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 EFFICIENCYSTANDARDS 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 ImpactAnalysis
November 1993

u.S. Department of Energy Assistant Secretary, Energy Efficiency & Renewable Energy Office of Codes and Standards Washington, DC 20585 DOElEE-0009 Vol.1of3ACKNOWLEDGEMENT

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 ofContents
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
.............................. 5-75.3 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 ofHouseholds
......................... 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 ofthe 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 ofImpact
.................................. 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 ofModules
.............................. 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

11C.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 Avoided Transmission/Distribution System Capital Costs
................................................ E-10 EA.1 Value ofReliability Savings
. . . . . . . . . . . . . . . . . . . . . . . . . . . . E-lO EA.2 Amount ofReliability Savings
......................... E-12 EA.3 Avoided Transmission and Distribution Capital Costs
.......... E-15

iiiENVIRONMENTAL 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 FOREIGHTTYPES OF CONSUMERPRODUCTS: ROOMAIR 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

ivREGULATORY 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 ofthe 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 ofStandards 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

v4.3 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 ofProposed Standards on Consumers
.................. Raid-37 4.4 Effects ofProposed Standards on Manufacturers
................ Raid-38 4.5 Effects ofProposed 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 ofAlternative 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 TableE.3 Table E.4 TableE.5 Table E.6 TableE.7 TableE.8 Table E.9 Table E.11 Table 9 

### List ofTables

Product Types and Number ofProduct" 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 ofMarket Controlled by the LargestProducers ofEach Product
. .. 4-12 Acquisitions ofAppliance Manufacturers
........................ 4-24 Demographic Projections
.................................... 5-2 Housing Starts, 1980-2015 (Thousands)
.......................... 5-2 House Retirement Rates (Percent of Stockper Year)
................. 5-3 Disposable Personal Income (1990 Dollars)
....................... 5-3 Average Residential Energy Prices (1990 Dollars per Million Btu)
....... 5-4 Relative ElectricityPrice by End Use (AverageResidential 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 perMillion Btu)
....... 5-9 Parameters ofThermal 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 oflID units
...... . . . . . . . . . . . . . . . . •D-4 Regional Appliance Saturations (in Fraction ofTotal)
................ E-2 " Regional Population-Weighted Heating and Cooling LoadHours
......... 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 ofResource Energy Saved
........................................... E-17 Total Utility Avoided Costs: Base Case (Dollars per MMBtu ofResource 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-LongRun
........... 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. • 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 

## CHAPTER 1. EXECUTIVE SUMMARY

TheEnergy Policy and Conservation Act (P.L. 94-163), as amended, establishes energy conservation standards for 12 ofthe 13 types ofconsumerproducts specifically covered by the Act. The legislation requires the Department of Energy (DOE) to consider new or amended standards for these and othertypes ofproducts at specified times. DOEis currently considering amending standards for seven types ofproducts: 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 ofthe 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 ConsumerAnalysis, whichforecasts appliance sales, efficiencies, energy use, and consumer expenditures. • A ManufacturerAnalysis, which provides an estimate ofmanufacturers' responses to the proposed standards. Their responses are quantified by changes in several financial performance measures for a prototypical firm.

• AnImpactAnalysis, whichprovides an integratedframeworkfor assessing the costs andbenefits ofappliance standards. TheImpactAnalysis includes: (1) aLife-Cycle CostAnalysis thatevaluates the savings in operating expenserelative to an increase in purchase price for individual consumers; (2) a UtilityAnalysis that measures the impacts of the altered energy-consumption patterns on electric utilities; (3) an Industry ImpactAnalysis that shows the financial and competitive impacts on the respective appliance manufacturing industry; and (4) a Cost-Benefit Analysis that collects the results ofall the analyses into the net benefits andcosts from a national perspective. TheEngineeringAnalysis segregatesproducttypes into separate classes to whichdifferent energy conservation standards apply. For each appliance class, baseline units are chosen representingrelatively low-efficiency units currentlybeing manufactured. The analysis identifies a series ofdesign options to improve energyefficiency andestimates the factory costs to produce them. Design options are added individually orin combination to the baseline unit to evaluate units that might be produced in response to standards. Factory costs are then marked up to
1-1consumer 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 constructedfor each class. The paybackperiods 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 ofnew appliances by class, as well as appliance usage levels inresponse to changes in projectedenergyprices and incomes. These results are used to project energy use and consumerexpenditures on fuel andequipment. The net present value of the standards is computed by discounting the differences in the time streams ofthese 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. Todothis, two measures ofimpactare tracked for the industry as a whole and for any segments that may exist: (1) retum-on-equity (ROE) and (2) netincome. ROE provides the primary measure ofprofitability; gross margin, return-on-assets (ROA), and return-on-sales (ROS) are alsoreported. 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 ofthe industry as a whole and ofspecific segments ofthe industry to make the one-time investments required to meet the new standards is examined. Second, ifstandards 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 ofthe proposed standards. The Utility Analysis focuses on revenue changes, avoided costs, andreductions in peak electric loads. Utility-avoided costs represent the marginal value oflower fuel consumption aswell as the marginal value of lower investment in new generating plants. Reduction in peak loads can lead to deferring the construction ofnew 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 ApplianceEnergy Conservation Actof 1987 (P.L. 100-12) andby the NationalApplianceEnergy Conservation Amendments of 1988 (P.L. 100-357), provides energy conservation standards for 12 ofthe 13 types of consumer products1 

### covered by the Act, and authorizes the Secretary of Energy toprescribe amendedornew energy standards foreachtype (orclass) ofcoveredproduct. The assessmentofthe proposedstandards forthe eighttypes ofproducts2

### presentedin this document is designed to evaluate their economic impacts according to the criteria in the Act. Itincludes an engineering analysis ofthe costandperformance ofdesign options to improve the efficiency ofthe products; forecasts ofthe number and average efficiency ofproducts sold, the amount of energy the products will consume, and their prices and operating expenses; a determination ofchange in investment, revenues, and costs to manufacturers ofthe products; a calculation ofthe costs and benefits to consumers, electric utilities, and the nation as a whole; and an assessment ofthe environmental impacts of the proposed standards. 

### 2.2 STRUCTURE OF THE DOCUMENT This technical support documentconsists ofnine volumes (one general and one for each product). Volume A, General Methodology, provides a general description of the analytic approach, including the structure ofthe major models. Volumes B through I are each specific to aproduct. Each volume contains the data and documentation specific to the analysis ofone product, and the results ofthat 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 ofinterest.

1The 13 products covered in the legislation are: (1) refrigerators, refrigerator-freezers, and freezers; (2) room air conditioners; (3) central airconditioners and central air-conditioning heatpumps; (4) waterheaters; (5) furnaces; (6) dishwashers; (7) clothes washers; (8) clothes dryers; (9) directheatingequipment; (10)kitchen ranges andovens; (11) pool heaters; (12). television sets; and (13) fluorescent lamp ballasts. 2 The 8 types ofproducts analyzed in this rulemaking are: (1) waterheaters; (2) directheating 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. 

### 2-1Volume 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 ofthe documents. Analytic Approach: summarizes the methodology used in 

### the analysis; discusses the components ofthe 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 ofthe 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 Methodologyforuncertainty analysis ofmaximum technologicallyfeasibleenergy efficiency. Appendix B Overview offorecasting models: structure ofthe 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 ofmanufacturer 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 ofthe analysis in terms ofchanges in gross margin, return on sales, return on equity, return on assets, total assets, and labor requirements; andpresents the method for conducting a sensitivity analysis ofthe results. Appendix D Maintenance costs are derived for several components common to more than one appliance type. Appendix E Electric utility impact modeling.

2-2Environmental 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 particularproduct. 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 CaseForecasts: describes nationalforecastofenergyconsumption,efficiency of new units, units installed in households, and annual appliance sales in the absence ofnew regulations. ProjectedNational Impacts ofStandards: discusses the choice ofstandards levels to be analyzed and the projected impacts of each standard level. The chapter summarizestheenergy savings byfuel andproduct; the sales, averageefficiencies, purchase costs, and operating costs of new and replacement appliances; the net present benefit ofthe standards; and the results ofthe sensitivity analyses. Life-Cycle Costs and Payback Periods: describes the effects of standards on individual purchasers and users ofappliances. It compares the life-cycle cost of appliances and othermeasures ofconsumerimpact with andwithout the proposed standards. Impacts ofStandardsonManufacturers: describes the analysis methodology, short and long-run impacts, and sensitivity analysis. Impact ofStandards onElectric Utilities: describes the effects ofstandards onthe ,_ electric utility industry, focusing on marginal costs of electricity, generating capacity growth, changes inregional capacity and energy demand, andchanges in utility revenues and costs. EnvironmentalEffects:describes standards-inducedchanges inemissionsofoxides of carbon, sulfur, and nitrogen from combustion of fossil fuels for electricity generation and in homes. Appendices A through C contain more detailedinformation on the models and databases used in the economic analysis.

2-3Appendix 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 ForecastingModel (LBL-REM orEPRI COMMEND): gives adetaileddescription 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 ofthe model to estimate fmandal impacts of the imposition ofstandards 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 onthe 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

111 

### CHAPTER 3. ANALYTIC APPROACH

3.1INTRODUCTION 

### The economic impacts ofnew oramended 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 ofenergy-saving design options are used to generate the relationships that are inputs to the other analyses ofthe impacts of setting standards at various efficiency levels. Identification of product classes, baseline units, design options, and maximum technologicallyfeasible efficiencies isbasedoninformation gatheredfrom the trade associations, manufacturers, discussions with researchers, a literature survey, and comments on the Advance Notice ofProposed 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 arecombined with theefficiencyestimates to generate the cost-efficiencyrelationships. The relationships between manufacturer costs and efficiency that are presented in this chapter are usedthroughout the otherparts ofthe analysis. The engineering analysis alsoincludes determination of maintenance and installation costs for each ofthe design options studied (see
. Appendix F ofthis volume and Chapter 1ofVolumes B through I). The manufacturer analysis. uses manufacturercosts to produceretailprices. Retailprices, installation costs, andmaintenance 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 ofProduct Classes

Product Type Number ofClasses 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 8This 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 fora 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 ofminimum efficiency standards. Quantitative estimates ofthe impacts ofstandards are calculated from the outputs ofthe 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 ofthe 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 ofvariation. 

### 3.2 OVERVIEW OF ANALYTIC FRAMEWORK The impact of appliance efficiency standards is determined by comparing projections of a wide range ofeconomic variables under the existing legislation with the projections under the proposed standards. Foreach ofthe eightproducts 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 ofthe energy consumption and economic variables in the base and standards cases provide quantitative estimates of the impacts of the standards. To evaluate the significance ofthe differences, a sensitivity analysis is performedon the key parameters and assumptions.

3-2The 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 ofdesign options to improve appliance efficiency; • A ConsumerAnalysis 

### at two levels: national aggregate impacts, andimpacts 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 ofConserved Energy (CCE), which evaluate the savings in operating expenses relative to increases in purchase price; • AManufacturerAnalysis, 

### which provides an estimate ofmanufacturers' response to the proposed standards. Their response is quantified by changes in several measures offinancial performance for a firm;
• An Industry ImpactAnalysis, 

### 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 • ARegulatory ImpactAnalysis, 

### which collects the results ofall 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, andimpact analysis is done. Foreach ofthe eight products, the analysis is performed for a base case plus several trial levels of standards. The selection oftrial standard levels by class is described in Chapter 3 for eachproduct-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 3-4Figure 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, andManufacturer Analyses. The Engineering Analysis establishes appliance designs andrelated 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 betweentheprices andefficienciesofdesignoptions,the consumeranalysisforecasts sales and efficiencies ofnew and replacement appliances. These data are used as inputs tothe Manufacturer Analysis to determine fmancial impacts on typical firms within the industry. The consumeranalysis also forecasts energy savings andconsumerexpenditures for thepurchase and operation ofthe appliances. Consumerexpenditures are usedin 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. Threetimeframes are consideredbythe analysis. First, the analysisofconsumerandutility impacts extends over a time frame consistent with the life ofthe products and includes the time requiredto approach market saturation. This time frame extends to2030 and the standardscorne into effect in 1996. Second, the Manufacturer Analysis is performedfor 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 ofthe 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 Analysisdevelops cost andefficiency datafor a setofdesign options withineach applianceclass. These data are the output ofthe engineeringperformance andcostmodels discussedlaterin this section. TheEngineering Analysis is performedin 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 ofproduct types into separate classes to which different energy-efficiency standards apply. DOE differentiates classes by the type ofenergy used (oil, natural gas, orelectricity), and capacity orperformance-relatedfeatures that provide utility to the consumer and affect efficiency. For specific appliances, classes are defmedusingdatacollectedin discussions withappliancemanufacturers, trade associations,other interestedparties, and from commentsreceivedon the Advance Notice ofProposedRulemaking.
3-5Those 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 ofeach 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 specificproducttypes, andfrom public comments on the Notice ofProposed 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 atthe present time orthat are presentin prototypes are considered. They are selected afterdiscussions with experts and an extensive literature review. The efficiency improvement and manufacturer cost ofdesign options added to the baseline unit are calculated. 

### Maximum Technologically Feasible Designs Foreach 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 foreachproductclass. Themaximum technologically feasible efficiency level is one that can be reached by the addition ofdesign 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 andfrom engineering calculations. 

### Cost Estimates The manufacturer cost data are obtained through a lengthyprocess that includedmeetings andtours atmanufacturingfacilities, submission offonnal requests to manufacturers for costing data, and review of the data received. Estimates of manufacturer cost are also received in response to the Notice ofProposed Rulemaking. Ineach product-specific volume, Appendix A contains detailed incremental cost data disaggregated into labor, purchased parts, materials, shipping/packaging and tooling.
3-6Cost-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 Theresults ofthe Engineering Analysis are summarizedin the cost-efficiencyrelationships 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 associatedwith specific design options are also estimated. The price-efficiencyrelationships are a fundamental input to the Consumer Analysis. 

### Assumptions in theAnalysis 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 ofproducts on the market in 1990 and consultations with manufacturers. As part of the Consumer Product Efficiency Standards program, DOE was required to establish testprocedures 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.
3-7Data 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 EngineeringAnalysis 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 ofpurchased materials andparts 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

. publisheddata. 

### Outputsfrom the EngineeringAnalysis 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 ofthe Results Experimentaldataon efficiencies are available for some ofthe 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 .PowerResearch Institute). COMMEND forecasts energy use by enduse andbuildingtype. The model covers eleven building types, ten end uses, andthree fuel types. Version 3.2 was usedfor this analysis (1).

3The energy factor is a measurement of energy efficiency derived from the DOE test procedure for each product. 3-8Forecast 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 ofequipment basedon life-cycle-costcriteria. There are four groups ofdecision-makers, each with a different discount rate. Short-run price elasticities for utilization ofenergy services are included. Interactions between lighting and space-conditioning energy use are also handled within the model. 

### Representation ofTechnologies In COMMEND, technology options are represented as a technology trade-off curve that shows the efficiency attainable for a given equipment cost. Technology trade-offcurves 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-offcurves are defined to model equipment markets for technologies using different fuel types. A trade-offcurve 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 ofthe different component technologies; these densities are then converted to EUIs using building operating hours developed from EIA data (2). 

### Development ofCOMMEND Input The basic relation modeled by COMMEND is that lighting energy use is the product of floor area (in square feet for a given enduse) 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 oflighting EUIs. These EUIs were then used to calibrate COMMEND anddevelop reference forecasts againstwhich the netimpacts ofthe policies were estimated. The base case lighting EUIs were then modified to reflect the impact oflighting equipment and building standards. The difference in forecasts between the base case and the policy is the net energy impact.

3-9Turnover 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, andHigh-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 LightingEquipment 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 ofballasts,4 and corresponds to the average fixture turnoverrate inrenovatedbuildings. 

### 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'sAnnual 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 aredefinitiondiscrepancies inthe buildingtypes usedbyCOMMEND 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, andmarketequipmentcostreductions thattake the marketfrom the 1986 stateto 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 of42% 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, andHigh-Efficiency are used to gauge the impacts ofpolicy actions. In all threebaselines, tradeoffcurves for all enduses other than lighting remain stationary. FortheNo Programs baseline, the technology trade-offcurve 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. 3-10For 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

. TheBase 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 Forthe High-Efficiency baseline, the 42% costreduction (because ofDSM programs, etc.) in the efficient end ofthe lighting technology trade-offcurve is left in place after 1995, and the trade-offcurveremains in its 1995 state. Forthe Base Case baseline, most ofthe pricereduction at the efficient endofthe trade-offcurve (30 ofthe 42%) is removed after 1995 to model a case in which utilities no longer provide DSM incentives. The remaining cost reductions are left in placein orderto simulate the permanentprice effectthatDSM programs andothermarketforces will have on the market for efficient technologies. In addition, theBase Case baseline includes small savings from state building codes that are introduced before 1995 and are expected to persist (see "Representation ofPolicies" below for a discussion of standards implementation in the model). Thesethreebaselineforecasts provideaframeworkfor analyzing abroad spectrumoffuture 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 othercases representtwo bounds ofthe forecast range. TheHigh-Efficiency baseline extends the 1995 market forecast to 2030, based on utility DSM programs, the EnvironmentalProtectionAgency (EPA) GreenLightsProgram, theFederalEnergyManagement Program (FEMP) relighting program and existing lighting regulations. This case represents an aggressive levelofincentives, butnot a "maximum efficiency" scenario. TheBase 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 ofdecisionmakers at the low-efficiency end ofthe lighting technology trade-offcurve. 3.3.3 

### LBL Residential Energy Model (LBL.REM) Purpose TheLBL-REMmodels the appliancepurchase choices made inhouseholds, as well as these households' subsequent usage behavior and energy consumption. See Appendix B for details about the model. Engineering,economic, anddemographic dataareusedinLBL-REM. Theengineeringdata for appliances are describedin Section 3.3.1. Additional data includeengineering dataregarding alternativebuilding shellconstructionmeasures andcosts,unitenergyconsumptionandefficiency ofappliances, age distribution ofexisting appliance stock, and retirement functions. Economic data include projected energy prices and household income and models ofenergy investment, appliance purchase, and usage behavior (including fuel and technology choice for each enduse).
3-11Demographic 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 forecastperiod, 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. 

### HistoricalDevelopment 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) developedthe ftrst model to integrate these twoimportant aspects ofenergy demand, the Engineering-Economic Model ofResidential 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 ojthe 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 appropriatetoeachenduse: electricity, natural gas (utility gas), heating oil, andliquidpetroleum 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 ofthe five types of activities is shown in Figure 

## :?.2.

5The model forecasts beginning in 1980. Historical data from 1980-1990 are used to check the validity ofthe 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 

## LBLRESIDENTIALENERGY MODEL DATA: 

### DEMOGRAPIDCS

ALGORITHMS:

OUTPUT: 

### ANNUALENERGY CONSUMPTION •ByEnd Use • ByFuel • ByBuildingType 

### ENGINEERING

FUEL CHOICE

FUEL EXPENDITURES • ByEnd Use •ByFuel •ByBuildingType 

### ECONOMICS

EQUIPMENT AND BUILDING STOCKS

CAPITAL COSTS • ByEndUse • By Fuel •ByBuildingType


### Figure 3.2 Activities in the LBL Residential Energy Model
3-13Housing 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 StockSubmodel This submodel prepares data about the housing stock projection forthe main model. The initial numberofoccupiedhouseholds, by type, is taken from the 1980 Census ofPopulation 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 ofthe population in each state. The method is described fully in Appendix B. 

### Efficiency ChoiceAlgorithm Historicalefficiency data are available for selectedyears through 1991 for each ofthe eight products. After 1991, efficiency improvements are projected as a function ofdesigns 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 ofa 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, ifenergy 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 ofthe incremental costs ofthermal 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 TheLBL-REM projectsthe averageefficiencyofnewproducts,e.g., gas-firedwaterheaters, purchased each year in the absence of additional federal regulations, as described above. A distribution ofunit 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 ofthe 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 ofstandards. 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 equipmentefficiency is used to calculate this year's thermal integrity. 

### 3-14Turnover 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 ofappliances in stockis characterizedfrom industry data about historical annual shipments and retirements. The fraction ofeach 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 ofpotential purchasers ofan appliance in new homes is equal to the number ofnew homes constructed each year. The number ofpotential purchasers in existing houses is equal to the number ofretiring appliances, plus some fraction ofthose 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 ofthe total thatchoose each class, e.g., gas-fired, oil-flred, electric, orgas instantaneous waterheaters, is specifiedexogenously. Long-term market share elasticities have been assumed withrespecttoequipmentprice, operating expense, andincome. Standards are expectedtocreate 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. Forranges and ovens, room airconditioners, directheating equipment, and mobile home furnaces, these elasticities are assumed to be non-zero.

7 For example, a cohort consists ofall 8-year-old gas-fired water heaters. Up to 30 cohorts, one for eachyearofpurchase, are tracked for each purpose. 3-15Energy 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 (existingornew) is the product ofthe VECpercycle (accountingforefficiencychanges) andusage factor, e.g., cyclesperyear. Thecorrespondingenergy consumptionfor all households is the consumption perhouse times the numberofhouseholds ofthat type and vintage, times the fraction ofhouseholds that owns that appliance.s Aggregate energy consumption is obtainedfrom summations overintermediateresults. For example, national electricity consumption for residential water heaters in a particularyear 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 theAnalysis 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.

8Forspace-conditioning(heatingand cooling),twoadditionaltermsaremultipliedbytheintermediate results: relative floor area (square feet ofconditioned space), and relative thermal integrity (a measure of the effect ofbuilding shell characteristics on energy consumption). 3-16Assumption

• 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 projectedto 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 DataResources, 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/EIAAnnual 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 ofthe 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/EIAAnnualEnergy 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 AnnualEnergy 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 ofhomes is projected to continue to improve.

• The lifetimes ofappliances are projected to remain the same as empirically observedin the past, independent ofenergy efficiency.

Data Sources Justification Research on equipment sales for competing alternatives and on historical efficiencychoicesindicatesthatoperating expenses are significant variables. Industry is competitive and historically has responded to changes in consumer demand. No evidence suggests an endto the responsiveness. Increasing energy prices and awareness on the part ofconsumers have resulted in modified construction practices and in home retrofits. ·Building codes under consideration,utilitycompanyincentives, 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.

TheLBL-REMtakes the energyefficiencies ofnew equipmentfrom theEngineeringAnalysis. Thepurchasepriceoftheseproducts is derivedfrom the factory costsuppliedby theEngineering Analysis and adjusted for manufacturer and dealer markups in the ManufacturerAnalysis. 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 ofpopulation, housing starts, income, andenergyprices are taken from Census, DataResources, Inc., and DOE, respectively. Appendix B contains a more detailed discussion ofthe data used in the model.

3-18Model 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 ofresidential 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 ofstandards are provided annually to the end 'ofthe period. Netpresentvalue (NPV) ofstandards is evaluatedfor eachregulatedproduct and for the end use(s) comprising the regulated and competing products. 

### Cost-BenefitAnalysis 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 ofdiscounted increases in equipment prices. DOE has determined that 6% real is the appropriate discountrate for calculating benefits to the consumer. The NPV ofstandards is also calculated atconsumerdiscountrates of2% 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 ofstandards on the other. Energy savings are the net ofany adjustments households make in

### changing their technology orfuel choice orfrom using appliances differently. Forexample, 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. NPVexcludesthese secondaryeffects. Basecasepurchasebehavior,withoutmarketshare changes, is assumed in calculating the NPV, because any market share shift reflects the consumer'sjudgment 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 normalizingto shipments, the results wouldbe erroneous: if

### standards causeddecreasedpurchase ofaproduct, this would appear as an economic benefit, namely less money spent on purchasing 3-19and 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) ConceptualApproach TheManufacturerImpactAnalysis estimates both the short- andlong-runimpacts ofnew 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 ManufacturerImpact Model (LBL-MIM), which is a spreadsheetcomposed ofthirteen 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 eachby explicitlyevaluatingthe standarderrorsinthe estimates ofinputvariables andthenusing 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 ofimpactare tracked for the industry: (1) shipments, (2) price, (3) revenue, (4) net income, and (5) return on equity (ROE). ROE is the primarymeasure ofprofitability, 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 ofthe impact of standards on competitiveness. A short-run calculation is also made. Ifthe long-run calculation indicates a decrease in sales, thepredictedpriceisreducedbyan amountdeterminedby theindustry'sobservedbehavior during historical downturns in sales. The adjusted price is used to calculate the other measures ofimpact.

9Without nonnalization, thegreatesteconomicbenefit wouldbeobtainedby standardsthatresulted in no future purchases ofthe product. Then, no money would be spent onpurchasing the product, or onoperatingexpenses, and the valueofthe savings would equal the amountofmoney thatwould have been spent without the standards. Clearly, this would be a misrepresentation ofthe NPV ofstandards. 3-20Opeeraling 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 infixed 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 ojthe LBL-MIM Although LBL-MIM examines many aspects ofan industry, its most importantjob is to compute the effect on profits of a cost increase caused by standards. To accomplish this, the model first estimates how much ofthe cost increase is passed on in the form ofhigher prices. (An accompanying effectis the decreasein operatingexpense due to standards, which makes the product more desirable to the consumer and, thus, increases demand for the product.) Then, it estimateshow this price increase and the accompanyingreduction ofoperatingexpenseinfluence demand. Finally, given the new price and level of demand, it computes a new level ofprofit. These calculations use estimatedlong-run costs. TheLBL-MIM makes an additional calculation to correctfor any short-run difficulties the industry may have in achieving its long-run markup. Itis 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. Thebasic behaviorofthe modelcan be understoodonly inlightofthe following standard economic consideration. In the longrun, an increase in

### fixed costs will 

### not bepassed on via a priceincrease exceptwhenitis sufficientto cause some firms to leavethe industry.to Thisresult is derived from the assumption that firms act individually to maximize profits and from the definitionoffixed costs as those that are notproportional to output. Themathematical derivation is given in Appendix C ofthis 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 theAnalysis This section begins with a series ofshort discussions ofdifferent aspects ofthe modeling procedure that require careful interpretation (such as "long-run variable cost") or simplifying assumptions. This is followed by a list ofmore detailed assumptions. 

### The long-run costs. The LBL-MIM requires input 'of long-run costs. A fmn is in long-run equilibrium when ithas optimal productive capacity. Thus, during a recession (when fmns experience excess capacity), firms are not in long-run equilibrium, and the theory ofprice 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, itbecomes less competitive and the remaining firms can increase their markup over variable costs, thus, indirectly covering their increased fixed costs. 3-23capital 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. Atypicalfirm is ahypothetical firm that is describedbyindustry-averagevalues. Each one is modeled as if it produced only products of the industry being analyzed. This procedure isjustifIedby the observation thatreal 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 ofthe various productlines within an industry. Therefore, datafor the various products are aggregated before the markupis computedand applied. However, once the aggregate calculationshave been made, individual prices are computed for the various classes ofproducts. How standards are implemented in the model. 

### A change in standards affects the model in three distinct ways. In 

### general, stricter standards willrequire additional investment, willraise 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 costis fIrst evaluated from engineering data, and then amortized by takinginto account the life ofthe investment, the date atwhich itis made, tax laws, andthe appropriate cost offunds. 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 ofinvestment tracked by the LBL-MIMis the change inthe demand for cash that accompanies a change in revenues. Financial inputs. 

### Several simplifying assumptions are used in the process ofgenerating 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 ofthe 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.

3-24Impact 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 

### ImpactAnalysis: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, ofthe 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, thevarious types ofsensitivity analysisperformedonthe model'sresults are discussed. This analysis is crucial for understanding the accuracy and reliability of the model'spredictions, andincludes the use ofalternative scenarios, sensitivitychecks onindividual inputs, and Monte Carlo simulations involving the random selection ofall control inputs.
3.3.4.1 Long-Run Impact Analysis 

### Implementation The heart ofthe 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, orcapital 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 ofprice 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 ofthe long run. The fraction of increased costs of production (due to more stringent standards) that manufacturers are able to pass onis determinedby the nature ofthe cost, i.e., whetheritis 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 ofdemand 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 ofunits shipped (quantity). Price and quantity, together withproduction costs, determine netincome (profit), andthese, together with changes in capital stock (not shown), determine return-on-equity (ROE). As showninFigure 3.4,the twocostchanges donottranslate directlyintopricechanges.
3-25The 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. Foreach product, a set ofengineering 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 ManufacturerImpactModelcalculates a weighted average ofthe two costs andthe energy usage for the relevant engineering designs. The weights used correspond to the relative numbers of shipments for each engineering design as determinedby LBL-REM. Another and more subtle point is that markup is determined by price elasticity, and because life-cycle-costelasticityis constant,priceelasticitychanges with a changein eitherprice or operating expense. This change in markup can have an important impact on ROE. Themarkup discussedhere is a markup over

### economic variable costs, which is abroader category than the typical business definition ofvariable 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. Themanufacturer markup listed is 

### notused in calculating the manufacturerprices for the 1996 base case orthe standardlevels. Inthese cases, the manufacturerprice 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.Typicalresults ofan analysis ofa standard at a particular level might be the following: wholesale price increases 10%, sales decline 2%, revenue increases 8%, net income increases 12%, andROEincreases 0.5%. Associated with each ofthese numbers is a standarderror. 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 itwill decrease. One could also conclude that the chanceofprofits declining by more than 2% is only 2.3%. 

### 3.3.4.2 Short-Run Impacts Inthe shortestrun, capacity is fixed; however, the short run ofinterestis, atthe 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 itwill alleviate any tendency toward excess capacity resulting from standards.

IlPor a thorough description ofhow markups are derived and used in LBL-MIM, see Appendix C.2.I. 3-26In 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 

### Inorderto simplify the analysis and to take acautious 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 ofits present capacity, and ifthe 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 causedby 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 ofthe standards levels. This demand shortfall seems to present all ofthe 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-runpredictionis for a decline in demand of2%, 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 isjusta first approximation, and the model does better, as follows: the short-runprice response factor (SRPR) of0.03 means that the short-run price will be 0.06% lowerthan predictedwith the long-run price formula ifthe

### actualdecline in demandis 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-runprediction, but not by a full 0.06%. Short-run demand and price must be determined simultaneously, which occurs in the short-run module ofLBL-MIM. Short-runprices,profitlevels, and othervariables will gradually, overaperiodofseveral 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 ofthe nature ofthe informationrequired andthe desire ofsources toprotect proprietary information, many of the data used by the model represent uncertain estimates. Therefore, the effectofthese uncertainties on the accuracy ofthe model'spredictions is analyzed. Two types of questions need to be answered. First, how does a particular input variable contribute to the uncertainty ofthe outputs? Second, how uncertain is the estimate ofaparticular output variable?

3-27Sensitivity 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 Themodel's sensitivity to inputs is measured by the impactofa change ofone standard error(S.E.) inthe inputvariable. This analysis is presented onlyfor the impactofinput variables on ROE; this standardizes and simplifies the process. The results are displayed in a sensitivity chart, an example ofwhich is given and explained below. 

### Table 3.2 Sensitivity ofROE to 1 S.E. Change in Control Variable, Direct Heating Equipment

Scenario = Primary Control Variables Possible StandardsLevels 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 thesensitivitiesofROE to the control panelinputs definedbelow: 1. IPE is the industryprice elasticity. 2. RD is the consumer discount rate. 3. ECC is the firm's real equity cost ofcapital. 4. EP is the firm's economic profit. 5. FCA is the percentage ofa 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 ofone-time capital costs inducedby standards. 8. dVC.Nistheincreaseinvariablecostsnecessarytoreachastandardslevel. 3-28Note 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 thatfor CC.N anddVC.N the change ofone 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'svalueis. Note thatthe changein profitis simply the difference betweenlong-run ROE andbase caseROE. Note also that, as expected, increasing the absolute values ofsome variables increases ROE and, for other variables, decreases ROE. The comparison ofthe 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 ofinputvariables wouldhave the greatest payoff. Because of the differences in the sensitivities among input variables, it will generally be found that one to three ofthe 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 Foreach applianceclass andhypothetical standards level,hundreds ofdifferent 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. Ifthe 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 deviationsthe 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 ofinterest. 

### Uncertainty ofOutputs (Monte Carlo Analysis) Outputuncertainty is most directly addressedby the Monte Carlo analysis. This analysis assigns an uncertainty to each ofthe nine control-panelinput variables andthen chooses a value for each based on this uncertainty. The model is then solved using these randomly chosen 3-29variables. 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 ofprice, 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 ofeach output variable are computed. The standard deviations ofthe Monte Carlo output variables are the best estimate ofthe true uncertainty ofthe model's predictions. However, this does not mean they are absolutely reliable because they are based on estimates ofthe uncertainty ofthe inputs, and these are often not based on something as objective as the standard error ofa regression coefficient. In short, ifthe uncertainty in the inputs is misperceived, the estimate ofuncertainty 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.

3-30Assumptions 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 ofequity 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 standardeconomic theory, inwhich 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 previousjustiftcation.
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 productioncapacityof 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 thatthe business cycle does not influence the consumers' elasticity of demand. There is no evidence one way or the other on this.

TheLBL-MIM tracks five essentialoutputvariables, with emphasis on ROE. The otherfour 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 partofits 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 ofpayback periods, and throughout the LBL-REM. Allofthese values are estimates that necessarilyreflect someuncertainty. Estimates ofthese uncertainties and identification oftheir sources comprise another set of model outputs. Under each estimate ofone ofthe five main output variables, the output table displays that variable's change from its base level and the standard error ofthe estimate ofthat change. TheLBL-MIM also computes the effect on ROE of a one-standard-deviation change in each of the control variables. This is reportedfor each standardlevelin a sensitivity chart, designedtohelp discover the source ofuncertainty in the output variables.

3-32Data 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 TheLBL-MIM needs datathatcharacterizeboth aparticularindustry andtypicalfIrms within that industry. Estimates ofdata 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 ofmodel 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 ofthe impactofthe 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 ofstandards. The Utility Analysis also quantifIes impacts on the electric utility industry's need for new generating capacity. Finally, the Utility Analysis calculates net impact ofstandards on utility revenues. Appendix E of this volume contains more information on the model and data used to perform these calculations. TheUtility Analysis adopts the convention that the value ofelectricity savingscanbe broken down into energy (or marginal cost) savings and reliability (or capacity) savings. The energy impactmeasures production costs avoided by reducedelectrical demands, valuedatthe marginal energy costs ofthe utility. The reliability impact measures the value ofreduced loads during system peak periods, which is, by convention, valued at the cost ofa 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 ofheating, cooling, and baseload energy. For the three product types under consideration, only baseload energy is used. The fraction ofthe electricitythatwouldhave tobe generated atthemarginfrom 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 ofa 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 averagedover an 3-33on-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 peakdemand. 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 ofthe 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. IfNERC predicts an adequatereserve margin in a region for a given year, no reliability value is given to the peak load savings in the region. The netrevenue loss is equal to the difference between the revenue reductions and avoided costs. Revenuereduction is calculated by multiplying the change in electricity consumption by the average utility rate. Avoided costs are calculatedfrom the change in electricity consumption multiplied by the per-unit, avoided costs. The inputs neededfor the utility impact model include electricity savings, conservation load factors, state-levelutilityfuel prices, average electricityprices, appliance saturations,efficiencies, hours-of-use, and electricity generation by fuel type and capacity need by NERC region. The outputs ofthe analysis are the fuel savings, the reduction in the needfor new generatingcapacity, 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 theAnalysis 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 ofenergy savings, utility marginal costs, and the substance of NERC forecasts.

3-34Assumption

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 basedon a simplified loadduration 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 consumptionof 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 neededto converttotal 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 ofthe 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 insufficientinformation 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 ofutility 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 ofthis variation for utility generating systems in different NERCregions has not been calculated. A 10,000 Btu/kWh heat rate is typical for many different types ofgenerating units.
TheNERC forecasts are widely accepted. They are the only forecasts of adjusted reserve margin at the regional level.

One measure ofthe effect ofproposed 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 LCCis the sum ofthe purchase price andthe operating expense discounted over the lifetime ofthe appliance. Itis calculated at the average efficiency for each class in the year standards are imposed using real consumer discount rates of2%, 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 ofthe LBL-REM. The life-cycle cost analysis also examines the payback periods and the cost of conserved energy associatedwith the standards. Details ofthe LCCcalculation may be found in Chapter4 ofeach product-specific volume. An additional measure of economic impact, payback period, is useful in evaluating the impacts onconsumers ofefficiency standards. The paybackperiodmeasures the amount oftime it takes to recover additional investment in increased efficiency through lower operating costs. Numerically. itis theratioofthe increase in purchaseprice between the base and standards cases to the decrease in annual operating expenditures. Both the numerator and denominator ofthis expression are evaluated at the average efficiency in the year standards come into effect and at energy prices in that year.

3-363.4 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 itpossibletoplacebounds onthe uncertaintyinthe results ofthe analysis and to gain an understanding ofwhich variables are most important in producing these results. Sensitivity analyses are developedin a series ofdistinct steps. Foreach computermodel 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 ofeach important parameter is then estimated by running the model for the base case and standards cases. The significance ofthe results is assessed in terms of the magnitude ofchange in each model's outputs. Sensitivity runs are made with the LBL-REM to explore the effects ofuncertainty in the equipment and operating cost ofthe products being analyzed. The costs ofthe 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 ofthe 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 displayedinthe outputtable. Foreach control variable, the Monte Carlo sectionrandomly picks a value near the best estimate ofits true value. As a result, the outputs ofMonte Carlo vary from run to run. Afterfinishing a batch ofruns (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 ofthe model are analyzed to determine the sensitivity ofthe 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 variablethat affects thetwocases similarly willhavelittle effectontheimpactofstandards,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 ofthe impacts ofstandards are discussedin 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 forthe sensitivityrun are lessthan those forthereference case,reflecting the smallermagnitude ofavailable 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 ofthe analyzed products. It then presents a briefdescription ofmarket shares followed by a discussion ofmergers and acquisitions inthese industries, and fmally a comparison ofconsumer and producerprice indices. 4.1 DEMAND 

### Units ShippedandHousing Starts One of the primary components driving the demand of most appliances is housing starts. Although the changes in shipments ofappliances are notentirely theresultofchanges inhousing starts, housing starts can be used to predict shipments ofmost appliances for two reasons. First, almost every new house requires a new set ofappliances, 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 goodindicatorofthe strength ofthe economy as a whole. A healthy new-housing market is likely to correspond to a healthy economy, and a healthy economy encouragesreplacement and discretionary purchases ofappliances. Therefore, there is generally a strong correlation between new-housing starts and appliance shipments. For similar reasons, there are strong (ifnot even stronger) correlations betweenmobile homeproduction and the sales ofmobile home furnaces, andbetween new commercial floor space and fluorescent lamp ballast shipments. Figures 4.1 through 4.8 show annual shipments comparedto 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 ofmany ofthese appliances, which is seen clearly in the nearly exactparallel ofthe 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 ofimportance in determining shipments ofthe 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 ofthese factors are used by the LBL-REM in predicting appliance shipments in the base case and new standards case. The Manufacturers ImpactAnalysis uses the LBL-REMestimates inits analysis offinancial impacts on manufacturers.

4-1As 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 ofthe appliances show a strong correlation with housing starts. Several interestingpoints emergefrom these graphs. In the case ofdirectheating equipment (Figure 4.2), there is a strong correlation with housing starts from 1969 to 1979 for the total (primarily composed ofwall furnaces with lesser correlations for room heaters). From about 1978 to the present, the shipments ofdirect heating in general (and floor furnaces in particular) have been declining. Meanwhile, the shipments ofcentral heatingequipmenthave beenrising andprobably havebeen absorbing the market share ofdirectheatingequipment. This observationcorresponds 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 ofgrowth in the late 1960s to early 1970s, the popularity ofmicrowave ovens increased steeply in the 1980s, probably due in part to the greater convenience, improvement ofthe technology, and lowerprices. In fact, the saturation rate ofmicrowave ovens rose from 1 % in 1973 to 66% in 1987, according to industry trade sources. The sales ofroom airconditioners have little correlation with housing starts. Weexpectthat this is because room air conditioners are bought primarily in the aftermarket andhave 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, colortelevisions have greatly outstripped the sales ofblack-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 ofa particular type. In general, ifmost homes own an appliance but few homes have more than one unit, we expect that the appliance will have a relatively low elasticity ofdemand because the appliance is usually considered a necessity. This observation corresponds with the relatively low price elasticity ofcertainproducts such as waterheaters andranges and ovens and the higher price elasticity ofproducts such as televisions and microwave ovens.

4-24000

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 

### 2000 0 :::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: GAMAandCurrent Industrial Reports 

### Year**1NpIIar~** Figure 4.1 Range & Shipments Compared to Housing Starts 

### 4-34000

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: GAMAandCUrrentIndustrial Reports 1h\pIIIIr.grf/lllellllp
.... ___ HousIng Starta --0- Wall Furnac:ea 0 Total DHE -It:- Floor Furnaces

... Room Healers 

### -+- CenIraIHeatIng

...

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. 4-45000

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 MAand 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 .... HousIngStarts _.£;- Gas ··0·· ElectrIc ··.·011**O+--.--.-~~~~~~~~~~~~~~~==~~~~~F=~~~-+--+--+~** 

### 68 70 

### 72 74 76 

### 78 

### 80 82 

### 84 

### 86 88 90

Source: GAMAand CurrentIndustrial Reports 

### Year Water Heater Shipments Compared to Housing Starts 

### 4-515000

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 endCUrrentIncbtrfaI 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► l n duabW 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 CurrentIndustrislReports Room Air Conditioner Shipments Compared to Housing Starts 

### 4-725000

20000+ 0 0 0 lz 15000 co 0) c 0

5000+--- Housing Starts

A Cola ...0.... B/W

_J=
- .-- - a--- -- - _.®------ M--w- 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.ur8ntInduBttftIlRepotf1I Figure 4.6 TV Shipments Compared to Housing Starts 

### 4-8300

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:::-:**~::..** _MHProd 

# I Source: NCSBCSandthelndustry. '-

_______________ ----l Figure 4.7 

### Mobile Home Furnaces Compared to Mobile Home Production. 

### 4-92000 --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 CurrentIndustrialReports Fluorescent Lamp Ballast Shipments Compared to New Commercial Floorspace 

### 4-10Table 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 ColorTelevision 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. *Initialfigure is for the stockofdwellings, the secondisfor new construction. RECS figuresincludemobilehomes, 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 ofthe market controlled by the two, four, and five largest producers in each industry. 4-11Most 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% 

### ofthe market for each ofthese products. Market concentration plays a partin the analysis because it may indicate the existence ofmarket 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 ofMarket Controlled by the LargestProducers ofEach 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. Mostproducts are producedby afew manufacturers, each ofwhich have large market shares.

12Marketpoweris the ability to setpricewithina limitedrange. Moretechnically,itis the freedom thatresults from facing a demand curve that is not infinitely elastic. This means that ifprice is raised by some small amount, sales will notfall to zero, and ifprice is lowered by some small amount, sales will notbecome 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: ApplianceManufacturer 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 ownstheJenn-Air. Hardwick, and Magic Chefbrand names Electroluxownsthe Frigidaire and Tappan brand names Raytheon owns the Caloric brand name. GE includes whatwas Ropar. Source: ApplianceManufacturer


### 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: ApplianceManufacturer 

### Figure 4.12 Microwave Oven 

### Firm Market Shares-1991 4-16 Sanyo Fisher 8%

Raytheon5%

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: Discussionswith the industryand 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

USStove 

### 1%Suburban 7% Locke 26%

LVTin

6% 6% Empire 13%

Perfection 13%

Source: Industryconsultantand LBLstaff. Figure 4.16 Direct Heating Equipment: Room HeaterFirm 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

LVTin 8.6% Perfection 6.6%

Empire 25.4% Other Locke 3% 3.6%

Source: IndustryconsuHant and LBLstaff Figure 4.17 Direct Heating Equipment: Wall Heater Firm Market Share-1989 4-21 Williams 52.8%

# Direct Heating Equipment 1989

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 LBLstaff Perfection 10% Martin 16%

Locke 14.8%



### Figure 4.18 Direct Heating Equipment: Floor Furnace Firm Market Shares 

### -1989 4-224.3 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 Theappliance-manufacturingindustryhashad acontinuinghistory ofconsolidation. 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 ofhome appliances for product diversification and can therefore offer a complete set ofappliances to consumers, an important feature inthe 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 ofappliance manufacturers covered in this analysis. Table 

### 4.4 Acquisitions ofAppliance 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 RockwellInternational 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 (FordMotor 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

4-23Table 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 ofAppliance 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 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 Maytag 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 Maytag 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) Maytag Co. Merger with Chicago PaCific Corp. 1988 Fedders Emerson Quiet Kool 1990 Hayward Pools ComfortZone 1991

4-244.4 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 producerprices, 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 componentprices during the 1970s and 1980s, the industry has, through increasedproductivity, 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 ofmillions ofdollars 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 PPIfor eachproduct groupingis defined as the PPIfor thatproductgrouping, holding the overall PPIconstant at 1982

## =100. In other words, this figure indicates the relative increase ordecrease in theprice index ofa 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),

4-25150

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. Bureauof LaborStatIstics Figure 4-19 National PPI and CPI for All Household Appliances 4-26150

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. BureauofLaborStatI8tk:8 

### Year Figure 4.20 National CPI and PPI Averages Compared to Household Cooking Equipment PPI 4-27150

120+--®--. PPI --k- CPI ...o... Radios/TVs, etc. (3651)

0.-

30+

+---^-t01 69 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.nauofLaborStatlstlca 

### Figure 4.21 National CPI and PPI Averages Compared to TV Product Group PPI 4-28150

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 ofLaborStatistics 

### Figure 4.22 CPI and PPI National Averages Compared to RAC Product Group PPI 4-29130+

90 t C

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

...@... HHApp NEe (3639)**50+--------+--------+-------~--------1-------~--------~------~------~** 83 

### 85 87 89 91

U.S. BureauofLaborStatistics 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. BureauofLaborStatistics Year Figure 4.24 National CPI and PPI Compared to DHE and Pool Heater Product Group PPI 4-3171 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 ofLaborStatiatice 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 offederal energy efficiency standards requires comparing projected U.S. residentialenergy consumption with andwithoutthe standards. Thecases without standards are referred to as base caseprojections. 

### These base caseprojections are comparedto 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, mixofhouse types (single-family, multifamily, mobile homes), andbuilding shell thermal characteristics. Additional (product-specific) variables, described in Chapter 2 ofeach product-specificvolume, includeappliancepurchases,equipmentprices,unitenergyconsumption, 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. Theseprojections 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 ofeach product-specific volume.) 5.1.1 

### Population Projection Thepopulation projection for the U.S. is basedon BureauofCensus datatothe year2010 (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 inpopulation is at 0.5% per year from 2010 to 2030. 

### 5.1.2 Occupied Households Thenumber andtype ofoccupiedhouseholds for the base year (1980) are from the 0.1% Public Use MicroSample (PUMS) ofthe 1980 Decennial Census. The popUlation projection is used to estimate the housing stock in each state each year, as described in Appendix B.



### 5-1Table 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 ofCommerce, Bureau ofthe Census. 

### 5.1.3 Housing 

### Starts Single-Family andMultifamily 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 ofmobile 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.
5-2Table 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 CurrentBusiness; (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 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 ofCurrent Business (13), convertedto 1990dollars using the ConsumerPrice Index-Urban. For 1987-2015, the projection from the DOEAnnual 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 ofthe 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)Surveyo/CurrentBusiness; (1987-2015)DOE/EIAAnnualEnergy Outlook1991; (2016-2030) LBLextrapolation. 

### 5.1.5 Residential Energy Prices Past energy prices (1980-1988) are from the DOEMonthly EnergyReview (15). Energy prices for 1989 and 1990 as well as pr:ojected energy prices (1991-2010) are from DOEAnnual Energy Outlook 1991 (13). Extrapolation ofenergyprices (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. 5-3These 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/EIAAnnual Energy Outlook 1991. (2011-2030) extrapolated by LBL.

5-4

-it) These multipliers captureregional differences inresidentialrates and appliance holdings, as well as effects ofrate structure. (For example, ifelectric heatis common in areas with lower residential rates, then the effective rate for electric space heating is lower than the average residentialrate. Also, ifthe block structure offers decliningrates forincreasedconsumption,then end uses associated with higher consumption, including space heating, will have lowereffective rates.) The multipliers are derived from the 1987 Residential Energy Consumption Survey. Table 

### 5.5 Average Residential Energy Prices (1990 Dollars per Million Btu)
Electricity1 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 EnergyReview. (1989-2010) OOE/EIAAnnual Energy Outlook 1991. (2011-2030) extrapolated by LBL.

5-4Table 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 Table5.7 shows theforecasted total U.S. commercial sectorfloor space (includingfederal buildings) by building type usedfor this analysis, basedon data developed by Pacific Northwest Laboratories (PNL) (16). Floor space growth drives the overall growth in demand for energy services- in the COMMEND model. Floorspace values given by EPRIin the COMMENDdefaultinputfiles are slightlylower 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 ofcurrently existing commercialbuildings.Thesurvival functions specifiedinCOMMENDcharacterizetheretirement rate ofbuildings 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.

5-5A 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). Afterthe year 2000, a GNP growthrate of2.1% peryearis assumed (based on ABO 1992projections), whichyields a 1.8% annual growthinfloor 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 vintageinformationon when buildings were built. Thesedata,togetherwithdefaultCOMMEND building decay functions, yield estimated commercial building starts listed in Table 5.8. The COMMEND building decay functions provide the buildingretirementrates shown in Table 5.9.

5-6Table 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 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 EnergyReview 

### (MER), August, 1992.(18) 

### Price forecasts are taken from the EIA Annual Energy Outlook (ABO), 1992, 

### extrapolated to
5-72030. 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 theAnnual 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 calculatedbasedon the trend given by the input files supplied with the COMMEND program. Electricitycosts are projectedpricesfor 1995from theAnnualEnergy Outlook 1991. The commercial electricity price is $0.0773 perkWh (1990 dollars). Fornatural gas pricesbeyond2010, thepricetrajectoryfrom theNationalEnergy 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 supplyIdemand 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 ofCOMMEND use the fuel oilprice as an indication oftheprice level ofpetroleum 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 usedtocalculate the contribution ofeachproduct to the mix. Itis assumed that propane represents all ofLPG, fuel oil represents all ofthe distillates, and all ofthe residual oil used in the commercial sector is oflow sulfur content.

5-8Table 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 EnergyReview. May 1991. (1991-2030) DOEIEIAAnnual 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:

• LowerEquipmentPrice. The price ofthe 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.

• HigherEquipmentPrice. Theprice ofthe baselineunitfor each class is increased 15%. The incremental price associated with each otherdesign option is increased by the uncertainty interval estimated for the incremental cost.

• LowerEnergy Price. Assume lower energy prices. Starting from 1992 to 2030,


### 5-9electricity 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% higherthan those in the ABO 1991 forecast. Y
• High EquipmentEfficiency. 

### Assume continuing future improvementin appliance efficiencies at a rate of2% 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) ofVolumes 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

Manyoftheinputs tothecalculations ofenergy savings for eachdesign optionhave some degree of uncertainty. This uncertainty of the inputs affects the level of confidence in the estimate ofenergy 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 ofenergy savings, a risk-analysis program (22) was used to quantify the**un~ertainty.** Therisk-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 ofinput values chosen matched the probabilities specified in the input distribution. Thebaseline was assumedto beexactforpurposes ofthis study, therefore no distribution was appliedto any ofthe inputs on the baseline models. The uncertainty on inputs for combined orstackeddesignoptions included, orwere appliedontop of, theuncertainties ofpreviousdesign options. Often different design options affected different parts of the energy consumption calculations. Anexample ofthis wouldbe applying heattraps to a gas-fired storage waterheater 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 ofthe 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 ofall the design options thatwere appliedbefore the maximumtechnologically feasible designoption. Theupper and lower values reportedfor the uncertainty ofthe efficiency indicator span the middle 95% of all values resulting from the uncertainty analysis. Forroom 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 ofuncertainty on the inputs.
A-IMost 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 ofthe input values were assumed to have a normal distribution, with the selected value being the mean ofthe distribution. The standard deviation of the distribution was then specified as the difference between the largest and/orthe smallest values consideredpossible 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% ofthe possible values in a normal distribution. This was done to eliminate potential outliers that could unnaturally skew the results.

Dl8tributlon of InputC Distributionof Input B 

# I**~** 

# I**~ ~** 

# ~~n 

# '\,., Energy Distributionof InputA

### ~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



# ---**~** A 

## 1**~**

3 d



### Figure 

### A.I Schematic of @RISK Uncertainty Analysis for Spreadsheet Calculations

A-2