DOE/EE-0009

Vol.i of 3

TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY STANDARDS

FOR CONSUMER PRODUCTS:

ROOM AIR CONDITIONERS, WATER HEATERS, DIRECT

HEATING EQUIPMENT, MOBILE HOME FURNACES,

KITCHEN RANGES AND OVENS, POOL HEATERS,

FLUORESCENT LAMP BALLASTS & TELEVISION SETS.

VOLUME 1: METHODOLOGY

Including:

Environmental Assessment (DOE/EA-0819)

Regulatory Impact Analysis

November 1993

U.S. Department of Energy

Assistant Secretary,

Energy Efficiency & Renewable Energy

Office of Codes and Standards

Washington, DC 20585

TECHNICAL SUPPORT DOCUMENT: 

ENERGY EFFICIENCY STANDARDS 

FOR CONSUMER PRODUCTS: 

ROOM AIR CONDITIONERS, WATER HEATERS, DIRECT 

HEATING EQUIPMENT, MOBILE HOME FURNACES, 

KITCHEN RANGES AND OVENS, POOL HEATERS, 

FLUORESCENT LAMP BALLASTS & TELEVISION SETS. 

VOLUME 1: METHODOLOGY 

Including: 

Environmental Assessment (DOE/EA-0819) 

Regulatory Impact Analysis 

November 1993 

u.S. Department of Energy 

Assistant Secretary, 

Energy Efficiency & Renewable Energy 

Office of Codes and Standards 

Washington, DC 20585 

DOElEE-0009 

Vol.1of3 
ACKNOWLEDGEMENT

This document was prepared for the Department by staff members of the

Energy Analysis Program at Lawrence Berkeley Laboratory.

ACKNOWLEDGEMENT 

This document was prepared for the Department by staff members of the 

Energy Analysis Program at Lawrence Berkeley Laboratory. 
Volume A

Volume A 
Table of Contents

CHAPTER 1. EXECUTIVE SUMMARY .................................. 1-1

CHAPTER 2. INTRODUCTION ........................................ 2-1

2.1 PURPOSE AND SCOPE ................................ 2-1

2.2 STRUCTURE OF THE DOCUMENT .......................... 2-1

CHAPTER 3. ANALYTIC APPROACH .................................. 3-1

3.1 INTRODUCTION ........................................ 3-1

3.2 OVERVIEW OF ANALYTIC FRAMEWORK .................... 3-2

3.3 MODELS, DATA, AND ASSUMPTIONS ....................... 3-5

3.3.1 Engineering Cost and Performance Models ................... 3-5

3.3.2 EPRI COMMEND ................................... 3-8

3.3.3 LBL Residential Energy Model (LBL-REM) ................ 3-11

3.3.4 LBL Manufacturer Impact Model (LBL-MIM) ............... 3-20

3.3.4.1 Long-Run Impact Analysis ...................... 3-25

3.3.4.2 Short-Run Impacts ............................ 3-26

3.3.4.3 Sensitivity Analysis .......................... 3-27

3.3.5 Utility Impact Model ................................. 3-33

3.3.6 Life-Cycle Cost Analysis .............................. 3-36

3.4 SENSITIVITY ANALYSIS ... .. ........................... 3-37

CHAPTER 4. INDUSTRY PROFILE FOR LBL-MIM ANALYSIS ................ 4-1

4.1 DEMAND ............................................. 4-1

4.2 SUPPLY ............................................. 4-11

4.3 MERGERS AND ACQUISITIONS .......................... 4-23

4.4 PRICES .............................................. 4-25

CHAPTER 5. DEVELOPMENT OF BASE CASE FORECASTS ................. 5-1

5.1 ECONOMIC AND DEMOGRAPHIC DATA: RESIDENTIAL ........ 5-1

5.1.1 Population Projection ................................. 5-1

5.1.2 Occupied Households ................................. 5-1

5.1.3 Housing Starts ...................................... 5-2

5.1.4 Disposable Personal Income ............................ 5-3

5.1.5 Residential Energy Prices .............................. 5-3

5.2 ECONOMIC AND DEMOGRAPHIC DATA: COMMERCIAL ........ 5-5

5.2.1 Commercial Floor Space Projections ....................... 5-5

5.2.2 Building Starts ...................................... 5-6

5.2.3 Commercial Energy Prices .............................. 5-7

i

Table of Contents 

CHAPTER 1. EXECUTIVE SUMMARY .................................. 1-1 

CHAPTER 2. INTRODUCTION ........................................ 2-1 

2.1 PURPOSE AND SCOPE ................................ 2-1 

2.2 STRUCTURE OF THE DOCUMENT .......................... 2-1 

CHAPTER 3. ANALYTIC APPROACH .................................. 3-1 

3.1 INTRODUCTION ........................................ 3-1 

3.2 OVERVIEW OF ANALYTIC FRAMEWORK .................... 3-2 

3.3 MODELS, DATA, AND ASSUMPTIONS ....................... 3-5 

3.3.1 Engineering Cost and Performance Models ................... 3-5 

3.3.2 EPRI COMMEND ................................... 3-8 

3.3.3 LBL Residential Energy Model (LBL-REM) ................ 3-11 

3.3.4 LBL Manufacturer Impact Model (LBL-MIM) ............... 3-20 

3.3.4.1 Long-Run Impact Analysis ...................... 3-25 

3.3.4.2 Short-Run Impacts ............................ 3-26 

3.3.4.3 Sensitivity Analysis .......................... 3-27 

3.3.5 Utility Impact Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 3-33 

3.3.6 Life-Cycle Cost Analysis .............................. 3-36 

3.4 SENSITIVITY ANALYSIS ................................ 3-37 

CHAPTER 4. INDUSTRY PROFILE FOR LBL-MIM ANALYSIS ................ 4-1 

4.1 DEMAND ............................................. 4-1 

4.2 SUPPLY ............................................. 4-11 

4.3 MERGERS AND ACQUISITIONS .......................... 4-23 

4.4 PRICES .............................................. 4-25 

CHAPTER 5. DEVELOPMENT OF BASE CASE FORECASTS ................. 5-1 

5.1 ECONOMIC AND DEMOGRAPHIC DATA: RESIDENTIAL ........ 5-1 

5.1.1 Population Projection ................................. 5-1 

5.1.2 Occupied Households ................................. 5-1 

5.1.3 Housing Starts ...................................... 5-2 

5.1.4 Disposable Personal Income ............................ 5-3 

5.1.5 Residential Energy Prices .............................. 5-3 

5.2 ECONOMIC AND DEMOGRAPHIC DATA: COMMERCIAL ........ 5-5 

5.2.1 Commercial Floor Space Projections ....................... 5-5 

5.2.2 Building Starts ...................................... 5-6 

5.2.3 Commercial Energy Prices .............................. 5-7 
5.3 SENSITIVITY ANALYSIS OF THE BASE CASE ................. 5-9

5.3.1 Residential Sector Sensitivity Cases ........................ 5-9

5.3.2 Commercial Sector Sensitivity Cases ...................... 5-10

APPENDIX A. UNCERTAINTY ANALYSIS FOR ENERGY EFFICIENCY ........ A-1

APPENDIX B. FORECASTING MODELS ............................... B-1

B.1 LBL RESIDENTIAL ENERGY MODEL .......................... B-1

B. 1. 1 Overview ......................................... B -I

B.1.2 Housing .......................................... B-2

B.1.2.1 Number of Households ......................... B-2

B.1.2.2 Housing Starts ............................... B-3

B.1.3 Building Shell Thermal Integrity ......................... B-3

B.1.4 Equipment Efficiency ................................. B-4

B.1.5 Market Shares ...................................... B-4

B.1.6 Usage Behavior ..................................... B-4

B.1.7 Equipment Turnover ................................. B-5

B.1.8 Outputs .......................................... B-5

B.1.9 Changes Since Previous Technical Support Document .......... B-6

B.1.10 Input Data ........................................ B-6

B.2 COMMEND END-USE PLANNING SYSTEM ..................... B-6

B.2.1 Discussion of the COMMEND Market Framework ............. B-7

B.2.2 COMMEND Forecasting Framework ..................... B-13

APPENDIX C. LBL MANUFACTURER IMPACT MODEL .................... C-1

C.1 OVERVIEW OF MANUFACTURER IMPACT MODEL ............ C-1

C.1.1 Purpose .......................................... C-1

C.1.2 The Economic Approach .............................. C-1

C.1.3 Measures of Impact .................................. C-2

C.1.4 Methodology ...................................... C-2

C.2 STRUCTURE OF THE LBL-MIM ............................ C-4

C.2.1 Economic Theory and Assumptions ....................... C-4

C.2.2 Structural Assumptions .............................. C-13

C.2.3 Description of Modules .............................. C-14

C.2.3.1 Control Panel ............................... C-17

C.2.3.2 The Monte Carlo Module ...................... C-18

C.2.3.3 The Accounting Module ....................... C-18

C.2.3.4 The Engineering Inputs Module .................. C-19

C.2.3.5 The Standards Levels Module .. ................. C-20

C.2.3.6 The Costs, Sales, and Revenue Module . ............ C-21

C.2.3.7 The Financial Module ........ ................. C-23

C.2.3.8 The One-Time Cost Amortization Module . .......... C-24

C.2.3.9 The Long-Run Model Module ................... C-27

C.2.3.10 The Short-Run Model Module ....... ............ C-31

11

5.3 SENSITMTY ANALYSIS OF THE BASE CASE . . . . . . . . . . . . . . . . . 5-9 

5.3.1 Residential Sector Sensitivity Cases . . . . . . . . . . . . . . . . . . . . . . . . 5-9 

5.3.2 Commercial Sector Sensitivity Cases ...................... 5-10 

APPENDIX A. UNCERTAINTY ANALYSIS FOR ENERGY EFFICIENCY ........ A-I 

APPENDIX B. FORECASTING MODELS ............................... B-1 

B.l LBL RESIDENTIAL ENERGY MODEL .......................... B-1 

B.l.l Overview ......................................... B-1 

B.l.2 Housing .......................................... B-2 

B.L2.1 Number of Households ......................... B-2 

B.L2.2 Housing Starts ............................... B-3 

B.L3 Building Shell Thermal Integrity ......................... B-3 

B.l.4 Equipment Efficiency ................................. B-4 

B.l.5 Market Shares ...................................... B-4 

B.L6 Usage Behavior ..................................... B-4 

B.L7 Equipment Turnover ................................. B-5 

B.l.8 Outputs .......................................... B-5 

B.L9 Changes Since Previous Technical Support Document .......... B-6 

B.l.lO Input Data ........................................ B-6 

B.2 COMMEND END-USE PLANNING SYSTEM ..................... B-6 

B.2.l Discussion of the COMMEND Market Framework ............. B-7 

B.2.2 COMMEND Forecasting Framework ..................... B-13 

APPENDIX C. LBL MANUFACTURER IMPACT MODEL .................... C-l 

C.l OVERVIEW OF MANUFACTURER IMPACT MODEL ............ C-l 

C.l.l Purpose .......................................... C-l 

C.L2 The Economic Approach .............................. C-l 

C.l.3 Measures of Impact .................................. C-2 . 

C.L4 Methodology ...................................... C-2 

C.2 STRUCTURE OF THE LBL-MIM ............................ C-4 

C.2.l Economic Theory and Assumptions ........ '.' ............. C-4 

C.2.2 Structural Assumptions .............................. C-13 

C.2.3 Description of Modules .............................. C-14 

C.2.3.l Control Panel ............................... C-17 

C.2.3.2 The Monte Carlo Module ...................... C-18 

C.2.3.3 The Accounting Module ....................... C-18 

C.2.3.4 The Engineering Inputs Module .................. C-19 

C.2.3.5 The Standards Levels Module ................... C-20 

C.2.3.6 The Costs, Sales, and Revenue Module ............. C-2l 

C.2.3.7 The Financial Module ......................... C-23 

C.2.3.8 The One-Time Cost Amortization Module ........... C-24 

C.2.3.9 The Long-Run Model Module ................... C-27 

C.2.3.1O The Short-Run Model Module ................... C-3l 

11 
C.2.4 Model Inputs ........ .............................. C-31

C.2.4.1 Input Generating Assumptions .................... C-31

C.2.4.2 Control Factors in the Control Panel ............... C-32

C.2.4.3 Engineering Data . ............................ C-33

C.2.4.4 Costs, Sales, and Revenues ...................... C-34

C.2.4.5 Financial Inputs ... ........................... C-35

C.2.5 Model Outputs ..... ............................... C-37

C.2.5.1 Control Panel Outputs .................. ....... C-37

C.2.5.2 Accounting Summary Outputs ... ................. C-38

C.2.5.3 Economic Analysis Outputs ............. ......... C-39

C.2.5.4 Monte Carlo Outputs .......................... C-39

C.2.5.6 Retail Prices ................................. C-40

C.3 INPUT DATA AND DATA DEVELOPMENT FOR THE EIGHT

PRODUCTS ............................................ C-40

APPENDIX D. MAINTENANCE COSTS FOR COMMON APPLIANCE

COMPONENTS ..............................................D-1

D.1 INTRODUCTION ........................................ D-1

D.2 STANDING PILOT IGNITION SYSTEMS ...................... D-2

D.3 INTERMITTENT IGNITION DEVICES (IIDs) ................... D-2

D.4 FORCED COMBUSTION/DRAFT FANS ....................... D-4

D.5 CONDENSING FURNACE MAINTENANCE .................... D-5

D.6 COMBUSTION BOX DAMPERS ............................. D-5

D.7 TWO-STAGE OPERATION AND FULL MODULATION ........... D-5

APPENDIX E. ELECTRIC UTILITY IMPACT MODELING .................... E-1

E.1 INTRODUCTION .......................................... E-1

E.2 BACKGROUND AND CONVENTIONS .......................... E-1

E.3 ENERGY SAVINGS ........................................ E-5

E.4 Reliability Savings and Avoided Transmission /Distribution System Capital

Costs ................................................ E-10

E.4.1 Value of Reliability Savings ............................ E-10

E.4.2 Amount of Reliability Savings ......................... E-12

E.4.3 Avoided Transmission and Distribution Capital Costs .......... E-15

iii

C.2A Model Inputs ...................................... C-31 

C.2A.1 Input Generating Assumptions .................... C-31 

C.2A.2 Control Factors in the Control Panel ............... C-32 

C.2A.3 Engineering Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-33 

C.2.4A Costs, Sales, and Revenues . . . . . . . . . . . . . . . . . . . . . . C-34 

C.2A.5 Financial Inputs .............................. C-35 

C.2.5 Model Outputs .................................... C-37 

C.2.5.1 Control Panel Outputs ......................... C-37 

C.2.5.2 Accounting Summary Outputs .................... C-38 

C.2.5.3 Economic Analysis Outputs ...................... C-39 

C.2.5A Monte Carlo Outputs .......................... C-39 

C.2.5.6 Retail Prices ...... '. ......................... C-40 

C.3 INPUT DATA AND DATA DEVELOPMENT FOR THE EIGHT 

PRODUCTS ........................................... C-40 

APPENDIX D. MAINTENANCE COSTS FOR COMMON APPLIANCE 

COMPONENTS .............................................. D-1 

D.1 INTRODUCTION ........................................ D-1 

D.2 STANDING PILOT IGNITION SYSTEMS ...................... D-2 

D.3 INTERMITTENT IGNITION DEVICES (lIDs) ................... D-2 

DA FORCED COMBUSTION/DRAFT FANS ....................... D-4 

D.5 CONDENSING FURNACE MAINTENANCE .................... D-5 

D.6 COMBUSTION BOX DAMPERS ............................. D-5 

D.7 TWO-STAGE OPERATION AND FULL MODULATION ........... D-5 

APPENDIX E. ELECTRIC UTILITY IMPACT MODELING .................... E-1 

E.1 INTRODUCTION .......................................... E-1 

E.2 BACKGROUND AND CONVENTIONS .......................... E-1 

E.3 ENERGY SAVINGS ........................................ E-5 

EA Reliability Savings and A voided Transmission/Distribution System Capital 

Costs ................................................ E-10 

EA.1 Value of Reliability Savings . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-lO 

EA.2 Amount of Reliability Savings ......................... E-12 

EA.3 Avoided Transmission and Distribution Capital Costs .......... E-15 

iii 
ENVIRONMENTAL ASSESSMENT FOR PROPOSED ENERGY CONSERVATION

STANDARDS FOR EIGHT TYPES OF CONSUMER PRODUCTS: ROOM AIR

CONDITIONERS, WATER HEATERS, DIRECT HEATING EQUIPMENT,

MOBILE HOME FURNACES, RANGES AND OVENS, POOL HEATERS,

FLUORESCENT BALLASTS, AND TELEVISIONS .....................

1 INTRODUCTION AND NEED FOR PROPOSED ACTION ................... 1

2 METHODS OF ESTIMATING ENVIRONMENTAL IMPACTS ................. 2

2.1 Baseline Emissions .......................................... 3

2.2 Sulfur and Nitrogen Oxide Emissions ............................. 4

2.3 Carbon Dioxide Emissions ..................................... 5

3 RESULTS ....................................................... 7

3.1 ROOM AIR CONDITIONERS .................................. 7

3.1.1 Sulfur and Nitrogen Oxide Emissions ...................... 7

3.1.2 Carbon Dioxide Emissions .............................. 8

3.2 WATER HEATERS ...................................... 15

3.2.1 Sulfur and Nitrogen Oxide Emissions ...................... 15

3.2.2 Carbon Dioxide Emissions ............................. 15

3.3 DIRECT HEATING EQUIPMENT ............................ 24

3.3.1 Sulfur and Nitrogen Oxide Emissions ...................... 24

3.3.2 Carbon Dioxide Emissions ............................. 24

3.4 MOBILE HOME FURNACES ............................... -30

3.4.1 Sulfur and Nitrogen Oxide Emissions ...................... 30

3.4.2 Carbon Dioxide Emissions ............................. 30

3.5 RANGES AND OVENS ................................... 37

3.5.1 Sulfur and Nitrogen Oxide Emissions ...................... 37

3.5.2 Carbon Dioxide Emissions ............................. 38

3.6 POOL HEATERS ........................................ 54

3.6.1 Sulfur and Nitrogen Oxide Emissions ...................... 54

3.6.2 Carbon Dioxide Emissions ............................. 54

3.7 FLUORESCENT BALLASTS ............................... 59

3.7.1 Sulfur and Nitrogen Oxide Emissions ...................... 59

3.7.2 Carbon Dioxide Emissions ............................. 60

3.8 TELEVISIONS ..................................... 68

3.8.1 Sulfur and Nitrogen Oxide Emissions ...................... 68

3.8.2 Carbon Dioxide Emissions ............................. 68

iv

lG

ENVIRONMENTAL ASSESSMENT FOR PROPOSED ENERGY CONSERVATION 

STANDARDS FOR EIGHT TYPES OF CONSUMER PRODUCTS: ROOM AIR 

CONDITIONERS, WATER HEATERS, DIRECT HEATING EQUIPMENT, 

MOBILE HOME FURNACES, RANGES AND OVENS, POOL HEATERS, 

FLUORESCENT BALLASTS, AND TELEVISIONS ..................... 1 

1 INTRODUCTION AND NEED FOR PROPOSED ACTION ................... 1 

2 METHODS OF ESTIMATING ENVIRONMENTAL IMPACTS ...... ; . . . . . . . . .. 2 

2.1 Baseline Emissions .............. . . . . . . . . . . . . . . . . . . . . . . . . . . .. 3 

2.2 Sulfur and Nitrogen Oxide Emissions ............................. 4 

2.3 Carbon Dioxide Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 5 

3 RESULTS ....................................................... 7 

3.1 ROOM AIR CONDITIONERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 7 

3.1.1 Sulfur and Nitrogen Oxide Emissions ...................... 7 

3.1.2 Carbon Dioxide Emissions .............................. 8 

3.2 WATER HEATERS ...................................... 15 

3.2.1 Sulfur and Nitrogen Oxide Emissions. . . . . . . . . . . . . . . . . . . . .. 15 

3.2.2 Carbon Dioxide Emissions ............................. 15 

3.3 DIRECT HEATING EQUIPMENT ........... '" .. , ........... 24 

3.3.1 Sulfur and Nitrogen Oxide Emissions. . . . . . . . . . . . . . . . . . . . .. 24 

3.3.2 Carbon Dioxide Emissions ............................. 24 

3.4 MOBILE HOME FURNACES ............................... -30 

3.4.1 Sulfur and Nitrogen Oxide Emissions . . . . . . . . . . . . . . . . . . . . .. 30 

3.4.2 Carbon Dioxide Emissions ............................. 30 

3.5 RANGES AND OVENS ................................... 37 

3.5.1 Sulfur and Nitrogen Oxide Emissions. . . . . . . . . . . . . . . . . . . . .. 37 

3.5.2 Carbon Dioxide Emissions ............................. 38 

3.6 POOL HEATERS ........................................ 54 

3.6.1 Sulfur and Nitrogen Oxide Emissions. . . . . . . . . . . . . . . . . . . . .. 54 

3.6.2 Carbon Dioxide Emissions ............................. 54 

3.7 FLUORESCENT BALLASTS ............................... 59 

3.7.1 Sulfur and Nitrogen Oxide Emissions. . . . . . . . . . . . . . . . . . . . .. 59 

3.7.2 Carbon Dioxide Emissions ............................. 60 

3.8 TELEVISIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 68 

3.8.1 Sulfur and Nitrogen Oxide Emissions. . . . . . . . . . . . . . . . . . . . .. 68 

3.8.2 Carbon Dioxide Emissions ............................. 68 

iv 
REGULATORY IMPACT ANALYSIS ................................. Raid-1

1. INTRODUCTION .............................................. Raid-1

2. DESCRIPTION AND ANALYSIS OF MAJOR ALTERNATIVES ............ Raid-3

2.1 Comparative Analysis of the Alternatives ...................... Raid-3

2.2 No-Action Alternative .......... .. ....................... Raid-9

2.2.1 Energy Prices .................................... Raid-9

2.2.2 Increasing Consumer Information ...................... Raid-10

2.2.3 Existing Mandatory Standards ........................ Raid-12

2.2.4 Quantitative Description ............................ Raid-12

2.3 Tax Credits to Consumers ............................... Raid-13

2.3.1 Discussion ...................................... Raid-13

2.3.2 Quantitative description ............................ Raid-13

2.4 Tax Credits to Manufacturers ............................ Raid-14

2.4.1 Discussion ...................................... Raid-14

2.4.2 Quantitative Description ............................ Raid-14

2.5 Rebates to Consumers .................................. Raid-15

2.5.1 Discussion ...................................... Raid-15

2.5.2 Quantitative Description ............................ Raid-16

2.6 Prescriptive Standards .................................. Raid-16

2.6.1 Discussion ...................................... Raid-16

2.6.2 Quantitative Description ............................ Raid-17

2.7 Voluntary Energy Efficiency Targets ....................... Raid-17

2.7.1 Discussion ...................................... Raid-17

2.7.2 Quantitative Description ............................ Raid-18

2.8 Enhanced Consumer Education and Labeling Programs .......... Raid-18

2.8.1 Discussion ...................................... Raid-18

2.8.2 Quantitative Description ............................ Raid-19

2.9 Performance Standards ................................. Raid-20

2.9.1 Discussion ...................................... Raid-20

2.9.2 Quantitative Description ............................ Raid-20

3. COSTS AND BENEFITS OF ALTERNATIVE STANDARDS LEVELS ...... Raid-20

3.1 Choice of Standards Levels to Analyze ...................... Raid-21

3.2 Impacts on Consumers .................................. Raid-22

3.3 Impacts on Manufacturers ............................... Raid-25

3.4 Impacts on Electric Utilities .............................. Raid-31

3.5 National Impacts ..................................... Raid-32

3.5.1 Energy Savings ............................. Raid-32

3.5.2 Net Present Value of Standards ....................... Raid-33

3.5.3 Environmental Impacts ......................... Raid-33

4.ECONOMIC IMPACTS OF THE PROPOSED AND ALTERNATIVE STANDARDStaid-36

4.1 Introduction ......................................... Raid-36

4.2 Methodology ........................................ Raid-36

v

REGULATORY IMPACf ANALYSIS ................................. Raid-1 

1. INTRODUCTION .............................................. Raid-1 

2. DESCRIPTION AND ANALYSIS OF MAJOR ALTERNATIVES ............ Raid-3 

2.1 Comparative Analysis of the Alternatives ...................... Raid-3 

2.2 No-Action Alternative ................................... Raid-9 

2.2.1 Energy Prices .................................... Raid-9 

2.2.2 Increasing Consumer Information . . . . . . . . . . . . . . . . . . . . . . Raid-lO 

2.2.3 Existing Mandatory Standards ........................ Raid-12 

2.2.4 Quantitative Description ............................ Raid-12 

2.3 Tax Credits to Consumers ............................... Raid-13 

2.3.1 Discussion ...................................... Raid-13 

2.3.2 Quantitative description ............................ Raid-13 

2.4 Tax Credits to Manufacturers ............................ Raid-14 

2.4.1 Discussion ...................................... Raid-14 

2.4.2 Quantitative Description ............................ Raid-14 

2.5 Rebates to Consumers .................................. Raid-15 

2.5.1 Discussion ...................................... Raid-15 

2.5.2 Quantitative Description ............................ Raid-16 

2.6 Prescriptive Standards .................................. Raid~ 16 

2.6.1 Discussion ...................................... Raid-16 

2.6.2 Quantitative Description ............................ Raid-17 

2.7 Voluntary Energy Efficiency Targets ....................... Raid-17 

2.7.1 Discussion...................................... Raid-17 

2.7.2 Quantitative Description ............................ Raid-18 

2.8 Enhanced Consumer Education and Labeling Programs .......... Raid-18 

2.8.1 Discussion...................................... Raid-18 

2.8.2 Quantitative Description ............................ Raid-19 . 

2.9 Performance Standards ................................. Raid-20 

2.9.1 Discussion ...................................... Raid-20 

2.9.2 Quantitative Description ............................ Raid-20 

3. COSTS AND BENEFITS OF ALTERNATIVE STANDARDS LEVELS ...... Raid-20 

3.1 Choice of Standards Levels to Analyze ...................... Raid-21 

3.2 Impacts on Consumers .................................. Raid-22 

3.3 Impacts on Manufacturers ............................... Raid-25 

3.4 Impacts on Electric Utilities .............................. Raid-31 

3.5 National Impacts ..................................... Raid-32 

3.5.1 Energy Savings ............................. Raid-32 

3.5.2 Net Present Value of Standards ....................... Raid-33 

3.5.3 Environmentallmpacts ........................ Raid-33 

4.ECONOMIC IMPACfS OF THE PROPOSED AND ALTERNATIVE STANDARD~aid-36 

4.1 Introduction ......................................... Raid-36 

4.2 Methodology ........................................ Raid-36 

v 
4.3 Effects of Proposed Standards on Consumers .................. Raid-37

4.4 Effects of Proposed Standards on Manufacturers ................ Raid-38

4.5 Effects of Proposed Standards on GNP and Employment .......... Raid-39

4.5.1 Overview ...................................... Raid-39

4.5.2 Crowding Out ................................... Raid-39

4.5.3 The Multiplier .................... .. ............. Raid-40

4.5.4 The Rational Expectations View ...................... Raid-40

4.5.5 Efficiency ...................................... Raid-40

4.5.6 Demand Effects and the Retail Sector ................... Raid-41

4.5.7 Demand Effects and the Manufacturer Sector ............. Raid-41

4.5.8 Demand Effects and the Energy Industry ................ Raid-41

4.5.9 Summary and Conclusions .......................... Raid-42

4.5.10 Trade ........................................ Raid-42

4.6 Impact of Alternative Regulations .......................... Raid-42

4.6.1 Tax Credits ..................................... Raid-42

4.6.2 Consumer Rebates ................................ Raid-43

4.6.3 Prescriptive Standards .............................. Raid-43

4.6.4 Voluntary Efficiency Targets ......................... Raid-43

4.6.5 Enhanced Labeling and Consumer Education .............. Raid-44

References .......................................... .. ........... R-1

vi

r z,

4.3 Effects of Proposed Standards on Consumers .................. Raid-37 

4.4 Effects of Proposed Standards on Manufacturers ................ Raid-38 

4.5 Effects of Proposed Standards on GNP and Employment .......... Raid-39 

4.5.1 Overview ...................................... Raid-39 

4.5.2 Crowding Out ................................... Raid-39 

4.5.3 The Multiplier ................................... Raid-40 

4.5.4 The Rational Expectations View ...................... Raid-40 

4.5.5 Efficiency ...................................... Raid-40 

4.5.6 Demand Effects and the Retail Sector ................... Raid-41 

4.5.7 Demand Effects and the Manufacturer Sector ............. Raid-41 

4.5.8 Demand Effects and the Energy Industry ................ Raid-41 

4.5.9 Summary and Conclusions .......................... Raid-42 

4.5.10 Trade ........................................ Raid-42 

4.6 Impact of Alternative Regulations .......................... Raid-42 

4.6.1 Tax Credits ..................................... Raid-42 

4.6.2 Consumer Rebates ................................ Raid-43 

4.6.3 Prescriptive Standards .............................. Raid-43 

4.6.4 Voluntary Efficiency Targets ......................... Raid-43 

4.6.5 Enhanced Labeling and Consumer Education .............. Raid-44 

References ....................................................... R-l 

vi 
List of Tables

Table 3.1 Product Types and Number of Product Classes ..................... 3-1

Table 3.2 Sensitivity of ROE to 1 S.E. Change in Control Variable, Direct Heating

Equipment ............................................. 3-28

Table 4.1 Market Saturations ........................................ 4-11

Table 4.2 Market Saturations ........................................ 4-11

Table 4.3 Percent of Market Controlled by the Largest Producers of Each Product ... 4-12

Table 4.4 Acquisitions of Appliance Manufacturers ........................ 4-24

Table 5.1 Demographic Projections .................................... 5-2

Table 5.2 Housing Starts, 1980-2015 (Thousands) .......................... 5-2

Table 5.3 House Retirement Rates (Percent of Stock per Year) ................. 5-3

Table 5.4 Disposable Personal Income (1990 Dollars) ....................... 5-3

Table 5.5 Average Residential Energy Prices (1990 Dollars per Million Btu) ....... 5-4

Table 5.6 Relative Electricity Price by End Use (Average Residential Electricity Price

= 1.00) ................................................. 5-5

Table 5.7 Commercial Floor Space Projections (Billions of square feet) ........... 5-6

Table 5.8 Commercial Building Starts, 1980-2030 (Billions of square feet) ......... 5-7

Table 5.9 Building Retirement Rates ................................... 5-8

Table 5.10 Average Commercial Energy Prices (1990 Dollars per Million Btu) ....... 5-9

Table B.1 Parameters of Thermal Integrity/Cost Curves by House Type ........... B-3

Table C. 1 Accounting Module ....................................... C-19

Table C.2 Equations Used in the Costs, Sales, and Revenue Module ... .......... C-22

Table C.3 Financial Module ......................................... C-23

Table C.4 One-Time Cost Amortization Module ........................... C-25

Table D.1 Data and Calculations Used to Obtain Maintenance Costs (1990 $) ....... D-2

Table D.2 Calculations for Mean Lifetime of IID units ....................... D-4

Table E.1 Regional Appliance Saturations (in Fraction of Total) ................ E-2

Table E.2 Regional Population-Weighted Heating and Cooling Load Hours ......... E-3

Table E.3 Regional Energy Savings Fractions ............................. E-4

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

Table E.5 Conservation Load Factors for Electrical Products .................. E-14

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

Table E.7

That Need Capacity ....................................... E-15

Electric Utility Avoided Energy Costs: Base Case (Dollars per MMBtu of

Table E.8

Resource Energy Saved) .................................... E-16

Electric Utility Avoided Reliability Costs (Dollars per MMBtu of Resource

Table E.9

Energy Saved ........................................... E-17

Total Utility Avoided Costs: Base Case (Dollars per MMBtu of Resource

Energy Saved) ........................................... E-18

Table E.11

Table 9

Total Utility Avoided Costs: Low Price Case (Dollars per MMBtu of

Resource Energy Cost: ................................... E-20

Televisions Output Table: Primary Scenario-Long Run ........... Riad-27

Vii

Table 3.1 

Table 3.2 

Table 4.1 

Table 4.2 

Table 4.3 

Table 4.4 

Table 5.1 

Table 5.2 

Table 5.3 

Table 5.4 

Table 5.5 

Table 5.6 

Table 5.7 

Table 5.8 

Table 5.9 

Table 5.10 

Table B.1 

Table C.1 

Table C.2 

Table C.3 

Table C.4 

Table D.1 

Table D.2 

Table E.1 

Table E.2 

Table E.3 

Table E.4 

Table E.5 

Table E.6 

Table E.7 

Table E.8 

Table E.9 

Table E.11 

Table 9 

List of Tables 

Product Types and Number of Product" Classes ..................... 3-1 

Sensitivity of ROE to 1 S.E. Change in Control Variable, Direct Heating 

Equipment ............................................. 3-28 

Market Saturations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 4-11 

Market Saturations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 4-11 

Percent of Market Controlled by the Largest Producers of Each Product . .. 4-12 

Acquisitions of Appliance Manufacturers ........................ 4-24 

Demographic Projections .................................... 5-2 

Housing Starts, 1980-2015 (Thousands) .......................... 5-2 

House Retirement Rates (Percent of Stock per Year) ................. 5-3 

Disposable Personal Income (1990 Dollars) ....................... 5-3 

Average Residential Energy Prices (1990 Dollars per Million Btu) ....... 5-4 

Relative Electricity Price by End Use (Average Residential Electricity Price 

= 1.00) .................................................. 5-5 

Commercial Floor Space Projections (Billions of square feet) ........... 5-6 

Commercial Building Starts, 1980-2030 (Billions of square feet) ......... 5-7 

Building Retirement Rates .•................................. 5-8 

Average Commercial Energy Prices (1990 Dollars per Million Btu) ....... 5-9 

Parameters of Thermal Integrity/Cost Curves by House Type ........... B-3 

Accounting Module ....................................... C-19 

Equations Used in the Costs, Sales, and Revenue Module ............. C-22 

Financial Module ......................................... C-23 

One-Time Cost Amortization Module ........................... C-25 

Data and Calculations Used to Obtain Maintenance Costs (1990 $) •...... D-2 

Calculations for Mean Lifetime of lID units ...... . . . . . . . . . . . . . . . . • D-4 

Regional Appliance Saturations (in Fraction of Total) ................ E-2 " 

Regional Population-Weighted Heating and Cooling Load Hours ......... E-3 

Regional Energy Savings Fractions ............................. E-4 

Utility Marginal OGFs, and Year of Capacity Need by NERC Region ..... E-9 

Conservation Load Factors for Electrical Products .................. E-14 

Sums of AC and Baseload Energy Savings Fractions for NERC Regions 

That Need Capacity ....................................... E-15 

Electric Utility Avoided Energy Costs: Base Case (Dollars per MMBtu of 

Resource Energy Saved) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-16 

Electric Utility Avoided Reliability Costs (Dollars per MMBtu of Resource 

Energy Saved ........................................... E-17 

Total Utility Avoided Costs: Base Case (Dollars per MMBtu of Resource 

Energy Saved) ........................................... E-18 

Total Utility Avoided Costs: Low Price Case (Dollars per MMBtu of 

Resource Energy Cost: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-20 

Televisions Output Table: Primary Scenario-Long Run ........... Riad-27 

vii 
CHAPTER 1. EXECUTIVE SUMMARY

The Energy Policy and Conservation Act (P.L. 94-163), as amended, establishes energy

conservation standards for 12 of the 13 types of consumer products specifically covered by the

Act. The legislation requires the Department of Energy (DOE) to consider new or amended

standards for these and other types of products at specified times. DOE is currently considering

amending standards for seven types of products: water heaters, direct heating equipment, mobile

home furnaces, pool heaters, room air conditioners, kitchen ranges and ovens (including

microwave ovens), and fluorescent light ballasts and is considering establishing standards for

television sets. This Technical Support Document presents the methodology, data, and results

from the analysis of the energy and economic impacts of the proposed standards.

The economic impact analysis is performed in four major areas:

• An Engineering Analysis, which establishes technical feasibility and product

attributes, including energy performance and costs of alternative design options,

which improve appliance efficiency.

• A Consumer Analysis, which forecasts appliance sales, efficiencies, energy use, and

consumer expenditures.

• A Manufacturer Analysis, which provides an estimate of manufacturers' responses

to the proposed standards. Their responses are quantified by changes in several

financial performance measures for a prototypical firm.

• AnImpact Analysis, which provides an integrated framework for assessing the costs

and benefits of appliance standards. The Impact Analysis includes: (1) a Life-Cycle

Cost Analysis that evaluates the savings in operating expense relative to an increase

in purchase price for individual consumers; (2) a Utility Analysis that measures the

impacts of the altered energy-consumption patterns on electric utilities; (3) an

Industry Impact Analysis that shows the financial and competitive impacts on the

respective appliance manufacturing industry; and (4) a Cost-Benefit Analysis that

collects the results of all the analyses into the net benefits and costs from a national

perspective.

The Engineering Analysis segregates product types into separate classes to which different

energy conservation standards apply. For each appliance class, baseline units are chosen

representing relatively low-efficiency units currently being manufactured. The analysis identifies

a series of design options to improve energy efficiency and estimates the factory costs to produce

them. Design options are added individually or in combination to the baseline unit to evaluate

units that might be produced in response to standards. Factory costs are then marked up to

1-1

CHAPTER 1. EXECUTIVE SUMMARY 

The Energy Policy and Conservation Act (P.L. 94-163), as amended, establishes energy 

conservation standards for 12 of the 13 types of consumer products specifically covered by the 

Act. The legislation requires the Department of Energy (DOE) to consider new or amended 

standards for these and other types of products at specified times. DOE is currently considering 

amending standards for seven types of products: water heaters, direct heating equipment, mobile 

home furnaces, pool heaters, room air conditioners, kitchen ranges and ovens (including 

microwave ovens), and fluorescent light ballasts and is considering establishing standards for 

television sets. This Technical Support Document presents the methodology, data, and results 

from the analysis of the energy and economic impacts of the proposed standards. 

The economic impact analysis is performed in four major areas: 

• An Engineering Analysis, which establishes technical feasibility and product 

attributes, including energy performance and costs of alternative design options, 

which improve appliance efficiency. 

• A Consumer Analysis, which forecasts appliance sales, efficiencies, energy use, and 

consumer expenditures. 

• A Manufacturer Analysis, which provides an estimate of manufacturers' responses 

to the proposed standards. Their responses are quantified by changes in several 

financial performance measures for a prototypical firm. 

• An Impact Analysis, which provides an integrated framework for assessing the costs 

and benefits of appliance standards. The Impact Analysis includes: (1) a Life-Cycle 

Cost Analysis that evaluates the savings in operating expense relative to an increase 

in purchase price for individual consumers; (2) a Utility Analysis that measures the 

impacts of the altered energy-consumption patterns on electric utilities; (3) an 

Industry Impact Analysis that shows the financial and competitive impacts on the 

respective appliance manufacturing industry; and (4) a Cost-Benefit Analysis that 

collects the results of all the analyses into the net benefits and costs from a national 

perspective. 

The Engineering Analysis segregates product types into separate classes to which different 

energy conservation standards apply. For each appliance class, baseline units are chosen 

representing relatively low-efficiency units currently being manufactured. The analysis identifies 

a series of design options to improve energy efficiency and estimates the factory costs to produce 

them. Design options are added individually or in combination to the baseline unit to evaluate 

units that might be produced in response to standards. Factory costs are then marked up to 

1-1 
consumer prices, and life-cycle cost curves are constructed for each class. The payback periods

associated with each incremental change in efficiency are also calculated.

The Consumer Analysis focuses on national energy savings and economic impacts on

consumers up to the year 2030. It forecasts efficiencies and sales of new appliances by class,

as well as appliance usage levels in response to changes in projected energy prices and incomes.

These results are used to project energy use and consumer expenditures on fuel and equipment.

The net present value of the standards is computed by discounting the differences in the time

streams of these expenditures in the "with" and "without" new standards cases.

The Manufacturer Impact Analysis estimates the overall impact of new or amended

standards on manufacturers. The analysis examines long-run impacts on (1) profitability, (2)

growth, and (3) competitiveness. To do this, two measures of impact are tracked for the industry

as a whole and for any segments that may exist: (1) return-on-equity (ROE) and (2) net income.

ROE provides the primary measure of profitability; gross margin, return-on-assets (ROA), and

return-on-sales (ROS) are also reported. The analysis also shows total assets, shipments, average

prices, and revenues. Assets and income are the measures of growth (positive or negative).

Two short-run impacts are also analyzed. First, the ability of the industry as a whole and

of specific segments of the industry to make the one-time investments required to meet the new

standards is examined. Second, if standards result in decreased sales for the particular industry

being analyzed, the analysis examines the possibility of price-cutting while the industry is

adjusting to a lower sales volume.

The impacts on individual consumers are evaluated by determining the changes in

life-cycle cost resulting from the imposition of the proposed standards.

The Utility Analysis focuses on revenue changes, avoided costs, and reductions in peak

electric loads. Utility-avoided costs represent the marginal value of lower fuel consumption as,

well as the marginal value of lower investment in new generating plants. Reduction in peak

loads can lead to deferring the construction of new generating capacity. Because fuel costs and

the need for additional capacity are region-specific, the Utility Analysis is conducted on a

regional basis.

1-2

1,

consumer prices, and life-cycle cost curves are constructed for each class. The payback periods 

associated with each incremental change in efficiency are also calculated. 

The Consumer Analysis focuses on national energy savings and economic impacts on 

consumers up to the year 2030; It forecasts efficiencies and sales of new appliances by class, 

as well as appliance usage levels in response to changes in projected energy prices and incomes. 

These results are used to project energy use and consumer expenditures on fuel and equipment. 

The net present value of the standards is computed by discounting the differences in the time 

streams of these expenditures in the "with" and "without" new standards cases. 

The Manufacturer Impact Analysis estimates the overall impact of new or amended 

standards on manufacturers. The analysis examines long-run impacts on (1) profitability, (2) 

growth, and (3) competitiveness. To do this, two measures of impact are tracked for the industry 

as a whole and for any segments that may exist: (1) retum-on-equity (ROE) and (2) net income. 

ROE provides the primary measure of profitability; gross margin, return-on-assets (ROA), and 

return-on-sales (ROS) are also reported. The analysis also shows total assets, shipments, average 

prices, and revenues. Assets and income are the measures of growth (positive or negative). 

Two short-run impacts are also analyzed. First, the ability of the industry as a whole and 

of specific segments of the industry to make the one-time investments required to meet the new 

standards is examined. Second, if standards result in decreased sales for the particular industry 

being analyzed, the analysis examines the possibility of price-cutting while the industry is 

adjusting to a lower sales volume. 

The impacts on individual consumers are evaluated by determining the changes in 

life-cycle cost resulting from the imposition of the proposed standards. 

The Utility Analysis focuses on revenue changes, avoided costs, and reductions in peak 

electric loads. Utility-avoided costs represent the marginal value of lower fuel consumption aswell as the marginal value of lower investment in new generating plants. Reduction in peak 

loads can lead to deferring the construction of new generating capacity. Because fuel costs and 

the need for additional capacity are region-specific, the Utility Analysis is conducted on a 

regional basis. 

1-2 
CHAPTER 2. INTRODUCTION

2.1 PURPOSE AND SCOPE

The Energy Policy and Conservation Act (P.L.94-163), as amended by the National

Appliance Energy Conservation Act of 1987 (P.L. 100-12) and by the National Appliance Energy

Conservation Amendments of 1988 (P.L. 100-357), provides energy conservation standards for

12 of the 13 types of consumer products' covered by the Act, and authorizes the Secretary of

Energy to prescribe amended or new energy standards for each type ( or class) of covered product.

The assessment of the proposed standards for the eight types of products2 presented in this

document is designed to evaluate their economic impacts according to the criteria in the Act.

It includes an engineering analysis of the cost and performance of design options to improve the

efficiency of the products; forecasts of the number and average efficiency of products sold, the

amount of energy the products will consume, and their prices and operating expenses; a

determination of change in investment, revenues, and costs to manufacturers of the products; a

calculation of the costs and benefits to consumers, electric utilities, and the nation as a whole;

and an assessment of the environmental impacts of the proposed standards.

2.2 STRUCTURE OF THE DOCUMENT

This technical support document. consists of nine volumes (one general and one for each

product). Volume A, General Methodology, provides a general description of the analytic

approach, including the structure of the major models. Volumes B through I are each specific

to a product. Each volume contains the data and documentation specific to the analysis of one

product, and the results of that analysis. A reader interested in one product will benefit from

reading Volume A for general methodology and the specific volume containing analysis results

for the product of interest.

t The 13 products covered in the legislation are: (1 ) refrigerators, refrigerator-freezers, and freezers; (2) room

air conditioners; (3) central air conditioners and central air -conditioning heat pumps; (4) water heaters; (5) furnaces;

(6) dishwashers; (7) clothes washers; (8) clothes dryers; (9) direct heating equipment; (10) kitchen ranges and ovens;

(11) pool heaters; (12). television sets; and (13 ) fluorescent lamp ballasts.

2 The 8 types of products analyzed in this rulemaking are: (1) water heaters ; (2) direct heating equipment; (3)

mobile home furnaces; (4) pool heaters; (5) room air conditioners; (6) kitchen ranges and ovens (including

microwave ovens); (7) televisions; and (8) fluorescent lamp ballasts.

2-1

1

CHAPTER 2. INTRODUCTION 

2.1 PURPOSE AND SCOPE 

The Energy Policy and Conservation Act (P.L.94-163), as amended by the National 

Appliance Energy Conservation Act of 1987 (P.L. 100-12) and by the National Appliance Energy 

Conservation Amendments of 1988 (P.L. 100-357), provides energy conservation standards for 

12 of the 13 types of consumer products1 covered by the Act, and authorizes the Secretary of 

Energy to prescribe amended or new energy standards for each type (or class) of covered product. 

The assessment of the proposed standards for the eight types of products2 presented in this 

document is designed to evaluate their economic impacts according to the criteria in the Act. 

It includes an engineering analysis of the cost and performance of design options to improve the 

efficiency of the products; forecasts of the number and average efficiency of products sold, the 

amount of energy the products will consume, and their prices and operating expenses; a 

determination of change in investment, revenues, and costs to manufacturers of the products; a 

calculation of the costs and benefits to consumers, electric utilities, and the nation as a whole; 

and an assessment of the environmental impacts of the proposed standards. 

2.2 STRUCTURE OF THE DOCUMENT 

This technical support document consists of nine volumes (one general and one for each 

product). Volume A, General Methodology, provides a general description of the analytic 

approach, including the structure of the major models. Volumes B through I are each specific 

to a product. Each volume contains the data and documentation specific to the analysis of one 

product, and the results of that analysis. A reader interested in one product will benefit from. 

reading Volume A for general methodology and the specific volume containing analysis results 

for the product of interest. 

1 The 13 products covered in the legislation are: (1) refrigerators, refrigerator-freezers, and freezers; (2) room 

air conditioners; (3) central air conditioners and central air-conditioning heat pumps; (4) water heaters; (5) furnaces; 

(6) dishwashers; (7) clothes washers; (8) clothes dryers; (9) direct heating equipment; (10) kitchen ranges and ovens; 

(11) pool heaters; (12). television sets; and (13) fluorescent lamp ballasts. 

2 The 8 types of products analyzed in this rulemaking are: (1) water heaters; (2) direct heating equipment; (3) 

mobile home furnaces; (4) pool heaters; (5) room air conditioners; (6) kitchen ranges and ovens (including 

microwave ovens); (7) televisions; and (8) fluorescent lamp ballasts. 

2-1 
Volume A contains five chapters and five appendices:

Chapter 1 Executive Summary: summarizes major results.

Chapter 2 Introduction: outlines the structure of the documents.

Chapter 3 Analytic Approach: summarizes the methodology used in the analysis; discusses

the components of the analysis and their interrelationships; describes the models

used, their data requirements, and their outputs; and identifies the primary

assumptions of the analysis. This chapter also provides an overview of the

methodology employed to examine the sensitivity of the results to changes in the

key assumptions, parameters, and exogenous forecasts used in the analysis.

Chapter 4 Industry Profile: discusses appliance saturations, market shares, and industry

structure.

Chapter 5 Development of Base Case Forecasts: discusses population projections, housing

starts, commercial floorspace projections, and energy prices.

The appendices in Volume A cover the following topics:

Appendix A Methodology for uncertainty analysis of maximum technologically feasible energy

efficiency.

Appendix B Overview of forecasting models: structure of the LBL Residential Energy Model

(LBL-REM) and the EPRI COMMEND programs. The principal components of

the models are discussed and the component interrelationship is explained. In

Volumes B through I, Appendix B describes and defines the inputs to the model

specific to each respective end use.

Appendix C Overview of manufacturer impact analysis (LBL-MIM): outlines how an industry

profile of the manufacturing firms affected is constructed; describes how the

effects on appliance manufacturers of imposing standards by product are

quantified; defines the results of the analysis in terms of changes in gross margin,

return on sales, return on equity, return on assets, total assets, and labor

requirements; and presents the method for conducting a sensitivity analysis of the

results.

Appendix D Maintenance costs are derived for several components common to more than one

appliance type.

Appendix E Electric utility impact modeling.

2-2

Is,

Volume A contains five chapters and five appendices: 

Chapter 1 

Chapter 2 

Chapter 3 

Chapter 4 

Chapter 5 

Executive Summary: summarizes major results. 

Introduction: outlines the structure of the documents. 

Analytic Approach: summarizes the methodology used in the analysis; discusses 

the components of the analysis and their interrelationships; describes the models 

used, their data requirements, and their outputs; and identifies the primary 

assumptions of the analysis. 'This chapter also provides an overview of the 

methodology employed to examine the sensitivity of the results to changes in the 

key assumptions, parameters, and exogenous forecasts used in the analysis. 

Industry Profile: discusses appliance saturations, market shares, and industry 

structure. 

Development of Base Case Forecasts: discusses population projections, housing 

starts, commercial floorspace projections, and energy prices. 

The appendices in Volume A cover the following topics: 

Appendix A Methodology for uncertainty analysis of maximum technologically feasible energy 

efficiency. 

Appendix B Overview of forecasting models: structure of the LBL Residential Energy Model 

(LBL-REM) and the EPRI COMMEND programs. The principal components of 

the models are discussed and the component interrelationship is explained. In 

Volumes B through I, Appendix B describes and defines the inputs to the model 

specific to each respective end use. 

Appendix C Overview of manufacturer impact analysis (LBL-MIM): outlines how an industry 

profile of the manufacturing firms affected is constructed; describes how the 

effects on appliance manufacturers of imposing standards by product are 

quantified; defines the results of the analysis in terms of changes in gross margin, 

return on sales, return on equity, return on assets, total assets, and labor 

requirements; and presents the method for conducting a sensitivity analysis of the 

results. 

Appendix D Maintenance costs are derived for several components common to more than one 

appliance type. 

Appendix E Electric utility impact modeling. 

2-2 
Environmental Assessment. Quantifies impacts of standards on emissions of carbon dioxide,

nitrogen oxides, and sulfur oxides.

Regulatory Impact Analysis.

Volumes B through I each correspond to a particular product. Each volume is organized

into eight chapters and three appendices.

Chapter 1 Engineering Analysis: contains detailed energy use and cost information.

Chapter 2 Base Case Forecasts: describes national forecast of energy consumption, efficiency

of new units, units installed in households, and annual appliance sales in the

absence of new regulations.

Chapter 3 Projected National Impacts of Standards: discusses the choice of standards levels

to be analyzed and the projected impacts of each standard level. The chapter

summarizes the energy savings by fuel and product; the sales, average efficiencies,

purchase costs, and operating costs of new and replacement appliances; the net

present benefit of the standards; and the results of the sensitivity analyses.

Chapter 4 Life-Cycle Costs and Payback Periods: describes the effects of standards on

individual purchasers and users of appliances. It compares the life-cycle cost of

appliances and other measures of consumer impact with and without the proposed

standards.

Chapter 5 Impacts of Standards on Manufacturers: describes the analysis methodology, short

and long-run impacts, and sensitivity analysis.

Chapter 6 Impact of Standards on Electric Utilities: describes the effects of standards on the

electric utility industry, focusing on marginal costs of electricity, generating

capacity growth, changes in regional capacity and energy demand, and changes in

utility revenues and costs.

Chapter 7 Environmental Effects: describes standards-induced changes in emissions of oxides

of carbon, sulfur, and nitrogen from combustion of fossil fuels for electricity

generation and in homes.

Appendices A through C contain more detailed information on the models and databases

used in the economic analysis.

2-3

Environmental Assessment. Quantifies impacts of standards on emissions of carbon dioxide, 

nitrogen oxides, and sulfur oxides. 

Regulatory Impact Analysis. 

Volumes B through I each correspond to a particular product. Each volume is organized 

into eight chapters and three appendices. 

Chapter 1 

Chapter 2 

Chapter 3 

Chapter 4 

Chapter 5 

Chapter 6 

Chapter 7 

Engineering Analysis: contains detailed energy use and cost information. 

Base Case Forecasts: describes national forecast of energy consumption, efficiency 

of new units, units installed in households, and annual appliance sales in the 

absence of new regulations. 

Projected National Impacts of Standards: discusses the choice of standards levels 

to be analyzed and the projected impacts of each standard level. The chapter 

summarizes the energy savings by fuel and product; the sales, average efficiencies, 

purchase costs, and operating costs of new and replacement appliances; the net 

present benefit of the standards; and the results of the sensitivity analyses. 

Life-Cycle Costs and Payback Periods: describes the effects of standards on 

individual purchasers and users of appliances. It compares the life-cycle cost of 

appliances and other measures of consumer impact with and without the proposed 

standards. 

Impacts of Standards on Manufacturers: describes the analysis methodology, short 

and long-run impacts, and sensitivity analysis. 

Impact of Standards on Electric Utilities: describes the effects of standards on the ,_ 

electric utility industry, focusing on marginal costs of electricity, generating 

capacity growth, changes in regional capacity and energy demand, and changes in 

utility revenues and costs. 

Environmental Effects: describes standards-induced changes in emissions of oxides 

of carbon, sulfur, and nitrogen from combustion of fossil fuels for electricity 

generation and in homes. 

Appendices A through C contain more detailed information on the models and databases 

used in the economic analysis. 

2-3 
Appendix A Energy Use and Cost Data: provides a breakdown of the costs for each design

option.

Appendix B Forecasting Model (LBL-REM or EPRI COMMEND): gives a detailed description

of the data used to calculate consumer impacts. The input data stream for the

base case runs is shown and changes to the input data for the standards cases are

indicated.

Appendix C Manufacturer Impact Analysis: provides a detailed description of the model to

estimate financial impacts of the imposition of standards on the manufacturers of

covered products. The appendix shows the structure of the model, the data

sources used, and detailed outputs of base case, standards case, and sensitivity

analysis runs.

2-4

ze

Appendix A Energy Use and Cost Data: provides a breakdown of the costs for each design 

option. 

Appendix B Forecasting Model (LBL-REM or EPRI COMMEND): gives a detailed description 

of the data used to calculate consumer impacts. The input data stream for the 

base case runs is shown and changes to the input data for the standards cases are 

indicated. 

Appendix C Manufacturer Impact Analysis: provides a detailed description of the model to 

estimate fmandal impacts of the imposition of standards on the manufacturers of 

covered products. The appendix shows the structure of the model, the data 

sources used, and detailed outputs of base case, standards case, and sensitivity 

analysis runs. 

2-4 
CHAPTER 3. ANALYTIC APPROACH

3.lINTRODUCTION

The economic impacts of new or amended energy efficiency standards depend largely on

the relationship between the cost of a consumer product and its energy efficiency. The costefficiency relationships are determined for each product class based on engineering analyses.

DOE has segregated product types into classes to which different energy efficiency standards

might apply. The product types and the number of classes of each product type are shown in

Table 3.1. Within each class, the energy-conserving designs are ordered by cost-effectiveness;

then combinations of energy-saving design options are used to generate the relationships that are

inputs to the other analyses of the impacts of setting standards at various efficiency levels.

Identification of product classes, baseline units, design options, and maximum

technologically feasible efficiencies is based on information gathered from the trade associations,

manufacturers, discussions with researchers, a literature survey, and comments on the Advance

Notice of Proposed Rulemaking (ANOPR). Energy use data have been obtained from AHAM

(Association of Home Appliance Manufacturers), GAMA (Gas Appliance Manufacturers

Association), FTC (Federal Trade Commission), manufacturers, and simulation models. Sources

of factory cost data include manufacturers, suppliers, and the ANOPR comments. The cost

estimates are combined with the efficiency estimates to generate the cost-efficiency relationships.

The relationships between manufacturer costs and efficiency that are presented in this

chapter are used throughout the other parts of the analysis. The engineering analysis also includes

determination of maintenance and installation costs for each of the design options studied (see

Appendix F of this volume and Chapter 1 of Volumes B through I). The manufacturer analysis

uses manufacturer costs to produce retail prices. Retail prices, installation costs, and maintenance

costs in conjunction with the corresponding efficiencies are used in the forecasting models to

forecast sales and efficiencies and to calculate life-cycle costs and payback periods.

Table 3.1 Product Types and Number of Product Classes

Product Type Number of Classes

Fluorescent Lamp Ballasts

Television Sets

Water Heaters

Pool Heaters

Direct Heating Equipment

Mobile. Home Furnaces

Room Air Conditioners

Kitchen Ranges and Ovens

3-1

11 1

CHAPTER 3. ANALYTIC APPROACH 

3.1INTRODUCTION 

The economic impacts of new or amended energy efficiency standards depend largely on 

the relationship between the cost of a consumer product and its energy efficiency. The costefficiency relationships are determined for each product class based on engineering analyses. 

DOE has segregated product types into classes to which different energy efficiency standards 

might apply. The product types and the number of classes of each product type are shown in 

Table 3.1. Within each class, the energy-conserving designs are ordered by cost-effectiveness; 

then combinations of energy-saving design options are used to generate the relationships that are 

inputs to the other analyses of the impacts of setting standards at various efficiency levels. 

Identification of product classes, baseline units, design options, and maximum 

technologically feasible efficiencies is based on information gathered from the trade associations, 

manufacturers, discussions with researchers, a literature survey, and comments on the Advance 

Notice of Proposed Rulemaking (ANOPR). Energy use data have been obtained from AHAM 

(Association of Home Appliance Manufacturers), GAMA (Gas Appliance Manufacturers 

Association), FfC (Federal Trade Commission), manufacturers, and simulation models. Sources 

of factory cost data include manufacturers, suppliers, and the ANOPR comments. The cost 

estimates are combined with the efficiency estimates to generate the cost-efficiency relationships. 

The relationships between manufacturer costs and efficiency that are presented in this 

chapter are used throughout the other parts of the analysis. The engineering analysis also includes 

determination of maintenance and installation costs for each of the design options studied (see . 

Appendix F of this volume and Chapter 1 of Volumes B through I). The manufacturer analysis. 

uses manufacturer costs to produce retail prices. Retail prices, installation costs, and maintenance 

costs in conjunction with the corresponding efficiencies are used in the forecasting models to 

forecast sales and efficiencies and to calculate life-cycle costs and payback periods. 

Table 3.1 Product Types and Number of Product Classes 

Product Type Number of Classes 

Fluorescent Lamp Ballasts 

Television Sets 

Water Heaters 

Pool Heaters 

Direct Heating Equipment 

Mobile. Home Furnaces 

Room Air Conditioners 

Kitchen Ranges and Ovens 

3-1 

10 

2 

4 

1 

16 

2 

12 

8 
This chapter briefly describes how the economic analysis of standards was performed.

Section 3.2 presents an overview of the analytic methodology and discusses the major

components of the analysis: the Engineering Analysis, the Consumer Analysis (national and

individual), the Manufacturer and Industrial Analyses, and the Utility Analysis. The emphasis

is on how these components fulfill the seven legislative requirements listed in the Introduction.

This section discusses the interrelationships among the components that ensure consistency

throughout the analysis.

Section 3.3 describes the computerized models used in the analysis. The models predict

consumer, manufacturer, and utility responses to future changes in the economy, including the

imposition of minimum efficiency standards. Quantitative estimates of the impacts of standards

are calculated from the outputs of the models. The models utilized in the analysis are:

• Engineering Cost and Performance Models;

• Consumer Impact Models;

• Manufacturer Impact Models;

• Utility Impact Model.

The function, data sources, assumptions, and validity of the results for each model are

discussed in the product-specific volumes (Volumes B through I).

Section 3.4 discusses the sensitivity analysis performed on the economic impacts and

focuses on the methods used to determine the parameters that have the largest effects and to

determine their range of variation.

3.2 OVERVIEW OF ANALYTIC FRAMEWORK

The impact of appliance efficiency standards is determined by comparing projections of

a wide range of economic variables under the existing legislation with the projections under the

proposed standards. For each of the eight products analyzed, these projections are first made for

a base case (existing legislation) using the analytic models described below. In each productspecific volume, Chapter 2 describes the data and assumptions used to calculate the baseline

forecasts. The calculations are then repeated imposing the proposed standards as discussed in

Chapter 3. The differences between the projections of the energy consumption and economic

variables in the base and standards cases provide quantitative estimates of the impacts of the

standards. To evaluate the significance of the differences, a sensitivity analysis is performed on

the key parameters and assumptions.

3-2

2 IL

This chapter briefly describes how the economic analysis of standards was performed. 

Section 3.2 presents an overview of the analytic methodology and discusses the major 

components of the analysis: the Engineering Analysis, the Consumer Analysis (national and 

individual), the Manufacturer and Industrial Analyses, and the Utility Analysis. The emphasis 

is on how these components fulfill the seven legislative requirements listed in the Introduction. 

This section discusses the interrelationships among the components that ensure consistency 

throughout the analysis. 

Section 3.3 describes the computerized models used in the analysis. The models predict 

consumer, manufacturer, and utility responses to future changes in the economy, including the 

imposition of minimum efficiency standards. Quantitative estimates of the impacts of standards 

are calculated from the outputs of the models. The models utilized in the analysis are: 

• Engineering Cost and Performance Models; 

• Consumer Impact Models; 

• Manufacturer Impact Models; 

• Utility Impact Model. 

The function, data sources, assumptions, and validity of the results for each model are 

discussed in the product-specific volumes (Volumes B through I). 

Section 3.4 discusses the sensitivity analysis performed on the economic impacts and 

focuses on the methods used to determine the parameters that have the largest effects and to 

determine their range of variation. 

3.2 OVERVIEW OF ANALYTIC FRAMEWORK 

The impact of appliance efficiency standards is determined by comparing projections of 

a wide range of economic variables under the existing legislation with the projections under the 

proposed standards. For each of the eight products analyzed, these projections are fIrst made for. 

a base case (existing legislation) using the analytic models described below. In each productspecific volume, Chapter 2 describes the data and assumptions used to calculate the baseline 

forecasts. The calculations are then repeated imposing the proposed standards as discussed in 

Chapter 3. The differences between the projections of the energy consumption and economic 

variables in the base and standards cases provide quantitative estimates of the impacts of the 

standards. To evaluate the significance of the differences, a sensitivity analysis is performed on 

the key parameters and assumptions. 

3-2 
The economic impact analysis is performed in seven major areas:

An Engineering Analysis, which establishes technical feasibility and product

attributes including costs of design options to improve appliance efficiency;

• A Consumer Analysis at two levels: national aggregate impacts, and impacts on

individuals. The national aggregate impacts include LBL Residential Energy

Model (LBL-REM) forecasts of appliance sales, efficiencies, energy use, and

consumer expenditures. The individual impacts are analyzed by Life-Cycle Cost

(LCC), Payback Periods, and Cost of Conserved Energy (CCE), which evaluate

the savings in operating expenses relative to increases in purchase price;

• A Manufacturer Analysis, which provides an estimate of manufacturers' response

to the proposed standards. Their response is quantified by changes in several

measures of financial performance for a firm;

• An Industry Impact Analysis, which shows financial and competitive impacts on

the appliance industry;

• A Utility Analysis that measures the impacts of the altered energy-consumption

patterns on electric utilities;

• An Environmental Effects Analysis, which estimates changes in emissions of

carbon dioxide, sulfur oxides, and nitrogen oxides, because of reduced energy

consumption in the home and at the power plant; and

• A Regulatory Impact Analysis, which collects the results of all the analyses into

the net benefits and costs from a national perspective.

A simplified diagram of the analysis is shown in Figure 3.1. Each type of analysis is

performed for the eight types of products. If appliances having the maximum efficiency

improvement that is technologically feasible show no significant energy savings in the

Engineering Analysis, then the legislation requires that no modified standard be prescribed for

that product type. If the appliance efficiency can be increased to produce significant energy

savings, then a detailed energy savings, economic, and impact analysis is done. For each of the

eight products, the analysis is performed for a base case plus several trial levels of standards.

The selection of trial standard levels by class is described in Chapter 3 for each product-specific

volume.

3-3

:-3

The economic impact analysis is performed in seven major areas: 

• An Engineering Analysis, which establishes technical feasibility and product 

attributes including costs of design options to improve appliance efficiency; 

• A Consumer Analysis at two levels: national aggregate impacts, and impacts on 

individuals. The national aggregate impacts include LBL Residential Energy 

Model (LBL-REM) forecasts of appliance sales, efficiencies, energy use, and 

consumer expenditures. The individual impacts are analyzed by Life-Cycle Cost 

(LCC), Payback Periods, and Cost of Conserved Energy (CCE), which evaluate 

the savings in operating expenses relative to increases in purchase price; 

• A Manufacturer Analysis, which provides an estimate of manufacturers' response 

to the proposed standards. Their response is quantified by changes in several 

measures of financial performance for a firm; 

• An Industry Impact Analysis, which shows financial and competitive impacts on 

the appliance industry; 

• A Utility Analysis that measures the impacts of the altered energy-consumption 

patterns on electric utilities; 

• An Environmental Effects Analysis, which estimates changes in emissions of 

carbon dioxide, sulfur oxides, and nitrogen oxides, because of reduced energy 

consumption in the home and at the power plant; and 

• A Regulatory Impact Analysis, which collects the results of all the analyses into 

the net benefits and costs from a national perspective. 

A simplified diagram of the analysis is shown in Figure 3.1. Each type of analysis is 

performed for the eight types of products. If appliances having the maximum efficiency 

improvement that is technologically feasible show no significant energy savings in the 

Engineering Analysis, then the legislation requires that no modified standard be prescribed for 

that product type. . If the appliance efficiency can be increased to produce significant energy 

savings, then a detailed energy savings, economic, and impact analysis is done. For each of the 

eight products, the analysis is performed for a base case plus several trial levels of standards. 

The selection of trial standard levels by class is described in Chapter 3 for each product-specific 

volume. 

3-3 
Engineering

Analysis

Manufacturer

Analysis

Life Cycle Cost

Analysis

Forecasts

Paybacks

Utility

Analysis

• Manufacturer Cost vs. Efficiency

• Retail Price vs. Efficiency

• Manufacturer Impacts

• Life Cycle Costs

• National Energy

• Net Present Value to Society

►- • Consumer Payback

• Generating Capacity

Environmental • Emissions (C02, NOx, S02) Analysis

Figure 3.1 Analytic Framework for the Appliance Standard Analysis

3-4

Engineering 110. 

Analysis -.... • Manufacturer Cost vs. Efficiency 

." 

Manufacturer .... -,.. • Retail Price vs. Efficiency 

Analysis ""- ". • Manufacturer Impacts 

... Life Cycle Cost 110. " .. Analysis .". • Life Cycle Costs 

...,... .. • National Energy ..... Forecasts ,. 10. 

... • Net Present Value to Society 

to. Paybacks ..... -.... -,.. • Consumer Payback 

to. Utility 110. 

-.... Analysis ,... • Generating Capacity 

..... Environmental !Io. ... Analysis ",.. • Emissions (C02, NOx, S02) 

Figure 3.1 Analytic Framework for the Appliance Standard Analysis 

3-4 
Figure 3.1 illustrates the interaction among the Engineering, Consumer, and Manufacturer

Analyses. The Engineering Analysis establishes appliance designs and related attributes such as

efficiency and costs. Based on these costs, the Manufacturer Analysis predicts retail prices for

use in the consumer analysis (the Life-Cycle Cost Analysis and Forecasts). Based on the

relationship between the prices and efficiencies of design options, the consumer analysis forecasts

sales and efficiencies of new and replacement appliances. These data are used as inputs to the

Manufacturer Analysis to determine financial impacts on typical firms within the industry. The

consumer analysis also forecasts energy savings and consumer expenditures for the purchase and

operation of the appliances. Consumer expenditures are used in the Life-Cycle Cost Analysis to

determine consumer impacts. Changes in sales, revenues, investments, and marginal costs of

utilities are calculated from the energy savings in the Utility Analysis.

Three time frames are considered by the analysis. First, the analysis of consumer and utility

impacts extends over a time frame consistent with the life of the products and includes the time

required to approach market saturation. This time frame extends to 2030 and the standards come

into effect in 1996. Second, the Manufacturer Analysis is performed for a typical year after the

imposition of standards. Third, the Engineering Analysis examines the technical feasibility of

improving the efficiency of appliances before the standards come into effect within the next

three years.

3.3 MODELS, DATA, AND ASSUMPTIONS

3.3.1 Engineering Cost and Performance Models

The Engineering Analysis provides information on efficiencies, manufacturing costs, and

other appliance characteristics for use in other components of the analysis. Appliance features

that provide utility to the consumer are incorporated into the analysis through the creation of

appliance classes. Classes are a subset of appliance types. For example, a water heater is an

appliance type, but a gas-fired storage water heater is an appliance class. The Engineering

Analysis develops cost and efficiency data for a set of design options within each appliance class.

These data are the output of the engineering performance and cost models discussed later in this

section. The Engineering Analysis is performed in seven steps: (1) select appliance classes; (2)

select baseline units; (3) select design options within each class; (4) determine maximum

technically feasible designs; (5) calculate the efficiency improvement provided by each design

option; (6) develop cost estimates; (7) generate cost-efficiency relationships.

Appliance Classes

The first step in the Engineering Analysis is the segregation of product types into separate

classes to which different energy-efficiency standards apply. DOE differentiates classes by the

type of energy used (oil, natural gas, or electricity), and capacity or performance-related features

that provide utility to the consumer and affect efficiency. For specific appliances, classes are

defined using data collected in discussions with appliance manufacturers, trade associations, other

interested parties, and from comments received on the Advance Notice of Proposed Rulemaking.

3-5

Figure 3.1 illustrates the interaction among the Engineering, Consumer, and Manufacturer 

Analyses. The Engineering Analysis establishes appliance designs and related attributes such as 

efficiency and costs. Based on these costs, the Manufacturer Analysis predicts retail prices for 

use in the consumer analysis (the Life-Cycle Cost Analysis and Forecasts). Based on the 

relationship between the prices and efficiencies of design options, the consumer analysis forecasts 

sales and efficiencies of new and replacement appliances. These data are used as inputs to the 

Manufacturer Analysis to determine fmancial impacts on typical firms within the industry. The 

consumer analysis also forecasts energy savings and consumer expenditures for the purchase and 

operation of the appliances. Consumer expenditures are used in the Life-Cycle Cost Analysis to 

determine consumer impacts. Changes in sales, revenues, investments, and marginal costs of 

utilities are calculated from the energy savings in the Utility Analysis. 

Three time frames are considered by the analysis. First, the analysis of consumer and utility 

impacts extends over a time frame consistent with the life of the products and includes the time 

required to approach market saturation. This time frame extends to 2030 and the standards corne 

into effect in 1996. Second, the Manufacturer Analysis is performed for a typical year after the 

imposition of standards. Third, the Engineering Analysis examines the technical feasibility of 

improving the efficiency of appliances before the standards corne into effect-within the next 

three years. 

3.3 MODELS, DATA, AND ASSUMPTIONS 

3.3.1 Engineering Cost and Performance Models 

The Engineering Analysis provides information on efficiencies, manufacturing costs, and 

other appliance characteristics for use in other components of the analysis. Appliance features 

that provide utility to the consumer are incorporated into the analysis through the creation of 

appliance classes. Classes are a subset of appliance types. For example, a water heater is an 

appliance type, but a gas-fired storage water heater is an appliance class. The Engineering 

Analysis develops cost and efficiency data for a set of design options within each appliance class. 

These data are the output of the engineering performance and cost models discussed later in this 

section. The Engineering Analysis is performed in seven steps: (1) select appliance classes; (2) 

select baseline units; (3) select design options within each class; (4) determine maximum 

technically feasible designs; (5) calculate the efficiency improvement provided by each design 

option; (6) develop cost estimates; (7) generate cost-efficiency relationships. 

Appliance Classes 

The first step in the Engineering Analysis is the segregation of product types into separate 

classes to which different energy-efficiency standards apply. DOE differentiates classes by the 

type of energy used (oil, natural gas, or electricity), and capacity or performance-related features 

that provide utility to the consumer and affect efficiency. For specific appliances, classes are 

defmed using data collected in discussions with appliance manufacturers, trade associations, other 

interested parties, and from comments received on the Advance Notice of Proposed Rulemaking. 

3-5 
Those classes for which no DOE test procedure has been specified are omitted. The appliance

classes covered by the analysis are listed in Chapter 1 of each product-specific volume.

Baseline Units

A baseline unit is the starting point for analyzing design options for improving energy

efficiency. To select a baseline unit, the Engineering Analysis uses information gathered from

trade organizations, manufacturers, consultants with expertise in specific product types, and from

public comments on the Notice of Proposed Rulemaking . For each product class, the baseline

unit generally represents a model with the maximum allowable energy use specified by the

National Appliance Energy Conservation Act (NAECA). Other than efficiency, features are

representative of the class as a whole.

Design Options

The Engineering Analysis identifies individual or combinations of design options with a

potential for improving energy efficiency. Design options that are commercially available at the

present time or that are present in prototypes are considered. They are selected after discussions

with experts and an extensive literature review. The efficiency improvement and manufacturer

cost of design options added to the baseline unit are calculated.

Maximum Technologically Feasible Designs

For each product class, a maximum technologically feasible design option or combination

of design options is identified. This option, or combination of options, results in the highest

energy efficiency for each product class. The maximum technologically feasible efficiency level

is one that can be reached by the addition of design options, both commercially feasible and in

prototypes, to the baseline units. This design must be possible to assemble, but not necessarily

to manufacture in large numbers. Economic criteria are applied to all design options in other

parts of the analysis.

Efficiency Calculation

For each of the product classes, the efficiency levels corresponding to various design

options are determined from manufacturer data and from engineering calculations.

Cost Estimates

The manufacturer cost data are obtained through a lengthy process that included meetings

and tours at manufacturing facilities, submission of formal requests to manufacturers for costing

data, and review of the data received. Estimates of manufacturer cost are also received in

response to the Notice of Proposed Rulemaking. In each product-specific volume, Appendix A

contains detailed incremental cost data disaggregated into labor, purchased parts, materials,

shipping/packaging and tooling.

3-6

Those classes for which no DOE test procedure has been specified are omitted. The appliance 

classes covered by the analysis are listed in Chapter 1 of each product-specific volume. 

Baseline Units 

A baseline unit is the starting point for analyzing design options for improving energy 

efficiency. To select a baseline unit, the Engineering Analysis uses information gathered from 

trade organizations, manufacturers, consultants with expertise in specific product types, and from 

public comments on the Notice of Proposed Rulemaking. For each product class, the baseline 

unit generally represents a model with the maximum allowable energy use specified by the 

National Appliance Energy Conservation Act (NAECA). Other than efficiency, features are 

representative of the class as a whole. 

Design Options 

The Engineering Analysis identifies individual or combinations of design options with a 

potential for improving energy efficiency. Design options that are commercially available at the 

present time or that are present in prototypes are considered. They are selected after discussions 

with experts and an extensive literature review. The efficiency improvement and manufacturer 

cost of design options added to the baseline unit are calculated. 

Maximum Technologically Feasible Designs 

For each product class, a maximum technologically feasible design option or combination 

of design options is identified. This option, or combination of options, results in the highest 

energy efficiency for each product class. The maximum technologically feasible efficiency level 

is one that can be reached by the addition of design options, both commercially feasible and in 

prototypes, to the baseline units. This design must be possible to assemble, but not necessarily 

to manufacture in large numbers. Economic criteria are applied to all design options in other· 

parts of the analysis. 

Efficiency Calculation 

For each of the product classes, the efficiency levels corresponding to various design 

options are determined from manufacturer data and from engineering calculations. 

Cost Estimates 

The manufacturer cost data are obtained through a lengthy process that included meetings 

and tours at manufacturing facilities, submission of fonnal requests to manufacturers for costing 

data, and review of the data received. Estimates of manufacturer cost are also received in 

response to the Notice of Proposed Rulemaking. In each product-specific volume, Appendix A 

contains detailed incremental cost data dis aggregated into labor, purchased parts, materials, 

shipping/packaging and tooling. 

3-6 
Cost-Efficiency Relationships

The results of the Engineering Analysis are summarized in the cost-efficiency relationships

showing the efficiency and manufacturer cost of the design options for each appliance class.

Manufacturer and dealer markups derived in the Manufacturing Analysis are applied to the

factory costs to arrive at the purchase price of the appliance. Additional installation costs

required for some designs are included in the purchase price. Additional maintenance costs

associated with specific design options are also estimated. The price-efficiency relationships are

a fundamental input to the Consumer Analysis.

Assumptions in the Analysis

Justifications for the most important assumptions of the Engineering Analysis are listed

below.

Assumption Justification

• Industry production processes can Manufacturing costs for small manufacturers

be characterized in terms of are highly varied because of dependence on

medium and large manufacturing purchased parts and sensitivity to labor and

facilities. distribution costs. An effort was made to

obtain these data, but they were not made

available by small firms. Because of this

difficulty and the fact that they represent a

small fraction of total shipments for most of

the product classes, analysis concentrated on

the medium and large manufacturers.

• Baseline units can be selected to The baseline unit is the starting point to

represent typical product offerings which design options are added to create

within a class. higher efficiency units. The selection of

baseline units is based on studies of products

on the market in 1990 and consultations with

manufacturers.

• Unit energy consumption is based As part of the Consumer Product Efficiency

on DOE usage estimates, unless Standards program, DOE was required to

otherwise noted. establish test procedures to determine average

consumer utilization and energy usage for the

covered products. Test procedures for each

product type have been promulgated and

were employed to determine unit energy

consumption throughout the Engineering

Analysis.-

3-7

Cost-Efficiency Relationships 

The results of the Engineering Analysis are summarized in the cost-efficiency relationships 

showing the efficiency and manufacturer cost of the design options for each appliance class. 

Manufacturer and dealer markups derived in the Manufacturing Analysis are applied to the 

factory costs to arrive at the purchase price of the appliance. Additional installation costs 

required for some designs are included in the purchase price. Additional maintenance costs 

associated with specific design options are also estimated. The price-efficiency relationships are 

a fundamental input to the Consumer Analysis. 

Assumptions in the Analysis 

Justifications for the most important assumptions of the Engineering Analysis are listed 

below. 

• 

• 

• 

Assumption 

Industry production processes can 

be characterized in terms of 

medium and large manufacturing 

facilities. 

Baseline units can be selected to 

represent typical product offerings 

within a class. 

Unit energy consumption is based 

on DOE usage estimates, unless 

otherwise noted. 

Justification 

Manufacturing costs for small manufacturers 

are highly varied because of dependence on 

purchased parts and sensitivity to labor and 

distribution costs. An effort was made to 

obtain these data, but they were not made 

available by small firms. Because of this 

difficulty and the fact that they represent a 

small fraction of total shipments for most of 

the product classes, analysis concentrated on 

the medium and large manufacturers. 

The baseline unit is the starting point to 

which design options are added to create 

higher efficiency units. The selection of 

baseline units is based on studies of products 

on the market in 1990 and consultations with 

manufacturers. 

As part of the Consumer Product Efficiency 

Standards program, DOE was required to 

establish test procedures to determine average 

consumer utilization and energy usage for the 

covered products. Test procedures for each 

product type have been promulgated and 

were employed to determine unit energy 

consumption throughout the Engineering 

Analysis. 

3-7 
Data Sources

Shipment data are based on information from industry sources and published data from

industry trade associations. Costs of purchased materials and parts are based on quotations from

product manufacturers and suppliers of these items. Data on engineering and labor costs are

taken from on-site visits to manufacturing plants and from manufacturer information. Data

characterizing baseline units for each class are based on information from industry sources and

published data.

Outputs from the Engineering Analysis

For each combination of design options considered in the analysis, the models and data

provide:

• energy efficiency (expressed as the DOE energy factor3 and/or unit energy

consumption);

• increased material, labor, and investment costs by product class for medium and

large manufacturers;

• annual energy consumption per unit (based on DOE test procedures);

• the relationship between cost and energy use by product class; and

• other information on product characteristics such as appliance lifetimes,

installation costs, and maintenance costs.

Validation of the Results

Experimental data on efficiencies are available for some of the design options studied. For

the others, engineering calculations based on physical principles are performed.

3.3.2 EPRI COMMEND

COMMEND is a commercial end-use forecasting program supported by EPRI (Electric

.Power Research Institute). COMMEND forecasts energy use by end use and building type. The

model covers eleven building types, ten end uses, and three fuel types. Version 3.2 was used for

this analysis (1).

3The energy factor is a measurement of energy efficiency derived from the DOE test procedure for

each product.

3-8

Data Sources 

Shipment data are based on infonnation from industry sources and published data from 

industry trade associations. Costs of purchased materials and parts are based on quotations from 

product manufacturers and suppliers of these items. Data on engineering and labor costs are 

taken from on-site visits to manufacturing plants and from manufacturer infonnation. Data 

characterizing baseline units for each class are based on information from industry sources and 

. published data. 

Outputs from the Engineering Analysis 

For each combination of design options considered in the analysis, the models and data 

provide: 

• energy efficiency (expressed as the DOE energy factor3 and/or unit energy 

consumption); 

• increased material, labor, and investment costs by product class for medium and 

large manufacturers; 

• annual energy consumption per unit (based on DOE test procedures); 

• the relationship between cost and energy use by product class; and 

• other infonnation on product characteristics such as appliance lifetimes, 

installation costs, and maintenance costs. 

Validation of the Results 

Experimental data on efficiencies are available for some of the design options studied. For 

the others, engineering calculations based on physical principles are perfonned. 

3.3.2 EPRI COMMEND 

COMMEND is a commercial end-use forecasting program supported by EPRI (Electric 

.Power Research Institute). COMMEND forecasts energy use by end use and building type. The 

model covers eleven building types, ten end uses, and three fuel types. Version 3.2 was used for 

this analysis (1). 

3The energy factor is a measurement of energy efficiency derived from the DOE test procedure for 

each product. 

3-8 
Forecast fuel prices and floor space are exogenous inputs to COMMEND. COMMEND

models fuel switching and selection of equipment based on life-cycle-cost criteria. There are four

groups of decision-makers, each with a different discount rate. Short-run price elasticities for

utilization of energy services are included. Interactions between lighting and space-conditioning

energy use are also handled within the model.

Representation of Technologies

In COMMEND, technology options are represented as a technology trade-off curve that

shows the efficiency attainable for a given equipment cost. Technology trade-off curves are

defined for at least one equipment market for each energy end use. COMMEND defines only

one curve for the lighting end use. For heating, however, separate trade-off curves are defined

to model equipment markets for technologies using different fuel types. A trade-off curve for

a particular building type is defined by the year, equipment cost level, average Energy Use

Intensity (EUI kWh/sq ft-year), and an elasticity representing the cost-efficiency relationship.

Several equipment components make up the lighting end use: lamps, ballasts, fixtures, and

controls. With COMMEND's present structure, it is not possible to represent the actual

technology options for lighting directly; instead, technology options have to be mapped onto the

technology trade-off curves for the single lighting end use by analyses performed outside

COMMEND. Lighting component spreadsheets are used to derive lighting power densities

(watts/sq ft) for each building type based on the energy characteristics and penetrations of the

different component technologies; these densities are then converted to EUIs using building

operating hours developed from EIA data (2).

Development of COMMEND Input

The basic relation modeled by COMMEND is that lighting energy use is the product of

floor area (in square feet for a given end use) and energy use per unit area (in kilowatt-hours per

square foot) for that end use, or EUI, summed over all building types.

The analysis integrated a variety of data: current market shares, recent usage trends for

various lighting products, and the LBL engineering analysis wattages and costs. The results of

this integration were used first to develop a base case set of lighting EUIs. These EUIs were

then used to calibrate COMMEND and develop reference forecasts against which the net impacts

of the policies were estimated. The base case lighting EUIs were then modified to reflect the

impact of lighting equipment and building standards. The difference in forecasts between the

base case and the policy is the net energy impact.

Forecast fuel" prices and floor space are exogenous inputs to COMMEND. COMMEND 

models fuel switching and selection of equipment based on life-cycle-cost criteria. There are four 

groups of decision-makers, each with a different discount rate. Short-run price elasticities for 

utilization of energy services are included. Interactions between lighting and space-conditioning 

energy use are also handled within the model. 

Representation of Technologies 

In COMMEND, technology options are represented as a technology trade-off curve that 

shows the efficiency attainable for a given equipment cost. Technology trade-off curves are 

defined for at least one equipment market for each energy end use. COMMEND defines only 

one curve for the lighting end use. For heating, however, separate trade-off curves are defined 

to model equipment markets for technologies using different fuel types. A trade-off curve for 

a particular building type is defined by the year, equipment cost level, average Energy Use 

Intensity (EUI kWh/sq ft-year), and an elasticity representing the cost-efficiency relationship. 

Several equipment components make up the lighting end use: lamps, ballasts, fixtures, and 

controls. With COMMEND's present structure, it is not possible to represent the actual 

technology options for lighting directly; instead, technology options have to be mapped onto the 

technology trade-off curves for the single lighting end use by analyses performed outside 

COMMEND. Lighting component spreadsheets are used to derive lighting power densities 

(watts/sq ft) for each building type based on the energy characteristics and penetrations of the 

different component technologies; these densities are then converted to EUIs using building 

operating hours developed from EIA data (2). 

Development of COMMEND Input 

The basic relation modeled by COMMEND is that lighting energy use is the product of 

floor area (in square feet for a given end use) and energy use per unit area (in kilowatt-hours per 

square foot) for that end use, or EUI, summed over all building types. 

The analysis integrated a variety of data: current market shares, recent usage trends for 

various lighting products, and the LBL engineering analysis wattages and costs. The results of 

this integration were used first to develop a base case set of lighting EUIs. These EUIs were 

then used to calibrate COMMEND and develop reference forecasts against which the net impacts 

of the policies were estimated. The base case lighting EUIs were then modified to reflect the 

impact of lighting equipment and building standards. The difference in forecasts between the 

base case and the policy is the net energy impact. 

3-9 
Turnover of Lighting Equipment

At present, only one lifetime may be specified for the lighting end use in the model even

though lamps usually have to be replaced much more frequently than ballasts and fixtures.

Discrete lighting component representation is expected to be a feature of COMMEND Version

4.0.

A lifetime of 15 years is used in this analysis. Fifteen years approximates the average

lifetime of ballasts,4 and corresponds to the average fixture turnover rate in renovated buildings.

Annual Operating Hours

To represent hours per year that commercial sector lighting equipment is operating for each

building type, building operating hours from NBECS 1986 are used. (2)

Calibration

As a first step in the calibration process, comparisons between COMMEND inputs and

NBECS 1986, EIA's Annual Energy Outlook 1991 (AEO) (3), and State Energy Data Report

Consumption, 1960-1989 (SEDR) (4) data were made in order to verify default assumptions in

the model. Although there are definition discrepancies in the building types used by COMMEND

and NBECS, as well as a 10% disagreement over the commercial building stock total, EUIs for

most building types and total consumption across segments correlate well.

The calibration for the lighting end use incorporated available data on the state of the

market in 1986 and the predicted state in 1995. Trends in the characteristics of the lighting

end-use trade-off curves were defined to capture ongoing incentive programs, existing energy

regulations, and market equipment cost reductions that take the market from the 1986 state to the

predicted 1995 state. To achieve this transformation, existing programs and regulations were

assumed to reduce the cost of high-efficiency technology options by 30% initially, and by an

additional 1% per year, for a total of 42% from 1983 to 1995. These assumptions are close to

what is happening in the market in the form of cost reductions from utility rebate programs,

which typically offer 3-4 cents/kWh saved to their participants.

Baseline Forecasts

As described in Volume B, Chapter 2, three different baselines, No Programs, Base Case,

and High-Efficiency are used to gauge the impacts of policy actions . In all three baselines, tradeoff curves for all end uses other than lighting remain stationary . For the No Programs baseline,

the technology trade-off curve for the lighting end use is also held stationary at its 1983 level

throughout the forecast period.

415 years = ballast rated lifetime/fluorescent lighting annual operating hours.

3-10

z,,

Turnover of Lighting Equipment 

At present, only one lifetime may be specified for the lighting end use in the model even 

though lamps usually have to be replaced much more frequently than ballasts and fixtures. 

Discrete lighting component representation is expected to be a feature of COMMEND Version 

4.0. 

A lifetime of 15 years is used in this analysis. Fifteen years approximates the average 

lifetime of ballasts,4 and corresponds to the average fixture turnover rate in renovated buildings. 

Annual Operating Hours 

To represent hours per year that commercial sector lighting equipment is operating for each 

building type, building operating hours from NBECS 1986 are used. (2) 

Calibration 

As a frrst step in the calibration process, comparisons between COMMEND inputs and 

NBECS 1986, EIA's Annual Energy Outlook 1991 (AEO) (3), and State Energy Data Report 

Consumption, 1960-1989 (SEDR) (4) data were made in order to verify default assumptions in 

the model. Although there are definition discrepancies in the building types used by COMMEND 

and NBECS, as well as a 10% disagreement over the commercial building stock total, EUIs for 

most building types and total consumption across segments correlate well. 

The calibration for the lighting end use incorporated available data on the state of the 

market in 1986 and the predicted state in 1995. Trends in the characteristics of the lighting 

end-use trade-off curves were defined to capture ongoing incentive programs, existing energy 

regulations, and market equipment cost reductions that take the market from the 1986 state to the 

predicted 1995 state. To achieve this transformation, existing programs and regulations were 

assumed to reduce the cost of high-efficiency technology options by 30% initially, and by an 

additional 1% per year, for a total of 42% from 1983 to 1995. These assumptions are close to 

what is happening in the market in the form of cost reductions from utility rebate programs, 

which typically offer 3-4 centslkWh saved to their participants. 

Baseline Forecasts 

As described in Volume B, Chapter 2, three different baselines, No Programs, Base Case, 

and High-Efficiency are used to gauge the impacts of policy actions. In all three baselines, tradeoff curves for all end uses other than lighting remain stationary. For the No Programs baseline, 

the technology trade-off curve for the lighting end use is also held stationary at its 1983 level 

throughout the forecast period. 

415 years = ballast rated lifetime/fluorescent lighting annual operating hours. 

3-10 
For the High-Efficiency baseline, the 42% cost reduction (because of DSM programs, etc.)

in the efficient end of the lighting technology trade-off curve is left in place after 1995, and the

trade-off curve remains in its 1995 state. For the Base Case baseline, most of the price reduction

at the efficient end of the trade-off curve (30 of the 42%) is removed after 1995 to model a case

in which utilities no longer provide DSM incentives. The remaining cost reductions are left in

place in order to simulate the permanent price effect that DSM programs and other market forces

will have on the market for efficient technologies. In addition, the Base Case baseline includes

small savings from state building codes that are introduced before 1995 and are expected to

persist (see "Representation of Policies" below for a discussion of standards implementation in

the model).

These three baseline forecasts provide a framework for analyzing a broad spectrum of future

market scenarios. When the No Programs forecast is compared to the other two baselines, the

effect of utility demand-side programs plus existing regulations and market forces can be

estimated. The two other cases represent two bounds of the forecast range. The High-Efficiency

baseline extends the 1995 market forecast to 2030, based on utility DSM programs, the

Environmental Protection Agency (EPA) Green Lights Program, the Federal Energy Management

Program (FEMP) relighting program and existing lighting regulations. This case represents an

aggressive level of incentives, but not a "maximum efficiency" scenario. The Base Case baseline

accounts for the currently widespread DSM activity, but assumes that these programs do not

persist at their current high level.

COMMEND uses an average consumer discount rate of 28% to model commercial

consumer decision-making.

Representation of Policies

Component efficiency standards are modeled by limiting the choice of decisionmakers at

the low-efficiency end of the lighting technology trade-off curve.

3.3.3 LBL Residential Energy Model (LBL-REM)

Purpose

The LBL-REM models the appliance purchase choices made in households, as well as these

households' subsequent usage behavior and energy consumption. See Appendix B for details

about the model.

Engineering, economic, and demographic data are used in LBL-REM. The engineering data

for appliances are described in Section 3.3.1. Additional data include engineering data regarding

alternative building shell construction measures and costs, unit energy consumption and efficiency

of appliances, age distribution of existing appliance stock, and retirement functions. Economic

data include projected energy prices and household income and models of energy investment,

appliance purchase, and usage behavior (including fuel and technology choice for each end use).

3-11

For the High-Efficiency baseline, the 42% cost reduction (because of DSM programs, etc.) 

in the efficient end of the lighting technology trade-off curve is left in place after 1995, and the 

trade-off curve remains in its 1995 state. For the Base Case baseline, most of the price reduction 

at the efficient end of the trade-off curve (30 of the 42%) is removed after 1995 to model a case 

in which utilities no longer provide DSM incentives. The remaining cost reductions are left in 

place in order to simulate the permanent price effect that DSM programs and other market forces 

will have on the market for efficient technologies. In addition, the Base Case baseline includes 

small savings from state building codes that are introduced before 1995 and are expected to 

persist (see "Representation of Policies" below for a discussion of standards implementation in 

the model). 

These three baseline forecasts provide a framework for analyzing a broad spectrum of future 

market scenarios. When the No Programs forecast is compared to the other two baselines, the 

effect of utility demand-side programs plus existing regulations and market forces can be 

estimated. The two other cases represent two bounds of the forecast range. The High-Efficiency 

baseline extends the 1995 market forecast to 2030, based on utility DSM programs, the 

Environmental Protection Agency (EPA) Green Lights Program, the Federal Energy Management 

Program (FEMP) relighting program and existing lighting regulations. This case represents an 

aggressive level of incentives, but not a "maximum efficiency" scenario. The Base Case baseline 

accounts for the currently widespread DSM activity, but assumes that these programs do not 

persist at their current high level. 

COMMEND uses an average consumer discount rate of 28% to model commercial 

consumer decision-making. 

Representation of Policies 

Component efficiency standards are modeled by limiting the choice of decisionmakers at 

the low-efficiency end of the lighting technology trade-off curve. 

3.3.3 LBL Residential Energy Model (LBL.REM) 

Purpose 

The LBL-REM models the appliance purchase choices made in households, as well as these 

households' subsequent usage behavior and energy consumption. See Appendix B for details 

about the model. 

Engineering, economic, and demographic data are used in LBL-REM. The engineering data 

for appliances are described in Section 3.3.1. Additional data include engineering data regarding 

alternative building shell construction measures and costs, unit energy consumption and efficiency 

of appliances, age distribution of existing appliance stock, and retirement functions. Economic 

data include projected energy prices and household income and models of energy investment, 

appliance purchase, and usage behavior (including fuel and technology choice for each end use). 

3-11 
Demographic data include number of households by type, projected housing starts and

demolitions, and appliance holdings.

Historical Development

Early energy-demand modeling focused on engineering estimates or on the relationship

between energy consumption and economic growth. In the 1970s, Oak Ridge National

Laboratory (ORNL) developed the first model to integrate these two important aspects of energy

demand, the Engineering-Economic Model of Residential Energy Use. (5)

The ORNL Model was brought to LBL in 1979 and adapted to the analysis of federal

appliance conservation standards. Further extensive changes were made at LBL from 1979 to

the present, resulting in the LBL-REM.(6) Many of these changes have already been

documented. (7) More recent changes for this rulemaking are described in Appendix B.

Structure of the Model

The LBL-REM segments annual energy consumption into house types, end uses, and fuel

types. The house types include single-family, multifamily, and mobile homes. Calculations are

performed separately for existing and new housing construction each year during the forecast

period, 1980-2030.5 The end uses are space-heating (including room and central), airconditioning, water heating, refrigeration, freezing, cooking, dish-washing, clothes-washing,

clothes-drying, televisions, lighting, and miscellaneous. Up to four fuels are considered, as

appropriate to each end use: electricity, natural gas (utility gas), heating oil, and liquid petroleum

gas (LPG). The national version of the model, which treats the country as a single region, is

used in the analysis.

The model projects five types of activities: technology/fuel choice, building shell thermal

integrity choice, appliance efficiency choice, usage behavior, and turnover of buildings and

appliances. The interrelationship of the five types of activities is shown in Figure 3.2.

5 The model forecasts beginning in 1980. Historical data from 1980-1990 are used to check the validity of the

forecast.

3-12

Demographic data include number of households by type, projected housing starts and 

demolitions, and appliance holdings. 

Historical Development 

Early energy-demand modeling focused on engineering estimates or on the relationship 

between energy consumption and economic growth. In the 1970s, Oak Ridge National 

Laboratory (ORNL) developed the ftrst model to integrate these two important aspects of energy 

demand, the Engineering-Economic Model of Residential Energy Use. (5) 

The ORNL Model was brought to LBL in 1979 and adapted to the analysis of federal 

appliance conservation standards. Further extensive changes were made at LBL from 1979 to 

the present, resulting in the LBL-REM.(6) Many of these changes have already been 

documented. (7) More recent changes for this rulemaking are described in Appendix B. 

Structure oj the Model 

The LBL-REM segments annual energy consumption into house types, end uses, and fuel 

types. The house types include single-family, multifamily, and mobile homes. Calculations are 

performed separately for existing and new housing construction each year during the forecast 

period, 1980-2030.5 The end uses are space-heating (including room and central), airconditioning, water heating, refrigeration, freezing, cooking, dish-washing, clothes-washing, 

clothes-drying, televisions, lighting, and miscellaneous. Up to four fuels are considered, as 

appropriate to each end use: electricity, natural gas (utility gas), heating oil, and liquid petroleum 

gas (LPG). The national version of the model, which treats the country as a single region, is 

used in the analysis. 

The model projects five types of activities: technology/fuel choice, building shell thermal 

integrity choice, appliance efficiency choice, usage behavior, and turnover of buildings and· 

appliances. The interrelationship of the five types of activities is shown in Figure :?.2. 

5 The model forecasts beginning in 1980. Historical data from 1980-1990 are used to check the validity of the 

forecast. 

3-12 
LBL RESIDENTIAL ENERGY MODEL

DATA:

OUTPUT:

ANNUAL ENERGY

CONSUMPTION

• By End Use

• By Fuel

• By Building Type

FUEL

EXPENDITURES

• By End Use

• By Fuel

• By Building Type

CAPITAL

COSTS

• By End Use

• By Fuel

• By Building Type

Figure 3.2 Activities in the LBL Residential Energy Model

3-13

LBL RESIDENTIAL ENERGY MODEL 

DATA: 

DEMOGRAPIDCS 

ALGORITHMS: 

OUTPUT: 

ANNUAL ENERGY 

CONSUMPTION 

• By End Use 

• By Fuel 

• By Building Type 

ENGINEERING 

FUEL CHOICE 

FUEL 

EXPENDITURES 

• By End Use 

• By Fuel 

• By Building Type 

ECONOMICS 

EQUIPMENT 

AND BUILDING 

STOCKS 

CAPITAL 

COSTS 

• By End Use 

• By Fuel 

• By Building Type 

Figure 3.2 Activities in the LBL Residential Energy Model 

3-13 
Housing Stock Submodel

This submodel prepares data about the housing stock projection forthe main model. The

initial number of occupied households, by type, is taken from the 1980 Census of Population and

Housing and an exogenous projection of housing starts is obtained. The housing submodel

determines the projected housing stock for each year from 1981-2030 by house type, based on

the projected age distribution of the population in each state. The method is described fully in

Appendix B.

Efficiency Choice Algorithm

Historical efficiency data are available for selected years through 1991 for each of the eight

products. After 1991, efficiency improvements are projected as a function of designs available

(technological change) and of electricity, oil, or gas prices. If energy prices increase, the

life-cycle cost of more efficient designs will increase more slowly than that of less efficient

designs, making the more efficient designs more attractive. When the life-cycle cost of a more

efficient design falls below the life-cycle cost of the current average design, then the more

efficient design is projected to be purchased. Conversely, if energy prices decline, the model

projects no further efficiency change.

Thermal Integrity

The projection of investment in thermal integrity measures in new houses is based on a

life-cycle cost calculation analogous to that done for equipment efficiencies. Engineering

estimates of the incremental costs of thermal integrity measures are used together with current

fuel prices and a market discount rate for each house type and heating fuel type.

Modeling Efficiency Standards

The LBL-REM projects the average efficiency of new products, e.g., gas-fired water heaters,

purchased each year in the absence of additional federal regulations, as described above. A

distribution of unit energy consumptions (UEC) is constructed around the projected average unit

energy consumption for each class, based on efficiency distributions previously observed in the

marketplace. New federal standards would eliminate at least part of the distribution. A new

distribution is constructed in which all sales below the standards are increased in efficiency to

meet the standards. The new shipment-weighted average efficiency then characterizes the

efficiency of new units in that year. The same process is applied to all years after

implementation of the standards. The model is then run again, for the standards case, with the

adjusted average efficiencies, to calculate any changes in market shares, usage behavior, or

investment in building shell thermal improvements that may occur as a result of standards, and

to calculate the net energy savings.

6 The equipment efficiency and thermal integrity decisions are not solved simultaneously, but

recursively. The previous year's thermal integrity is assumed in projecting this year's equipment

efficiency; then this year's equipment efficiency is used to calculate this year's thermal integrity.

3-14

3c1.

Housing Stock Submodel 

This submodel prepares data about the housing stock projection forthe main model. The 

initial number of occupied households, by type, is taken from the 1980 Census of Population and 

Housing and an exogenous projection of housing starts is obtained. The housing submodel 

determines the projected housing stock for each year from 1981-2030 by house type, based on 

the projected age distribution of the population in each state. The method is described fully in 

Appendix B. 

Efficiency Choice Algorithm 

Historical efficiency data are available for selected years through 1991 for each of the eight 

products. After 1991, efficiency improvements are projected as a function of designs available 

(technological change) and of electricity, oil, or gas prices. If energy prices increase, the 

life-cycle cost of more efficient designs will increase more slowly than that of less efficient 

designs, making the more efficient designs more attractive. When the life-cycle cost of a more 

efficient design falls below the life-cycle cost of the current average design, then the more 

efficient design is projected to be purchased. Conversely, if energy prices decline. the model 

projects no further efficiency change. 

Thermal Integrity 

The projection of investment in thermal integrity measures in new houses is based on a 

life-cycle cost calculation analogous to that done for equipment efficiencies.6 Engineering 

estimates of the incremental costs of thermal integrity measures are used together with current 

fuel prices and a market discount rate for each house type and heating fuel type. 

Modeling Efficiency Standards 

The LBL-REM projects the average efficiency of new products, e.g., gas-fired water heaters, 

purchased each year in the absence of additional federal regulations, as described above. A 

distribution of unit energy consumptions (VEC) is constructed around the projected average unit 

energy consumption for each class, based on efficiency distributions previously observed in the 

marketplace. New federal standards would eliminate at least part of the distribution. A new 

distribution is constructed in which all sales below the standards are increased in efficiency to 

meet the standards. The new shipment-weighted average efficiency then characterizes the 

efficiency of new units in that year. The same process is applied to all years after 

implementation of the standards. The model is then run again, for the standards case, with the 

adjusted average efficiencies, to calculate any changes in market shares, usage behavior, or 

investment in building shell thermal improvements that may occur as a result of standards. and 

to calculate the net energy savings. 

6 The equipment efficiency and thermal integrity decisions are not solved simultaneously, but 

recursively. The previous year's thermal integrity is assumed in projecting this year's equipment 

efficiency; then this year's equipment efficiency is used to calculate this year's thermal integrity. 

3-14 
Turnover of Appliance Stocks

The initial age distribution of appliances in stock is characterized from industry data about

historical annual shipments and retirements. The fraction of each product that retires each year

is based on the number of years since purchase for each age cohort 7 Each age cohort is

associated with an average efficiency; when older appliances retire, they are also typically

recognized as less efficient.

The number of potential purchasers of an appliance in new homes is equal to the number

of new homes constructed each year. The number of potential purchasers in existing houses is

equal to the number of retiring appliances, plus some fraction of those households that did not

previously own the product.

Calculation of Market Shares

Potential buyers may make no purchase or may buy any competing technology within an

end use. For each of the eight products, the decision to purchase or not is modeled, and the

fraction of the total that choose each class, e.g., gas-fired, oil-fired, electric, or gas instantaneous

water heaters, is specified exogenously. Long-term market share elasticities have been assumed

with respect to equipment price, operating expense, and income. Standards are expected to create

lower operating expenses and increased equipment prices. The percentage changes in these

quantities are used, together with the elasticities, to determine changes in market share resulting

from standards. Higher equipment prices will decrease market shares, while lower operating

expense will increase market shares. The net result depends on the standard level selected and

associated equipment price and operating expense.

Usage Behavior

For some products, changing the operating expense results in changes in usage behavior.

These changes are modeled with usage elasticities in operating expense and income. For

fluorescent light ballasts, televisions, water heaters, and pool heaters, these elasticities are

assumed to be zero. For ranges and ovens, room air conditioners, direct heating equipment, and

mobile home furnaces, these elasticities are assumed to be non-zero.

7 For example, a cohort consists of all 8-year-old gas-fired water heaters. Up to 30 cohorts, one for

each year of purchase, are tracked for each purpose.

3-15

Turnover of Appliance Stocks 

The initial age distribution of appliances in stock is characterized from industry data about 

historical annual shipments and retirements. The fraction of each product that retires each year 

is based on the number of years since purchase for each age cohort'? Each age cohort is 

associated with an average efficiency; when older appliances retire, they are also typically 

recognized as less efficient. 

The number of potential purchasers of an appliance in new homes is equal to the number 

of new homes constructed each year. The number of potential purchasers in existing houses is 

equal to the number of retiring appliances, plus some fraction of those households that did not 

previously own the product. 

Calculation of Market Shares 

Potential buyers may make no purchase or may buy any competing technology within an 

end use. For each of the eight products, the decision to purchase or not is modeled, and the 

fraction of the total that choose each class, e.g., gas-fired, oil-flred, electric, or gas instantaneous 

water heaters, is specified exogenously. Long-term market share elasticities have been assumed 

with respect to equipment price, operating expense, and income. Standards are expected to create 

lower operating expenses and increased equipment prices. The percentage changes in these 

quantities are used, together with the elasticities, to determine changes in market share resulting 

from standards. Higher equipment prices will decrease market shares, while lower operating 

expense will increase market shares. The net result depends on the standard level selected and 

associated equipment price and operating expense. 

Usage Behavior 

For some products, changing the operating expense results in changes in usage behavior. c 

These changes are modeled with usage elasticities in operating expense and income. For 

fluorescent light ballasts, televisions, water heaters, and pool heaters, these elasticities are 

assumed to be zero. For ranges and ovens, room air conditioners, direct heating equipment, and 

mobile home furnaces, these elasticities are assumed to be non-zero. 

7 For example, a cohort consists of all 8-year-old gas-fired water heaters. Up to 30 cohorts, one for 

each year of purchase, are tracked for each purpose. 

3-15 
Energy Consumption Calculations

The total energy consumption per house for each end use and fuel by house type and

vintage (existing or new) is the product of the UEC per cycle (accounting for efficiency changes)

and usage factor, e.g., cycles per year. The corresponding energy consumption for all households

is the consumption per house times the number of households of that type and vintage, times the

fraction of households that owns that appliance.8

Aggregate energy consumption is obtained from summations over intermediate results. For

example, national electricity consumption for residential water heaters in a particular year is the

sum, over house types, classes, and vintages, of electricity consumption by water heaters.

National residential electricity consumption in that year is the sum of electricity consumption

over all end uses.

Assumptions in the Analysis

The Consumer Analysis assumes that decisions on the purchase and use of appliances

depend on operating expenses, household income, and appliance prices. Manufacturers are

projected to respond to the demand for more efficient products by incorporating technologically

feasible and cost-effective design options in new units. Exogenous forecasts of population

growth, housing starts, personal income and energy prices from published sources are utilized.

Justifications for the most important assumptions in the Consumer Analysis are listed below.

8 For space-conditioning (heating and cooling), two additional terms are multiplied by the intermediate

results: relative floor area (square feet of conditioned space), and relative thermal integrity (a measure of

the effect of building shell characteristics on energy consumption).

3-16

Energy Consumption Calculations 

The total energy consumption per house for each end use and fuel by house type and 

vintage (existing or new) is the product of the VEC per cycle (accounting for efficiency changes) 

and usage factor, e.g., cycles per year. The corresponding energy consumption for all households 

is the consumption per house times the number of households of that type and vintage, times the 

fraction of households that owns that appliance.s 

Aggregate energy consumption is obtained from summations over intermediate results. For 

example, national electricity consumption for residential water heaters in a particular year is the 

sum, over house types, classes, and vintages, of electricity consumption by water heaters. 

National residential electricity consumption in that year is the sum of electricity consumption 

over all end uses. 

Assumptions in the Analysis 

The Consumer Analysis assumes that decisions on the purchase and use of appliances 

depend on operating expenses, household income, and appliance prices. Manufacturers are 

projected to respond to the demand for more efficient products by incorporating technologically 

feasible and cost-effective design options in new units. Exogenous forecasts of population 

growth, housing starts, personal income and energy prices from published sources are utilized. 

Justifications for the most important assumptions in the Consumer Analysis are listed below. 

8 For space-conditioning (heating and cooling), two additional terms are multiplied by the intermediate 

results: relative floor area (square feet of conditioned space), and relative thermal integrity (a measure of 

the effect of building shell characteristics on energy consumption). 

3-16 
Assumption

• Population growth is projected to follow

Census projections to the year 2000 and

population growth is projected to slow

from 2000 to 2015.

• The fraction of the population in each age

group in each state that lives in a particular

house type is projected to remain constant

over time.

• Housing starts in future years are projected

to range from 900,000 to 1,100,000 singlefamily units; 300,000 to 530,000

multifamily units, and 170,000 to 210,000

mobile homes.

• Real disposable personal income is

projected to. grow from $3.9 trillion in

1990 to $7.7 trillion in 2030 (in 1990

dollars). This corresponds to disposable

personal income per household growing

from $41,500 in 1990 to $59,400 in 2030.

• In real terms, residential electricity prices

are projected to increase at an average

annual rate of 0.3% from 1989 to 2010,

and 0.6% from 2011 to 2030. Residential

natural gas prices are projected to increase

at an average annual rate of 2.1 % from

1989 to 2010, and 2.2% from 2011 to

2030. Residential distillate prices are

projected to increase at an average annual

rate of 1.5% from 1989 to 2010, and 1.3%

from 2011 to 2030.

Justification

A Census projection of population by

state, by age group, benchmarked to

the 1980 Census count, provides the

basis for the projection to 2000. The

projection given in the 1985 OBERS

BEA Regional Projections is consistent

with Census projections to 2000 and

shows slower growth after 2000.

As the age distribution and the

geographic distribution of the

population change, the mix of house

types will also change.

A long-term forecast from Data

Resources, Inc., represents a state-ofthe-art projection for single-family and

multifamily units. The projection for

mobile homes is based on the

population projection.

The DOE/EIA Annual Energy Outlook

1991 (8) provides the projection to

2010, which is extrapolated to 2030.

The extrapolation is linear, based on

the 2010 to 2015 trend.

The DOE/EIA Annual Energy Outlook

1991 provides the projection to 2010.

Assumption 

• Population growth is projected to follow 

Census projections to the year 2000 and 

population growth is projected to slow 

from 2000 to 2015. 

• The fraction of the population in each age 

group in each state that lives in a particular 

house type is projected to remain constant 

over time. 

• Housing starts in future years are projected 

to range from 900,000 to 1,100,000 singlefamily units; 300,000 to 530,000 

multifamily units, and 170,000 to 210,000 

mobile homes. 

• Real disposable personal income is 

projected to. grow from $3.9 trillion in 

1990 to $7.7 trillion in 2030 (in 1990 

dollars). This corresponds to disposable 

personal income per household growing 

from $41,500 in 1990 to $59,400 in 2030. 

• In real terms, residential electricity prices 

are projected to increase at an average 

annual rate of 0.3% from 1989 to 2010, 

and 0.6% from 2011 to 2030. Residential 

natural gas prices are projected to increase 

at an average annual rate of 2.1 % from 

1989 to 2010, and 2.2% from 2011 to 

2030. Residential distillate prices are 

projected to increase at an average annual 

rate of 1.5% from 1989 to 2010, and 1.3% 

from 2011 to 2030. 

3-17 

Justification 

A Census projection of population by 

state, by age group, benchmarked to 

the 1980 Census count, provides the 

basis for the projection to 2000. The 

projection given in the 1985 OBERS 

BEA Regional Projections is consistent 

with Census projections to 2000 and 

shows slower growth after 2000. 

As the age distribution and the 

geographic distribution of the 

population change, the mix of house 

types will also change. 

A long-term forecast from Data 

Resources, Inc., represents a state-ofthe-art projection for single-family and 

multifamily units. The projection for 

mobile homes is based on the 

popUlation projection. 

The DOE/EIA Annual Energy Outlook 

1991 (8) provides the projection to 

2010, which is extrapolated to 2030. 

The extrapolation is linear, based on 

the 2010 to 2015 trend. 

The DOE/EIA Annual Energy Outlook 

1991 provides the projection to 2010. 
Assumption

• Appliance purchase decisions are based on

operating expenses, as well as on

equipment price.

• Manufacturers are projected to respond to

consumer demand for energy efficiency.

Engineering designs that are

technologically feasible and attractive to

purchasers are assumed to be available.

• Thermal integrity of homes is projected to

continue to improve.

• The lifetimes of appliances are projected to

remain the same as empirically observed in

the past, independent of energy efficiency.

Data Sources

Justification

Research on equipment sales for

competing alternatives and on historical

efficiency choices indicates that operating

expenses are significant variables.

Industry is competitive and historically

has responded to changes in consumer

demand. No evidence suggests an end to

the responsiveness.

Increasing energy prices and awareness

on the part of consumers have resulted in

modified construction practices and in

home retrofits. Building codes under

consideration, utility company incentives,

and projected long-term energy price

increases suggest that further thermal

improvements are likely.

Retirement functions are based on

reconciliation of appliance stocks and

shipments. Lifetimes may include

engineering and economic determinants,

which are not separated in existing

studies.

The LBL-REM takes the energy efficiencies of new equipment from the Engineering Analysis.

The purchase price of these products is derived from the factory cost supplied by the Engineering

Analysis and adjusted for manufacturer and dealer markups in the Manufacturer Analysis. Data

on housing and equipment stocks as well as historical data on housing starts and new equipment

purchases come from the 1980 census and industry trade associations. Exogenous projections

of population, housing starts, income, and energy prices are taken from Census, Data Resources,

Inc., and DOE, respectively. Appendix B contains a more detailed discussion of the data used

in the model.

3-18

I fi

Assumption 

• Appliance purchase decisions are based on 

operating expenses, as well as on 

equipment price. 

• Manufacturers are projected to respond to 

consumer demand for energy efficiency. 

Engineering designs that are 

technologically feasible and attractive to 

purchasers are assumed to be available. 

• Thermal integrity of homes is projected to 

continue to improve. 

• The lifetimes of appliances are projected to 

remain the same as empirically observed in 

the past, independent of energy efficiency. 

Data Sources 

Justification 

Research on equipment sales for 

competing alternatives and on historical 

efficiency choices indicates that operating 

expenses are significant variables. 

Industry is competitive and historically 

has responded to changes in consumer 

demand. No evidence suggests an end to 

the responsiveness. 

Increasing energy prices and awareness 

on the part of consumers have resulted in 

modified construction practices and in 

home retrofits. ·Building codes under 

consideration, utility company incentives, 

and projected long-term energy price 

increases suggest that further thermal 

improvements are likely. 

Retirement functions are based on 

reconciliation of appliance stocks and 

shipments. Lifetimes may include 

engineering and economic determinants, 

which are not separated in existing 

studies. 

The LBL-REM takes the energy efficiencies of new equipment from the Engineering Analysis. 

The purchase price of these products is derived from the factory cost supplied by the Engineering 

Analysis and adjusted for manufacturer and dealer markups in the Manufacturer Analysis. Data 

on housing and equipment stocks as well as historical data on housing starts and n.ew equipment 

purchases come from the 1980 census and industry trade associations. Exogenous projections 

of population, housing starts, income, and energy prices are taken from Census, Data Resources, 

Inc., and DOE, respectively. Appendix B contains a more detailed discussion of the data used 

in the model. 

3-18 
Model Outputs

The principal outputs from the LBL-REM for each year are:

• Energy consumption by end use and fuel;

• Per-unit equipment price and operating expense by product;

• Total residential energy consumption by fuel;

• Projected annual shipments of residential appliances; and

• Differences in these quantities between a base case and each standards case.

'These outputs are provided annually (or for selected years) and cumulatively for the period

1996-2030. Energy savings from implementation of standards are provided annually to the end

of the period. Net present value (NPV) of standards is evaluated for each regulated product and

for the end use(s) comprising the regulated and competing products.

Cost-Benefit Analysis

The costs and benefits of the proposed standards from a national perspective are

quantified by calculating a net present value. The NPV is the sum of discounted savings in

operating expenses minus the sum of discounted increases in equipment prices.

DOE has determined that 6% real is the appropriate discount rate for calculating benefits

to the consumer. The NPV of standards is also calculated at consumer discount rates of 2% and

10% as sensitivity analyses.

Different assumptions about technology choice are invoked when calculating energy.

savings of standards on the one hand, and when calculating the net present benefit of standards

on the other. Energy savings are the net of any adjustments households make in changing their

technology or fuel choice or from using appliances differently. For example, if, after standards,

a regulated product captures larger market shares (without displacing consumption of another

fuel), then the net energy savings will be diminished. The energy savings are calculated as the

net result of standards, accounting for such secondary effects as shifts in market share.

NPV excludes these secondary effects. Base case purchase behavior, without market share

changes, is assumed in calculating the NPV, because any market share shift reflects the

consumer's judgment that change is worth more than the direct energy savings associated with

keeping market share constant. NPV is calculated from per unit changes in equipment and

operating costs, multiplied by standards case shipments. If the NPV was calculated without

normalizing to shipments, the results would be erroneous: if standards caused decreased purchase

of a product, this would appear as an economic benefit, namely less money spent on purchasing

3-19

2^l

Model Outputs 

The principal outputs from the LBL-REM for each year are: 

• Energy consumption by end use and fuel; 

• Per-unit equipment price and operating expense by product; 

• Total residential energy consumption by fuel; 

• Projected annual shipments of residential appliances; and 

• Differences in these quantities between a base case and each standards case. 

'These outputs are provided annually (or for selected years) and cumulatively for the period 

1996-2030. Energy savings from implementation of standards are provided annually to the end 

'of the period. Net present value (NPV) of standards is evaluated for each regulated product and 

for the end use(s) comprising the regulated and competing products. 

Cost-Benefit Analysis 

The costs and benefits of the proposed standards from a national perspective are 

quantified by calculating a net present value. The NPV is the sum of discounted savings in 

operating expenses minus the sum of discounted increases in equipment prices. 

DOE has determined that 6% real is the appropriate discount rate for calculating benefits 

to the consumer. The NPV of standards is also calculated at consumer discount rates of 2% and 

10% as sensitivity analyses. 

Different assumptions about technology choice are invoked when calculating energy 

savings of standards on the one hand, and when calculating the net present benefit of standards 

on the other. Energy savings are the net of any adjustments households make in changing their 

technology or fuel choice or from using appliances differently. For example, if, after standards, 

a regulated product captures larger market shares (without displacing consumption of another 

fuel), then the net energy savings will be diminished. The energy savings are calculated as the 

net result of standards, accounting for such secondary effects as shifts in market share. 

NPV excludes these secondary effects. Base case purchase behavior, without market share 

changes, is assumed in calculating the NPV, because any market share shift reflects the 

consumer's judgment that change is worth more than the direct energy savings associated with 

keeping market share constant. NPV is calculated from per unit changes in equipment and 

operating costs, multiplied by' standards case shipments. If the NPV was calculated without 

normalizing to shipments, the results would be erroneous: if standards caused decreased purchase 

of a product, this would appear as an economic benefit, namely less money spent on purchasing 

3-19 
and use of the appliance;9 and (2) if standards caused an increase in purchases, this would be

counted incorrectly as a cost, when it reflects consumers' preference for the post-standards

product.

3.3.4 LBL Manufacturer Impact Model (LBL-MIM)

Conceptual Approach

The Manufacturer Impact Analysis estimates both the short- and long-run impacts of new

or amended standards on profitability (return on equity) and many other variables for each

industry under consideration. All computations used in this analysis are carried out by the

Manufacturer Impact Model (LBL-MIM), which is a spreadsheet composed of thirteen modules.

Figure 3.3 lists these and displays their interactions. In addition to computing estimated values

for the above-mentioned descriptive variables, the LBL-MIM also computes standard errors for

each by explicitly evaluating the standard errors in the estimates of input variables and then using

Monte Carlo simulations.

Measures of Impact

Three types of long-run impacts are analyzed: (1) profitability, (2) growth, and (3)

competitiveness. To do this, the following five measures of impact are tracked for the industry:

(1) shipments, (2) price, (3) revenue, (4) net income, and (5) return on equity (ROE).

ROE is the primary measure of profitability, although gross margin, return on assets, and

return on sales are also reported. Changes in assets and revenue are the measures of growth

(positive or negative). Because all the industries analyzed are in an historical process of

consolidation, any decrease in ROE will tend to hasten this process; thus, ROE also provides an

indication of the impact of standards on competitiveness.

A short-run calculation is also made. If the long-run calculation indicates a decrease in

sales, the predicted price is reduced by an amount determined by the industry's observed behavior

during historical downturns in sales. The adjusted price is used to calculate the other measures

of impact.

9 Without normalization, the greatest economic benefit would be obtained by standards that resulted

in no future purchases of the product . Then, no money would be spent on purchasing the product, or

on operating expenses, and the value of the savings would equal the amount of money that would have

been spent without the standards. Clearly, this would be a misrepresentation of the NPV of standards.

3-20

C/ 6

and use of the appliance;9 and (2) if standards caused an increase in purchases, this would be 

counted incorrectly as a cost, when it reflects consumers' preference for the post-standards 

product. 

3.3.4 LBL Manufacturer Impact Model (LBL.MIM) 

Conceptual Approach 

The Manufacturer Impact Analysis estimates both the short- and long-run impacts of new 

or amended standards on profitability (return on equity) and many other variables for each 

industry under consideration. All computations used in this analysis are carried out by the 

Manufacturer Impact Model (LBL-MIM), which is a spreadsheet composed of thirteen modules. 

Figure 3.3 lists these and displays their interactions. In addition to computing estimated values 

for the above-mentioned descriptive variables, the LBL-MIM also computes standard errors for 

each by explicitly evaluating the standard errors in the estimates of input variables and then using 

Monte Carlo simulations. 

Measures of Impact 

Three types of long-run impacts are analyzed: (1) profitability, (2) growth, and (3) 

competitiveness. To do this, the following five measures of impact are tracked for the industry: 

(1) shipments, (2) price, (3) revenue, (4) net income, and (5) return on equity (ROE). 

ROE is the primary measure of profitability, although gross margin, return on assets, and 

return on sales are also reported. Changes in assets and revenue are the measures of growth 

(positive or negative). Because all the industries analyzed are in an historical process of 

consolidation, any decrease in ROE will tend to hasten this process; thus, ROE also provides an 

indication of the impact of standards on competitiveness. 

A short-run calculation is also made. If the long-run calculation indicates a decrease in 

sales, the predicted price is reduced by an amount determined by the industry's observed behavior 

during historical downturns in sales. The adjusted price is used to calculate the other measures 

of impact. 

9 Without nonnalization, the greatest economic benefit would be obtained by standards that resulted 

in no future purchases of the product. Then, no money would be spent on purchasing the product, or 

on operating expenses, and the value of the savings would equal the amount of money that would have 

been spent without the standards. Clearly, this would be a misrepresentation of the NPV of standards. 

3-20 
Opeeraling

Cost

tong-Run

calculations

Demand

Revenue

b-i71-0 7-1,

Fixed

Cost

Total

cost

4-Z

Net Income

ROE

Variable

Cost

Price

Figure 3.3 Conceptual Approach to the Manufacturer Impact Model

3-21

Industry Data

Markup

Operating 

Cost 

Inputs 

Long-Run 

Calculations 

Demand 

Revenue 

C8pilal 

Cost 

Net Income 

ROE 

Total 

Cost 

Paris & 

Labor 

Variable 

Cost 

Price 

Figure 3.3 Conceptual Approach to the Manufacturer Impact Model 

3-21 

Fixed 

Cost 

InduslJy Data 

Variable 

Cost 

Economic 

Profit 
Operating

cost

Markup

Production

costs

Demand

curve

Price

Figure 3.4 How Standards Cause a Change 'in ROE.

3-22

Quantity

Profit

ROE

lt.,

Operating 

cost 

Markup 

-

Production 

costs 

> 

Demand 

curve 

,if 

Price 

~ 

Figure 3.4 How Standards Cause a Change' in ROE. 

3-22 

Profit 

Quantity ... J\ , 

ROE 

, 
Description of the LBL-MIM

Although LBL-MIM examines many aspects of an industry, its most important job is to

compute the effect on profits of a cost increase caused by standards . To accomplish this, the

model first estimates how much of the cost increase is passed on in the form of higher prices.

(An accompanying effect is the decrease in operating expense due to standards, which makes the

product more desirable to the consumer and, thus, increases demand for the product .) Then, it

estimates how this price increase and the accompanying reduction of operating expense influence

demand. Finally, given the new price and level of demand, it computes a new level of profit.

These calculations use estimated long-run costs . The LBL-MIM makes an additional calculation

to correct for any short-run difficulties the industry may have in achieving its long-run markup.

It is important to note from the start that the model does not assume perfect competition,

nor does it assume that the level of profit will remain unchanged. The results of the model

depend entirely on the input values.

The basic behavior of the model can be understood only in light of the following standard

economic consideration. In the long run, an increase in fixed costs will not be passed on via a

price increase except when it is sufficient to cause some firms to leave the industry.10 This result

is derived from the assumption that firms act individually to maximize profits and from the

definition of fixed costs as those that are not proportional to output. The mathematical derivation

is given in Appendix C of this volume.

A second influence on profits is the change in purchases resulting from both the change

in price and the change in operating expense. These tend to offset each other because price

increases lower quantity demanded, and increased efficiency increases demand (by lowering

operating expense). The LBL-MIM takes these effects into account through the use of two

demand elasticities which play an important role in determining the outcome and are therefore

examined more closely in the sensitivity analysis.

Assumptions in the Analysis

This section begins with a series of short discussions of different aspects of the modeling

procedure that require careful interpretation (such as "long-run variable cost") or simplifying

assumptions. This is followed by a list of more detailed assumptions.

The long-run costs. The LBL-MIM requires input of long-run costs. A firm is in

long-run equilibrium when it has optimal productive capacity. Thus, during a recession (when

firms experience excess capacity), firms are not in long-run equilibrium, and the theory of price

as a markup over long-run costs may not apply. Long-run fixed costs are those that remain

unchanged when a firm moves from one long-run equilibrium to another; they do not include

10 When firms leave the industry, it becomes less competitive and the remaining firms can increase

their markup over variable costs, thus, indirectly covering their increased fixed costs.

3-23

U

Description oj the LBL-MIM 

Although LBL-MIM examines many aspects of an industry, its most important job is to 

compute the effect on profits of a cost increase caused by standards. To accomplish this, the 

model first estimates how much of the cost increase is passed on in the form of higher prices. 

(An accompanying effect is the decrease in operating expense due to standards, which makes the 

product more desirable to the consumer and, thus, increases demand for the product.) Then, it 

estimates how this price increase and the accompanying reduction of operating expense influence 

demand. Finally, given the new price and level of demand, it computes a new level of profit. 

These calculations use estimated long-run costs. The LBL-MIM makes an additional calculation 

to correct for any short-run difficulties the industry may have in achieving its long-run markup. 

It is important to note from the start that the model does not assume perfect competition, 

nor does it assume that the level of profit will remain unchanged. The results of the model 

depend entirely on the input values. 

The basic behavior of the model can be understood only in light of the following standard 

economic consideration. In the long run, an increase in fixed costs will not be passed on via a 

price increase except when it is sufficient to cause some firms to leave the industry.to This result 

is derived from the assumption that firms act individually to maximize profits and from the 

definition of fixed costs as those that are not proportional to output. The mathematical derivation 

is given in Appendix C of this volume. 

A second influence on profits is the change in purchases resulting from both the change 

in price and the change in operating expense. These tend to offset each other because price 

increases lower quantity demanded, and increased efficiency increases demand (by lowering 

operating expense). The LBL-MIM takes these effects into account through the use of two 

demand elasticities which play an important role in determining the outcome and are therefore 

examined more closely in the sensitivity analysis. 

Assumptions in the Analysis 

This section begins with a series of short discussions of different aspects of the modeling 

procedure that require careful interpretation (such as "long-run variable cost") or simplifying 

assumptions. This is followed by a list of more detailed assumptions. 

The long-run costs. The LBL-MIM requires input 'of long-run costs. A fmn is in 

long-run equilibrium when it has optimal productive capacity. Thus, during a recession (when 

fmns experience excess capacity), firms are not in long-run equilibrium, and the theory of price 

as a markup over long-run costs may not apply. Long-run fixed costs are those that remain 

unchanged when a fum moves from one long-run equilibrium to another; they do not include 

10 When firms leave the industry, it becomes less competitive and the remaining firms can increase 

their markup over variable costs, thus, indirectly covering their increased fixed costs. 

3-23 
capital costs that are proportional to output. The latter are considered long-run variable

(marginal) costs and are marked up and passed on. To the extent that costs are not strictly

proportional to output, but decrease on average with quantity produced, there are fixed costs.

Also, a cost that does not depend on the scale of production, i.e., an engineering cost, is a

long-run fixed cost.

Use of "typical" firm. The analysis estimates the impact of the standards by using

"typical" firms. A typical firm is a hypothetical firm that is described by industry-average values.

Each one is modeled as if it produced only products of the industry being analyzed. This

procedure is justified by the observation that real firms generally are organized into autonomous

divisions, each of which produces a different type of appliance and one of which generally

corresponds to the industry being modeled. Also, these divisions are generally run on a

profit-and-loss basis similar to a firm. It is not possible to analyze inter-divisional interactions

within real firms.

Aggregation. Generally, it has been impossible to obtain data on cross-elasticities of the

various product lines within an industry. Therefore, data for the various products are aggregated

before the markup is computed and applied. However, once the aggregate calculations have been

made, individual prices are computed for the various classes of products.

How standards are implemented in the model. A change in standards affects the model

in three distinct ways. In general, stricter standards will require additional investment, will raise

production costs, and will affect revenue through both price and demand.

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 date at which it is made, tax laws, and the appropriate cost

of funds. An additional, and sometimes larger, investment takes place as the old inventory is

replaced with more expensive new units. The model assumes that the ratio of inventory to

revenue remains unchanged; in this way both changes in quantity sold and unit value are taken

into account. A third form of investment tracked by the LBL-MIM is the change in the demand

for cash that accompanies a change in revenues.

Financial inputs. Several simplifying assumptions are used in the process of generating

inputs for the LBL-MIM. First, all firms within a particular industry segment (e.g., large

manufacturers of water heaters) are assumed to have the same cost structure. Second, it is

assumed that financial data collected for the parent company are representative of the specific

product division being studied. These data come from publicly available sources such as Value

Line, Standard and Poor's, Moody's, and company reports.

3-24

h1i

capital costs that are proportional to output. The latter are considered long-run variable 

(marginal) costs and are marked up and passed on. To the extent that costs are not strictly 

proportional to output, but decrease on average with quantity produced, there are fIxed costs. 

Also, a cost that does not depend on the scale of production, i.e., an engineering cost, is a 

long-run fIxed cost. 

Use of "typical" firm. The analysis estimates the impact of the standards by using 

"typical" fIrms. A typical firm is a hypothetical firm that is described by industry-average values. 

Each one is modeled as if it produced only products of the industry being analyzed. This 

procedure is justifIed by the observation that real fIrms generally are organized into autonomous 

divisions, each of which produces a different type of appliance and one of which generally 

corresponds to the industry being modeled. Also, these divisions are generally run on a 

profIt-and-loss basis similar to a fIrm. It is not possible to analyze inter-divisional interactions 

within real firms. 

Aggregation. Generally, it has been impossible to obtain data on cross-elasticities of the 

various product lines within an industry. Therefore, data for the various products are aggregated 

before the markup is computed and applied. However, once the aggregate calculations have been 

made, individual prices are computed for the various classes of products. 

How standards are implemented in the model. A change in standards affects the model 

in three distinct ways. In general, stricter standards will require additional investment, will raise 

production costs, and will affect revenue through both price and demand. 

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 date at which it is made, tax laws, and the appropriate cost 

of funds. An additional, and sometimes larger, investment takes place as the old inventory is 

replaced with more expensive new units. The model assumes that the ratio of inventory to 

revenue remains unchanged; in this way both changes in quantity sold and unit value are taken 

into account. A third form of investment tracked by the LBL-MIM is the change in the demand 

for cash that accompanies a change in revenues. 

Financial inputs. Several simplifying assumptions are used in the process of generating 

inputs for the LBL-MIM. First, all firms within a particular industry segment (e.g., large 

manufacturers of water heaters) are assumed to have the same cost structure. Second, it is 

assumed that [mandal data collected for the parent company are representative of the specifIc 

product division being studied. These data come from publicly available sources such as Value 

Line, Standard and Poor's, Moody's, and company reports. 

3-24 
Impact Analysis: An Overview

The next section provides a general discussion of the methods of the impact analysis

together with some cautions about interpreting the results. Most, but not all, of the discussion

applies to all eight products.

The significance of long-run impacts and how they are analyzed are discussed below.

These impacts are most important simply because they will either persist or be so severe as to

cause a restructuring of the industry. The meaning and analysis of short-run impacts are also

discussed. Additionally, the various types of sensitivity analysis performed on the model's results

are discussed. This analysis is crucial for understanding the accuracy and reliability of the

model's predictions, and includes the use of alternative scenarios, sensitivity checks on individual

inputs, and Monte Carlo simulations involving the random selection of all control inputs.

3.3.4.1 Long-Run Impact Analysis

Implementation

The heart of the model is the long-run impact analysis. It assumes that enough time has

passed for any changes in demand to have been accommodated by changes in the industry's

productive capacity. Thus, in the long-run, capacity, or capital stock, is. variable. Because capital

is sometimes thought to be the only fixed cost, it might seem that fixed costs would be zero in

the long run. This is not the case. For the purpose of price determination, whenever there are

returns to scale, there are, effectively, fixed costs. This means that if the cost of meeting

standards is less than proportional to plant capacity, there are fixed costs. Design costs would

typically fall into this category. See Appendix C.2.1 of this volume for a more detailed

discussion of the long run.

The fraction of increased costs of production (due to more stringent standards) that

manufacturers are able to pass on is determined by the nature of the cost, i.e., whether it is fixed

or variable, the price elasticity of demand, and the consumer discount rate as seen by the

price-setting firm. Once the price increase has been determined , it and the operating expense

decrease (obtained from engineering data) are used with the price elasticity of demand and the

discount rate to determine the change in sales. These mechanisms are presented in Figure 3.4,

which displays the most basic economic forces analyzed by the LBL-MIM.

Instead, the reduction in operating costs makes the appliance more desirable, thereby

shifting the demand curve out (e.g., an increase in production costs changes the marginal cost

curve). Together, these determine price and the number of units shipped (quantity). Price and

quantity, together with production costs, determine net income (profit), and these, together with

changes in capital stock (not shown), determine return-on-equity (ROE).

As shown in Figure 3.4, the two cost changes do not translate directly into price changes.

3-25

b5

Impact Analysis: An Overview 

The next section provides a general discussion of the methods of the impact analysis 

together with some cautions about interpreting the results. Most, but not all, of the discussion 

applies to all eight products. 

The significance of long-run impacts and how they are analyzed are discussed below. 

These impacts are most important simply because they will either persist or be so severe as to 

cause a restructuring of the industry. The meaning and analysis of short-run impacts are also 

discussed. Additionally, the various types of sensitivity analysis performed on the model's results 

are discussed. This analysis is crucial for understanding the accuracy and reliability of the 

model's predictions, and includes the use of alternative scenarios, sensitivity checks on individual 

inputs, and Monte Carlo simulations involving the random selection of all control inputs. 

3.3.4.1 Long-Run Impact Analysis 

Implementation 

The heart of the model is the long-run impact analysis. It assumes that enough time has 

passed for any changes in demand to have been accommodated by changes in the industry's 

productive capacity. Thus, in the long-run, capacity, or capital stock, is. variable. Because capital 

is sometimes thought to be the only fixed cost, it might seem that fixed costs would be zero in 

the long run. This is not the case. For the purpose of price determination, whenever there are 

returns to scale, there are, effectively, fixed costs. This means that if the cost of meeting 

standards is less than proportional to plant capacity, there are fixed costs. Design costs would 

typically fall into this category. See Appendix C.2.1 of this volume for a more detailed 

discussion of the long run. 

The fraction of increased costs of production (due to more stringent standards) that 

manufacturers are able to pass on is determined by the nature of the cost, i.e., whether it is fixed 

or variable, the price elasticity of demand, and the consumer discount rate as seen by the 

price-setting firm. Once the price increase has been determined, it and the operating expense 

decrease (obtained from engineering data) are used with the price elasticity of demand and the 

discount rate to determine the change in sales. These mechanisms are presented in Figure 3.4, 

which displays the most basic economic forces analyzed by the LBL-MIM. 

Instead, the reduction in operating costs makes the appliance more desirable, thereby 

shifting the demand curve out (e.g., an increase in production costs changes the marginal cost 

curve). Together, these determine price and the number of units shipped (quantity). Price and 

quantity, together with production costs, determine net income (profit), and these, together with 

changes in capital stock (not shown), determine return-on-equity (ROE). 

As shown in Figure 3.4, the two cost changes do not translate directly into price changes. 

3-25 
The LBL-MIM simulates standards as follows. For each product, a set of engineering

designs is specified. For each design, the engineering analysis provides a per-unit cost, a

one-time capital cost, and an energy usage. The two costs effectively determine the production

costs used in Figure 3.4 and the energy usage determines the operating expense. The

Manufacturer Impact Model calculates a weighted average of the two costs and the energy usage

for the relevant engineering designs. The weights used correspond to the relative numbers of

shipments for each engineering design as determined by LBL-REM.

Another and more subtle point is that markup is determined by price elasticity, and

because life-cycle-cost elasticity is constant, price elasticity changes with a change in either price

or operating expense. This change in markup can have an important impact on ROE.

The markup discussed here is a markup over economic variable costs, which is a broader

category than the typical business definition of variable costs. The manufacturer markup listed

in the model and the technical support document is used only to calculate the baseline model

price (calculated by multiplying the baseline manufacturer cost by the manufacturer markup).

This is called the calibration case in LBL-MIM. The manufacturer markup listed is not used in

calculating the manufacturer prices for the 1996 base case or the standard levels. In these cases,

the manufacturer price is calculated from the economic markup11, which, as described above, is

dependent on elasticities, discount rates, and price and operating expense changes. Thus prices

at different standards levels do not exactly reflect the manufacturer cost times the manufacturer

markup.

Typical results of an analysis of a standard at a particular level might be the following:

wholesale price increases 10%, sales decline 2%, revenue increases 8%, net income increases

12%, and ROE increases 0.5%. Associated with each of these numbers is a standard error. The

errors might be as follows: 4% on price, 1% on sales, 4% on revenue, 10% on net income, and

1.5% on ROE. To interpret the prediction for ROE, one could then conclude (by checking a

table of the normal distribution) that there is about a 69% chance that ROE will increase, and.

a 31% chance that it will decrease. One could also conclude that the chance of profits declining

by more than 2% is only 2.3%.

3.3.4.2 Short-Run Impacts

In the shortest run, capacity is fixed; however, the short run of interest is, at the shortest,

approximately three years. During this time some change in capacity may occur, and demand,

apart from the change induced by standards, will change. Demand for each appliance is

projected to grow by approximately 2% during the three years, so to that extent it will alleviate

any tendency toward excess capacity resulting from standards.

11For a thorough description of how markups are derived and used in LBL-MIM, see Appendix

C.2.1.

3-26

V6,

The LBL-MIM simulates standards as follows. For each product, a set of engineering 

dl1signs is specified. For each design, the engineering analysis provides a per-unit cost, a 

one-time capital cost, and an energy usage. The two costs effectively determine the production 

costs used in Figure 3.4 and the energy usage determines the operating expense. The 

Manufacturer Impact Model calculates a weighted average of the two costs and the energy usage 

for the relevant engineering designs. The weights used correspond to the relative numbers of 

shipments for each engineering design as determined by LBL-REM. 

Another and more subtle point is that markup is determined by price elasticity, and 

because life-cycle-cost elasticity is constant, price elasticity changes with a change in either price 

or operating expense. This change in markup can have an important impact on ROE. 

The markup discussed here is a markup over economic variable costs, which is a broader 

category than the typical business definition of variable costs. The manufacturer markup listed 

in the model and the technical support document is used only to calculate the baseline model 

price (calculated by multiplying the baseline manufacturer cost by the manufacturer markup). 

This is called the calibration case in LBL-MIM. The manufacturer markup listed is not used in 

calculating the manufacturer prices for the 1996 base case or the standard levels. In these cases, 

the manufacturer price is calculated from the economic markupll, which, as described above, is 

dependent on elasticities, discount rates, and price and operating expense changes. Thus prices 

at different standards levels do not exactly reflect the manufacturer cost times the manufacturer 

markup. 

Typical results of an analysis of a standard at a particular level might be the following: 

wholesale price increases 10%, sales decline 2%, revenue increases 8%, net income increases 

12%, and ROE increases 0.5%. Associated with each of these numbers is a standard error. The 

errors might be as follows: 4% on price, I % on sales, 4% on revenue, 10% on net income, and 

1.5% on ROE. To interpret the prediction for ROE, one could then conclude (by checking a 

table of the normal distribution) that there is about a 69% chance that ROE will increase, and 

a 31 % chance that it will decrease. One could also conclude that the chance of profits declining 

by more than 2% is only 2.3%. 

3.3.4.2 Short-Run Impacts 

In the shortest run, capacity is fixed; however, the short run of interest is, at the shortest, 

approximately three years. During this time some change in capacity may occur, and demand" 

apart from the change induced by standards, will change. Demand for each appliance is 

projected to grow by approximately 2% during the three years, so to that extent it will alleviate 

any tendency toward excess capacity resulting from standards. 

IlPor a thorough description of how markups are derived and used in LBL-MIM, see Appendix 

C.2.I. 

3-26 
In order to simplify the analysis and to take a cautious approach, the analysis has ignored

the ability of the industry to actively or passively adjust its capacity relative to demand. The

estimated short-run effect is what would happen if the industry converted, as a result of

standards, all of its present capacity, and if the demand curve did not shift except as an indirect

consequence of standards.

The short-run analysis is based on the analogy between a change in demand caused by

standards and one caused by the business cycle. The business cycle periodically presents the

industry with a fairly sharp decrease in demand that is much greater than the decrease predicted

for any of the standards levels. This demand shortfall seems to present all of the opportunities

for price competition that would accompany a shortfall resulting from a change in standards.

Data for the last few business cycles (18 years) have been analyzed to determine the ratio

between the decline in demand (industry-wide) and the induced decline in price.

If, for example, the long-run prediction is for a decline in demand of 2%, then, using the

above figure, the short-run price could be predicted to be 0.06% (0.03 x 2%) lower than the

long-run price. Actually, this is just a first approximation, and the model does better, as follows:

the short-run price response factor (SRPR) of 0.03 means that the short-run price will be 0.06%

lower than predicted with the long-run price formula if the actual decline in demand is 2%. This

is different from the long-run prediction of a 2% decline, because the short-run price fall will

keep the long-run demand fall from fully materializing. Thus, in the short run, the actual fall in

demand will be less than the long-run prediction of 2%, and the actual fall in price will be

greater than the long-run prediction, but not by a full 0.06%. Short-run demand and price must

be determined simultaneously, which occurs in the short-run module of LBL-MIM.

Short-run prices, profit levels, and other variables will gradually, over a period of several

years, approach their long-run values. How long this will take depends on the industry, on the

fact that the implementation of standards is announced three or four years in advance, and on

how fast (and if) the demand for the product is growing. These factors are discussed in the

sections on individual products.

3.3.4.3 Sensitivity Analysis

Both because of the nature of the information required and the desire of sources to protect

proprietary information, many of the data used by the model represent uncertain estimates.

Therefore, the effect of these uncertainties on the accuracy of the model's predictions is analyzed.

Two types of questions need to be answered. First, how does a particular input variable

contribute to the uncertainty of the outputs? Second, how uncertain is the estimate of a particular

output variable?

3-27

07

In order to simplify the analysis and to take a cautious approach, the analysis has ignored 

the ability of the industry to actively or passively adjust its capacity relative to demand. The 

estimated short-run effect is what would happen if the industry converted, as a result of 

standards, all of its present capacity, and if the demand curve did not shift except as an indirect 

consequence of standards. 

The short-run analysis is based on the analogy between a change in demand caused by 

standards and one caused by the business cycle. The business cycle periodically presents the 

industry with a fairly sharp decrease in demand that is much greater than the decrease predicted 

for any of the standards levels. This demand shortfall seems to present all of the opportunities 

for price competition that would accompany a shortfall resulting from a change in standards. 

Data for the last few business cycles (18 years) have been analyzed to determine the ratio 

between the decline in demand (industry-wide) and the induced decline in price. 

If, for example, the long-run prediction is for a decline in demand of 2%, then, using the 

above figure, the short-run price could be predicted to be 0.06% (0.03 x 2%) lower than the 

long-run price. Actually, this is just a first approximation, and the model does better, as follows: 

the short-run price response factor (SRPR) of 0.03 means that the short-run price will be 0.06% 

lower than predicted with the long-run price formula if the actual decline in demand is 2%. This 

is different from the long-run prediction of a 2% decline, because the short-run price fall will 

keep the long-run demand fall from fully materializing. Thus, in the short run, the actual fall in 

demand will be less than the long-run prediction of 2%, and the actual fall in price will be 

greater than the long-run prediction, but not by a full 0.06%. Short-run demand and price must 

be determined simultaneously, which occurs in the short-run module of LBL-MIM. 

Short-run prices, profit levels, and other variables will gradually, over a period of several 

years, approach their long-run values. How long this will take depends on the industry, on the 

fact that the implementation of standards is announced three or four years in advance, and on 

how fast (and if) the demand for the product is growing. These factors are discussed in the 

sections on individual products. 

3.3.4.3 Sensitivity Analysis 

Both because of the nature of the information required and the desire of sources to protect 

proprietary information, many of the data used by the model represent uncertain estimates. 

Therefore, the effect of these uncertainties on the accuracy of the model's predictions is analyzed. 

Two types of questions need to be answered. First, how does a particular input variable 

contribute to the uncertainty of the outputs? Second, how uncertain is the estimate of a particular 

output variable? 

3-27 
Sensitivity charts

The model's sensitivity to inputs is measured by the impact of a change of one standard

error (S.E.) in the input variable. This analysis is presented only for the impact of input variables

on ROE; this standardizes and simplifies the process. The results are displayed in a sensitivity

chart, an example of which is given and explained below.

Table 3.2 Sensitivity of ROE to 1 S.E. Change in Control Variable, Direct Heating Equipment

Scenario = Primary

Control Variables Possible Standards Levels

Name Value Changed 1 2 3 4 5

IPE -0.300 -0.690 0.06% 0.07% 0.15% -1.65% -4.27%

RD 50.00% 114.96% 0.00% 0.15% 0.22% 1.32% 2.81%

ECC 0.068 0.075 0.00% 0.00% 0.00% -0.01% -0.04%

EP 0.008 0.018 -0.01% -0.01% -0.02% 0.07% 0.15%

FCA 0.098 0.157 0.00% 0.00% -0.01% 0.06% 0.13%

F1X 0.200 0.348 -0.09% -0.10% -0.14% -0.17% -0.30%

CC.N 0.394 0.480 -0.02% -0.03% -0.04% -0.07% -0.16%

dVC.N 5.124 6.872 0.00% 0.01% 0.01% 0.05% 0.01%

Table 3.2 lists the sensitivities of ROE to the control panel inputs defined below:

1. IPE is the industry price elasticity.

2. RD is the consumer discount rate.

3. ECC is the firm's real equity cost of capital.

4. EP is the firm's economic profit.

5. FCA is the percentage of a firm's costs that are fixed before standards.

6. F1X is the percentage of one-time costs (i.e., costs that are induced by

standards) that are fixed.

7. CC.N is the estimate of one-time capital costs induced by standards.

8. dVC.N is the increase in variable costs necessary to reach a standards level.

3-28

,, Si

Sensitivity charts 

The model's sensitivity to inputs is measured by the impact of a change of one standard 

error (S.E.) in the input variable. This analysis is presented only for the impact of input variables 

on ROE; this standardizes and simplifies the process. The results are displayed in a sensitivity 

chart, an example of which is given and explained below. 

Table 3.2 Sensitivity of ROE to 1 S.E. Change in Control Variable, Direct Heating Equipment 

Scenario = Primary 

Control Variables Possible Standards Levels 

Name 

IPE 

RD 

ECC 

EP 

FCA 

F1X 

CC.N 

dVC.N 

Value Changed 1 2 3 4 5 

-0.300 -0.690 0.06% 0.07% 0.15% -1.65% -4.27% 

50.00% 114.96% 0.00% 0.15% 0.22% 1.32% 2.81% 

0.068 0.075 0.00% 0.00% 0.00% -0.01% -0.04% 

0.008 0.018 -0.01% -0.01% -0.02% 0.07% 0.15% 

0.098 0.157 0.00% 0.00% -0.01% 0.06% 0.13% 

0.200 0.348 -0.09% -0.10% -0.14% -0.17% -0.30% 

0.394 0.480 -0.02% -0.03% -0.04% -0.07% -0.16% 

5.124 6.872 0.00% 0.01% 0.01% 0.05% 0.01% 

Table 3.2 lists the sensitivities of ROE to the control panel inputs defined below: 

1. IPE is the industry price elasticity. 

2. RD is the consumer discount rate. 

3. ECC is the firm's real equity cost of capital. 

4. EP is the firm's economic profit. 

5. FCA is the percentage of a firm's costs that are fixed before standards. 

6. F1X is the percentage of one-time costs (Le., costs that are induced by 

standards) that are fixed. 

7. CC.N is the estimate of one-time capital costs induced by standards. 

8. dVC.N is the increase in variable costs necessary to reach a standards level. 

3-28 
Note that for CC.N and dVC.N the change of one standard error from the mean is applicable for

standards level 2 only. These two variables are dollar values rather than percentages , and thus

their value and the change in one standard error are different at each standards level.

To construct the table, each control variable is first set to its normal value . Next, one

at a time, each is increased in absolute value by one standard error and the change in profit is

recorded. Then the variable is returned to its normal value and the next variable is tested. The

matrix in the table lists the percentage point change in ROE as a result of the change of one

standard error in the variable's value. The change in ROE is listed for each control panel

variable and at each standard level being analyzed.

Since each variable has its own standard error, the sensitivity reported in the table

measures both how sensitive the model is to a change in the variable , and how uncertain the

variable's value is. Note that the change in profit is simply the difference between long-run ROE

and base case ROE. Note also that , as expected, increasing the absolute values of some variables

increases ROE and, for other variables, decreases ROE.

The comparison of the various inputs' contributions to the uncertainty in ROE focuses

attention on the parts of the model that should be examined most closely to determine their

accuracy and on the parts where improvement in the certainty of input variables would have the

greatest payoff. Because of the differences in the sensitivities among input variables, it will

generally be found that one to three of the inputs will dominate the model's uncertainty, in the

sense that perfecting all the rest of the inputs would make only a negligible difference to the

model's accuracy.

Alternative Scenarios

For each appliance class and hypothetical standards level, hundreds of different scenarios

are run for the Monte Carlo analysis described in a following section. However, a few scenarios

that involve different demand elasticities are singled out for special attention.

If the reader wishes to estimate some other scenario not reported here, he or she can

often simply use the sensitivity charts. To do this, first look up how sensitive ROE is to a one

standard deviation change in the control variables that are changed by the proposed scenario.

Then estimate by how many standard deviations, the desired scenario variables differ from the

primary scenario control variables and calculate a proportional change in ROE. Lastly, add the

changes in ROE resulting from the different variables . This method should be reasonably

accurate for changes in the control variables that are likely to be of interest.

Uncertainty of Outputs (Monte Carlo Analysis)

Output uncertainty is most directly addressed by the Monte Carlo analysis. This analysis

assigns an uncertainty to each of the nine control-panel input variables and then chooses a value

for each based on this uncertainty. The model is then solved using these randomly chosen

3-29

Note that for CC.N and dVC.N the change of one standard error from the mean is applicable for 

standards level 2 only. These two variables are dollar values rather than percentages, and thus 

their value and the change in one standard error are different at each standards level. 

To construct the table, each control variable is first set to its normal value. Next, one 

at a time, each is increased in absolute value by one standard error and the change in profit is 

recorded. Then the variable is returned to its normal value and the next variable is tested. The 

matrix in the table lists the percentage point change in ROE as a result of the change of one 

standard error in the variable's value. The change in ROE is listed for each control panel 

variable and at each standard level being analyzed. 

Since each variable has its own standard error, the sensitivity reported in the table 

measures both how sensitive the model is to a change in the variable, and how uncertain the 

variable's value is. Note that the change in profit is simply the difference between long-run ROE 

and base case ROE. Note also that, as expected, increasing the absolute values of some variables 

increases ROE and, for other variables, decreases ROE. 

The comparison of the various inputs' contributions to the uncertainty in ROE focuses 

attention on the parts of the model that should be examined most closely to determine their 

accuracy and on the parts where improvement in the certainty of input variables would have the 

greatest payoff. Because of the differences in the sensitivities among input variables, it will 

generally be found that one to three of the inputs will dominate the model's uncertainty, in the 

sense that perfecting all the rest of the inputs would make only a negligible difference to the 

model's accuracy. 

Alternative Scenarios 

For each appliance class and hypothetical standards level, hundreds of different scenarios 

are run for the Monte Carlo analysis described in a following section. However, a few scenarios 

that involve different demand elasticities are singled out for special attention. 

If the reader wishes to estimate some other scenario not reported here, he or she can 

often simply use the sensitivity charts. To do this, fIrst look up how sensitive ROE is to a one 

standard deviation change in the control variables that are changed by the proposed scenario. 

Then estimate by how many standard deviations the desired scenario variables differ from the 

primary scenario control variables and calculate a proportional change in ROE. Lastly, add the 

changes in ROE resulting from the different variables. This method should be reasonably 

accurate for changes in the control variables that are likely to be of interest. 

Uncertainty of Outputs (Monte Carlo Analysis) 

Output uncertainty is most directly addressed by the Monte Carlo analysis. This analysis 

assigns an uncertainty to each of the nine control-panel input variables and then chooses a value 

for each based on this uncertainty. The model is then solved using these randomly chosen 

3-29 
variables. All of the important outputs are tabulated on the Monte Carlo page of the model.

These outputs are changes from the base case of price, shipments, revenue, net income, long-run

ROE, and short-run ROE. Next, new values of the input variables are drawn from the same

distribution. The model is run again and the new outputs recorded. This cycle can be repeated

as many as 400 times (or more). After a sufficient number of runs, the mean and standard

deviations of each output variable are computed.

The standard deviations of the Monte Carlo output variables are the best estimate of the

true uncertainty of the model's predictions. However, this does not mean they are absolutely

reliable because they are based on estimates of the uncertainty of the inputs, and these are often

not based on something as objective as the standard error of a regression coefficient. In short,

if the uncertainty in the inputs is misperceived, the estimate of uncertainty in the outputs cannot

be relied upon. Nonetheless, the standard errors generated by the Monte Carlo procedure are

useful for the interpretation of the LBL-MIM outputs.

3-30

<o

variables. All of the important outputs are tabulated on the Monte Carlo page of the model. 

These outputs are changes from the base case of price, shipments, revenue, net income, long-run 

ROE, and short-run ROE. Next, new values of the input variables are drawn from the same 

distribution. The model is run again and the new outputs recorded. This cycle can be repeated 

as many as 400 times (or more). After a sufficient number of runs, the mean and standard 

deviations of each output variable are computed. 

The standard deviations of the Monte Carlo output variables are the best estimate of the 

true uncertainty of the model's predictions. However, this does not mean they are absolutely 

reliable because they are based on estimates of the uncertainty of the inputs, and these are often 

not based on something as objective as the standard error of a regression coefficient. In short, 

if the uncertainty in the inputs is misperceived, the estimate of uncertainty in the outputs cannot 

be relied upon. Nonetheless, the standard errors generated by the Monte Carlo procedure are 

useful for the interpretation of the LBL-MIM outputs. 

3-30 
Assumptions in the Analysis

Assumption

• Firms maximize revenues minus

economic costs (R-EC), where EC

includes the cost of equity and all taxes.

• The life-cycle-cost elasticity of demand

experienced by a single firm is constant.

This means that price elasticity will tend

to decrease with operating cost. Note

also that since price elasticity is finite,

firms have market power.

Costs are assumed to have two

components: one fixed, and one

proportional to quantity.

• Assets are assumed to be a linear

function of sales.

• The debt-to-equity ratio remains constant

while the firm finances any investment

necessary to meet standards.

• The additional costs of parts, materials,

and labor are assumed to be long-run

marginal costs.

3-31

Justification

This follows from the assumptions of

standard economic theory, in which firms

are rational profit maximizers.

There is no reason to assume that lifecycle-cost elasticity either increases or

decreases with cost; thus, constancy is

the base assumption.

A linear cost function approximates any

smooth curve over a small region; since

quantity changes very little with

standards, the linear approximation

should be adequate.

Same as previous justification.

Firms attempt to maintain a relatively

constant overall debt-to-equity ratio,

which they believe is optimal. Although

specific new expenditures (such as those

induced by standards) may be financed

primarily by debt or by equity, in the

long run the debt-to-equity ratio will be

restored to its desired value.

These costs are proportional to

production except for cases in which

there are discounts for large quantity

purchases. Industry sources state that at

the scales of production under

consideration, these discounts are very

nearly exhausted,

^6/

Assumptions in the Analysis 

Assumption 

• Firms maXImlze revenues minus 

economic costs (R-EC), where EC 

includes the cost of equity and all taxes. 

• The life-cycle-cost elasticity of demand 

experienced by a single flrm is constant. 

This means that price elasticity will tend 

to decrease with operating cost. Note 

also that since price elasticity is flnite, 

ftrms have market power. 

• Costs are assumed to 

components: one ftxed, 

proportional to quantity. 

have 

and 

two 

one 

• Assets are assumed to be a linear 

function of sales. 

• The debt-to-equity ratio remains constant 

while the ftrm finances any investment 

necessary to meet standards. 

• The additional costs of parts, materials, 

and labor are assumed to be long-run 

marginal costs. 

3-31 

Justification 

This follows from the assumptions of 

standard economic theory, in which ftrms 

are rational proftt maximizers. 

There is no reason to assume that lifecycle-cost elasticity either increases or 

decreases with cost; thus, constancy is 

the base assumption. 

A linear cost function approximates any 

smooth curve over a small region; since 

quantity changes very little with 

standards, the linear approximation 

should be adequate. 

Same as previous justiftcation. 

Finns attempt to maintain a relatively 

constant overall debt-to-equity ratio, 

which they believe is optimal. Although 

speciftc new expenditures (such as those 

induced by standards) may be ftnanced 

primarily by debt or by equity, in the 

long run the debt-to-equity ratio will be 

restored to its desired value. . 

These costs are proportional to 

production except for cases in which 

there are discounts for large quantity 

purchases. Industry sources state that at 

the scales of production under 

consideration, these discounts are very 

nearly exhausted. 
Assumption Justification

A percentage (that varies from industry Although industry informants have stated

to industry) of capital costs are long-run that one-time capital costs will be

fixed costs. proportional to the production capacity of

the plant in question, it seems inevitable

that there will be some associated

engineering costs that are not. These will

be long-run fixed costs.

Any fall in demand caused by standards To the extent that a normal demand

(because of a price increase) will affect shortfall reduces the firm's short-run

prices in the industry by the same marginal cost, the demand decrease

amount as a normal fall in demand caused by standards will be

experienced during the business cycle. indistinguishable. The crucial

assumption here is that the business cycle

does not influence the consumers'

elasticity of demand. There is no

evidence one way or the other on this.

Outputs of the LBL-MIM

The LBL-MIM tracks five essential output variables, with emphasis on ROE. The other four

variables are price, sales, revenue, and net income. Base case and short- and long-run values of

these variables are reported on the control panel. In addition, LBL-MIM constructs an output

table that shows the long-run values of the variables at each of the standard levels under

consideration. LBL-MIM also produces a simplified income statement. As part of its algorithm,

it calculates the markup from manufacturers' costs to consumer purchase price. The purchase

price is used in the calculation of price-efficiency curves, the life-cycle-cost curves, and the

calculation of payback periods, and throughout the LBL-REM.

All of these values are estimates that necessarily reflect some uncertainty. Estimates of these

uncertainties and identification of their sources comprise another set of model outputs. Under

each estimate of one of the five main output variables, the output table displays that variable's

change from its base level and the standard error of the estimate of that change. The LBL-MIM

also computes the effect on ROE of a one-standard-deviation change in each of the control

variables. This is reported for each standard level in a sensitivity chart, designed to help discover

the source of uncertainty in the output variables.

3-32

S

Assumption 

• A percentage (that varies from industry 

to industry) of capital costs are long-run 

fixed costs. 

• Any fall in demand caused by standards 

(because of a price increase) will affect 

prices in the industry by the same 

amount as a normal fall in demand 

experienced during the business cycle. 

Outputs of the LBL-MIM 

Justification 

Although industry informants have stated 

that one-time capital costs will be 

proportional to the production capacity of 

the plant in question, it seems inevitable 

that there will be some associated 

engineering costs that are not These will 

be long-run fixed costs. 

To the extent that a normal demand 

shortfall reduces the firm's short-run 

marginal cost, the demand decrease 

caused by standards will be 

indistinguishable. The crucial 

assumption here is that the business cycle 

does not influence the consumers' 

elasticity of demand. There is no 

evidence one way or the other on this. 

The LBL-MIM tracks five essential output variables, with emphasis on ROE. The other four 

variables are price, sales, revenue, and net income. Base case and short- and long-run values of 

these variables are reported on the control panel. In addition, LBL-MIM constructs an output 

table that shows the long-run values of the variables at each of the standard levels under 

consideration. LBL-MIM also produces a simplified income statement As part of its algorithm, 

it calculates the markup from manufacturers' costs to consumer purchase price. The purchase 

price is used in the calculation of price-efficiency curves, the life-cycle-cost curves, and the 

calculation of payback periods, and throughout the LBL-REM. 

All of these values are estimates that necessarily reflect some uncertainty. Estimates of these 

uncertainties and identification of their sources comprise another set of model outputs. Under 

each estimate of one of the five main output variables, the output table displays that variable's 

change from its base level and the standard error of the estimate of that change. The LBL-MIM 

also computes the effect on ROE of a one-standard-deviation change in each of the control 

variables. This is reported for each standard level in a sensitivity chart, designed to help discover 

the source of uncertainty in the output variables. 

3-32 
Data Sources

The LBL-MIM needs data that characterize both a particular industry and typical firms within

that industry. Estimates of data are based on information from five general sources: (1) the

Engineering Analysis, (2) the Consumer Analysis, (3) industry consultants, (4) public financial

data, and (5) industry profiles. A detailed discussion of model inputs for each product appears

in Appendix C for that product's volume.

3.3.5 Utility Impact Model

The Utility Analysis serves several purposes in the assessment of the impact of the proposed

standards. It contributes to quantifying energy savings by determining the reduction in fossil

fuels used for electricity generation; this reduction also serves as input to the Environmental

Assessment. By calculating utility avoided costs, the Utility Analysis provides marginal

electricity costs to be used in evaluating the societal benefits of standards. The Utility Analysis

also quantifies impacts on the electric utility industry's need for new generating capacity.

Finally, the Utility Analysis calculates net impact of standards on utility revenues. Appendix E

of this volume contains more information on the model and data used to perform these

calculations.

The Utility Analysis adopts the convention that the value of electricity savings can be broken

down into energy (or marginal cost) savings and reliability (or capacity) savings. The energy

impact measures production costs avoided by reduced electrical demands, valued at the marginal

energy costs of the utility. The reliability impact measures the value of reduced loads during

system peak periods, which is, by convention, valued at the cost of a combustion turbine. This

convention is justified in Appendix E. The analysis calculates the avoided costs per. kWh of

energy saved. These values are used to calculate societal benefits from reduced electricity

consumption.

The utility impact model calculates avoided energy costs based on a disaggregation of the

generation fuel mix to the National Electric Reliability Council (NERC) regions and a simplified

load duration curve for each region. First, the model allocates national electricity savings

forecast by the LBL-REM to NERC regions in proportion to their 1980 consumption of heating,

cooling, and baseload energy. For the three product types under consideration, only baseload

energy is used. The fraction of the electricity that would have to be generated at the margin from

oil and gas is calculated from total regional oil and gas generation and the simplified load

duration curve. Projected utility natural gas and coal prices, weighted by the oil and gas

marginal fraction and the non-oil and gas marginal fraction, respectively, are used to calculate

utility marginal energy costs during the forecast period.

The avoided-capacity cost calculation in the model is based on conservation load factors

(CLFs) for the energy savings attributable to the standards, as well as the cost of a combustion

turbine. A conservation load factor is defined as the average load savings of a conservation

measure (in kilowatts) divided by its peak-load savings. The peak savings are averaged over an

3-33

^3

Data Sources 

The LBL-MIM needs data that characterize both a particular industry and typical fIrms within 

that industry. Estimates of data are based on information from fIve general sources: (1) the 

Engineering Analysis, (2) the Consumer Analysis, (3) industry consultants, (4) public fInancial 

data, and (5) industry profiles. A detailed discussion of model inputs for each product appears 

in Appendix C for that product's volume. 

3.3.5 Utility Impact Model 

The Utility Analysis serves several purposes in the assessment of the impact of the proposed 

standards. It contributes to quantifying energy savings by determining the reduction in fossil 

fuels used for electricity generation; this reduction also serves as input to the Environmental 

Assessment. By calculating utility avoided costs, the Utility Analysis provides marginal 

electricity costs to be used in evaluating the societal benefits of standards. The Utility Analysis 

also quantifIes impacts on the electric utility industry's need for new generating capacity. 

Finally, the Utility Analysis calculates net impact of standards on utility revenues. Appendix E 

of this volume contains more information on the model and data used to perform these 

calculations. 

The Utility Analysis adopts the convention that the value of electricity savings can be broken 

down into energy (or marginal cost) savings and reliability (or capacity) savings. The energy 

impact measures production costs avoided by reduced electrical demands, valued at the marginal 

energy costs of the utility. The reliability impact measures the value of reduced loads during 

system peak periods, which is, by convention, valued at the cost of a combustion turbine. This 

convention is justified in Appendix E. The analysis calculates the avoided costs per. kWh of 

energy saved. These values are used to calculate societal benefIts from reduced electricity 

consumption. 

The utility impact model calculates avoided energy costs based on a disaggregation of the 

generation fuel mix to the National Electric Reliability Council (NERC) regions and a simplified 

load duration curve for each region. First, the model allocates national electricity savings 

forecast by the LBL-REM to NERC regions in proportion to their 1980 consumption of heating, 

cooling, and baseload energy. For the three product types under consideration, only baseload 

energy is used. The fraction of the electricity that would have to be generated at the margin from 

oil and gas is calculated from total regional oil and gas generation and the simplifIed load 

duration curve. Projected utility natural gas and coal prices, weighted by the oil and gas 

marginal fraction and the non-oil and gas marginal fraction, respectively, are used to calculate 

utility marginal energy costs during the forecast period. 

The avoided-capacity cost calculation in the model is based on conservation load factors 

(CLFs) for the energy savings attributable to the standards, as well as the cost of a combustion 

turbine. A conservation load factor is defined as the average load savings of a conservation 

measure (in kilowatts) divided by its peak-load savings. The peak savings are averaged over an 

3-33 
on-peak period from 8 A.M. to 8 P.M. This wide on-peak period yields a conservative estimate

for peak-load savings that accounts for coincidence with system peak demand.

The CLFs, calculated using annual average appliance load curves from Southern California

Edison Company data, are used to characterize the peak demand savings of a conservation

measure. (4) They are used to convert the reliability value of the standards into the per kWh

values described above. The analysis assumes that the load shapes of each one of the eight

product types do not change as these appliances are made more efficient.

The NERC forecasts of capacity requirements for each region are used to account for

regional variations in reserve margin. If NERC predicts an adequate reserve margin in a region

for a given year, no reliability value is given to the peak load savings in the region.

The net revenue loss is equal to the difference between the revenue reductions and avoided

costs. Revenue reduction is calculated by multiplying the change in electricity consumption by

the average utility rate. Avoided costs are calculated from the change in electricity consumption

multiplied by the per-unit, avoided costs.

The inputs needed for the utility impact model include electricity savings, conservation load

factors, state-level utility fuel prices, average electricity prices, appliance saturations, efficiencies,

hours-of-use, and electricity generation by fuel type and capacity need by NERC region. The

outputs of the analysis are the fuel savings, the reduction in the need for new generating capacity,

the net change in revenues, and the avoided energy and capacity costs for heating, cooling, and

baseload appliances per million Btu of resource energy. These marginal costs are used to

calculate societal costs and benefits of standards.

Assumptions in the Analysis

Justifications for the most important assumptions in the Utility Analysis are listed below.

These assumptions include the shape of regional load-duration curves, factors that remain

constant through the analysis, the regional distribution of energy savings, utility marginal costs,

and the substance of NERC forecasts.

3-34

L^ N

on-peak period from 8 A.M. to 8 P.M. This wide on-peak period yields a conservative estimate 

for peak-load savings that accounts for coincidence with system peak demand. 

The CLFs, calculated using annual average appliance load curves from Southern California 

Edison Company data, are used to characterize the peak demand savings of a conservation 

measure. (4) They are used to convert the reliability value of the standards into the per kWh 

values described above. The analysis assumes that the load shapes of each one of the eight 

product types do not change as these appliances are made more efficient. 

The NERC forecasts of capacity requirements for each region are used to account for 

regional variations in reserve margin. If NERC predicts an adequate reserve margin in a region 

for a given year, no reliability value is given to the peak load savings in the region. 

The net revenue loss is equal to the difference between the revenue reductions and avoided 

costs. Revenue reduction is calculated by multiplying the change in electricity consumption by 

the average utility rate. A voided costs are calculated from the change in electricity consumption 

multiplied by the per-unit, avoided costs. 

The inputs needed for the utility impact model include electricity savings, conservation load 

factors, state-level utility fuel prices, average electricity prices, appliance saturations, efficiencies, 

hours-of-use, and electricity generation by fuel type and capacity need by NERC region. The 

outputs of the analysis are the fuel savings, the reduction in the need for new generating capacity, 

the net change in revenues, and the avoided energy and capacity costs for heating, cooling, and 

baseload appliances per million Btu of resource energy, These marginal costs are used to 

calculate societal costs and benefits of standards. 

Assumptions in the Analysis 

Justifications for the most important assumptions in the Utility Analysis are listed below, 

These assumptions include the shape of regional load-duration curves, factors that remain 

constant through the analysis, the regional distribution of energy savings, utility marginal costs, 

and the substance of NERC forecasts. 

3-34 
Assumption

The analysis calculates the period during

which oil and gas or coal are the

marginal fuels for electricity generation

based on a simplified load duration curve

(LDC) for each NERC region. It

therefore assumes that this LDC

accurately reflects the distribution of

fuels on the margin.

The oil and gas fractions (OGFs)

calculated for all the NERC regions

remain constant throughout the analysis

period.

• Energy savings accrue in each region in

proportion to the region's consumption of

heating, cooling, and baseload energy in

1980.

3-35

Justification

There is little information by NERC

region on which fuels are marginal.

Since NERC supplies the total amount of

fuel burned for generation, a method is

needed to convert total generation into an

estimate of marginal generation, and

using an LDC is the usual way to derive

such an estimate. Because oil and gas

are most expensive, they will be used by

utilities to meet the peak load-hours;

hence, oil and gas are assumed to be at

the top of the load duration curve. The

other fuel on the margin is coal, since

nuclear and hydro resources are usually

base-loaded.

Constant OGFs are assumed to simplify

computation. The OGFs are calculated

using the simplified LDCs described

above and by averaging NERC forecasts

of the total amount of oil and gas

generation for the years 1990 to 1995.

There are no forecasts for the amount of

oil and gas generation after 1995.

Because these calculations are highly

uncertain, and because no other forecasts

are available, the analysis assumes

constant OGFs.

This approach is an approximation made

necessary by insufficient information and

for computational convenience.

Information is available on average

saturations and appliance efficiencies, by

state, that can be aggregated to NERC

regions.

65

Assumption 

• The analysis calculates the period during 

which oil and gas or coal are the 

marginal fuels for electricity generation 

based on a simplified load duration curve 

(LDC) for each NERC region. It 

therefore assumes that this LDC 

accurately reflects the distribution of 

fuels on the margin. 

• The oil and gas fractions (OGFs) 

calculated for all the NERC regions 

remain constant throughout the analysis 

period. 

• Energy savings accrue in each region in 

proportion to the region's consumption of 

heating, cooling, and baseload energy in 

1980. 

3-35 

Justification 

There is little information by NERC 

region on which fuels are marginal. 

Since NERC supplies the total amount of 

fuel burned for generation, a method is 

needed to convert total generation into an 

estimate of marginal generation, and 

using an LDC is the usual way to derive 

such an estimate. Because oil and gas 

are most expensive, they will be used by 

utilities to meet the peak load-hours; 

hence, oil and gas are assumed to be at 

the top of the load duration curve. The 

other fuel on the margin is coal, since 

nuclear and hydro resources are usually 

base-loaded. 

Constant OGFs are assumed to simplify 

computation. The OGFs are calculated 

using the simplified LDCs described 

above and by averaging NERC forecasts 

of the total amount of oil and gas 

generation for the years 1990 to 1995. 

There are no forecasts for the amount of 

oil and gas generation after 1995. 

Because these calculations are highly 

uncertain, and because no other forecasts 

are available, the analysis assumes 

constant OGFs. 

This approach is an approximation made 

necessary by insufficient information and 

for computational convenience. 

Information is available on average 

saturations and appliance efficiencies, by 

state, that can be aggregated to NERC 

regions. 
Assumption

• Utility marginal energy costs are

calculated using the sum of utility natural

gas and coal prices weighted by the oil

and gas fraction and the non-oil and gas

fraction, respectively. This approach

assumes that all marginal generation has

the same heat rate.

• The NERC forecasts of changes in

adjusted reserve margin are accurate.

3.3.6 Life-Cycle Cost Analysis

Justification

Different generating units have different

heat rates, but the extent of this variation

for utility generating systems in different

NERC regions has not been calculated. A

10,000 Btu/kWh heat rate is typical for

many different types of generating units.

The NERC forecasts are widely accepted.

They are the only forecasts of adjusted

reserve margin at the regional level.

One measure of the effect of proposed standards on consumers is the change in operating

expenses as compared to the change in purchase prices. These changes are quantified by the

difference in life-cycle costs between the base and standards cases for the appliance classes

analyzed. The LCC is the sum of the purchase price and the operating expense discounted over

the lifetime of the appliance. It is calculated at the average efficiency for each class in the year

standards are imposed using real consumer discount rates of 2%, 6%, and 10%. The purchase

price is based on the factory costs in the Engineering Analysis and includes a factory markup

plus a distributor and retailer markup. Maintenance and installation costs are included, when

appropriate. The operating expense is calculated using the unit energy consumption data in the

LBL-REM. Energy prices and appliance usage are taken from the results of the LBL-REM. The

life-cycle cost analysis also examines the payback periods and the cost of conserved energy,

associated with the standards. Details of the LCC calculation may be found in Chapter 4 of each

product-specific volume.

An additional measure of economic impact, payback period, is useful in evaluating the

impacts on consumers of efficiency standards. The payback period measures the amount of time

it takes to recover additional investment in increased efficiency through lower operating costs.

Numerically, it is the ratio of the increase in purchase price between the base and standards cases

to the decrease in annual operating expenditures. Both the numerator and denominator of this

expression are evaluated at the average efficiency in the year standards come into effect and at

energy prices in that year.

3-36

Assumption 

• Utility marginal energy costs are 

calculated using the sum of utility natural 

gas and coal prices weighted by the oil 

and gas fraction and the non-oil and gas 

fraction, respectively. This approach 

assumes that all marginal generation has 

the same heat rate. 

• The NERC forecasts of changes in 

adjusted reserve margin are accurate. 

3.3.6 Life-Cycle Cost Analysis 

Justification 

Different generating units have different 

heat rates, but the extent of this variation 

for utility generating systems in different 

NERC regions has not been calculated. A 

10,000 Btu/kWh heat rate is typical for 

many different types of generating units. 

The NERC forecasts are widely accepted. 

They are the only forecasts of adjusted 

reserve margin at the regional level. 

One measure of the effect of proposed standards on consumers is the change in operating 

expenses as compared to the change in purchase prices. These changes are quantified by the 

difference in life-cycle costs between the base and standards cases for the appliance classes 

analyzed. The LCC is the sum of the purchase price and the operating expense discounted over 

the lifetime of the appliance. It is calculated at the average efficiency for each class in the year 

standards are imposed using real consumer discount rates of 2%, 6%, and 10%. The purchase 

price is based on the factory costs in the Engineering Analysis and includes a factory markup 

plus a distributor and retailer markUp. Maintenance and installation costs are included, when 

appropriate. The operating expense is calculated using the unit energy consumption data in the 

LBL-REM. Energy prices and appliance usage are taken from the results of the LBL-REM. The 

life-cycle cost analysis also examines the payback periods and the cost of conserved energy 

associated with the standards. Details of the LCC calculation may be found in Chapter 4 of each 

product-specific volume. 

An additional measure of economic impact, payback period, is useful in evaluating the 

impacts on consumers of efficiency standards. The payback period measures the amount of time 

it takes to recover additional investment in increased efficiency through lower operating costs. 

Numerically. it is the ratio of the increase in purchase price between the base and standards cases 

to the decrease in annual operating expenditures. Both the numerator and denominator of this 

expression are evaluated at the average efficiency in the year standards come into effect and at 

energy prices in that year. 

3-36 
3.4 SENSITIVITY ANALYSIS

Sensitivity studies are performed to determine how changes in technical and operational

parameters affect key engineering and economic indicators used in the evaluation of appliance

standards. This makes it possible to place bounds on the uncertainty in the results of the analysis

and to gain an understanding of which variables are most important in producing these results.

Sensitivity analyses are developed in a series of distinct steps. For each computer model in the

analysis, critical input parameters are identified and reasonable ranges of variation are

determined. The sensitivity of the model to changes in the value of each important parameter

is then estimated by running the model for the base case and standards cases. The significance

of the results is assessed in terms of the magnitude of change in each model's outputs.

Sensitivity runs are made with the LBL-REM to explore the effects of uncertainty in the

equipment and operating cost of the products being analyzed . The costs of the baseline unit as

well as the incremental costs of design options are varied. Other sensitivity runs examine

alternative efficiency trends in the absence of new standards.

Sensitivity analysis of the results of the LBL-MIM is conducted mainly from a sensitivity

chart that reports the sensitivity of ROE to each of the nine control variables. Because the

LBL-MIM is a non-linear model, the effects of these input uncertainties may not be additive.

A Monte Carlo analysis addresses this problem by computing standard errors, which are

displayed in the output table. For each control variable, the Monte Carlo section randomly picks

a value near the best estimate of its true value. As a result, the outputs of Monte Carlo vary

from run to run. After finishing a batch of runs (typically 100 to 400), the LBL-MIM calculates

means and standard deviations for the five main output variables.

The results of the sensitivity analyses are examined to extract two types of information.

First, the base case runs of the model are analyzed to determine the sensitivity of the forecasts

to exogenous variables and assumptions. Second, the sensitivity analysis of the impacts of

standards is performed by examining the differences between the base and standards cases. A

variable that affects the two cases similarly will have little effect on the impact of standards, even

though it might have a significant effect on the absolute forecasts. The primary interest in the

second type of sensitivity analysis is in variables and assumptions that affect the two cases

differently. The sensitivity analyses of the impacts of standards are discussed in the chapters on

these impacts.

The EPRI COMMEND sensitivity run for the fluorescent ballast analysis was the "HighEfficiency baseline." Standards analyzed under the reference baseline ("Base Case") show the

savings potential if other existing incentives (utility DSM, EPA's Green Lights, and FEMP's

Relighting Initiative) are removed from the market after 1995. Standards analyzed under the

"High-Efficiency baseline" show the effects of the persistence of existing incentives programs

after 1995. The savings for the sensitivity run are less than those for the reference case, reflecting

the smaller magnitude of available savings to be captured by standards. See Volume B, Chapter

2, for further explanation of the commercial sector baselines.

3-37

3.4 SENSITIVITY ANALYSIS 

Sensitivity studies are performed to determine how changes in technical and operational 

parameters affect key engineering and economic indicators used in the evaluation of appliance 

standards. This makes it possible to place bounds on the uncertainty in the results of the analysis 

and to gain an understanding of which variables are most important in producing these results. 

Sensitivity analyses are developed in a series of distinct steps. For each computer model in the 

analysis, critical input parameters are identified and reasonable ranges of variation are 

determined. The sensitivity of the model to changes in the value of each important parameter 

is then estimated by running the model for the base case and standards cases. The significance 

of the results is assessed in terms of the magnitude of change in each model's outputs. 

Sensitivity runs are made with the LBL-REM to explore the effects of uncertainty in the 

equipment and operating cost of the products being analyzed. The costs of the baseline unit as 

well as the incremental costs of design options are varied. Other sensitivity runs examine 

alternative efficiency trends in the absence of new standards. 

Sensitivity analysis of the results of the LBL-MIM is conducted mainly from a sensitivity 

chart that reports the sensitivity of ROE to each of the nine control variables. Because the 

LBL-MIM is a non-linear model, the effects of these input uncertainties may not be additive. 

A Monte Carlo analysis addresses this problem by computing standard errors, which are 

displayed in the output table. For each control variable, the Monte Carlo section randomly picks 

a value near the best estimate of its true value. As a result, the outputs of Monte Carlo vary 

from run to run. After finishing a batch of runs (typically 100 to 400), the LBL-MIM calculates 

means and standard deviations for the five main output variables. 

The results of the sensitivity analyses are examined to extract two types of information. 

First, the base case runs of the model are analyzed to determine the sensitivity of the forecasts 

to exogenous variables and assumptions. Second, the sensitivity analysis of the impacts of 

standards is performed by examining the differenc~s between the base and standards cases. A 

variable that affects the two cases similarly will have little effect on the impact of standards, even 

though it might have a significant effect on the absolute forecasts. The primary interest in the 

second type of sensitivity analysis is in variables and assumptions that affect the two cases 

differently. The sensitivity analyses of the impacts of standards are discussed in the chapters on 

these impacts. 

The EPRI COMMEND sensitivity run for the fluorescent ballast analysis was the "HighEfficiency baseline." Standards analyzed under the reference baseline ("Base Case") show the 

savings potential if other existing incentives (utility DSM, EPA's Green Lights, and FEMP's 

Relighting Initiative) are removed from the market after 1995. Standards analyzed under the 

"High-Efficiency baseline" show the effects of the persistence of existing incentives programs 

after 1995. The savings for the sensitivity run are less than those for the reference case, reflecting 

the smaller magnitude of available savings to be captured by standards. See Volume B, Chapter 

2, for further explanation of the commercial sector baselines. 

3-37 
CHAPTER 4. INDUSTRY PROFILE FOR LBL-MIM ANALYSIS

This section begins by examining housing starts and appliance saturation rates, with the

primary focus on annual unit shipments for each of the analyzed products. It then presents a

brief description of market shares followed by a discussion of mergers and acquisitions in these

industries, and finally a comparison of consumer and producer price indices.

4.1 DEMAND

Units Shipped and Housing Starts

One of the primary components driving the demand of most appliances is housing starts.

Although the changes in shipments of appliances are not entirely the result of changes in housing

starts, housing starts can be used to predict shipments of most appliances for two reasons . First,

almost every new house requires a new set of appliances, so fluctuations in housing starts cause

fluctuations in shipments of most common appliances. (In fact, most of the fluctuation in

shipments of products being analyzed can be attributed to the fluctuations in housing starts.)

Second, housing starts are a good indicator of the strength of the economy as a whole. A healthy

new-housing market is likely to correspond to a healthy economy, and a healthy economy

encourages replacement and discretionary purchases of appliances. Therefore, there is generally

a strong correlation between new-housing starts and appliance shipments . For similar reasons,

there are strong (if not even stronger) correlations between mobile home production and the sales

of mobile home furnaces, and between new commercial floor space and fluorescent lamp ballast

shipments.

Figures 4.1 through 4.8 show annual shipments compared to housing starts between 1970 to.

1989 for most products. Figure 4.7 shows mobile home furnaces versus new mobile home

production. Figure 4.8 shows annual fluorescent lamp ballast shipments and new commercial

construction between 1980 and 1989. There is a strong correlation between housing starts and

sales of many of these appliances, which is seen clearly in the nearly exact parallel of the graphs

in the figure.

Other factors such as age of the current stock of appliances, purchase prices, fuel costs,

consumer incomes, and climate conditions have varying degrees of importance in determining

shipments of the various products covered by the National Appliance Energy Conservation Act

of 1987 (NAECA). Further, some appliances have strong demand in the replacement market or

in the aftermarket, and thus would be less correlated with new housing starts. Some of these

factors are used by the LBL-REM in predicting appliance shipments in the base case and new

standards case. The Manufacturers Impact Analysis uses the LBL-REM estimates in its analysis

of financial impacts on manufacturers.

4-1

CHAPTER 4. INDUSTRY PROFILE FOR LBL-MIM ANALYSIS 

This section begins by examining housing starts and appliance saturation rates, with the 

primary focus on annual unit shipments for each of the analyzed products. It then presents a 

brief description of market shares followed by a discussion of mergers and acquisitions in these 

industries, and fmally a comparison of consumer and producer price indices. 

4.1 DEMAND 

Units Shipped and Housing Starts 

One of the primary components driving the demand of most appliances is housing starts. 

Although the changes in shipments of appliances are not entirely the result of changes in housing 

starts, housing starts can be used to predict shipments of most appliances for two reasons. First, 

almost every new house requires a new set of appliances, so fluctuations in housing starts cause 

fluctuations in shipments of most common appliances. (In fact, most of the fluctuation in 

shipments of products being analyzed can be attributed to the fluctuations in housing starts.) 

Second, housing starts are a good indicator of the strength of the economy as a whole. A healthy 

new-housing market is likely to correspond to a healthy economy, and a healthy economy 

encourages replacement and discretionary purchases of appliances. Therefore, there is generally 

a strong correlation between new-housing starts and appliance shipments. For similar reasons, 

there are strong (if not even stronger) correlations between mobile home production and the sales 

of mobile home furnaces, and between new commercial floor space and fluorescent lamp ballast 

shipments. 

Figures 4.1 through 4.8 show annual shipments compared to housing starts between 1970 to. 

1989 for most products. Figure 4.7 shows mobile home furnaces versus new mobile home 

production. Figure 4.8 shows annual fluorescent lamp ballast shipments and new commercial 

construction between 1980 and 1989. There is a strong correlation between housing starts and 

sales of many of these appliances, which is seen clearly in the nearly exact parallel of the graphs 

in the figure. 

Other factors such as age of the current stock of appliances, purchase prices, fuel costs, 

consumer incomes, and climate conditions have varying degrees of importance in determining 

shipments of the various products covered by the National Appliance Energy Conservation Act 

of 1987 (NAECA). Further, some appliances have strong demand in the replacement market or 

in the aftermarket, and thus would be less correlated with new housing starts. Some of these 

factors are used by the LBL-REM in predicting appliance shipments in the base case and new 

standards case. The Manufacturers Impact Analysis uses the LBL-REM estimates in its analysis 

of financial impacts on manufacturers. 

4-1 
As expected, most of the appliances show a strong correlation with housing starts. Several

interesting points emerge from these graphs. In the case of direct heating equipment (Figure 4.2),

there is a strong correlation with housing starts from 1969 to 1979 for the total (primarily

composed of wall furnaces with lesser correlations for room heaters). From about 1978 to the

present, the shipments of direct heating in general (and floor furnaces in particular) have been

declining. Meanwhile, the shipments of central heating equipment have been rising and probably

have been absorbing the market share of direct heating equipment. This observation corresponds

with reports from the industry which also state that the primary market for direct heating

equipment is now replacement rather than new sales.

The explosive growth in microwave ovens is displayed in Figure 4.3. After a slow period

of growth in the late 1960s to early 1970s, the popularity of microwave ovens increased steeply

in the 1980s, probably due in part to the greater convenience, improvement of the technology,

and lower prices. In fact, the saturation rate of microwave ovens rose from 1% in 1973 to 66%

in 1987, according to industry trade sources.

The sales of room air conditioners have little correlation with housing starts. We expect that

this is because room air conditioners are bought primarily in the aftermarket and have a strong

seasonal component in their purchase.

Similar to microwave ovens, television sets have experienced explosive growth over the

twenty years as noted in Figure 4.5. In particular, color televisions have greatly outstripped the

sales of black-and-white televisions.

Appliance Saturation Rates

The saturation rate is the percentage of homes that own a particular appliance. Tables 4.1

and 4.2 list the saturation rates for the various analyzed appliances in selected years during the

past 20 years. Note that the data do not reflect the fact that some homes have more than one

appliance of a particular type. In general, if most homes own an appliance but few homes have

more than one unit, we expect that the appliance will have a relatively low elasticity of demand

because the appliance is usually considered a necessity. This observation corresponds with the

relatively low price elasticity of certain products such as water heaters and ranges and ovens and

the higher price elasticity of products such as televisions and microwave ovens.

4-2

As expected, most of the appliances show a strong correlation with housing starts. Several 

interesting points emerge from these graphs. In the case of direct heating equipment (Figure 4.2), 

there is a strong correlation with housing starts from 1969 to 1979 for the total (primarily 

composed of wall furnaces with lesser correlations for room heaters). From about 1978 to the 

present, the shipments of direct heating in general (and floor furnaces in particular) have been 

declining. Meanwhile, the shipments of central heating equipment have been rising and probably 

have been absorbing the market share of direct heating equipment. This observation corresponds 

with reports from the industry which also state that the primary market for direct heating 

equipment is now replacement rather than new sales. 

The explosive growth in microwave ovens is displayed in Figure 4.3. After a slow period 

of growth in the late 1960s to early 1970s, the popularity of microwave ovens increased steeply 

in the 1980s, probably due in part to the greater convenience, improvement of the technology, 

and lower prices. In fact, the saturation rate of microwave ovens rose from 1 % in 1973 to 66% 

in 1987, according to industry trade sources. 

The sales of room air conditioners have little correlation with housing starts. We expect that 

this is because room air conditioners are bought primarily in the aftermarket and have a strong 

seasonal component in their purchase. 

Similar to microwave ovens, television sets have experienced explosive growth over the 

twenty years as noted in Figure 4.5. In particular, color televisions have greatly outstripped the 

sales of black-and-white televisions. 

Appliance Saturation Rates 

The saturation rate is the percentage of homes that own a particular appliance. Tables 4.1 

and 4.2 list the saturation rates for the various analyzed appliances in selected years during the 

past 20 years. Note that the data do not reflect the fact that some homes have more than one 

appliance of a particular type. In general, if most homes own an appliance but few homes have 

more than one unit, we expect that the appliance will have a relatively low elasticity of demand 

because the appliance is usually considered a necessity. This observation corresponds with the 

relatively low price elasticity of certain products such as water heaters and ranges and ovens and 

the higher price elasticity of products such as televisions and microwave ovens. 

4-2 
4000

3000

2000

1000

68 70 72 74

0

Source: GAMA and Current Indu * Reports

76

Figure 4.1 Range & Shipments Compared to Housing Starts

78

Year

80 82 84 86 88 90

4-3

4000~---------------------------------------------------------------' 

1-11- Starts ~ Electric Ranges ---0-- Gas Ranges 

'iii" 

0 

0 

~ 3000 ,.. 

c 

;::.. 

(I) 

1: 

cil 

C) 

c 

en 

:::::J 

0 2000 

:::t: 

"C 

C 

as 

(I) 

'E 

CD 

E 

Co 

::2 

en 1000 

~"" '. '\ '. 

/ \... '\ 

,t' 'B\ \\~ 1lI-'-'~ ./,,@ 

/ '<#.. r /0(11" 

/ \ ".. /JJ·"I· .. e -.... ': ...... ,B .....•. ~-···I::-.. , / \ '\......." / · .. e···· ........ ~ ~ "-

/ \ 'e ~ \\........... //e"/ '~ 

-J '\', I \-0,,/ ~ ',-,1 "'. / .-'-liB 

0+--+--+--+--+-~--4-~--~~--~--~~--~~--r--+--+--+--+--+--+-~ 

68 70 72 74 76 78 80 82 84 86 88 90 

Source: GAMA and Current Industrial Reports 

Year 1NpIIar~ 

Figure 4.1 Range & Shipments Compared to Housing Starts 

4-3 
4000

3000--

Figure 4.2

2000+

...... 0.

.. 0....... a.....

69 71 73 75 77 79 81

Source: LAMA and Current Industrial Reports

-Z-- Housing Starts --Q~~ Wall Fumwn o Total DHE

-f- Floor Furnaces -4-- Room Heaters -4--- Central Heating

Year

0.... ... q...........0...........

87 89

Direct Heating Equipment Shipments Compared to Housing Starts. DHE = Direct Heating Equipment.

4-4

4000.-----------------------------------~------------------------------, 

'Cii" 3000 o 

o 

q 

,.-

E: 

==-

... .0... . .... c'< 

71 73 75 77 

Source: GAMA and CUrrent Industrial Reports 

1h\pIIIIr.grf/lllellllp .... 

___ HousIng Starta --0- Wall Furnac:ea 0 Total DHE 

-It:- Floor Furnaces ... Room Healers -+- CenIraI HeatIng 

... 0 ........ ·0 ........ 0···.. 0· •.. ' ... -0 ••••....... 0.· ........ . 

79 81 83 85 87 89 

Year 

Figure 4.2 Direct Heating Equipment Shipments Compared to Housing Starts. DRE = Direct Heating Equipment. 

4-4 
5000

4000+

3000+

^- Hai** Starts

-A- Gas

^.. Electric

Oil

2000+

1000+

0 1& -------- 0 *.-- ............ : -. z . 41 °. * ........ ®........ * i

68 70 72 74 76 78 80 82 84 86 88 90

Source: e MA and Current Industrial Reports Year

Figure 4.3 Water Heater Shipments Compared to Housing Starts

4-5

'Ci) 

8 

q 

..... 

Figure 4.3 

5000,----------r------------------------------------------------------~ 

4000 

3000 

2000 

1000 

.... HousIng Starts 

_.£;- Gas 

··0·· ElectrIc 

··.·011 

O+--.--.-~~~~~~~~~~~~~~~==~~~~~F=~~~-+--+--+~ 

68 70 72 74 76 78 80 82 84 86 88 90 

Source: GAMA and Current Industrial Reports Year 

Water Heater Shipments Compared to Housing Starts 

4-5 
15000

12000+

9000 +

---®--- Housing Starts

-A- Microwaves

6000+

3000+

04-

68 70 72 74 76 78 80 82 84 86 88 90

Year

Source: Apphrm and Ourtetd lndu*W Repod5

Figure 4.4 Microwave Shipments Compared to Housing Starts

4-6

~. 

15~.-------------.---------------------------~--------------------, 

---11---· Housing Starts 

-k- Microwaves 

12~ 

3000 

-111-_____ .---&-------111----&-__ • '..... ,. - .. --. 

'111-------l1li-./ 

O+--+~~~--~~--~_r~--~~--~_+--+-_+--+__+--r__r--~~~r_~ 

68 70 72 74 

Source: App/ItIncfI end CUrrent IncbtrfaI Reporla 

76 78 

Year 

Figure 4.4 Microwave Shipments Compared to Housing Starts 

4-6 

80 82 84 86 88 90 
6000

4000

2000

68

0

---- Housing Starts

-A- RACs

70 72 74 76 78

Year

80 82 84 86 88

Source: Appliance and ax► lnduabW Repots

Figure 4.5 Room Air Conditioner Shipments Compared to Housing Starts

a

90

4-7

~ ~ u; 

::l 

o 

J: 

"C 

a 

6000.---------------------------~--------------~----------------~ 

···-111-··· Housing Starts 

---£:- RACs 

4000 

E 2000 

CD 

E 

a. 

::c 

C/) 

Figure 4.5 

0+__+--+__+--4_-4--4-~--~--~~--~~--~_+--+__+--+__+--+__+--4_~ 

68 70 72 74 76 78 80 82 84 86 88 90 

Year 

Source: AppITancB and Current Industrisl Reports 

Room Air Conditioner Shipments Compared to Housing Starts 

4-7 
25000

20000+

0

0

0

lz 15000

co

0)

c

0

5000+

--- Housing Starts

A Cola

...0.... B/W

.-- - a--- -- - _.®------ M--w-_J= -

0'

68 70 72 74 76 78 80 82 84 86 88 90

Year

Some: Appllsn©e end Cumanth dusmrs► Reporta

Figure 4.6 TV Shipments Compared to Housing Starts

.-a- ,.w.- - ff 4...._..E-_._

4-8

'0' 

o 

o 

q 

--

i 

Ci) 

C) 

c: 

'US 

::J 

o 

:c 

"t:I 

fa 

-E 

Q) 

E 

0-

:t: 

en 

25000,--------------.--------------------------------------------------~ 

-II- Housing Starts 

---A- Color 

···e···· B/W 

20000 

15000 

10000 

I 

5000 ....... d 

O+-~--;-~r__r--r__+--+_~--~--r__r--r__+--+_~--~--r__r--+__+--+_~ 

68 70 72 74 76 78 80 82 84 86 88 90 

Year 

Soun:e: AppIIIfIICfJ and Cl.ur8nt InduBttftIl Repotf1I 

Figure 4.6 TV Shipments Compared to Housing Starts 

4-8 
300

4

200

100

------------

79

0

0 ..................... .................... a....... ............ p................... 0........ -._..-

p .............

/

81 83 0

Year

I

0

i

87

0 ..................a

i i

89

-11- Electric -6-011 ...,... Now MH Prod

-f- Gas °*-• Total

Figure 4.7 Mobile Home Furnaces Compared to Mobile Home Production.

Sowce: NCSBCS and the Industry.

4-9

A

:c 200 

r:: 

o 

U 

:::J 

"'0 

e 

0-

J: 

:::::E 

"'0 

a 

s 100 r:: 

CD 

E 

Co 

:2 

en 

/- ~. 8 __ ... 111 .. ·_··-·-·.-.. 11." ---... 

~ " .. /., ', ....... 

/ / .... -----..... _-- 1lIII• ----...... ~.~-.... -.-.. -.-.. - ""---------..... IIIII~---- "" 

e ............... ",,· .. 0 .................... & .. ·· .. ·_··· .. ·· .. ··0 .. ·· ............... -0....... .. ........ 0· .. ··· .. · ...... " ... '0 ...... · ...... ···· .... ·· .. ·19 .... · .. · .......... ··0 ................... 0 

O+-----+-----~----~----~----~----~----~----;-----;-----;---~ 

79 81 83 85 87 89 

Year I :: :-: ~::.. _MH Prod I 

Source: NCSBCSandthelndustry. '-_______________ ----l 

Figure 4.7 Mobile Home Furnaces Compared to Mobile Home Production. 

4-9 
2000

--M--- Comm. Floorspace

A Ballasts

1600+

1200+

800+

400

0+

75 77 79 81 83 85 87

Year

Source: COMMEND, PNL, and Currant IndusMal Reports

Figure 4.8 Fluorescent Lamp Ballast Shipments Compared to New Commercial Floorspace

4-10

I 

o 

o 

u: 

2000.-------------~--------------------------------------------------~ 

1600 

...... Comm. FIoorspace 

-.- Ballasts 

(ij ·e 1200 

Q) 

E 

E 

o 

(,) 

~ Z 800 

"'C 

a 

s 

c 

Q) 

E 

c. 

:.2 

C/) 

Figure 4.8 

---.&.--.-~ 

400 

o+-----~--~-----+-----+----~----~----+-----~--~~--~-----+----~ 

75 77 79 81 83 85 87 

Year 

Source: COMMEND, PNL, and Current Industrial Reports 

Fluorescent Lamp Ballast Shipments Compared to New Commercial Floorspace 

4-10 
Table 4.1. Market Saturations

Product

1973 1978 1983 1987

Room Air Conditioners 30 28 27 26

Water Heaters (Electric) 46 48 47 46

Water Heater (Gas) 53 52 53 53

Ranges (Electric) 47 51 58 59

Ranges (Gas) 52 48 43 42

Microwave Ovens 1 7 33 66

Televisions (Color) 67 85 93 96

Source: Appliance, September 1989.

Table 4.2. Market Saturations*

Product

1981 1984 1987

Pool Heaters (Gas) 0.6/0.5 1/1 2/3

Direct Heating Equipment 6/0.4 5/0.4 4/0.5

Color Television Sets 87/88 88/88 93/97

Electric Stove 54/29 54/79.5 57177

Electric Oven 52/74 49/73 57/75

Gas Stove 46/21 45/20 2.7/21

Gas Oven 40/17 42/18 41/21

Microwave 17/23 34/51 61/76

Room Air Conditioners 32/17 30/19 30/16

Source: RECS, 1981, 1984, 1987.

*Initial figure is for the stock of dwellings, the second is for new construction. RECS figures include mobile homes,

and single-and multi-family dwellings.

4.2 SUPPLY

Market concentration

Market concentration is the extent to which market share is controlled by the largest firms

in an industry. Table 4.3 shows the market concentration for the products according to the

degree of the market controlled by the two, four, and five largest producers in each industry.

4-11

Table 4.1. Market Saturations 

1973 1978 1983 1987 

Product % % % % 

Room Air Conditioners 30 28 27 26 

Water Heaters (Electric) 46 48 47 46 

Water Heater (Gas) 53 52 53 53 

Ranges (Electric ) 47 51 58 59 

Ranges (Gas) 52 48 43 42 

Microwave Ovens 1 7 33 66 

Televisions (Color) 67 85 93 96 

Source: Appliance, September 1989. 

Table 4.2. Market Saturations* 

1981 1984 1987 

Product % % % 

Pool Heaters (Gas) 0.6/0.5 1/1 2/3 

Direct Heating Equipment 6/0.4 5/0.4 4/0.5 

Color Television Sets 87/88 88/88 93/97 

Electric Stove 54/29 54/79.5 57/77 

Electric Oven 52/74 49/73 57/75 

Gas Stove 46/21 45/20 2.7/21 

Gas Oven 40/17 42/18 41/21 

Microwave 17/23 34/51 61/76 

Room Air Conditioners 32/17 30/19 30/16 

Source: RECS, 1981, 1984, 1987. 

*Initial figure is for the stock of dwellings, the second is for new construction. RECS figures include mobile homes, 

and single-and multi-family dwellings. 

4.2 SUPPLY 

Market concentration 

Market concentration is the extent to which market share is controlled by the largest fmns 

in an industry. Table 4.3 shows the market concentration for the products according to the 

degree of the market controlled by the two, four, and five largest producers in each industry. 

4-11 
Most of the products show a high level of market concentration. With the exception of

microwave ovens and television set manufacturers, the markets concentration of the top five

producers are at least 85%. The largest producer controls from 40% to more than 50% of the

market for each of these products. Market concentration plays a part in the analysis because it

may indicate the existence of market power12 in a particular industry. Firms with the largest

market shares may have some market power, a factor that affects the markups used by these

firms both in the base case and the standards case.

Table 4.3 Percent of Market Controlled by the Largest Producers of Each Product

Product

Number of Companies

Top Two Top Four Top Five

Water Heaters 49 87 100

Ranges (Electric) 60 92 99

Ranges (Gas) 52 92 95

Microwave Ovens 35 61 70

Room Air Conditioners 55 81 87

Fluorescent Ballasts 60 95 100

Pool Heaters 66 96 100

Direct Heating Equipment (Room) 54 80 86

Direct Heating Equipment (Wall) 77 92 96

Direct Heating Equipment (Floor) 48 79 89

Televisions 35 55 62

Figures 4.9 to 4.18 give a picture of the manufacturer market shares for each of the eight

products. Most products are produced by a few manufacturers, each of which have large market

shares.

12Market power is the ability to set price within a limited range. More technically, it is the freedom that results

from facing a demand curve that is not infinitely elastic. This means that if price is raised by some small amount,

sales will not fall to zero, and if price is lowered by some small amount, sales will not become essentially infinite.

4-12

Most of the products show a high level of market concentration. With the exception of 

microwave ovens and television set manufacturers, the markets concentration of the top five 

producers are at least 85%. The largest producer controls from 40% to more than 50% of the 

market for each of these products. Market concentration plays a part in the analysis because it 

may indicate the existence of market power12 in a particular industry. Firms with the largest 

market shares may have some market power, a factor that affects the markups used by these 

finns both in the base case and the standards case. 

Table 4.3 Percent of Market Controlled by the Largest Producers of Each Product 

Number or Companies 

Top Two Top Four Top Five 

Product % % % 

Water Heaters 49 87 100 

Ranges (Electric) 60 92 99 

Ranges (Gas) 52 92 95 

Microwave Ovens 35 61 70 

Room Air Conditioners 55 81 87 

Fluorescent Ballasts 60 95 100 

Pool Heaters 66 96 100 

Direct Heating Equipment (Room) 54 80 86 

Direct Heating Equipment (Wall) 77 92 96 

Direct Heating Equipment (Floor) 48 79 89 

Televisions 35 55 62 

Figures 4.9 to 4.18 give a picture of the manufacturer market shares for each of the eight 

products. Most products are produced by a few manufacturers, each of which have large market 

shares. 

12Market power is the ability to set price within a limited range. More technically, it is the freedom that results 

from facing a demand curve that is not infinitely elastic. This means that if price is raised by some small amount, 

sales will not fall to zero, and if price is lowered by some small amount, sales will not become essentially infmite. 

4-12 
Water Heater Firm Market Shares - 1991

Rheem

21.8%

.

-------------------- ..............

Mor-Flo

21.8%

State Industries

26.7%

Source: Appliance Manufacturer

Figure 4.9 Water Heater Firm Market Shares-1991

4-13

.......................

.......................

:............................ .................................... ........................

.............................. ........ .......

........................ --

............................................................................................

..................

------------------

..................................................................................

....................... ......

.

............................................................

..........................................

...

...........................

..................................

........................ .....

................. ...

71

Water Heater Firm Market Shares - 1991 

Mor-Fia 

21.8% 

Rheem 

21.8% 

Source: Appliance Manufacturer 

State Industries 

26.7% 

Figure 4.9 Water Heater Firm Market Shares-1991 

4-13 

A.O. Smith 

15.8% 

Bradford-White 

12.9% 
Electric Range Firm Market Shares - 1991

GE

30%

Maytag owns the Jenn-Air, Hardwick, and Magic Chef brand names

Electrolux owns the Frigidaire and Tappan brand names

Raytheon owns the Caloric brand name.

GE includes what was Rop3r.

Source: Appliance Manufacturer

Figure 4.10 Electric Range Firm Market Shares-1991

4-14

-1z

Electric Range Firm Market Shares - 1991 

Whirlpool 

30% 

GE 

30% 

Maytag 

17% 

Maytag owns the Jenn-Air. Hardwick, and Magic Chef brand names 

Electrolux owns the Frigidaire and Tappan brand names 

Raytheon owns the Caloric brand name. 

GE includes what was Ropar. 

Source: Appliance Manufacturer 

Figure 4.10 Electric Range Finn Market Shares-1991 

4-14 

Electrolux 

15% 
Gas Range Firm

Market Shares - 1991

Raytheon

22%

Maytag

28%

Source: Appliance Manufacturer

Figure 4.11 Gas Range Firm Market Shares-1991

4-15

Brown

3%

Peerless-Premier

3% Others

2%

-13

Electrolux 

24% 

Gas Range Firm 

Market Shares - 1991 

Raytheon 

22% 

Maytag 

28% 

Source: Appliance Manufactursr 

Figure 4.11 Gas Range Finn Market Shares-1991 

4-15 

GE 

,8% 

Brown 

3% 

Peerless-Premier 

3% 

2% 
Microwave Oven Firm

Market hares - 1991

Goldstar 10%

.............................

............................................................

..................................................................

------------------------------------------------------------------------

.............................................................................

TI 11111

Matsushita 18%

Source: Appliance Manufacturer

Figure 4.12 Microwave Oven Firm Market Shares-1991

4-16

Electrolux 9%

Sanyo Fisher 8%

Maytag 6%

Raytheon 5%

Whirlpool 3%

Toshiba 1%

Others 7%

gal

Microwave Oven Firm 

Market Shares - 1991 

Goldstar 10% 

Samsung 16%LJ~.~~~~~ 

Sharp 17% 

Matsushita 18~ 

Source: Appliance Manufacturer 

Figure 4.12 Microwave Oven Firm Market Shares-1991 

4-16 

Sanyo Fisher 8% 

Raytheon 5% 

Toshiba 1% 
1991 A Firm Market Shares

Source: Appliance Manufacturer

Figure 4.13 Room Air Conditioner Firm Market Shares-1991

4-17

1991 RAG Firm Market Shares 

Others 

Raytheon 5% 

F ' d' 4% ~....., rle rich . 

Matsushita 

6% 

Carrier/UT 

7% 

4% 

Whirlpool 

19% 

Source: Appliance Manufacturer 

Electrolux 

20% 

Figure 4.13 Room Air Conditioner Firm Market Shares-1991 

4-17 
1 990 Fluorescent Lamp Ballast

Firm Market Shares

Valmont

30%

Source: Lighting Research Institute

Figure 4.14 Fluorescent Lamp Ballast Firm Market Shares-1990

4-18

Magnetek 

30% 

1990 Fluorescent Lamp Ballast 

Firm Market Shares 

Valmont 

30% 

Advanced 

30% 

Source: Ughting Research Institute 

Figure 4.14 Fluorescent Lamp Ballast Firm Market Shares-1990 

4-18 

Robertson 

5% 
Pool Heater Firm Market Shares ®1 1

ComfortZone

22%

Raypak

31%

=------------------------------------------------- ---- --

Source: Discussions with the industry and LBL staff.

Figure 4.15 Pool Heater Firm Market Shares-1991

4-19

-71

Pool Heater Firm Market Shares - 1991 

ComfortZone 

22% 

Hydrotech 

8% 

Anderson 

4% 

Raypak 

31% 

Source: Discussions with the industry and LBL staff. 

Figure 4.15 Pool Heater Firm Market Shares-1991 

4-19 

._-----------.---_ ..... -.-... _._.-

... --......... ---.. _ .. 
Direct Heating Equipment

Room eater Firm Market Share

1991

Locke

26%

Martin

28%

Source: Industry consultant and LBL staff.

Figure 4.16 Direct Heating Equipment: Room Heater Firm Market Share-1991

4-20

Martin 

28% 

Direct Heating Equipment 

Room Heater Firm Market Share 

1991 

US Stove 

1% Suburban 

7% 

Locke 

26% 

LV Tin 

6% 

6% 

Empire 

13% 

Perfection 

13% 

Source: Industry consultant and LBL staff. 

Figure 4.16 Direct Heating Equipment: Room Heater Firm Market Share-1991 

4-20 
Direct Heating Equipment

Wall Heater Firm Market Shares

1989

Source: Industry consultant and LBL staff

Figure 4.17 Direct Heating Equipment: Wall Heater Firm Market Share-1989

4-21

Williams

52.8%

Direct Heating Equipment 

Wall Heater Firm Market Shares 

1989 

LV Tin 

8.6% 

Perfection 

6.6% 

Empire 

25.4% 

Other 

Locke 3% 

3.6% 

Source: Industry consuHant and LBL staff 

Figure 4.17 Direct Heating Equipment: Wall Heater Firm Market Share-1989 

4-21 

Williams 

52.8% 
Direct Heating Equipment 19 89

Floor Furnace Firm Market Shares

Empire

19%

Williams

29.1%

Source: Industry consultant and LBL staff

Figure 4.18 Direct Heating Equipment: Floor Furnace Firm Market Shares -1989

4-22

Direct Heating Equipment 1989 

Floor Furnace Firm Market Shares 

Williams 

29.1% 

Suburban 

4% 

Empire 

19% 

LV Tin 

7% 

Source: Industry consultant and LBL staff 

Perfection 

10% 

Martin 

16% 

Locke 

14.8% 

Figure 4.18 Direct Heating Equipment: Floor Furnace Firm Market Shares -1989 

4-22 
4.3 MERGERS AND ACQUISITIONS

The appliance-manufacturing industry has had a continuing history of consolidation. Mergers

and acquisitions have two purposes: First, they produce large corporations with the financial

resources and stability to be successful in an increasingly competitive market. Second, mergers

and acquisitions mean manufacturers can have a complete line of home appliances for product

diversification and can therefore offer a complete set of appliances to consumers, an important

feature in the builder market. There is increasing worldwide competition in the major appliance

market, so mergers or acquisitions are likely to continue. Table 4.4 is a partial list of major

acquisitions of appliance manufacturers covered in this analysis.

Table 4.4 Acquisitions of Appliance Manufacturers

Buyer Purchase Year

Whirlpool Heil-Quaker 1964

Raytheon Co. Amana Refrigeration, Inc. 1965

White Consolidated Inc. Franklin Appliance Div. (Studebaker Corp.) 1967

White Consolidated Inc. Kelvinator Appliance Div.

(American Motor Corp.)

1968

Emerson Electric Co. In-Sink-Erator 1968

Magic Chef, Inc. Gaffers & Sattler 1968

Magic Chef, Inc. Dixie-Narco, Inc. 1968

Magic Chef, Inc. Johnson Corp. 1971

AB Electrolux (Sweden) Eureka 1974

Rockwell International Admiral Co. 1974

White Consolidated Inc. Westinghouse Electric Corp.'s

Major Appliance Operations

1975

Caloric Corp. (Raytheon) Glenwood Range Co. 1978

White Consolidated Inc. Philco Appliance Business

(Ford Motor Co.)

1979

White Consolidated Inc. Frigidaire Appliance Business

(General Motors Corp.)

1979

Magic Chef, Inc. Admiral Div. (Rockwell) 1979

Carrier Jenn-Air 1979

Raytheon Co. Modem Maid Co. 1979

Raytheon Co. Speed Queen (McGraw Edison) 1979

United Technologies Corp. Carrier 1979

AB Electrolux (Sweden) Tappan 1981

4-23

vl

4.3 MERGERS AND ACQUISITIONS 

The appliance-manufacturing industry has had a continuing history of consolidation. Mergers 

and acquisitions have two purposes: First, they produce large corporations with the financial 

resources and stability to be successful in an increasingly competitive market Second, mergers 

and acquisitions mean manufacturers can have a complete line of home appliances for product 

diversification and can therefore offer a complete set of appliances to consumers, an important 

feature in the builder market. There is increasing worldwide competition in the major appliance 

market, so mergers or acquisitions are likely to continue. Table 4.4 is a partial list of major 

acquisitions of appliance manufacturers covered in this analysis. 

Table 4.4 Acquisitions of Appliance Manufacturers 

Buyer Purchase Year 

Whirlpool Heil-Quaker 1964 

Raytheon Co. Amana Refrigeration, Inc. 1965 

White Consolidated Inc. Franklin Appliance Div. (Studebaker Corp.) 1967 

White Consolidated Inc. Ke1vinator Appliance Div. 1968 

(American Motor Corp.) 

Emerson Electric Co. In-Sink-Erator 1968 

Magic Chef, Inc. Gaffers & Sattler 1968 

Magic Chef, Inc. Dixie-Narco, Inc. 1968 

Magic Chef, Inc. Johnson Corp. 1971 

AB Electrolux (Sweden) Eureka 1974 

Rockwell International Admiral Co. 1974 

White Consolidated Inc. Westinghouse Electric Corp.'s 1975 

Major Appliance Operations 

Caloric Corp. (Raytheon) Glenwood Range Co. 1978 

White Consolidated Inc. Philco Appliance Business 1979 

(Ford Motor Co.) 

White Consolidated Inc. Frigidaire Appliance Business 1979 

(General Motors Corp.) 

Magic Chef, Inc. Admiral Div. (Rockwell) 1979 

Carrier Jenn-Air 1979 

Raytheon Co. Modern Maid Co. 1979 

Raytheon Co. Speed Queen (McGraw Edison) 1979 

United Technologies Corp. Carrier 1979 

AB Electrolux (Sweden) Tappan 1981 

4-23 
Table 4.4 Acquisitions of Appliance Manufacturers - Con't.

Buyer Purchase Year

Dart & Kraft Hobart Corp. 1981

Magic Chef, Inc. Revco 1981

Maytag Co. Hardwick Stove 1981

Maytag Co. Jenn-Air (Carrier/United Techn) 1982

Hobart (Dart & Kraft) Chambers Corp. (Rangaire Corp.) 1983

Magic Chef, Inc. Toastmaster 1983

Admiral (Magic Chef) Warwick Manufacturing Co. 1985

Chicago Pacific Corp. The Hoover Co. 1985

Masco Corp. Thermador-WasteKing (NI Ind.) 1985

AB Electrolux (Sweden) White Consolidated Industries 1986

Maytag Co. Magic Chef, Inc. 1986

Chicago Pacific Corp. Rowenta Group (West Germany)

(Allegheny Int'l and Rothmans

Deutschland GmbH)

1986

Whirlpool Corp. Kitchen-Aid Division

(Dart & Kraft, Inc.)

1986

General Electric Co. RCA 1986

Inter-City Gas Corp. (Canada) Heil-Quaker Home Systems, Inc.

(Whirlpool)

1986

Emerson Electric Co. Kitchen-Aid dishwashing manufacturing facility

(Whirlpool)

1986

White Consolidated Ind. (Electrolux) Design & Manufacturing Corp. 1987

Thomson S.A. (France) GE/RCA consumer electronics 1988

Toastmaster Div. Mgt. team buys division from Maytag Co. 1988

Speed Queen Holiday-Hammond 1988

Whirlpool Corp. Roper Corp.'s brand name 1988

General Electric Co. Roper Corp.'s physical assets

(inc. manufacturing facilities)

1988

Maytag Co. Merger with Chicago Pacific Corp. 1988

Fedders Emerson Quiet Kool 1990

Hayward Pools ComfortZone 1991

4-24

^2

Table 4.4 Acquisitions of Appliance Manufacturers - Con't. 

Buyer Purchase Year 

Dart & Kraft Hobart Corp. 1981 

Magic Chef, Inc. Revco 1981 

May tag Co. Hardwick Stove 1981 

May tag Co. Jenn-Air (Carrier/United Teehn) 1982 

Hobart (Dart & Kraft) Chambers Corp. (Rangaire Corp.) 1983 

Magic Chef, Inc. Toastmaster 1983 

Admiral (Magic Chef) Warwick Manufacturing Co. 1985 

Chicago Pacific Corp. The Hoover Co. 1985 

Masco Corp. Thermador-WasteKing (NT Ind.) 1985 

AB Electrolux (Sweden) White Consolidated fudustries 1986 

May tag Co. Magic Chef, Inc. 1986 

Chicago Pacific Corp. Rowenta Group (West Germany) 1986 

(Allegheny Int'l and Rothmans 

Deutschland GmbH) 

Whirlpool Corp. Kitchen-Aid Division 1986 

(Dart & Kraft, Inc.) 

General Electric Co. RCA 1986 

Inter-City Gas Corp. (Canada) Heil-Quaker Home Systems. Inc. 1986 

(Whirlpool) 

Emerson Electric Co. Kitchen-Aid dishwashing manufacturing facility 1986 

(Whirlpool) 

White Consolidated Ind. (Eleetrolux) Design & Manufacturing Corp. 1987 

Thomson S.A. (France) GE;RCA consumer electronics 1988 

Toastmaster Div. Mgt. team buys division from May tag Co. 1988 

Speed Queen Holiday-Hammond 1988 

Whirlpool Corp. Roper Corp.'s brand name 1988 

General Electric Co. Roper Corp. 's physical assets 1988 

(inc. manufacturing facilities) 

May tag Co. Merger with Chicago PaCific Corp. 1988 

Fedders Emerson Quiet Kool 1990 

Hayward Pools ComfortZone 1991 

4-24 
4.4 PRICES

Appliance prices have, on average, risen more slowly than aggregate indexes measuring

consumer and producer prices, with direct heating equipment being an exception, and television

sets showing a strong drop in the 1980s. The appliance industry has a long history of price

stability even through fluctuations in the business cycle. Despite increased energy and

component prices during the 1970s and 1980s, the industry has, through increased productivity,

kept price increases lower than increases in the Consumer Price Index (CPI). In recent years,

the industry has made a strong push toward automation to keep costs down, spending hundreds

of millions of dollars to automate production facilities.

Figure 4.19 shows the Producer Price Index (PPI) for all commodities and for household

appliances for the years 1984 to 1991. The PPI for household appliances increased about half

as much as the PPI for all commodities. Figures 4.20 to 4.25 show the real PPI for various

product groupings compared to the CPI and the household appliance group as a whole. The real

PPI for each product grouping is defined as the PPI for that product grouping, holding the overall

PPI constant at 1982 = 100. In other words, this figure indicates the relative increase or decrease

in the price index of a product grouping, using the PPI for all commodities as the yardstick. The

subsequent figures and their products are

• Figure 4.20: Household Cooking Equipment (kitchen ranges and ovens and

microwave ovens),

• Figure 4.21: Household Audio and Video Equipment (including TVs),

• Figure 4.22: Refrigeration and Heating Equipment (including RACs),

• Figure 4.23: Household Appliances Not Elsewhere Classified (including water

heaters),

• Figure 4.24: Heating Equipment, Except Electric and Warm Air Furnaces

(including direct heating equipment and pool heaters), and

• Figure 4.25: Power, Distribution, and Specialty Transformers (including

fluorescent lamp ballasts).

4-25

4.4 PRICES 

Appliance prices have, on average, risen more slowly than aggregate indexes measuring 

consumer and producer prices, with direct heating equipment being an exception, and television 

sets showing a strong drop in the 1980s. The appliance industry has a long history of price 

stability even through fluctuations in the business cycle. Despite increased energy and 

component prices during the 1970s and 1980s, the industry has, through increased productivity, 

kept price increases lower than increases in the Consumer Price Index (CPD. In recent years, 

the industry has made a strong push toward automation to keep costs down, spending hundreds 

of millions of dollars to automate production facilities. 

Figure 4.19 shows the Producer Price Index (PPI) for all commodities and for household 

appliances for the years 1984 to 1991. The PPI for household appliances increased about half 

as much as the PPI for all commodities. Figures 4.20 to 4.25 show the real PPI for various 

product groupings compared to the CPI and the household appliance group as a whole. The real 

PPI for each product grouping is defined as the PPI for that product grouping, holding the overall 

PPI constant at 1982 = 100. In other words, this figure indicates the relative increase or decrease 

in the price index of a product grouping, using the PPI for all commodities as the yardstick. The 

subsequent figures and their products are 

• Figure 4.20: Household Cooking Equipment (kitchen ranges and ovens and 

microwave ovens), 

• Figure 4.21: Household Audio and Video Equipment (including TVs), 

• Figure 4.22: Refrigeration and Heating Equipment (including RACs), 

• Figure 4.23: Household Appliances Not Elsewhere Classified (including water 

heaters), 

• Figure 4.24: Heating Equipment, Except Electric and Warm Air Furnaces 

(including direct heating equipment and pool heaters), and 

• Figure 4.25: Power, Distribution, and Specialty Transformers (including 

fluorescent lamp ballasts), 

4-25 
150

125 -F

.. E-- PPI

--A--- All HH App

100

751 '

84 85 86 87

Year

Source: U.S. Bureau of Labor Statfstice

Figure 4-19 National PPI and CPI for All Household Appliances

88 89 90 91

4-26

~ 

-II 

C\I 

co 

0) 

-

~ "C 

.5 

150r-----------.---------------~-----------------------------. 

125 

100 

..... -. PPI 

.---'---- All HH App 

..----

,-----"'-- .111-----

_--II--.----------i. 

..... -..--------

t--.. -."--.. -."-II-.... -11"_-..... ------- ~ ......... ----. ~--------1.~········------II·---·---·----· -;.-----.----

75+-------~------~--------+-------~------~--------+-------~ 

84 85 86 87 88 89 90 91 

Year 

. Source: u.s. Bureau of Labor StatIstics 

Figure 4-19 National PPI and CPI for All Household Appliances 

4-26 
150

120+

-9-- PPI

-A- CPI

...a.. HH Cooking Egmt

............ ? ............ ...

90+

30+

0 i--I

76 77 78 79 80 81

Source: U.S. Bureau of Labor Stattetlce

82 83 84 85 86 87 88 89 90 91

Year

Figure 4.20 National CPI and PPI Averages Compared to Household Cooking Equipment PPI

4-27

150.-------------~------------------------------------------------~ 

-fII-- PPI 

--+- cPt 

.@. HH Cooking Eqmt 

120 

g 90 ,.... 

II 

i ,.... 

-

30 

O+---~---r---+----~--+---~--~--~----~--+---~---+--~----~~ 

76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 

Source: u.s. Bureau of Labor StatI8tk:8 Year 

Figure 4.20 National CPI and PPI Averages Compared to Household Cooking Equipment PPI 

4-27 
150

120+

--®--. PPI

--k- CPI

...o... Radios/TVs, etc. (3651)

0.-

30+

01 +---^-t69 71 73 75 77 79 81 83 85 87 89 91

Year

Source: U.S. Sueau of Labor Statistics

Figure 4.21 National CPI and PPI Averages Compared to TV Product Group PPI

4-28

0 

0 

..... 

II 

~ 0) 

..... 

-

~ -g 

150,----------------,-------------------------------------------------, 

120 

90 

60 

30 

--II!--- PPI 

,\- CPI 

·--0·-- RadiosfTVs, etC. (3651) 

.......... $-... 

O+_~--~--r__+--~--r_-+--+_~--_r--+_~--~--r__+--~--r__+--+_~--_r~ 

69 71 73 75 77 79 81 83 85 87 89 91 

Year 

Source: u.s. Bl.nau of Labor Statlstlca 

Figure 4.21 National CPI and PPI Averages Compared to TV Product Group PPI 

4-28 
150

120+

60+

30+

-A CPI

..0.° Refrigeration (3585)

- PPI

80 82 84 86 88 90

Year

U.S. Bureau of Labor Statistics

Figure 4.22 CPI and PPI National Averages Compared to RAC Product Group PPI

4-29

6' 

o 

,... 

II 

~ CD 

... 

-

150~--------------~--------------------------------------------------~ 

-111- PPI 

-Ir- CPI 

.. @ ... Refrigeration (3585) 

120 

90 

60 

30 

0+-----+-----+-----+-----+-----+-----+-----+-----4-----4-----4-----4---~ 

78 80 82 84 86 88 90 

Year 

U.S. Bureau of Labor Statistics 

Figure 4.22 CPI and PPI National Averages Compared to RAC Product Group PPI 

4-29 
130+

90

C t

70+

504-

83

--a- PPI

f CPI

...0 HH App NEC (3639)

85 87

U.S. Bureeu of Labor Sfabebce Year

Figure 4.23 National CPI and PPI Averages Compared to Water Heater Product Class PPI

4-30

89 91

130 

......... : 

110 .. - ...... -..... @ ............... _ ..... , ..... ==---_.----.... , ....... . So 

T"" 

II 

C\I 

co 

0) 

T"" 

-

~ 90 

"0 

C 

-II- PPI 

70 -.l- CPI 

... @... HH App NEe (3639) 

50+--------+--------+-------~--------1-------~--------~------~------~ 

83 85 87 89 91 

U.S. Bureau of Labor Statistics Year 

Figure 4.23 National CPI and PPI Averages Compared to Water Heater Product Class PPI 

4-30 
............. -I^

a--- PPI

f CPI

°4-° Heating Egmt (3433)

83

source: U.S. Bureau of Labor stedistics Year

87

Figure 4.24 National CPI and PPI Compared to DHE and Pool Heater Product Group PPI

89 91.

4-31

S' 

0 

.,... 

II 

t\I 

00 

0) 

,.... 

-

~ 

"2 

170 

140 

110 .. , 

80 

. ,'1')" 

...... 

.............. ...... $ ..... 

............. 0··· 

........ "".$" .. 

" ..... " ..... 0·· .. · 

. $' .. , ....... e" ..... $ .... --." .. "." ." .. ·0'" 

--······--111--·----· __ ·_11111 __ -----·---·-111-~------~-..... -

-.,.-~ ...... ....--

--.--- PPI 

,,"CPI 

...... -~.~---~--~ •............ -.. -

... $... Heating Eqrnt (343S) 

.':' 

50+------r----~------+------r----~------+------r----~------+-----~ 

81 83 85 87 89 91. 

Source: u.s. Bureau of Labor Statistics Year 

Figure 4.24 National CPI and PPI Compared to DHE and Pool Heater Product Group PPI 

4-31 
71 73 75 77 79 81 83 85 87 89

Source: U.S. Bureau of Labor Statietice Year

Figure 4.25 National CPI and PPI Averages Compared to Ballasts Product Group PPI

91

4-32

150~--------------~------------------------------------------------~ 

120 

6' 90 o 

,... 

II 

~ en 

,... 

-

~ 

-g 60 

30 

---11--- PPI 

--*-_: CPI 

---e--- Transformers (3612) 

0+--+--4---~~--+--+--4---~-+--+-~---r--~-+--+-~---r--r--+--~~--~ 

69 71 73 75 77 79 81 83 85 87 89 91 

Source: u.s. BuI'8lW of Labor Statiatice Year 

Figure 4.25 National CPI and PPI Averages Compared to Ballasts Product Group PPI 

4-32 
CHAPTER 5. DEVELOPMENT OF BASE CASE FORECASTS

Analyzing impacts of federal energy efficiency standards requires comparing projected

U.S. residential energy consumption with and without the standards. The cases without standards

are referred to as base case projections. These base case projections are compared to projections

of conditions that would be likely to prevail if standards were enacted (see Chapter 3). The

difference between the two projections is defined as the impact of standards.

Projections are made for a number of demographic, economic, and energy variables,

which hold true for all end uses, including energy prices, household income, housing stock,

housing starts, mix of house types (single-family, multifamily, mobile homes), and building shell

thermal characteristics. Additional (product-specific) variables, described in Chapter 2 of each

product-specific volume, include appliance purchases, equipment prices, unit energy consumption,

and aggregate residential energy consumption by fuel type.

5.1 ECONOMIC AND DEMOGRAPHIC DATA: RESIDENTIAL

The LBL-REM (described in Volume A, Appendix B) is used to derive projections of

residential energy demand. These projections are dependent on assumptions about future years,

including the size and age distribution of the population, number of annual housing starts,

disposable personal income, and energy prices. These data are described in Tables 5.1 through

5.6. (Economic data specific to each product, such as equipment price, initial market shares,

elasticities for market shares, efficiency choices, and usage behaviors are described in Appendix

C of each product-specific volume.)

5.1.1 Population Projection

The population projection for the U.S. is based on Bureau of Census data to the year 2010

(10). The population projections are consistent with those used by DOE in projecting energy

prices. Projections for later years are obtained by extrapolation, assuming that the annual rate of

increase in population is at 0.5% per year from 2010 to 2030.

5.1.2 Occupied Households

The number and type of occupied households for the base year (1980) are from the 0.1%

Public Use MicroSample (PUMS) of the 1980 Decennial Census. The population projection is

used to estimate the housing stock in each state each year, as described in Appendix B.

CHAPTER 5. DEVELOPMENT OF BASE CASE FORECASTS 

Analyzing impacts of federal energy efficiency standards requires comparing projected 

U.S. residential energy consumption with and without the standards. The cases without standards 

are referred to as base case projections. These base case projections are compared to projections 

of conditions that would be likely to prevail if standards were enacted (see Chapter 3). The 

difference between the two projections is defined as the impact of standards. 

Projections are made for a number of demographic, economic, and energy variables, 

which hold true for all end uses, including energy prices, household income, housing stock, 

housing starts, mix of house types (single-family, multifamily, mobile homes), and building shell 

thermal characteristics. Additional (product-specific) variables, described in Chapter 2 of each 

product-specific volume, include appliance purchases, equipment prices, unit energy consumption, 

and aggregate residential energy consumption by fuel type. 

5.1 ECONOMIC AND DEMOGRAPHIC DATA: RESIDENTIAL 

The LBL-REM (described in Volume A, Appendix B) is used to derive projections of 

residential energy demand. These projections are dependent on assumptions about future years, 

including the size and age distribution of the population, number of annual housing starts, 

disposable personal income, and energy prices. These data are described in Tables 5.1 through 

5.6. (Economic data specific to each product, such as equipment price, initial market shares, 

elasticities for market shares, efficiency choices, and usage behaviors are described in Appendix 

C of each product-specific volume.) 

5.1.1 Population Projection 

The population projection for the U.S. is based on Bureau of Census data to the year 2010 

(10). The population projections are consistent with those used by DOE in projecting energy 

prices. Projections for later years are obtained by extrapolation, assuming that the annual rate of 

increase in population is at 0.5% per year from 2010 to 2030. 

5.1.2 Occupied Households 

The number and type of occupied households for the base year (1980) are from the 0.1 % 

Public Use MicroSample (PUMS) of the 1980 Decennial Census. The popUlation projection is 

used to estimate the housing stock in each state each year, as described in Appendix B. 

5-1 
Table 5.1 Demographic Projections

U.S.

Population

(millions)

Occupied

Households

(millions)

Singlefamily

(millions)

Multifamily

(millions)

Mobile

Homes

(millions)

1980 226.9 80.4 53.8 22.7 3.9

1985 238.1 87.1 58.4 24.5 4.2

1990 249.9 94.4 63.3 26.5 4.6

1995 259.6 101.3 67.9 28.4 5.0

2000 267.7 107.8 72.3 30.3 5.3

2005 275.1 114.3 76.6 32.1 5.6

2010 282.1 117.2 78.5 32.9 5.8

2015 289.2 120.1 80.5 33.7 5.9

2030 311.7 129.5 86.8 36.4 6.4

Source: U.S. Department of Commerce, Bureau of the Census.

5.1.3 Housing Starts

Single-Family and Multifamily

Figures for past (1980-1985) housing starts by state come from the National Association

of Home Builders (NAHB) data (11). Projections (1986-2015) by region come from Data

Resources, Inc (12). Housing starts in later years (2016-2030) are based on changes in the

housing stock and estimated retirement rates, described in Appendix B.

Mobile Homes

Past mobile home placements for residential use are taken from the U.S. Bureau of.

Census (1980-1986) data. In other years, mobile home placements are calculated from changes

in the projected stock of mobile homes and estimated retirement rates.

Table 5.2 Housing Starts, 1980-2015 (Thousands)

Singlefamily

Multifamily

Mobile

homes Total

1980 852 460 222 1534

1985 1072 670 283 2025

1990 895 298 200 1393

1995 1056 531 214 1801

2000 1021 403 200 1624

2005 937 414 192 1543

2010 909 443 183 1535

2015 901 444 177 1522

2030 971 478 191 1640

Sources: (1980-1985) NAHB; (1986-2015) Data Resources Inc.; (2016-2030) LBL.

5-2

9 ti

Table 5.1 Demographic Projections 

u.s. Occupied Single- Multi- Mobile 

Population Households family family Homes 

(millions) (millions) (millions) (millions) (millions) 

1980 226.9 80.4 53.8 22.7 3.9 

1985 238.1 87.1 58.4 24.5 4.2 

1990 249.9 94.4 63.3 26.5 4.6 

1995 259.6 101.3 67.9 28.4 5.0 

2000 267.7 107.8 72.3 30.3 5.3 

2005 275.1 114.3 76.6 32.1 5.6 

2010 282.1 117.2 78.5 32.9 5.8 

2015 289.2 120.1 80.5 33.7 5.9 

2030 311.7 129.5 86.8 36.4 6.4 

Source: U.S. Department of Commerce, Bureau of the Census. 

5.1.3 Housing Starts 

Single-Family and Multifamily 

Figures for past (1980-1985) housing starts by state come from the National Association 

of Home Builders (NAHB) data (11). Projections (1986-2015) by region come from Data 

Resources, Inc (12). Housing starts in later years (2016-2030) are based on changes in the 

housing stock and estimated retirement rates, described in Appendix B. 

Mobile Homes 

Past mobile home placements for residential use are taken from the U.S. Bureau of 

Census (1980-1986) data. In other years, mobile home placements are calculated from changes 

in the projected stock of mobile homes and estimated retirement rates. 

Table 5.2 Housing Starts, 1980-2015 (Thousands) 

SiIigle- Multi- Mobile 

family family homes Total 

1980 852 460 222 1534 

1985 1072 670 283 2025 

1990 895 298 200 1393 

1995 1056 531 214 1801 

2000 1021 403 200 1624 

2005 937 414 192 1543 

2010 909 443 183 1535 

2015 901 444 177 1522 

2030 971 478 191 1640 

Sources: (1980-1985) NAHB; (1986-2015) Data Resources Inc.; (2016-2030) LBL. 

5-2 
Table 5.3 House Retirement Rates (Percent of Stock per Year)

Single-Family 0.62%

Multifamily 0.82%

Mobile Home 2.50%

5.1.4 Disposable Personal Income

Disposable personal income (1980-1986) is from the Survey of Current Business (13),

converted to 1990 dollars using the Consumer Price Index-Urban. For 1987-2015, the projection

from the DOE Annual Energy Outlook 1991 is used (14), similarly converted to 1990 dollars.

For 2016-2030, the projection is extended, assuming a linear increase in disposable personal

income, with the annual increase being the average of the increases from 2010 to 2015.

Table 5.4 Disposable Personal Income (1990 Dollars)

Total

(Billions)

Per Occupied Household

(Thousands)

1980 2890 35.9

1985 3435 39.4

1990 3916 41.5

1995 4227 41.7

2000 4665 43.3

2005 5126 44.8

2010 5592 47.7

2015 6056 50.4

2030 7692 59.4

Sources: (1980- 1986) Survey of Current Business; (1987-2015) DOE/EIA Annual Energy Outlook 1991; (2016-2030)

LBL. extrapolation.

5.1.5 Residential Energy Prices

Past energy prices (1980-1988) are from the DOE Monthly Energy Review (15). Energy

prices for 1989 and 1990 as well as projected energy prices (1991-2010) are from DOE Annual

Energy Outlook 1991 (13). Extrapolation of energy prices (2011-2030) assumes average annual

increases of 0.6% per year for electricity, 2.2% per year for natural gas, 1.3% per year for

distillate, and 2.2% per year for LPG.

Electricity prices are further adjusted to reflect differences in effective price by specific

end uses. Table 5.6 shows multipliers for obtaining effective prices for space heating, water

heating, air conditioning, and other end uses from the average sector prices in Table 5.5.

5-3

q3

Table 5.3 House Retirement Rates (Percent of Stock per Year) 

Single-Family 

Multifamily 

Mobile Home 

5.1.4 Disposable Personal Income 

0.62% 

0.82% 

2.50% 

Disposable personal income (1980-1986) is from the Survey of Current Business (13), 

converted to 1990 dollars using the Consumer Price Index-Urban. For 1987-2015, the projection 

from the DOE Annual Energy Outlook 1991 is used (14), similarly converted to 1990 dollars. 

For 2016-2030, the projection is extended, assuming a linear increase in disposable personal 

income, with the annual increase being the average of the increases from 2010 to 2015. 

Table 5.4 Disposable Personal Income (1990 Dollars) 

Total Per Occupied Household 

(Billions) (Thousands) 

1980 2890 35.9 

1985 3435 39.4 

1990 3916 41.5 

1995 4227 41.7 

2000 4665 43.3 

2005 5126 44.8 

2010 5592 47.7 

2015 6056 50.4 

2030 7692 59.4 

Sources: (1980-1986) Survey o/Current Business; (1987-2015) DOE/EIAAnnual Energy Outlook 1991; (2016-2030) 

LBL extrapolation. 

5.1.5 Residential Energy Prices 

Past energy prices (1980-1988) are from the DOE Monthly Energy Review (15). Energy 

prices for 1989 and 1990 as well as pr:ojected energy prices (1991-2010) are from DOE Annual 

Energy Outlook 1991 (13). Extrapolation of energy prices (2011-2030) assumes average annual 

increases of 0.6% per year for electricity, 2.2% per year for natural gas, 1.3% per year for 

distillate, and 2.2% per year for LPG. 

Electricity prices are further adjusted to reflect differences in effective price by specific 

end uses. Table 5.6 shows multipliers for obtaining effective prices for space heating, water 

heating, air conditioning, and other end uses from the average sector prices in Table 5.5. 

5-3 
These multipliers capture regional differences in residential rates and appliance holdings,

as well as effects of rate structure. (For example, if electric heat is common in areas with lower

residential rates, then the effective rate for electric space heating is lower than the average

residential rate. Also, if the block structure offers declining rates for increased consumption, then

end uses associated with higher consumption, including space heating, will have lower effective

rates.) The multipliers are derived from the 1987 Residential Energy Consumption Survey.

Table 5.5 Average Residential Energy Prices (1990 Dollars per Million Btu)

Electricity) Natural Gas Distillate Oil LPG

Past

1980 22.44 5.71 11.16 12.37

1985 27.75 7.21 9.41 9.25

1990 23.74 5.69 7.64 8.31

Projected

1991 23.87 5.76 7.57 8.41

1992 24.01 5.84 7.51 8.52

1993 24.14 5.91 7.45 8.63

1994 24.27 5.98 7.38 8.73

1995 24.41 6.06 7.32 8.85

1996 24.28 6.15 7.39 8.98

1997 24.15 6.25 7.45 9.12

1998 24.02 6.34 7.52 9.26

1999 23.90 6.44 7.59 9.40

2000 23.77 6.54 7.66 9.55

2005 24.74 7.98 8.65 11.65

2010 25.44 8.90 9.24 12.99

2015 26.16 9.93 9.87 14.49

2020 26.90 11.07 10.54 16.16

2025 27.66 12.35 11.26 18.02

2030 28.44 13.77 12.03 20.10

13412 Btu/kWh.

Source: (1980-1988) DOE Monthly Energy Review.

(1989-2010) DOE/EIA Annual Energy Outlook 1991.

(2011-2030) extrapolated by LBL.

5-4

-it)

These multipliers capture regional differences in residential rates and appliance holdings, 

as well as effects of rate structure. (For example, if electric heat is common in areas with lower 

residential rates, then the effective rate for electric space heating is lower than the average 

residential rate. Also, if the block structure offers declining rates for increased consumption, then 

end uses associated with higher consumption, including space heating, will have lower effective 

rates.) The multipliers are derived from the 1987 Residential Energy Consumption Survey. 

Table 5.5 Average Residential Energy Prices (1990 Dollars per Million Btu) 

Electricity 1 Natural Gas Distillate Oil LPG 

Past 

1980 22.44 5.71 11.16 12.37 

1985 27.75 7.21 9.41 9.25 

1990 23.74 5.69 7.64 8.31 

Projected 

1991 23.87 5.76 7.57 8.41 

1992 24.01 5.84 7.51 8.52 

1993 24.14 5.91 7.45 8.63 

1994 24.27 5.98 7.38 8.73 

1995 24.41 6.06 7.32 8.85 

1996 24.28 6.15 7.39 8.98 

1997 24.15 6.25 7.45 9.12 ' 

1998 24.02 6.34 7.52 . 9.26 

1999 23.90 6.44 7.59 9.40 

2000 23.77 6.54 7.66 9.55 

2005 24.74 7.98 8.65 11.65 

2010 25.44 8.90 9.24 12.99 

2015 26.16 9.93 9.87 14.49 

2020 26.90 11.07 10.54 16.16 

2025 27.66 12.35 11.26 18.02 

2030 28.44 13.77 12.03 20.10 

13412 BtulkWh. 

Source: (1980~1988) DOE Monthly Energy Review. 

(1989-2010) OOE/EIA Annual Energy Outlook 1991. 

(2011-2030) extrapolated by LBL. 

5-4 
Table 5.6 Relative Electricity Price by End Use (Average Residential Electricity

Price = 1.00)

Electricity Natural Gas

Space Heating 0.90 0.98

Water Heating 0.90 0.98

Air Conditioning 1.00 NA

Other End Uses 1.04 1.20

Source: LBL derived from 1987 RECS.

5.2 ECONOMIC AND DEMOGRAPHIC DATA: COMMERCIAL

EPRI COMMEND (described in Volume A, Appendix B) provides projections of

commercial energy demand . These projections depend upon commercial floorspace, GNP, and

energy prices. Appendix C of Volume B describes the inputs specific to lighting.

5.2.1 Commercial Floor Space Projections

Floor Space Data

Table 5.7 shows the forecasted total U.S. commercial sector floor space (including federal

buildings) by building type used for this analysis, based on data developed by Pacific Northwest

Laboratories (PNL) (16). Floor space growth drives the overall growth in demand for energy

services, in the COMMEND model.

Floor space values given by EPRI in the COMMEND default input files are slightly lower

than the values published in the AEO 1992 (17), the NES (18), and the values used by PNL.

Because these other sources are in close agreement, the PNL floor space data series was chosen

in this study (as it provides the most detail). PNL data give the vintages of currently existing

commercial buildings. The survival functions specified in COMMEND characterize the retirement

rate of buildings and are used to estimate annual additions from 1920 to 1989. For the future,

the growth rate of total commercial floorspace is related to the Gross National Product (GNP)

growth rate through the regression equation calculated by PNL, which is based on data for

1963-1986.

Aln(St) = 0.117 Aln(GNP) + 0.863[ Aln (S)]t_i (1)

where A ln(S)=a dimensionless growth rate relating the stock S in the year t to the stock in

the previous year, t - 1.

5-5

Table 5.6 Relative Electricity Price by End Use (Average Residential Electricity 

Price = 1.00) 

Space Heating 

Water Heating 

Air Conditioning 

Other End Uses 

Source: LBL derived from 1987 RECS. 

Electricity 

0.90 

0.90 

1.00 

1.04 

Natural Gas 

0.98 

0.98 

NA 

1.20 

5.2 ECONOMIC AND DEMOGRAPHIC DATA: COMMERCIAL 

EPRI COMMEND (described in Volume A, Appendix B) provides projections of 

commercial energy demand. These projections depend upon commercial floorspace, GNP, and 

energy prices. Appendix C of Volume B describes the inputs specific to lighting. 

5.2.1 Commercial Floor Space Projections 

Floor Space Data 

Table 5.7 shows the forecasted total U.S. commercial sector floor space (including federal 

buildings) by building type used for this analysis, based on data developed by Pacific Northwest 

Laboratories (PNL) (16). Floor space growth drives the overall growth in demand for energy 

services- in the COMMEND model. 

Floor space values given by EPRI in the COMMEND default input files are slightly lower 

than the values published in the AEO 1992 (17), the NBS (18), and the values used by PNL. 

Because these other sources are in close agreement, the PNL floor space data series was chosen 

in this study (as it provides the most detail). PNL data give the vintages of currently existing 

commercial buildings. The survival functions specified in COMMEND characterize the retirement 

rate of buildings and are used to estimate annual additions from 1920 to 1989. For the future, 

the growth rate of total commercial floorspace is related to the Gross National Product (GNP) 

growth rate through the regression equation calculated by PNL, which is based on data for 

1963-1986. 

Mn(St) = 0.117 .1ln(GNP) + 0.863[ Mn(S)]t_l (1) 

where ~ in(S)=a dimensionless growth rate relating the stock S in the year t to the stock in 

the previous year, t - 1. 

5-5 
A In S t = In S t - In St-1 = In St

S(t-1)

(2)

The GNP forecasting is based on DRI numbers (to the year 2000) published in the 1987 AEO

(Case A). After the year 2000, a GNP growth rate of 2.1% per year is assumed (based on AEO

1992 projections), which yields a 1.8% annual growth in floor space. This total forecasted figure

is distributed to building types based on a fixed ratio (the average of the last three years'

additions distribution) and is kept constant throughout the forecasting period.

By the year 2030, 75% (98 billion sq ft) of the commercial floor space will have been

built after 1986. All buildings, however, will have an opportunity for lighting system changes

by 2030.

Table 5.7 Commercial Floor Space Projections (Billions of square feet)

Year

Small Large Restau- WareOffice Office rant Retail Grocery house School College Health Lodging Misc. Total

1980 4.95 4.95 1.18 11.55 0.81 8.10 6.35 2.71 1.87 3.31 10.52 56.30

1985 5.60 5.60 1.22 12.16 0.83 8.99 6.31 2.70 2.04 3.57 10.75 59.78

1990 6.13 6.13 1.29 13.06 0.88 9.96 6.52 2.80 2.20 3.81 11.25 64.02

1995 6.64 6.64 1.39 14.15 0.95 10.79 7.07 3.03 2.38 4.13 12.18 69.34

2000 7.21 7.21 1.51 15.37 1.03 11.72 7.68 3.29 2.58 4.48 13.24 75.34

2005 7.86 7.86 1.65 16.75 1.12 12.78 8.37 3.59 2.82 4.89 14.43 82.10

2010 8.58 8.58 1.80 18.28 1.23 13.94 9.13 3.92 3.07 5.33 15.74 89.60

2015 9.38 9.38 1.97 19.99 1.34 15.25 9.99 4.28 3.36 5.83 17.22 98.00

2020 10.25 10.25 2.15 21.85 1.47 16.67 10.92 4.68 3.67 6.37 18.82 107.12

2025 11.21 11.21 2.35 23.89 1.60 18.22 11.93 5.12 4.02 6.97 20.58 117.10

2030 12.25 12.25 2.57 26.11 1.75 19.92 13.04 5.59 4.39 7.62 22.49 127.98

Sources: PNL, DRI, NBECS, 1986.

5.2.2 Building Starts

Building starts are calculated using PNL 1989 commercial building data with historic

vintage information on when buildings were built. These data, together with default COMMEND

building decay functions, yield estimated commercial building starts listed in Table 5.8. The

COMMEND building decay functions provide the building retirement rates shown in Table 5.9.

5-6

tU

(2) 

The GNP forecasting is based on DRI numbers (to the year 2000) published in the 1987 ABO 

(Case A). After the year 2000, a GNP growth rate of 2.1 % per year is assumed (based on ABO 

1992 projections), which yields a 1.8% annual growth in floor space. This total forecasted figure 

is distributed to building types based on a fixed ratio (the average of the last three years' 

additions distribution) and is kept constant throughout the forecasting period. 

By the year 2030, 75% (98 billion sq ft) of the commercial floor space will have been 

built after 1986. All buildings, however, will have an opportunity for lighting system changes 

by 2030. 

Table 5.7 Commercial Floor Space Projections (Billions of square feet) 

Small Large Restau- WareYear Office Office rant Retail Grocery house School College Health Lodging Misc. Total 

1980 4.95 4.95 1.18 11.55 0.81 8.10 6.35 2.71 1.87 3.31 10.52 56.30 

1985 5.60 5.60 1.22 12.16 0.83 8.99 6.31 2.70 2.04 3.57 10.75 59.78 

1990 6.13 6.13 1.29 13.06 0.88 9.96 6.52 2.80 2.20 3.81 11.25 64.02 

1995 6.64 6.64 1.39 14.15 0.95 10.79 7.07 3.03 2.38 4.13 12.18 69.34 

2000 7.21 7.21 1.51 15.37 1.03 11.72 7.68 3.29 2.58 4.48 13.24 75.34 

2005 7.86 7.86 1.65 16.75 1.12 12.78 8.37 3.59 2.82 4.89 14.43 82.10 

2010 8.58 8.58 1.80 18.28 1.23 13.94 9.13 3.92 3.07 5.33 15.74 89.60 

2015 9.38 9.38 1.97 19.99 1.34 15.25 9.99 4.28 3.36 5.83 17.22 98.00 

2020 10.25 10.25 2.15 21.85 1.47 16.67 10.92 4.68 3.67 6.37 18.82 107.12 

2025 11.21 11.21 2.35 23.89 1.60 18.22 11.93 5.12 4.02 6.97 20.58 117.10 

2030 12.25 12.25 2.57 26.11 1.75 19.92 13.04 5.59 4.39 7.62 22.49 127.98 

Sources: PNL, DRI, NBECS, 1986. 

5.2.2 Building Starts 

Building starts are calculated using PNL 1989 commercial building data with historic 

vintage information on when buildings were built. These data, together with default COMMEND 

building decay functions, yield estimated commercial building starts listed in Table 5.8. The 

COMMEND building decay functions provide the building retirement rates shown in Table 5.9. 

5-6 
Table 5.8 Commercial Building Starts, 1980 -2030 (Billions of square feet)

Small Large Restau- WareYear Office Office rant Retail Grocery house School College Health Lodging Misc. Total

1980 0.166 0.166 0.026 0.300 0.017 0.265 0.067 0.030 0.040 0.077 0.184 1.338

1985 0.246 0.246 0.033 0.388 0.022 0.349 0.070 0.032 0.054 0.086 0.186 1.714

1990 0.126 0.126 0.040 0.363 0.032 0.128 0.223 0.099 0.046 0.085 0.359 1.626

1995 0.189 0.189 0.042 0.421 0.029 0.273 0.211 0.090 0.063 0.105 0.338 1.950

2000 0.211 0.211 0.047 0.477 0.032 0.307 0.236 0.100 0.069 0.120 0.377 2.188

2005 0.238 0.238 0.052 0.526 0.035 0.340 0.250 0.107 0.078 0.139 0.409 2.411

2010 0.261 0.261 0.055 0.561 0.037 0.379 0.264 0.113 0.088 0.157 0.441 2.617

2015 0.304 0.304 0.060 0.615 0.041 0.429 0.280 0.120 0.097 0.168 0.479 2.896

2020 0.327 0.327 0.066 0.678 0.045 0.475 0.292 0.126 0.104 0.177 0.514 3.131

2025 0.344 0.344 0.073 0.740 0.050 0.521 0.316 0.136 0.112 0.191 0.558 3.386

2030 0.375 0.375 0.080 0.810 0.055 0.557 0.351 0.151 0.122 0.208 0.609 3.693

Sources: PNL, COMMEND decay functions

Table 5.9 Building Retirement Rates

Building Type

Percentage

Age Remaining Age

Percentage

Remaining Age

Percentage

Remaining

Years % Years % Years %

Small Office 15 99 36 50 80 20

Large Office 15 99 36 50 80 20

Restaurant 15 99 36 50 80 20

Retail 15 99 36 50 80 20

Grocery 15 99 36 50 80 20

Warehouse 15 99 48 50 90 20

School 15 99 48 50 90 20

College 15 99 48 50 90 20

Health 15 99 48 50 90 20

Lodging 15 99 48 50 90 20

Miscellaneous 15 99 48 50 90 20

Sources: COMMEND default values

5.2.3 Commercial Energy Prices

Table 5.10 shows the energy price forecasts used in this analysis. Non-electric energy

prices are important here because they affect the choice of,heating energy in COMMEND

forecasts, which ultimately influences the estimates of HVAC interactions. Real commercial

prices for electricity, natural gas, and fuel oil used in the analysis are in constant 1990 dollars.

Prices prior to 1990 are taken from the EIA Monthly Energy Review (MER), August, 1992.(18)

Price forecasts are taken from the EIA Annual Energy Outlook (AEO), 1992, extrapolated to

5-7

ri

Table 5.8 Commercial Building Starts, 1980-2030 (Billions of square feet) 

Small Large Restau- WareYear Office Office rant Retail Grocery house School College Health Lodging Misc. Total 

1980 0.166 0.166 0.026 0.300 0.017 0.265 0.067 0.030 0.040 0.077 0.184 l.338 

1985 0.246 0.246 0.033 0.388 0.022 0.349 0.070 0.032 0.054 0.086 0.186 l.714 

1990 0.126 0.126 0.040 0.363 0.032 0.128 0.223 0.099 0.046 0.085 0.359 l.626 

1995 0.189 0.189 0.042 0.421 0.029 0.273 0.211 0.090 0.063 0.105 0.338 l.950 

2000 0.211 0.211 0.047 0.477 0.032 0.307 0.236 0.100 0.069 0.120 0.377 2.188 

2005 0.238 0.238 0.052 0.526 0.035 0.340 0.250 0.107 0.078 0.139 0.409 2.411 

2010 0.261 0.261 0.055 0.561 0.037 0.379 0.264 0.113 0.088 0.157 0.441 2.617 

2015 0.304 0.304 0.060 0.615 0.041 0.429 0.280 0.120 0.097 0.168 0.479 2.896 

2020 0.327 0.327 0.066 0.678 0.045 0.475 0.292 0.126 0.104 0.177 0.514 3.131 

2025 0.344 0.344 0.073 0.740 0.050 0.521 0.316 0.136 0.112 0.191 0.558 3.386 

2030 0.375 0.375 0.080 0.810 0.055 0.557 0.351 0.151 0.122 0.208 0.609 3.693 

Sources: PNL, COMMEND decay functions 

Table 5.9 Building Retirement Rates 

Percentage Percentage Percentage 

Building Type Age Remaining Age Remaining Age Remaining 

Years % Years % Years % 

Small Office 15 99 36 50 80 20 

Large Office 15 99 36 50 80 20 

Restaurant 15 99 36 50 80 20 

Retail 15 99 36 50 80 20 

Grocery 15 99 36 50 80 20 

Warehouse 15 99 48 50 90 20 

School 15 99 48 50 90 20 

College 15 99 48 50 90 20 

Health 15 99 48 50 90 20 

Lodging 15 99 48 50 90 20 

Miscellaneous 15 99 48 50 90 20 

Sources: COMMEND default values 

5.2.3 Commercial Energy Prices 

Table 5.10 shows the energy price forecasts used in this analysis. Npn-electric energy 

prices are important here because they affect the choice of' heating energy in COMMEND 

forecasts, which ultimately influences the estimates of HV AC interactions. Real commercial 

prices for electricity, natural gas, and fuel oil used in the analysis are in constant 1990 dollars. 

Prices prior to 1990 are taken from the EIA Monthly Energy Review (MER), August, 1992.(18) 

Price forecasts are taken from the EIA Annual Energy Outlook (ABO), 1992, extrapolated to 

5-7 
2030. The deflator values for the years 1973-1990 are taken from the MER. For the years

1991-2030, the deflators are calculated based on the trend given by the input files supplied with

the COMMEND program.

Electricity costs are projected prices for 1995 from the Annual Energy Outlook 1991. The

commercial electricity price is $0.0773 per kWh (1990 dollars).

For natural gas prices beyond 2010, the price trajectory from the National Energy Strategy

(NES) (9) is used, scaled to the AEO 1992 price in 2010. This price path is based on output

from the FOSSIL2 partial equilibrium model and incorporates information about the

supply/demand balance for natural gas. The growth in natural gas prices is expected to flatten

out in 2015, according to the NES prediction.

The default input files of COMMEND use the fuel oil price as an indication of the price

level of petroleum products. This is improved by defining a mix of products (distillate fuel,

kerosene, LPG, and residual fuel) and calculating a price index for this mix. The consumption

levels given in the State Energy Data Report, Consumption Estimates 1960-1989 (SEDR),(21)

for the year 1989 are used to calculate the contribution of each product to the mix. It is assumed

that propane represents all of LPG, fuel oil represents all of the distillates, and all of the residual

oil used in the commercial sector is of low sulfur content.

5-8

2030. The deflator values for the years 1973-1990 are taken from the MER. For the years 

1991-2030, the deflators are calculated based on the trend given by the input files supplied with 

the COMMEND program. 

Electricity costs are projected prices for 1995 from the Annual Energy Outlook 1991. The 

commercial electricity price is $0.0773 per kWh (1990 dollars). 

For natural gas prices beyond 2010, the price trajectory from the National Energy Strategy 

(NBS) (9) is used, scaled to the ABO 1992 price in 2010. This price path is based on output 

from the FOSSIL2 partial equilibrium model and incorporates information about the 

supply Idem and balance for natural gas. The growth in natural gas prices is expected to flatten 

out in 2015, according to the NBS prediction. 

The default input files of COMMEND use the fuel oil price as an indication of the price 

level of petroleum products. This is improved by defming a mix of products (distillate fuel, 

kerosene, LPG, and residual fuel) and calculating a price index for this mix. The consumption 

levels given in the State Energy Data Report, Consumption Estimates 1960-1989 (SEDR),(21) 

for the year 1989 are used to calculate the contribution of each product to the mix. It is assumed 

that propane represents all of LPG, fuel oil represents all of the distillates, and all of the residual 

oil used in the commercial sector is of low sulfur content. 

5-8 
Table 5.10 Average Commercial Energy Prices (1990 Dollars per Million Btu)

Electricity* Natural Gas Distillate Oil

1980 25.51 5.24 8.76

1985 26.62 6.44 7.36

1986 24.92 5.84 5.12

1987 23.62 5.29 5.12

1988 23.01 4.97 4.57

1989 22.25 4.85 4.52

1990 21.41 4.86 5.29

1991 21.87 4.91 5.42

1992 22.29 4.97 5.48

1993 22.62 5.03 5.51

1994 22.61 5.09 5.53

1995 22.59 5.17 5.54

1996 22.51 5.30 5.65

1997 22.19 5.44 5.69

1998 22.07 5.58 5.77

1999 21.74 5.73 5.84

2000 21.60 5.87 5.85

2005 22.02 6.85 6.85

2010 22.32 7.81 7.41

2015 22.69 8.84 8.34

2020 23.07 9.10 9.39

2025 23.44 9.54 10.57

2030 23.82 9.96 11.88

*33412 Btu/kWh.

Sources: (1980-1990) DOE/EIA Monthly Energy Review, May 1991.

(1991-2030) DOE/EIA Annual Energy Outlook, 1992

5.3 SENSITIVITY ANALYSIS OF THE BASE CASE

5.3.1 Residential Sector Sensitivity Cases

The sensitivity cases for the residential sector are defined as follows:

• Lower Equipment Price. The price of the baseline unit for each class is reduced

15%. The incremental price associated with each other design option is reduced

by the uncertainty interval estimated for that incremental cost.

• Higher Equipment Price. The price of the baseline unit for each class is increased

15%. The incremental price associated with each other design option is increased

by the uncertainty interval estimated for the incremental cost.

• Lower Energy Price. Assume lower energy prices. Starting from 1992 to 2030,

5-9

ii q

Table S.10 Average Commercial Energy Prices (1990 Dollars per Million Btu) 

Sources: 

1980 

1985 

1986 

1987 

1988 

1989 

1990 

1991 

1992 

1993 

1994 

1995 

1996 

1997 

1998 

1999 

2000 

2005 

2010 

2015 

2020 

2025 

2030 

*33412 Btu/kWh. 

Electricity* 

25.51 

26.62 

24.92 

23.62 

23.01 

22.25 

21.41 

21.87 

22.29 

22.62 

22.61 

22.59 

22.51 

22.19 

22.07 

21.74 

21.60 

22.02 

22.32 

22.69 

23.07 

23.44 

23.82 

Natural Gas 

5.24 

6.44 

5.84 

5.29 

4.97 

4.85 

4.86 

4.91 

4.97 

5.03 

5.09 

5.17 

5.30 

5.44 

5.58 

5.73 

5.87 

6.85 

7.81 

8.84 

9.10 

9.54 

9.96 

(1980-1990) DOEIEIA Monthly Energy Review. May 1991. 

(1991-2030) DOEIEIA Annual Energy Outlook, 1992 

Distillate Oil 

8.76 

7.36 

5.12 

5.12 

4.57 

4.52 

5.29 

5.42 

5.48 

5.51 

5.53 

5.54 

5.65 

5.69 

5.77 

5.84 

5.85 

6.85 

7.41 

8.34 

9.39 

10.57 

11.88 

5.3 SENSITIVITY ANALYSIS OF THE BASE CASE 

5.3.1 Residential Sector Sensitivity Cases 

The sensitivity cases for the residential sector are defined as follows: 

• Lower Equipment Price. The price of the baseline unit for each class is reduced 

15%. The incremental price associated with each other design option is reduced 

by the uncertainty interval estimated for that incremental cost. 

• Higher Equipment Price. The price of the baseline unit for each class is increased 

15%. The incremental price associated with each other design option is increased 

by the uncertainty interval estimated for the incremental cost. 

• Lower Energy Price. Assume lower energy prices. Starting from 1992 to 2030, 

5-9 
electricity prices are 3% lower, while gas and distillate prices are 5% lower than

those in the AEO 1991 forecast.

• Higher Energy Price. Assume higher energy prices. Starting from 1992 to 2030,

electricity prices are 3% higher, while gas and distillate prices are 5% higher than

those in the AEO 1991 forecast.

• High Equipment Efficiency. Assume continuing future improvement in appliance

efficiencies at a rate of 2% per year.

• Market Discount Rates Decline. Assume that market discount rates used to

determine future efficiency choices are declining over time by 2% per year, i.e.,

efficiency improvements appear in the marketplace sooner.

The results of these six sensitivity cases are presented in Sections 2.3 (base case

sensitivity) and Sections 3.6 (standards impacts) of Volumes B through I.

5.3.2 Commercial Sector Sensitivity Cases

The sensitivity case for the commercial sector is defined as follows:

• Lower Equipment PricelHigh Efficiency Case. The price of energy-efficient

lighting is lower, simulating continuing DSM (demand -side management)

programs by electric utility companies or a permanent price drop by lighting

manufacturers.

5-10

)od

electricity prices are 3% lower, while gas and distillate prices are 5% lower than 

those in the ABO 1991 forecast. 

• Higher Energy Price. Assume higher energy prices. Starting from 1992 to 2030, 

electricity prices are 3% higher, while gas and distillate prices are 5% higher than 

those in the ABO 1991 forecast. Y 

• High Equipment Efficiency. Assume continuing future improvement in appliance 

efficiencies at a rate of 2% per year. 

• Market Discount Rates Decline. Assume that market discount rates used to 

detennine future efficiency choices are declining over time by 2% per year, i.e., 

efficiency improvements appear in the marketplace sooner. 

The results of these six sensitivity cases are presented in Sections 2.3 (base case 

sensitivity) and Sections 3.6 (standards impacts) of Volumes B through I. 

5.3.2 Commercial Sector Sensitivity Cases 

The sensitivity case for the commercial sector is defined as follows: 

• Lower Equipment Price/High Efficiency Case. The price of energy-efficient 

lighting is lower, simulating continuing DSM (demand-side management) 

programs by electric utility companies or a permanent price drop by lighting 

manufacturers. 

5-10 
APPENDIX A. UNCERTAINTY ANALYSIS FOR ENERGY

EFFICIENCY

Many of the inputs to the calculations of energy savings for each design option have some

degree of uncertainty. This uncertainty of the inputs affects the level of confidence in the

estimate of energy savings for design options. As design options are combined, the effects of

the uncertainty on the inputs are also combined, so that the uncertainty in the energy savings

becomes larger and larger.

In order to understand the effects that the uncertainty of input values had on the

calculation of energy savings, a risk-analysis program (22) was used to quantify the uncertainty.

The risk-analysis program was an add-in to the spreadsheets used to generate the cost/efficiency

tables shown in the TSD (See Figure A.1.) It allowed the inputs to the calculations to be

specified by an assumed distribution of values with associated probabilities. After the

probabilities were specified, the energy-savings spreadsheet was recalculated several hundred

times, randomly choosing a value from each distribution. The selection was done so that the

distribution of input values chosen matched the probabilities specified in the input distribution.

The baseline was assumed to be exact for purposes of this study, therefore no distribution

was applied to any of the inputs on the baseline models. The uncertainty on inputs for combined

or stacked design options included, or were applied on top of, the uncertainties of previous design

options. Often different design options affected different parts of the energy consumption

calculations. An example of this would be applying heat traps to a gas-fired storage water heater

and then applying a submerged combustion chamber. The heat traps reduce the standby loss,

while the submerged combustion chamber increases the recovery efficiency. In these cases, the

uncertainties from previous design options were used in the calculations, even though that part

of the calculation was not being changed by the current design option.

The results of the uncertainty analysis were reported only for the maximum

technologically feasible design options. These results include the uncertainties of all the design

options that were applied before the maximum technologically feasible design option. The upper

and lower values reported for the uncertainty of the efficiency indicator span the middle 95% of

all values resulting from the uncertainty analysis.

For room air conditioners, the method was modified slightly to accommodate the use of

a simulation model. An input file to the simulation model was constructed for the baseline and

each design option. The data in the input files specified the properties and operating conditions

of a room air conditioner. The risk-analysis program was used to generate several hundred

simulation model input files. The distribution of values in the input files that were used to

specify changes from the baseline by the design options reflected the probability distribution

entered into the risk-analysis software. The combined results of all the simulation runs thus

reflected the impact of uncertainty on the inputs.

A-1

j(

APPENDIX A. UNCERTAINTY ANALYSIS FOR ENERGY 

EFFICIENCY 

Many of the inputs to the calculations of energy savings for each design option have some 

degree of uncertainty. This uncertainty of the inputs affects the level of confidence in the 

estimate of energy savings for design options. As design options are combined, the effects of 

the uncertainty on the inputs are also combined, so that the uncertainty in the energy savings 

becomes larger and larger. 

In order to understand the effects that the uncertainty of input values had on the 

calculation of energy savings, a risk-analysis program (22) was used to quantify the un~ertainty. 

The risk-analysis program was an add-in to the spreadsheets used to generate the cost/efficiency 

tables shown in the TSD (See Figure A.I.) It allowed the inputs to the calculations to be 

specified by an assumed distribution of values with associated probabilities. After the 

probabilities were specified, the energy-savings spreadsheet was recalculated several hundred 

times, randomly choosing a value from each distribution. The selection was done so that the 

distribution of input values chosen matched the probabilities specified in the input distribution. 

The baseline was assumed to be exact for purposes of this study, therefore no distribution 

was applied to any of the inputs on the baseline models. The uncertainty on inputs for combined 

or stacked design options included, or were applied on top of, the uncertainties of previous design 

options. Often different design options affected different parts of the energy consumption 

calculations. An example of this would be applying heat traps to a gas-fired storage water heater 

and then applying a submerged combustion chamber. The heat traps reduce the standby loss, 

while the submerged combustion chamber increases the recovery efficiency. In these cases, the 

uncertainties from previous design options were used in the calculations, even though that part 

of the calculation was not being changed by the current design option. 

The results of the uncertainty analysis were reported only for the maximum 

technologically feasible design options. These results include the uncertainties of all the design 

options that were applied before the maximum technologically feasible design option. The upper 

and lower values reported for the uncertainty of the efficiency indicator span the middle 95% of 

all values resulting from the uncertainty analysis. 

For room air conditioners, the method was modified slightly to accommodate the use of 

a simulation model. An input file to the simulation model was constructed for the baseline and 

. each design option. The data in the input files specified the properties and operating conditions 

of a room air conditioner. The risk-analysis program was used to generate several hundred 

simulation model input files. The distribution of values in the input files that were used to 

specify changes from the baseline by the design options reflected the probability distribution 

entered into the risk-analysis software. The combined results of all the simulation runs thus 

reflected the impact of uncertainty on the inputs. 

A-I 
Most of the input values were assumed to have a normal distribution, with the selected

value being the mean of the distribution. The standard deviation of the distribution was then

specified as the difference between the largest and/or the smallest values considered possible and

the mean. The distributions were typically truncated at two standard deviations from the mean.

This truncation excludes the smallest 2.28% and the largest 2.28% of the possible values in a

normal distribution. This was done to eliminate potential outliers that could unnaturally skew

the results.

Figure A.1 Schematic of @RISK Uncertainty Analysis for Spreadsheet Calculations

A-2

Most of the input values were assumed to have a normal distribution, with the selected 

value being the mean of the distribution. The standard deviation of the distribution was then 

specified as the difference between the largest and/or the smallest values considered possible and 

the mean. The distributions were typically truncated at two standard deviations from the mean. 

This truncation excludes the smallest 2.28% and the largest 2.28% of the possible values in a 

normal distribution. This was done to eliminate potential outliers that could unnaturally skew 

the results. 

Dl8tributlon of Input C 

Distribution of Input B I ~ I ~ ~ 

~~n '\,., Energy 

Distribution of Input A ~s 

I " baseline""" a b c d =f(a.b.c.cl) 

~ 1 ~ b r~ _1 d A 

............ 2 a i'A 1 d A 

--- 3 ~ A 1 d ~ 

Figure A.I Schematic of @RISK Uncertainty Analysis for Spreadsheet Calculations 

A-2