,922 0.07% 2015 4,020 0 4,020 0.05% 2020 1,354 0 1,354 0.02% 2025 0 0 0 0.00% 2030 0 0 0 0.00% Cumulative S02reduction, 1996-2030 = 120,177 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation (tons) Emissions 1996 1,911 0 1,911 0.02% 2000 3,586 0 3,586 0.05% 2005 5,165 0 5,165 0.07% 2010 5,146 0 5,146 0.07% 2015 3,727 0 3,727 0.05% 2020 1,354 0 1,354 0.02% 2025 0 0 0 0.00% 2030 0 0 0 0.00% CumulativeNOx reduction, 1996-2030 = 99,428 tons


### 68Table 58 Reduction of Pollutants for Televisions - Level Two CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 981 0 981 0.04% 2000 2,482 0 2,482 0.10% 2005 3,937 0 3,937 0.14% 2010 4,241 0 4,241 0.13% 2015 3,977 0 3,977 0.11% 2020 2,943 0 2,943 0.07% 2025 2,195 0 2,195 0.05% 2030 2,268 0 2,268 0.05% Cumulative CO2 reduction, 1996-2030 = 108 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 5,509 0 5,509 0.04% 2000 8,912 0 8,912 0.10% 2005 11,965 0 11,965 0.14% 2010 11,067 0 11,067 0.13% 2015 8,361 0 8,361 0.11% 2020 4,974 0 4,974 0.07% 2025 2,879 0 2,879 0.05% 2030 2,266 0 2,266 0.05% Cumulative SO2 reduction, 1996-2030 = 261,149 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change

(tons) Power Plant

Emissions 1996 3,458 0 3,458 0.04% 2000 6,635 0 6,635 0.10% 2005 9,627 0 9,627 0.14% 2010 9,618 0 9,618 0.13% 2015 7,751 0 7,751 0.11% 2020 4,974 0 4,974 0.07% 2025 3,155 0 3,155 0.05% 2030 2,786 0 2,786 0.05% Cumulative NOx reduction, 1996-2030 = 225,217 tons



### 69 

### ^5? Table 58 ReductionofPollutants for Televisions

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 981 0 981 0.04% 2000 2,482 0 2,482 0.10% 2005 3,937 0 3,937 0.14% 2010 4,241 0 4,241 0.13% 2015 3,977 0 3,977 0.11% 2020 2,943 0 2,943 0.07% 2025 2,195 0 2,195 0.05% 2030 2,268 0 2,268 0.05% Cumulative C02reduction, 1996-2030

### = 108 milliontons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 5,509 0 5,509 0.04% 2000 8,912 0 8,912 0.10% 2005 11,965 0 11,965 0.14% 2010 11,067 0 11,067 0.13% 2015 8,361 0 8,361 0.11% 2020 4,974 0 4,974 0.07% 2025 2,879 0 2,879 0.05% 2030 2,266 0 2,266 0.05% Cumulative S02reduction, 1996-2030

### = 261,149 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation (tons) Emissions 1996 3,458 0 3,458 0.04% 2000 6,635 0 6,635 0.10% 2005 9,627 0 9,627 0.14% 2010 9,618 0 9,618 0.13% 2015 7,751 0 7,751 0.11% 2020 4,974 0 4,974 0.07% 2025 3,155 0 3,155 0.05% 2030 2,786 0 2,786 0.05% CumulativeNOxreduction, 1996-2030

### = 225,217 tons


### 69 Table 58 ReductionofPollutants for Televisions

- Level Two C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net % U.S. C02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 981 0 981 0.04% 2000 2,482 0 2,482 0.10% 2005 3,937 0 3,937 0.14% 2010 4,241 0 4,241 0.13% 2015 3,977 0 3,977 0.11% 2020 2,943 0 2,943 0.07% 2025 2,195 0 2,195 0.05% 2030 2,268 0 2,268 0.05% Cumulative C02reduction, 1996-2030

### = 108 milliontons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 5,509 0 5,509 0.04% 2000 8,912 0 8,912 0.10% 2005 11,965 0 11,965 0.14% 2010 11,067 0 11,067 0.13% 2015 8,361 0 8,361 0.11% 2020 4,974 0 4,974 0.07% 2025 2,879 0 2,879 0.05% 2030 2,266 0 2,266 0.05% Cumulative S02reduction, 1996-2030

### = 261,149 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation (tons) Emissions 1996 3,458 0 3,458 0.04% 2000 6,635 0 6,635 0.10% 2005 9,627 0 9,627 0.14% 2010 9,618 0 9,618 0.13% 2015 7,751 0 7,751 0.11% 2020 4,974 0 4,974 0.07% 2025 3,155 0 3,155 0.05% 2030 2,786 0 2,786 0.05% CumulativeNOxreduction, 1996-2030

### = 225,217 tons


### 69Table 59 Reduction of Pollutants for Televisions - Level Three CO2 Reduction (in Thousand Tons) Abated from Abated from Net % U.S. CO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 1,902 0 1,902 0.08% 2000 5,115 0 5,115 0.20% 2005 8,364 0 8,364 0.29% 2010 9,052 0 9,052 0.28% 2015 8,987 0 8,987 0.25% 2020 8,146 0 8,146 0.21% 2025 7,555 0 7,555 0.17% 2030 7,774 0 7,774 0.16% Cumulative CO2 reduction, 1996-2030 = 263 million tons of CO2
SO2 Reduction (in Tons) Abated from Abated from Net % U.S. SO2 Year Power Plant

Generation In-Home

Generation Change Power Plant Emissions 1996 10,680 0 10,680 0.08% 2000 18,367 0 18,367 0.20% 2005 25,420 0 25,420 0.29% 2010 23,620 0 23,620 0.28% 2015 18,891 0 18,891 0.25% 2020 13,767 0 13,767 0.21% 2025 9,908 0 9,908 0.17% 2030 7,767 0 7,767 0.16% Cumulative SO2 reduction, 1996-2030 = 599,166 tons
NOx Reduction (in Tons) Abated from Abated from Net %U.S. NOx Year Power Plant

Generation In-Home

Generation Change

(tons) Power Plant

Emissions 1996 6,704 0 6,704 0.08% 2000 13,673 0 13,673 0.20% 2005 20,453 0 20,453 0.29% 2010 20,526 0 20,526 0.28% 2015 17,515 0 17,515 0.25% 2020 13,767 0 13,767 0.21% 2025 10,855 0 10,855 0.17% 2030 9,547 0 9,547 0.16% Cumulative NOx reduction, 1996-2030 = 527,742 tons



### 70 Table 59 ReductionofPollutants forTelevisions

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,902 0 1,902 0.08% 2000 5,115 0 5,115 0.20% 2005 8,364 0 8,364 0.29% 2010 9,052 0 9,052 0.28% 2015 8,987 0 8,987 0.25% 2020 8,146 0 8,146 0.21% 2025 7,555 0 7,555 0.17% 2030 7,774 0 7,774 0.16% CumulativeC02reduction, 1996-2030= 263 milliontons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 10,680 0 10,680 0.08% 2000 18,367 0 18,367 0.20% 2005 25,420 0 25,420 0.29% 2010 23,620 0 23,620 0.28% 2015 18,891 0 18,891 0.25% 2020 13,767 0 13,767 0.21% 2025 9,908 0 9,908 0.17% 2030 7,767 0 7,767 0.16% Cumulative S02reduction, 1996-2030 = 599,166 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation (tons) Emissions 1996 6,704 0 6,704 0.08% 2000 13,673 0 13,673 0.20% 2005 20,453 0 20,453 0.29% 2010 20,526 0 20,526 0.28% 2015 17,515 0 17,515 0.25% 2020 13,767 0 13,767 0.21% 2025 10,855 0 10,855 0.17% 2030 9,547 0 9,547 0.16% CumulativeNOx reduction, 1996-2030 = 527,742 tons


### 70 Table 59 ReductionofPollutants forTelevisions

- Level Three C02Reduction (inThousandTons) Abatedfrom Abatedfrom Net %U.S. CO2 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 1,902 0 1,902 0.08% 2000 5,115 0 5,115 0.20% 2005 8,364 0 8,364 0.29% 2010 9,052 0 9,052 0.28% 2015 8,987 0 8,987 0.25% 2020 8,146 0 8,146 0.21% 2025 7,555 0 7,555 0.17% 2030 7,774 0 7,774 0.16% CumulativeC02reduction, 1996-2030= 263 milliontons ofC02

S02Reduction (inTons) Abatedfrom Abatedfrom Net % U.S. S02 Year PowerPlant In-Home Change PowerPlant Generation Generation Emissions 1996 10,680 0 10,680 0.08% 2000 18,367 0 18,367 0.20% 2005 25,420 0 25,420 0.29% 2010 23,620 0 23,620 0.28% 2015 18,891 0 18,891 0.25% 2020 13,767 0 13,767 0.21% 2025 9,908 0 9,908 0.17% 2030 7,767 0 7,767 0.16% Cumulative S02reduction, 1996-2030 = 599,166 tons

NOxReduction (inTons) Abatedfrom Abatedfrom Net %U.S.NOx Year PowerPlant In-Home Change PowerPlant Generation Generation (tons) Emissions 1996 6,704 0 6,704 0.08% 2000 13,673 0 13,673 0.20% 2005 20,453 0 20,453 0.29% 2010 20,526 0 20,526 0.28% 2015 17,515 0 17,515 0.25% 2020 13,767 0 13,767 0.21% 2025 10,855 0 10,855 0.17% 2030 9,547 0 9,547 0.16% CumulativeNOx reduction, 1996-2030 = 527,742 tons


### 704 REFERENCES

1. National Appliance Energy Conservation Act, (NAECA). 1987. Public Law 100-12. 2. 1991. Improving Technology: Modeling Energy Futures for National Energy Strategy. Report to the 1991 National Energy Strategy, SR/NES/90-01. Service

3. J. Koomey. 1990. Comparative Analysis of Monetary Estimates of External Environmental Costs Associated with Combustion of Fossil Fuels. Lawrence Berkeley Laboratory Report, LBL-28313, Berkeley CA, July, 1990.
4. Ibid.

71

3C) 

### 4 REFERENCES

1. National Appliance Energy Conservation Act, (NAECA). 1987. Public Law 100-12. 2. 1991. Improving Technology: Modeling Energy FuturesforNationalEnergy Strategy. Service Report to the 1991 National Energy Strategy, SR/NES/90-01.

3. J. Koomey. 1990. ComparativeAnalysisof Monetary Estimates of ExternalEnvironmental Costs Associatedwith Combustion of FossilFuels. Lawrence BerkeleyLaboratory Report, LBL-28313, Berkeley CA, July, 1990. 4. Ibid.



### 71 

### 4 REFERENCES

1. National Appliance Energy Conservation Act, (NAECA). 1987. Public Law 100-12. 2. 1991. Improving Technology: Modeling Energy FuturesforNationalEnergy Strategy. Service Report to the 1991 National Energy Strategy, SR/NES/90-01.

3. J. Koomey. 1990. ComparativeAnalysisof Monetary Estimates of ExternalEnvironmental Costs Associatedwith Combustion of FossilFuels. Lawrence BerkeleyLaboratory Report, LBL-28313, Berkeley CA, July, 1990. 4. Ibid.



### 71