APPENDIX B. FORECASTING MODELS

B.1 LBL RESIDENTIAL ENERGY MODEL

B.1.1 Overview

The LBL Residential Energy Model (LBL-REM) provides projections of the important

characteristics of the residential appliance market. The LBL-REM utilizes a database of

significant detenninants of the current residential appliance market, as well as parameters

characterizing market decisions that will affect the energy consumption offuture appliances. This

appendix describes the LBL-REM, the types of data it uses, the calculations it perfonns, 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 fonnats, 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 tis:

(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

EE-RM-90-201 COMMENT OOA1

APPENDIX B. FORECASTING MODELS 

B.1 LBL RESIDENTIAL ENERGY MODEL 

B.1.1 Overview 

The LBL Residential Energy Model (LBL-REM) provides projections of the important 

characteristics of the residential appliance market. The LBL-REM utilizes a database of 

significant detenninants of the current residential appliance market, as well as parameters 

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 perfonns, 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 fonnats, 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 tis: 

(B.l) 

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 
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 of that fuel can be thought of as being separated into three elements-a usage

elasticity (Eu)' a technical efficiency elasticity (Ee), and an equipment/fuel choice (market share)

elasticity (Ems):

(B.2)

In fact, the LBL-REM also includes "cross-price" effects in the elasticity of market share

Ems' Le., the effect of changes in the operating expense of one fuel on the market shares of

competing fuels and technologies.

B.t.2 Housing

The housing submodel uses: (1) population projections, which are more stable than

projections 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.t.2.t 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

EE-RM-90-201 COMMENT OOA1

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 of that fuel can be thought of as being separated into three elements-a usage 

elasticity (Eu)' a technical efficiency elasticity (Ee), and an equipment/fuel choice (market share) 

elasticity (Ems): 

(B.2) 

In fact, the LBL-REM also includes "cross-price" effects in the elasticity of market 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 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.l.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.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 r TBETA ]TALFA [ TINF]

TINa = TINa + LTBETA+C 1 - TINa

(B.3)

where TIN is the thermal performance (relative heating energy normalized to 1980 existing

houses); C is the change in the initial cost of the structure due to the change in TIN; and TINF,

TALFA, and TBETA are parameters.

Table B.l shows values of the parameters TINF/TINO' TALFA, and TBETA.

Table B.1 Parameters of Thermal 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

EE-RM-90-201 COMMENT OOA1

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 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 of the parameters TINPITINO' TALPA, and TBETA. 

Table B.1 Parameters of Thermal 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 
Thermal 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.

8.1.4 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 defmes 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.

Bol.S 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 1.

Bolo6 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 1.

B-4

EE-RM-90-201 COMMENT OOA1

Thermal 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 defmes 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.S 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 1. 

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 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);

• ave,rage 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

. ...,

EE-RM-90-201 COMMENT OOA1

B.I.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.I.S 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); 

• ave,rage 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 
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 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 (to Annual 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.I.IO Input Data

The complete input listing is available on electronic medium in ASCII fonnat. The input

database includes demographic, economic, and engineering data. The input listing for products

in the current proposed rule is reproduced in Appendix B in the respective volumes for each

product.

B.2 COMMEND END-USE PLANNING SYSTEM·

The COMMEND end-use planning system provides a framework for organizing and

analyzing commercial-sector market data. COMMEND was developed by the Electric Power

Research Institute (EPRI) for use by its member utilities. The main analysis uses are load

forecasting 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 (RPI216) in 1981 to develop and transfer end-use analysis tools, market

infonnation, and data-gathering strategies to the industry. At the core of this effort is the

COMMEND framework, the COMMEND programs, and their supporting databases.

The COMMEND Framework. The COMMEND framework segments the commercial market

by building type and end use. The framework is illustrated in Figure B-1. The detailed focus

is driven naturally by emerging market issues and analysis needs.

B-6

EE-RM-90-201 COMMENT OOA1

B.l.9 Changes Since Previous Technical Support Document 

The previous Technical Support Document was concerned with dishwashers, 

clothes washers, 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 (to Annual Energy Outlook 1991) (see 

Volume A, Chapter 5). 

(5) Equipment lifetimes have been re-estimated tor water heaters, to distinguish 

among fuel types (see Volume F, Appendix B). 

(6) Initial stock and new market shares for space heating and cooling are revised, 

based upon 1987 Residential Energy Consumption Survey, industry shipments data, 

and (where applicable) revised equipment lifetimes (see Volumes H and I, 

Appendix B). 

B.l.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-l. The detailed focus 

is driven naturally by emerging market issues and analysis needs. 

B-6 
The primary use of the COMMEND framework is long-tenn forecasting. However, the

market databases that result from model implementation are vital inputs to a wide variety of

planning and analysis activities.

Figure B.l COMMEND Framework

Uses for Market Data

• Forecasting

• Demand-Side Planning

• Integrated Planning

• Marketing

BUILDINGS

Small Ottlce

Luce Ottlce

Restaurant.

Retail

Grocery

Warehouse

Schools

Colleees

. HealUl

Lod:in~

~Uscellaneous

END USES

Space Heat

Cootinc

Ventilation

Wat.er Heat

Coold.nC

Refriceration

UChUnC

Miscellaneous

FUELS

Electricit.y

Natural Gas

Fuel Oil

The COMMEND Models. The COMMEND 2.0 model was 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 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

EE-RM-90-201 COMMENT OOA1

The primary use of the COMMEND framework is long-tenn forecasting. However, the 

market databases that result from model implementation are vital inputs to a wide variety of 

planning and analysis activities. 

Figure B.l COMMEND Framework 

BUILDINGS 

Small Ottlce 

Luce OCfice 

Restaurant. 

Retail 

Grocery 

Warehouse 

Schools 

Colleges 

. Health 

Lod:in: 

~Uscellaneous 

END USES 

Space Heat 

Coollnc 

Ventilation 

Water Heat 

CookiDC 

RetrlceratioD. 

UghUng 

Miscellaneous 

Uses for Market Data 

• Forecasting 

• Demand-Side Planning 

• Integrated Planning 

• Marketing 

FUELS 

Electricity 

Natural Gas 

Fuel Oil 

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 organlZtng market 

information. The purpose of the following discussion is to describe this framework, and to 

introduce the main analysis concepts. The focus is on the description of current energy-use 

patterns. 

B-7 
Market Segments

The purpose 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

EE-RM-90-201 COMMENT OOA1

Market 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 
These 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

EE-RM-90-201 COMMENT OOA1

These 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 (HV AC) 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 
These 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 EDI 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 EDI 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 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

EE-RM-90-201 COMMENT OOA1

These 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 equipment 

decisions are being made and new technologies can penetrate the market rapidly. In slowgrowing areas, relatively few equipment decisions are made, and they are restricted to 

replacement and retrofit in the existing stock. 

Energy Intensity. The term energy intensity applies to total energy use per square foot for all 

end uses. For example: 

B-10 
A typical office building intensity of 18 kWh/foot for electricity and 45 kBtu/foot for

natural gas.

A typical restaurant intensity is 36 kWh/foot for electricity and 140 kBtu/foot for natural

gas.

The numerator in these intensity ratios is annual energy use. The denominator is total

square footage.

Trends in energy intensItIes reflect changes in fuel shares, changes in equipment

efficiencies, and changes in usage levels. At a point in time, the efficiency and usage factors are

captured by the average EDI 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 atypical square foot, the EDI is a standardized

concept.

EDI values embody an average level of service and average equipment efficiency. There

are several options for units of measurement. The standard approach is to develop electric values

in kWh/foot and fossil fuel values in kBtu/foot.

B-l1

EE-RM-90-201 COMMENT OOA1

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 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 DEC. 

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-l1 
For each end use, EDI values will differ across building types and across fuels. For

example, for space heating in offices, suppose that:

The electric EDI is 20 kEtu/foot (about 6 kWh/foot) and

The gas EDI is 50 kEtu/foot.

This difference in EDI values across fuels reflects differences in equipment efficiencies, in

the thermal features 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

EE-RM-90-201 COMMENT OOA1

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

Cool

Light

ELECTRICITY

(Total =7.6 TWll)

Space

Heat

Space

Heat

Misc.

Cool 11.Hel\t

NATURAL GAS

(Total =22.4 TBTU)

Spl\ce

Hent

"CHest

FUEL OIL

(Tolal .= 3.4 TBTU)

Misc.

B.2.2 COMMEND Forecasting Framework

For the base year, the market profiles discussed above provide a detailed depiction of

energy-use patterns at the end-use level. The purpose 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

EE-RM-90-201 COMMENT OOA1

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

Cool 

Light 

ELECTRICITY 

(Total = 7.6 TWll) 

Space 

Heat 

Misc. 

Spl\ce 

Hent 

Space 

Heat 

Cool l'f'. He 1\ t 

NATURAL GAS 

(Total = 22.4 TBTU) 

W.Heat 

FUEL OIL 

(Total .= 3.4 TBTU) 

B.2.2 COMMEND Forecasting Framework 

Misc. 

For the base year, the market profiles discussed above provide a detailed depiction of 

energy-use patterns at the end-use level. The purpose 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 
Figure B.3 COMMEND Framework for Long-Tenn Forecasting

Market Decisions

@sage /)f

...." ·"·"l 1" o

oo

End-Use·

Segment

I

I

I! I

Market

Framework

Within each market segment or model cell, COMMEND computes energy sales using the

central energy equation. This equation sums across all building vintages as follows:

(B.5)

This equation defines annual energy sales for each fuel (j) as the sum across vintages (v)

of the product of four factors. Starting from the right-hand end, these factors are:

Floor stock of vintage v (F),

The share of vintage v space using fuel f equipment (Sj'v)'

EDI for fuel f equipment in vintage v space (Ej'v)' and

Utilization rate for fuel f equipment in vintage space (Uj'v).

This definition holds in each forecast year for each fuel.

All end-use models use this type 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

EE-RM-90-201 COMMENT OOA1

Figure B.3 COMMEND Framework for Long-Term Forecasting 

Market 

Framework 

End-Use· 

Segment 

Market Decisions 

o 

o o @sage 

/ .... · .. · .. 1 )f " 1 II 

Within each market segment or model cell, COMMEND computes energy sales using the 

central energy equation. This equation sums across all building vintages as follows: 

(B.5) 

This equation defines annual energy sales for each fuel (j) as the sum across vintages (v) 

of the product of four factors. Starting from the right-hand end, these factors are: 

Floor stock of vintage v (F), 

The share of vintage v space using fuel f equipment (Sf)' 

EUI for fuel f equipment in vintage v space (Ejv)' and 

Utilization rate for fuel f equipment in vintage space (Ujv). 

This definition holds in each forecast year for each fuel. 

All end-use models use this type 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 
construction 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 8.4 COMMEND Forecast Framework

Cenlrnl

Energy

Equalion

Floor

Slock

Fuel

Sllares

Ehislicities

Energy Prices

Employmenl.

Popululion •...

Energy Prices

Technology Dala

1---1 Discounl Rales _______

Elasticities

Floor Stock

The floor stock component of COMMEND is used to organize infonnation about the

existing floor stock and to forecast future stock levels. The floor stock outlook embodies the

utility planning assumptions about growth in economic activity for the commercial sector. This

outlook will be tightly linked to population growth, employment growth, and regional income.

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

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

A historicalfloor stock 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

EE-RM-90-201 COMMENT OOA1

construction 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 

Employmenl. 

Popululion •... 

Floor 

Slock 

Fuel 

Energy Prices 

Technology Dala 

Discount Rales 

~ Sllares 

1---1 _______ 

Elasticities ~ 

Energy Prices 

Ehislicities 

Level 

Floor Stock 

CenlrOoI 

Energy 

Equation 

The floor stock component of COMMEND is used to organize infonnation about the 

existing floor stock and to forecast future stock levels. The floor stock outlook embodies the 

utility planning assumptions about growth in economic activity for the commercial sector. This 

outlook will be tightly linked to population growth, employment growth, and regional income. 

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

Base year floor stock (e.g., 1987). 

A historical floor stock 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 
A flexible forecasting framework is provided. Two general approaches can be used.

In the flow 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, 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 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

EE-RM-90-201 COMMENT OOA1

A flexible forecasting framework is provided. Two general approaches can be used. 

In the flow 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, 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 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 
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 LCe

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 EDI 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

EE-RM-90-201 COMMENT OOA1

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 LCe 

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 EDI 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 
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

appliaIlcce replacement. They reflect the presence of barriers to fuel conversion when

equipment is replaced. Eur inertial factors apply to efficiency changes at the time of

equipment replacement

Office Equipment and Miscellaneous Equipment EUI Growth. These parameters 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

EE-RM-90-201 COMMENT OOA1

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 HV AC 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 

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 

Office Equipment and Miscellaneous Equipment EUI Growth. These parameters 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 ofincentive 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

EE-RM-90-201 COMMENT OOA1

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 
Figure 8.S Forecast of Electricity Sales by End Use (gWh) (l% of U.S. Total)

200

199

2500

GWH

3500,----------------------....

3000

2000

1500

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

B-20

EE-RM-90-201 COMMENT OOA1

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

GWH 

3500 r---------------------------------------------~ 200 

3000 199 

2500 

, 

2000 

1500 

.:: :~ ~ 

':::~t~::;~ : __ ~;~I~ __ ;!~~II·~ __ ~~~_W~~~L_~.~. L_~~~ S.He:a~ Ven~ Cool lI".Heat Coole Retrig Li,h~ Mille 

B-20 
APPENDIX C. LBL MANUFACTURER IMPACT MODEL

C.I OVERVIEW OF MANUFACTURER IMPACT MODEL

C.I.I Purpose

The Lawrence Berkeley Laboratory Manufacturer Impact Model (LBL-MIM) collects into

one spreadsheet all the calculations necessary to determine the impact 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.I.2 The Economic Approach

The impact of efficiency standards on manufacturers depend on the interaction of three

factors: (1) the costs imposed by the change in standards, (2) the price elasticity of a single

typical finn, and (3) the industry's price and operating cost elasticities of demand. The

LBL-MIM integrates and analyzes these four factors as they apply to a single typical finn.

A change in standards will generally create three types of costs 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 of new products. And __.'.

third, new products will require re-tooling and new equipment (capital) costs. The incorporation

of yeaS 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 finns 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-l

EE-RM-90-201 COMMENT OOA1

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 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.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 of costs 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 of new products. And __ ._ 

third, new products will require re-tooling and new equipment (capital) costs. The incorporation 

of YCGS 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 fmn' 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-I 
assumption, all firms change the prices and operating costs of their appliances by the same

amount (Le., the industry acts in unison). When this happens, sales for each fIrm are determined

by the industry elasticities.

C.I.3 Measures 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.IA Methodology

How stanikzrds are implemented in the model

Generally, a change in standards affects the model in three distinct ways. Standards

require additional investment, raise production costs, and affect revenue.

.I

\. The most obvious investment induced by standards is the purchase of new plants and

equipment. This cost is fIrst evaluated from engineering data, and then 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 i~ made as the old inventory is replaced with

more expensive new units. The model assumes that previous inventory ratios are maintained..-.-

A third form 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

EE-RM-90-201 COMMENT OOA1

assumption, all finns change the prices and operating costs of their appliances by the same 

amount (Le., the industry acts in unison). When this happens, sales for each fInn are determined 

by the industry elasticities. 

C.l.3 Measures 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.l.4 Methodology 

How stanilllrds are implemented in the model 

Generally, a change in standards affects the model in three distinct ways. Standards 

require additional investment, raise production costs, and affect revenue. 

f 

\. The most obvious investment induced by standards is the purchase of new plants and 

equipment. This cost is fIrst evaluated from engineering data, 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 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 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 detennined by computing the 

markup over long-run marginal costs, and then using the markup to detennine an optimal price. 

Shipments (demand) is detennined by price and operating cost elasticities, coupled with the 

changes in price and operating costs resulting from the standards. ' , 

C-2 
R

/

/

/.

/

/

/

/

/

/

Figure C.I

Q*

Output (Q)

Profit Maximization and Fixed Costs

C-3

EE-RM-90-201 COMMENT OOA1

/ 

/ 

R 

Q* 

Output (Q) 

Figure C.I Profit Maximization and Fixed Costs 

C-3 
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 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 finn entry into or exit·from the industry.

Use ofprototypical finns

Because the engineering and financial data for most finns are proprietary, LBL-MIM

models a prototypical finn. In many cases, this firm must be thought of as representing an

autonomous division of a larger finn. A prototypical finn is a hypothetical finn that is

representative of the industry. Prototypical finns are defined by parameters that are important

for detennining the impacts of standards and are consistent with data for the portion of the

industry they represent. Important parameters used in the model include the cost structure of the

finns, profitability ratios, relative costs of complying with the new standard, and marketing

strategies.

Product segmentation

The LBL-MIM assumes that the product market is segmented. Thus, 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.! Economic Theory and Assumptions

The basic question that LBL-MIM must answer is: how much of the cost increases

caused by standards can be passed through to consumers? If less than the nonnal pass-through

occurs, profits will suffer, while an above normal pass-through will increase profits. To answer

this question precisely, it is necessary to compute a markup; but, before that, a more intuitive

understanding 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

EE-RM-90-201 COMMENT OOA1

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 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 finn entry into or exit· from the industry. 

Use of prototypical firms 

Because the engineering and financial data for most finns are proprietary, LBL-MJM 

models a prototypical finn. In many cases, this finn must be thought of as representing an 

autonomous division of a larger finn. A prototypical finn is a hypothetical finn that is 

representative of the industry. Prototypical finns are defined by parameters that are important 

for detennining the impacts of standards and are consistent with data for the portion of the 

industry they represent. Important parameters used in the model include the cost structure of the 

finns, profitability ratios, relative costs of complying with the new standard, and marketing 

strategies. 

Product segmentation 

The LBL-MIM assumes that the product market is segmented. Thus, 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.! Economic Theory and Assumptions 

The basic question that LBL-MIM must answer is: how much of the cost increases 

caused by standards can be passed through to consumers? If less than the nonnal pass-through 

occurs, profits will suffer, while an above nonnal pass-through will increase profits. To answer 

this question precisely, it is necessary to compute a markup; but, before that, a more intuitive 

understanding 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 
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 priceP

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 finn it can have no effect on price), which would

certainly drive prices down. The reverse argument holds with price below marginal cost, so, in

either case, price will inevitably change to equal marginal cost. The second fact is derived in

the next section. The third point is most easily understood by examining Figure C.I, which

shows a revenue curve and two total-cost curves that differ only in their fixed components.

How will a firm respond to an increase in fixed cost (represented by the upward shift

displayed in the 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 finn

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

13oywo interpretations may legitimately be given to this mathematical result First, one may say that given the

demand function, marginal cost determines price and fixed costs are not passed on. Alternatively, one may argue

that fixed costs indirectly determine the elasticity 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 foUowing: iffixed costs are not covered, then economic profits will be negative and some rums will leave

the industry; this will reduce competition and thus reduce each rum's elasticity of demand. Conversely, iffixed costs.

are more than covered, positive economic profits will induce entry and elasticity and markup over marginal cost will

be reduced.

C-5

EE-RM-90-201 COMMENT OOA1

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.I, which 

shows a revenue curve and two total-cost curves that differ only in their fixed components. 

How will a firm respond to an increase in fixed cost (represented by the upward shift 

displayed in the 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 finns in the industry 

remains fixed. In the long run, capacity may be adjusted, but the number of firms remains 

constant. In the very long run, firms may enter or leave the industry, with the result that an 

individual firm's market power, as measured by its price elasticity of demand, may change. 

Thus, in the very long run, markup may change. 

In the long run, price is a markup over long-run marginal cost, while in the short run, 

price is a markup over short-run marginal cost. Since in the long run, but not in the short run, 

capital is considered a marginal cost, how can the two pricing rules be reconciled? If a finn 

finds 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 

13<ywo interpretations may legitimately be given to this mathematical result First, one may say that given the 

demand function, marginal cost detennines price and fixed costs are not passed on. Alternatively, one may argue 

that fixed costs indirectly detennine the elasticity 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 lInn'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 
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. .

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 (Le., unit cost decreases as quantity decreases) in the production of the

appliances analyzed. Consequently, each producing finn must not face an infinite elasticity of

demand because a small increase in price charged will 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 finns. 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 finns 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 =btO + btlQ

C-6

(C.I)

EE-RM-90-201 COMMENT OOA1

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. . 

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 0/ 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 = btO + btlQ (C.I) 

C-6 
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 of capital is included in economic cost, a f"mn 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-(R-C)=O

dQ

(C2)

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 markl;t equilibrium

takes the value e. Elasticity of demand is the percentage change in demand caused by a 1%

change in price P, and consequently is negative. Mathematically, this is expressed as follows:

dQ

dP

p

Q

=e (C.3)

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

mathematical manipulations. From the two supply assumptions, and denoting dR I dQ by R' and

dP I dQ by p', we have:

d

- [ R - btO - btlQ ] =0

dQ

(C.4)

14 If we assume that cost is an arbitrary function of Q, C(Q.R), our fmal 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

EE-RM-90-201 COMMENT OOA1

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 of capital is included in economic cost, a fInn with an ROE equal to the 

cost of capital will find its economic profit to be zero. If, however, a flrm can earn a higher 

ROE, it is assumed that it will, in which case R-C will be positive. The firm can maximize 

profit by picking either a quantity to produce (Q) or, equivalently, a price to sell it at, but it 

cannot choose both. The following computations assume that profit is maximized with respect 

to Q. This gives the foilowing first-order condition: 

~(R-C)=O dQ 

(C.2) 

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 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 by R' and 

dP I dQ by P', we have: 

d - [ R - btO - btlQ ] = 0 

dQ 

(C.4) 

14 If we assume that cost is an arbitrary function of Q, C(Q.R), our fmal 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 
By definition:

1 _A R -IJI

R=P·Q

R 1- P +P

dQ P --dP Q

R/=~+p e

p=_e_btl

l+e

(C.5)

(C.6)

(C.?)

(C.8)

(C.9)

(C.lO)

(C.II)

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

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

e

p=-

1+e

(C.12)

EE-RM-90-201 COMMENT OOA1

By definition: 

R=P'Q 

R '- P +P 

dQ P --dP Q 

~ R' =P' oQ+P 

R' =P +P 

e 

p=_e_btl 

l+e 

(C.S) 

(C.6) 

(C.7) 

(C.8) 

(C.9) 

(C.lO) 

(C. II) 

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

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

e 

p=-

1 +e 

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

e = -1, then J.l is undefined, since the firm makes more the less it sells.

A small rearrangement 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 J.l and fixed

costs are not passed on at all.

It is now easy to derive another representation of J.l which is used in the calibration stage

of the LBL-MIM to estimate J.l from Eq. C.l:

Using Eq. C.5:

so

and

Finally,

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

R =P-Q =pbtlQ

(R-C) =pbtlQ-btO-bt1Q

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

=1+ (R-C)+btO

p bt1Q

C-9

(C.14)

(C.15)

(C. 16)

(C. 17)

(C.18)

EE-RM-90-201 COMMENT OOA1

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 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 J.l and fixed 

costs are not passed on at all. 

It is now easy to derive another representation of J.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. 14) 

Using Eq. C.S: 

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

so 

(R -C) =pbtlQ-btO-btlQ (C. 16) 

and 

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

Finally, 

= 1 + (R -C)+btO 

p btlQ (C.18) 

C-9 
This 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 btlQ is replaced by C-btO, and the relation, R-C =EP, where EP is economic

profit, is used twice. The new form of the equation is:

1 EP+btO

)1= +----

R-EP-btO

(C.19)

Markup is detennined by revenue, economic profit, and fixed costs. Generally, it has been found

that fixed costs are large relative to economic profit, so the numerator is controlled largely by

the fixed cost. Thus, markup is primarily determined by the size of fixed cost relative to

revenue.

Oligopsony Power 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.IS 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 ,"-

firms 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 iftheoretical 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

15nte 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 on

Energy Efficiency Standards for Clothes Dishwashers, Clothes Washers and Clothes Dryers," CE-RM88-101, October to, 1989.

C-lO

EE-RM-90-201 COMMENT OOA1

This 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 btlQ is replaced by C-btO, and the relation, R-C = EP, where EP is economic 

profit, is used twice. The new form of the equation is: 

1 EP+btO 

).1= +----

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. IS 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 inc1udes '--

firms 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 

15nte 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 on 

Energy Efficiency Standards for Oothes Dishwashers, Clothes Washers and Oothes Dryers," CE-RM88-101, October 10, 1989. 

C-lO 
demand it faces, we can find the single-fInn demand elasticity that corresponds to the observed

markup. This same elasticity is also implied by the industry elasticity and a Cournot oligopoly

with some hypothetical number 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 Coumot oligopoly facing a competitive, not oligopolistic. demand for its

product. but we use the "effective" number of firms rather than the actual number of fInns 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 finns is computed from markup and industry demand elasticity. A

well-known result for a Cournot oligopoly is:

where:

e=NF*e·1

the firm elasticity of demand,

the industry elasticity of demand, and

number of firms.

(C.20)

We can solve for NF if the two el~sticities are known. We have an estimate of ei' and can

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

e=~1-p

(C.21)

·Thus. we can find the markup, pt, in Eq. C.21 from Eq. CA. if we know marginal cost c.

Marginal cost can be found from the firm's return on equity, its fixed costs, and the following

price identity: rc/Q=P-c+FC/Q, where FC is fixed cost. This is just a restatement of the

definition of economic profit, which is revenue minus costs. Noting that revenue. R. equals P *Q.

we have:

C-ll

EE-RM-90-201 COMMENT OOA1

demand it faces, 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 of flrms. 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 Coumot oligopoly facing a competitive, not oligopolistic, demand for its 

product, but we use the "effective" number of firms rather than the actual number of flnns 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 proflt and flxed costs, and how 

the "effective" number of flnns 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 of demand, 

the industry elasticity of demand, and 

number of flrms. 

(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 of economic profit, which is revenue minus costs. Noting that revenue, R, equals P *Q, 

we have: 

C-ll 
p p

p=-------

c p_ FC_"::'

Q Q

R

R-FC-TI (C.22)

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

C.9 and C.lO for e, we can now fmd the number of firms, NF:

NP- P

ei '(l-p)

(C.23)

We find that given two elasticities estimated from actual data, NF is higher than the actual

number of finns observed.

To summarize, we frrst look at a typical firm's profit and fixed costs. In order to recover

fixed costs, a finn must mark up over variable costs, and if it can mark up enough, it will make

above "normal" ROE. From these considerations, we find its markup. The markup indicates a

finn's industry demand elasticity and the effective number of frrms 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 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, ff is 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

EE-RM-90-201 COMMENT OOA1

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 of firms, NF: 

NF- P 

ei '(1-p) 

(C.23) 

We find that given two elasticities estimated from actual data, NF is higher than the actual 

number of finns observed. 

To summarize, we first look at a typical finn's profit and fixed costs. In order to recover 

fixed costs, a finn must mark up over variable costs, and if it can mark up enough, it will make 

above "nonnal" ROE. From these considerations, we find its markUp. The markup indicates a 

finn's industry demand elasticity and the effective number of finns 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.S but from a modified price elasticity. 

The new price elasticity is obtained as follows. First, the original price and operating cost ----

elasticities are used to detennine a life-cycle cost and an elasticity with respect to that cost. A 

demand curve with constant life-cycle cost elasticity is then computed. It takes this fonn: 

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

where P is price, F is operating (fuel) cost, ff is 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 
B·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 btO, 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. CA, 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

EE-RM-90-201 COMMENT OOA1

e = 

B·P 

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 btO, 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. CA, 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 
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 fInn 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 finn is assumed to maximize revenue minus economic cost

(R-EC).

Debt-to-equity ratio. The debt-to-equity ratio is assumed to remain constant while the

fInn finances any investment necessary to meet standards. It is generally assumed that finns

have some DIE ratio that they consider optimal, and there is no reason for standards to change

this fIgure.

Amortization 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

EE-RM-90-201 COMMENT OOA1

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 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 

ftrm ftnances any investment necessary to meet standards. It is generally assumed that fums 

have some DIE ratio that they consider optimal, and there is no reason for standards to change 

this ftgure. 

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 ftxed 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 
MIM Modules and Primary Routes of Data Flow

Engineering

Inputs 1-----,

4

Standards

Cost One-Time

Conversion Financial )0 Cost ArnorLevels 7 tization 5 12 8

1 I'

Costs, Sales

Revenues

6

.,

Long-Run

Model

9

1r

•..

, ,~

Accounting Control Short-Run Charts

Panel Model

3 1 10 11

1

Monte

Carlo

2

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

C-15

EE-RM-90-201 COMMENT OOA1

MIM Modules and Primary Routes of Data Flow 

Engineering 

Inputs -----, 

4 

Standards ~ 

Levels 

5 

1 

Costs, Sales 

Revenues 

6 

, 

Accounting 

3 

Cost 

Conversion 

12 

., 

Long-Run 

Model 

1 r 

Control 

Panel 

1 

Monte 

Carlo 

9 

1 

2 

Financial 

7 

Short-Run 

Model 

10 

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

C-15 

One-Time 

)0 Cost Arnortization 8 

I' 

... . 

~ 

Charts 

11 
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

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

EE-RM-90-201 COMMENT OOA1

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 

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 detennined 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 fInn. 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 
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 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.t Control Panel

The control panel displays the nine most important input variables of the model and

allows the user to easily adjust them. These variables are described below. The Input Values

are the best available estimates of the relevant variables.

The control panel has one control for each input variable. The controls, found in the

column labeled entrl, allow the Input Values to be modified to become the Model Values before

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

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

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

a Model Value. Because the error distributions associated with the Input Values are not normal,

they are not changed by exactly one standard deviation when the control is set to plus or minus' ---

I. A control value of ± 1 actually changes an Input Value by an amount sufficient to make it

just as unlikely as a point that is one standard deviation from the mean using a normal

distribution. Consider an Input Value that is known to be strictly positive and have a mean of

5 and a 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.lmean) 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

EE-RM-90-201 COMMENT OOA1

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 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.t Control Panel 

The control panel displays the nine most important input variables of the model and 

allows the user to easily adjust them. These variables are described below. The Input Values 

are the best available estimates of the relevant variables. 

The control panel has one control for each input variable. The controls, found in the 

column labeled entrl, allow the Input Values to be modified to become the Model Values before 

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

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

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

a Model Value. Because the error distributions associated with the Input Values are not normal, 

they are not changed by exactly one standard deviation when the control is set to plus or minus' ---- . 

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.lmean) 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 
second 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 (Le., 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 frrm's

financial position. The income statement and financial ratios are presented for the 1989

calibration case, a 1996 base case that includes the impact 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

EE-RM-90-201 COMMENT OOA1

second 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 (Le., 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 (BCC), 

4. Economic profit: the industry's ROE minus normal ROE (BP), 

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 frrm' s 

financial position. The income statement and financial ratios are presented for the 1989 

calibration case, a 1996 base case that includes the impact 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 
Table C.I Accounting Module

Item Fonnula

Earnings Before Interest and Taxes

Total Expenses

Interest

Taxes

Net Income

Gross Margin

Return on Sales

Return on Assets (ROA)

Return on Equity (ROE)

EBIT =Revenue - Total Expenses

Does not include interest 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 IncomelRevenue

(EBIT - Taxes - (Tax rate mb Interest))/Assets

This calculation eliminates interest tax shield

Net IncomelEquity

A second table at the bottom of the Accounting Summary page lists financial data for the

fmn 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" (i-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 uve (Unit Variable Cost): Engineering input data. Each

"increment" is measured relative to the previous engineering design. The numbers refer

to 1989 dollars per unit produced and are displayed only to the nearest whole dollar.

2. Additional uve above Level 0 cost (VCS.E): "Additional" UVC is measured relative to

the 1989 calibration case, which is shown as level O. In other words, this table is a

cumulative version of the above "incremental" table.

C-19

EE-RM-90-201 COMMENT OOA1

Table C.I Accounting Module 

Item Fonnula 

Earnings Before Interest and Taxes 

Total Expenses 

Interest 

Taxes 

Net Income 

Gross Margin 

Return on Sales 

Return on Assets (ROA) 

Return on Equity (ROE) 

EBIT = Revenue - Total Expenses 

Does not include interest 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" (i-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 uve (Unit Variable Cost): Engineering input data. Each 

"increment" is measured relative to the previous engineering design. The numbers refer 

to 1989 dollars per unit produced and are displayed only to the nearest whole dollar. 

2. Additional uve above Level 0 cost (VCS.E): "Additional" DYC is measured relative to 

the 1989 calibration case, which is shown as level O. In other words, this table is a 

cumulative version of the above "incremental" table. 

C-19 
3. 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 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.2.3.5 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

EE-RM-90-201 COMMENT OOA1

3. 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 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 

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 
The 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 DVC (dVeB), the weighted

average change in price, the new weighted average price (PB), the firm's total revenues

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

shipments (QB). PB, QB and RB are actually computed in the Long-Run module.

The first stage in this module is setting up the 1990 calibration case, which uses known

information about industry shipments, product class market shares, prototypical firm market

shares, markups for each 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

EE-RM-90-201 COMMENT OOA1

The 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 (dVeB), the weighted 

average change in price, the new weighted average price (P B), the firm's total revenues 

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

shipments (QB). P B, 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 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 
Table C.2 Equations Used in the Costs, Sales, and Revenue Module

Item

Size

Industry Shipments

Relative Shipments (Q%)

Shipments (Q.I)

Price (p.I)

Revenue (R.I)

Markup (M.I)

Unit Variable Cost (UVC.I)

Weighted UVC

Weighted Change in UVC

Weighted Change in Price

Weighted Change in Price

Mark.B

New Price (pi.B)

Operating Cost Ratio

New Shipments (Qi.B)

New Revenue (Ri.B)

Weighted Operating Cost Ratio

Calibmtion Case Formula or Definition

Prototypical firm market share

Annual industry shipments for a product class

Product class market share

(product class units sold/total units sold)

Product class shipments for a protypical firm

Retail price/retail market

Price mb Shipments

1 + (P.I - BB)/AA

P.I/M.I

UVC mb Relative Shipments (Q%)

Change in UVC mb Relative shipments (Q%)

Change in Price mb Relative shipments (Q%)

Wghtd dp=q%*M.I mb Change in UVC

(p.B-P)/IWghtd dP

P.I+Mark.B*M. 1mb Change in UVC

1996 VEC/1990 VEC

Opeating Cost Ratio mb Relative Shipments (Q%)

Q%*Q.B

Pi.B * Qi.B

The next stage in the Cost, Sales, and Revenue Module calculates the changes necessary

to produce the 1996 base case (1990 NAECA standards) and the 1996 new standards case. The

calibration case is used as a base, and the changes in unit variable cost and unit energy

consumption are fed in from the REM panel. From this input, new unit variable costs and prices

are calculated for each 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

EE-RM-90-201 COMMENT OOA1

Table C.2 Equations Used in the Costs, Sales, and Revenue Module 

Item 

Size 

Industry Shipments 

Relative Shipments (Q%) 

Shipments (Q.l) 

Price (p.I) 

Revenue (R.I) 

Markup (M.I) 

Unit Variable Cost (UVC.l) 

Weighted UVC 

Weighted Change in UVC 

Weighted Change in Price 

Weighted Change in Price 

Mark.B 

New Price (pi.B) 

Operating 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.I - BB)/AA 

P. l/M.1 

UVC mb Relative Shipments (Q%) 

Change in UVC mb Relative shipments (Q%) 

Change in Price mb Relative shipments (Q%) 

Wghtd dp=q%*M.l mb Change in UVC 

(p.B-P)/IWghld dP 

P.l+Mark.B*M. 1mb Change in UVC 

1996 VEC/1990 VEC 

Opeating 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 
The 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

Depreciation

(DR:R =Depreciable assets as a

percentage of revenues

Pre-Tax Equity Cost of Capital (pECC)

Weighted Average Cost of Capital (WACC)

Mter-Tax WACC

Return on Equity (ROE)

(BCC =Equity Cost of Capital)

(This ROE figure is the expected ROE

for a prototypical firm.)

Formula

Average depreciation rate x DR:R

Equity Cost of Capital/(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 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.I.

C-23

EE-RM-90-201 COMMENT OOA1

The 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 

Depreciation 

(DR:R = Depreciable assets as a 

percentage of revenues 

Pre-Tax Equity Cost of Capital (PECC) 

Weighted Average Cost of Capital (W ACC) 

Mter-Tax WACC 

Return on Equity (ROE) 

(BCC = Equity Cost of Capital) 

(This ROE figure is the expected ROE 

for a prototypical firm.) 

Formula 

Average depreciation rate x DR:R 

Equity Cost of Capital/(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 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.I. 

C-23 
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.2.3.8 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.I? 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

EE-RM-90-201 COMMENT OOA1

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 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.I7 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 
meeting 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 CA.

Table C.4 One-Time Cost Amortization Module

Item

DR [ER]

T

ECC

PECC

WACC

ATWACC

ATR

CCi

EEi

WCCi

WEEi

LTC

LTE

EMRT

EMRL

BN

RTL

PVD

OCS

OCTL

TBS

PVCC

APVCC

CCLF

EELF

AFB

APC

B:T

AFF

Definition

Tax rate (34%)

Equity cost of capital

Pre-tax ECC

Weighted average CC

After-tax WACC

Continuous ATWACC

Capital cost i years before standards

Engineering expense i years before standards

(see I-X Cost module)

(see I-X Cost module)

(see I-X Cost module)

(see I-X Cost module)

"Tax benefit rate"

Remaining tax life

Present value 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

(see One-Time Cost module)

(see One-Time Cost module)

(see One-Time Cost module)

Average asset factor

C-25

Formula

Debt [Equity] / (Debt + Equity)

ECC/(l - T)

DR*/ + ER * PECC

WACC* (l -1)

In(l + ATWACe)

CCi * exp (ATR * i)

EEl * exp (ATR *i)

Sum of WCCi over 4 years

Sum of WEEi over 4 years

exp (-ATR*TL)

exp (-ATR*L)

T/I'L

max (TL - AGE, 0)

- BN * (I -EMR1)/ATR

- (I - %NC) * exp (-ATR * (L - AGE»

(l - %NC) * BN * (1 - EMR1)/ATR

-RTL * BN * (I - %NC)

1+ PVD + OCS + OCTL +TBS

PVCC * LTC

ATR * APVCCC/(1 - EMRL)

LTE * (I -1) * ATR/(l - EMRL)

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

1- EMRL

I - ATR * TL * EMRT/(I - EMRT)

1- EMRL

(I - EMRT)/(ATR/BN + I - EMRT)

B:T * AFB + (l - B:T) * APC

EE-RM-90-201 COMMENT OOA1

meeting 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 

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 of capital 

Pre-tax ECC 

Weighted average CC 

After-tax WACC 

Continuous A TW 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 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 

(see One-Time Cost module) 

(see One-Time Cost module) 

(see One-Time Cost module) 

Average asset factor 

C-25 

Formula 

Debt [Equity] / (Debt + Equity) 

ECC/(l - T) 

DR*I + ER * PECC 

WACC* (1 -1) 

In(1 + ATW ACC) 

CCi * exp (ATR * i) 

EEl * exp (ATR * i) 

Sum of WCCi over 4 years 

Sum of WEEi over 4 years 

exp (-ATR*TL) 

exp (-ATR*L) 

T/TL 

max (TL - AGE, 0) 

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

- (1 - %NC) * exp (-ATR * (L - AGE» 

(1 - %NC) * BN * (1 - EMR1)/ATR 

-RTL * BN * (1 - %NC) 

1 + PVD + OCS + OClL +TBS 

PVCC * LTC 

ATR * APVCCC/(1 - EMRL) 

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

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

1 - EMRL 

1 - ATR * lL * EMRT/(l - EMRT) 

1 - EMRL 

(1 - EMRT)/(ATR/BN + 1 - EMRT) 

B:T * AFB + (1 - B:T) * APC 
Table C.4 should be read in conjunction with the One-Time Cpst Amortization module.

Note that the calculations are all done with continuous interest and discount rates, which makes

it necessary to convert A1WACC to a continuous equivalent cost of capital (ATR) by taking the

natural log of (l + ATWACC). The continuous rate is equivalent to, and for small rates it is

numerically close to, the annual rate. The continuous time formula for the present value of $Y

received t years in the future is Y 'exp( -rt), where r is the discount rate and exp denotes

exponentiation. For a constant stream of income at $Y per year for L years, the present value

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

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

constructed by summing the following five present values: (1) The initial cost, which is

normalized to 1 in 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

1 -r(T-t)

DF(t) =_-e_-=_

l-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

EE-RM-90-201 COMMENT OOA1

Table C.4 should be read in conjunction with the One-Time Cpst Amortization module. 

Note that the calculations are all done with continuous interest and discount rates, which makes 

it necessary to convert A1WACC to a continuous equivalent cost 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 - (J-exp( -rL)Yr. 

The most important output of this module is the amortized cost factor eClF', which is 

constructed by summing the following five present values: (1) The initial cost, which is 

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 C.4. 

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 

1 -r(T-t) 

DF(t) =_-e_-=_ 

l-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 
PV(DF(t)) =PV(AAV)

where PV() is the L-year present value operator, and AAV is the average asset value

corresponding to DF(t). This equation can be solved for AAV, to find the following:

-rT

AAV= 1 [I_rT e ]

1-e -rL I-e -rT

This would be the end of the problem-AAV would be the sought-for average asset

factor-except for the fact that the 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 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 R:T gives the fraction of the total stream made up of tax benefits. The

factors APR and AFC 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 R:T as the weight for

AFR.

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.I.

C-27

EE-RM-90-201 COMMENT OOA1

PV(DF(t)) =PV(AAV) 

where PV() is the L-year present value operator, and AA V is the average asset value 

corresponding to DF(t). This equation can be solved for AA V, 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-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 R:T gives the fraction of the total stream made up of tax benefits. The 

factors APR and AFC 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 R:T as the weight for 

AFR. 

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 
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 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), EI for economic income, and,ul for markup above marginal cost,,u - 1 .

Calibration. The calibration stage estimates a typical finn's markup over unit variable

costs using the following basic economic equation:

Markup = (Fixed Cost + Economic Profit)

VaI-iable Cost

(In this equation, fixed and variable cost are components 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 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 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, Le., those that are tax-deductible. FCA is also used to

determine the proportion of fixed (total) costs in revenues. Interest loss resulting from cash

holdings and a part of general and administrative expenses given by RGA account for

revenue-related costs. All other costs are marginal, and their amount is deduced from the fInn'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 =(1-T) ·TC.F+A.F .ER .ECC

where T is the tax rate, ER is the ratio of equity 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

EE-RM-90-201 COMMENT OOA1

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 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), 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 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 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 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 fInn'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 = (1-T) ·TC.F +A.F . ER . ECC 

where T is the tax rate, ER is the ratio of equity 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 
finish 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 =l-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 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 of DVC (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 IX =IC =1 ·DR -A-C:R'R-(I-ICash)

Pre-interest Cost =PIC =TC-IC

C-29

EE-RM-90-201 COMMENT OOA1

finish 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 tqtal 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, LA.B. This apparent discrepancy is necessary 

because the costs associated with the new assets are computed separately in the One-Time Cost 

Amortization module and added to the economic cost. 

Total costs are the same as during calibration, except that new variable costs are added 

into TC.Q. Economic costs are computed from A and TC as before, except for the addition of 

amortized capital costs (LCC), which are divided between fixed and variable according to the 

input parameter F lX, 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 of DVC (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 IX =IC =1 ·DR 'A -C:R'R'(1 -ICash) 

Pre-interest Cost =PIC =TC-IC 

C-29 
Pre-tax Cost = PTC =TC

Taxes = TAX =(R-TC) °T

Equity = EQ=ERoA

Economic Income =EJ =NI - EQ °ECC and

Net Income =NI =R-TC-TAX

Return on Equity = ROE =ECC+ EI

EQ

For the base case the above formulas hold with the exception of the 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 oP.B

UVC.B =UVC+DVC.B

VCGS.B =UVC.B oQ.B

IX depreciation =XID.B =CC.BjL

IX interest=XII.B=LA.B ·DR or °LTC

IX equity cost=XlI.B=LA.B °ER °ECC

IX tax benefit=XIT.B =LCC.B +LEE.B-XID.B-XlI.B-XIE.B

IC.B=I °DR o(A.B -LA.B)-C:R °R.B o(I-ICASH)

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

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

C-30

EE-RM-90-201 COMMENT OOA1

Pre -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 

Return on Equity = ROE =ECC+ EI 

EQ 

For the base case the above fonnulas 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 =(pl +1) -EC.BQ 

R.B=Q.B -P.B 

UVC.B =UVC+DVC.B 

VCGS.B =UVC.B -Q.B 

IX depreciation =XID.B =CC.B/L 

IX interest=XlI.B=LA.B -DR -I -LTC 

IX equity cost=XlI.B=LA.B -ER -ECC 

IX tax benefit=XI T.B =LCC.B +LEE.B -XID.B -XlI.B -XIE.B 

IC.B=I -DR -(A.B -LA.B)-C:R -R.B -(l-ICASH) 

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

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

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

ROE.B =NI.B/EQ.B

In the fonnula for Q.B above, LCC stands for life-cycle cost. This fonnula is equivalent

to the more general one used in the model when the shape parameter (ro) for the demand

function is set to one. Setting ro to one simply implements the assumption 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, oui' estimates will converge to

a stable answer. At this point, the price correction exactly agrees with the estimated fall in

demand, and the estimated demand agrees with the price. At no other values of price and

quantity is this true.

Once price and quantity are known, all other variables are easily computed using

procedures that have already been described.

C.2.4 Model Inputs

C.2.4.1 Input Generating Assumptions

Many 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

EE-RM-90-201 COMMENT OOA1

NI=R -TC-TAX -LCC.B-LEE.B 

ROE.B =NI.BIEQ.B 

In the fonnula for Q.B above, LCC stands for life-cycle cost. This fonnula is equivalent 

to the more general one used in the model when the shape parameter (ro) for the demand 

function is set to one. Setting ro to one simply implements the assumption 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 
Elasticities 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.

Financial data. 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 fInns 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 finn 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 detennining 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 detennining 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 tenns (Le., with inflation subtracted). It is an after-tax rate, which means that

it is the rate of return the company must earn for its shareholders after its taxes have been

accounted for. Data values come from calculations using standard fInance equations and public

fInancial data.

C-32

EE-RM-90-201 COMMENT OOA1

Elasticities 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. 

Financial data. 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 (Le., with inflation subtracted). It is an after-tax rate, which means that 

it is the rate of return the company must earn for its shareholders after its taxes have been 

accounted for. Data values come from calculations using standard fInance equations and public 

fInancial data. 

C-32 
Economic profit. Economic profit is the profit earned by a finn 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-runfixed part ofone-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

EE-RM-90-201 COMMENT OOA1

Economic profit. Economic profit is the profit earned by a finn 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-runfixed 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 
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 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 ofretooling, 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 flrms in the analysis is assigned a

market share appropriate to that segment of the industry. For example, flve large water heater

manufacturers may each be assigned a 20% market share. The manufacturer market share,

combined with the total industry shipments, gives a firm's annual shipments. This figure,

combined with the product class market shares, gives a prototypical firm's annual shipments for

each product class.

C-34

EE-RM-90-201 COMMENT OOA1

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 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 
Markups. The markup is the figure used by the manufacturer to determine the finn'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.

Initial prices. 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%.N).

C.2.4.5 Financial Inputs

Rates offinancial costs:

After-tax equity cost ofcapital. 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 tenns (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 lost on cash. Companies hold a portion of their current assets in the fonn of cash

in bank accounts, marketable securities, and other liquid holdings, all lumped under the tenn

"cash." 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 fonn 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

finn in the industry. It is obtained by assuming straight-line depreciation and an average tax life

of eight years for the f1IlJl's fixed assets. It is then revised by analyzing actual depreciation data

from the industry under consideration.

Tax rate. This rate is the average tax rate faced by an industry in 1996. It is based on

past data and recent revisions to the tax law.

C-35

EE-RM-90-201 COMMENT OOA1

Markups. The markup is the figure used by the manufacturer to determine the finn'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. 

Initial prices. 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%.N). 

C.2.4.S 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 tenns (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 lost on cash. Companies hold a portion of their current assets in the fonn of cash 

in bank accounts, marketable securities, and other liquid holdings, all lumped under the tenn 

"cash." 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 fonn 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 

finn in the industry. It is obtained by assuming straight-line depreciation and an average tax life 

of eight years for the finn'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 ofrevenue:

Cash. Companies hold a portion of their current assets in the fonn of cash in bank

accounts, marketable securities, and other liquid holdings, all lumped under the term "cash." In

the model, this input is expressed as a percent 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 ofall costs and depreciable assets. This input is explained in the Control Panel

section of C.2.3.1.

Fixed part ofone-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

EE-RM-90-201 COMMENT OOA1

Assets and costs as a percent of revenue: 

Cash. Companies hold a portion of their current assets in the fonn of cash in bank 

accounts, marketable securities, and other liquid holdings, all lumped under the tenn "cash." In 

the model, this input is expressed as a percent 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 engineenng 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 
Markup 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 fpr 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.S Model Outputs

C.2.S.1 Control Panel Outputs

The Control Panel outputs are the summary outputs of the model. This output section

enables the reader or model user to see at a glance the main short-run and long-run impacts of

standards on a few key variables: shipments, price, revenue, net income, and return on equity.

C-37

EE-RM-90-201 COMMENT OOA1

Markup 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 fpr 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.S Model Outputs 

C.2.S.1 Control Panel Outputs 

The Control Panel outputs are the summary outputs of the model. This output section 

enables the reader or model user to see at a glance the main short-run and long-run impacts of 

standards on a few key variables: shipments, price, revenue, net income, and return on equity. 

C-37 
For 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 [mancial 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

EE-RM-90-201 COMMENT OOA1

For 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 fmancial 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.S.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 
Gross 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)/R.

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

EE-RM-90-201 COMMENT OOA1

Gross 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)/R. 

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.S.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.S.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 
of 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

EE-RM-90-201 COMMENT OOA1

of 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.S.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 
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.

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 + dfD 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 (Cmaint ) 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

EE-RM-90-201 COMMENT OOA1

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. 

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 + dfn 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 (Cmaint ) 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 
Table D.I Data and Calculations Used to Obtain Maintenance Costs (1990 $)

(Co') (cJ (n) (Cmaint)

San Francisco National Maintenance Present Value of

Component Name Regional Cost Average Cost Lifetime Maintenance Cost Data Source

Standing Pilot $73.1 $53.5 10.2 $30 survey

Intennittent Ignition $249.0 $182.0 na $81 or $55 survey + analysis

Combustion Fan $149.2 $109.2 11.1 $57 survey

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 ofpilot 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 (lIDs)

lIDs are common to 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-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 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 that HSI ignitor breakage rates will have been significantly

reduced by the time the subject standards take effect (24).

Regardless of the 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 of cost (Co) values.

D-2

EE-RM-90-201 COMMENT OOA1

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

(Co') (CO> (n) (Cmaint) 

San Francisco National Maintenance Present Value of 

Component Name Regional Cost Average Cost Lifetime Maintenance Cost Data Source 

Standing Pilot $73.1 $53.5 10.2 $30 survey 

Intennittent Ignition $249.0 $182.0 na $81 or $55 survey + analysis 

Combustion Fan $149.2 $109.2 11.1 $57 survey 

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 (lIDs) 

lIDs are common to 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-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 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 that HSI ignitor breakage rates will have been significantly 

reduced by the time the subject standards take effect (24). 

Regardless of the 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 of cost (Co) values. 

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

maintenance organization survey:

Sensor replacement service call:

Control module replacement service call:

Total Cost:

$79

$170

$249

These values 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 (n). The value of n was estimated from information obtained

from lID manufacturers and not from the survey results. There were several reasons for using

alternative lifetime infonnation in this case. The fIrst reason has been mentioned above.

Secondly, it appeared that TID systems in particular are still developing rapidly, and that the

estimates for lifetimes obtained from services organizations would be faulty if based on

experience from the past.

The lifetime 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 of lID 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 lID 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 ftrst year of operation is below

2.5%. The value 2.5 was used for this analysis. Failures in the ftrst year are

covered under warranty and do not represent a cost to the consumer.

Item 3: After 10 years of production, yearly TID sales for the aftennarket (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 lID failure at some time during the 18 years of lID life. Results (in Column

E) are calculated for appliances with a 15-year life (speciftcally, direct heating equipment) and

for appliances with lifetimes greater than or equal to 18 years (speciftcally, mobile home

D-3

EE-RM-90-201 COMMENT OOA1

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 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 (n). The value of n was estimated from information obtained 

from lID manufacturers and not from the survey results. There were several reasons for using 

alternative lifetime information in this case. The flrst reason has been mentioned above. 

Secondly, it appeared that TID systems in particular are still developing rapidly, and that the 

estimates for lifetimes obtained from services organizations would be faulty if based on 

experience from the past. 

The lifetime 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 of lID 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 lID 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 ftrst year of operation is below 

2.5%. The value 2.5 was used for this analysis. Failures in the ftrst year are 

covered under warranty and do not represent a cost to the consumer. 

Item 3: After 10 years of production, yearly TID 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 lID failure at some time during the 18 years of lID life. Results (in Column 

E) are calculated for appliances with a 15-year life (speciflcally, direct heating equipment) and 

for appliances with lifetimes greater than or equal to 18 years (speciftcally, mobile home 

D-3 
furnaces). 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 lID maintenance were calculated as follows:

Appliances with 15 years avg. life:

Appliances with 18 or more years of life:

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

$182 x ($.44/$1.0) = $81

Table D.2 Calculations for Mean Lifetime of lID units

Year Mter Yearly Fraction of Present Value Average Present Value of Summation of Present

Purchase Units Failing (F) Discount Factor (t) Maintenance ($lxFxt) Value of Maintenance

Column A Column B Column C Column D Column E

1 0.025 na na

2 0.01 0.890 $0.009

3 0.01 0.840 $0.008

4 0.01 0.792 $0.008

5 0.01 0:747 $0.007

6 0.01 0.705 $0.007

7 0.01 0.665 $0.007

8 0.0175 0.627 $0.011

9 0.0275 0.592 $0.016

10 0.045 0.558 $0.025

11 0.0575 0.527 $0.030

12 0.075 0.497 $0.037

13 0.0875 0.469 $0.041

14 0.105 0.442 $0.046

15 0.115 0.417 $0.048 $0.30

16 0.1225 0.394 $0.048

17 0.1275 0.371 $0.047

18 0.135 0.350 $0.047 $0.44

D.4 FORCED COMBUSTIONIDRAFf FANS

The lifetime and cost values shown in Table D.I for "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

EE-RM-90-201 COMMENT OOA1

furnaces). 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 lID 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 lID units 

Year Mter Yearly Fraction of Present Value Average Present Value of Summation of Present 

Purchase Units Failing (F) Discount Factor (t) Maintenance ($lxFxt) Value of Maintenance 

Column A Column B Column C Column D Column E 

1 0.025 na na 

2 0.01 0.890 $0.009 

3 0.01 0.840 $0.008 

4 0.01 0.792 $0.008 

5 0.01 0:747 $0.007 

6 0.01 0.705 $0.007 

7 0.01 0.665 $0.007 

8 0.0175 0.627 $0.011 

9 0.0275 0.592 $0.016 

10 0.045 0.558 $0.025 

11 0.0575 0.527 $0.030 

12 0.075 0.497 $0.037 

13 0.0875 0.469 $0.041 

14 0.105 0.442 $0.046 

15 0.115 0.417 $0.048 $0.30 

16 0.1225 0.394 $0.048 

17 0.1275 0.371 $0.047 

18 0.135 0.350 $0.047 $0.44 

D.4 FORCED COMBUSTIONIDRAFf FANS 

The lifetime and cost values shown in Table D.I for "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 
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 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.l under

"Condensing Design."

D.6 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.I as 40% of the lID costs. Thus, two costs are shown

for two-stage maintenance costs, one for l5-year lifetime equipment (e.g., direct heating

equipment) and one for 18-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.I were estimated at two times the incremental cost

of maintenance for two-stage furnaces.

D-5

EE-RM-90-201 COMMENT OOA1

establish both bearing ternperature and actual hours of operation per year (or per furnace 

lifetime). 

D.S CONDENSING FURNACE MAINTENANCE 

The rnaintenance contractors that responded to questions about condensing furnace 

maintenance cited a number 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.l under 

"Condensing Design." 

D.6 COMBUSTION BOX DAMPERS 

Among the survey respondents, there was no rnaintenance experience with cornbustion 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 lirnit switch. The rnost likely components to fail were presumed to be 

the motor and the limit switch. Service lifetirne was estimated at 10 years, and a maintenance 

call (based on San Francisco area costs) was estimated to have a $75 labor charge (which was 

the average minimum labor 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.I were estimated. For two-stage operations, incremental 

maintenance requirements were assurned to arise solely from control components necessary to 

the implementation of two-stage operations, and the maintenance costs for these control 

components were estirnated in Table D.I as 40% of the lID costs. Thus, two costs are shown 

for two-stage maintenance costs, one for l5-year lifetime equipment (e.g., direct heating 

equipment) and one for 18-year or longer lifetime equiprnent (e.g., mobile home furnaces). 

For full or continuous rnodulation, incrernental maintenance costs were also attributed to 

control components. The costs in Table D.I were estimated at two times the incrernental cost 

of maintenance for two-stage furnaces. 

D-5 
APPENDIX E. ELECTRIC UTILITY IMPACT MODELING

E.! INTRODUCTION

This appendix summarizes the methodology used to calculate utility avoided costs for

energy savings that result from the imposition of appliance efficiency standards. The second

section explains the conventions and background information used in the analysis, and the third

and fourth sections explain the basis for valuing the energy and reliability savings of the

standards. The goal of this analysis is to calculate avoided costs, in dollars per million Btu

(MMBtu) of electricity saved, that can be used to value electricity savings from the societal

perspective. These 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 (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 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 derIDed as all other

appliances affected by standards.

Regional variation in energy consumption and peak demand is accounted for by use of

energy savings fractions, which are 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.

E-l

EE-RM-90-201 COMMENT OOA1

APPENDIX E. ELECTRIC UTILITY IMPACT MODELING 

E.1 INTRODUCTION 

This appendix summarizes the methodology used to calculate utility avoided costs for 

energy savings that result from the imposition of appliance efficiency standards. The second 

section explains the conventions and background infonnation 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 (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 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 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 a relative measure of heating, cooling, and "baseload" energy 

consumption (and hence energy savings) attributable to each region. Calculating these fractions 

requires regional infonnation 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. 

E-l 
Most ofthe infonnation used to calculate the energy savings fractions (ESFs) is contained

in Tables E.l and E.2. The Heating Load Hours (HLH) and Cooling Load Hours (CLH) are

calculated as population-weighted averages of state-level data. Alaska and Hawaii are not

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.! Regional Appliance Saturations (in Fraction of Total)

Central Heat Primp

Aitt Room Heating Electric

NERCRegion Households Air Resistance

NPCC 11,328,877 0.008 0.045 0.001 0.009

MAAC 9,259,883 0.021 0.040 0.002 0.008

ECAR 11,686,342 0.028 0.033 0.004 0.012

MAIN 7,042,024 0.020 0.025 0.001 0.005

spp 6,039,959 0.027 0.024 0.002 0.009

SERC 15,379,811 0.078 0.055 0.017 0.044

ERCOT 5,485,273 0.031 0.020 0.002 0.013

MAPP 3,792,833 0.011 0.012 0.001 0.003

WSCC 16,357,454 0.043 0.025 0.006 0.030

Alaska & 475,103 0.000 0.000 0.000 0.000

Hawaii

National . 86,847,558 0.267 0.281 0.036 0.132

Total

(a) Includes heat pumps

B-2

EE-RM-90-201 COMMENT OOA1

Most of the infonnation used to calculate the energy savings fractions (ESFs) is contained 

in Tables E.l and E.2. The Heating Load Hours (HLH) and Cooling Load Hours (CLH) are 

calculated as population-weighted averages of state-level data. Alaska and Hawaii are not 

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) 

Central Heat Primp 

A.itt Room Heating Electric 

NERCRegion Households Air Resistance 

NPCC 11,328,877 0.008 0.045 0.001 0.009 

MAAC 9,259,883 0.021 0.040 0.002 0.008 

ECAR 11,686,342 0.028 0.033 0.004 0.012 

MAIN 7,042,024 0.020 0.025 0.001 0.005 

spp 6,039,959 0.027 0.024 0.002 0.009 

SERC 15,379,811 0.078 0.055 0.017 0.044 

ERCOT 5,485,273 0.031 0.020 0.002 0.013 

MAPP 3,792,833 0.011 0.012 0.001 0.003 

WSCC 16,357,454 0.043 0.025 0.006 0.030 

Alaska & 475,103 0.000 0.000 0.000 0.000 

Hawaii 

National . 86,847,558 0.267 0.281 0.036 0.132 

Total 

(a) Includes heat pumps 

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

Saturation

Central Air Room Air weifhted

NERC Region HLH CLH CLH CH

NPCC 2506 437 324 342

MAAC 2358 600 448 499

ECAR 2404 573 479 522

MAIN 2322 583 467 519

SPP 1646 1061 984 1024

SERC 1378 1276 964 1147

ERCOT 1250 1500 1250 1403

MAPP 2604 509 449 477

WSCC 2408 501 408 466

Alaska and 1126 1170 1170 1170

Hawaii

National Average 2101 763 613 686

B-3

EE-RM-90-201 COMMENT OOA1

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.3 Regional Energy Savings Fractions

NERC Region Heating Cooling Baseload

NPCC 0.073 0.049 0.131

MAAC 0.055 0.064 0.107

ECAR 0.110 0.083 0.135

MAIN 0.026 0.037 0.082

spp 0.027 0.069 0.070

SERC 0.314 0.498 0.178

ERCOT 0.026 0.083 0.064

MAPP 0.009 0.009 0.044

WSCC 0.359 0.109 0.189

Total 1.000 1.000 1.000

The equations used to derive the energy savings fractions (ESFs) in Table E.3 are:

SatHPi SatE~ l-n-l. x + X HLJ-l· ........ '1 EftHP EffER"'1

ESFHtg =----->--,..------""'"'""':----

N [satHPi SatE~ J . [~x EftHP + EffER x HL~ ]

Sat (HPi = CAq) x CAC CLIft + SatRACi x RAC CL~ HR· x

1 EffCAC EffRAC

ESFClg = ---~--r--------------------~

N Sat (HPi + CAq) x CAC C~ SatRAq x RAC CL~ tr [~x " EffCAC + EffRAC

E-4

EE-RM-90-201 COMMENT OOA1

Table E.3 Regional Energy Savings Fractions 

NERC Region Heating Cooling Baseload 

NPCC 0.073 0.049 0.131 

MAAC 0.055 0.064 0.107 

ECAR 0.110 0.083 0.135 

MAIN 0.026 0.037 0.082 

spp 0.027 0.069 0.070 

SERC 0.314 0.498 0.178 

ERCOT 0.026 0.083 0.064 

MAPP 0.009 0.009 0.044 

WSCC 0.359 0.109 0.189 

Total 1.000 1.000 1.000 

The equations used to derive the energy savings fractions (ESFs) in Table E.3 are: 

SatHPi SatE~ l-n-l. x + X HLl-l. ........ '1 EftHP EffER'" 

ESFHtg = ----->--,...------""""-.:----

N [satHPi SatE~ J . ~ [~x EftHP + EfiER x HL~ ] 

Sat (HPi = CAq) x CAC CLIft + SatRACi x RAC CL~ HH· x 

1 EffCAC EffRAC 

ESFClg = ---~--r--------------------~ 

N Sat (HPi + CAq) x CAC C~ SatRAq x RAC CL~ tr [~x " EffCAC + EffRAC 

E-4 
where:

= SatE~ = SatHP·1 = Sat(HPi + CAq) = SatRAq = HL~ = CACCL~ = RACCL~ = N = EftER = 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 (l~

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 (0 & 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.l 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

A tanQ =_

B

E-5

EE-RM-90-201 COMMENT OOA1

where: 

~ 

SatE~ 

SatHP· 1 

Sat(HPi + CAq) 

SatRAq 

HL~ 

CACCL~ 

RACCL~ 

N 

EftER 

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 (l~ 

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 (0 & 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" OGFe OGFt can then be related to 

OGF m with a simplified load duration curve (LDC) for each region. Figure G.l 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 

A tan!} =_ 

B 

E-5 
and B = 1.0, which implies that

A =B tanQ =tanQ

The nonnalized 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' =O.5(BQ )B'

B' can thus be expressed as follows:

2OGF~

B' =OGFill =......,....----",.....,t,....., (tanQ)

E-6

EE-RM-90-201 COMMENT OOA1

and B = 1.0, which implies that 

A = B tanQ = tanQ 

The nonnalized 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 = O.SA 'B' = O.5(BQ )B' 

B' can thus be expressed as follows: 

2OGF~ 

B' = OGF m = --,-----",.....,t,....., 

(tanQ) 

E-6 
Simplified Load Duration Curve

1.0

"0

t':l

0

a

0

...

"0

<l)

N

.--t':l

e

0

zo

0

0

t

A

!

1.0

Normalized Duration (in fraction of a year)

Figure E.1 Simplified Load-Duration Curve

E-7

EE-RM-90-201 COMMENT OOA1

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 of a year) 

Figure E.1 Simplified Load-Duration Curve 

E-7 
The appropriate value for A = tan n is detennined empirically: when OOFt 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,

Nonnalized peak demand = 1.4 X mean load

and

Nonnalized minimum load = 0.6 X mean load.

A weighted-average marginal energy cost is detennined for each of the NERC regions

using the above fonnulas, 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 E.4 shows the

annual average OaFm for each NERC region.

E-8

EE-RM-90-201 COMMENT OOA1

The appropriate value for A = tan n is detennined empirically: when OGFt ~ 0.4, OGF m = 1.0. 

The value of A must therefore be greater than or equal to O.S. Note that when A = 0.8, 

Nonnalized peak demand = 1.4 X mean load 

and 

Nonnalized minimum load = 0.6 X mean load. 

A weighted-average marginal energy cost is detennined for each of the NERC regions 

using the above fonnulas, 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 OGF m is typically higher in summer than in winter. The 

annual average OGF m is approximately 52% when calculated using the energy savings fractions 

for heating, cooling, and baseload energy. Cooling MECs are calculated using a national OGFm 

of 60 percent to account for the seasonal differences in marginal fuel usage. Table E.4 shows the 

annual average OaF m for each NERC region. 

E-S 
Table E.4 Utility Marginal ooFs, 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

i ooFm = L.J ESFi x ooFm i=1

NooFm = 1 - OGFm

MECj = (OGFm x gas pricej + NooFm x coal price + Variable 0& M) x 1.06

where:

i denotes a particular NERC region,

ESFi represents the Energy Savings Fraction for region i, and

1.06 adjusts for transmission and distribution losses.

E-9

EE-RM-90-201 COMMENT OOA1

Table E.4 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 = L.J " ESF i i x OGFm 

i=1 

NOGF m = 1 - OGF m 

MECj = (OGF m x gas pricej + NOGF 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 
Variable 0 & M is calculated using references (27) and (28). Average 0 & M (fixed and

variable) for all fossil-fuel frred steam-electric powerplants for investor-owned utilities in 1990

is reported in (30) as 0.5¢/kWh (in 1990 dollars). This can be split into fixed and variable

components using (27). The analysis uses the fraction of 0 &M that is variable for a

supercritical coal steam unit (50%) and for a natural gas Combustion Turbine (20%), weights

these by the appropriate marginal oil and gas fractions, and applies the resulting percentage

(variable 0 & M as a percent of total 0 & M) to the total 0 & M (0.5¢/kWh). This calculation

results in a variable 0 & M cost of 0.33¢/kWh or 0.29 $/MMBtu of resource energy (in 1990

dollars).

E.4 Reliability Savings and Avoided TransmissionlDistribution System Capital Costs

E.4.! 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

frred 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-IO

EE-RM-90-201 COMMENT OOA1

Variable 0 & M is calculated using references (27) and (28). Average 0 & M (fixed and 

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 of total 0 & M) to the total 0 & M (0.5¢/kWh). This calculation 

results in a variable 0 & M cost of 0.33¢/kWh or 0.29 $/MMBtu of resource energy (in 1990 

dollars). 

E.4 Reliability Savings and A voided TransmissionlDistribution 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 base load 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-IO 
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.

The following two rules are applied to detennine 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 reserves

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 0.4 shows the year that each region will need capacity based

on these rules.

The calculations in this analysis follow standard industry practice developed for

detennining the value 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 BCC

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 "1evelizes" 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%

offorecasted 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.

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 of capital are 10% for debt and

preferred stock and 13.4% for common stock.

E-ll

EE-RM-90-201 COMMENT OOA1

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. 

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 reserves 

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 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 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 BCC 

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 tenns, 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. 

18 An 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. 

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 of capital are 10% for debt and 

preferred stock and 13.4% for common stock. 

. E-ll 
E.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 8760hours 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

EE-RM-90-201 COMMENT OOA1

E.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: 

eLF = 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 
CLF x 8760

= 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 standards on gas-fired mobile home furnaces and gas-fired direct heating equipment are

valued using avoided costs that include only the avoided energy and O&M costs. There is no

capacity component to the avoided costs for these end uses.

20rhis approach implicitly assumes that transmission capacity within each region allows most efficient 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

EE-RM-90-201 COMMENT OOA1

CLF x 8760 = 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.S. 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 TV s 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 standards on gas-fired mobile home furnaces and gas-fired direct heating equipment are 

valued using avoided costs that include only the avoided energy and O&M costs. There is no 

capacity component to the avoided costs for these end uses. 

2O-:rhis approach implicitly assumes that transmission capacity within each region allows most efficient 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 
Table E.5 Conservation Load Factors for Electrical Products

Appliance/Product

RAC

Water heating

Cooktops/ovens/microwaves

TVs policy 1 (standby energy use)

Other TV policies (not standby)

Fluorescent ballasts

CLF

0.14

0.89

0.46

1.00

0.86

057

The calculation of avoided reliability costs can be summarized in the following equations:

ARC . = Capacity Value 1 kWh 106

clgJ CLFcooling x 8760 x 11,500 Btu x MMBtu x 1.2 x L ESFC1gj

ARCBaseloadj

where:

ARCClg ,Baseloadj

Capacity Value

11,500 Btu/kWh

CLFBaseload

LESFClgj

LESFBaseloadj

1.2

Capacity Value 1 kWh 106

= CLFBaseload x 8760 x 11,500 Btu x MMBtu x 1.2 x ESFBaseloadj

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

= value of 1 kW 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 ill year j,

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

=reserve margin adjustment needed for adequate reliability.

E-14

EE-RM-90-201 COMMENT OOA1

Table E.S Conservation Load Factors for Electrical Products 

Appliance/Product eLF 

RAC 

Water heating 

Cooktops!ovens!microwaves 

0.14 

0.89 

0.46 

TVs policy 1 (standby energy use) 

Other TV policies (not standby) 

Fluorescent ballasts 

1.00 

0.86 

057 

The calculation of avoided reliability costs can be summarized in the following equations: 

ARC . = Capacity Value 1 kWh 106 

clgJ CLF

cooling 

x 8760 x 11,500 Btu x MMBtu x 1.2 x L ESFC1gj 

ARCBaseloadj = 

where: 

ARCClg ,Baseloadj 

Capacity Value 

11 ,500 Btu/kWh 

,eLF Base/oad 

LESFC/

gj 

LESF Base/oadj 

1.2 

Capacity Value 1 kWh 106 

CLFBaseload x 8760 x 11,500 Btu x MMBtu x 1.2 x k ESFBaseloadj 

= 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 base load appliances, 

= sum of cooling ESFs for regions that need capacity ill year j, 

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

= reserve margin adjustment needed for adequate reliability. 

E-14 
Capacity savings are peak load savings for only those regions that need capacity in a

given year. Load savings in regions that do not need capacity contribute to deferring capacity

additions in future years, but this analysis does not account for the value of such deferrals.

Table E.6 Sums of AC and Baseload Energy Savings Fractions for NERC Regions That Need

Capacity

Year

1996

1997

1998

1999

2000

Regions

Cooling Baseload Needing

ESF ESF Capacity

0.693 0.552 5

1.703 0.552 6

0.703 0.596 6

1.000 1.000 9

1.000 1.000 9

E.4.3 Avoided Transmission and Distribution Capital Costs

Recently, utility planners have begun to 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 a!. (37) review the most recent studies on this topic, and settle on a range for T

& D savings of from $10 to $40/kW-yr. Krause et a!. also cite reasons why this range is

probably too low, and suggest that further work will probably raise these values. For example,

they cite marginal T & D cost estimates of nine different utilities that (on average) totaled more

than $l00/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.ll show the sum of

avoided costs for the base-, high-, and low-energy-price cases.

E-15

EE-RM-90-201 COMMENT OOA1

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 of AC and Baseload Energy Savings Fractions for NERC Regions That Need 

Capacity 

Year 

1996 

1997 

1998 

1999 

2000 

Cooling 

ESF 

0.693 

l.703 

0.703 

l.000 

1.000 

Baseload 

ESF 

0.552 5 

0.552 6 

0.596 6 

l.000 9 

1.000 9 

E.4.3 Avoided Transmission and Distribution Capital Costs 

Regions 

Needing 

Capacity 

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 $l00/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.ll show the sum of 

avoided costs for the base-, high-, and low-energy-price cases. 

E-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

EE-RM-90-201 COMMENT OOA1

Table E.7 Electric Utility Avoided Energy Costs: Base Case (Dollars per MMBtu of 

Resource Energy Saved) 

Year Avoided Energy A voided 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.8 Electric Utility Avoided Reliability Costs (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

Avoided Reliability

Cost: Heating

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Avoided Reliability

Cost: Cooling

0.270

0.421

0.421

1.516

1.793

1.793

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

2.436

E-17

Avoided Reliability

Cost: Baseload

0.049

0.086

0.086

0.247

0.309

0.308

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

0.457

EE-RM-90-201 COMMENT OOA1

Table E.8 Electric Utility Avoided Reliability Costs (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 

A voided 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 

A voided 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 

00457 
Table E.9 Total Utility Avoided Costs: Base Case (Dollars per MMBtu of Resource Energy

Saved)

Year Total Avoided Cost Total Avoided Cost Total Avoided Cost

Heating Cooling Base10ad

1990 2.488 2.920 2.535

1991 2.654 3.263 2.741

1992 2.836 3.473 2.925

1993 3.035 4.797 3.287

1994 3.252 5.326 3.568

1995 3.490 5.601 3.806

1996 3.622 6.398 4.089

1997 3.761 6.560 4.229

1998 3.907 6.730 4.377

1999 4.060 6.909 4.532

2000 4.221 7.097 4.694

2001 4.389 7.294 4.865

2002 4.567 7.502 5.045

2003 4.753 7.720 5.233

2004 4.949 7.948 5.431

2005 5.154 8.189 5.639

2006 5.370 8.442 5.857

2007 5.596 8.707 6.086

2008 5.834 8.986 6.327

2009 6.085 9.279 6.580

2010 6.347 9.588 6.846

2011 6.624 9.912 7.126

2012 6.914 10.252 7.420

2013 7.219 10.610 7.728

2014 7.539 10.986 8.053

2015 7.876 11.382 8.394

E-18

EE-RM-90-201 COMMENT OOA1

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

Saved) 

Year Total A voided Cost Total Avoided Cost Total Avoided Cost: 

Heating Cooling Baseload 

1990 2.488 2.920 2.535 

1991 2.654 3.263 2.741 

1992 2.836 3.473 2.925 

1993 3.035 4.797 3.287 

1994 3.252 5.326 3.568 

1995 3.490 5.601 3.806 

1996 3.622 6.398 4.089 

1997 3.761 6.560 4.229 

1998 3.907 6.730 4.377 

1999 4.060 6.909 4.532 

2000 4.221 7.097 4.694 

2001 4.389 7.294 4.865 

2002 4.567 7.502 5.045 

2003 4.753 7.720 5.233 

2004 4.949 7.948 5.431 

2005 5.154 8.189 5.639 

2006 5.370 8.442 5.857 

2007 5.596 8.707 6.086 

2008 5.834 8.986 6.327 

2009 6.085 9.279 6.580 

2010 6.347 9.588 6.846 

2011 6.624 9.912 7.126 

2012 6.914 10.252 7.420 

2013 7.219 10.610 7.728 

2014 7.539 10.986 8.053 

2015 7.876 11.382 8.394 

E-18 
Table E.I0 Total Utility Avoided Costs: High Price Case (Dollars per MMBtu of

Resource Energy Saved)

Year Total Avoided Cost: Total Avoided Cost: Total Avoided Cost:

Heating Cooling Baseload

1990 2.531 2.972 2.579

1991 2.708 3.327 2.795

1992 2.902 3.551 2.991

1993 3.115 4.891 3.367

1994 3.348 5.439 3.665

1995 3.604 5.736 3.921

1996 3.825 6.638 4.295

1997 4.065 6.920 4.538

1998 4.325 7.224 4.800

1999 4.606 7.554 5.085

2000 4.911 7.912 5.394

200l 5.241 8.300 5.728

2002 5.598 8.720 6.089

2003 5.985 9.176 6.482

2004 6.405 9.669 6.907

2005 6.860 10.204 7.367

2006 7.353 10.784 7.866

2007 7.887 11.413 8.407

2008 8.467 12.095 8.994

2009 9.094 12.835 9.630

2010 9.775 13.637 10.319

2011 10.513 14.506 11.066

2012 11.313 15.449 11.877

2013 12.181 16.472 12.756

2014 13.122 17.581 13.709

2015 14.142 18.784 14.742

E-19

EE-RM-90-201 COMMENT OOA1

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.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

EE-RM-90-201 COMMENT OOA1

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