### " ., ·l ; II M 

# /tH£E

·Preliminary Technical Support Document: Energy Efficiency Standards for Consumer Products:

. Clothes Washers (TSD)

October 1998

U.S
..Department ofEnergy Assistant Secretary, Energy Efficiency & 

### Renewable Energy Office of Codes and Standards Washington, DC 20585This document was prepared for the Department ofEnergy by staffmembers ofthe Lawrence Berkeley National Laboratory and Arthur D. Little, Inc.• TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1. 1.1 Overview .·... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 1.2 Structure ofthe Document
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3 CHAPTER 2: ANALYTICAL FRAMEWORK
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.1 Background
...................·............................... 2-3 2.2 Preliminary Market and Technology Assessment
....................... 2-3 2.2.1 Market Assessment
.......................................· 2-3 2.2.2
.Technology Assessment
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3 2.2.3 Preliminary Base Case Shipments Forecast
..................... 2-4 2.3 Screening Analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 2.3.1 Product Classes.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 2.3 .2 Baseline Units
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 2.3.3 Design Options/Efficiency Levels
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5 2.3.4 Proprietary Designs
.......................................· 2-5 2.4 Engineering Analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5 2.4.1 Energy Savings Potential and Manufacturing Costs.
. . . . . . . . . . . . . . 2-5 2.5. Life-Cycle Cost arid Payback Analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6 2.5. l LCC Spreadsheet Model
.......................·. . . . . . . . . . . . 2-6 2.5.2 Price
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7 2.5.2.1 Retail Price
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7 2.6 Preliminary National Impacts Analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8 2.6.1 National Energy Savings (NES) Spreadsheet Model
.............. 2-8 2.6.2 Shipments
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9 2.6.3 Indirect Employment Impacts
.. '.
............................ 2-9 2.7 · Consumer Analysis............................................ 2-10 2.7.1 Purchase Price Increases
.................... ; . . . . . . . . . . . . . 2-10 2.7.2 Consumer Participation
.................................... 2-10 2.8 Manufacturer Impact Analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-10 2.8.1 Industry Cash Flow
...................................... 2-12 2.8.2 Manufacturer Sub-Group Analysis
.......................... 2-12 2.8.3 Interview Process
................. ·.... . . . . . . . . . . . . . . . . . . 2-12 2.9 Competitive Impact Assessment
.................................·. 2-13 2.10 Utility Analysis............................................... 2-14 2.11 Environmental Analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15 2.12 Regulatory Impact Analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15 CHAPTER 3: PRELIMINARY MARKET 

### & TECHNOLOGY ASSESSMENT
......... 3-1 3.1 Introduction
...................................·. . . . . . . . . . . . . . . . 3-1 3.2 Product Classes
.**~** . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

### 3.3 Manufacturers and Market Shares
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 3.4 Historical Shipments.
. . • . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . 3-2 3.5 Retailers
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3 3.6 Price Distribution
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4 3.7 Product Characteristics.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4 3.8 Market Saturation
. . . . . . . . . . . . . . . . . . . . . . . . . . . • . . . . . . . . . . . . . . . . . . 3-7 3.9 Voluntary Programs (to increase water efficiency 

### I promote horizontal axis machines)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-8 3.10 Fuel Use
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

### 3-8 • 3.11 Industry Cost Structure
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9 3.12 Technology Assessment
......... .'
....·. . . . . . . . . . . . . . . . . . . . . . . . . . 3-11 3.13 Energy Efficiency
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-11 CHAPTER 4: ENGINEERING ANALYSIS
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.1 Introduction
................................................. ; 4-1 4.2 Product Classes
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.3 Baseline Efficiency
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.4 Cost-Efficiency Data Collection
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2 4.4.l Characterizing Uncertainty
................................. 4-2 4.4.2 Variability in costs between Manufacturers
..................... 4-3 4.4.3 Manufacturer assumptions for Supplying Data
........... : ...... 4-4 4.5 Data Aggregation
.................................... : . . . . . . . . . 4-5

. 4.6 Retail Price
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-11

4.7 Proprietary Design
..............................·. . . . . . . . . . . . . . 4-11 CHAPTER 5: MANUFACTURING COST ASSESSMENT
.......... : ........ : ..... 5-1 5.1 Jntroduction
..... ·....................... . . . . . . . . . . . . . . . . . . . . . . 5-1 5.2 Program Objective
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 5.3 Phase I Objectives
. . . . • . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 5.4 Assumptions
................................................ ·.·. 5-2 5.4.1 Baseline Definition
........................**~.** 

### . . . . . . . . . . . . . 5-2 5.4.2 Manufacturing Assumptions
................................ 5-2 5.4.3 Conventions for Manufacturing Cost Estimates
.................. 5-3 5.5 Approach
........................ _

. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4 5.5.1 Bill ofMaterials
......................................... · 5-4 5.5.2 Fabrication Processes
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 5.5.3 Assembly Processes
. . . . . . . . . . ... . . . . . . . . . . . . . . . . . . . . . . . . . . 5-7 5.5.4 Baseline Manufacturing Assumptions
. . . . . . . . . . . . . . . . . . . . . . . . . 5-7 5.6 Results
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8 5.7 Variability and Uncertainty in Manufactllring Cost
..................... 5-10 5.7.1 Key Parameters
.... ".................................... 5-10 5.7.2 Sensitivity Analysis...................................... 5-10

II

# .. 5.7.3 Monte Carlo Simulation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14 5.8 Conclusion and Phase II Program Plan
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-15 CHA.PTER 6: MARK-UPS FOR PRICE DETERMINATION
. . . . . . . . . . . . . . . . . . . . . . . 6-1 6.1 Overview
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . .. . 6:-1 6.2 Retail Mark-ups
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2 6.2.1 Background
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 6-2· 6.2.2 Retail Price Information
. '.
........................... : . . . . . 6-3 6.2.2.1 Consumer Expenditure Surveys
. . . . . . . . . . . . . . . . . . . . . . . 6-3 6.2.2.2 Dealerscope Merchandising Statistical & Marketing Reports
. 6-4 6.2.2.3 ELCAP .Clothes Washer Sales Information
. . . . . . . . . . . . . . 6-4 6.2.3 Manufacturer Price Information
............................. 6-5 6.2.3. l Current Industrial Reports
. . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5 6.2.3.2 

### AHAM Fact Book
...·. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-6 6.2.4 Retail Mark-ups from Manufacturer and Retail Price Information
. . . . 6-6 6.2.5 Retail Mark-ups Estimated from SECs Financial Statements
....... 6-8 6.3 Sales Tax
............................... ·. . . . . . .. . . . . . . . . . . . . . . 

## 6-9 6.4 Manufacturer Mark-ups
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-10 6.4.1 Background
........................·................... 6-10 6.4.2 Manufacturer Price and Mark-up for the Baseline Standard Clothes Washer
........................................ : 6-10 6.4.3 Manufacturer Price and Mark-ups for Different Efficiency Levels
... 6-12 6.4.3.1 Pre-ANOPRAnalysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-12 6.4.3.2 Post-ANOPR Analysis
......................·. . . . . . 6-12 CHAPTER 7: LIFE-CYCLE COST and PAYBACK PERIOD
....................... 7-1 7.1 Introduction
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1 7.1.1 General Approach for LCC and Distribution PBP
................ 7-1 7.2 Life-Cycle Cost (LCC)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2 7.2.l Definition
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2 7.2.2 LCC Inputs............................................. 7-3 

### 7.2.2.l Purchase expense · . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

### 7-3 7.2.2.2 Lifetime
........................... ; ..... .'
. . . . . . . 7-4 7.2.2.3 Cycles peryear ...................**~** .....· . . . . . . . . . . 7-5 7.2.2.4 Energy per cycle
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6 7.2.2.5 Electricity rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-7 7.2.2.6 Gas rate .................................... , . . 7-12 7.2.2.7 Oil prices.....................
. . . . . . . . . . . . . . . . . . 7-17 7.2.2.8 Waterpercycle
.................................. 7-18 7.2.2.9 Water and wastewater removal rates .................. 7-19 7.2.2.10 Discount rate....... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-20 7.2.2.11 Fuel use ofwater heater and clothes dryer
.............. 7-23 7.2.2.12 Year to start date
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-24
iii

### 7.2.2.13 Base case design
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-24 7.2.2.14 Standard case design
. . . . ... . . . . . . . . . . . . . . . . . . . . . . . . 7-24 7.2.3 LCC results and preliminary output. . . . . . . . . . . . . . . . . . . . . . . . . . 7-25 7.3 Distribution Payback Period
............................ , . . . . . . . . 7-28 7.3.1 Metric
.................... ."........
. . . . . . . . . . . . . . . . . . . . 7-28 7.3.2 Inputs
. . . . . . . . . . . . . . . . . . ... . . . . . . . . . . . . . . . . . . . . . . ... . . . . 7-28 7.3.2 Preliminary Results
. : . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-29 7.4 Rebuttable PBP
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-30 7.4.1 Metric...
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-30 7.4.2 Inputs
....................... -. . . . . . . . . . . . . . . . . . . . . . . . . 7.31 7.4.3 Preliminary Results
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.32 7.5 User Instructions for Spreadsheet
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-32 CHAPTER 8: NATIONAL ENERGY IMPACTS
................................ 8-1 8.1 Introduction
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -8-1 8.1.1 Methodology and Definitions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1 8.2 National Energy/Water Savings (NES)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1 8.2.1 Definition
..........**~** 

### ..................... : . . . . . . . . . . . . . . 8-1 8.2.2 NES Inputs
........................ : . . . . . . . . . . . . . . . . . . . . 8-3 8.2.2.1 Annual Energy Consumption (A£C)
.............. 8-3 8.2.2.2 National Energy Savings (NES)
. . . . . . . . . . . . . . . . . . 8-4 8.2.2.3 Source Energy Conversion (src_conv)
............. 8-4 8.2.2.4 Stock ofClothes Washers by Vintage (STOCKv)
..... 8-5 8.2.2.5 Cycles per Year (IlcyciJ
. . . . . . . . . . . . . . . . . . . . . . . . . 

### 8-6 8.2.2.6 Energy per Cycle (UEC)
............-: . . . . . . . . . . 8-6 8.2.2.7 Annual Water Consumption (AWC)
. . . . . . . . . . . . . . 8-8 8.2.2.8 Water per Cycle (UWC)
. . . . . . . . . . . . . . . . . . . . . . . 8-8 8.2.2.7 Shipments
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-9 8.2.2.8
.Voluntary Programs...
. . . . . . . . . . . . . . . . . . . . . . . 8-16 8.2.2.9 Elasticities
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-16 8.2.3 Preliminary Results
......................**~** 

### . . . . . . . . . . . . . . . 8-17 8.3 Net PresentValue (NPV)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-17 8.3.1 NPV Metric
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-17 8.3.2 NPV Inputs..................
. . . . . . . . . . . . . . . . . . . . . . . . . . 8-18 8.3.2.1 Discount Factor
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-19 8.3.2.2 Present Value ofCosts (PVC)
.......... -. . . . . . . . 8-19 8.3.2.3 Present Value ofSavings (PVS)
................. 8-20 8.3.2.4 Total Equipment Cost
............... , . • . . . . . . 8-20 8.3.2.5 Total Operating Cost Savings
. . . . . . . . . . . . . . . . . . 8-21

- 8.4 Preliminary Output for NES & NPV.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-21 8.4.1 Assumptions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-21 8.4.2 Base**Cas~** 

### Shipments
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-22

iv

### 8.4.3 National Energy Savings and Net Present Value from Possible Standards
.... , . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-22 8.5 ., User Instructions for Spreadsheet
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-24 8.6 Indirect Employment Impacts
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-25 8.6.1 Introduction
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-25 8.6.2 Assumptions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-26 8.6.3 Methodology
...... : ........................ : ........... 8-26 CHAPTER9: MANUFACTURER IMPACT ANALYSIS
......................... 9-1 9."l Introduction
........... .- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1 . 9.2 Phase 1: Industry Characterization
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1 9.2.1 Tax Rate
..................... · ................ ; . . . . . . . . . 9-2 9.2.2 Working Capital
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-3 9.2.3 Selling, General and Administration (SG&A)
................... 9-3 9.2.4 Research and Development (R&D)
........................... 9-4 9.2.5 Depreciation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-5 9.2.6 Capital Expenditures............
. . . . . . . . . . . . . . . . . . . . . . . . . . 9-6 9.2.7 Cost ofCapital/Discount Rate........
. . . . . . . . . . . . . . . . . . . . . . . 9-6 9.3 Phase 2: Industry Cash Flow.............
. . . . . . . . . . . . . . . . . . . . . . . . 9-11 9.4 Phase 3: Subgroup Analysis and Manufacturer biterviews
............... 9-13 CHAPTER 10: CONSUMER ANALYSIS
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-1

- 10.1 Consumer Issues
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-1 10.1.1 Focus Groups and Conjoint Analysis
...... ; . . . . . . . . . . . . . . . . . . 10-2 10.2 Consumer Sub-Group Analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ·10-2 10.2.1 Life-Cycle Cost Analysis (LCC)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-2 CHAPTER 11: UTILITY IMPACT ANALYSIS
.......... : . . . . . . . . . . . . . . . . . . . . . 11-1 11.1 Introduction
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11-1 11.2
. Methodology
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11-2 11.3 Results
.................................................... 11-2

. . 

### CHAPTER 12: ENVIRONMENTAL ASSESSMENT
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-1 12.1 Introduction
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-1 12.2 Methodology
. , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-1
· 12.3 Results
. . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-2 CHAPTER 13: REGULATORY IMPACT ANALYSIS
........................... 13-1 13.l Introduction
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13-1 13.2 Methodology
.......... '.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13-1 APPENDIX A: APPROACH FOR UNCERTAINTY AND VARIABILITY
............. A-1 A. l Introduction
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

v

### A.2 Uncertainty
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . A-1 A.3 Variability
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .•. . . . . . . . . . . . . . . . . . . A-1 A.4 Approaches to Uncertainty and Variability
........................... A-2 APPENDIX B: CLOTHES WASHER MANUFACTURERS
........................ B-1 APPENDIX C: PRELIMINARY LIFE-CYCLE-COST AND PAYBACK PERIOD RESULTS
................................· ....... , . . . . . . . . . . C-1 C. l Life-Cycle Cost Results
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1 C.2 Payback Period Results
.......................,.... ."
............ : C-11 APPENDIX D: NATIONAL ENERGY SAVINGS (NES) SPREADSHEET OUTPUT
.... D-1 APPENDIX E: GOVERNMENT REGULATORY IMPACT MODEL (GRIM)
.......... E-1 E.1 Introduction and Purpose
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1 E.2 Model Description
. . . . . . . . . . . . . . ..............................·. E-1 E.3 Income Statement
.............**~** 

### .................... : . . . . . . . . . . . E-6 E.4 Major Assumptions
. . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-7 APPENDIX F: CONSUMER RESEARCH REPORT
.............................. F-1 APPENDIX G: MANUFACTURER INTERVIEW GUIDE
......................... G-1 G.1 Introduction
.................................................. G-1 G.2 Current Organizational Characteristics
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G-1 G.3 Industry Infrastructure
.......................................... G-1 G.4 Manufacturer Cash Flow Analysis.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G-2 G.5 Competitive Impacts Assessment·
.....................-. ............ G-3. G.6 Employment Impacts Assessment
.................................. G-4 G.7 Manufacturing Capacity lmpa,cts Assessment
......................... G-4 APPENDIX H: LIST OF ACRONYMS AND ABBREVIATIONS
.................... H-1

viTable 2.1 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 3.8 Table 3.9 Table 3.10 Table 3.11 Table 3.12 ·Table 3.13

Table 3.14 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 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 

### LIST OF TABLES Comparison of 1994 and 1998 Non-Regulatory Alternatives to Standards
.. 2-16 Market Share
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 Industry Unit Shipments
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2 Washer Shipments by Access
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3 Washer Sales by Type ofOutlet
. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . 3-3 Clothes Washer Unit Sales by Price Range
. . . . . . . . . . . . . • . . . . . . . . . . . . . 3-4 Wa5her Characteristics
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5 Washer Shipment by Capacity...................................... 3-5 Brand Names, Price Level and Market Share ofMajor Manufacturers
. . . . . . 3-6 Washer Market Share by Brand
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7 Market Saturation by Fuel Type among Housing Units with Clothes Washers
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9 Information from the 1992 Census ofManufactures (MC91-I-36B)
........ 3-9 Summary ofSEC 10-K reports ofAppliance Manufacturers
. . . . . . . . . . . . . 3-10· Product Characteristics ofClothes Washers Tested
.................... 3-12 Summary ofAppendix J and J1 Test Results
. . . . . . . . . . . . . . . . . . . . . . . . . 3-12 Energy-use Data.......................
. . . . . . . . . . . . . . . . . . . . . . . . 4-5 Aggregated Cost Data using "Most Likely" Values
. . . . . . . . . . . . . . . . . . . . . 4-7 Industry Cost Distributions for 5%, 10%, 15%, 20% and 25% Improvements applied to an existing V-axis Clothes Washer
.............. 4-9 Industry Cost Distributions for 35%, 40%, 45% and 50% Improvements applied to a H-axis Clothes Washer
. . . . . . . . . . . . . . . . . . . . 4-10 Major Manufacturing Processes
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 Key Assumptions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8 Cost Components for Baseline V-axis Clothes Washer ($/Unit)
........... 5-8 Overhead Cost ofBaseline Clothes Washer
. . . . . . . . . . . . . . . . . . . . . . . . . . 5-9 Fixed Costs (millions) for Baseline V-axis Clothes Washer
............... · 5-9- Monte Carlo Assumptions for Key Parameters
. . . . . . . . . . . . . . . . . . . . . . . 5-14 Retail Price ofClothes Washers fyom 1986 through 1994 obtained from the Consumer Expenditure Surveys
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4 Retail Price ofClothes Washer from 1986 through 1995 estimated from Dealerscope Merchandisings Annual**Stati~tical** Reviews
...... : . . . . . . . . . 6-4 Shipment Weighted Retail Prices ofClothes Washers from 1994 through 1997 based on ELCAP Retail Sales Data
...................... 6-5 Manufacturer Prices ofClothes Washers from 1986 through 1996 estimated from Current Industrial Reports
. . . . . . . . . . . . . . . . . . . . . . . . . . . 6:-5 Manufacturer Prices ofClothes Washers from 1986. through 1996 estimated using data from the AHAM Fact Book
. . . . . . . . . . . . . . . . . . . . . . 6-6 Manufacturer Mark-ups for Clothes Washers from 1986 through 1994 using Retail Price from the Consumer Expenditure Surveys and Manufacturer Prices from the Current Industrial Reports
. . . . . . . . . . . . . . . . 6-7
viiTable 6.7 Table 6.8 Table 6.9 Table 7.1 Table 7.2 Table 7.3 Table 7.4 .Table 7.5 Table 7.6 Table 7.7 Table 7,.8 Table 8.1 · Table 8.2 Table 8.3 Table 8.4 Table 8.5 Table 8.6 Table 8.7 Table 8.8 Table 8.9 Table 8.10 Table 9.1 Table 9.2 Table 9.3 Table 9.4 Table 9.5 Table 9.6 Table 9.7 Table 9.8 Table 9.9 Table 9.10 Table 11.1 Table 12.l Table 

### 13.l TabkD.1.1 Table D.1.2 Retail Mark-ups estimated from Different Data Sources
............... .'. 6-7 Gross Margins for Retailers ofElectronics, Home Office Equipment

.and Major Appliances
................. .'
. . . . . . . . . . . . . . . . . .. . . . . . . . . 6-9 Manufacturing Cost ofa Baseline Clothes Washer estimated from the Industry Cost Structure
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-11 Quantities in LCC Spreadsheet
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-3 Summary ofNominal**Interes~** Rates
............................... 7-20 Distribution ofDiscount Rates
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-22 Shares ofWater Heaters and Dryers by Fuel Type
.................... 7-23 Summary ofLCC Results
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-27 Summary ofPayback Period Results
................................. 7-30 Rebuttable Presumption Payback in Years
. . . . . . . . . . . . . . . . . . . . . . . . . . 7-32 Quantities and Locations in Life-Cycle Cost Spreadsheet
................ 7-34 National Energy/Water Saving Inputs
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-3 Variables accounted for by different forecast approaches
. . . . . . . . . . . . . . . 8-15 Percent ofAnnual Clothes Washer Shipments having Energy Consumption Equivalent to Horizontal Axis Washers
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-16 Net Present Value Inputs
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-18 Assumptions Used for Generating Preliminary National Impacts
. . . . . . . . . 8-21 Preliminary National Energy Savings Results (2003 to 2030 cumulative)
... 8-22 · NES Results: 20% Improvement (MEF = 1.021)
..................... 8-23 Efficiency Level: Energy and Water Savings (20% Improvement)·
........ 8-23 National Energy Saving Quantities and their Locations in the National Energy Savings Spreadsheet
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-25 Net Present Value Quantities and their Locations in the National Energy Savings Spreadsheet
.........................·.... . . . . . . . . . . . . . . 8-25 Average Income Tax Rates
....................................... 9-2 Average Working Capital as a Percentage ofRevenues
.................. 9-3 Average SG&A Expense as a Percentage ofRevenues
. . . . . . . . . . . . . . . . . . 9-4 Average R&D Costs as a Percentage ofRevenues
..................... 9-5 Average Depreciation as a Percentage ofRevenues
..............·...... 9-5 Average Capital Expenditures as a Percentage ofRevenues
. . . . . . . . . . . . . . 9-6 Cost ofDebt and Debt Ratio
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7 Average Rates ofReturn (1990-96) and Risk PremiU1lls (1926-94) on Some Securities (figures in percent per year)
....................... 9-9 Average Beta Values for Two Appliance Manufacturers
. . . . . . . . . . . . . . . . 9-10 Comparison ofFinancial Data
........................·. . . . . . . . . . . . 9-11 Example Output Table ofNEMS-NAECA Results for Utility Analysis
..... 11-3 Example Output Table ofNEMS-NAECA Results 

### for Environmental Analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-2 Non-regulatory Alternatives
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13-1 5% Efficiency Level: Cost and Net Present Values
..................... D-2 5% Efficiency Level: Energy and Water Savings
....................... D-2
viiiTable D.1.3 Table D.1.4 Table D.1.5 Table D.1.6 Table D.1.7 Table D.1.8 Table D.1.9 Table D.1.10 Table D.1.11 Table D.1.12 Table D.1.13 Table D.1.14 Table D.1.15 Table D.1.16 TableD.1.17 Table D.1.18 10% Efficiency Level: Cost and Net Present Values
.................... D-3 10% Efficiency Level: Energy and Water Savings
...................... D-3 15% Efficiency Level: Cost and Net Present Values
.................... D-4 15% Efficiency Level: Energy and Water Savings
...................... D-4 20% Efficiency Level: Cost and Net Present Values
...... · .............. D-5 20%**Effic~ency** Level: Energy and Water Savings
...................... D-5 25% Efficiency Level: Cost and Net Present Values
............... : .... D-6 25% Efficiency Level: Energy and Water Savings
....................... D-6 35% Efficiency Level: Cost and Net Present Values
.................. .- . D-7 35% Efficiency Level: Energy and Water Savings
...................... D-7 40% Efficiency Level: Cost and Net Present Values
.................... D-8 40% Efficiency Level: Energy and Water Savings
...................... D-8 45% Efficiency Level: Cost and Net Present Values
.................... D-9 45%· Efficiency Level: Energy and Water Savings
...................... D-9 50% Efficiency Level: Cost and Net Present Values
................... D-10 50% Efficiency Level: Energy an.d Water Savings
..................... D-10

ix

### Figure 2.1 Figure 2.2 · Figure 3.1 · Figure 3.2 Figure 4.1 Figure 4.2 Figure 4.3 Figure 5.1 Figure 5.2 Figure 5.3 Figure 5.4 Figure 5.5 Figure 5.6 Figure 5.7 Figure 5.8 Figure 5.9 Figure 5.10 Figure 5.11 Figure 6.1 Figure 6.2 Figure 7.1 Figure 

### 1.2 Figure 7.3 Figure 7.4 Figure 7.5 Figure 7.6 Figure 7.7 Figure 7.8 Figure 7.9 Figure 7.10 ·Figure 7.11 Figure 7.12 Figure 7.13 Figure 7.14 Figure 8.1 Figure 8.2 Figure 8.3 Figure 8.4 Figure 8.5 

### LIST OF FIGURES· Clothes Washer Rulemaking Framework
............................ 2-2 Flow diagram ofthe manufacturer impact analysis methodology
.......... 2-11 Water heater and dryer fuel use ........**~...** 

### . . . . . . . . . . . . . . . . . . . . . . . 3-8 Washing machine industry cost structure as a percentage ofrevenues ...... 3-10 Illustrative input distributions. for incremental manufacturing costs......... 4-3 Industry cost distribution obtained by sampling each manufacturers cost distributions
.....................................·. . . . . . . . . 4-7 Cost-Efficiency graph for clothes washers.
. . . . . . . . . . . . . . . . . . . . . . . . . . 4-11 Full production cost ........................................**~** 

### . . . 5-3 Manufacturing cost assessment approach
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4 Structured bill ofmaterials
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 Fabrication process cycle time estimates
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6 Fabrication process equipment and labor requirements
.................. 5-6 Sensitivity analysis on labor rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-11 Sensitivity analysis on actual plant output. . . . . . . . . . . . . . . . . . . . . . . . . . . 5-12 Sensitivity analysis on total investment cost . . . . . . . . . ... . . . . . . . . . . . . . . 5-12 Sensitivity analysis on equipment depreciation life.
. . . . . . . . . . . . . . . . . . . . 5-13 Sensitivity analysis on maintenance.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14 Monte Carlo simulation ofaggregated baseline cost . . . . . . . . . . . . . . . . . . . 5-15 Average costs and mark-ups for the baseline clothes washer
.............. 6-1 Retail mark-ups estimated from different data sources .................. 6-8 Percent ofclothes washers surviving since year oforiginal purchase
........ 7-5 Percent ofwasher owners by the number ofwasher cycles per year ........ 7-6 Energy use per cycle vs. modified energy factor (met)
. . . . . . . . . . . . . . . . . . 7-7 Distribution of 1997 residential electricity prices
. . . . . . . . . . . . . . . . . . . . . . 7-8 Alternative electricity price trends
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7:-12 Distribution of 1997 residential gas prices
. . . . . . . . . . . . . . . . . . . . . . . . . . 7-13 Alternative gas price trends
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-16 ·Alternative oil price trends
............. ·. . . . . . . . . . . . . . . . . . . . . . . . . 7-1 7 Water use per cycle vs. MEF
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . 7-18 Distribution ofnet water and sewer charges 

### in 1994 (expressed in 1997$)
. . 7-19 Distribution ofConsumer Discount Rates
. . . . . . . . . . . . . . . . . . . . . . . . . . 7-22 Frequency chart ofLCC differences for 25% efficiency improvement level
.. 7-26 Cumulative chart ofLCC differences for 25% efficiency improvement level. 7-27 Distribution ofPBPs for 25% efficiency improvement level
............. 7-29. ARIMA model sales forecast
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-10 Muti-variate time series fit sales forecast
. . . . . . . . . . . . . . . . . . . . . . . . . . . 8-11 Saturation/Lifetime model sales forecast
. . . . . . . . . . . . . . . . . . . . . . . . . . . 8-12. Accounting model sales forecast
. . . . . . . . . . . . . . . . . . . . . . . . ....·. . . . . 8-14 Visual comparison offorecasting models
.... ; . . . . . . . . . . . . . . . . . . . . . . 8-15

xFigure 8.6 Figure 9.1 Figure 9.2 Figure C.l Figure C.2 Figure C.3 Figure C.4 Figure C.5 Figure C.6 Figure C.7 Figure C.8 Figure C.9 Figure C.10 Figure C.11 Figure C.12 Figure C.13 Figure C.14 Figure C.15 Figure C.16 Figμre C.17 Figure C.18 Figure C.19 Figure C.20 Figure C.21 Figure C.22 Figure C.23 Figure C.24 Figure C.25 Figure C.26 Figure C.27 Figure D.1 Figure D.2 Figure D.3 Figure D.4 Figure D.5 Figure D.6 Figure D.7 Figure D.8 Figure D.9 Net Present Annual Values ofa Clothes Washer Standardv with a 20% Efficiency Improvement
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-23 Using GRIM to perform an industry cash flo.w analysis
................. 9-12 Summary offinancial inputs to GRIM
.......... : . . . . . . . . . . . . . . . . . . . 9-12 5% Efficiency Level: Frequency Chart ofLCC Differences
............... C-2 5% Efficiency Level: Cumulative Chart ofLCC Differences
.............. C-2 10% Efficiency Level: Frequency Chart ofLCC Differences
.............. C-3
· 10% Efficiency Level: Cumulative Chart ofLCC Differences
............. C-3

15% Efficiency Level: Frequency Chart ofLCC Differences
.............. C-4 15% Efficiency Level: Cumulative Chart ofLCC Differences
............. C-4 20% Efficiency Level: Frequency Chart ofLCC Differences
........**~** ..... C-5 20% Efficiency Level: Cumulative Chart ofLCC Differences
.............**C-~** 25% Efficiency Level: Frequency Chart ofLCC Differences
.............. C-6 25% Efficiency Level: Cumulative Chart ofLCC Differences
............. C-6 35% Efficiency Level: Frequency Chart ofLCC Differences
.............. C-7 35% Efficiency Level: Cumulative Chart ofLCC Differences
............. C-7 40% Efficiency Level: Frequency Chart ofLCC Differences
.............. C-8 40% Efficiency Level: Cumulative Chart ofLCC Differences
.............. C-8 45% Efficiency Level: Frequency Chart ofLCC Differences
. . . . . . . . . . . . . . C-9 45% Efficiency Level: Cumulative Chart ofLCC Differences
............. C-9 50% Efficiency Level: Frequency Chart ofLCC Differences.
. . . . . . . . . . . . C-10 50% Efficiency Level: Cumulative Chart ofLCC Differences
............ C-10 5% Efficiency Level: Distribution ofPayback Periods
.................. C-11 10% Efficiency Level: Distribution ofPayback Periods.
. . . . . . . . . . . . . . . . C-11 15% Efficiency Level: Distribution ofPayback Periods.
. . . . . . . . . . . . . . . . C-12 20% Efficiency Level: Distribution ofPayback Periods
................. C-12 25% Efficiency Level: Distribution ofPayback Periods.
. . . . . . . . . . . . . . . . C-13 30% Efficiency Level: Distribution ofPayback Periods
................. C-13 40% Efficiency Level: Distribution ofPayback Periods
................. C-14 45% Efficiency Level: Distribution of**Payba~k** Periods
................. C-14 50% Efficiency Level: Distribution ofPayback Periods
................. C-15 5% Efficiency Level: Net Saving
................................... D-2 10% Efficiency Level: Net Saving
.................................. D-3 15% Efficiency Level: Net Saving
.................................. D-4 20% Efficiency Level: Net Saving
.................................. D-5 25% Efficiency Level: Net Saving.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-6 35% Efficiency Level: Net Saving.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-7 40% Efficiency Level: Net Saving
......... · .................... .'
.. : . D-8 45% Efficiency Level: Net Saving
.................................. D-9 50% Efficiency Level: Net Saving
.... : . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1 O

xiCHAPTER 1: INTRODUCTION ll.1 OVERVIEW Part B of Title III of the Energy Policy and Conservaticm Act, Public Law 94-163, as amended by the National· Energy Conservation Policy Act, Public Law 95-619, the National Appliance Energy Conservation Act, Public Law 100-12, the. National Appiiance Energy**ConservationAmendme~ts** of 1988, Public Law 100-357, and the Energy Policy Act of 1992, Public Law 102-486, (the ActorEPCA), created the Energy Conservation Program for Various Consumer Products other than Automobiles. 42 U.S.C. §6291-6309. The National. Appliance Energy Conservation Act of 1987 amended the Act to impose prescriptive standards (design· feature requirements) for cIOthes washers as part of the energy conservationprogram for consumer products. EPCA, §325(g); 42 U.S.C. §6295 (g). The design feature requirement that clothes washers shall have an unheated rinse option was effective for
· appliancesmanufactured on or after January 1, 1988. The Act required the Department to conduct

a rulemaking by January 1, 1990, to detemrine 

### if the above mentioned standards should be amended. The Actprovidedthat any amendment to the standards would apply to products manufactured three years afterthe rulemaking. The Final Rule was issued on May 14, 1991, and is effective.forproducts manufacturedon orafterMay 14, 1994, (hereinafterreferred to as the May 1991 Final Rule). 56 FR 22279. The Act also. requires the Departmentto conduct a subsequent rulemaking no later than five years after the date ofpublication ofthe previous final rule. Before the Department detemrines whether or not an energy conservation standard is economicallyjustified, itmust first solicit comments onthe proposed standard. EPCA, §325 (p), 42 U.S.C. §(;295 (p). Any new or amended standard is required to be designed so as to achieve the maximumimprovementin energyefficiencythatistechnologically feasible and economically justified. EPCA, §325(0)(2), 42 U.S.C. §6295 (o)(2). After reviewing comments on the proposal, the Department must then determine that the benefits ofthe standard exceed its burdens based to the grt!atest extent practicable, on a weighing ofthe'following seven factors: (1) The economic impact ofthe standard onthe manufacturers and onthe consumers of the products subject to such standard; (2) The savings in operating costs throughout the estimated average life ofthe covered product in the type (or class) compared to any increase in the price, initial charges, or maintenance expensesforthe coveredproductsthatare likelytoresultdirectly from the impositionof the standard; (3) The total projected amount ofenergy, or as applicable, water, savings likely to result directly from the imposition ofthe standard; (4) Anylessening ofthe utility orthe performance ofthe covered products likely to result from the imposition ofthe standard;
1-1

### (5) The impact ofany lessening ofcompetition, as determined in writing by the Attorney· General, that is likely to result from the imposition ofthe standard; (6) The need for national energy and water conservation; and (7) Other factors the Secretary considers relevant.

The Department initiated a clothes washer rulemaking to determine ifthe standards (design feature requirements)·imposed by the Act should be amended. The Department published an Advance Notice ofProposed Rulemaking (ANOPR) (53 FR 17712, May 18, 1988), a Notice of Proposed Rulemaking (NOPR) (54 FR 32744, August 9, 1989), and the May 1991 Final Rule. The May 1991 Final Rule mandated performance-based energy conservation standards for clothes washers. The standards specified a minimum energy factor (EF) for two ofthe five classes ofclothes washers (top-loading1

### standard andtop-loading compact). The energy conservation standards in the. May 1991 Final Rule are effective for products manufactured on or after May 14, 1994. On November 14, 1994, the Department issued an ANOPR to begin the second review of energyefficiency standards for clothes washers, dishwashers and clothes dryers. The methodologies presented in this report outline analytical approaches designed to evaluate the economic impacts according to the criteria in the Act. It 

### includes the approach for an engineering analysis of the manufacturingcost and clothes washer performance data; for forecasting the number and efficiency of products sold, for determining prices and operating expenses; for determining change in investment, revenues, and costs to manufacturers of the products; for calculating the costs and benefitsto consumers, and the nation

# as a whole; and for assessing the environmental impacts ofthe proposed standards. The analytical approaches outlined in this report are provided in order to solicit comments from stakeholders onthe proposed approaches to be used inthis rulemaking. Alternative approaches
· can be explored or proposed approaches can be modified and refined. Alsoincluded inthis preliminary Technical Support Document (TSD) are preliminaryresults for the Life-Cycle Cost (LCC) and National Energy Savings (NES) Analyses. Data from the Engineering Analysis is also provided.

1-2; 

### 1.2 STRUCTURE OF THE DOCUMENT This preliminary TSD consists ofthirteen Chapters and eight Appendices. Chapter 1 Introduction: outlines the structure ofthe document. Chapter 2 Analytical Framework: describes the new rulemaking process step-by-step. Chapter 3 Preliminary Marketing and Technology Assessment: characterizes the relevant product markets and existing technology options including prototype designs. Chapter 4 Engineering Analysis: contains detailed energy use and cost information. Chapter 5 Manufacturing Cost Assessment: determines the manufacturing cost by physically taking apart washers. Chapter 6 Mark-ups for Price Determination: establishes a range of mark-ups from manufacturing cost to retail price. Chapter 7 · Life-CycleCost and Payback Period: describes the effects ofstandards on individual purchasers and users ofthe appliances. Itcompares the life-cycle cost ofappliances and other measures ofconsumer impact with and without the proposed standards. Chapter 8 National Energy Impacts: describes national forecast of energy consumption, efficiencyofnew units, units installed in households and annual appliance sales in the absence (or presence) of new regulations. Indirect employment impacts are also evaluated. Chapter 9 Manufacturer Impact Analysis: describes the financial impact on manufactures. Chapter 10 Consumer Analysis: describes the affect on the utility and performance ofmore efficientwashers as experienced by consumers. Evaluates the attributes ofwashers, or what consumers value in a washing machine. Also evaluated is the effect of standards on subgroups such as different household income levels. · Chapter 11 UtilityImpact Analysis: determines the decrease in electricity and gas needed. For electricity it determines the primary energy source for electricity generation. Chapter 12 Environmental Assessment: determines the reduction in emissions due to higher efficiency standards. Chapter 13 Regulatory Impact Analysis: analysis impact of non-regulatory alternatives to standards.
1-3

### Appendix A Approach for Uncertainty and Variability Appendix B List OfClothes Washer Manufacturers Appendix C Life-cycle-cost and payback results Appendix D National Energy Savings results ·. Appendix E Government Regulatory Impact Model (GRIM) Appendix F Consumer Research Report Appendix G Manufacturer Interview Guide Appendix H List ofAcronyms and Abbreviations

1-4

### CHAPTER 2: ANALYTICAL FRAMEWORK
In prescribing new or amended energy efficiency appliance standards, the Departmenfmust achieve the maximum improvement in energy efficiency that is technologically feasible and economicallyjustified. This sectionprovides a description ofthe analytical framework to be used by**tht~** Departmentindeveloping standards and assessingthe impacts. Itdiscussesthe majorcomponents ofthe**analyti~al** framework and the interrelationships amongthe componentsthatensure consistency throughout the analyses. A diagram ofthe analytical framework is shown in Figure 2.1. The analyses performed are:
• A Preliminary Market and Technology Assessment to characterize the relevant product markets and existing technology options including prototype designs. (Subtask 1 in Fig. 2.1) · 

### • A Screening Analysis to review each technology option and determine if it is practicable to manufacture, install and service, would adversely impact product ·utility or product availability, or would have adverse impacts on health and safety. (Subtask 2 in Fig. 2:1) • An Engineering Analysis to determine the maximum technologically feasible energy efficiency level and develop cost-efficiency relationships to show the manufacturer cost of achieving increased efficiency. (Subtask 3 in Fig. 2.1) • A LCC and PaybackAnalysesto calculatethe savingsinoperatingcosts throughoutthe estimated average life ofthe covered product compared to any increase in the price, initial charges, or maintenanceexpensesfor the productlikely to result directly from the impositionof the standard. (Subtask 5 in Fig. 2.1) • A Preliminary National Impacts Ailalysis to assess the aggregate impacts at the national level of consumerpayback, net present value (NPV) oftotal consumer LCC, national energy and water savings and national employment. (Subtask 

### 8 in Fig. 2.1) • A Consumer Analysis to evaluate variations in household characteristics (e.g., energy prices, appliance use behavior, installation costs) that might cause a standard to impact particular consumersub-populations suchas low-incomehouseholdsdifferentlythantheoverallpopulation. (Subtask 10 in Fig. 2.1) • A Manufacturer Impact Analysis (MIA) to estimate the financial impact of standards on manufacturersandto calculateimpactsoncompetition,employment,andmanufacturingcapacity. (Subtask 

### 11 in Fig. 

### 2.1) • A Utility Analysis to estimate the effects ofproposed standards on electric and gas utilities. (Subtask-_12 in Fig. 2.1) • AnEnvironmental Analysis toprovide estimates ofchanges in emissions ofpollutants (sulfur and nitrogen oxides and carbon dioxide). (Subtask 13 in Fig. 2.1) • A Regulatory Impact Analysis to present major alternatives to proposed standards that could achieve substantially the same regulatory goal at a lower cost. (Subtask 14 in Fig. 2.1)

2-1Aooroac·hes
lnduatry Review Ouantit.lltiYe Anatyai1 Qualitative Anatyait- Interviews

Cost Modeling Approach Etf1caency·Level Approach

Market Survey Approach Expert Forecaata Key-Inouts
IdentifyFinns/ Producta Hialorical tnformation Mar1<et/ Firm Segmentation Exishng Technology'Options Prototype De1ign1 I I 1. I I I : EfficiencylPerfontlance i I 

### --·---·-----··-··-··r··---· Energy/Water mes I

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Identification & Screening Analysis

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Coot (C), Price (P). Quantily (Q), Financial (F) I ;**~** I I I I Manufacturer Impact · Analysis (Industry profile, Industry cash flow. Sub-group analysis) Industry Cash-flow wilt\ Uncertainty
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: 

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### Figure 2.1 Clothes Washer Rulemaking Framework

2-2 '-"

### 2.1 BACKGROUND In September 1995, the Department announced a formal effort to consider further improvements to the process used to develop appliance efficiency standards, calling on energy efficiency groups, manufacturers, trade association, state agencies, utilities and other**inter~sted** partiestoprovideinputto guide the Department. OnJuly 15, 1996, the Departmentpublished a Final Rule: Proceduresfor Consideratio.nofNeworRevisedEnergyConservationStandardsfor Consumer Products (hereinafterreferred to as the Process Rule). 61 FR 36974. The Process Rule outlines the procedural improvements identified bythe interested parties. The process improvement effort also • included a review of the: 1) economic models; 2) analytical tools; 3) methodologies; 4) non-regulatory approaches; and 5) prioritization offuture rules. The Department will also take into account uncertainty and variability by doing scenario or probability analysis. For a more detailed discussion see Appendix A. The clothes washer standardsrulemaking is the first rule. to be developed under the Process Rule. The Department developed the analytical framework for the clothes washer rulemaking in responseto stakeholderconcerns regardingthe application of the Process Rule to the clothes washer rulemaking. This analytical framework was presented during a clothes washerworkshopheld onJuly 23, 1997. Itdescribes the methodology, the analytical tools and the relationship between the various analyses. The following sections provide a general description ofthe different analytical components

. ofthe rulemaking framework.



### 2.2 PRELIMINARY MARKET AND TECHNOLOGY ASSESSMENT Thepreliminarymarketandtechnologyassessmentcharacterizestherelevantproductmarkets and existing technology options including prototype designs.


### 2.2.1 Market Assessment Wheninitiating a standards rulemaking, the Departmentdevelops information onthe present and past industry ,structure and market characteristics ofthe product(s) concerned. This activity consists of.both quantitative and qualitative efforts to assess the industry and products based on public.b' available information. Issues to be addressed include: 1) manufacturer market share and characteristics; 2) trends in the number of firms; 3) the financial situation ofmanufacturers; 4) existingnon-regulatory efficiency improvement initiatives; and 5) trends in product characteristics and retail markets. The information collected serves as resource material to be used throughout the rulemaking. · 

### 2.2.2 Technology Assessment Informationrelative to existing technology options and prototype designs are used as inputs to the screening analysis. In consultation with interested parties, the Department develops a list of 2-3design options for consideration. All technologically feasible design options are candidates in this initial assessment.


### 2.2.3 Preliminary Base Case Shipments Forecast The Department develops a base case forecast ofproduct shipments in the absence ofnew standards. This forecast requires an assessment ofthe impacts ofpast and existing non-regulatory efforts by manufacturers, utilities and other interested parties. DOE considers information on the actual impacts of such initiatives to date, and also considers information presented regarding the possibleimpacts thatany existinginitiatives mighthave inthe future. Such information could include a demonstration ofthe steps manufacturers, distribution channels, utilities or others will take to realize.such voluntary efficiency improvements. The base case shipmentsforecast is used as input to the national impacts analysis, in which a forecast ofannual shipments and their weighted average energy efficiency is needed to the year 2030.


### 2.3 SCREENING ANALYSIS The screening analysis reviews various technologies with regard to whether they: a) are impracticableto manufacture, install and service; b) have an adverse impact on product utility or product availability; and c) have adverse impacts on health and safety. The screening analysis establishesproductclasses, baseline units, and efficiency levels (or combinations ofdesign options) for further analysis.


### 2.3.1 Product Classes Producttypes are divided into classes usingthe following criteria: a) thetype ofenergy used; b) capacity; and c) performance-related features that affect consumer utility orefficiency. Different energy efficiency standards will apply to different product classes. 

### In general, classes are defined usinginformationobtainedindiscussionswithappliancemanufacturers, trade associations, and other interested parties.


### 2.3.2 Baseline Units Inorderto analyze designoptions forenergyefficiencyimprovements,the Departmentdefines a baseline unit. For eachproductclass, the assumed baseline unit is a unit that minimally exceeds the existingstandard. To determine the characteristics ofthe baseline unit in this screening analysis, the Department will gather Information from trade organizations, manufacturers, and consultants with expertise in specific product types.
2-4

### 2.3.3 Design Options/Efficiency Levels Following the development of an initial list of design options. during the technology a.ssessment and the screening analysis, the Department, in consultation with interested ·pa.rties, will
· select appropriate efficiency levels (or combinations ofdesign options) for data collection.



### 2

..3.4 Proprietary Designs
·•· In its analysis, the Department considers all design options that are commercially available or present in a working prototype, including proprietary designs. Proprietary designs are fully considered in the Department's engineering and economic analyses.


### 2.4
. ENGINEERING ANALYSIS The engineering analysis first determines the maximum technologically feasible energy efficiencylevel and then develops cost-efficiency relationships to show the manufacturer costs of achieving increased efficiency.


### 2.4.1 Energy Savings Potential and Manufacturing Costs The engineering analysis estimates the energy savings potential of the individual or combinationsofdesign options not eliminated in the previous screening analysis. The Department, in consultation with stakeholders, uses the most appropriate means available to determine energy consumption, including an overall system approach or engineering modeling. Ranges and uncertainties in performance are established. The energy savings measures developed in the engineering analysis are combined with end-user costs in the LCC analysis. The engineering analysis involves adding individual or combinations ofdesign options to the baselineunit. A cost-efficiencyrelationship is developed to showthe manufacturer cost ofachieving increasedefficiency. The efficiency levels corresponding to various design option combinations are. determined from manufacturer data submittals and from DOE**engin~ering** 

### calculations. The Act requires that, in considering any new or amended standards, the Department must considerthose that "shall be designed to achieve the maximum improvement in energy efficiency that the Secretary determines is technologically feasible and economically justified." EPCA, §325(1)(2)(A), 42 U

### .S.C. §6295(1)(2)(A). Therefore an essential role ofthe engineering analysis consists ofidentifying the maximum technologically feasible level. The maximum technologically foasible level is one that can be reached by the addition ofefficiency improvements and/or design options, both commercially feasible and in prototypes, to the baseline units. The Departmentbelieves that the design options comprising the**maxir~1um** 

### technologically feasible level must have been physically demonstrated in at least a prototype form 

### to be considered technologically feasible.
2-5

### Three methodologies can be used to generate the manufacturing costs**n~eded** 

### for the engineering analysis. These methods include: 1) the desigri-option approach, reporting the incremental costs ofadding design options to a baseline model; 2) the efficiency-level approach, reporting relative costs ofachieving energy efficiency improvements; 3) and the cost-assessment approach which requires a "bottoms-up" manufacturing cost assessment based on a detailed bill of materials. The Department considers public comments in determining the best approach for a rulemaking. 

### If

theefficiency-levelapproachis used,theDepartmentwill selectappropriateefficiency levels for data collection on the basis of: 1)

### energy savings potential identified from engineering models; , 2) observation ofexistingproducts onthe market; and/or 3) information obtained for the technology assessment. Stakeholders 

### will be consulted on the efficiency level selection.**Th~** 

### use ofa design-option approach provides useful information such as the identification of potentialtechnologicalpathsmanufacturerscoulduseto achieve increasedproductenergy efficiency. It also allowsthe use ofengineering models to simulate the energy consumption ofdifferent design configurations under various user profiles and applications. However, the Department recognizes that the manufacturer cost information derived in the design-option approach does not reflect the variabilityin design strategies and cost structures that can exist between manufacturers. Therefore, the Departmentmayderive additionalmanufacturingcostestimatesfrom otherapproachesdeveloped in consultation with interested parties.


### 2.5 LIFE-CYCLE COST (LCC) AND PAYBACK ANALYSES In determining economicjustification, the Act directs the Department to consider a number of different factors, including theeconomic impact ofpotential standards on consumers. The Act also establishes a rebuttable presumption that a standard is economically justified ifthe additional product costs attributedto the standard are lessthanthree timesthe value ofthe first yearenergy cost savings. EPCA, §325(o)(2)(B)(iii), 42 U

### .S.C. §6295 ( o){2)(B)(iii). To consider these requirements the Departmentcalculates changes in LCC to the consumers that. are likely to result from the proposed standai-d and two different simple payback periods: distributions ofpayback periods, and a payback period (which follows the test procedure without variation) calculated for purposes ofthe rebuttable presumptionclause. The effect ofstandards on individualconsumers includes a change in operating expense (usually decreased) and a change in purchase price (usually increased). The net effect is analyzed by calculating the change in LCC as compared to the base case.. Inputs to the LCC calculation include the installed consumer cost (purchase price plus installation cost), operating expenses (energy, water, sewer, and maintenance costs), lifetime ofthe appliance, and a discount rate. 

### 2.5.1 LCC Spreadsheet Model The LCC analysis is conducted using a spreadsheet model developed in Microsoft Excel for Windows95, combined with Crystal BallTM (acommercially available softwareprogram). The Model 2-6---- ---------

uses a Monte Carlo simulation to perform the analysis considering uncertainty and variability. The spreadsheetis organized so that ranges (distributions) can be entered for each input variable needed to perform the calculations. Based on the results ofthe LCC analysis, DOE selects candidate standard levels for a more detailed analysis. The range ofcandidate standard levels typically includes: (I) the most energyefficientcombination ofdesign options ormost energy-efficient level; (2) the combination ofdesign options or efficiency level with the lowest LCC; and (3) the combination of design options or efficiencylevels withapayback.period of notmorethanthreeyears. Additionally, candidate standard levelsthatincorporate noteworthytechnologies orfill in large gaps between efficiency levels ofother c:andidate standards levels may be selected. Paybackis calculated based onthe same inputs used forthe LCC analysis (with the difference that the values are based only onthe first yearthe standard takes affect). The output is a distribution ofpayback periods. The mean payback period is also reported. Additional information is available in the LCC spreadsheet but is not reported in the this preliminary TSD. 

# This data includes charts of cash flow taking into account the changing annual fuel prices. The payback for purposes ofthe rebuttable presumption clause is calculated on the LCC spreadsheetbutwithoutusing any distributions orCrystal BalfM. Paybackperiods are first calculated

.between the new standard level and each washer efficiency being sold in the year 2003. The paybacksare then weighted and averaged according to the percentage ofeach washer efficiency sold before a new standard is enacted. Rather than distributions, single point values for the inputs are used. These values (including cycles per year, electric fuel source, etc.) will correspond to those outlined in the DOE test procedure, Appendix 

### JI. The result is a single payback value and not a distribution. The payback is calcuiated for theexpected effective year ofthe standard (e.g., 2003).


### 2.5.2 Price Manufacturer and retail prices are needed to determine how manufacturing costs and associatedmargins, associatedwith standards, arepassed-throughfrom manufacturersto consumers, as this 

### has an impactonbothconsumers andmanufacturers. Consumer and manufacturer economics are linked and inversely related. For this reason, retail prices used for the LCC analysis need to be reconciledwith manufacturer prices. Chapter 6 provides a detailed description ofthe approach used for determining industry-average prices and mark-ups.


### 2.5.2.1 Retail Price Retail prices are needed for a base case, absent new standards, and for all efficiency levels to be considered. Several approaches can be used to obtain these retail prices. Potential approaches include: 1) conducting a survey ofexisting prices on the market; 2) surveying manufacturers and other market experts to predict consumer willingnessto pay higher prices; and 3) applying various
2-7mark-ups overmanufacturing costs. Consistent withthe Process Rule, and building on the estimates generated by various approaches, retail prices would be described within a range ofuncertainty.


### 2.6 PRELIMINARY NATIONAL IMPACTS ANALYSIS The national impacts analysis assesses the netpresent value (NPV) oftotal consumer LCC, energy (and water, if appropriate) savings and indirect employment impacts. A. preliminary assessment ofthe aggregate impacts at the national level isconducted. for the ANOPR. Analyzing impacts ofFederal energy-efficiency standards requires a comparison ofprojected U.S. residential energyconsumptionwithand without standards. Thebasecase, whichis theprojected US residential energy consumption without standards, includes the mix ofefficiencies being sold at the time the standardbecomes effective. Sales projectionstogetherwith efficiency levels of the washers sold, are important inputs to determine the total energy consumption due to clothes washers under both base case and standards case scenarios. The differences between the base case and standards case provides the energy and cost savings. Depending on the analysis method used, the sales under a standards case projection may differ from those ofa base case projection. The Departmentestimatesnational energyand water, ifapplicable, consumptionfor eachyear beginning with the expected effective date of the standards. National annual energy and water savings are calculated asthe difference between two projections: a base case and a standards case. Analysis includes estimated energy savings by fuel type for electricity, natural**ga~d** oil. Energy consumptionand savings are estimatedbasedonsite energy (kWhof electricity, millionBtuof natural gas or oil used in the home), then the electricity consumption and savings are converted to source. energy.Measures ofimpact reported include the riet present value (NPV) oftotal consumer LCC, national energy savings (and water savings, ifappropriate) and indirect employment impacts. Each ofthe above are determined for selected trial standarci levels. These calculations are done by the use ofa spreadsheettool called the National Energy Savings (NES) Spreadsheet Model, which has been developed for all the standard rulemakings and tailored to each specific appliance rulemaking.**2~6.1** 

### National Energy Savings (NES) Spreadsheet Model In order ·to make the analysis more accessible and transparent to all stakeholders, a spreadsheet model was developed using Microsoft Excel in Windows 95 to calculate the national energy (and water savings, ifappropriate) and the national economic costs and savings from new standards. Input quantities can be changed within the spreadsheet. Unlike the LCC analysis, in the NES Spreadsheet, distributions are not used for inputs oroutputs. Sensitivities can be demonstrated by running different scenarios.

2-8

### 2.6.2 Shipments One of the more important components of any estimate of future impact is shipments; Forecasts ofshipments for the base case and the standard case need to be obtained as an input to the

.NES. The most basic method for forecasting future shipments is a simple saturation-based method

whichassumes saturations remain unchanged and solves for a growth rate in shipments sufficient to keep saturations constantin light ofpopulation growth. There are several factors that can make this estimateinaccurate. These factors include possible changes in: the number of households, saturation levels,appliance lifetimes, prices (including operating costs), and consumerdecisions about whether

.. to repair rather than replace an appliance. Because ofthese complexities, and to improve on the forecasts, four different statistical models were studied. These are described in detail in Chapter 8 ofthis document.


### :Z.6.3 Indirect Employment Impacts TheJuly 1996 Process Rule includes employmentimpacts amongthe factors to beconsidered. in selecting a proposed standard. The Department will estimate the impacts of standards on employment for appliance manufacturers, relevant service industries, energy suppliers, and the**c~conomy** 

### in general. Employment impacts are separated into indirect and direct impacts. Direct employirent impacts would result if standards lead to a change in the number of employees at manufacturing plants and related supply and seniice firms. Direct impacts will be discussed in the sectiononmanufacturerimpactanalysis. Indirectimpacts are impacts on the national economy other 1han

### inthe manufacturing sectorbeingregulated. Indirectimpacts may result from both expenditures shifting among goods (substitution effect), and income changing, which will lead to a change in overall expenditure levels (income effect). Indirect employment impacts from standards are defined as netjobs eliminated or created in the general economy as a consequence ofincreased spending on purchase price ofappliances and reduced household spending on energy. New appliance standards are expected to increase the purchaseprice ofappliances (retail priceplus sales 

### tax, and installation). The same standards are also c::xpected to decrease energy consumption, and therefore reduce household expenditures for energy. Overtime, the increased purchase price is paid back through energy savings. The savings in energy expenditures may be spent on other items. Using an input/output model ofthe U.S. economy, this analysis seeks to estimate the effects on different sectors, and the net impact on jobs. National impactswill be estimated for major sectors ofthe U.S. economy. Public and commercially available data sources and software will be utilized to estimate employment impacts. At least three scenarios will be analyzed to bound the range of uncertainty in future energy prices. All methods and documentation will be made available for review.

2-9

### 2.7 CONSUMER ANALYSIS The consumer analysis evaluates impacts to any identifiable groups or consumers, such as ·consumersofdifferent income levels, who may be disproportionately affected by any national energy efficiency standard level. TheDepartment could evaluate variations in regional energy prices;water and sewer prices, variationsin energy use and variations in installation costs that might affect the netpresent value of a standard to consumer sub-populations. To the extent possible, DOE obtains estimates of the variabilityin each input quantity and considers this variability inits calculation ofconsumer impacts. The analysis is structured to answer questions such as: How many households are better-offwith standards and by how much? How many households are not better offand by how much? The variability in each input quantity and likely sources ofinformation are discussed with stakeholders. Variations in energy use for a particular appliance can depend on factors such as: climate, type ofhousehold, people in household, etc. Annual energy use can be estimated by a _calculation based on an accepted test procedure or it can be measured directly in the field. The Department performs sensitivity analyses to consider how differences in energy use will affect sub-groups of consumers. The impact on consumer sub-groups will be determined using the LCC spreadsheet model.


### 2.7.1 Purchase Price Increases The Department will be sensitive to purchase price increases to avoid ·negative impacts to identifialie population groups such as low-income households. Additionally, the Department will assess the likely impacts ofpurchase price increases on product sales and fuel switching.


### 2.7.2 Consumer Participation The Department seeks to inform and involve consumers and consumerrepresentatives inthe process ofdeveloping standards. This includes notification ofconsumer representatives during the rulemaking process and where appropriate, seeking direct consWiler input.


### 2.8 MANUFACTURER IMPACT ANALYSIS The manufactt.i.rer analysis estimates the financial impact ofstandards on manufacturers and calculates impacts on competition, employment, andmanufacturing capacity. Priorto initiating the detailed manufacturing impact analysis the Department will prepare an approach document and have it available for review. While the general framework will serve as a guide, the Department intends to tailor the methodology for each rule on the basis ofstakeholder commeμts. The document will outline procedural steps and outline issues ·for consideration. Three 2-10

## .. important elements of the approach consist of the preparation of an industry cash-flow, the**d1~velopment** ofa process to consider sub-group cash-flow, and the design ofan interview guide. The policies outlined in the Process Rule required substantial revisions to the analytical framework to be used in performing manufacturer impact analysis for each rulemaking. In the approach document, the Department will describe andobtain comments on the methodology.to be used in performing the manufacturer impact analyses. The manufacturer impact analyses will be conducted in three phases (see Figure 2.2). Phase 1 consistsoftwo activities, namely, preparation ofan industry characterization and identification ofissues. The second phase has as its focus the larger industry. In this phase, the GRIM will be used to perform an industry cash flow analysis. Phase 3 involves repeating the process de·scribed in Phase 2 (the industry cash-flow analysis) but on different sub-groups of manufacturers. Phase 3 also entails calculating additional impacts on competition, employment, and manufacturing capacitr.

ANO PR Screening Analysis



### l§,I.ll.!§41!.!.f@i@b------·· 

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## IUfOI.!.fll!.,!.ft;§l;WfMb•,_----··

Indirect Employment Impacts Impacts on Utilities Impacts on the Environment and Energy Security Manufacturer Impact Analyses

Estimate Manufacturer Costs Estimate Prices and Shipments Cash Flow Analysis Impacts on Competition Impacts on Employment**lmpac~s** on Manufacturing 

### Figure 2.2 Flow diagram ofthe manufacturer impact analysis methodology

2-11

### 2.8.1 Industry Cash Flow A change in standards affects the analysis inthree distinct ways. Increased levels ofstandards willrequire additional investment, will raise production costs, and will affect revenue through higher prices and, possibly, lower quantities sold. To quantify these changes the Department performs an industry cash flow analysis using the GRIM. Usually this analysis will use manUfacturing costs, shipmentsforecasts, and price forecasts developed for the otheranalyses. Financial information, also required as an input to GRIM, will be developed based on publicly available data and confidentially submitted manufacturer information. · The GRIM analysis uses a numberoffactors- annual expectedrevenues; manufacturercosts suchas cost ofsales, selling and general administration costs, taxes, and capital expenditures related to**~epreciation,** 

### newstandards, and maintenance- to arrive ata series ofannual cash flows beginning from before implementation of standards and continuing explicitly for several years after implementation. Themeasure ofindustry netpresentvalues are calculatedby discountingthe annual cash flows from the period before implementation of standards to some future point in time. Appendix E provides a description of

## GRIMs operating principles.


### 2.8.2 Manufacturer Sub-Group Analysis Using industry**·~verage"** 

### cost values is not adequate for assessing the variation in impacts among sub-groups of manufacturers. Smaller manufacturers,· niche players or manufacturers exhibitingacoststructurelargelydifferentfrom industry averagescouldbe morenegatively impacted. Ideally,the Departmentwould considerthe impact onevery firm individually. In highly concentrated industriesthis may be possible. In industries having numerous participants, the Department will use the results ofthe industry characterization to group manufacturers exhibiting similar characteristics. The financial analysis ofthe 'prototypical"firm performed inthe Phase 2 industry analysis can serve as a benchmark against which manufacturer sub-groups can be analyzed. The manufacturing cost data collected for the engineering analysis will be used to the extent practicalin the sub-group impact analysis. To be useful, however, this data should be disaggregated to reflect the variability in costs between relevant sub-groups offirms. The Department will conduct detailed interviews with as many manufacturers as is possible to gain insight into the potential impacts ofstandards. During'these interviews, the Department will solicitthe information necessary to evaluate cashflows and to assess competitive, employment and capacity impacts. Firm-specific cumulative burden will also be considered.


### 2.8.3 Interview Process Therevised rulemaking process provides for greaterpublic input and for improved analytical approaches, with particular emphasis on earlier and more extensive information gathering from interested parties. The· proposed three-phase manufacturer impact analysis process will draw on 2-12multiple infomiation sources, including structured interviews with manufacturers and a broad cross-sectionofinterestedparties. Interviews may beconducted in any and all phases ofthe analyses as determined in Phase 1 ofMIA. The interviewprocess has a key role in the manufacturer impact analyses, since it provides an opportunity for interested parties.to privately express their views on important issues. A key characteristic ofthe interview process is that it is designed to allow confidential information to be considered in the rulemaking decision. · Theinitial industry characterizationwill collectinformation from relevantindustry andmarket publications,industry trade organizations, company financial reports, and product literature. This informationwill aidinthe development ofdetailed and focused questionnaires, as needed, to perform allphases of the manufacturerimpactanalyses. Itis theintention ofthe Departmentthatthe contents of questionnaires and the list of interview participants be publicly vetted prior to initiating the interview process. ThePhase 3 (sub-groupanalysis)questionnairewill solicitinformationonthe possibleimpacts ofpotential efficiency levels on manufacturing costs, product prices, and sales. Evaluation ofthe. possible impacts on direct employment, capitalassets, and industry competitiveness will also draw heavily on the information gathered during the interviews. The questionnaires will solicit both qualitative and quantitative information. Supporting information will be requested whenever applicable. Interviews will be conducted according to DOE procedures. Interviews will be scheduled wellin advance inorderto provideevery opportunity forkey individualsto be available for comment. Although a written response to the questionnaire is acceptable, an interactive interview process is preferred because it helps clarify responses and provides the opportunity for additional issues to be identified. Interview participants will be requested to identify all confidential information provided in writingor orally. Approximately two weeks following the interview, an interview summary will be providedto give participants the opportunity to confirm the accuracy and protectthe confidentiality ofall collected information. All the information transmitted will be considered, when appropriate, inDOEs decision-makingprocess. However, confidential informationwill notbe made available in the public record. · DOEwill collatethe completed interviewquestionnaires and prepare a summary of the major issues and outcomes. The Department will seek comment onthe outcome ofthe interviewprocess. 

### 2
..9· COMPETITIVE IMPACT ASSESSMENT Legislation directs the Department to cons.iderany lessening ofcompetition that is likely to result from standards. It further directs the Attorney General to gauge the impacts, ifany, ofany lessening of competition. DOE will make an effort to gather and report firm-specific financial 2-13

### informationandimpacts. The competitive analysis will focus on assessing the impacts to smaller, yet significant, manufacturers. The assessment will be based on manufacturing cost data and on information collected from interviews with manufacturers, consistent with Phase 3 of the
. manufacturer impact analyses. The Department ofJustice (DOJ) has offered to help in drafting
·questionsto be used in the manufacturer interviews. These questions will pertainto the assessment ofthe likelihood ofincreasesin market concentration levels and other markei conditions that could lead to anti-competitive pricing behavior. The manufacturer interviews will focus on gathering information that would help in assessing asymmetrical cost increases to some manufacturers, increased proportion of fixed costs pc)tentially increasing business risks, and potential barriers to market entry (proprietary technologies, etc.).


### 2.10 UTILITY ANALYSIS The utility analysis estimates the effects ofproposed standards on electric and gas utilities. The Department proposes .to use a version ofEnergy Information Administrations (EIA)

. widely recognized National Energy Modeling System (NEMS) for the utility and environmental analyses. NEMS is a large multi-sectoral partial equilibrium model ofthe U.S. energy sectorthat has been developed over several years by the EIA primarily for the purpose ofpreparing the Annual Energy Outlook (AEO). NEMSproduces a widely recognizedbaseline forecast forthe U.S. through 2020 and is available inthe public domain. The version ofNEMS to be used for appliance standards analysis will be called NEMS-NAECA, and will be based on the AE098 version witji minor modifications.' · NEMS offers a sophisticatedpicture of the effect ofappliance standards since its scale allows it to measure the interactions between the various energy supply a.ild demand sectors and the economy as a whole. In addition, the scale ofNEMS permits analysis ofthe effects ofstandards on both the electric and gas utility industries: To analyze the effect of standards, NEMS-NAECA is first run exactly .as it would be to produce an AEO forecast, then a second runis conducted withresidential energy usage reduced by the amount ofenergy (gas, oil, and electricity) saved due to appliance standards for the appliance beinganalyzed. The energy savings input is obtained from the NES spreadsheet. Outputs available are the same as those inthe original NEMS model includingresidential energyprices, generation and installed capacity (and in the case ofelectricity, which primary fuel is used for generation).

1 EIA approves use ofthe name NEMS only to describe an AEO version ofthe model without any modification to code or data. Since, in this work, there will be some minor code modifications and the model will be run under various policy scenarios that deviate from AEO assumptions, DOE proposes use ofthe name NEMS-NAECA for the model as used here. 2-14

### · 2.11 ENVIRONMENT AL ANALYSIS TheEnvironmentalAssessmentisrequiredpursuanttotheNational Environmental PolicyAct of 1969 (NEPA)( 42 U.S.C. 4321 et seq.), regulations ofthe Council on Environmental Quality (49 CFRparts· 1500-1508), the Departmentregulations for compliance with NEPA ( 10 CFR part 1021) and the Secretarial Policy on the National Environmental Policy Act (June 1994). The main**environmen~l** concern addressed is emissions from fossil fuel-fired electricity ·generation. Power plant emissions include oxides ofnitrogen (NQ) and sulfur (S0 2), as well as '.• carbon dioxide (CC>i). The first two are major causes ofacid precipitation, which can affect humans byreducingtheproductivity offarms, forests and fisheries, decreasing·recreational opportunities and degrading susceptible buildings and monuments. NOx istlso a precursor gas to urban smog and is particularly detrimental to air quality during hot, still weather. CQ emissions contribute to raising the global temperature via the 'greenhouse effect." The long-term consequences of higher temperaturesmayinclude perturbed airand oceancurrents, perturbedprecipitationpatterns, changes inthe gaseous equilibrium betweenthe atmosphere and the biosphere, and the melting ofsome ofthe ice now covering polar lands and oceans, causing a rise in sea level. The ·Department proposes to use EIA

# s widely recognized NEMS for the appliance environmentalanalyses. The version ofNEMS to be used for appliance standards analysis will be calledNEMS-NAECA, and will be based on the AE098 version with minor modifications. NEMSNAECA is runexactlythe**s~e** as the original NEMS exceptthatresidential energy usage is reduced by the amount ofenergy (gas, oil, and electricity) saved due to appliance standards for the appliance being analyzed. The input of energy savings are obtained from the NES spreadsheet. For the environmentalanalysis,the outputis the forecasted physical emissions. Thenetbenefits ofa standard will be the difference between emissions estimated by the AE098 version ofNEMS-NAECA and those it estimates with a standard in place.


### 2
.. 12 REGULATORY IMPACT ANALYSIS DOE will be preparing a draft regulatory analysis pursuant to E.O. 12866, 'Regulatory Planning and Review," which will be subject toreview under the Executive Order by the Office of InformationandRegulatoryAffairs (OIRA)58FR51735 (October4, 1993). Eightmajoralternatives were identified by DOE as representing feasible policy options to achieve consumer product energy efficiency. Each alternative will be evaluated in terms ofability to achieve significant energy savings at a reasonable cost and will be compared to the effectiveness ofthe rule. Under the Process Rule policies, the Department is committed to continually explore nonregulatory alternativesto standards. InTable 2.1 belowis a discussion ofwhatwas examined in 1994 and what is being proposed for this rulemaking. The Department will seek comments on this approach.

2-15Table 2.1 Comparison of 1994 and 1998 Non-Regulatory Alternatives to Standards Alternatives Examined in 1994 Alternatives to Examine in 1998 

### -No action -No new regulatory action -Consumer tax 

### credits -Consumer tax 

### credits -Manufacturer tax 

### credits -Manufacturer tax credits -Performance standards -Perfom1ance standards -Consumer rebates -Rebates -Prescriptive standards

-Voluntary standards -Voluntary energy efficiency targets -Enhanced labeling and consumer education
-Mass Government purchases -Early Replacement

2-16

### CHAPTER 3: MARKET AND TECHNOLOGY ASSESSMENT
3.1 INTRODUCTION This chapter provides a profile ofthe clothes washer industry in the United States. The preliminary market and technology assessment presentedin this chapter is developed from publicly availableinformation. This information will be helpful in identifying the major manufacturers and theirproduct characteristics which form the basis for the engineering and the life-cycle-cost analysis. Present andpast industry structure and industry financial information will help the Department in the process ofconducting the manufacturer impact analysis. 

### 3.2 PRODUCT CLASSES The Association ofHome Appliance Manufacturers (AHAM) defines a compact washer as ''Combination ofcabinet width of24 inches or less and a washer tub capacity ofless than 2.0 cubic feet and/or a dryer drum capacity ofless than 4.4 cubic feet" [I]. Using the AHAM definition, ApplianceMagazine Statistical Review shows for 1995 compact washer sales accounted for 

### 2.8% (200,000) ofthe total residential washer market[2]. For the year 1996, washer imports were 2.3% ofdomestic sales. Imports tend to be compact horizontal axis machines (H-axis) [3].


### 3.3 MANUFACTURERS AND MARKET SHARES There are five major manufacturers ofclothes washers in the United States. Their market shares have remained fairly steady in the last seven years, as can be seen from Table 3.1. Each manufacturer offers multiple brand names. Some of the brand names come from independent appliance manufacturers which have been acquired over time.

Manufacturer
Whirlpool Maytag General Electric Electrolux (Frigidaire) Raytheon/ Goodman (Speed Queen) 

### Table 3.1 Market Share Appliance Magazine Appliance Magazine 1990 1995

[4] [5] 51% 52% 18% 20% 16% 15% 12% 9% 3% 4%



### 3-1 Appliance Magazine 1997 [6] 52% 19% 17% 7% 5%

### 3.4 HISTORICAL SHIPMENTS Clothes washer shipments in the U.S. have been increasing at an average rate of2.15% over the last 10 years. Table 3.2 provides industry unit shipments and industry dollar sales of clothes washers for the ten year period 1987-1996.

Year

1987 1988
. 1989 1990 1991 1992 1993 1994 1995 1996 

### Table 3.2 Industry Unit Shipments [7] Shipments Estimated Industry (Domestic+ Dollar Sales

Exports) (Manufacturer (in OOO's) dollar value in $mil)

5,998 6,190 6,252 6,192 1,175.2 6,197 1,783.5 6,515 1,898.0 6,793 1,986.l 7,035 2,106.3 6,901 2,090.2 7,129 2,049.0 Shipments (Exports) (in OOO's)

247.6 403.3 385.3 601.2 692.7 760.8 769.7 894.2 824.6 917.6


### In recent years the sales of horizontal axis washers have been increasing. Traditionally horizontal axis machines have accounted for no more than 1 or 2 percent market share. Recent publicallyavailable data shows a horizontal axis market share of5.4% 

### (see Table 3.3). 

### This datamay not include all major retailers and therefore has a margin oferror. Inthe last year sales have picked up dueto U.S. companiesenteringthehorizontal axis market. Highefficiencywasher saleshavebeen encouraged by regional rebate programs often combined with a rating system developed by the Consortium for Energy Efficiency (CEE). Other voluntary programs have encouraged the sales of resource efficient clothes washers. [8] The Energy Star program also promotes high efficiency washers. MajorU

# .s: manufacturers offeringH-axis machines atthe time of this TSD (October 1998) are as follows: Maytag, Electrolux (Frigidaire), and General Electric. Goodman Manufacturing (Amana) also had a horizontal axis machine on the market but at the time this document was written was not 

### in production. Whirlpool recently introduced an energy efficient vertical axis (Y-axis) machine on the market. General Electric offers a horizontal axis machine made by another manufacturer. 

### .,

3-2

## -. Table**3~3** 

### Washer Shipments by Access [9]

3.5 RETAILERS Access Top Load/Vertical Front Load/ Horizontal 

### First Quarter 1998 94.6% 5.4%

, Distributersare virtually non-existent inthis market. Manufacturers deal directly with retail outlets or buying groups formed by smaller appliance stores (see Table 3.4). (10] 

### Table 3.4 Washer Sales**b~** 

### Type ofOutlet [11) Outlet 1997 Market Share Mass Merchandiser 32.6 Appliance Store 28.8 CE Superstore 10.4 Department Store 7.9 Furniture Store 5.0 Discount Dept. Store 3.7 Home Center 3.7 Hardware Store 1.1 Home Builder 1.0 Warehouse Club 1.0 Kitchen Remodeler 0.2 Catalog Showroom 0.1 Landlord 0.1 Mail Order/Direct 0.1 Other 4.3

3-3

### 3.6 PRICE DISTRIBUTION Table 3.5 lists the clothes washer unit sales in the U.S. by price range. Clothes washer retail or consumer prices are required for conducting the life-cycle-cost analysis and manufacturer prices are required for identifying the financial impact ofstandards on manufacturers. Chapter 6 ofthis document provides detailed information on both retail and manufacturer prices and markups
. • 

### Table 3.5 Clothes Washer Unit Sales by Price Range [12] Price Range Market Share 1997

. Under$200 1.2% $200-299 6.3% $300-399 38.2% $400-499 31.7%

. $500-599 13.7% $600-699 5.6% $700-799 0.7% $800 and above 2.6%



### 3.7 PRODUCT CHARACTERISTICS Clothes washers ofdifferent capacities and configurations are sold in the market., A survey 

### by Elrick and Lavidge [13] forthe year 1995, provided information in Table 3.6 below. More recent data, in Table 3.7, shows the market share between 16-pound and 18-pound 

### & over capacities.

3-4 ;

### "

:. 

### Table 3.6 Washer Characteristics (1995) Features Units Sold Average Price Type Regular Side by Side 97.6% $373 Door Top Load 99.9% $384 Front Load 0.1% $668 Capacity Large Load (15-18 lbs.) 35.6% $355 Extra Large Load (19+ lbs.) 64.1% $401


### Table 3.7 Washer Shipment by Capacity [14] Capacity 1997 Market Share 16-pound 19% 18-pound and over 81%
Table 3.8 provides the brand names used by the different manufacturers and the price level for a given brand. Manufacturers also build dryers, usually marketed as a set washer with matching dryer. See Appendix B for a list ofmanufacturers and addresses.

3-5

### Table 3.8 Brand Names, Price Level and Market Share ofMajor Manufactures (15] Parent Company Brand Price Level Whirlpool Sears/Kenmore Varied· Whirlpool Mid-priced Roper Value Kitchen Aid High Maytag Maytag High Admiral Mid-low Magic Chef Value Norge/Crosley Value General Electric General Electric High/Mid. Hot Point Value RCA Mid Electrolux Frigidaire Mid-High White Westinghouse Value Kelvinator Value. Tappan Mid Gibson Value Raytheon 1 Amana Mid Speed Queen· Value Source: DealerScope, "Statistical Survey and Report:, May 1996, P.60 Quality levels taken from Smith Barney Whirlpool/Maytag Company Report 1117196.

1 Raytheon sold the Amana brand to Goodman Holdings, Inc. and licences the Speed Queen brand to them. 3-6Table 3.9 Washer Market Share by Brand 

### f161 Brand 1997 Market Share(%) Sears/Kenmore 25.6 Whirlpool 20.4 Maytag 17.1 General Electric 12.4 Amana 4.5 Frigidaire 4.3 Roper 3.3 Hotpoint 2.8 White-Westinghouse 2.1 Admiral 1.3 Speed Queen 1.2 Gibson 1.0 Kitchen Aid 1.0 Magic Chef 0.5 RCA 0.5 Montgomery Ward 0.4 Kelvinator 0.3 Asko 0.2 Crosley 0.2 Other 1.9


### 3.8 MARKET SATURATION The saturationofclothes washers varies withthe type ofhousingand income level.The 1990 Census gives a clothes washerpenetration of76%. Appliance Magazine gives a saturation for 1997 of79.7%.[17]



### 3-7

### 3.9 VOLUNTARYPROGRAMS(TOINCREASEWASHEREFFICIENCY/PROMOTE HORIZONTAL AXIS MACHINES) The Department researched volwitazy programs. Research included regional programs that encouraged the buying of high efficiency clothes washers through rebates, education and demonstrationprojects. National efforts such as Energy Starare also sununarized. The entire report is located in Appendix F. ·

40 35 c 30 0 ;I! 25 

## .a ;J 20 

# - c GI 15**~** GI 0.. 10 5 0 Eled. Oil WaterHeater Fuel 

### Figure 3.1 Water Heater and Dryer Fuel Use
3.10 FUEL USE Saturation ofFuel Type

Beet Or}9' Fuel

Althoughclotheswashers are allelectricpowered,mostof theenergyused by clotheswashers is hot water from a water heater. This for the most part is either gas or electric water heaters. The reduction in drying clothes due to greater moisture extraction in the clothes washer is also a contributionto clothes washer energy savings and is therefore also shown in the chart below.[18] Figure 3.1 below is for households having a clothes washer and is taken from EIA 1995 Databook which uses data collected in 1993.

3-8

### •**~** 

### Table 3.10 Market Saturation by Fuel Type Amone Housine Units with Clothes Washers Water Heater Fuel Percent Dryer Fuel Type Percent Electricity Gas None piped gas 52.97 31.14 17.3 4.53 electricity 38.81 35.3 0.49 3.03 fuel oil 4.36 3.33 0.18 0.85 !pg 3.32 2.53 0.79 other 0.46 0.36 0.05 0.04
none 0.08 0.01 0.07


### 3.11 INDUSTRY COST STRUCTURE Based on publicly available information from thtCensus ofManufactures (Table 3.11) and the Securities and Exchange Commission (SEC) 10-Kreports filed by publicly owned manufacturers (summarized in Table 3.12), the washing machine industry cost structure was developed. 

### Table 3.11 Information from the 1992 Census ofManufactures (MC91-I-36B) Census Year Cost ofMaterials as a Cost ofPayroll for Cost ofTotal Payroll percent ofvalue of production workers as a (production+admin.) as a shipments percent ofvalue of percent ofvalue of shipments shipments (percent) (percent) (percent) 1992 52 10:2 12.7 1987 54 12.3 15.3 1982 53 12.3 15.8

3-9

### Table 3.12 Summary ofSEC 10-K reports ofappliance manufacturers Appliance Manufacturers 1992-1996 % of Sales Net Sales $52,381.7 100% Cost ofSales $38,877.8 74.2% Selling, General & Admin. $9,574.0 18.3% Research & Development $953.7 1.8% Depreciation $1816.3 3.5% Amortization, Tooling $0.0 0.0% Amortization, Intangibles $154.8 0.3% Income from Cont. Operations $3143.0 6.0% Income before Interest, Taxes, Extr. $2952.8 5.6% Items Net Income $1094.2 2.1% Capital Expenditures $2,368.0 4.4%
The resulting cost structure is shown in Figure 3.2. This a composite for the industry as a whole; no single firm may have these results. In developing the cost structure for the washing machineindustry, it is assumed that the washing machine industry is a microcosm ofthe total major

-appliance industry.

- ·

R&D Income _from Operations s:o%

1.8% 

## ..--.===
11.7%

10.2% Material 52.0%



### Figure 3.2 Washing machine iiidustry cost structure as a percentage ofrevenues 3-10 

### -.A detailed financial analysis ofthe clothes washerindustryto identify key financial inputs like cost ofcapital, working capital, depreciation, capitalexpenditures etc. is presented in Chapter 9 of this document.


### 3.12 TECHNOLOGY ASSESSMENT Theefficiencyofsomeclotheswashers currentlyonthemarkethavebeenimprovedbyadding design options such as:
• adaptive fill control, temperature sensing to set wash temperatures more precisely and to lower the water temperature (to allow lower water temperatures), • features that promote greater moisture removal for the final spin cycle; such as higher spin speeds, longer spin speeds and • horizontal axis designs (also referred to as tumble wash designs). More detail on clothes washer technology is available 

### in two DOE reports: Draft Report on the Preliminary Engineering Analysis for Clothes Washers [19] and Draft Report on Design Options for Clothes Washers [20]
.. · 

### 3.13 ENERGY EFFICIENCY The test procedure for which this rulemaking will be based on will only take effect when a new standard is enacted. Therefore, efficiency data publicall)available is still based on the current testprocedure referred to as Appendix J. Inorderto get an ideaofcurrent efficiencies onthe market usingthe future test procedure metric ofJI and howthey correlate to current test procedure ratings using EF, nine clothes washers weretested to the new test procedure Appendix J 1. A summary of the washers tested are shown in Table 3.13. Testresults for Appendix J and JI are shown in Table 3.14.

3-11

### Table 3.13 Product characteristics ofclothes washer tested Clothes Washer Type Capacity Warm Rinse Extra Rinse (cu.ft.) A Y-axis 3.20 

### No No B Y-axis 3.20 Yes 

### No 

# c Y-axis 2.45 Yes· Yes D H-axis 2.85 Yes 

### No E Y-axis 3.08 Yes 

### No F Y-axis 3.28 

### No Yes G Y-axis 3.02 

### No No H H-axis 2.54 Yes 

### No I Y-axis 2.93 Yes 

### No
T bl 3 14 S

a e . : 

### ummaryof 

### A d" J d Jl t t It 

### .ppen ix an es resu s Clothes Washer EF 

### % Efficient MEF 

### % Efficient (cu.ft./kWh/ overNAECA (cu.ft./k Wh/ overAHAM cycle) minimum cycle) baseline (EF=l.18) (MEF=0.817) A 1.44 18.1 1.05 22.1 B 1.50 21.3. 

# - - 

# c 1.160 0 0.822" 0.6 D 2.339 49.6 1.516 46.1 

### E 1.166 0 0.946 13.6 F 1.326 11.0 1.159 29.5 G 1.149 0 1.098 25.6 H 3.929 70.0 1.470 44.4 I 1.141 0 0.851 4.0 ITS, 1998, Energy Efficiency Testing on Clothes Washers, Report No. 198004015-00l, Intertek Testing Services, Cortland, NY.

3-12

### JREFERENCES 1. Conversation with Mary K. Matimore, Manager Market Data, AHAM, 7 July 1997. 

### 2. Appliance Magazine, January 1997, p. 38 3. Association ofHome Appliance Manufacturers (AHAM), 1997Major Appliance Industry FactBook. 4. Appliance Magazine, September 1990 

### 5. Ibid. 6. Appliance Magazine, September 1998 7. AssociationofHome Appliance Manufacturers. 1997.1997MajorApplianceIndustry Fact Book Chicago, IL. 8. RevisedDraft Report on ConsumerResearch for Clothes Washers; April.3, 1998, Comment #85, Docket# EE-RM-94403 9. Ibid. [Dealerscope referenced Intellect ASW] 

### IO. EPRI, DistributionAnalysis: THELMA, P. 1-4. 11. Dealerscope, June 1998, North American Publishing Company, p. 23 

### 12. Ibid.

. 13. Elrick and Lavidge, 1996. One page memo - Washer Sales Percentage ofUnits Sold by Features. 14. Dealerscope, June 1998, North**Am~rican** Publishing Company, p. 23 15. EPRI, MarketingStrategies forHorizontalAxis Washers, December 1996, p.2-8, EPRI TR107612, Prepared 

### by Dove Associates, Inc. Boston, Massachuse_tts. 16. Dealerscope, June 1998, North American Publislting Company, p. 23 17. Appliance Magazine, September 1998, p. 72 18. EIA, RECS 1995 · 19. DraftReporton thePreliminaryEngineeringAnalysis for Clothes Washers, DOE Comment 55B, Docket No. EE_RM_94-403, November 15, 1996

3-1320. 

### DraftReport on Design Options for Clothes Washers, DOE Comment 55C, Docket No.**EE-RM-94~403,** November 15, 1996

. 3-14

### CHAPTER4: ENGINEERING ANALYSIS
4.1 INTRODUCTION TheEngineeringAnalysis determines the maximum technologically feasible energy efficiency leveland develops cost-efficiencyrelationships

## to showthe manufacturercosts ofachievingincreased efficiency. As described later in this section, manufacturers were asked to provide clothes washer. performance and manufacturing cost information for conducting the engineering analysis. Data was**-~** 

### collectedutilizingthe efficiency-levelapproach. Theclothes washermanufacturersremarkedthatthis approach was the most suitable approach for showing the manufacturing cost ofachieving increased efficiency and also provided.a high degree ofconfidentiality ofdesign. This approach consists of lettingthe manufacturers supplycost and corresponding efficiency data using any design options or designoption combinations they choose. The collected information describes manufacturer specific costs ofachieving predetermined efficiency levels. This format has the benefit ofbeing entirely compatible with data requirements for manufacturer impact analysis.


### 4.2 PRODUCT CLASSES For this rulemaking theDepartment is proposing the following classes: Compact (less than 2.0 

### ft. 3 

### capacity) and Standard (2.0 ft. 3 

### or greater capacity). Based on current information, the Department believes that there is no basis for separate product classes for H- and Y-axis clothes washers. Recent and near-term product offerings, and working prototypes ofH- and Y-axis clothes washers demonstrate large energy savings while maintaining important product features. The Department is proposing to eliminate certain previously defined classifications (Semi-Automatic Top-Loading, Front-Loading and Suds Saving) because they do not offer any added utility which is inherentlyless energy efficient and therefore would require protection from the energy conservation standards. In the May 1991 Final Rule, these classes were not subject to minimum energy c:onservation standards because they represented a small portion of the market, and there was i:nsufficientinformation to analyze them. However, the 1988 standard requiring an unheated rinse
. option is still applicableto these classes. The**D~partment**

### has furtherreviewedthis topic and believes that these products should be subject to the minimum energy conservation standards applicable to either compact or standard clothes washers.


### 4.3 BASELINE UNIT A baseline unit is the starting point for analyzing design options for improving energy efficiency. In this engineering analysis, the baseline unit represents a typical model with an energy efficiency.no lower than the minimum required by the amended National Appliance Energy Conservation Act (NAECA). New minimum standard for clothes washers (top loading, 1.6 cu.ft.

4-1or greater cap.acity) was set at Energy Factor (EF) = 1.18 cu.ft/kWhper cycle and became effective on May 14, 1994. The Department issued two new test procedures during the course of this rulemaking: Appendices'1"and 

### '1 l "62FR45484 [1]. (See Sectionl.B.2. onTestProcedure). The Engineering Analysispresented inthis document is based onthe Appendix JI testprocedure. This testprocedure calculates a MEF (Modified Energy Factor) descriptor. Unlike its EF predecessor, the MEF uses remaining moisture content (RMC) to account for energy saved due to lower drying times and temperature use factors (TUFs). Using cloth loads and different water temperatures are among the manyothersubstantivedifferences betweentheJ andJ 1testprocedures. Giventhesedifferenttesting methods and variables, there is no computational relationship between the EF and MEF descriptors. In9rderto determinethe MEFvalue for the baseline unit, clotheswashermanufacturers were asked to take a representative clothes washer with an EF as close as possible to 1.18 (current minimum EF) and perform the new J 1 procedure. 

### Ifno clothes washer was available with an EF value close to 1.18, they were asked to adjust the water volume, machine energy, and/or hot water volume to obtain an EF of 1.18. Five manufacturers (Amana, Frigidaire, GEA, Maytag and Whirlpool)submitted datato the Association ofHome Appliance Manufacturers (AHAM). AHAM mathematicallyaveraged these values to derive an industry average MEF value of 0.817 for the baseline unit (based on an EF=l.18) [2].


### 4.4
. COST-EFFICIENCY DATA COLLECTION Manufacturingcost and efficiency data was collected utilizing the efficiency level approach, in cooperation with AHAM. The manufacturers provided cost and corresponding efficiency data using any design options or design option·combinations they choose. The collected information describesmanufacturer specific costs ofachieving predetermined efficiency levels in addition to the energy-usecharacteristics. AHAMmembersprovided costs andenergydatafor the following levels: Baseline, 5%, 10%, 15%, 20% and 25% improvement applied in an existing Y-axis clothes washer and improvements of35%, 40%, 45%, and 50% applied in a H-axis clothes washer. Manufacturers believethat the disclosure oftheir individual costs is notpractical giventhe proprietary nature ofthe information. Hence they agreed to provide detailed cost information to AHAM which would then aggregate this data and provide 'industry"costs to DOE. 

### 4.4.1 .Characterizing Uncertainty In the manufacturing cost information previously supplied by manufacturers (Comment#38, AHAM), a confidence level was provided to characterize the probability of achieving the costs reported. Buildingonthis framework, DOEproposedmodificationtothe mostrecentdatacollection so that the cost estimates reported would be bound by a range. DOE requested manufacturers to provide a range of costs associated with achieving each efficiency level. Separate ranges were requested for variable and fixed costs.

### For example, for characterizing variable costs, each manufacturer was asked to provide its best point estimates ("most likely" values) for incremental material, labor and overhead costs for achievinga particular efficiency level. These disaggregated costs were then added to arrive at the "most likely" total variable cost for that manufacturer. In addition manufacturers were asked to indicate their maximum and minimum total variable costs to meet the efficiency level. Triangular distributionscreated using minimum, maximum, and "most likely"values were used to characterize the uncertainty associated withthe manufacturing costestimates. Similarprocedure was adopted for fixed costs. Figure 4.1 shows illustrative input distributions for incremental manufacturing costs. This figure assumes that data is provided by three manufacturers.

Efficiency Level1

Efficiency Level2 0

0 2 3 4 Incremental Cost ($)

Incremental Cost ($) 5 6 7

7**Manu~** Mkt 8 acturer Share A 50% B 30% c 20%

8 

### Figure 4.1 Illustrative input distributions for incremental manufacturing costs

4.4.2 Variability in Costs between Manufacturers Legend



## - -·-·



# --

The clothes washer manufacturing costs previously submitted to DOE (Comment#38, AHAM) demonstrated large variability in 

### some cases. This variability was expressed using the standard deviation in previous data submittals. DOE will not be using standard deviation figures to conductits subgroup analysis sincethesemay mask serious costimpacts, particularlyto smallerfirms. The Department requested disaggregated cost data from all manufacturers. Cost distributions, both variable and fixed, for the industry for achieving a particular efficiency level were obtained by applyingshipment-based weights to each manufacturers cost distribution. This approach captures the richness of the data provided by manufacturers and ensures that the final cost distributions incorporate each individual manufacturers costdistributions thereby accounting for variability in the data submittals.

4-3

### Table 4.1 Energy-use Data Product Characterisics Energy Use Water Use Percent MEF**TotalEne~** Clothes Hot Machine


### ...**_J:?.l::Y.~~**

### ..- Water Water ··------·----· 

## ------ -- ...

# ______ ···--··-··---····- ···································- . ····-······ lmprovemen wl dryer Container Water Energy Enerizy Use Use CU.ft./kWhfcyclE kWh/cycle cu.ft. kWh/cycle kWh/cycle kWh/cycle ga/./cyc gal.I cu

ft. [I] [2] [3] [4) [SJ [6) [7] [8]
. [9] Baseline 0.817 3227 2.847 1.587 0209 1.43 39.181 13.779 5 0.860 3.165 2.817 1.543 0209 1.413 38.613 13.732 10 0.908 3.017 2.822 1.408 0209 1.4 38.613 13.71 15 0.961 2.833 2.832 1216 0209 1.407 38.621 13.67 20 1.021 2.739 2.893 l.113 0218 1.408 38.446 13.342 25 1.089
. 2292 2.866 0.715 0.304 1273 26.6 9.22 35 1257 1.866 2.749 0.462 0.133 127 21.03 7.661 40 1.362 1.859 2.749 0.462 0.133 1263 21.03 7.661 45 1.485 1.651 2.736 0.429 0.114 1.107
. 23.405 8.57 50 1.634 1.574 2.736 0.413 0.114 1.047 23.405 8.57



### For data in columns [3), [4], [5), [6],[7),[8], and [9], companies were asked to approximate the energy component that they would use to develop the MEF shown in column [2). Where this data includes multiple products with varying clothes container capacities, for instance, a shipment weighted average within the product mix could be used.
4.5 

### DATAAGGREGATION Arthur D. Little prepared a spreadsheet to aggregate cost data obtained from various manufacturers. AHAM used this spreadsheet to aggregate the cost data obtained from the manufacturers. This aggregated data was then presented to DOE. This procedure helped address concerns ofmanufacturers concerning the proprietary nature ofthe manufacturing costs and their disclosure
.

. The aggregation spreadsheet takes each manufacturers total variable (labor, material and overhead) and total fixed (tooling, investment, R&D, and marketing) costs characterized using triangular distributions and aggregatesthem applying shipment-based weights to arrive at the total variableand fixed cost distributions respectively for the industry for achieving each efficiency level. The aggregation spreadsheet was developed using Microsoft Excel and Crystal BaIIM [3] software and has the ability oftreating custom distributions also. The following procedure is used for arriving at the industry incremental total variable cost distribution for a particular efficiency level from individual manufacturers'cost submittals:

4-50 

### •

• Each manufacturers incremental material, labor and tooling costs are added to arrive at the manufacttirers "most likely"incremental total variable cost. The "most likely" incremental total variable cost along with the minimum and maximum values ofincremental total variable costs, as reported by manufacturers, are used to create triangular distribution. for the incremental total variable cost. Samplesare drawn from each manufacturers incremental total variable cost distributions to create an industry incremental total variable cost distribution. The number of times a particular manufacturers cost distribution is sampled relative .to others is dependent on its relative market share (when compared to other manufacturers). Figure 4.2 graphically illustrates individual manufacturers' incremental total variable cost distributions and the industry total incremental variable cost distribution thus obtained (here it is assumed that the industry is made up oftwo manufacturers with eachhaving a 50% market share). The final cost distributioncan also be presented using percentile values-. 0, 10, 25, 50, 75, 90 and I00 percentiles. Crystal BallTM software was used for specifying individual distributions and for samplingthemto arrive atthe final distribution. Crystal BallTM providesthe flexibility of reporting the final distribution graphically or by specifying percentile values. AHAM provided aggregated industry cost data using percentile values. Thisprocedurewas used for arrivingattheindustry incrementaltotal variablecostdistribution for each effici.ency level. Industry incremental total fixed cost distributionare also obtained using the same steps as for the variable cost. In addition, shipment weighted averages ofthe most likely costs, both fixed and variable, were also calculated for each efficiency level as point values for fo.cremental costs.

4-6 -.Manufacturer A Efficiency Manufacturer B Level X
0 

## 1 I

### ~

### ~ 4 5 6 I 

## t Q I I I I I I Incremental Cost ($) I I I I 

# _,_, _, _, _, ;e-1**~I** ;;e I :;e I :ie1 ;;e1 ;;el o I**~I ~I** **~I** 01 ,,, I 01 81 

# -, :-1 -i**~,** !:::.1 !!?..1**~I** I I

### ~ 

## :s ftl .D 

## e Total Rel. Q. Cll Probability > ;:: = 1.0 ftl Cii a::: 1.00 2.75 4.50 6.24 8.00 Incremental Cost ($) 

### Figure 4.2 Industry cost distribution obtained by sampling each manufacturers cost distributions The results after aggregation are presented in Tables 4.2 through 4.4. Table 4.2 gives the industry cost data using "most likely"values. Tables 4.3 and 4.4 present detailed cost distributions for total variable, total fixed, and incremental total manufacturing costs using percentile values.


### Table 4.2 Aggregated Cost Data using "Most Likely" Values Product Characterisics Variable Costs Total Total Incremen. No.of Percent MEF Cost Per Unit Variable Fixed Mfg. Cost Respondents Improvement Materials Labor Overhead Costs Costs Cll.ft.lk:Wltlcycle ($per Unit) ($per Unit) ($per Unit) ($per Unit) [I) (2] (12] (13) [14) (JS] [22] (23] Baseline 0.817 5 0.860 $0.01 $0.01 $0.00 $0.02 $0.07 $0.09 5 10 0.908 $0.25 $0.04 $0.01 $0.30 $0.61 $0.91 5 IS 0.961 $3.18 $0.10 $0.04 $3.32 $1.01 $4.33 

### s 20 l.021 $10.71 ($0.02) ($1.80) $8.89 $6.21 $15.10 5 2S l.089 $36.14 $8.61 $0.09 $44.84 $19.29 $64.13
. 5 3S 1.257 $86.73 $10.94 $7.78 $105.4S $22.72 $128.17
. 

### s 40 1.362 $87.15 $10.94 $7.78 $105.87 $22.72 $128.S9 

### s 4S 1.485 $12S.S8 $1S.52 $14.SI $155.61 $24.67 $180.28 4 

### so 1.634 $132.40 $15.68 $14.51 $162.59 $24.67 $187.26 4

4-7

### For column [ 12], companies were asked to indicate incremental costs associated with materials that would be needed to achieve the particular standards levels in $ 

### per washer unit. For column (13), companies were asked to indicate the incremental cost oflabor associated with the new technology to reach the particular standard levels in $ 

### per washer unit. For column [14], companies were asked to indicate the incremental overhead cost associated with this standard level. On column (14), for example, additional electricity to produce new components may be considered as overhead. For each of these columns, entries on the baseline line are not necessary. ·However, companiesthatmay h,ave variable costincreases necessary to bring all production up to**th~** 

### minimum ofMEF=0.817 were asked to include this cost in the 5% improvement line.

4-8i**~---.,....-----~--------** ------ ---------·



### Table 4.3 Industry Cost Distributions for 5%, 10%, 15% 20% and 25% Improvements applied to an existing V-axis Clothes Washer Product Characterisics Total Total Incremental Percent Percentile MEF Variable · Fixed Manufacturing Improvement Costs U) Costs (q) Costs (b) cu.ft./kWhlcycle ($per Unit) ($per Unit) ($per Unit) Baseline 0.817 5 100 (Max.) 0.860 $0.24 $0.87 $1.J l 90 $0.01 $0.18 $0.18 75 $0.01 $0.01 $0.02 50 $0.01 $0.01 $0.02 25 $0.01 $0.01 $0.02 IO . $0.01 $0.01 $0.02 0 $0.00 $0.00 $0.00 IO IOO <Max.) 0.908 $6.75 $10.84 $12.84 90 $1.26 $1.09 $2.71 75 $0.49 $0.30 $0.96 50 $0.01 $0.06 $0.07 25 $0.01 $0.05 $0.06 10 $0.01 $0.01 $0.02 0 $0.00 $0.00 $0.00 15 100 (Max.) 0.961 $40.00 $10.84 $46.19 90 $3.96. $4.36 $14.08 75 $0.76
. $0.30 $0.97 50 $0.65 $0.15 $0.80 25 $0.58 $0.12 $0.72 10 $0.01 $0.01 $0.02 0 $0.00 $0.00 $0.00 20 100 (Max.) 1.021 $70.00 $20.60 $79.45 90 $27.78 $15.63 $38.48 75 $14.13 $11.75 $27.74 50 $0.72 $0.33 $1.06 25 $0.65 $0.26 $0.95 IO ($0.53) $0.24 $0.90 0 ($12.20) $0.21 $0.80 25 100 <Max.) l.089 $83.28 $27.63 $102.63 90 $70.40 $25.36 $93.97 75 $67.24 $24.04 $90.85 50 $63.46 $22.14 $86.26 25 $15.11 $14.39 $29.37 IO ($0.30) $14.07 $16.13 0 ($12.20) $4.85 $1.28



### 4-9

### Table 4.4 

### Industry Cost Distributions for 35o/o, 40°/o, 45% 

### and 50°/o 

### Improvements applied to a H-axis Clothes Washer Product Characterisics · Total Total Incremental Percent Percentile MEF Variable Fixed Manufacturing Improvement Costs {j) Costs (q) Costs {b) cu.ft./kWh/cyc/e ($per Unit) ($per Unit) ($per Unit) Baseline 0.817 35 100 (Max.) 1.257 $280.00 $101.60 $381.60 90 $228.80 $25.28 $254.01 75 $118.63 $23.29 $143.34 50 $69.06 $21.41 $90.56 25 $63.59 $19.44 $85.30 IO $60.38 $17.20 $81.74 0 $52.03 $15.06 $67.43 40 100 (Max.) 1.362 $290.00 $101.60 $391.60 90 $229.28 $25.28 $254.44 75 $117.17 $23.30 $143.57 50 $69.05 $21.41 $90.54 25 $63.73 $19.44 $85.28 10 $60.44 $17.15 $81.74 0 $52.03 $15.06 $67.43 45 100 (Max.) IA85 $300.00 $101.60 $401.60 90 $239.76 $28.23 $266.26 75 $227.29 $23.70 $251.73 50 $128.38 $19.89 $153.71 25 $79.38 $18.02 $97.37 10 $69.25 $16.95 $87.36 0 $52.30 $15.06 $68.26 50 100 (Max.) 1.634 $300.00 $101.60 $401.60 90 $239.46 $28.29 $266.11 75 $227.31 $23.76 $251.70 50 $132.89 $19.89 $157.87 25 $95.45 $18.00 $113.81 10 $84.95 $16.94 $102.98 0 $66.16 $15.06 $82.12

4-10

### The AHAM data submittal was used to develop a cost-efficiency graph. This graph is presented in Figure 4.3 

### AHAM Clothes Washer Data 450 400 4-
;;; 350

...... en 

### !!? 300 

# - 

# - •**~** 250 

## • • )( 4-100% (.) • 90% :! 200 X75% c ><50% cu &25% 

### E 150 )( )( )( 

### f 

# - • 10% (,) 

# • .. •0% 

### .E 100**~** >< 

### • ·SWA 4- ,..... 

# • • 

# • 50 4- 

# •

0



### .... CIC ..... ..... O> 

### .... N Ll)**~** ..... co 0 co N co LO co co <""> CIC co O> 0) C! C! C'!**~** -.i: <'! d d d d ..... ..... (Baseline) (5%) (10%) (15%) (20%). (25%) (35%) (40%) (45%) (50%) Modified Energy Factor 

### ll?igure 4.3 Cost-Efficiency graph for clothes washers·


### 4.6 RETAILPRICE The results ofthe Engineering Analysis are summarized in the Cost-Efficiency graph using percentilevalues, as presented in

### Figure 4.3. Manufacturerand retail markups, discussed in Chapter 

### 6 ofthis document, 

### will be applied to these total incremental cost distributions to determine the purchase price (i.e. price to the consumer) ofclothes washers. These consumer prices will theme used along with energy-use data to perform LCC analysis.


### 4l.7 PROPRIETARY DESIGN At the November 1996 workshop, it was acknowledged that Whirlpool had four patented proprietaryprototype designs which include both V- and H-axis platforms. Whirlpool indicated that these were working prototypes. In a letter dated November 29, 1996, DOE communicated the patents numbers ofWhirlpools designs to the workshop participants. (DOE, No. 57). During the 4-11workshop, Whirlpool asked that the designs be included in the rulemaking analysis. Whirlpool also indicatedthatitwould be appropriatetoconductan independent study to estimatethe manufacturing costs ofthe new designs. (Whirlpool, No. 55L at 169). Whirlpool didnot see the practicality ofeach manufacturer estimating the cost ofthe Whirlpool designs, since estimates by other manufacturers would only be based on patent information and could not be expected to produce consistency in approach or a high degree ofaccuracy. (Whirlpool, No. 69 at 4). · Maytagcommented thatthe Whirlpool designs needed to be subjected to a full and complete engineering and cost analysis by DOE. Maytag requested that all manufacturers be given the opportunity to participate inthis process since the costofapplying these designs to a manufacturers own basic washer design varies greatlyfrom manufacturer to manufacturer. (Maytag, No. 64 at l). GEAalso statedthatthe analysis neededto be expandedto coverthe designs disclosedby Whirlpool.

_ It further stated that only a revised method focusing on the technical know-how, manufacturing

capabilitiesand economic strengths ofindividual manufacturers would permit the proper evaluation ofthe impacts on**·~typical** manufacturers." (GEA, No. 63 at 7). Inresponse the Department conducted a 'tear-down"manufacturing cost assessment ofone ofthe V-axis Whirlpool prototype. The main objective ofthe manufacturing cost assessment, is to quantify the differential manufacturing costs o#>roducing high efficiency clothes washers based on (l) the Whirlpool proprietary V-axis design, and (2) commercially available V- and H-axis designs. See Chapter 5 for further details.

. 4-12REFERENCES

1 I. U.S. Department ofEnergy (DOE) 1997. 

### Energy Conservation Programfor Consumer Products: Test Procedurefor Clothes Washers and Reporting Requirementsfor Clothes Washers, Clothes Dryers, andDishwashers. 2. Association ofHomeAppliance Manufacturers (AHAM). December 2, 1997. Letter from Wayne Morris (Technical Director--Regulatory Affairs, AHAM) to Bryan Berringer (Program Manager, Clothes Washer Rulemaking, U.S. DOE). 3. Crystal BalfM.

4-13

### CHAPTERS: MANUFACTURING COST ASSESSMENT
5.1 INTRODUCTION At the November 1996 clothes washer'workshop, it was acknowledged that Whirlpool has four patented proprietary designs which include both V- and H-axis designs. In order to include Whirlpools proprietary V-axis designs in the rulemaking analysis the Department ofEnergy decided to conduct a 'tear-down" manufacturing cost assessment ofthe Whirlpool Y-axis prototype. Arthur D. Little (AOL) was chosen tp conduct this study. This chapter presents the Phase I ofthis study.


### 5.2 PROGRAM OBJECTIVE The main objective of this project is to quantify the differential manufacturing costs of producinghigh efficiency clothes ·washers based on ( 1)the Whirlpool proprietary V-axis design, and (2) commercially available H-axis designs. The overall project consists oftwo phases: 

### Phase Iprovides a detailed and objective costestimate for two state-of-the-art, high volume, V-axis washer as a baseline for further analysis. The major objective of this phase is to obtain stakeholder comment on the costing methodology and baseline costs. 

### Phase II develops a differential cost estimate for the proprietaryY-axis design and for two commerciallyavailable H-axis clothes washer_, relative to the baseline clothes washer evaluated in Phase I. This chapter describesthefinal results of Phase I. Several manufacturing plant visits were · conducted along with process equipment and supplier interviews, sensitivity analysis on critical cost

parameters, and incorporation of stakeholder suggestions from the March 11-12, 1998, workshop.


### 5.3 PHASE I OBJECTIVES
• Developa clear and consistent manufacturing cost assessment methodology for clothes washers. • Provide detailed manufacturing cost estimate of a high production volume 'typical" clothes washer for a baseline estimate. • Report the results ofthe analysis in a way that preserves confidentiality ofdata. • Obtain input from all stakeholders as to the validity of the methodology and on the 'reasonableness"ofthe baseline manufacturing cost estimates.

5-1

### 5.4 ASSUMPTIONS 5.4.1 Baseline Definition· Two clothes washers were selected to be used in the baseline cost assessment. They are amongthe top selling machines as reported in a recent market surveyby Intellect, Inc. ·Two machines were disassembled, rather than one, in orderto reflect product differences between**manufac~urers** in the baseline V-axis washer, and to then aggregate the actual component costs to maintain confidentiality. Each clothes washer was analyzed and costed separately. The costs were then
. averaged at the sub-assembly level and by category. Since different manufacturers have significantly differentdesigns, sub-assemblies aredefinedby function (i.e. suspension, tubandbasket,transmission and 111otor, etc.), rather than by specific components. In Phase II, the differential costs will be calculated by estimating thecosts ofthe proprietary Y-axis design, and two commercially available H-axis designs. This will be done on an add and omit basis using each baseline V-axis machine. From this analysis, an aggregated differential cost will be generated. Once again, the results will be aggregated at the sub-assembly level and by category. In addition, each machine will be tested to obtain its energy consumption, according to DOE test procedure, Appendix J1. The following is a list ofthe key characteristics describing the baseline clothes washers: Baseline**Cloth~s** Washer Characteristics: • 27"overall width • 3**wash/rin~e** temperature settings • Unheated rinse • 3 Load Sizes Settings • 2**wash/~pin** speeds • 8 or more wash cycles • Super capacity wash basket


### 5.4.2 Manufacturing Assumptions Key assumptions used inthe Phase I manufacturing cost assessment include:
• An annual clothes washer production volume of 1.5 million units. Future analysis· will include separate baseline cost estimates for a smaller and larger volume manufacturer. • Capacity utilization at 95 percent. (Sensitivity to capacity utilizationwill be conducted in order to understand the impact on the baseline cost.)
. • Investmentfor a ':greenfield"site, including all new equipment, tooling, installation, and building. • Full or semi-automation ofthe manufacturing facility fonriajor fabrication processes (painting, stamping, welding etc.). • Complete manual final assembly to reflect the need for flexibility as multiple models are built. It is understood that the cost ofspecific models (or individual manufacturers) will vary from
5-2 ,•this baseline, depending on elements such 

### as the products precise characteristics, actual manufacturingprocesses, and the**produc~** mix in the factory (multiple clothes washer and dryer products). However, 

### as this set ofassumptions will remain constant for the Phase**~I** analysis, the differential cost estimation methodology reduces the impact ofplant-specific characteristics.


### 5.4.3 Conventions for Manufacturing Cost Estimates The cost categories usedto summarize the manufacturing costs developed in Phase I closely parallel the cost categories used in the AHAM data collection. Direct material, direct labor and overhead(includinginvestmentdepreciation)comprisethefulfproductioncosts. Othercostelements (Sales General 

### & Administration, R&D, interest, etc.) are groupedunder the non-production cost
- category. TQgether these costs make up the full cost ofthe product 

### as shown 

### in Figure 5.1. The manufacturingcostassessmentwill developthefull productioncostestimates. Non-productioncosts will be added based on a top-down analysis ofindustry structure·.

· Full Production Cost 1 Non.Production Cost

1 Tax Reform Act of 1986, essentially, requires companies to measure cost of goods sold as the full production cost of the goods so 

### Figure 5.1 Full production cost

5-3

### 5.5 APPROACH The manufacturing cost assessment methodology used is a detailed, component-focused, activity based technique for rigorously calculating the manufacturing cost of a product (direct materials, direct labor and some overhead costs). This methodology has been successfully applied by Arthur D. Little to products at various stages ofdevelopment, from early R&D to production. Figure 5.2 shows the three major steps in generating a baseline manufacturing cost.

Raw 

### Fabrication Finished Materials Materials



## - Processes 

# - Bill of 

## - Assembly Materials Purchased Parts 

### Processes



# - Figure 5.2 

### Manufacturing cost assessment approach
5.5.1 Bill 

### ofMaterials (BOM) The first step in the baseline cost assessment was the creation ofa complete and structured billofmaterials from the physicaI teardownof two highvolumeV-axis clothes washers. To conduct the tear down, two Y-axis clothes washers were purchased and completelydismantled. Each part was then characterized accordingto weight, dimensions, material and quantity. A separate BOM for each clothes washer was created. TheBOMincorporatesallmaterials, components, andfasteners withestimatesof rawmaterial costs or purchased part costs, as appropriate. Assumptions on the sourcing ofparts and in-house fabricationwere based.on previous industryexperience, recent infolJllation intrade publications and discussions with major clothes washer manufacturers. To improve our understanding of the industrys current manufacturing practices, interviews and plant visits were conducted at major clotheswashermanufacturingfacilities. Thesevisits focused onobserving and characterizingcurrent manufacturing practicesfor the majority ofthe parts in the baseline clothes washers. For internally fabricated parts, manufacturing processes were identified on the 

### BO:M for each component. Cost estimates for raw materials and purchased components were drawn from Arthur D. Littles manufacturingdatabasesand supplementedwithinformationobtainedfrommanufacturerand supplier sources. Adjustments to the cost estimates for purchased parts and raw materials were made, as appropriate, to include price discounts typically available at the purchase volumes in question. Figure 5.3 is a sectionfrom the structuredbill ofmaterials ofone baseline clothes washer. This section of the structured·bill of materials lists the part number (arbitrarily defined during

. disassembly), description, material category, sourcing category (fabricated or vended), quantity,

dimensions, weight, percent ofscrap in processing, and estimated material cost.

5-4UNI I I I llub & tsaSKet A11SemD1y I Part Flnol ou 1388.00 y OD or Welrbl (lb•) &crap M1ceri1I No. Dcscrlptin 1800.00 Coterory 

## ' • Widlb Tltkou Leartb Steel Otber ".4 Cost, S 1.01 "l>b "'°"" 1 22.5 0.09 22.5 5.520**J."'<1~1:l't':t;f:** $3.31 1.03 Water Level 11ox · r"""'c 1 2.1:. O.Oo:> 1.t:> 0.066**,1,;<::.:?}i$,~'.S:~f.** I0.04 1.02 "l>b to S.spensoon Sear Ribber 1 2.6 0.3 1.22:> 0.066**~"1~~Q~,%~,f.** S0.03 1.01 Bleach tube Ribber 1 0.6 o.w :>.2 0.036**~i@.Ml~~li.** S0.02 1.30 Backet kEv 1.201 BaSl<l>t I fnamld Seel I I 1 I 20.5 I 0.055 I 18 I 13.500 I I 15% I $4.13 1 n1 Lint Collector I l'laSlic I I 1 I I I I I u...**,~ l~i~.&.;:~t•**  I0.29 1.211 Bani Balancing IU\gw/WBter In it I Plastic I I 1 I 20.5 I 0.09 I 2 I I 1.400**l'lil<ffi'.l~lll** S0.84
1.SOA Fabnc 01spenaer • lDp t"laStic 3.81 0.05 1.6 0.108**~~** S0.05**~stic** 1 3 u.u:. 

### "·" O.u74**-~~~~** 0.03 1."'1C Fabnc Oispen•r· Bollom F'laslic 1 4 u.06 5 0.148**~~1~£** 0.07

1.51 Clutch Cover lastic a.z:> 0.11:> 0.9 0.030**g~;··-f.!t~** 0.02 1.52 Clutch Cover Seal rubber 3.15 0.16 0.010**~~** 0.01 1.75 LowerAoltatorAny UICI Bottom Ao1tator 1:1eamg t'lastic l**2.~:.** u.2s v.w1o 'Rl'!r.'MG Ml.U1 1.59 Bottom Allitator Plastic 1 13.5 0.15 1a 1.816**~ft=~;bf.:** S0.82 1.58 '-'>pelfLower Agitator 11eanng Plastic 1 2.6 U.U:> 0.1**·~** 0.016**~** ...**~~~~~;** I0.01 1.57 '-'>per Agitator Plastic 1 3.75 0.1 11.:> 0.934 Wi<SW. S0.42 1.15 Aaltator Clutch Aacv**1.'.~~~:-~~:f,~~L-?** ..**~·f;;:.;~~J;~~~:.~M>1t.'1~1t,.~~lli~~"f~~·~~~J.v:~r·~'(~~:,::-1\=;~t~T.ft:?fa:~~l'f~~%-~~a:s.N!f-0f£:·ffa** 1.85 Aaltatar Clutch "'-•- 1.561 Clutch Body I rwmic I I 1 I 2.83 I o.08 I 2.26:. I I 0.0:>2**H~=~=/1{.!'i~I** S0.02 1.551 Ratchet Sops I I 4 I 0.4 I u.1:> I 0.9 I I 0.016 I**}1:0.~!~'t~1** I0.01 1.541Cluteh Bottom Cover I nasllC I I 1 I 1.75 I u.o:. I**o.~** I I O.uua**1t~~~'.j?.;1** SO.OU Sub-Totals 22 13.50 10.79 S10.12 

### Figure 5.3 

### Structu.red bill ofmaterials

5.5.2 

### Fabrication Processes Aftereachdetailed BOMwas completed, the majormanufacturingprocesses wereidentified. These processes are listed in Table 5.1. 

### Table 5.1 ·MajorManufacturing Processes**m011~~4f~~~W'5%t~<eftSS~efF.abiiCatiODV~~~ft~j~~~!£4tZ9-ff.5~,~~~~&1.$£ifilS1fili{~ti~l!?ill~ft7!i~S%~SSeiiiblflJOlilihg,£fug~f:!~-t~;** Fixturing Washing Adhesive Bonding Stamping/Pressing (Large, Medium, Small) Powder Coating Spot Welding Brake Forming Enameling Seam Welding Cut & Shearing Deburring Integral Fastener (i.e. clinching) Machining Other Fasteners Injection Molding (Large>1500 ton, Small<800 ton) Press Fitting Casting In5pection & Testing Informationonequipmentandtoolingcosts,typicalprocessingcycletimes, andmaterialsused for fabrication were obtained from the ADL manufacturing databases. Equipment suppliers were contacted for further details concerning equipment capabilities and processing parameters (cycle times, scrap rates, etc.). The fabrication process cycle times were directly entered in the BOM and linkedto the specificparts being fabricated. Eachparthad a processingtime and laborcost assigned based on an overall labor rate. For this analysis $24.00 was usedas the average fully burdened labor rate based on typical hourlywages and benefits for union employees. Sensitivity analysis was conducted onthis important 5-5parameterand is presented in Section 5.7.2. Thisparameterwas investigated becauselaborrates can vary considerably, depending on geographic location, availability ofskilled labor, and level ofunion**represen~tion.** Figure 5.4 contains an excerpt ofthe non-assemblyprocessing times (seconds per process) associated with each component listed on the left. This section ofthe model has all the processes · listed inTable 5.1, but for brevity a single page was presented in Figure 5.4. Figure 5.5 is from the equipmentsummary section, whichincludes total equipment and summarized laborrequirements for each fabrication process.
1.00 I I I tTub & Baskat Assembly Part Flnal &SI 1388.00 No. Ducrlnlion 1800.00 1.01 1Ub 1.03 Water Level Bi:u 1.02 lUb to S.spension Seal 1.07
. Bleach tube

1.49 Un_,. A111tator Assv 1.50A Fabric Dispenser-Top 1.SOE Fabric Dispenser - Cun 1.SOC Fabric Disoenser-Bottom

1.51 Clutch Cover**--~~~~··¥~~~ -~~ttk~$** **~~~}.7.-~ ¥-~it~** **~~~r~~- ·~~~~** **Mi..~"l'~ i.~~~:** 

. 1.52 Clutch Cover Seal**~~ ~"'"'M"""°** **~·~** ·**~?ff.'i'MWi.J!!** 'IJii$:!.G1%'"' 1.75 Lower Aaltator**~**

1.85 Aaitator Clutch Assv 1.561 Clutch Ebdv 1.551 Ratchet Sops 1.541Clutch Ebttom Caver Sib-Totals
. 62.0 4.0 10.0 30.0 3.0 

### Figure 5.4 Fabrication process cycle time estimates

Figure 5.5 Fabrication process equipment and labor requirements

5-6In addition to the process time and _labor costs associated with-fabrication, labor times for assembly associated with each part is included.
5.5.3 Assembly Processes Inthe final step ofthe costa5sessment, assembly times and associated direct laborcosts were estimated. Assembly cycle times were primarily derived from reversing the disassembly of the selected tear-down clothes washers. The assembly sequence was built into the structured BOM, culminating with final assembly, testing and packaging. Oncethe costestimates for eachmachine was finalized, a detailed summary was prepared for ' relevant components, subassemblies and processes. The structured bill ofmaterials and manufacturingprocess summaries provide detailed direct material,direct labor, indirectmaterial, and investmentcosts. Indirectlabor costs were estimated by subtractingthedirectlaborhours, determined fromthebottoms-upanalysis, from thecurrentave.rage total labor hours per unit (1.8 hours). The current average labor content was derived from a top down analysis ofseveral household laundry equipment manufacturing plants and can be confirmed withdata from the 1992 Census ofManufacturers [1]. Although the 1992 Census would indicate an averagelaborcontentinthe household laundryequipment segmentto be2.1 labor-hours perunit, we believe 1.8 hours is more accurate, as a savings of0.3 hours perunit, or approximately 14% for the industry over the past 6 years, could be expected. This generated an indirect labor-hour per unit, whichwas multiplied by the average laborrate ($24) in orderto estimatethe indirect laborcost. The average direct labor hour perunit was 1.3 hours; therefore the indirect labor content was calculated to be on average 0.5 labor hours per unit. The indirect labor is included in the overhead estimate, along with indirect materials, utilities, depreciation, maintenance, taxes, and insurance. The direct labor cost total is approximately $31.20 per unit, while the indirect labor costs are estimated to be $11.96 per unit at the fully burdened $24 per hour rate. ·
5.5.4 Baseline Manufacturing Assumptions Table 5.2 lists the key manufacturing assumptions used in the baseline manufacturing cost assessment.



### -S-7

### a e 

# . ey 

### T bl 52 K As

### sumptlons**-.:.~ ~**  •' ,,......

# 'WI·~~**~·· ~~Pt'-~~~~~~~** **~E~~mr~,.~.~~~~;r~w~11~** "" ' Work Days per Year 249 davs Avera2e Shifts Der davs 2.0 shifts• Runtime per Shift 8 hrs Lot Siz.e 1 workdav Eauipment Uptime 90% Assembly Downtime 20% Wa2es $24 per hour Capital Recovery Rate 0%/not used in analvsis Eauipment Depreciation Life 10.1 vears Total Plant Deoreciation (includes building and conveyor) 17.1 years Maintenance 4% ofEauipment Cost Utilities 1.7% ofMaterials Cost Taxes 0.9% ofFactory Cost Insurance 0.8% ofFactorv Cost Capacitv Utilization 95% *Fabrication, 2.5 Shifts, Assembly 1.0 Shifts 

### These financial and operational assumptions are based on general industry practices, determined from manufacturer interviews, 1992 U.S. census data, and internal Arthur D. Little sources. 5.6
. RESULTS Table 5.3 presents a summary ofthe full production cost, built up from material, direct labor

.artd overhead costs, for thebaseline V-axis clothes washer. These costshave been aggregated based on the two machines ·that were dismantled. 

### Table 5.3 Cost Components for Baseline V-Axis Clothes**Was~er** 

### ($/Unit)**~~;Qc?JX~!suii:Weffii>fi~~~~~¢~fJi1'.YM:aie'fiil1it@ti:Dite~\t.a6or.".:7rf0rov.effi'~ll'<f;ii;(i;ts,~iFUiiffirb"'dlfc'.ti6'tiz;w5f11tJ~~** Tub & Basket $17.64 $8.56 $11.99 $38.18 Transmission/Motor $35.78 $7.38 $15.09 $58.26 Suspension $5.57 $2.52 $3.99 $12.08 Pumps, Controls, and Valves $33.13 $3.38 $3.67 $40.18 Cabinet $18.10 $5.50 $13.85 $37.45 Misc. & Final Assembly $10.75 $3.90 $1.77
. $16.42**~~1.'*i#r1Xcid,~~'fr'Jl:~~1#~".\rr'f¥f's"'t2if9·:;:r:~~~f0¥"'~1·'!:?<;~..J\.%~5"'0~1!;'6°~f@t~6iFW&*-~2·"0"2<,'i'~,.<.fr~~****t2 ~gJ,,'-l[)t8** **~~:.:.x.»~**......**~<;.:;m. 4'(:W:;.:~;<.<;;$u~..>.~~** .........,M'·" , •**':.:f~.-;-:"9~il~~** ....**~:ww.v;.;~ :~--~-**  ...**~;$7J:g[_')_~** . "1**1.J~i;,:;,,..,.~~~!'l-,.·-.v'=""'",..,....;,.~-.;J** ,**~{,,.,.,.~**.. :<¥
...**~"-'>«:{,.'-::.~·-··~** 

### A detailed breakdown ofthe overhead costs and the key assumptions for each cost category are presented in Table 5.4. These costs are estimated based on the bottoms up cost analysis and typical values for the appliance manufacturing industry. Taxes are not directly**rela~ed** 

### to a product design, therefore no change between the baseline and any higher efficiency washers is anticipated. Also, maintenance, utilities, and insurance are primarily related to equipment quantity and cost of investmentand therefore are indirectly driven by product designs. We have included these costs to**~** 

### estimatethe production costofthe baseline but anticipate that variationwith future higher efficiency designs will have a secondary effect on differential cost. 

### 5-8

### Table 5.4 Overhead Cost ofBaseline Clothes Washer**~1IkWI~~i€0S11Ci~goeyr#?Sit~~~r~~~!~~TCOset~'l~~r£~i~i~1~~3~;@h~t~~118s~\1~::t:\~iBi0~2if~W~~ifJY~** Indirect Labor $11.96 Teardown analysis Indirect Materials' $17.45 Teardown analysis Maintenance $3.25 4.0% oftotal yearly capital investment Utilities $2.35 1.7% ofmaterial costs Depreciation $11.90 Teardown analysis Taxes ·$1.82 0.9% offull production cost Insurance $1.62 0.8% offull production cost**~oYir<b-~%1111*~~~~-~~~~~@J!lr~~~Yk~:,<4~fffe1:l:~~11~T~1tE~itliii7~1~~Ji~~~**

Although the overhead costs are slightly higher in this analysis than the top-down industry analysis,much ofthis difference cari be attributed to the significant investment in a greenfield site. The allocation of direct and indirect labor can 3Iso vary with any single companys individual practices.·Fixedinvestmentsinequipmentandtoolinghavebeenestimatedbasedontherequirements derivedfrom the componentcycletimes inproductionvolumes. Productionvolumes, inconjunction with equipment process times, will determine the numberofpieces ofequipment necessary and the plant investment costs. For the sizingofthe plant and the number ofpieces ofequipment, we have assumed anup-time of90%. Table 5.5 shows the investment for a plantcapacity of 1.5 million units per year. 

### Table 5.5 Fixed Costs millions for Baseline V-Axis**Cloth~s** 

### Washer



### ..**,s11i>assemDJiesTf!itftt:;~'Wikl&.~J.:~~r<E9·wp1ii'ent'-&~t<1oling1\I~ui.iding:a.1111tco11veiot:":i\Jr"tL1ltr;toi8iiDvesimenf~';'.~'**

Tub & Basket
.$36.58 $34.85 $71.43 Transmission/Motor $37.82 $36.04 $73.86 Suspension $7.35 $7.01 $14.36 Pumps, Controls, and Valves $3.56 $3.39 $6.96 Cabinet $30.16 28.74 $58.90 Misc. & Final Assembly $0.42 0.40 $0.82**19".,.i.'""'"'c,;.y&c!tltttl··"·'·"*",*'•V*"""*-M"'~,.,qw.w,._1,,zt~·:i,c:ts9"ih~lli'""'"~i,£.e"'JiW,·,$"''l'O'****v,,34'flW·'1··li'"•"'·"·"·'!.~!'t!~~~2""'L'1;~illi···~:i,"i!l;:rr.'Cim****~~'Q1.cu:./~.:.f~h~~Vit~~lnfuli,~t_Wf4~*llfa.ti',i.~~.;;** ·**.~. on&~G~Wi~~-NJltJ** . .**1~** ...**~~- ~** **on.tl~<&<iillt<~j.p· ~~x~z:m** **_on~t:T~** Thebuildinginvestmentcurrentlyassumes 1,000,000squarefeet at$100/sq.

### ft. and 10.7miles ofconveyor at an average price $185/foot. A multiplier of 1.6 (additional 40% for installation and 20% for auxiliary equipment) was used on the purchased equipment costs to calculate the total equipment investment. Because ofthe high volume production,hard tooling is included in indirect material costs and is amortized on a yearly basis. We have assumed that 

# the useful life of the equipment and building on average is 17.l years (which agrees with the 1992 Census of· Manufactureri; [ 1]) and we used a simple straightline depreciation assumption to calculatethe yearly amortizedperunit cost. These assumptions canvarygreatly, and have a significanteffectonthetotal investmentand the depreciation element ofthe overhead costs. Because ofthis, these assumptions willbethe subject offurther sensitivity analysis. Also we have not included the cost ofcapital inthe products full production cost, since this cost is an interest which is considered inthe estimate ofthe non-productioncost. Forfuture analysis,the methodologycanapplya capital recoveryfactor (CRF) to the total investment in order to estimate the yearly cost ofcapital, but at this time we have not included it in the baseline units full production cost. Thefull costof theproduct, whichincludes 

### SG&A, R&D,**~d** interest costs (Figure 5.1), 

### will
5-9be arrived at using the industry cost structure developed 

### in Section 3.10 ofthis document. This manufacturing cost assessment will estimate the direct materials, direct labor and overhead for a baselineunit which, according to the industry cost structure, represents approximately 77% ofthe full cost ofa product.


### 5.7 VARIABILITY AND UNCERTAINTY IN MANUFACTURING COST Since the current cost assessment represents only a fraction ofthe baseline clothes washer products available, and the.estimates have inherent variability and uncertainty, we have conducted extensivesensitivity and cost simulations to understand the impact ofthis variability and uncertainty onthe baseline manufacturing cost. This analysis will provide us with a greaterunderstanding ofthe · range ofprobable costs, and to identify key cost elements may require further critical review and investigation. We use primarily two methods to analyze the variability and uncertainty in the manufacturing cost assessment:
• Sensitivityanalysisis usedtoqtiantifytheimpactof variability and uncertainty for any one specific parameter.
• To understandtheinteractionand impactof multipleparameters weuseMonte Carlo simulations. This analytical technique allows us to input a range ofdata with probability distributions and generate an estimated range for the baseline manufacturing cost with a quantifiable level of certainty.


### 5.7.1 Key Parameters The physical teardown and activity based cost estimating conducted on two actual clothes washersremovesmuchof theuncertaintyaboutmaterials,components, andmanufacturingprocesses. Many of the manufacturing processes, however, carry some variability based on individual manufacturers and product differentiation. This results i_n some variation in the total capital investment, the utilization ofthat capital, maintenance:;osts, and equipment uptime. Also average labor rates for different manufacturers can vary, so sensitivity analysis to determine the impact on costs is warranted. Inadditionto the variability in for example, inputparameters, a certain degree ofuncertainty accompanies the estimation of manufactμring process capabilities and equipment investment estimates. The impact ofthis uncertainty is quantified by using sensitivity analysis.


### 5.7.2 Sensitivity Analysis Wehave conductednumerous sensitivity analyses and have selectedthe mostappropriate and
5-10significantparameters that will impact full production cost. The sensitivity results are presented for an aggregated baseline Y-axis machine. LaborRate The 1992 Census data for household laoodry equipment manufacturers indicates an average fullyburdened laborrate of$18 perhour while some uriion wages could be

## as high as $28 perhour. Sensitivity analysis was conducted on this parameter and the results are shown in Figure 5.6
.



## ...**~** 1215.00 In 

## 3

.. 12'0.00 Cll CL 

# - :g aas.oo (,) c**~** UDD.00 u**~** 'C 

## e StOS.00 Cl. 0 

## "' ::I Stl0.00

$115.00 

### " 

# .. .. .. Labor Rate (USO/hr) 

### ·Figure 5.6 Sensitivity analysis on labor rate . 21

The manufacturing cost model indicates that a $17 to $28 labor rate variation would result In approximately a $20 differential in full production cost. Capacity Utilization (Plant Output) Oneimportant source ofvariability is the utilization ofa plants capacity. Sensitivity analysis on capacity utilization is presented 

### in Figure 5.7.

5-11215.00**~** 210.00 i i i u 205 00 

# I
. l**~** 200.00 :>

1115.00

180.00

I00.000 1,000,000 1.100.000 1.200,000 1,300,000 1.400,000 Actual WHhera Bullt per Year In 1.IMM p.a. Faclllty 

### Figure 5.7 Sensitivity analysis on actual plant output 1.500.000

If

the greenfield baseline clothes washermanufacturing plant slipped to 60% ofcapacity this would result in an increase ofapproximately $10 per unit. Total Investment There is a considerable amount ofuncertainty in estimating the equipment requirements of the total plant and the total cost. Sensitivity analysis was conducted on this parameter. In the sensitivityanalysis total investment was varied considerably from -50% to+150% ofthe aggregated baseline cost estimate of$226 million. The results are presented in Figure 5.8.

$220.00

S215.00 -! $210.00 i i. i**~** 1205.00 

### f 

### ! f: 1200.00 ::>

1195.00

50% 10% 10% 80% 

### '°"' 100% 110% 120% 130% 140% 150% l•'ff•tnwnt F•ctor (PHcent of E•tlm•t•d lnvestm•nt) 

### Figure 5.8 Sensitivity analysis on total investment cost 5-12The x-axis representsa**percentag~** ofthe baseline cost ofapproximately $226 million. It is apparent from the sensitivity analysis that this large variation in equipment cost could have a significant impact ($15 out of$202) onthe full production cost. 

### EquipmentAverage Depreciation Life The depreciation life ofa piece ofmanufacturing equipmenthas been investigated, and there stillremains a significant degree ofvariability between manufacturers. To understand the impact of this variability, sensitivity analysis was conducted and the results are presented in Figure 5.9
..

$220.00

1215.00 

### -! 1210.0D 

# ' l i ': $205.00 R 

# I
L i $200.00 ::>

1195.00

1100.005014

- 10%

- -· 100% 110% 120% 130% 1'0% 150% A""V- Oeprec.. tfon Ltfe Percent of .. ttmet.) 

### Figure5.9 Sensitivityanalysis on equipmentdepreciation life We assumed that the average depreciation life on the equipment could vary from 7 to 15 years, which translates into a cost differential ofapproximately $11 per unit. Maintenance Maintenance cost values do not change dramatically 

### in the appliance industry but the uncertainty in.the estimates warranted an investigation ofits significance (see Figure 5.10).

5-13l205.00

s:zoc.oo .i 

### i i s=.oo 

### J 

# I J ROZ.00 

# .. 2 

# ..
SZOl.00

szao.ao2.5
... •.SY. ..ln.teuttee u Perc.ntage of e:qulpaaent Depreciation 

### Figure 5.10. Sensitivity analysis on maintenance 5.0%

Ourcostassessmentwould indicatethatwithintherange oftypical maintenance costs (2-6% ofequipment investment) there is not a significant impact on full production cost (approximately a range of$2). 

### 5.7.3 Monte Carlo Simulation To evaluate eachoftheseplus several otherparameters simultaneously and to understand the cumulative effects of variability and uncertainty we conducted a Monte Carlo simulation. This process generates random inputs within a prescribed distribution which then completes numerous iterations(10,000)to quantifytherange of possibleoutcomes andtheprobability ofeachoccurrence. For the simulation we made the fol.lowing assumptions:

Labor Rate Uniform $17 to $28 per hour Plant Output Triangular 900,000 to 1.5 million 1.425 million Equipment Depreciation Life Uniform 7 to 15 Total Investment Multiplier Triangular 50%to 150% 100% Maintenance Uniform 2.5% to 5.5% Equipment Uptime · Triangular 70%to95% 90% Utilities Triangular 1%to 3.5% 1.7% By randomly generating values in the key parameters of Table 5.6, a distribution of full production cost was generated as shown 

### in Figure 5.11. Based on this analysis, Figure 5.11 shows 5-14-- ------- ----------------------------- --

that the variation inkey parameters mentioned inTable 5.6 for.washer manufacturing results in an aggregated baseline clothes washer cost between $188 and $217 with 95% certainty. This range represents a variationof+or- $16 perunit (8.5%). This analysis canberepeated for differentranges in the key parameters, alternative probability distribution functions and for additional parameters if
. warranted. · · Forecast: Aggregated Baseline Cost 10,000 Trials Frequency Chart .023
.017

### ~ 

# - .c .012 

# = .c c 

# ... .006 

### c.
.000**~** $180.00 $191.25 $202.50 ($/washer) $213.75 $225.00
Between $188 and $217 with 95% certainty 

### Figure 5.11 Monte Carlo simulation ofaggregated baseline cost
5.8 CONCLUSION AND PHASE II PROGRAM PLAN 20 Outliers 232

- 174 

# ..,, 

# ... n

- 116 .c 

# = n 

## =

- 58**~**

- 0

Arthur D. Little has completed a manufacturing cost assessment of two Y-axis baseline clothes washers and estimates the aggregated full production cost at 

### $202.57. Sensitivity analysis andMonte Carlo simulations wouldindicatethecostestimates would fall between $188 and 217 with 95% certainty. This estimate is based on the key assumptions outlined in this Phase I report, interviews and feedback from stakeholders and Arthur D. Littles industry knowledge. Basedonadditional feedback from any stakeholders, ArthurD. Little canconductalternative analyses on the baseline clothes washer, butat this time, we have initiated Phase II to estimate the differential costs of several higher efficiency designs relative to the aggregated baseline cost developed in Phase I.

5-15REFERENCES I. 

### U:S. Department ofCommerce, Bureau ofCensus. 1995. 1992 Census of Manufacturers, 

### MC92-I-36B. Washington, D.C.

5-16

### CHAPTER 6: MARK-UPS FORPRICE DETERMINATION
6.1 OVERVIEW The following figure illustrates the representative prices and.mark-ups for a typical V-axis**cloth~s** washer being sold in the U.S. marketplace:

Manufacturing Manufacturer Cost**1------'----~i** Price 

## t------''---...-' $211.50 $285.90 Retail Price $400.25 

### Figure 6.1 Average costs and mark-ups for the baseline clothes washer Consumer Price $421.10

To perform Pre-ANOPR analysis, the Department proposes a 2-step mark-up approach where: (a) the manufacturing costs (i.e., full production costs) are marked up to the manufacturer price 'ilsing a manufacturer mark-up; and (b) the manufacturerprice is marked up by a retail mark-up to arrive at the retail price. The price paid by the consumer includes the sales tax in addition to the retail price. This sales tax is accounted for by using a sales tax mark-up over the retaiprice ofthe clothes washer. The Department proposes a fixed retail mark-up of 1.40, and a fixed mark-up of 1.052 to cover the sales tax. The manufacturer mark-up over full production costs will be bound by 

# a maximum value of1.35whichmaintains**in~ustry** (manufacturer) coststructure, and a minimumvalue of 1.00 whichrepresents apass-through offull production costs including depreciation ofnewcapital but no recuperation ofnon-production costs. Based on the information above, the total mark-up from full production costs to consumer

1 In this description a typical unit refers to a high production volume model with high market share (industry average); notnecessarily a minimally complying unit under NAECA, or a unit with an MEF value of0.817 (AHAM baseline). Currently no data exists that would allow the Departmecyt to associate sales information on the basis ofthe MEF descriptor to be used in the present rulemaking. Inthe absence ofspecificprice information for a baseline (0.817 MEF) clothes washer, tvpical values will be used as default values for the baseline model Since the life-cycle-cost (LCC) analysis uses differential costs only, the absolute**vah.~e** ofthe baseline unit cosfhas no impact on LCC and payback analysis. The absolute valueof the baseline unitcostis important, however, ifprice survey information is used to compare prices derived from a mark-up over cost with prices observed in the market. 6-1price varies from a minimum of 1.473 to a maximum of 1.990. The Department proposes to characterize the total mark-up using a triangular distribution with the above stated minimum and maximum values and a most likely value which is the average ofthe two extremes. Thistotal mark-updistribution will be usedbythe Departmentas a means of estimating future consumer prices ofclothes washers. Section 6.2 of this document presents a discussion onretail mark-ups, presenting current and historicalretail mark-ups for clothes washers. Section 6.3 presents a briefdiscussion on sales tax and· its effect on consumer**pric~.** · Section 6.4 outlines the Departments methodology for estimating manufacturer mark-ups.


### 6.2 RETAIL MARK-UPS The.objective ofthis section is to estimate the manufacturerprice-to-retail price mark-up for clothes washers from the available sources ofprice information.
6.2.1 Background One measure ofthe effect ofappliance efficiency standards on the consumers is the change (increase) in the purchase price of the appliance. A more stringent standard usually results
. 

### iri increased manufacturing costs tothe manufacturers. These increased costs (and associated margins) are passed-through from the manufacturerstothe retailers and eventuallyto the consumerinthe form ofhigher purchase prices
.

.. Itis important to identify what proportion ofthe standards-induced costs will be reflected in retail prices, as this mechanism is important for the rulemaking in two ways: a lower margin over incremental manufacturing costs passed-through justifies a less stringent standard from a manufacturer impact standpoint; andhas the opposite effect ofloweringconsumerLCC thatjustifies a more stringent standard from a consumer perspective. Retailers .buy appliances (clothes washers inthis case) from manufacturers and sell them 

### to consumers at higher prices to cover their operation costs and profit margins. The amount by which the manufacturer price (price paid bythe retailer to the manufacturer) is marked up by the retailer to arrive at the retail price is referred to as the retail mark-up. Inthis sectionwe estimatethemanufacturerprice-to-retailprice mark-up for clothes washers (i.e., the retail mark-up) based on available historical market information. Sources ofinformation for the retail price ofclothes washers include:

6-2(1) Consumer Expenditure Surveys, conducted by the Bureau ofLabor Statistics in conjunction with the Bureau ofCensus [1].
. (2) Dealerscope Merchandisings Annual Statistical Surveys [2] (3) ELCAP price database [3] Sources ofinformati.on for the manufacturer price ofclothes washers include: {1) Current Industrial Reports, U.S. Department ofCommerce [4] (2) Association ofHome Appliance Manufacturerss Fact Book, 1997 [5] Sections 6.2.2 and 6.2.3 present the retail and manufacturer price data, respectively, along with short descriptions ofthe data collectionprocedures and the characteristics ofeach ofthese data sets. These data sets are then used to arrive at the retail mark-ups which are reported and compared in Section 6.2.4. Retail prices from each data source will be divided by manufacturer prices from each oftwo sources for manufacturer price, thereby resulting 

# m six different sets ofestimates for retail mark-ups. Section 6.2.5 presents an analysis offinanCial statements (SEC 10-K reports) ofa few appliance retailers to get an understanding ofgross margins in the appliance retail business.


### 6.2.2 Retail Price Information 6.2.2.1 Consumer Expenditure Surveys (CES) Consumer Expenditure Surveys (CES) provide information onthe buyinghabits ofAmerican consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. The survey consists oftwo components: a quarterly Interview Survey and a weekly Diary Survey, each with its own questionnaire and sample. Data collection is carried
·out by the Bureau ofCensus under contract with the Bureau ofLabor Statistics (BLS). The data are collected in independent Quarterly Interview and· Weekly Diary surveys of approximately 5,000 sample households. Each survey has its own independent sample, and each collectsdata onhousehold income and socioeconomic chaiacteristics. TheInterviewsurvey includes monthly out-of-pocketexpenditures, suchas housing, apparel, transportation, majorhome appliances, health care, insurance, and entertainment. The Diary survey includes weekly expenditures of frequently purchased items, such as food and beverages, .tobacco, personal care products, and nonprescription drugs and supplies. Table6.1 shows the unweighted average purchase price ofclothes washers as reported in the ·consumer expenditure surveys. These values represent the price to consumers net ofa trade-in and represent purchases by individuals. These valuesare reported for the period oftime 1986 through 1994. Table 6.1 also shows the retail price ofthese washers. The retail prices were estimated by subtracting the sales tax from the purchase price paid by consumers (based on sales tax rates and shipments ofclothes washers in all the 5_0 states, a shipment weighted average sales tax of5.2% was 6-3calculated). 

### Table 6.1

Retail Price 413.56


### 6.2.2.2 Dealerscope Merchandising Statistical and Marketing Reports Dealerscope is the monthly trade publication for retailers of consumer electronics, major appliances, and computers. The magazine covers product news, industry trends, and new technologies, along with reports on the marketing, branding, merchandising, and retail strategies of vendors and dealers: Dealerscopereportscontainone-yearandfive-yearunitshipmentsindustry-Wide andtheretail value ofthese shipments. 

### In these reports the data is based on information provided by leading manufacturers. Companies are asked to indicate how much the overall total shipments and average retail prices have changed for the industry, and for the product category. Manufacturers are not asked for the annual changes intheir owncompanies

## sshipments orinretail prices of theirownbrand of**merchandise~** Table 6.2 provides the retail price ofclothes washers estimated from the statistical reviews. The estimated retail value ofclothes washers shipped from factories is divided by the clothes washer units shipped industry wide to arrive at the per unit retail price.



### 6.2.2.3 ELCAP Clothes Washer Sales Information The ELCAP2 service collects sales data {retail prices) on**clothe~ashers** from a number of retailers- consumer electronics

-superstores, appliance stores, ·and mass merchants. Table 6.3 presents shipment weighted retail price for. the years 1994 through 1997. (The 1997 retail price is based on sales data through August 1997.) ·

2ELCAP service was discontinued atthe end of 1997. Clothes washer retail sales are currently being tracked by INTELLECT ASW.

- . 

### 6-4

### •. Table 6.3 Shipment Weighted Retail Prices ofClothes Washers from 1994 throu h 1997 based on ELCAP Retail Sales Data $! 

## !J.&1- ,.**at~r**

# ··

6.2.3 Manufacturer Price Information 6.2.3.1 Current Industrial Reports (CIR) The CIR program 

### has been providing monthly, quarterly, and annual measures ofindustrial activity for many years. The primary objective ofthe CIR program is to produce timely, accurate data onproduction and**shipme~ts** ofselected products. Thedata are used to satisfy economicpolicy needs and for market analysis, forecasting, and decision making in the private sector. The CIRs include statistics for the United States onthe quantity and value ofshipments along withcomparative data on exports, imports, and domestic output. The CIR program uses**~** unified data collection, processing, and publication system. The census bureau updates the surveypanels for mostreports annually and reconciles the estimates to the broader-based annual survey of manufactures and the census of manufactures. The census of manufactures provides a complete list ofall producers ofthe products covered by the CIRprogram and serves as the primary source for CIR sampling. The CIRprogram includes a group ofmandatory and voluntary surveys. Typically, the monthly and quarterly surveys are conducted on a voluntary basis. Those companies that choose not to respond to the voluntary surveys are required to submit a mandatory annual counterpart corresponding to the more frequent survey. The figures on quantity and value ofshipments represent physical shipments ofall products sold, transferred to other establishments ofthe same company or shipped on consignment, whether for domestic or export sale. The value represents the net sales price, f.o.b. plant, to the customer or branch to which the products are shipped, net ofdiscounts, allowances, freight charges, and returns. Shipments to a companys own branches are assigned the same value as comparable appropriate allocationofcompany overhead and profit. Productsbought and resold without further manufacture are excluded. Table 6.4 provides the estimated manufacturer price ofclothes washers. The value ofthe clothes washers shipped from factories is divided by the quantity ofshipments to arrive atthe per unit manufacturer price. 

### Table 6.4 Manufacturer Prices ofClothes Washers from 1986 through 1996 estimated from Current Industrial Reports
.

6-5

### 6.2.3.2 AHAM Fact Book The

### AHAM Fact Book is a comprehensive collection offacts defining the U.S. major home appliance industry, its market and its products. The Association lists both:
• the total manufacturers'sales ofclothes washers (excludes freight charges or extended warranty costs), and • the industry unit shipments (domestic and exports) ofclothes washers. The above stated informationcanbe used to arrive at an average per-unit manufacturerprice for clothes washers, presented in Table 6.5.

, 6.2.4 Retail Mark-ups from Manufacturer and Retail Price Information I
. Table 6.6 shows average retail mark-ups over the 9-year period, 1986 through 1994, for clothes washers sold in the U.S. The retail mark-ups in this table are obtained using manufacturer and retail price information obtained from governmental sources- retail price information from the Consumer Expenditure Surveys conductedbythe BureauofLabor Statistics; and manufacturerprice

- information from the Current Industrial Reports prepared by the Bureau ofCensus.

6-6

### Table 6.6 Retail Mark-ups for Clothes Washers from 1986 

### through 1994 

### using Retail Prices from the Consumer Expenditure Surveys, and

M f: 

### t 

### p· ti 

### th C tld t•IR 

### rts anu a:c urer rices rom e urren n us r1a epo Year Retail Manuf. Retail Price Price Markup (CES) (CIR) 1986 410.6 272.6 1.51 1987 410.9 266.9 1.54 1988 416.0 262.0 1.59 1989 420.4 277.3 1.52 1990 422.3 278.1 1.52 1991 422.1 275.9 1.53 1992 413.7 277.6 1.49 1993 398.7 283.8 1.40 1994 413.6 279.0 1.48
Table 6.7 gives the retail mark-ups estimated using price information from different data sources explained earlier inthe text, and the same information is presented graphically in Figure 6.2. For the period 1986 through 1996, the average retail mark-up varies from a minimum of 1.33 (in 1995) to a maximtim of 1.62 (in 1988).


### Table 6.7 Retail Mark-u s estimated from Different Data Sources Year Retail Retail Retail Retail Retail Retail Average Mark-up Mark-up Mark-up Mark-up Mark-up Mark-up Retail (Dealerscope) (Dealerscope) (CES) (CES) (ELCAP) (ELCAP) 

### Mark-up CIR AHA CIR A CIR 1986 1.65 1.61 1.55 1.51 1.58 1987 1.67 1.65 1.56 1.54 1.61 1988 1.64 1.68 1.55 1.59 1.62 1989 1.46 1.46 1.51 1.52 1.49 1990 1.43 1.47 1.47 1.52 1.47 1991 1.44 1.50 1.47 1.53 1.49 1992 1.31 1.38 1.42 1.49 1.40 1993 1.31 1.35 1.36 1.40 1.36 1994 1.32 1.41 1.38 1.48 1.31 1.41 1.39 1995 1.30 1.44 1.23 1.36 1.33 1996 1.36 1.41 1.39

6-71.8 1.7**~.6** 1.5 1.4 1.3 1.2 1.1 1.0

1986 1988 1990 1992 Year -._.Dealerscope-AHAM 

### --+- Dealerscope-CIR -CES-AHAM -*-CES-CIR -e- ELCAP-AHAM -+- ELCAP-CIR
1994 1996 

### Figure 6.2 Retail mark-ups estimated from different data sources
6.2.5 Retail Mark-ups estimated from SECs Financial Statements Information relating to the gross margins (or gross profit) of appliance retailers can be obtained from their SEC 10-K financial statements. Table 6.8 presents gross margins for some appliance retail companies (Best Buy, Circuit City Stores, Lowes, and Sun T.V. and Appliances). Most ofthese retailers sell electronics and home office equipment along with household appliances. The margins in Table 6.8 represent the average ofthe margins that retailers obtained from sales in all the above mentioned categories. On average, appliance sales are more profitable (i.e., obtain higher margins) than sales of personal computers; T.V.s and other electronics due to the intensely competitive and highly promotional pricing environment in the electronics retail**enviro~enf.** As a consequence, some retailers are trying to further increase their margins by expanding the appliance part oftheir business and by putting greater emphasis on the sale ofextended service contracts.4

3 Source: Standard and Poors Stock Reports on Circuit City Stores and Best Buy.
4 Source: Standard and Poors Stock Reports on Best Buy. 

### 6-8

### Table 6.8 Gross Margins for Retailers ofElectronics, Home Office Equipment · and Ma·orA liances**~~**
26.3% 15.0 16.2% 

### 9.0 1.19 25.7% 17.5 1.35 Typicallyintheretail industry, grossprofits, asreported inSEC 10-Kstatements, are obtained by subtracting the 'bost ofsales" from the ''net sales." ·Dividing gross profit by the net sales gives the gross margin. Gross margin represents the mark-up that retailers receive over the total ofthe: • price that the retailers pay to the manufacturers for buying the goods, • costs involved inthe process ofbuying the goods from manufacturers (these might iriclude some transportation costs, administration and labor costs), and • costs of maintaining and operatingwarehouseswherethe goods.are stocked afterthey are bought from manufacturers. From the above description ofgross margins, it canbe seen that the retail mark-ups (defined earlierinthis write-up as the ratio ofretail price to manufacturerprice) will be, in general, higherthan
· whatthe gross margins indicate. This is because inadditionto profits and SG&A, the retail mark-ups

will also have to cover the costs incurred by retailers for buying and warehousing activities. The buying and warehousing costs as a percentage ofthe net sales or revenues are not available.


### 6.3 SALES TAX Consumers pay sales tax in addition to the retail price atthe time ofpurchasing an appliance. This additional margin for sales 

### tax needs to be reflected in the mark-up from manufacturer price to consumer price. For the year 1995, the shipment weighted sales 

### tax on clothes washers was calculated to be 5.2%. This information is based onindividual sales taxrates for 50 states and weighted bythe clothes washer shipments (source: AHAM) in each ofthese states. Hence, a mark-up of 1.052 will be used to cover the sales 

### tax. In the year 1994, for which retail price is available from all the three sources, namely, CES [l), Dealerscope [2], and ELCAP [3], the average clothes washer retail priceis $400.25. Looking

.at Tables 6.2 and 6.3, there seems to be no discernible change in clothes washer retail prices since**1994~** Hence (in the absence ofprice information specific to clothes washers with an MEF value of

s Retail mark-up= 100/(100-Gross margin). Mark-up for clothes washers (appliances) should be higher given the product mix. 

### 6-90.817), the Department proposes to use $400.25 as the baseline retail price for the clothes washer rulemakinganalysis. Adding a 5.2% sales tax would result in a baseline consumer price o:f$421.10. Looking at Table 6.7, the retail mark-up, on average, in the past three years has been around 1.40. Adding a sales tax of5.2% ontop ofthe retail price would result in a manufacturer price-toconsumer price mark-up of 1.473. This mark-up of 1.473 is in agreement with the mark-up of 1.475 used in the 1991 report to AHAM [6] e.ntitled 'Financial Impact ofDOE Top Loading Horizontal Axis Standards on U.S. Washing Machine Manufacturers." Hence; the Department of Energy proposes using a retail mark-up ofl.40, and a sales tax mark-up of1.052 to arrive at the consumer price for the clothes washer rulemaking analysis.
6.4 MANUFACTURERMARK-UPS The objective ofthis section is to outline a methodology for estimating the mark-up from manufacturing cost to manufacturer price for the clothes washer rulemaking analysis.


### 6.4.1 Background Inordertomeetthe newappliance efficiency standards, manufacturers may have to introduce
· designchanges intheirproductlines. These design changes usually result in increased manufacturing

costs. The increased manufacturing costs are 'passed-through"from manufacturers to retailers and eventually to consumers in the form ofhigher purchase prices. In this section the manufacturing cost to.manufacturer price mark-up is estimated. This is referredto as the manufacturermark-up. The manufacturermark-up has an importantbearing onthe manufacturerss profitability, as a highermark-up suggests that manufacturers canpass-through the
. variablecosts and some ofthe product conversion.costs (the fixed costs) to retailers and eventually tci consumers; whereas a lower mark-up itμplies that manufacturers will not be able to fully recover the necessary investment in plant and equipment.
6.4.2 

### Manufacturer Price and Mark-up for the Baseline Standard Clothes Washer The average retail price ofa clothes washer in 1994 was $400.25 (as discussed in Section 6.3). Using a retail mark-up of 1.40 (also discussed in Section 6.3), the baseline manufacturer price would be $285.90. Manufacturing cost estimates for the baseline clothes washer can be estimated from the baselinemanufacturerprice ($285.90) andthe industry cost structure developed in Section3.10. This is shown in Table 6.9. The break-upof.the price is based on the assumption that in order to earn a satisfactory return, revenues from the sale ofgoods (and hence the manufacturerprice) mustbe large 6-10enough both (I) to recover costs and (2) to earn a profit that provides a satisfactory return on investment. 

### Table 6.9 Manufacturing Cost ofa BaseUne Clothes Washer estimated from the Industry Cost Structure**~iltrftmttii'1tit"** 

## ..**~ ~tf$ii;**  Material 

### f48.7 Labor 

### 29.3 Overhead 

### 33.5

The full production cost ofa baseline clothes washer is estimated to be$211.50. The full production cost (also called the inventory cost) and the non-production cost together constitute the 

### full cost ofa product. Full production cost is the sum ofthe direct material cost, the direct labor cost, and the overhead cost which includes depreciation 

### (-3.5% ofrevenues). The non-production costs include selling costs, general and administrative costs, research and development costs, and interest costs. The full cost ofa product and its components are shown in Chapter 5, Figure 5.1. Baseline manufacturer mark-up is defined as the increase in price overthe full production cost ofa baseline clothes washer, and is calculated as: 

### Baseline Manufacturer Mark-up = 

### Manufacturer Price Full Production Cost A baseline mark-up is applied overthe full production costto arrive atthe manufacturerprice at which the firm can recover all costs, both production and non-production, and earn a profit. In general, the manufacturer mark-up should ensure that the manufacturer price ofthe product is high enough (1) to recover the full cost ofthe product and (2) to yield a satisfactory profit. Such a price is called a

### normalprice. 6 A baseline manufacturer mark-up ofl.35 overfull production costis calculated from the cost . information in Table 6.9.

6 Some people question whether full costs are widely used as a basis for pricing. In a survey oflarge industrial companies, 85% reported that they do use full costpricing: (Source: V. Govindarajan and Robert N. Anthony, ''How Finns Use Cost Data in Pricing Decisions,"ManagementAccounting, July 1993, pp. 30-36.) 6-11

### 6.4.3 Manufacturer Price and Mark-ups for Different Efficiency Levels 6.4.3.1 Pre-ANOPR Analysis For the ANOPR analysis the Department will use an average manufacturer mark-up of 1.35 as an upper bound in conducting the LCC analysis. The Department will use a mark-up of 

### I.DO as a lowerbound. Thislowerboundrepresents pass-throughof incrementalfull productcosts, including depreciation ofnew capital expenditures. There is, however, no recuperation ofincremental nonproduction costs (which include R&D and marketing expenses). In orderto characterize the uncertainty inmanufacturer mark-ups, the Department proposes usingatriangwardistributioncharacterizedbyamaximummanufacturermark-upofl.35,aminimum manufacturer mark-up of 1.00,"and a most likely mark-up of 1.18 (the average). Adding a fixed mark-up to retail price (of 1.40) and then to consumer price (mark-up of 1.052), as discussed in Section 6.3, the total mark-up from manufacturing cost to consumer price would be bound by the following values:
. • minimum value of 1.00 x 1.40 x 1.052 = 1.473 • maximum value of 1.35 x 1.40 x 1.052 

## = 1.990 The Department proposes to**characteri~e** the total mark-up using a· triangular distribution with minimum, maximum, and most likely values of 1.473, 1.990, and 1.732, respectively.


### 6.4.3.2 Post-ANOPR Analysis Post ANOPR analyses will.use a consistent set ofassumptions for prices across all analyses·
·. sections (manufacturer impact, national benefits, and consumer impacts). Manufacturer and retail price will be related by a fixed mark-up of 1.40, as discussed in Section 6.3 ofthis document, and a sales tax mark-up of 1.052 will be applied to arrive at the consumer price. Whereas the development ofprice scenarios for the manufacturer impact analysis will be determined ata later date, one possible approach that is being considered is the approach used in the 1991 Arth:ur D. Little report to AHAM. This approach is discussed below: 

### • For the detailed economic analyses conducted Post-ANOPR, manufacturer mark-ups will be determined by conducting financial analysis. The Government Regulatory Impact Model (GRIM)will be used to carry outthe cash flow analysis. The GRIM is a standard annual cash flow analysis which**~es** price, quantity, and cost information to assess the impact of regulatoryconditions onmanufacturerincome and cashflow. The model calculatesthe actual cash flows, by year, and then determines the present value ofthose cash flows, both without regulations and with regulations.



### 6-12The post-standard prices required in order to achieve several scenarios will be found by running the GRIM and treating manufacturer price as variable. Scenarios that will be considered include: · (a) What price (mark-up) results in maintenance ofcuqent industry value? 

### (b) What price (mark-up) would reduce industry value to zero? (c) What price would result from pass-through ofincremental material, labor, and overhead costs only?

6-13

### REFERENCES l. 

### U.S. Department ofLabor, Bureau ofLabor Statistics. Consumer Expenditure Surveys, Washington, D.C. 2. DealerscopeConsumerElectronicsMarketplace.AnnualMajorAppliance**~tatistical**

### Survey andReport. 

### 3. Elrick & 

### Lavidge Computerized Audit Program (ELCAP) monitors the major domestic appliance industry in the U.S. INTELECT, a leading supplier of retail sales tracking information for the consumer electronics industry, acquired ELCAP on August 5, 1996. ' 

### 4. U.S. Department ofCommerce, Bureau ofCensus. Current Industrial Reports, 

### MA36F, Major Household Appliances. Washington, D.C. 5. Association ofHome Appliance Manufacfurers. 1996 and 1997MajorApplianceIndustry FactBook, 

### Chicago, IL. 6. Arthur D. Little, Inc. 1991. Financial Impact of DOE Top Loading Horizontal Axis Standards on U.S. Washing Machine Manufacturers. 

### Report to Association of Home Appliance Manufacturers Horizontal Axis Task Force. Cambridge, MA.

6-14**---------------~-----** --- - ----- - - --------



### CHAPTER 7: LIFE-CYCLE COSTS AND PAYBACK PERIOD
7.1 INTRODUCTION This chapter describes the method for analyzing the economic impacts ofpossible standards on individual consumers. The effect of standards on individual consumers includes a change in operating experue (usually decreased) and a change in purchaSe price (usually increased). This chapter describes three metrics used in the consumer analysis, to determine the effect ofstandards OJ;l 

### individual consumers:
• 

### •

• 

### Life-cyclecost (LCC) captures the tradeoffbetween purchase price and operating expenses for appliances. Paybackperiod (PBP) measures the amount of time it takes consumers to recover the assumed higher purchase expense of more energy-efficient equipment through lower operating costs. RebuttablePayback Periodis a special case. ofPBP. Where LCC and PBP are estimated over a range of inputs reflecting actual conditions, Rebuttable Payback Period is based on laboratory**c~nditions,** 

### specifically, DOE test procedure inputs. These are discussed in sections 7.2, 7.3 and 7.4 ofthis chapter, respectively. Inputs and preliminary results are presented for each metric. Key quantities, current assumptions and calculations are · detailed for each metric. The calculations discussed here are performed on a series ofMicrosoft

ExcelTM spreadsheets which are aceessible over the internet. Details and instructions for the spreadsheets are discussed in section 7.6. A more complete set ofresults is available inAppendix C.


### 7.1.1 General Approach for LCC and Distribution PBP In recognition that each household is unique, variability is analyzed in order to account for many ofthe differences among them by performing the calculations detailed here for a large sample ofindividualhouseholds. The results are expressed as the number ofhouseholds having impacts of particular magnitudes. The LCC and PBP model is being developed using Microsoft ExcelM in Windows 95TM, combined with Crystal BallTM (a commercially available add-in). Theanalysis explicitly specifiesboththe uncertainty and variabilitY inthe modelsinputs using probability distributions. A Monte Carlo simulation then takes thousands ofrandom samples from each probability distribution within the model to calculate the outputs. Each input probability distribution is sampled in a way that reproduces the distributions' shape. The distribution ofthe ·resultsthereforereflectstheprobabilityofthe valuesthatcould occurgiventhe range ofinputvalues. This techrlique helps provide an insight into the likelihood ofvarious possible results.

7-1Theresults aretypicallydisplayed as distributions of the impactexamined as comparedto the baseline. Animportance analysis can be produced which shows relative contribution by each ofthe inputs to the total range ofresults. A variety ofgraphic displays will be created to illustrate the
. implications ofthe analysis results. Examples would be: i) a cumulative probability distribution showingthe percentage ofAmerican households which would have a net saving by owning a more energyefficientappliance orii) a frequency chartthat depicts variation ofthe life-cycle cost for each efficiency level considered.


### 7.2 LIFE-CYCLE 

### COST (LCq.

·7.2.1 Defmition

·Life-cyclecostis the total consumerexpense overthe life ofan appliance, includingpurchase expense and operating expenses (including energy expenditures). Future operating expenses are discounted to the time ofpurchase, and summed over the lifetime oftheappliance. Life-cycle cost is defined by the following equation: 

### 0 LCC=P+'L I 

### (l+r)' (1)

The following quantities are required to determine life-cycle cost: P = Purchase expense 

### ($) 

# L. = Sum over the lifetime, from year 1 to year N, where N 

## =lifetime ofappliance (years) 0 

## = Annual operating expense 

### ($) 

### [Note: for clothes washers 0 

## = (cycles peryear)* {(energy per cycle) * (energy rate) 

### + (water per cycle) * (water rate)}]
r 

## = Discountrate (real)



### 7-2

### 7.2.2 LCC Inputs This sectionprovides information aboutthe quantities and assumptions used to calculate lifecycle cost for clothes washers. For each quantity, the discussion includes:
• definition •**~pproach** 

### • current assumption Inputs for the LCC analysis are shown in Table 7.1 below.

. .



### 7.2.2.1 Definition 

### Table 7.1 Quantities in LCC Spreadsheet Purchase expense ($) Lifetime (years) Cycles peryear**En~rgy** per cycle (kWh/cycle) Electricity rate (cents/kWh) Gas rate ($/MMBtu) Oil price ($/MMBtu) Waterper cycle (gallons) Water rate ($/thousand gallons) Discount rate Water Heater/ Dryer Share Year to Start Date Base Case Design Standard Case Design


### Purchase expense
Consumer expense for purchasing an appliance. · Approach 

### Consumer purchase expense includes the retail price ofthe appliance plus sales tax. 

### The baseline purchase .expense is the retail price plus sales tax for a clothes washer at the baseline efficiencylevel. Thebaselinepriceis a shipmentweighted average (SWA) price of washers currently being sold. Efficiency and price are not necessarily related for clothes washers; price is more a function offeatures and brand name.

7-3

### Example of Baseline Purchase Expense (all values in this description are rounded to the nearest dollar): Baseline retail price = $400 Sales tax (U.S. average)= 5.2% Therefore, baseline purchase expense = $421 The purchase expen.Se for each efficiency level is calculated as the sum ofbaseline purchase expense plus the incremental purchase expense for the efficiency level. .The incremental purchase expenseis the product ofincremental manufacturing cost, manufacturer variable markup, and retail variable markup, plusthe sales tax. The Association ofHome Appliance Manufacturers (AHAM) provided incremental manufactllring costs for 9 efficiency levels. For example, the incremental manufacturing cost (shipment-weighted average) for a 35% energy reduction from baselineis $128, with a custom distribution provided by AHAM. The mean manufacturervariable markup is 1.175 (range of 1.0 to 1.35, triangular distribution), retail variable markup is 1.40, and sales tax adds 5.2% ofthe retail price. The incremental purchase expense is: $128 x 1.175 x 1.40 x 1.052 = $222. The purchase expense for the unit having a 35% energy reduction is the baseline purchase expense plus the incremental purchase expense= $421 

### + 222 = $643. A distribution ofprices is used for the LCC analysis based on a distribution ofincremental manufacturingcosts and a distribution ofmarkups. Single values are shown in this discussion for illustration purposes only. 

### CurrentAssumptions AHAMdata is used for manufacturing costs (see Chapter 4). Arthur D. Little (ADL) report data is used for markups and sales tax (see Chapter 

### 6).


### 7.2.2.2 Lifetime 

### Definition The period oftime the clothes washer will provide service. 

### Approach Thelifetime is estimated bycomparinghistorical shipments to percentofhouseholds owning the appliance (MTSC 1980)[1]. 

### CurrentAssumption For clothes washers, lifetimes range from 12 to 16 years, with an average of 14.1 years. Figure 7.1 shows the percent ofwashers surviving since year oforiginal purchase. ·

7-4100% 90%

- 80%

- 

### '""' 

# = 70%

- 

# ·- ... 

# ·- t 60%

- 

# = ,.,, 50%

- 

# - =**~** 40%

- CJ 

# ..**~** 30%**~** 20%

- 10%

-

0%

- 

# ll

- I 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Years since original purchase 

### Figure 7.1 Percent ofclothes washers surviving since year oforiginal purchase 

### 7.2.2.3 Cycles peryear Definition 

### Loads oflaundry washed per household per year. Approach 

### The DOE test procedure assumes 392 cycles per year. In actuality, the number ofloads of laundry washed per household peryear depends upon the number ofpersons in the household and on other factors. A survey ofU.S. households indicates a relationship between family size and cycles per year [2]. · Using the sample ofhouseholds having both a clothes wa.Sher and a clothes dryer from the Energy Information Administration 1993 Residential Energy Consumption Survey (DOE/EIA 1995)[3], we assigned these cycles per year to each household according to the persons per household. The weighted average result is 379 cycles peryear for the U.S. mix ofhouseholds. This value agrees closely with (is 4% lowerthan) the DOE testprocedure. To the extent that households use different fuels for water heaters and clothes dryers, the cycles per year also differ among those households. CurrentAssumption 

### To simulate a sample ofhouseholds, each household is assigned a given number ofcycles per year based upon the following table (the values are then scaled so the average agrees with the test 7-5procedure assumption). Figure 7.2 shows the percent of households (i.e., the weighting of households) for a given number ofwasher cycles per year.



### · Persons per household 1

rtJ 

## ... G.l 40% 

# i 30% 0



## ... Cl.I .:: rtJ 0: 20%**~** . 

## ""' 

# = 

# ... = t 10%



## ... G.l**~** 2 3-4 5+

207 

### Cycles peryear 

### 200 310 

### 454 624

321 Washer Cycles 469 

### Cycles per year (scaled) 

### 207 321 

### 469 .645

645 

### Figure 7.2 Percent ofWasher Owners by the number ofwasher cycles peryear
7.2.2.4 Energy per cycle Definition Energy consumed per cycle. One cycle corresponds to one load oflaundry. 

### Approach In. accordance with the DOE test procedure (for modified energy factor, MEF), energy consumptionper cyclejncludes electricity usedbythe clothes washer, and.energy used bythe water heater to provide hot water, and energy used by the clothes dryer to dry the clothing. 

### CurrentAssumptions Each ofthese components ofenergy consumption per cycle is obtained from data submitted by AHAM, for both the baseline and the various efficiency levels (see engineering analysis). Figure 7.3 shows the energy use per cycle vs. MEF
.

. 7-6

## - " 3.5 3.2 3.2



# - 3.0 CJ ,,....**~** 2.5 

# -=**~** 

# - 2.0**~** fl'J 1.5 ::::> 

# to 1.0 

# ...**~** 

# =**~** 0.5 0.0 0.82 0.86 0.91 0.96 1.02 1.09 1.26 1.3 6 1.49 1.6 3 

### MEF Figure 7.3 Energy use per cycle vs. modified energy factor (mef) 

### 7.2.2.5 Electricity rate Definition Thevalueto a residential customerofsavingelectricity, expressed as centsperkilowatt-hour. 

### Issues Allhouseholds with clothes washers use electricity for the washer, and some households use electricityfor water heating and clothes drying as well. Showing the change the customer will see in his/her electricity bill due to increased energy efficiency is complex because: 1) electricity rate varies from house to house, depending upon the rate schedule charged by the utility and the consumptionpattern of the particularcustomer; and 2) future rates cannotbe known with certainty, especiallysince the marketis currentlyrestructuring and a growing number ofcon,sumers are able to choose among competing utilities. 

### A.pp

### roach
_ The variations in electricity rate are handled by separating the problem into three parts: a) variabilityin energy rate among households; b) value ofsavings; and c) future trends. Each ofthese parts is discussed and current assumptions described.

7-7 1'·**~** GI !! iii 

## e 0 

### .s 

### .., 

# - z: Cll 'ii**~** 

### a) Variability. · The Energy Information Administrations 1993 Residential Energy Consumption Survey gathered information on electricity bills which is used to represent the variability from house to house (DOE/EIA 1993)[4). An advantage of using a survey containinginformation on specific households is the ability to directly capture correlations among quantities. Such correlations are important forelectric and gas prices, for example, or among household characteristics (e.g., fuel ofthe waterheater and clothes dryer, persons per household) that affect energy consumption. Currentassumptionregardingvariabilitv: AllthosehouseholdsfromtheEnergyInformation Administrations 1993 Residential Energy Consumption Survey (RECS) survey which have both a clothes washer and a clothes dryer are used. Energy prices are reported inthe survey results for each household. These households are assigned weights to represent the U.S. Figure 7.4 shows residential electricity prices ranging from 2 to 17 cents/kWh. Prices are derived by scaling 1993 RECS householdenergy prices to 1997$ using the consumer price index arid then--to bring these prices in line with known national energy trends--scalinghe 1997$ prices by the ratio of 1997 to 1993 national energy prices as reported in AEO 1998. (DOE/EIA,1997)[5] 

### b) Value of Savings. The prices shown in Figure 7.4 are average prices including fixed charges. When a consumer saves electricity, the bill savings depend uponthemarginal energy chargeas reflected inthe utility rate schedule (cents perkilowatt-hour), which may be higher or lowerthanthe average rate inthe figure. Anexact accounting wouldrequire knowing for each household: the quantity and timing ofenergy used with and withoutthe saved energy,

4.5% 4.0% 3.5%
3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 0 2 Distribution of Residential Electricity Prices in 1997

4

- 6 8 10 12 14 cents/kWh 16 18 

### Figure 7.4 Distribution of 1997 residential electricity prices

- 7-8::: and the rate schedule (rate perkilowatt-hour as a function ofthe quantity ofkilowatt-hours used, including seasonal and/ortime ofday pricing). In general, such detailed information is not known. Three approaches are considered: 1) average rates, calculated as the ratio of household expendituresfor electricity (including fixed charges)to kWhconsumed; 2)partial adjustment ofknown inaccuracies by subtracting the average fraction corresponding to fixed charges from average rates; and 3) estimating the actual consumermarginal energyrate. We review the advantages and disadvantages ofeach ofthese approaches below: 1) Average rates. AdVantage: Simple. Available in published historical data and forecasts. Disadvantage: May over- or under-estimate the consumer marginal energy rate. 2) Subtract average fixed charge everywhere. Advantage: Approximately accounts for fixed charges. Edison Electric Institute (EEI) has assembled information on fixed charges for average general service residentialrates for a large number ofutilities that show fixed charges on average represent about 7.5% ofaverage rates.[6] Disadvantage: By reducing the valueofenergy savings in all cases, including those where the marginal rate is higher than the average price including fixed charges, this method under-estimates the actual consumer marginal energy rate in some cases. 3) Estimate distribution ofconsumer marginal energy rates. Advantage: Theoretically most accurate. Disadvantage: Practical difficulties: The rate schedules for households in the RECS**~urvey** are not known. The households, need to be assigned to utility service territories and to particular rate schedules within the utility. Month by month electricalconsumptionwouldbeneededto determinethemonthlymarginalrate, since energy is typically billed monthly, and rates may change seasonally. Rate schedule data does not exist for all utilities and, when available, is usually limited to general service residential customers. Current assumption regarding value ofsavings: Average rates are assumed in this analysis eventhoughitisrecognizedthatthe use of marginal energyrates would improvetheaccuracy ofthe analysis. At this time, however, the data needed to determine marginal rates is not readilyavailable. Moreover, inas much as it is unknown at this point ifremoving fixed costs is more or less reflective of**m~ginal** rates this intermediate step is not taken in this analysis. Developingconsumermarginalenergy rates: The Advisory Committee recommended that the Department use marginal energy rates rather than average energy rates, and the Departmenthas agreed. Inorderto developconsumermarginal energyrates, theDepartment proposes to collectdata on currentrate schedules and energy consumption. Those rates will 

### 7-9be assigned to a national sample of households, weighted to represent the total U.S. population of buildings and households. The result will be a weighted distribution of consumption by marginal rates. DOEproposes to obtain a sample ofresidential buildings from existing surveys, such as the RECS orfrom a commercially available database. RECS data has the advantage ofbeing in the public domain. In contrast, a commercially available database is likely to be more expensive,butmay have significant added value interms ofassigning the buildings to states or to utilities, including a broader sample ofthe population, permitting stratification ofthis larger sample to distinguish among some sub-populations. The commercially available databasealso containshousehold loadprofiles. Eachbuildingwillbe assignedto a geographic region(e.g., state orutility serviceterritory). Energy consumptionby month will be included inthe database for eachbuilding, inordertotreat seasonal changes inconsumptionandrates. RecentFederalsurveys (RECS) gatherinformationbyfuel onannualenergyconsumptionand total expenditures for energy. Total expenditures for energy included customer and other fixed charges, energy rates, demand charges, taxes, etc., but these were not tabulated separately from each other. These surveys gathered customer bills but did not extract informationon rate schedules, fixed charges ormarginal rates. The Departmentproposes to explore the feasibility ofextracting historical information on rate schedules, including the relationship between fixed charges and marginal rate.s to average prices. This effort, if successful, will provide information about the extent to which marginal rates differ from average prices, or from average prices less fixed charges. Givenrestructuring ofparts ofthe energy supply sector, customers may have more than one bill(e.g., one from the distribution company, and one ormore from generators or suppliers). To capture complete information, future surveys are expected to gather energy pricing informationdirectlyfrom customers,ratherthanfrom utilitiesorlocaldistributioncompanies. Themostefficientmeans to collectenergypricinginformationinthe future involves changing the currentprocessing ofthe billing information so as to gathermore detail from the bills, to include consumption by month and pricing information. The pricing information would includefor each customer, the rate schedule: marginal rates, fixed charges, demand charges, or time-of-use rates where applicable. The Department will express the need for these data in discussions with EIA about the design offuture surveys. Residentialelectricityrate scheduleswillbecollected from Federal databaseswhereavailable, commercial vendors, state regulatory agencies or directly from utilities. The information obtainedfor eachrate schedule will include any fixed charges (customercharges, etc.), block structure, and rate per kilowatt-hour (kWh) by block. Information from utilities or local distribution companies will be examined to determine: confirmation of the set of rate schedules, the number of**cu~tomers** by state using each rate schedule, the total electricity sales by state by rate schedule, and (if possible) monthly electricity sales by state by rate schedule.·
7-10**-----------~---** -------- -

Inthedatabase.ofbuildings, suchcharacteristics as energyconsumptionarid expenditures and numberofcustomers by state or utility will be used to map a rate schedule onto each ofthe buildingsinthe national sample. The marginal rate for each building will be the block from the rate schedule corresponding to that buildings monthly energy consumption. For life cycle savings calculations, the monthly energy savings will be estimated for each building. Those monthly energy savings for each building will then be evaluated for each building at the monthly marginal rate, using the rate schedule assigned to each building. 

### c) Future Trend. Eswnating future electricityrates is nowmore difficultthan ever. In some states, theelectricity supplyindustryis undergoingrestructuring. Previously, eachhousehold was assignedto a particularutilitycompany, andthe rates offeredbythatutility obtained from surveys. Inthe future, withrestructuring, households will be able to purchase electricityfrom a large set ofsuppliers.· A projected trend innational averageprices is appliedto eachhouseholds energy prices, after accountingfor 'Value ofsavings"(described above). In the spreadsheets, the user can select from the following scenarios: 1) Constant energy prices at 1997 values 2) Energy Information Administration Annual Energy Outlook 1998, High Economic Growth 3) EnergyInformationAdministrationAnnual Energy Outlook 1998, Reference Case 4) Energy Information Administration Annual Energy Outlook 1998, Low Economic Growth 5) Gas Research Institute (GRl)l998 Baseline Projection Figure 7.5 shows the trends for the last four ofthose projections. The values in later years (i.e. after 2015 for GRI and after 2020 for all others) are interpolated from their relative sources. Interpolationis needed because the sources used do not forecast beyond 2020 (or 2015 in the case of the GRI forecast). To arrive atvalues for these lateryears two techniques were used. Inthe three trends forecast by EIA (i.e., "High**Growth,"'~O** 1998,"and 'LowGrowth')thepricetrend inthe last five years ofthe forecast was used to**~stablish** prices in the years 2020-2030. With the GRI forecast prices are simply left constant at 2015 levels. Additional work is being conducted to refine these lateryear forecast values in line with methods currently used by the EIA to forecast fuel prices for the Federal Energy Management Program (FEMP). Electricity prices will be held constant at 2020 levels on the assumption that the transition to a restructured utility industry will have been completed.

7-11

### 9 

# E Iectricity Price · 8 7 6 

### ---H igh·Growth 

### -•-AEO 1998 5 

### ---GRI 

### 1998 ---Low Growth 4

### 1995
. 2000 2005 2010 2015 2020 2025 2030 Figure 7.5 Alternative electricity price trends
Current assumption regarding "future trend: 

### This analysis assumes the trend from AE098 Reference Case. 

### 7.2.2.6 Gas rate 

### Definition The value to a residential customer ofsaving gas, expressed as dollars per million Btu. 

### Issues Some households use gas for their water heater or clothes**dryer~** 

### Showing the change the cust<?merwill see in his/her gas bill due to increased energy efficiency is complexbecause: 1) gas rate varies from house to house, depending upon the rate schedule charged by the utility and the consumptionpattern of the particularcustomer; and 2).future rates cannotbe known with certainty. 

### Approach

· The variations in gas rate are handled by separating the problem into three parts: a) variability in energy price among households; b) value ofsavings; and c) future trends. Each of these parts is discussed and current assumptions described. 

### a) Variability
. . The Energy Information Administrations 

### 1993 Residential Energy Consumption Survey gathered information on gas bills which is used to represent the variabilityfromhouse to house (DOE/EIA 

### 1993). An 

### advantage ofusing a survey containing

. . 

### 7-12.!l 

# = Cl "ii 3': information on specific households is the ability to directly capture correlations among quantities. Suchcorrelations are important for electric and gas rates, for example, or among household characteristics (e.g., fuel of the water heater and clothes dryer, persons per household) that affect energy consumption. 

### Current assumption regarding variability: All those households from the Energy InformationAdministrations 1993 Residential Energy Consumption Survey (RECS) survey whichhavebotha clothes washer and a clothes dryer are used. Foreachhousehold, relevant energy prices are reported. These households are assigned weights to represent the U.S. Figure 7.6 shows residential gas prices ranging from 4.3 to 12.4 $/million BTU (MMBtu). Prices are derived by scaling 1993 RECS household energy prices to 1997$ using the consumer price index and then--to bring these prices in line with known national energy**trends-~scaling** the 1997$ prices by the ratio of 1997 to 1993 national energy prices as reported inAEO 1998. 

### b) Value of Savings. The prices shown in Figure 7.6 include average prices including fixed charges. When a consumer saves energy,the bill savings depend upon the marginal energy charge as reflected inthe utility rate schedule, which may be higher orlowerthanthe average rate in Figure 7.6. An exact accounting would require knowing·for each household: the quantity and timing ofenergy used with and without the saved energy, and the rate schedule (rate as a function of the quantityofenergy used, Including seasonal pricing). Ingeneral, such detailed information is not known.

0.60%

0.50%

0.40%

0.30%

0.20%

0.10%

0.00% 0 2 Distribution of Residential Gas Prices in 1997

3 4 5 6 7 8 9 10 dollar/MM Btu 11 12 13 Figure 7.6 Distribution of 1997 residential gas prices

7-131bree approaches are considered: (i) average .rates, calculated as the ratio ofhousehold expenditures for.gas (including fixed charges) to gas consumed; (ii) partial adjustment of known inaccuracies by subtracting the average fraction corresponding to fixed charges from averagerates; and (iii) estimating the actual consumer marginal energy rate. We review the advantages and disadvantages ofeach ofthese approaches below. 

### 5) Average rates. Advantage: Simple. Available in published historical data and in forecasts. Disadvantage: May over- or under-estimate the actual consumer marginal energy rate. 6) Subtract average-fixed charge everywhere. Advantage: Approximately accounts for fixed charges. The American Gas Association (AGA) 

### has assembled information on fixed charges for residential gas customers that show fixed charges on average represent about 12% of average rates. 1[7][8] Disadvantage: By reducing the value ofenergy savingsin all cases, including those where the marginal rate is higher than the average price including fixed charges, this method under-estimates the actual consumer marginal energy rate in some cases. 7) Estimate distribution ofconsumer marginal energy rates. Advantage: Theoretically most accurate. Disadvantage: The rate schedules for households in the RECS survey are notknown. Thehouseholds need to be assigned to utility service territories and to particular rate scheduleswithinthe utility. Monthbymonthgas consumptionis neededto determine the monthly marginal rate, since energy is typically billed. monthly, and rates may change seasonally. Rate schedule data does not exist for all utilities and, when available, is usually limited to general service residential customers. Current assumption regarding value of savings: Average rates are assumed in this · analysis even though it is recognized that the use ofmarginal energy rates would improve the accuracy ofthe analysis. At this time, however, the data needed to determine marginal rates is not readily available. Moreover, inasmuch as it is unknown at this point ifremoving fixed costs is more or less reflective ofmarginal rates this intermediate step is not taken in this analysis. Developingconsumermarginalenergyrates: Inorderto develop consumermarginal energy rates, the Department proposes to collect data on current rate schedules and energy consumption. Those rates will be assigned to a national sample ofbuildings, weighted to representthe total U.S. populationofbuildings. The result will be a weighted distribution of consumption by marginal rates.

1 More recent data has allowed for a revision in this number to 13.5%. 7-14DOE proposes to obtain a sample ofresidential buildings from existing surveys, such as the RECS or from a commercially available database. RECS data has the advantage ofbeing in the public domain. In contrast, a commercially available database is likely to be more expensive,but may have significant added value in terms ofassigning the buildings to states orto utilities, including a broader sampleofthe population, and permitting stratification of this larger sample todistinguish among some sub-populations. The commercially available database also contains household load profiles. Each building will be assigned to a· geographicregion (e.g., state orutility serviceterritory). Energyconsumptionby month will be included in the database for each building, in order to treat seasonal changes in consumption and rates. RecentFederal surveys (RECS)gatherinformationbyfuel onannualenergyconsumptionand total expenditures for energy. Total expenditures for energy included customer and other fixed charges, energy rates, demand· charges, taxes, etc. but these were not tabulated separately from each other. These surveys gathered customer bills but did not extract informationonrate schedules, fixed charges ormarginal rates. The Departmentproposes to explore the feasibility ofextracting historical information on rate schedules, including the relationship between fixed charges and marginal rates to average prices. This effort, if successful, will provide information about the extent to which marginal rates differ from average prices, or from average prices less fixed charges. Givenrestructuring of

## parts ofthe energy supply sector, customers may have more than one bill(e.g., one from the distribution company, and one ormore from generators or suppliers). To capture complete information, future surveys are expected to gather energy pricing informationdirectly from customers,ratherthanfrom utilities orlocal distributioncompanies. Themostefficientmeansto collectenergypricinginformationinthe future involves changing the current processing ofthe billing information so as to gathermore detail from the bills, to include consumption by month and pricing information. The pricing information would include for each customer, the rate schedule: marginal rates and fixed charges, where applicable. The Department will express the need for these data in discussions with EIA about the design offuture surveys. Residential natural gas rate schedules will be collected from Federal databases where available,commerciallyavailabledata, orstateregulatoryagencies. Theinformationobtained for eachrate schedule will include any fixed charges (customercharges,etc.), blockstructure, and rate pertherm by block. Information from utilities or local distribution companies will beexaminedto determine: confirmation ofthe setof rate schedules, the numberofcustomers bystateusingeachrate schedule, thetotal gas sales by state byrate schedule, and (if possible) monthly gas sales by state by rate schedule. Inthedatabaseofbuildings, suchcharacteristicsasenergyconsumptionand expenditures and numberofcustomers by state or utility will be used to map a rate schedule onto each ofthe buildings in the national sample. The marginal rate for each building.rill be**th~** block from
7-15

# =
..... 

# =**~**

## ~ 61"7 the rate schedule corresponding to that buildings monthly energy consumption. For life cycle savings calculations, the monthly energy savings 

### will be estimated for each building. Those monthly energy savings for each building will then be evaluated for each building atthe monthly marginal rate, using the rate schedule assigned to each building. 

### c) Future Trend. A projectedtrend in national average prices is applied to eachhouseholds energyprices, afteraccounting for "value ofsavings"(described above). Inthe spreadsheets, the user can select from the following scenarios: 1) Constant energy prices at 1997 values 2) Energy Information Administration Annual Energy Outlook 1998, High Economic Growth 3) EnergyInformationAdministrationAnnual Energy Outlook 1998, Reference Case 4) Energy Information Administration Annual Energy Outlook 1998, Low Economic Growth 5) Gas Research Institute 1998 Baseline Projection. Figure7.7 shows the projections. The values in lateryears (i.e. after 2015 for GRI and after 2020 for all others) are interpolated from their relative sources Interpolation is necessary because the sources used do not forecast beyond 2020 (or 201 S in the case of the GRI forecast). To arrive at values for these later years forecast prices are simply left constant at 2020 (2015 for GRI) levels. Additional work is being conducted to refine these later year forecast values in line with methods currently used by the EIA. Natural gas prices will be derived from the average growth rate for world prices over the years 2010 to 2020 and will be combined with regional price margins from the year 2020. 6.5
6.0
5
..5 5.0
4.5
4.0 

# Gas Price

--- High Grow th

. -•-AE 0 1998

--GRI 

### 1998 

## ---Low Growth 1995 2000 2005 2010 2015 2020 2025 2030 

### Figure 7.7 Alternative gas price trends 7-16Current assumption regardingfuture trend: This analysis assumes the trend from the AEO 1998 Reference Case
.. 

### 7.2.2.7 Oil Prices Definition Residential price ofdistillate. Approach Variabilityinpricesistakenfrom theRECS sample. No attemptismadeto define a consumer marginal energy rate. Figure7.8 shows the oil price timetrend. The values in later years (i.e. after 2015 for GRI and after 2020 for all others) a:re extrapolated from their relative sources. Extrapolationisnecessarybecausethe sources useddonotforecast beyond2020 (or2015 inthe case . ofthe GRI forecast). To arrive atvalues for these lateryears forecast prices are simply left constant at 2020 (2015 for GRI) levels. Additional work is being conducted to refine these lateryear forecast values in line with methods currently used by the EIA Forpetroleum products, the average growth rate for the world oil price overthe years 2010 to 2020 will be used in combination with the refinery and distribution markup from the year 2020 to determine the regional price forecasts.


### 9.0

- 8.5

- 8.0

-

6.0

- 5.5

- 

### 5.01995 I I 

### 2000 2005 Figure 7.8 Alternative oil price trends

# 0 ii Price

--- High Growth -•-AEO 1998 · 

## ---Low Growth --GRI 

### 1998

I I I I I 

### 2010 2015 2020 2025 2030

7-17

### CurrentAssumptions Variabilityin price from 1993 RECS sample ofhouseholds owning washers and dryers. The future trend is taken from the AEO 1998 Reference Case. 

### 7.2.2.8 Water per Cycle Definition Water consumed by clothes washer in one cycle. 

### Approach Data as submitted by AHAM. [9] 

### CurrentAssumption Use numerical values for baseline and eaclefficiency level, as provided by AHAM. Figure 7.9 shows the water use per cycle vs. modified energy factor (MEF).

45 40 39 39 39 39 38



### .35 

# -**~ ~**  

### 30**~** 

# - - = 25 t>J) 

# -**~** 

### 20 I'll**~** a. 

### 15**~** 

# - =**~** 10 5 0 0.82 0.86 0.91 0.96 1.02 1.09 1.26 . 1.36 1.49 1.63 

## MEF Figure 7.9 Water use per cycle vs. MEF

7-18

### 7.2.2.9 Water and wastewater removal rates 

### Definition Residential customer rate for water and wastewater (sewer) (expressed in $/ 

### thousand gallons), excluding fixed charges. 

### Issues Water is a significant cost of doing laundry. Different designs of washers have a large difference in water use. Approach 

### An existing survey of water and wastewater rates represents the population-weighted
. distributionofU.S. waterrates in 1994 [10] [11]. Research continues to: 1) update this estimate; 2) estimate historical change in these rates; and 3) obtain projections offuture change in·those rates. Figure7.10 shows the distribution ofthe sum ofwaterand wastewaterrates per 1000 gallons in 1994 expressed in·1997$.

8.0%
7.0%
6.0% 

## g 5.0%**~ ~**  .Q 4.0% 

### ·.:: 

# - en i5 3.0% 2.0% Net Water & Sewer Charges in 1994 In 1997$; Average= $3.18

o v m N**~** o v m N**~** o v m N**~** o v m N**~** o v m·N**~** o v m N**~** o v m 0 N v**~** m N v**~** m**~** v**~** m**~** M**~ ~**  0 M**~** m 0 N**~ ~**  0 N v**~ ~**  N v w 0 0 0 0 0**~ ~** **~ ~**  N N N N M M M M**~ ~** **~ ~** **~ ~** **~ ~** **~ ~**  m**~ ~** **~ ~** **~** 

### """"""""""""""""""""""""""""""""" 1997$/1000 gallons 

### .Figure 7.10 Distribution ofnet water and sewer charges in 1994 (expressed in 1997$)

7-19

### CurrentAssumption Use thel994 distribution ofU.S. values for water and wastewaterrates. The escalationrate ofwater is selectable by the user ofthe spreadsheet. Currently, the escalation rate is set at zero, pendingfurther research. The averagevalue forwaterpricecurrently usedis $3.18per 1000 gallons. 

### FutureAssumptions In some cases waterprices come with a fixed costto be connected, in some cases the rate is higher as usage increases and in other cases the rate deceases with increased water consumption
.. Analysis is being conducted to determine the marginal cost ofwater and.the rate of increase of marginalrates. Because this is expected to vary with region ofthe country, work is being done to get costs and escalation rates for different geographic regions. This data can then be mapped onto the RECS database and onto eachhousehold. The analysis consists ofupdating previous data from Ernst and Young report as adjusted by Al Dietemann, as well as new data obtained from the AmericanWaterWorksAssociation(A WW A). TheErnstandYoungdataisbeingupdatedwithdata from 125 utilities (usedintheoriginal study),includingtheirwater**rate/wastewat~r**disposal schedules andtheirforecasts for the future, as well as any historical information available. Historical datafrom previousRaftelis waterrate studies will also be used. These three sources will be combined into one database. A distribution of water**price~** (as iri the current analysis) will be used as well as a distribution ofescalation rates. To determine escalation rates, information on historical trends in marginalrates as well as the costto meet future Government water quality regulations will be taken into consideration
.. 

### 7.2.2.10 Discount Rate · Definition The rate at which future expenditures are discounted to establish their present value. 

### Approach A distribution ofdiscountrates was derived to represent the variability in financing methods consumers use in purchasing appliances. The resulting distribution of discount rates is used to calculatea distribution oflife-cyclecosts for clothes washers. Table 7.2 summarizesthe interestrate assumptions discussed below. 

### Table 

### 7.2 Summary ofNominal Interest Rates FinanCing Method Percent ofClothes Washer Sales Range ofReal Interest Rates Minimum Maximum Mean New home (after tax) 20% 1.60% 3.76% 2.68% Cash 32% 0.00% 3.00% 1.50% Credit Card 28% 6.00% 15.00% 10.5% Retailer Loan 20o/o 6.00% 15.00% 10.50%
7-20ConsumerPurchaseandFinancingMethods. Consumerspurchase appliances innewhomes and as retail purchases. The retail purchases are paid by cash, credit cards, or retailer loans. A stakeholdercomment indicated that for white goods about 40% ofretail purchases are paid in cash, 35% use credit cards, and 25% use retailer loans [12]. The same comment indicates 25% of appliancepurchasesare fornewhomes. (Forclothes washers,DOEestimatesthatpurchases for new homes are now about 20%.) Themethod ofpurchase used by**consum~rs** is assumed to be indicative ofthe source ofthe funds and the type offinancing used by these consumers. InterestRates. DOE estimated a range ofinterest rates that may reasonably be expected to apply in the future to different types ofconsumer savings orfinancing. These are estimates, based upon historical data andjudgment about how the future may differ from the historical period. For newhousing, DOE estimated nominal mortgage rates (5-8%), derived the after-tax rate assumingatax of28%, then subtracted the inflation rate (assume 2%). The result is a range ofreal mortgage rates of 1.60-3.76%. (Example: 5% 

## * (100%-28%)- 2% 

## = 1.6%) For cash, the minimum rate is 0%. This rate applies to purchasers making cash purchases without withdrawing from savings accounts. The maximum is taken to be the opportunity cost represented by the interest that could have been earned in a savings account. The historical nominal maximumsavings rate ranged from 4.5-5.5% from 1970 to 1986 (real rates of-8.27 to +3.58%). A real rate of3% as indicative ofthe maximum. · The interestrates for.retailer loa.nS and creditcards are assumed to have the same range. The minimumcreditcardrate is takenas 6% real. Introductoryrates onsome credit cards today are 5.9% nominal, but after the introductory period (often 6 months), the rate becomes much higher. Maximtnn rates are over 20% nominal. On the other hand, most retail purchasers will not make paymentsfor a clothes washer overthe entire lifetime ( 12-16 years) ofthe appliance. 

### If thepurchase is paid for initially by a credit card, but the consumer pays offthe balance in less than the life ofthe washer, then the effective interest rate is lower than the nominal credit card rate. The current assumption is a range of6-15% real. 

### CurrentAssumption
. Thereal interestrateassociatedwithfinancing anappliancepurchase orwiththe savingsfrom which the necessary funds were drawn are good indicators of the additional costs incurred by consumers who pay a higher first cost, putenjoy future savings. For this reason, it is assumed that these rates are appropriate discount rates for use inthis analysis. Table 7.3 summarizes the ranges of discountrates used inthe LCC distributionanalysis, derived from the analysis of financing methods described above. Figure 7.11 shows the distribution ofreal discount rates, ranging from 0 to 15%, with a mean of6%. The weights noted on the y-axis indicate percent ofpurchases. Tahl.e7.3 uses the datainTable 7.2 and accounts for overlap betweenthe newhome and cash interest rates.

7-21--- ---- ----**--~~---------**



### Table 7.3 Distribution ofDiscount Rates Minimum Value Maximt1Pl Value Probability 0.00% 1.60% 17% 1.60% 3.00% 28% 3.00% 3.76% 7% 3.76% 6.00% 0% 6.00% 15.00% 48%



## Consumer Real Discount Rate
20% 16%
rll 12% 

### ..... .c tlll 

# ....**~** 8%**~** 4% 0% 0% 2% 4% 6% 8% 10% 12% 14% 16% 

### Discount Rate Figure 7.11 Distribution ofConsumer Discount Rates
DOE recognizes that other factors might be considered 

### iri the esthnation ofreal consumer discountrates, such as the actual impacts ofappliance purchases onconsumer savings, indebtedness or consumption, and expressed or imputed consumer preferences. While such data, ifit were to become available, might provide a stronger analytical basis for DOEs choice ofdiscount rates, it is consideredunlikely that such data would have a significant effect on the range ofvalues considered in the current analysis.

7-22



## ...----------- ---- -- ------------------------------------ -



### 7.2.2.11 Fuel use ofwater heater and clothes dryer Definition The fuel used to heatwaterinthe waterheaterandthe fuel used to provide heatin the clothes dryer. 

### Approach
· Whenusing the Crystal BallTM distribution optiononthe spreadsheet, the distribution offuel used is determined bythe samplingof the RECS households. The fuel type used for the waterheater and the clothes dryer are providedin.the RECS database. Only those houses having both a clothes washer and dryer are included inthe sample. Households using either electricity, natural gas or fuel oilare sampled. TheDOEtestprocedure onwhichreported energyuse isbasedaccounts for the fact that not all clothes washed in a clothes washer are dried in a clothes dryer. The DOE test procedure assumes an electric water heater and an electric dryer. The DOE test procedure is used for the Rebuttable Payback calculation. 

### CurrentAssumption The distribution offuel use as provided by RECS is shown in Table 7.4. Currently only the households in the RECS data base using these fuels are sampled using the Monte Carlo method.


### Table 7.4 Shares in Households ofWater Heaters and Dryers by Fuel Type Water Heater Dryer Percent ofHouseholds Electric Electric 41.2% Electric 

### Gas 0.6% 

### Gas Electric 35.5% 

### Gas Gas 20.1%. Oil Electric· 2.5% Oil 

### Gas 0.1% Source: EIA, RECS 1993 

### PlannedFuture Changes Liquid petroleum gas, LPG (also commonly referred toas propane) will be added as a fuel choice to be consistent with·the water heater rulemaking.

7-23

### 7.2.2.12 Year to start date Definition This is the year at which a new standard is expected to become effective. 

### Approach The LCC is calculated for 

### all households as 

### ifthey each purchase a new washer in the year the standard takes affect. The cost ofappliances are based on this year, however, all dollar values are expressed in 1997 dollars. AnnuaI·energy prices are included for the life ofthe washer. 

### CurrentAssumption The new eriergy efficiency standardfor clothes washers is assumed to take effect in the year 2003.


### 7.2.2.13 Base Case Design Definition This is the cost and efficiency ofthe starting point to which different improvement levels of washers are compared. 

### Approach AHAM supplied cost and efficiency data for a baseline and nine higher efficiency levels. The user can select any level against·which to compare higher efficiency levels (see Engineering section). 

### CurrentAssumption The default assumption for the base case design is the baseline design option.


### 7.2.2.14 Standard case design Definition The improved efficiency level for comparison with the base case design. 

### Approach The spreadsheet user selects the level for the analysis. 

### CurrentAssumption Analysis is done for all levels for which data was provided.



### 7-24- --- - -- ----- --- -------- 

### -----

7.2.3 LCC results and preliminary output Titls section presents preliminary results for life-cycle cost (LCC) for the efficiency improvement**level~** specified in the Engineering Analysis (Chapter 4). Results presented here are based onthe inputs described in section 7.2.2. Sincethe value ofmost inputs are uncertain and must be represented by a distribution ofvalues rather than a discrete value, the results are presented here as a distribution ofvalues. Where distributions are shown, the mean value ofthe distribution is also presented. · LCC results are presented as differences in the LCC relative to the baseline clothes washer design. As mentioned previously, the LCC differences are depicted as a distribution ofvalues. The primaryresults are presented in two types ofcharts: 1) 

### afrequency chart showing the distribution of LCC differences With its corresponding probability of occurrence and 2) 

### acumulative chart showingthe cumulative distribution ofLCC differences along with the correspondingprobability of occurrence. In each chart, the mean LCC difference is provided along with the percent ofthe population for which the LCC will decrease. Inthe explanation below, thetwo charts depictingthe case fora 25% efficiency improvement levelareused (Figure 7.12 and 7.13). The 25% efficiency improvementlevel representsthe standard level at which the energy usage is reduced by 25%. In either chart (frequency or cumulative), the mean change (reduction of$205 in the examples here) is shown in a text box next to a vertical line atthatvalue onthe x-axis. The phrase 'Certaintyis 89.23% from -Infinityto $0"means that 89.23% ofhouseholds will have reduced LCC with the increased efficiency level compared to the baseline efficiency level.



### 10,000 Trials .047 

### Forecast: LCC Difference Frequency Chart 0 Outliers

- 471

### ~ 

# ·- .035-i------------------tttHffllfft-----I- 353.2**.024+---------------~** 

# .,,**~** 

### ro fffffffffl!fffhr----:---l- 235.5 

### .c c 

### ro ::i
.

.012-t-------------fflfffffffffftilffffffffffH+H----I- 117.7**~ .000-1-..,...~-----,,..---,-aaaa..~l&&Ul** ..........**l:l&LLl&.LllJ~111.1** ($1,500) ($1,063) ($625) ($188) $250 Certainty is 89.23% from -Infinity to $0 $ 

### Figure 7.12 Frequency chart ofLCC differences for 25% efficiency improvement level 7-25Figure 7.12 is an example of

### afrequency chart. They-axes showthe number ofhouseholds ('Frequency" at right y-axis) and percent ofall households ('Probability" at left y-axis). In this example, 10,000 households were examined ("l0,000 trials'') and all results are displayed 

### (''O outliers''). The x-axis is the difference in LCC between a baseline efficiency level and a higher efficiencylevel (in this example, 25% energy reduction). The x-axis begins with negative values on the left, which indicatethatstandardsforthosehouseholdsprovide savings (reducedLCC). Reduced LCC occurs when reduced operating expenses- energy and water- more than compensate for increased purchase expense. In Figure 7.12, going from the baseline efficiency level to the 25% efficiencyimprovementlevel provideshouseholds withanaverage LCCreductionof$205, andrange from reductions of $1486 (at the left) .to increases of $137 (at the right) depending upon the household. (The minimum and maximum values cannot be read with precision for the graph, but rather, the program provides them in a statistical summary.) 

### Forecast: LCC Difference .10,000 Trials Cumulative Chart 0 Outliers**1.000..r-----------------.------==~--i-** 10000**.0001-..,....~ ~-..,..--,....---~i&lllllld,lll.WWlilUJIJJI**  ($1,500) ($1,063) ($625) Mean= $205**WJi]!Dllllm~IJJ.DimrmDll,_l_** 

## o ($188) $250 Certainty is 89.23% from -Infinity to $0 $ 

# .,, .... n .Cl 

### c 

### n ::i

## ~**Fig~re** 

### 7.13 Cumulative chart ofLCC differences for 25% efficiency improvement level
The vertical axis in Figure 7.13 is the cumulative probability (left axis) or frequency (right axis) that the LCC difference will be less than the value on the horizontal axis.Starting at the left, there is a 0% probabilitythata householdwillhave a reduction inLCC largerthan $1486 in absolute value. Toward the middle, there is a 50% probability that a household will have a reduction in LCC largerthan about $164. Atthe right, there is a 100% probability that a household will have either a decrease in LCC or an increase in LCC ofless than $1377

· 2 See Table 7.5 for intermediate values 7-26AppendixC contains the.frequency and cumulative charts for all the efficiency levels. These charts provide more complete information than summarystatistics, but a summary ofthe change in LCC from the baseline by percentile groupings (i.e., ofthe distribution ofresults) is given below in Table 7.5. 

### T bl 7 5 S a e • ummaryo 

### fLCCR Its esu Percent Change in LCC from Baseline1 Percent Efficienc Shown by Percentiles ofthe Distribution ofResulti with LCC y Level (values in $) Less than Baseline 0 10 25 

### so 75 90 100 Mean 5 ($83) ($33) ($24) ($16) ($11) ($8) ($2) ($19) 100.0 10 ($232) ($82) ($55) ($36) ($23)
. ($15) $13 ($43) 99.5 15 ($402) ($140) ($90) ($55) ($33) ($19) $63 ($68) 95.6 20 ($504) ($161) ($98) ($55) ($26) $10 $129 ($67) 86.7 25 ($1,486) ($465) ($303) ($164) ($67) $4 $137
. ($205) 89.2 35 ($1,997) ($639) ($408) ($211) ($59) $79 $570 ($252) 83.4 40
. ($2,039) ($649) ($412) ($207) ($64) $75 $645 ($253) 83.7

. 45 ($2,068). ($606) ($365) ($155) $9 $159' $666 ($199) 73.6

50 ($2,075) ($617) ($374) ($156) $6 $153 $571 ($204) 74.2

1 The baseline LCC, based on SWA ofthe most likely costs, is $1,554 2 For sample size of 10,000 trials. Energy price trends are for AEO 1998. Operatingcosts include water prices. No escalator is assumed for waterprice.
Each row in Table 7.5 corresponds to the impacts on a population ofhouseholds from a possible standard level. For example, for a 25% efficiency increase, the sample household with the largest reduction in LCC saves $1486 compared to the baseline. This result is in the 

### 'O" percentile column. For the same 25% efficiency increase, the sample household with the largest increase in LCC, $137, is represented in the 

### "100" percentile column. In between, 

### 10% ofsample households lowerLCC by at least $465, 

### 20% ofsample households lowerLCC by at lea$t $303, 75% ofsample householdslowerLCC by at least $67, and 

### 90% ofhouseholds have eitherreduced LCC orincreases inLCC no greaterthan $4. The mean impact is an LCC reduced by $205 (in column "Mean"). The right-most column showsthe percent ofsample households for which the particular standard level would produce some reduction in LCC, e.g. 89.2% of sample households for an efficiency improvement of

### 25%. I

7-27

### 7.3 DISTRIBUTION PAYBACK PERIOD 

### 7.3.l l\1etric Thepaybackperiod (PBP) measures the amount oftime ittakes the consumerto recover the assumedhigherpurchaseexpenseof moreenergy-efficientequipmentthroughloweroperatingcosts. Numerically,thePBP isthe ratio oftheincrease inpurchase expense (i.e., from a less efficient design to a more efficientdesign) tothe decrease inannual operatingexpenditures. This type ofcalculation is known as a ''Simple"payback period, because is does not take into account changes in operating expenseovertime orthe time value ofmoney, that is, the calculation is done atan effective discount rate of0%. · PBP is found by solving the equation
(2)
for

### PAY, where 

### t:..P = difference in purchase expense betweenthe more efficient and the less efficient designoptions($), and 

### t:..0 1 =difference in annual operating expenses ($/a). PBPs are expressed in years. PBPs greater than the 

### life ofthe product mean that the increased purchase expense is not recovered in reduced operating expenses. 

### 7.3.2 Inputs The data inputs to PBP are the purchase expense for each design option and the annual (first year)operatingexpenditures for each design option. The inputs to the operating costs are the annual energy savings, the energy price, the change in annual water consumption, and the water price. The DistributionPBP uses the same inputs as the LCC analysis described in section 7.2 except for a few exceptions described below. Since this is a ''Simple"payback the fuel rates used are only for the year the standard takes effect, assumed here to be the year 2003. The price ofelectricity, gas and oil are those projected for that year. Similarly for water rates, a single value for the price ofwater and wastewater disposal is

. used. Discount rates are not used for the payback calculation
.

. . 7-28

### 7.3.3 Preliminary Results
. Figure7.14 is anexample ofa chart showing the distribution ofpaybackperiods for the 25% improvementefficiencylevel. Notethatthemeanvalue of thepaybackperiod distribution ( 4.4 years) is provided. AppendixC contains DistributionPBPcharts for all the efficiency levels. Again, these charts provide more complete information than summary statistics, but a summary ofpayback values by percentilegroupings (i.e., ofthe distribution of results) is givenbelowinTable 7.6. Eachchartis the result of 10,000 Monte Carlo runs or in other words, 10,000 samples from each ofthe distribution inputs.



### 10,000 Trials .054 .041

## ~ 

# - .Cl .027**~** .Cl 0 a.. .014 

# a. .000**~** 0.0 

### Forecast: Payback Frequency Chart

Mean= 4.4 8.8 17.5 years 26.3 

### Figure 7.14 Distribution ofPBPs for 25% efficiency improvement level

7-29 

### OOutliers 544**~** - 0 35.0 

# .,, """' n .c c n 

# =**~**

### T bl 7 6 S 

### a e 

# . ummaryof 

### P b kP . d R I ay ac er10 esu ts Percent Payback Period in Years Efficiency Shown by Percentiles ofthe Distribution ofResults1 Level 0% 10% 25% 50% 75% 90% 100% Mean

5 0.0 0.0 0.0 0.0 0.0 0.2 3.7 0.1 10 0.0 0.0 0.0 0.1 0.5 1.6 15:8 0.6 15 0.0 0.0 0.1 0.2 0.6 4.1 40.7 1.4' 20 0.0 0.1 0.2 0.5 5.2 10.8 57.9 3.6 25 '0.0 0.8 1.8 3.6 6.0 8.8 34.5 4.4 35 0,8 2.0 2.8 4.2 6.9 11.4 49.8 5.8 40 0.7 2.0 2.8 4.3 6.9 11.4 57.8 5.8 45 0.7 2.4 3.6 5.8 9.3 13.9 54.0 7.2 50 0.9 2.7 3.8 5.9 9.1 13.5 54.5 7.2 I For sample size of 10,000 tnals. Energy pncetrends are for AEO 1998. Operating costs include water prices. No escalator is assumed for water price. 

### 7.4 REBUTTABLEPBP Rebuttable PBPs are presented in order to provide the legally established rebuttable presumptionthat a energy efficiency standard is economicallyjustifiedif the additional productcosts . attributed to the standard are less thanthree times the. value ofthe first year energy cost savings (42 · U.S.C. §6295 ( o)(2)(B)(iii)). 

### 7.4.1 .l\tetric The basicequation for Rebuttable PBP is the same as that shown in section 7.3. Unlike the analyses 

### in sections 7.2 andsection 7.3, the Rebuttable PBP is not based on·distributions and does not utilize the CrystalBallTM option in the spreadsheet model. Rather than using distributions, the Rebuttable PBP is based on discrete values and is based on the DOE clothes washer test procedure assumptions (Appendix JI).

7-30

### 7.4.2 Inputs Inputs differ from the Distribution PBP in 

### that discrete values are used rather than distributions (where distributions are used in the LCC and Distribution PBP) for inputs. All dollar values are in 1997$.
• For purchase expense the shipment weighted average (SWA) ofmost likely (in the AHAM supplied Engineering data) is used as the input. • For the lifetime an average value of 14.1 years is used. • The cycles/year are 392, from DOE test procedure • Energy per cycle is obtained fromthe AHAM supplied data; for this parameter the original data was not submitted as a distribution, butas single discrete values for machine energy, hot water energy, and dryer energy, for every efficiency level. • Electric, gas and oil rates used are average values for the yearthe standard takes effect, i.e., AEO projections for the year 2003. • Water use per cycle is the gallons per cycle submitted by AHAM (see Engineering data). • Waterpluswastewaterrates isthe averageof anational distribution; $3 .18/1000 gallons. The value used did not assume an escalation rate, but more research is being conducted to determine this value. • A discount rate is not required in this calculation · • The fuel use is based on the test procedure asslimption ofan electric water heater and an electric dryer. • Year to start date assumed is 2003. Payback periods are first calculated between the new standard level and each washer efficiency being sold in the year 2003. The paybacks arahen weighted and averaged according to the percentage ofeach washer efficiencysold before a new standard is enacted. With the presently availabledata, the baseline efficiencylevel is weighted with a market share of95% and the horizontal axismarket share is weighted at 5%. Rather than distributions, single point values for the inputs are used. These values (including cycles peryear, electric fuel source, etc.) correspondtothose outlined in the DOE test procedure, discussed inAppendix 

### JI. The result is a single payback value and not a distribution ofPBPs. The payback is calculated for the expected effective year ofthe standard (e.g., 2003) and is presented below in Table 7.7. Data on a forecasted distribution ofclothes washer efficiencies in the year 2003 would be usefulin refining these calculations. ·

7-31

### 7.4.3 Preliminary Results Table 7.7 Rebuttable Presumption Paybackin Years Market Share 95% 5% Standard Level 0% to Standard 35% to Standard Weighted Payback 5 0.1 

### NA 0.1 10 0.2 

### NA 0.2 15 0.6 

### NA 0.6 20 1.8 

### NA 1.8 25 2.7 

### NA 2.7 35 3.7 

### NA 3.7 40 3.7 3.7 3.7 45 4.9 29.2 6.2 50 5.0 19.6 5.8



### 7.5 USER INSTRUCTIONS FOR SPREADSaEET Itis possibleto examine andreproducethedetailedresults obtained inthis partof the analysis using a Microsoft ExcelTM spreadsheet available onthe U.S. Department ofEnergy Office ofCodes and Standards website at: wwW.eren.doe.gov/buildings/codes_standards/. The LCC spreadsheet is called''LCC
_CW3.l .XLS". To executethe spreadsheets fully you will needbothMircosoftExcelTM and Crystal BallTM software. Both applications are commercially available. The spreadsheetspostedonthewebsiterepresentthelatestversions of the applicable models, and have been tested with both EXCEL97 and EXCEL95. 

### Basic Operating Instructions: 1. Once you have downloaded the LCC file from the Web, open the file using Excel. At the bottom, click on the tab for sheet 'LCC." 2. The screen will display two figures (''Cumulative Cost"and "Difference in Cumulative Cash Flow'') and two columns ofhighlighted data ('Baseline"and a given efficiency level thatthe user can determine by modifying the input assumptions). (Use Excels commands at the top "View/Zoom"to change the size ofthe display to make it fit your monitor.)

7-323. To see the assumptions, click on 'List lnpll:ts." 4. To change assumptions on 'List Inputs" click on the a5sumption you wish to change, and click on the new assumption from the menu. 5. · After selecting each assumption, click on 'List Inputs.'' 'Cancel" wiH erase your last set of changes to assumptions without recalculating results
.. 

### 6. Finally, to see results, click on 'Calculate."
. Using these steps you can explore outputs using a range ofalternative assumptions. To produce sensitivity results using Crystal BalTM , including the sort ofdistribution figures shownin Figures 7.12-7.14, you need simply select 'hm"fromthe Run menu onthe menu bar. To make basic changes in the run sequence, including altering the number of trials, select 'tun preferences"from theRunmenu. Once CrystalBallTM has completedits run sequenceit will produce a series ofdistributions. Usingthe menubars onthe distributionresults itis possibleto obtainfurther statisticalinformation. The time takento complete

## arun sequence canbe reduced by minimizing the CrystalBallTM windowinExcel. Formoreinformation,pleaseconsultthe CrystalBallTM usersguide or Help menu in Crystal BallTM
. Table 7.8 shows the quantities and their locations in the life-cycle cost spreadsheet (LCC_CW3.l.XLS). The first column (QUANTITY) shows the name ofthe quantity and the units (e.g., $ oryears). The remaining columns showthe location, includingthe sheet ahd, as appropriate, the ceH address or 'button" where the numerical values are located.

7-33

### Table 7.8 Quantities and Locations in Life-Cycle Cost Spreadsheet QUANTITY SHEET CELL(S) BUTTON Purchase expense ($) Lifetime (years) Cycles per year
· Energy per cycle (kWh/cycle)

Electricity rate (cents/kWh) Gas rate ($/MMBtu) Oil price ($/MMBtu) Water per cycle (gallons) Water rate ($/thousand gallons) Discount rate Water Heater/Dryer Type Year to Start Date
. Base Case Design Standard Case Design Manufacturers Cost Engineering Lifetime

Setup Engineering LCC LCC LCC Engineering Water Price LCC RECS households LCC· LCC LCC Engineering

7-34 ES-17 C3 E40 G,H&IS-17 'List Inputs" 'List Inputs" 'List Inputs" JS-17

B4 136
'List Inputs" 'List Inputs" 'List Inputs" K8-Q17

## REFERENCES 1. MTSC Inc./Management Technology Division 1980. Energy Capital in the U.S. Economy. Bethesda, MD: MTSC.**No~ember.** 2. Erikson, J.T. 1995.; Letter dated May 4, 1995; The Procter & Gamble Company; NonClassified Information 3. U.S. Department ofEnergy/Energy Information Administration (DOE/EIA) 1995. ResidentialEnergy Consumption Survey: HouseholdEnergy Consumption and Expenditures 1993. DOE/EIA-0321(93) October. 4. Ibid. 

# 5: U.S. Department ofEnergy/Energy Information Administration (DOE/EIA), 1997. AnnualEnergy Outlook 1998. DOE/EIA-0383(98) December. 6. Electric Edison Institute, 1997. Binder and spreadsheet by Steve Rosenstock, presented to DOE Advisory Committee on Appliance Standards, Subcommittee on Economic Analysis,_ September 26, 1997, and subsequent updates. 7. American Gas Association, 1997. Spreadsheet provided by Kirstie Kummer. 8. · American Gas Association, 1998.**Spreadshe~t** provided by Kirstie Kummer 9. Association ofHome Appliance Manufacturers (AHAM) comments to the Department of Energy, December 2, 1997; Docket No. EE-RM-94-403, Comment #83. 10. Ernst & Young LLP 1994. Ernst &Youngs 1994.National Water and Wastewater Rate Survey. Washington, D.C.: Ernst & Young. 11. Dietemann, A. 1995. Spreadsheet with title:Ernst & Young 1994 National Water and Wastewater Rate SurveyPopulation WeightedAverage Commodity Charge 12. Whirlpool Corporation**co~ents** to the Department ofEnergy, December 12, 1990, Docket No. CE-RM-90-201, p. 1&2, Attachment #1

7-35

### CHAPTER 8: NATIONAL ENERGY IMPACTS
8.1 INTRODUCTION This chapter describes the method for estimating the quantity and value offuture national energy savings (NES) from possible standards. The two metrics discussed in this chapter are:
• National Energy/Water Savings (section 8.2), and • Net Present Value (NPV) ofEnergy/Water Savings (section 8.3). All calculations are performed ona series ofMicrosoftExcel spreadsheets which are accessible over the Internet Basic outputs from the spreadsheets calculations are discussed in section 8.4. A more detailed set of results is available 

### in Appendix D. Access to and basic instructions for the spreadsheets are discussed in section 8.5. In addition, the methodology the Department will use to determine indirect employment impacts is discussed in section 8.6. · 

### 8.1.1 Methodology and Definitions Among the important drivers ofenergy consumption ofclothes washers are: (1) shipments ofclothes washers, (2) voluntaryprograms promotinghigherenergy efficiencyproducts, and (3)how consumers respond to any change in price or operating expense. The number ofclothes washers purchased in future years (shipments) are an important component ofany estimate offuture impacts, and several alternate methods forprojectingfuture shipments aredescribed. Voluntaryprograms may increasethe share ofenergy efficientproducts,priorto the implementation date ofany new standards, and their potential impact is accounted for. 

### If clothes washerprices are increased in response to new standards, then shipments may be influenced. The percent change in shipments due to a percent change in price is the price elasticity ofdemand for clothes washers. Similarly, as standards reduce pnergy consumption, operating expenses decline, which may also influence the number ofwashers purchased. Preliminary results are also described here including: energy consumption by fuel**typ~,** monetary value ofenergy savings, increased purchase prices, and the net present value (difference between value ofenergy savings and increased purchase prices).


### 8.2 NATIONAL ENERGY/WATER SAVINGS (NES) 8.2.1 Definition

· This section provides the definition ofnational energy savings.

1Waterheaters orclothes dryers may use one ofseveral fuel types (e.g. electricity, natural gas, LPG (propane), and--for water heaters--distillate oil). 

### 8-1. National annual energy savings are calculated as the difference between two projections: a base case (without new standards) and a standards case. Positive values ofNES correspond to energy**~avings,** that is, energy consumption with standards is less than energy consumption in the base case. (1) Cumulative energy savings are the sum over some period (e.g., 2000-2030) ofthe annual national energy savmgs. 

### (2)
The national annual energy consumption is calculated according to the following equati011: (3). The national water consumption is calculated according to the following equation: (4) For the above expressions, the quantities are required: AEC 

## =
AWC 

## = Ilcycte 

## = NES 

## = STOCKv 

## =

UECv 

## = Annual energyconsumptioneachyear(Quads), summed over vintages ofclothes washer stocks, STOCK.y. Annual water consumption each year 

### (I03 gallons), summed over vintages ofclothes washer stocks, STOCKv. Cycles per year (e.g., loads oflaundry) Annual national energy savings (Quads) Stock ofclothes washers (millions ofunits) ofvintage V surviving in the year for which annual energy consumption is being calculated. Vintages range from 0 (new)- to 16-yeais old. Energy consumption per clothes washer per cycle (kWh electricity, MMBtu gas or oil). [NOTE: electricity consumption is converted from site energy (kWh) to source energy (Quads) 

### by applying a time 

### 8-2

# ·uwev = 

## v = y = 

### 8.2.2 NES Inputs dependent conversion factor (I3tu/kWh). See quantity**·~rc_conv"** 

### below in discussion ofAEC.] Water consumption per clothes washer per cycle (gallons per cycle) ' ' 

### Year in which the washer was.purchased as a new unit. 

### year in the forecast (e.g., 2000-2030)

This section provides information about the quantities and assumptions used to calculate national energy/water savings for clothes washer standards. For each quantity, the discussion includes:
• definition
. • approach • current assumption
The inputs into the NES are listed in the table below.



### 8.2.2.1 Definition 

### Table 8.1 National Energy/Water Saving Inputs Input National Annual Energy Consumption (AEC) National Annual Energy Savings (NES) Source conversion factor (src_conv) Stock ofclothes washers (STOCKv) Cycles per year (Ilcyc1 .) Energy per Cycle (UEC) Water per Cycle (UWC). Shipments Effect ofVoluntary Programs Elasticities


### Annual Energy Consumption (AEC)

·National energy consumption associated with residential clothes washers and dryers. 

### 8-3

### Approach National energy consumption is the product ofenergy consumption per washer times the number ofclothes washers ofeach vintage. ·Tiris approach accounts for differences in unit energy consumption from year to year. Annual energy consumption per clothes washer is calculated as the product ofenergy per cycle times number ofcycles per year. 

### AEC=.

# L STOCKv 

## * (UEC

## *ncyc1e) (5) 

### CurrentAssumptions Energy consumption is calculated at the site (e.g., electricity in kWh consumed in the household). Primary energy consumption is calculated from site energy consumption by applying a conversion factor to account for losses, such as losses 

### in generation, transmission and distribution of electricity. 

### SeeSource Conversion Factor, below. 

### 8.2.2.2 National Energy Savings (NES) Definition Energy savings attributable to the new standards. 

### Approach Energy savings are calculated as the difference betweenprojected energy eonsumption in the Base Case (having no new standards) and the projected energy consumption in the Standards Case. 

### CurrentAssumptions Simple subtraction between two projections. 

### 8.2.2.3 Source Conversion Factor (src_conv) Definition For electricity, this is the factor by which site kWh is multiplied to obtain primary (source) Btu. The source conversion factor accounts for losses 

### in generation, transmission and distribution. For natural gas, this is the loss in transmission and distribution
.. 

### Approach Aftercalculating energy savings atthe site, multiply those site energy savings by a conversion factor to obtain primary energy consumption, usually expressed in Quads (quadrillion Btu). This conversion permits comparison across fuels by taking account ofthe heat content ofdifferent fuels and the efficiency ofdifferent energy conversion processes.



### 8-4

### CurrentAssumptions The source conversion factor is applied to site electricity, to convert from site kWhto source Btu. This analysis asswnes that the source conversion factor changes over time, and applies annual values. The annual values are the U.S. average conversion factors, calculated from AEO 98, Table 

### A4 [l]. FutureAssumptions DOE agrees with the Advisory Committees recommendation that the asswnption of a constant conversion factor should be dropped in favor of a time series projection of conversion factors, changing from year to year. For future conversion factors, DOE proposes to use the following method: (I) Startwith an integrated projection ofelectricity supply and demand (e.g., the NEMS Annual Energy Outlookreference case), and extract the source energy conswnption. (2) Estimateprojected energy savings due to possible standards for each year (e.g., using the National Energy Savings spreadsheet). (3) Feed these energy savings back to NEMS as a new scenario, specifically a deviation from the reference case, to obtain the corresponding source energy consumption. (4) Obtain the difference in source energy consumption between this standard level· scenario and the reference case. (5) Divide the source energy savings in Btu, adjusted for class specific transmission and distribution losses, by the site energy savings in kilowatt-hours to provide the time series ofconversion factors in Btu per kilowatt-hour. Since NEMS can not adjust for class specific transmission losses, this information will come from sources outside ofNEMS. For residential electricity customers, transmission losses might consist of about 3 percent (which basically gets you to a substation) and distribution losses may be about 7 percent (substation to house). For the above case, the conversion factor would be the marginalplantheat rate times I.I0. For natural gas, the transmission and distribution losses amount to about 

### IO% ofprimary energy, so primary energy is calculated from site energy by dividing site energy by 0.90. The base conversions may be based on the maximwn energy savings (e.g., 50% efficiency improvement) to avoid noise within NEMS. At small differences in energy savings, the accuracy within NEMS is less. NEMS drops the leastefficient power producers (cost wise) first, which are not necessarily_the least polluting power plants. The resulting conversion factors will change over time, and will account for the displacement ·ofgenerating sources. Furthermore, the National Energy Savings spreadsheet models will include a clearly defined column of conversion factors, one for each year of the projection. DOE and stakeholders can examine the effects ofalternative assumptions by replacing this column ofnumbers.



### 8-5

### 8.2.2.4 Stock ofClothes Washers by 

### Vintage (STOCKv) Definition
. Number ofclothes washers purchased in a particular year that survive in a later year. The vintage is the age ofthe washer (I-year old up to 16-year old). 

### Approach The NES spreadsheet keeps track ofthe number ofclothes washers purchased each year. Clothes washers are assumedtohave anincreasingprobability of retiring as they age. Theprobability ofsurvival as a :function ofyears since ptJ.rchase is the survival :function. The lifetime was estimated by comparing historical shipments to percent ofhouseholds currently owning the appliance [2]). 

### Example: Base Case cell C51 shows 6.5 million washers purchased as new units inyear 2000. Cell 063 shows 5.6 million ofthese will still be in use in year 2012. The vintage ofthese units in year 2012 is 12 years old (Cell 06). 

### CurrentAssumption For clothes washers, lifetimes range from 12 to 16 years, with an average of 14.1 years (see Chapter 7, section 7.2.2.2, lifetimes). 

### 8.2.2.5 Cycles per Year (DcydJ 

### Definition Loads.oflaundrywashed per household per year. 

### Approach The DOE test procedure assumes 392 cycles per year. In actuality, the number ofloads of laundry washed per household per year depends upon the number ofpersons in the household, and probably on other factors. · 

### CurrentAssumption
. Assume an average of392 cycles per year. 

### 8.2.2.6 Energy per Cycle (UEC) Definition Energy consumed per cycle. 

### Approach In accordance with the DOE test procedure (for modified e_nergy factor, MEF), energy consumption per cyCie includes electricity used by the clothes washer, and energy used by the water heater to provide hot water, and energy used by the clothes dryer to dry the clothing.

8-6.**~** The base case forecast ofunit energy consumption is comprised ofa weighted average of baselineefficiencylevel and35%efficiencyimprovementlevel (35% energyreduction from baseline). The relative weight is a user input, namely, the percent of new washers each year that will be horizontal axis. The unit energy consumption is composed of three components: water heating, machine (clothes washer), and dryer energy. Machine energy is electricity. Water heating energy depends upon the fuel type ofthe water heater. Dryer energy depends upon the fuel type ofthe dryer. (The DOEtestprocedure, AppendixJI assumes a valuebased onanelectric dryer; adjustments were made to this value for a gas dryer). Theunit energy consumptionis first calculated for an all-electric household,**UE~.** Foreach component (water.heat, dryer, and machine), the weighted average is calculated, given the share of new washers that are horizontal axis. Example: In 1995, 1.5% ofnewwashers are assumed to have an efficiency equivalentto horizontal axis. Electricity (kWh/Cycle) Share 98.5% (Baseline) 1.5% (Energy Efficiency equivalent to H-Axis) Water Heater

1.587 0.462 Weighted average 1.570 Machine 0.209 0.133 0.208 (See sheet "Base Case", cells AE46, AF46, AG46.) Dryer 1.430 1.270 1.428
Theaverage electricity consumptionpernewclothes washerdepends uponthe shares offuels for water heating and for clothes drying. The electricity consumed for water heating (calculated in the example above) is multiplied by the percent ofhouseholds having electric water heating, then summed togetherwiththeproductofelectricityperunit for dryertimes percentof households having electric dryers. Finally the machine energy perunit is added. The result is multiplied by cycles per year. UECE=(kWh WH*%WHE)+(kWh DR*%DR E)+kWh machine Example: Continuing with the example for year 1995,


## = = = (1.570 

## * 41.81%) 

### + (1.428 

## * 79.22%) 

### + 0.208 0.656 

### + 1.131 

### + 0.208 1.995 kWh/cycle (6)

Annual energy consumption is the product ofnumber ofcycles per year and unit energy consumption per cycle. 

### In the example for year 1995, AEC:: 

## = I\:ycle 

## * UEC 

## = 392 

## * 1.995 == 782 8-7kWh/year. (See sheet 'Base Case", cell AI46.) Energy consumption for natural gas and for oil are obtained by correcting for the difference inrecovery efficiency ofthe waterheater, and by converting units from kWhto MMBtu. For natural gas, energy consumption per cycle for water heating (expressed in MMBtu) is: {kWhWH

### I 0.15) 

## * (3412Btu/kWh)/ (1,000,000 Btu/MMBtu), where the denominator of0.75 is the recovery efficiency ofthe gas water heater. Gas consumption for clothes dryer is 1.12 

## * kWlfR 

## * (3412Btu/kWh)/( 1,000,000 Btu/MMBtu). For distillate oil, energy cbnsumption per cycle for water heating (expressed in MMBtu) is: (kWhWH 

### I 0.81) 

## * 3412Btu/kWh/ 1,000,000 Btu/MMBtu, \Vhere the denominator of0:81 is the recovery efficiency ofthe oil water heater. 

### CurrentAssumptions Each ofthe components ofenergy consumed per cycle is obtained from the data submitted by AHAM, for baseline and for eachof the efficiency levels. The percent ofhouseholds having water heaters ofeach fuel type and having clothes dryers ofeach fuel type is used to calculate the annual energy consumptionfor eachfuel type. A weighted average unitenergyconsumptionfor newclothes washers (including waterheat, clothes drying, and machine energy) is calculated for each year ofthe projection, using the percent ofnew washers that are horizontal axis selected by the user. 

### 8.2.2.7 Annual Water Consumption (AWC) Definition National water consumption associated with residential clothes washers. 

### Approach National water consumption is the product of water consumption per washer times the mnnber ofclothes washers ofeach vintage. This approach accounts for differences in unit water consumption for year to year. Annual water consumption per clothes washer is calculated 

### as the product ofwater per cycle time number ofcycles per year. 

### AWC= LSTOCKv *(UWC*ncyc1J (7)


### 8.2.2.8 Water per Cycle (UWC) Definition Water consumed per cycle. 8-8

### Approach Similar to the calculations forthe energy use per cycle, the base case forecast ofunit water consumption is comprised ofa weighted average ofbaseline water use and water use at the 35% efficiency improvement level. · · 

### CurrentAssumptions The water consumed per cycle is obtained from the data submitted by AHAM, for baseline and for each ofthe efficiency levels. A weighted unit water consumption for new clothes washers is calculated for eachyear ofthe projection, using the percent ofnewwashers that are horizontal axis selected by the user. · 

### 8.2.2.9 Shipments A key component in determining national energy savings lies in understanding the degree to which clothes washer shipments may change in the future. 

### As of 1993, 93% ofexisting single family homes, 84% ofexisting mobile homes, and 31% ofexisting multifamily homes had a clothes washer. [3]. Giventhese saturations the most basic method for forecasting future shipments would simply involveassuming saturations remain unchanged.and solving for a growth rate 

### in shipments sufficient to keep saturations constant in light ofpopulation growth. Such a basic calculation yields a growth rate of0.9%, withdomestic shipments totaling over 8.5 millionby 2030 (as compared with shipments · of6.2 million 

### in 1996). Naturally, however, there are several factors that could make this estimate inaccurate. These factors include possiblechanges 

### in: the number ofhouseholds, saturation levels, washer lifetimes, washer prices (including operating costs), andconsumer decisions. about whether to repair rather than replace a faulty clothes washer. Because of these complexities, and to improve on the simple saturation based method described above, four different statistical models were studied in order to better forecast clothes washer shipments. Each ofthe four models and their respective pros and cons are discussed here. 

### Auto-RegressiveMovingAverage (ARIMA) Model Under this model analysis ofa univariate time series is usedto predict future values. In the case ofclothes washers, then; only current and past sales data is used to predict future sales under this approach. 

### In conducting the univariate time series analysis, a variety offorms ofa linearprocess, termed an Auto Regressive-Moving Average (4.RIMA) process, are used to describe the time series of interest if the data show no apparent trends and/or seasonal· fluctuations. This approach is described by the following equation, where the decimal constants are used to fit the model to historical sales: 

### cwsa/es(t+1) = 

### 0.4196Z(t)

- 0.4372 Z(t-1) 

### + 0.0879 

### + Cwsaresft) where:**c~~es(tJ** 

### Z(t) Z(t-1) = CW sales 

### in year t =CW sales(t)

- CWsa1es(t-l) = CW sales(t-1)

- CW sales(t-2). 8-9 

### (8)9500 9000 8500 8000 7500


# - 7000 

### "" 

## = 6500 

## = = 

# - 6000 

# - "" 5500 GI 

# - CIS 5000 00 4500 4000 3500 3000 2500 1965 

# • • • ••• 

# •• • 

# • • 

# • • • 

# • • •••• ••• 

# • • 

# ••

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 Year 

### Figure 8.1 ARIMA model sales forecast The advantage ofthe ARIMA univariate approach is that only time series data are needed to run the model. The disadvantages ofthis approach are that (1) historical trends may not be a good guide to the future, and (2) the model cannot explicitly account for changes in the number of households, percent owning washers, price, or operating expense. ·Under this model, any future shipments would not affect shipments. 

### Multi-Variate Time Series Fit 

### In addition to the ARIMA univariate process for projecting sales, a multi-variate time series data analysis was also conducted based not only on clothes washer sales but number ofhouses as well. This approachis described by the following equation, where the decimal constants are used to fit the model to historical sales: 

### CWsa1eltJ =.CWsa1elt-l) 

### + 93.6708 

### + 0.6357 ·{HousesrocftJ -Housestocft-1)} (9) where:**C~a1elt)** 

### . 

## =Clothes washer sales at time t**C~a1~(t-l)** 

### =Clothes washer sales at timet-1 

### HousestocltJ = Housing stock attime t 

### Housesiocft-1) =Housing stock at time t-1 Figure 8.2 shows the sales forecast produced by the multi-variate inethod. Note that the models ability to "backcast" and fit actual shipments is shown as the difference between the black diamonds (actual shipments) and the black trend line**produc~d** 

### by the model.



### 8-109500 9000 8500 8000 7500**~** 7000 

# g 6500 

### c 6000 

## .!! 5500**~** 5000 4500 4000 3500 3000 

# • •**2500+-~-.-~--.-~--.-~-.-~--.~~~~..-~-.-~-.-~-r-~-.-~--.~~~--.** 1965 1970' 1975 '1980 1985 1990 1995 2000 2005 2010 2015 2020 2025. 2030 2035 Year 

### Figure 8.2 Multi-variate time series fit sales forecast The advantage ofthe multi-variate time series method is that only two time series are needed to build the model (i.e., clothes washer shipments from the previous year and the change 

### in the number of households from theprevious year).**Th~** disadvantages ofthis approachare that ( 1) again, historicaltrends may not be a good guide to the future, and (2) the model cannot explicitly account for replacement sales, changes in saturation, price, and operating cost. 

### Saturation/LifetimeModel A saturation/lifetime (SIL) model was developed as yet another alternative for forecasting sales. The equation for the 

### SIL model is as follows: 

### CWsales = 

### House(t)srock · Sat(t)cw

- House(t- l)srock · Sat(t-l)cw + 

### CWremovals (10)
where 

## CW..a1.,,, House(t)stock 

### Sat(tJcw House(t-1) stock 

### Sat(t-lJcw cw,emovals 

## =Clothes washer sales 

## =Housing sto.ck at time, t = Saturation ofclothes washers at time, 

### t 

## =Housing stock at time, t-1 

## = Saturation ofclothes washers·at time, t-1 

## =Clothes washers retired from service. The 

### SIL model assumes that the saturation of clothes washers varies with time. Clothes washer removals are based upon assumptions regarding the distribution ofclothes washer lifetimes, and the above functional form of the model allows for flexibility in that different assumptions 8-11regarding clothes washer saturations and lifetimes can be used in an attempt to get the best fit to historical data. Figure 8.3 shows two sets ofprojections from the SIL model based on slightly differing asswnptions regarding saturations and lifetimes. In the top forecast, saturations are estimated through the use ofa curve fit based on five years of saturation data from past Residential Energy Consuinption Surveys (RECS) [4]. So that saturations will not exceed 100%, the high end ofthe saturationcurve fit asymptotically approaches 100%. Clothes washer lifetime inthis forecast is taken
9500 9000 8500 8000 7500**~.** 7000 

### g 6500 

## = 

## e 6000 

### re 5500 "; VJ 5000

. 4500 • CWsales -Sat=5 point curve fit; Removal= 13.6 avg life

- Sat= auto-regres:Sive fit; Removal = separate auto & wringe

4000 • 3500 3000**2500-t--~-.-~-,-~-..-~--.~---,.--~r-~-.-~-.-~~~-..-~--r~~.--~-r-----.** 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 Year 

### Figure 8.3 Saturation/Lifetime model sales forecast
to be 13.6 years, derived from the best fit ofthis model to the shipment data.2 In contrast, the lower forecast forgoes· the curve fit and uses a saturation estimate based on an auto-regressive 

### fit. Clothes washer lifetimes in this forecast are differentiated on the basis ofvarious attributes, namely whether they are automatic or wringer washers. The advantages ofthe saturation/lifetime method are that (1) the method explicitly accounts for washer lifetimes, (2) housing and saturation stocks are.based only on time-series data, so that different housing and saturation fits can 

### be used to get ''good" fits to historical sales. The disadvantages ofthis approach are that (1) removals must 

### be based on asswnptions about lifetimes, and (2) the model cannot explicitly account for the impact ofprice and operating cost onhousing and saturation stocks.

2 The 13.6 years was mathematically determined to give the bestfit to historical data. In other parts ofthe TSD lifetimes from MTSC are used (see text). 

### 8-12

### AccountingModel The accounting model seeks to foreeast shipments by determining sales destined for new homes plus the additional sales meant to replace clothes washers being retired from service. For those sales meant for the replacementmarket, the model accounts for the impact**o~homes** which are being retired from the existing housing stock. The accounting model is described by the equation: 

### CWsales 

## = [Satnew home • Housestarts] + 

### [CW,emovals

- (Satstock home
. House,etired)] (11)

where**C~a1es** = Clothes washer sales 

### Sat new home = Saturation ofclothes washers in new housing starts 

### Housestarts =New housing starts 

### CWremovals = Clothes washers retired from service 

### Satstock home = Saturation ofclothes washers in existing housing stock 

### House,etired = Homes retired from existing housing stock
Clothes washer saturations in new housing starts are projected to have 1 % growth rates up to maximum saturations of 100% in single-family homes and 97% in both multi-family and mobile· homes. Saturations in the existing housing. stock as of 1980 are assumed to remain frozen at 1980

. levels of86.1 % in single
...family homes, .28.4% in multi-family homes, and 65.5% in mobile homes. Clothes washer removals are based upon a distribution ofclothes**wash~r** lifetimes (MTSC 1980)(5]. The retirement function has a relatively narrow distribution with a mean clothes washer.lifetime of. 14.1 years. Figure 8.4 provides projections ofhistorical and future sales based on the accounting model. As the figure makes clear, the accounting model accuratelyestimates sales up to the year 1990 but significantly underestimates sales afterthis date. A couple ofpotential causes for this error are the models inability to vaiy: 1) clothes washer lifetime and 2) the saturation ofwashers in both the existing housing stock and new housing starts. With regard to future sales estimates, because the assumed range oflifetimes is narrow, sales projections exhibit an oscillatory behavior.



### 8-139500 9000 8500 8000 7500**~** 7000 

### g 6500 

# = ::::, 6000

. I'll**~** 5500

CIS en 5000 4500 4000 3500 3000 2500 1980 1985 1990 1995 2000 Figure 8.4 Accounting model sales forecast 2005 Year 2010 2015 2020 2025 2030

The advantages ofthe accounting model are that (1) itis a straightforward and simple model, (2) it explicitly accounts for new washersseparately 

### in new houses and replacements, and (3) price and operating costs can be incorporate_d into saturation terms (i.e, in the term

### Sat new home). The disadvantages ofthe acc01μ1ting model are that (1) saturations ofwashers in new and stock homes must be forecast, (2) housing starts mustbe forecast (e.g., based onAEO projections), and removals must be based on assumptions about**life~es.**



### 8-14Figure 8.5 compares the results ofthe different forecasting approaches discussed here.
9500 9000 8500 8000 7500**~** 7000 

### g 6500 

## = 

## c 6000 

### ., Col 5500 "; v.i 5000 4500. 4000 3500 3000 • CWsales -Accounting -ARIMA

- - - Time Series -Sat/Life 1 --M- Sat/Life 2**2500+-~--...~~-.-~~.,--~-,-~~-r-~~.---~--r~~....-~----,~~--.-~~-.--~--,** 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 Year 

### Figure 8.5 Visual comparison offorecasting models
Table 8.2 shows the degree to which each approach accounts for different variables that impact actual shipments.

I 

### Table 8.2 Variables accounted for by different forecast approaches Model Variables Accounted For: washer sales number of saturation washer lifetime price & households operating cost ARIMA 

## x Multi-variate 

### x x Saturation/ 

## x 

## x x 

## x Life Accounting 

## x x x x x
In as much as the accounting model is the only approach that takes into account price and operating costs, the accounting model is the recommended model for forecasting clothes washer shipments inthis analysis. The other models do, however, provide important cross-checks to results obtained via the accounting model.
8-15·8.2.2.10 Voluntary Programs A separatereportdescribes voluntaryprogramsalready inplace, whichare promoting clothes washers with higher energy efficiency (see AppendixF)[6]. The impact ofthese programs has been expressed here as the percent of new clothes washers each year that will have efficiencies corresponding to those ofhorizonial-axis washers (35% energy efficiency improvement from the baseline washer design). The current spreadsheetprovides the user with the opportunity to specify the percent ofnew shipments that will achieve 35% energy efficiency improvement (relative to the baseline washer design). For illustrative purposes, Table 8.3 shows the values used in the draft spreadsheet.

- Table 8.3 Percent ofAnnual Clothes Washer Shipments having -Energy Consumption Equivalent to Horizollltal Axis Washers Year Percent ofshipments 1995 1.5% 2000 4% 2010 9% 2020 14% 2030 19%
8.2.2.11

- Elasticities Inprevious analysis, the future shipments ofclothes washers were assumed to depend, at least in part, on the purchase price ofthe clothes washers, and also on the operating expense. According to economic principles, increasingthe price ofclothes washers may decrease shipments. Bythe same principles,**d~creasing**the operatingexpense may increase shipments. Previous analysis also indicates

-that these responses are not equally weighted, that is, the response to price changes is often greater than responses to operating expenses. One way to express these economic responses is by means ofelasticities. An elasticity is the percent change in one quantity in response to a percent change in a driving variable. The price elasticityofdemand for clothes washers is the change in annual shipments (expressed as a percent of baseline shipments) corresponding to a percent change in price. For example, ifa 10% increase in price causes a 2% decrease 

### in shipments, then the price elasticity is -0.2 (i.e.: -2%/10%). Previous analysis did not include consumer expenditures for water and wastewater in operating expenses. Research is on-going to develop new estimates ofprice and operating expense elasticities to account for: (1) changing the definition of operating expense to include water and

. 8-16wastewater rates; 

### (2) changing the definition ofthe value ofenergy savings from average prices to· marginal rates; and (3) a longer time series to include more recent data.



### · 8.2.3 Preliminary Results See section 8.4.


### 8.3 NET PRESENT VALUE (NPV) 8.3.1 NPV Metric NPV is the value inthe present time ofa time series ofcosts and savings. Net present value is described by the equation: 

### NPV=PVS-PVC (12)
where: PVS 

## = present value ofsavings ('Total Fuel 

### & Water Savings')



## =



# L Tota/OperatingCostSavingsY*DiscountFactorY (13)

PVC =

y 

## = present value ofcosts ('Total Equipment Cost')


## =



# L Tota/EquipmentCostY*DiscountFactorY (14)
Years (from start date ofstandards to the year when units purchased in 2030 retire) The net present value is calculated from the projections of national expenditures for appliances, including purchase price and operating costs. Costs and savings are calculated as the difference between a new standards case and a base case without those new standards. Future costs and savings are discounted to the present. A discount factor is calculated from the discount rate and the number ofyears between the
·'present" (year to which the sum is being discounted) and the year in which the costs and savings occur. The net present value is the sum over time ofthe discounted net savings.



### 8-17

### CurrentAssumptions The assumptions are contained inthe terms PVC and PVS, which _are discussed below. Net present value (NPV) is the value today ofa future stream ofsavings less expenditures. 

### In the NES spreadsheet table labeled "Cost and Net Present Value,""fotal Fuel and Water Savings"is the presentvalue of the savings.·'Total Equipment Cost"is the present value ofincreased purchase prices. ''NetPresent Benefit"is the differencebetween 'Total Fuel and Water Savings"and 'Total Equipment Cost." 'Benefit/Cost Ratio" is the ratio of 'Total Fuel and Water Savings" to 'Total Equipment Cost."
8.3.2 NPV Inputs This sectionprovides information about the quantities and assumptions used to cii.lculate net present value for clothes washer standards. For each quantity, the discussion includes:
• definition • approach • current assumption Table 8.4 summarizes the inputs to the NPV calculation. 

### Table 8.4. Net Present Value Inputs Input Discount factor Net Present Value (NPVY Present Value ofCosts (PVC)4 Present Value ofSavings (PVSf Total Equipment Cosiy Total Operating Cost**Saving~**

3 Referred to in the spreadsheet as 'hetpresent benefit." 4 Referred to in the spreadsheet as "total equipment cost" (discounted). c 5 Referred to in the spreadsheet as ''total energy and water savings" (discounted). 8-18

### 8.3.2.1 Discount Factor Definition Thefactor by whichto multiply monetaryvalues inoneyear, inorderto determine thepresent value in a·different year. Discount Factor is also described by the equation: Discount Factor= 11(1 

### + DiscountRate)O'ear-presentyear) (15) 

### Approach For example, to discount monetary values in the year 2000 to the value in year 1997, assuming a discount rate of7% is l/(l.07J or 0.816. 

### CurrentAssumptiqns The discount rate is assumed to be 7% real.6 

# n.e present year is defined to be 1997, for consistency with the year in which the manufacturing cost data was collected. In the NOPR the present year is expected to be defined as 1998. · 

### 8.3.2.2 Present Value ofCosts (PVC) Definition. Totalequipmentcost, discountedtothe present, and summed overthe timeperiod (from start ofstandards to year 2030). 

### Approach Costs are typically increases in purchase price associated with the higher energy efficiency ofappliances purchased in the standards case compared to the base case. Costs are calculated as the difference in purchase price for new appliances purchased each year. ('tlel Unit Price'), multiplied 

### by the shipments in the standards case 7 ('Total Equipment Costs'). 

### CurrentAssumptions The chiefassumption made in calculating PVC lies in determining the discount factor to be applied. Here the discount factor is taken to be 7% (seealso Discount Factor, above).

·• 6Used by DOE in previous rulemaking for the National ImpactAnalysis. Higher and lowervalues can be used as sensitivities (i.e., to bound a range ofdiscount rates). 7 Counting the reduction in energy consumption from a reduction in shipments as a savings would be incorrect. Ifstandards cause a decrease in shipments, then using the lower shipments in the standards case reduces the NPVappropriately. To illustrate with an extreme example, ifstandards cause shipments to be zero, then NPV is zero, no matter what. the shipments were in the base case. Using the shipments from the standards case avoids miscounting any reduction in shipments due to standards as a savings. · 8-19

### ·8.3.2.3 Present Value ofSavings (PVS) Definition Annual operating cost savings (difference between base case and standards case) discounted to the present, and summed. 

### Approach
· Savings 

### ('\:lei Unit Op. Cost') are typically decreases in operating costs associated with the higher energy efficiency ofappliances purchased in the standards case compared to the base case. 'Total Operating Cost Savings" is the product of savings per unit times number ofunits ofeach vintage surviving in a particular year. Appiiances consume energy overtheir entire lifetime, in some cases including energy consi.imed after year 2030. Net savings eachyear.are calculated as the difference between Total Operating Cost Savings and Total Equipment Costs.8 The savings are calculated over the 

### life ofthe appliance, accounting for the energy and water rates each year. 

### CurrentAssumptions As with PVC, the chiefassumption made in calculating PVS lies in determining the discount factor to be applied. Here the discount factor is taken to be 7% (see also 

### DiscountFactor, above). In addition, see 

### Total Equipment Cost and 

### Total Operating Cost Savings, below. 

### 8.3.2.4 Total Equipment Cost Definition Annual changeinpurchaseprice (differencebetweenbasecase and standards case), multiplied by shipments in the standards case. 

### Approach Purchase price per clothes washerinthe standards case is subtracted from purchase price per clothes washer 

### in the base case for one year. The result is multiplied by the projected shipments 

### in that year. 

### CurrentAssumptions See section 8.2.2.8 for a discussion ofshipments. As noted, the accounting**~odel** is currently assumed to produce the best shipments forecast.

8 In the spreadsheet, Total Equipment Costs are expressed as a negative number (the difference between the base case and the standards case) then swnmed with Total Operating Cost Savings (the difference between base case and the standards case). 

### 8-20

### • ---- ----- - ---------- -- 

## ----------
8.3.2.5 Total Operating Cost Savings Definition
. Annual 

### national operating cost savings, calculated as the difference between total operating cost in the base case minus total operating cost in the standards case. 

### Approach Operating expense per clothes washer in the standards case is subtracted from operating expense per clothes washer in the base case for one year. The result is multiplied by the projected shipments in that year. Positive**valu~s** 

### are savings (e.g., operating costs in the standards case are lower than 

### in the base case). 

### CurrentAssumptions See Cyclesper Year and Energyper Cycle as discussed in Sections 8.2.2.5 and 8.2.2.6, respectively, and the discussion ofenergy prices in Chapter 7.


### 8.4 PRELIMINARY OUTPUT FORNES & 

### NPV The NES spreadsheets offer a range of possible outputs, all of which depend on the assumptions used in deriving the results. Table 8.5 details the assumptions used in the NES calculations in this analysis. 

### 8.4.1 Assumptions Table 8.5 Assumptions Used for Generating Preliminary National Impacts Fuel Price Water Price Discount Rate and the Year ofthe NPV Start Year for NewStandards Annual Real Change in Water & Sewer Cost (water price escalator) · · Manufacturing Cost Total Mark up on Manufacturer Costs Energy Consumption Data Clothes Washer Shipments Percent Horizontal-Axis Washers Primary Energy Conversion Factors EIA Annual Energy Outlook 1998 to the year 2020 and extrapolated to the year 2030 Average: $3.18 per 1000 gallons Discounted to the year 1997 (in 1997$) at 7 percent (real) 2003 0 percent Shipment-weighted average ofthe most likely (from AHAMdata) 1.731 AHAMdata Assumed same for standards and base case (inelastic to price and energy savings) 1.5 percent in 1995, increasing by 0.5 percent each year AE098



### 8-21Using these assumptions, we highlight preliminary output in key areas below. 

### 8.4.2 Base Case Shipments Theresults ofthe Accounting model are used to establish a base case shipment forecast. The base case forecast is shown in Figure 8.4.


### 8.4.3 National Energy Savings and Net Present Value from Possible Standards Preliminarynational energy savings and net present value results from the NES spreadsheet are shown in Table 8.6. More detailed results are also available in Appendix D. Results are cumulative to 2030 and are shown as absolute energy and water savings and as the discounted value ofthese savings in dollarterms. It should be noted that, currently, the NES spreadsheet uses a single value for the base case efficiencies rather than a distribution ofvalues. 

### Table 8.6 Preliminary National Energy Savings Results (2003 to 2030 cumulative) Efficiency Improvement Energy Savings Water Savings Net Present Value (NPV) over the Base Case Quads trillion '(billion 1997$) gallons 5% 0.36 0.05 $1.02 10% 1.18 0.46 $2.41 15% 2.18 0.45 $3.80 20% 2.66 0.59 $3.67 25% 5.09 10.13 $11.07 35% 7.85 14.62 $13.47 40% 7.90 14.62 $13.53 45% 9.49 12.47 $8.81 50% 10.06 12.47 $9.07
Inthis example (see Tables 8.7 and 8.8), the value ofsavings and costs are discounted at 7% to 1997 from 2003 to 2030 (in billion 1997$). The value oftotal energy and water savings is**$5~20** billion, the total equipment cost is $1.53 billion, and theresulting net present value is $3.67 billion. The corresponding ratio ofbenefits to costs is 3.40 to 1.

8-22 

# ..

.•

### Table 8.7 NES Results: 20°/o Improvement (MEF

## = 1.021) Cost and Net i>re5ent Values Discounted from 2003 at 7% real to 1997 (in billion 1997$) Total Energy & Water Savings Total Equipment Cost Net Present Benefit Benefit/Cost Ratio


### Table 8.8 Efficienc

rom 2003 to 2010 to 2020 to 2030 0.31 1.40 2.66 0.87 1.64 0.11 0.51 0.98 $5.50

. $1.53 $3.67 3.40

0.02 0.04 l

0.31 0.59 Figure 8.6, below, illustrates the typical pattern ofnational savings and costs resulting from standards over time.**Th~** heavy line runningjust below the energy and water savings bars indicates the undiscounted net national consumer impact ofstandards overtime. Figure 8.6 shows the nature ofnet savings for a 20% efficiency improvement relative to thebaseline. Alternate standard levels would yield different values.



### j 2000 1.0

- 0.8
0.6

- 0 c. 0.4

- r- 

### "' "' 0.2

- 0.0
-0.2

- -0.4

- 2005 2010 2015



# - - 

# - 2020 2025

':i f( "**~ ~** **~ ~**  ' fl'**·~** } ' 

# .
,.. 0**~"** < .**~ ~** **~ ~**  ii c::::::JPrice Change 1!!!!!!131 Energy & Water Saving --:-Net Saving 

### : ! " tf



### ..... 

# - 2030
1**~** t • 

### i ' t



### Figure 8.6 Net Present Annual Values ofa Clothes Washer Standard with a 20% Efficiency Improvement
8-23

### 8.5
. USER INSTRUCTIONS FOR SPREADSHEET Itis possible to examine and evenreproduce many ofthe detailed results obtained inthis part ofthe analysis using 

## a Microsoft Excel spreadsheet available on the U.S. Department of Energy Officeof Codesand Standardswebsiteat: www.eren.doe.gov/buildings/codes_standards/. TheNES spreadsheet is titled "NES_CWV2.LXLS ." The spreadsheets posted on the web site represenfthe latest versions ofthe applicable models, and have been tested with both EXCEL97 and EXCEL95. 

### Basic OperatingInstructions: 

### 1. Once you have downloaded the NES file from the Web, open the file using Excel. At the bottom, click on the tab for sheet "NES." 2.
. The screen will display two tables (''Energy Savings and Water Savings"and "Cost and Net Present Value') and a chart ("Cost and Savings ofNew Clothes Washers'). (Use Excels commands at the top "View/Zoom" to change the size ofthe display to make it fit your monitor.) 

### 3. To see the assumptions, click on 'List Inputs." 

### 4. To change assumptions on 'ListInputs" click on the assumption you wish to change, and click on the new asst.imption from the menu. 5. After selecting each assumption, click on ''ListInputs." 'Cancel" 

### will erase your last set of changes to assumptions without recalculating results. 

### 6. Finally, to see results, click on "Calculate." Using these steps you can explore outputs using a range ofalternative assumptions. Table 8.9 shows the quantities and their locations in the NES spreadsheet (NES_CWV2.l.XLS). For example, NES occurs in sheet 'Standards Case", in column AT (annual energy savings).

8-24 •.

•.-:.. 

### Table 8.9 National Energy Saving Quantities and their Locations in the National Energy
· Savings Spreadsheet QUANTITY SHEET COLUMN ROW National Annual Energy Consumption (AEC) Ba5e or Standards Case AO-AS 46-106 National Annual Energy Savings (NES) Standards Case AU 46-81 Source conversion factor (src_conv) Elec Conversion B 41-76 Stock ofclothes washers (STOCKy) Base or Standards Case D-AA9 46-81 Cycles per year (Ilc,c1J Setup D 27 Energy per Cycle (UEC) Engineering G-1 8-17
Table 8.10 shows the quantities and their locations in the national energy savings spreadsheet 

### (NES_CWV2.l.XLS).
. Table 8.10 Net Present Value Quantities and their Locations in the National Energy Savings Spreadsheet QUANTITY SHEET COLUMN ROW Discount factor Standards case BA 46-106 Net Present Value. (NPVY0 (Copy)NES N 21 (Original) Standards Case AM 87 Present Value ofCosts (PVC) 11 (Copy)NES N 20. (Original).Standards Case AM 86 Present Value ofSavings (PVSjl (Copy) NES N 19 (Original) Standards Case AM 85 Total Equipment Costy Standards case AY 46-106 Total Operating Cost Saving; Standards Case AW 46-106


### 8.6 INDIRECT EMPLOYMENT IMPACTS 8.6.1 Introduction Indirect impacts are defined as netjobs created or eliminated as a consequence ofreduced household spending on energy, and possibly increased spending on purchase price of appliances. National impacts will be estimated for major sectors ofthe U.S. economy. Public and commercially available data sources will be utilized to estimate employment impacts. Commercially available

9 These columns may be hidden. To displaythem, use theExcel command sequence Fonnat/Column/Unhide. 10 Referred to in the spreadsheet as 'het present benefit." 11 Referred to in the spreadsheet as "total equipment cost" (discounted). 12 Referred to in the spreadsheet as "total energy and water savings" (discounted). 

### 8-25software will be utilized. At least three scenarios will be analyzed to bound the range ofuncertainty in future energy prices. All methods and documentation will be made available for review. New appliance standards are expected to reduce household expenditures for energy. The IndirectEmploymentImpacts analysis isintendedto estimatenationaljobcreationoreliminationfrom possiblestandards,resultingfromreallocationof theassociatedconsumerexpenditures forpurchasing and operating appliances. · · 

### 8.6.2 Assumptions Household expenditures for energy exceed $100 billion per year (not counting gasoline consumption for transportation). New appliance standards are expected to decrease energy consumption, and therefore reduce household expenditures for energy. The same standards may increasethe p.urchase price ofappliances (retail price plus sales tax, and installation). Over time, the increased purchase price is paid back through energy·savings. The savings in energy expenditures may be spent on other items. Using an input/output model ofthe U.S. economy, this analysis seeks to estimate the effects on different sectors, and the net impact onjobs. A simplemodel mightinvolvereduced expenditures for energy, butreallocationof thatmoney toward otherexpenditures. Even savings ofa few dollars perhousehold peryear, taken in aggregate for the nation, may trigger some changes in employment While the creation ofa new industry is not anticipated, small increases injobs in many sectors may occur at the same time that decreases may occur for energy supply and appliance manufacturing employment This analysis is intended to quantify the indirect employmentimpacts from appliance standards (direct employmentimpacts will. be evaluated in the Manufacturer Impact Analysis). · 

### 8.6.3 Methodology Theexisting structureof the economyfrom 1977through 1994hasbeenderived from publicly availabledata. The Department ofEnergys Office ofBuilding Technologies, State and Community Programshas developed a spreadsheetmodelthatfocuses onthose sectorsmostrelevanttobuildings. This software (IMBUILD) is a PC-based economic analysis system that characterizes the interconnections among 35 sectors as national input-output structural matrices. The model can be applied to future time periods. The IMBUILD output includes employment, industry output, and wage income. The impacts ofnewappliance standards as household energy savings (reduced energy expenditures) and increased appliance purchase price will be estimated elsewhere (see ''National Energy Impacts') and then those changes in expenditures will be introduced to IMBUILD as perturbations on the existing economic flows. The net national impact onjobs by sector will be estimated. · .For example, appliance standards could increase retail prices and reduce household energy expenditures. In the short term, this may reduce jobs in appliance manufacturing (for example, Household Appliance sector) and energy supply (gas and electric utilities'sectors), while increasing
8-26

### expenditures on other consumer items, causingjob creation in other sectors, especially over the longer term.



### . 8-27

## REFERENCES 1. 

### U.S. Department ofEnergy/Energy Information Administration (DOE/EIA) 1997. Annual Energy Outlook 

### 1998. Washington, D.C.: DOE/EIA-0383(98) December, pp. 106-107. 2. MTSC Inc./ Management Technology Division 1980.Energy Capital in the U.S. Economy. 

### Bethesda, MD: MTSC. November. 3. U.S. Department of Energy/Energy Information Administration (DOE/EIA) 1995. ResidentialEnergyConsumptionSurvey:Household EnergyConsumptionand Expenditures 1993. 

### DOE/EIA-0321(93) October. 

### 4. Ibid. 

### 5. MTSC. 1980. Op. cit. 

### 6. Lawrence BerkeleyNational Laboratory (LBNL) 1998.Revised DraftReporton Consumer Research/or Clothes Washers. 

### Comment No.85. Docket No. EE-RM-94-403 Berkeley. April
. 3. (Currently available at: http://www.erendoe.govlbuildings/codes _standardslnotices/notcOO12/index.htn}



### 8-28

### CHAPTER 9: MANUFACTURER IMPACT ANALYSIS
9.1 INTRODUCTION The manufacturer impact analysis (MIA) estimates the financial impact of standards on manufacturersandcalculatesimpacts oncompetition, employment, and manufacturingcapacity. The Departments methodology for conducting the MIA for the clothes washerrulemaking is presented in detail in Chapter 2, Section 2.8. The MIA will be conducted in three phases: • · Phase l consists of two activities, namely, preparationofan industry characterizationand the identification ofissues. • Phase2 has as its focus the largerindustry. Inthisphase, the GovernmentRegulatory Impact Model (GRIM) will be used to perform an industry cash flow analysis. • Phase 3 involves repeatingthe process described in Phase 2 (the industry cash-flowanalysis) but on different sub-groups ofmanufacturers. Phase 3 also entails calculating additional impacts on competition, employment, and manufacturing capacity.


### 9.2 PHASE 1: INDUSTRY CHARACTERIZATION Priorto initiating the cash-flow analysis, financial data onvariables such as tax rate, working capital, depreciation, cost ofcapital, etc., need to be input into GRIM. To obtain estimates for these inputs, DOE has analyzed information available from publicly available sources of firm-specific financialinfonnationformajorclotheswashermanufacturers- SEC- I

### OKReports, Moody's Company Data Reports, Standard & Poor's (S&P) Stock Reports, and Value Line Reports. The financial reports ofthe five major U.S. clothes washer manufacturers were.studied. The
· top three clothes washer manufacturers by market share, namely, Whirlpool, Maytag and General

Electric,together accounted for 88 percentof all clotheswashersmanufactured inthe U.S. inthe year 1996. For Whirlpool and Maytag, home appliance manufacturing represents the major portion of their business and revenue generation. Hence financial data obtained from a top-down analysis of theirSEC 10-Kreports wouldberepresentative of the clotheswasherindustry (assumingthatclothes washers are a microcosm ofthe total major appliance industry). GE, however, is highly diversified, and its appliance manufacturing business-GE Appliance-accountedfor only a small percentage (8%) ofGE's total revenues in 1996. Similarly Amana (which was a part of Raytheon until recently when it was sold to Goodman Holdings) accounted for only about 12 percentofRaytheon's annual sales (and 6percent ofthe profits). Hence, like GE, the information obtained fromRaytheon's SEC 

### 10-K reports may not be representative of ·its appliance manufacturing business (i.e., ofAmana's**financi~** structure)
.

.Frigidaireis a subsidiary company ofElectrolux AB, a European company. Electrolux is the world's largest manufacturer ofhousehold appliances, such as white goods, vacuum cleaners and
9-1·outdoor products. MostofElectrolux'srevenues comefromoperationsoutsideNorthAmerica(59% from Europe, 6% from Asia, and 6.4% from Latin America) and hence financial data from Electrolux'sincome statements may notbe indicative ofthe business environmentinNorthAmerica.**Also~** differences in accounting principles for financial reporting purposes (between U.S. and other countries) might lead to a different financial structure. ValueLineand Standard& Poor's (S&P)compositeindustry statistics forthehome appliance industry were reviewed. Value Line's statistics for the home applianceindustry are a composite of the following companies: Whirlpool, Maytag, Fedders Corp., Black & Decker, Toro, and National

. 

### . Presto Industries. In S&P reports, appliance manufacturers are included in the "Household FurnishingsandAppliance" industry. The S&P statistics are a composite of thefollowing companies: Armstrong World, Basset Furniture, Fedders Corp., Maytag, Mohasco, Roper, Rubbermaid, Whirlpool, White Consolidated Industries, and Zenith Electronics. Given the composition ofthe industry-composites,Value Line's industry statistics are possibly more representative ofthe clothes washer industry.

. . The tables in the following sub-·sections present historical financial data obtained from the sources previously described. · 

### 9.2.1 Tax Rate Firms pay an annual tax, levied by the Federal and state governments, based on income. Taxableincome andthe income taxes paid are presented as separate line items ina company's income statement. Table 9.1 presents the effect_ive income tax rates for

# two major appliance manufacturers overtheperiod 1990-96. Italsopresentsthe industryaveragetaxrate whichis obtainedby weighting effective income tax rates offour clothes washer manufacturers by their clothes washer shipments. This information was obtained from the manufacturers' income statements, by dividing the income taxes paidby thetaxable income. Thetaxrate for the industry composite, as reported by Value Line, is in close agreement with that for the industry average. 

### T bl 91 A

a e 

# .**vera~e** 

### mcome tax rates Manufacturer Avg. Tax Rate (1990-96) A 46.5% B 45.4 % Industry Average 42.8% Value Line 40.0% The Department proposes using an income tax rate of42.8% as an input to GRIM. IQ.is value is based on the industry average tax rate. .


### 9-2

### 9.2.2 Working Capital Workingcapitalis defined asthe**differen~** betweena firm's short-term (orcurrent) assets and short-term (or current) liabilities. It is the part ofa company's capital that must be-maintained in order to undertake day-to-day operations ofthe company without experiencing liquidity problems. Because cash inflows and outflows are not synchronized, a firm needs to maintain access to cash either from its own liquidity portfolio, or from back-up liquidity. This capital cannot be used in the investing activities ofthe company. Table9.2 presents average workingcapitalmaintainedbytwo majorappliancemanufacturers as a percentage of theirannualrevenues. Data

### has beenaveraged overtheperiod 1990-96.·Working capitaldata is also presented forthe home appliance industry composite as reported by Value Line. Workingcapital is calculated by subtracting current liabilities from current assets ofthe firm, where the current assets and liabilities are obtained from the firm's annual balance sheets. 

### Table9 2 A 

# . vera2e W ki C . I or nj? ap1ta as a p

### ercenta2e ofR venues e Manufacturer Working Capital as % of Revenues (1990-96) A -0.70 B 16.09 Value Line 6.76 Clearlythere exists considerable variability in the working capital requirements ofvarious firms based on information obtained from SEC 1.0-K reports. Discussions With appliance industry analysts indicate that working capital requirements are in the 7-16% range. Based on this information,the Department will assume working capital to be 11.5% (average of7% and 16%) of revenues for input into GRIM. In general, companies will strive tolower their working capital needs to the lowest amount possible so as to free up as much cash flow as possible for revenue-generating investing activities. According to Standard & Poor's, in the last few years improved working-capital management techniques have greatly reduced the investment that might otherwise have been required.


### 9.2.3 Selling, General and Administration (SG&A) Table 9.3 presents average SG&A expenses for two major appliance manufacturers as a percentage_oftheir annual revenues. The data has been averaged over**seve~** years, 1990 through 1996. SG&Ainformation is presented as a line item in individual manufacturers' income statements, presented in separate SEC 10-K reports. 9-3

### Table 9 3 A 

# . veraee SG&AE 

### xpense as a p t 

### ercen aee o fR venues e Manufacturer SG&A as % ofRevenues (1990-96) A 18.43 B 17.01 Selling costs include any expenseor class ofexpense incurred iii selling or marketing. In a· manufacturing firm, selling 

### costs include both marketing (order getting) costs and logistics (order-filling)costs. Marketing

### costs includemarketresearch, advertising,point-of-salepromotions, and salespersons' compellSation and travel costs. Logistics costs include warehousing anddelivery costs as well as record-keeping costs associated with processing an order. General and Administrative(G&A) costs are expenses incurred for the general direction of the enterprise. G&A costs include the cost ofservice andstaffunits (such as the personnel office)**~d** general corporate costs, including compensation oftop managementand donations to charitable organizations. The Department proposes using a value of 18.0% (revenue weighted average ofthe two manufacturers

# in Table 9.3) ofrevenues for SG&A expense.as an input to GRIM.


### 9.2.4 Research and Development (R&D) Researchand development (R&D) costsarecostsincurred forthepurpose ofdevelppingnew or improved goods, processes or services. The results ofR&D efforts are increased revenues or lowercosts. Since these results will notoccur until future periods, often five ormore years after the researchprojectis started, a case couldbemade for capitalizingR&D costs and amortizingthem over the benefits period. However, this practice would be counterto the Generally Accepted Accounting Principles(GAAP) promulgated by the Financial Accounting Standards Board 

### (FASB). Instead, it requires that R&D costs be treated as period costs-that is, charged offas an expense ofthe current period. The reason given by the FASB for its requirement is that by their very nature, the future benefits to be derived from current R&D efforts are highly uncertain. Table 9.4 presents the R&D costs incurred by two major appliance manufacturers as a percentage oftheir annual revenues. TheR&D cost datahas been averaged overthe past four years, 1993 through 1996.



### 9-4

### Tabl 9 4 A e 

# . vera2e R&DCosts as a p t 

### ercen a2e ofR noes eve Avg. R&D Cost as % of Manufacturer Revenues (1993-96) A 2.01 B 1.50
Data for the period 1990-1992 is not available as the separate disclosure ofresearch and development expense is a relatively recent requirement. Formerly, most companies included this expenseas a part

### of general and administrative expenses. Becausethe amount spentonresearch and development can provide an important clue as to how energetic the company is in keeping its products and processes up to date, FASB requires that this amount be reported separately ifit is material. Based on the information in Table 9.4, the Department proposes using R&D expense as 1.80% (revenue weighted average ofthe two manufacturers inTable 9.4) ofrevenues for input into GRIM.
.
9.2.5 

### Depreciation Plantand manufacturing equipment assets have limited useful lives; that is, they will provide serviceto the firm overa limitednumberoffuture accountingperiods. A fraction ofthe cost ofthese assets· is therefore properly chargeable as expense .in each ofthe accounting periods in which the assets provide service to the firm. The accounting process for this gradual conversion ofplant and equipment capitalized cost into expense is called depreciation. Table 9.5 presents average depreciation expensefor two major appliance manufacturers as a percentage oftheir annual revenues. The data has been averaged over seven years, 1990 through 1996. Depreciation is presented as a separate line item in the manufacturers' income statementsis presented in SEC 10-K reports. 

### Table 9.5 

### A vera2e D 

### . ti E eprec1a on xpense as a p**ercenta~e** 

### ofRevenue Manufacturer Depreciation as % of Revenues (1990-96) A 3.54
I B 3.43 Value Line 3.66

9-5Based on information in Table 9.5, the Department proposes using depreciation expense as**3.~1%** (revenue weighted average ofthe two manufacturers) ofrevenues for input into GRIM.
9.2.6 

### Capital Expenditures These are the expenditures made for additions to fixed assets, like plant, equipment, etc., intendedto benefit future accounting periods (in contrast with other expenditures that benefit the current period). These expenditures are aimed at increasing the capacity, efficiency, life span, or economy ofthe operation ofan existing fixed asset, or, in some cases, to build entirely new assets. Table9.6 presents average capital expenditures for two major appliance manufacturers as a percentage oftheir annual revenues. The data 

### has been averaged.over seven years, 1990 through 1996. 

### Table 9.6 

### A vera2e C "t 

### IE api a xpen 1 

### res as a p t 

### ercen age ofRevenues Manufacturer Capital Expenditures as % ofRevenues (1990-96)
. A 4.47 B
. 4.33 Based on information in Table 9.6, the Department proposes using capital expenditures as 4.43% (revenue weighted average ofthe two manufacturers) ofrevenues for input into GRIM.
9.2.

### 7 Cost ofCapital/Discount Rate Cost ofcapital is a measure ofa company's cost ofusing capital that it raises eitherthrough loans (i.e., debt) or by issuing equity. It is the minimum rate ofreturn that must be earned on new investments thatwill not dilute the equity ofthe shareholders. This rate ofreturn is often referred to as the discount rate, hurdle rate, or the opportunity cost ofcapital. The company cost ofcapital is a weighted average ofthe returns that investors expect from variousdebt and equity securities issued bythe firm. Itis calculated as the costofdebtcapital (which is then adjusted for the income tax effect ofdebt financing) plus the cost ofequity capital, weighted by the relative amount ofeach in a company's capital structure. For the purpose ofcharacterizing financial impacts ofregulations omnanufacturers using GRIM, the Department proposes using the company cost ofcapital to discount future cash flows for arriving at the clothes washer.industry value. · ''

9-6In order to calculate the company cost ofcapital, it is essential to identify a company's cost of raising debt, costof raisingcapitalthroughissuingequity, andthe capital structure of the company. Capital structure ofa company gives information about the amount ofdebt versus the amount of equity in the company. 

### Cost ofdebt: Costofdebt is a weighted average cost ofdebt based on all forms ofdebt financing outstanding. Information on the amount ofdebt outstanding and the interest rate at which it was issuedwas obtainedfrom SEC 10-Kfinancial statements and Moody'sreports fortwopubliclytraded appliance manufacturers. Table9.7 presents the nominal1costofdebt and debtratios for two appliance manufacturers. The cost ofdebt is calculated in two ways: (a) As the weighted average cost ofall debt based onthe coupon yield, which is the yield that a company promises to pay on a bond's face value at the time ofissuance ofthe debt. This weightedaverage represents a company's nominal cost ofdebtcapital from the time it issued the debt to the present time. 

### (b) As the weighted average cost of all debt based on yield to maturity. Yield to maturity determinesthe rate ofreturn an investor will receive today ifa long-term bond is held to its maturitydate. This yield represents the nominal cost ofdebt to a company ifit needs to raise debt capital at the present time. Appendix E presents cost ofdebt calculations using both approaches (a) and 

### (b). While regulators often use anaverage stated actual costofdebt (as calculatedinapproach (a)) to determine cost ofdebt [I], it is preferable to use the current replacement cost ofdebt (market value ofdebt as ·calculatedby approach 

### (b)) for calculatingthe cost ofcapital for the purpose ofassessing the present valueofthe future cash flows orfor evaluating alternative investmentproposals offinancial policies ifthe policies or proposals involve debt financing.[!] Cash flows associated witlnew investments should be matched with the current or prospective cost of all capital associated with such investments. Debtratio is defined as the ratio oflong-term debt to total long-term capital (i.e., long-term debt plus equity), and illustrates a company's capital structure.


### Table 9.7 Cost ofDebt and Debt Ratio Manufacturer Cost ofdebt Cost ofdebt Debt Ratio (Coupon Yield) (Yield to Maturicy) (1990-96) A 8.27 6.92 0.38. B 9.01 7.07 0.49

1Nominal cost implies that the cost has not been adjusted to take account ofinflation. 

### 9-7Thereal costofdebtis obtainedby adjustingthe nominal value for inflation. Ave.rage annual inflation in the U.S. overthe period 1990-96 was 3.42% [2], and more recently, it was 2.2% [2] in theyear 1997. ForManufacturerA, 8.27% represents the average nominal costofdebtthat itraised
· through corporate bond offerings at different points in time over the past 10 years, a time frame

duringwhichthe interest rates and inflationrates have beenvarying; whereas, 6.92% represents the nonllnaIcost ofdebt ifManufacturer A raised its debt in 1997 when both interestrates and inflation were low. 

### In the long run, for raising debt required to meet regulations, it could be assumed that Manufacturer A's nominal cost of debt would fall between 7% and 9% provided there are no sharp changes in interest rates. The same argument holds for Manufacturer B, which would also be able to raise debt atthe 7-9% nominal rate. According to Statements ofManagement Accounting, cost of debtbasedonyieldtomaturitywouldprovide a betterestimate. However, itis difficultto estimate· the exact costs at which these companies could raise debt at some point oftime in the future. Another way oflooking atcorporate debt rates is by treating them as spreads (basis points) over the Treasury bill rates. Both manufacturers have similar bond ratings (Baal as provided by Moody's). Looking athistorical annualizedreturn spreads for corporate bonds, bothmanufacturers, with theirBaal ratings, would be able to raise 30-year debt instruments at approximately 120 basis points above U.S. Treasuries [3]. Looking at current interestrates, this would mean a nominal cost ofdebt in the range of7.3% for 30-year debt instruments. Adjusting for an inflation rate of2.2% in. 1997, the real cost ofdebt would be around 5%. Ibbotson Associates, a company that publishes industry specific financial infonnation on a quarterlybasis, reports a nominal costofdebt of7.14% 2 for thehome appliance industry forthe year 1997. TheJuly 1991 AHAMreport, entitled "FinancialImpactof DOETopLoadingHorizontalAxis Standards onWashing Machine Manufacturers," used a nominal costofdebt of9.2%. Adjusting for inflation(2.2% in 1997 and 4.2% in 1991), the real costs ofdebt as reported by Ibbotson Associates and the AHAM report would be 4.94% and 5.0%, respectively. Based on arguments above, the Department proposes to use 5% as the real cost ofdebt. Cost of 

### equity: Cost ofequity is the rate ofreturn that equity investors expect to earn on their investments in a company's stock. These expectations are reflected in the market price of the company'sstock. One way ofestimatingthe costofequity is by using the capital assetpricingmodel (CAPM). The CAPMis based onthe premisethat the expected orthe required rate ofretumon any finn'scommon stockequalstheriskless rate ofinterestplus a premiumforrisk. The CAPMis widely used by large corporations to estimate their costs ofequitY.

2Used with permission. ©1998 lbbotSon Associates, Inc. All rights**res~rved.** [Certain portions ofthis work were derived from copyrighted works ofRoger G. Ibbotson and Rex Sinquefield.]
3For more information on CAPM the readers are referred to Chapters 7,8 and 9 ofCorporate Finance by Brealy, R.A., and Myers, S.C.; and to article entitled Does capital assetpricing model work by Mullins, D.W., published in the Harvard Business Review. · 9-8Accordingto the CAPM, the costofequity, or, the expected return for equity, is determined as: 

### costofequity = 

### riskless rate+ beta* [risk premium] where: 

### • riskless rateis usually the short-term Treasury bill rate, orthe long-term government bond rate adjusted for the risk premium over Treasury bills, 

### • riskpremium is the difference betweenthe expected return on the market portfolio and the riskless rate, and 

### • beta is a measure ofthe sensitivity ofa company's stock to market movements. Table 9.8 presents yields on Treasury bills and government bonds, and the average risk premium associated with thesesecurities. Eachportfolio offers different degrees ofrisk. Treasury bills are about as safe an investment as· one can make.· There is little risk ofiefault, and their short maturitymeans thatthe prices ofTreasurybills arerelatively stable. However, investors cannotlock in a real .rate ofreturn; there is still some uncertainty about inflation. By switching to long-term government bonds, investors acquire an asset whose prices fluctuate as interest rates vary. (Bond prices fall when interest rates rise and rise when interest rates fall.) An investor who switches from government bonds to common stocks has a direct share in the risks ofthe enterprise, and hence acceptsthe additional defaultrisk. Investorswho acceptedtheextrariskofcommon stocksreceived, on average, a premium of8.5% a year over the return onTreasury bills. 

### Table 9.8 

### Average Rates ofReturn (1990-96) 

### and Risk Premiums (1926-94) 

### on Some Securities (fi!!ures in oercent 1>er vear) Portfolio Average Annual Rate ofReturn4 Average Risk Premiunr (Nominal) (Extra return vs. Treasury ' bills) (1990-96) (1926-94)



# . Treasury bills . 5.00 0 Government bonds (IO.yr.) 7.06 1.5 Common stocks 8.5 (S&P 500 index) Table9.9 presents averagevalues ofbetafortwo appliancemanufacturers. The datahasbeen averaged overthe period 1991-96. The values ofbeta are obtained from financial services provided by the DowJones Company. 1They measure the sensitivity ofa stock's price to overall fluctuations

4Based on data from the Federal Reserve**S~atistical** Release.**~used** with permission. ©1998 Ibbotson Associates,Inc. All rights reserved. [Certain portions ofthis work. were derived from copyrighted works ofRoger 

### G. Ibbotson and Rex Sinquefield.] 9-9inthe S&P 500 stockindex. Betameasures the sensitivity ofthereturn onanindividual stock to the return on the market portfolio. A stock.with a beta of 

### 1 tends to move up and down in the same percentageasthemarket. Stockswitha betacoefficientless than 1tendto move inpercentageterms less than the market. Similarly, a stockwith a beta that is higher than 1 will tend to move up and down more than the market. 

### Table9 9 A verae:e 

# . B VI 

### fi T A 

## r eta a ues or WO ,pp 1ance M facturers anu Manufacturer Beta (1991-96) A 0.94 B 0.90 Using CAPM to calculate the cost of equity, the following results are obtained for two appliance manufacturers: Manufacturer A: Cost ofequity= (7.06-1.5) 

### + 0.94 * (8.5) = 13.55 °/o [riskless rate] beta * [risk premium] Manufacturer B: Cost ofequity= (7.06-1.5) 

### + 0.90 

## * (8.5) = 13.21 °/o [riskless rate] beta * [risk premium] Adjustingfor inflation (averaging 3.42% peryear over the period 1990-96), the real cost of equity for Manufacturer A is 10.13o/o and for Manufacturer Bis

### 9.79%. Weighted Average Cost ofCapital (W ACC) is defined as: WACC = (1-tax rate)* cost ofdebt *·[debt/(debt+equity)] 

### + cost of.equity* [equity/(debt+equity)] Manufacturer A: Manufacturer B: WACC = [(1-0.465) * 5.00 * 0.38] 

### + [10.13 

## * (1-0.38)] = 

### 7.30°/c, WACC = [(1-0.454) * 5.00 * 0.49] 

### + [ 9.79 * (1-0.49)] = 

### 6.33°/o The weighted average cost ofcapital calculated accounts for inflation, as real values ofcost ofequity and debt are used in the calculation. In calculating WACC it is assumed that the two manufacturers maintain the samecapital structure in the future, i.e. future debt ratios are the same as those in the past (see Table 9.7). Independentestimatesof theWACCwereobtainedfromIbbotsonAssociates, a companythat tracks the industry cost ofcapital for over 300 industries. Their analysis reports a WACC of 11.6% for the household appliance industry (represented bythe SIC code 363) in the year 1997. Adjusting for inflation (2.2% in 1997), the WACC net ofinflationwould**b~.4%.** This analysis Is a composite 9-10ofeight companies: Whirlpool Corporation, Maytag Corporation, Royal Appliance Manufacturing Company, National Presto Industries Inc., Rival Company, Health 

### 0 Meter Products Inc., Salton/Maxim Housewares Inc., and Windmere-Durable Holdings. The July 1991 AHAMreport used a cost ofdebt of9.2% (bond rate) and a cost ofequity of 13% based on average return on equity earned by the Standard and Poor's 500 Industrial Average in 1990. Adjusting for tax effects and using a typical capital structure, the AHAM report calculated a total cost ofcapital of 10% before inflation. Adjusting for inflation (which was 4.2% in 1991) the AHAMreport used 6% as the discount rate for conducting industry cash flow analysis. Table 9. I0 compares the cost ofdebt, cost ofequity, and WACC calculated for manufacturers A and B to the household appliance industry composite values obtained from Ibbotson Associates and to the values used in the AHAM 1991 report. · 

### T bl 910 C

a 

### e 

# . ompar1son ofF" 

### . ID t manc1a aa Item Manufacturer Manufacturer Ibbotson Associates AHAM A B Industry composite 1991 Report (1997) Cost ofdebt* 5.00 5.00 4.94 5.20 Cost ofequity 13.55 13.21 15.87 13.00 Capital Structure 0.38 0.49 0.38 

### - (Debtffotal Value) WACC* 7.30 6.33 9.40 6.00 Beta 0.94 0.90 0.85 6


## - *Values have been adjusted for inflation. · The Departmentproposes using 7.25% (revenue weighted average ofthe WACC ofthe two manufacturers) as the real cost ofcapital for discounting future cash flows in GRIM. 

### 9.3 PHASE 2: INDUSTRY CASH FLOW TheDepartmentproposesto use the GRIMto calculatethe financial impactofnewstandards· on manufacturers ofclothes washers. The GRIM model will be used to conduct astandard annual cash flow analysisthat uses price, quantity, and cost information to assess the impact ofregulatory conditions on manufacturer income and cash flow. In Phase 2, GRIM will be used with industry6Used with permission. ©1998 Ibbotson**Asso~iates, Inc.~** All rights reserved. [Certain portions ofthis work were derived from copyrighted works ofRogerG. Ibbotson and Rex Sit:lquefield.). Forbeta values, Ibbotson Associates use theirmarket index as the composite monthly return ofNewYork Stock Exchange (NYSE) companies as developed by Center for Research in Security Prices at the University ofChicago. 9-11

---- - -----------average values of pre- and post-standards manufacturing costs, prices, shipments, and financial information to perform an industry cash flow analysis. This process is represented in Figure 9.1.

Price
Quantity

Cash Flow Manufacturing Cost 

# ...
Financial Information 

# ...

Figure 9.1 Using GRIM to perform an industry cash flow analysis
Basedoninformationpresented in Section9.2, the folloWing table illustrated a representative financial structure that 

### will be used for conducting the industry cash flow 

### analysis.

Scenario Description Base Year AMouncement Year Standard Year Tax Rate Discount Rate for NPV Inflation Rate Working Capital Standard SG&A Research and Development Ordinary Depreciation Ordinary Capital Expenditures Assumptions Page for Clothes Washers

.. Clothes'Washer8 ..



## ..::;:**·~>** ,J9.98 Beginningyear for analytical purposes ·**:.,/L'.:·~**.·. ·'>199!) Year in which the regulaJion announcement is made 

### +- :. "''L:: ,·: 2002 Year in which the regulation takes effect

....:- .'J2.8%

•'"'.v
..: ·.'7:25% .....

.. :0.0% perannum 11.50% of Revenue •.18.00% of Revenue
·. , L80% of Revenue 3.51% of Revenue

.. ..4A3% of Revenue 

### ·Figure 9.2 Summary ofFinancial Inputs to GRIM

9-12

### 9.4 PHASE 3: SUBGROUP ANALYSIS AND MANUFACTURER INTERVIEWS The Departmentproposes to conductinterviews withmanufacturerrepresentatives to gather key information on the effects that potential new energy efficiency standards for clothes washers mighthave ontheir company's revenues and finances, on their direct employment, capital assets and industry competitiveness. DOE 

### has developed an interview guide to assist in this process. The manufacturer interview guide is presented in Appendix G. 

### This interview guide provides a starting point to identify relevant issues and will help identify, both qualitatively and quantitatively, the impacts of possible candidate standard levels on individual ·manufacturers or sub-groups of manufacturers. The interview guide is structured into six different sections: (I) 

### Current Organizational Characteristics (2) Industry Infrastructure (3) Manufacturer Cash FlowAnalysis (4) Competitive Impacts Assessment (5) Employment Impacts Assessment, and (6) Manufacturing Capacity Impacts Assessment Once the interviews are conducted DOE will use the information obtained to identify groups ofmanufacturers representing similar characteristics, e.g. small vs. large manufacturers, full product vs. niche players or other relevant characteristics. DOE will collate the completed interview questionnaires and prepare a summary ofthe major issues and outcomes.



### 9-13

## REFERENCES 1. 

### National Association of Accountants. 1994. Statements on Management Accounting, StatementNumber 4A, Practices and Techniques: Cost of Capital. 

### 2. U.S. Department ofLabor, Bureau ofLabor Statistics. Consumer Price Index-A// Urban Consumers. 

### {Available at ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt). Washington, 

### D.C. 3. BridgeCorporationSpreadsforIndustrials,http://bondchannel.bridge.eom/publicspreads.cgi; and Federal Reserve Statistical Release,**http://www~bog.frb.fed.us/** 

### releases/h 15/

9-14 '!

### CHAPTER10: CONSUMER ANALYSIS
10.1 · CONSUMER ISSUES 

### As part ofthe process of setting a new energy efficiency standard for clothes washers, legislationrequires that "the Secretary consider, among other factors,
...ifany lessening ofthe utility or the performance ofthe products is likely to result from the imposition ofthe standard (42 U.S.C. Sec. 6295 (o) (2) 

### (B) (I) (IV)). In orderto better inform the clotheswasher standards rulemaking relatingto consumer issues, the Department decided to follow the recommendations made by the Consumer Subcommittee ofthe Advisory Committee in December of 1997: Adopt a three-step process ofobtaining background research, holding focus groups, and conducting interviews/surveys. The Department first conducted anextensive review ofsecondary sources ofinformation on consumers and clothes washers (see Appendix F). The information was compiled by the Lawrence BerkeleyNational Laboratory and made available at a March 11, 1998, publicworkshop attended by

.manufacturers, trade associations, energy efficiency groups, and other stakeholders. This study was the subject ofa reviewto determine ifsecondary studies provided adequate information toenable the Department to determine the value consumers placed on different clothes washer attributes. Itwasthe consensusof the workshopparticipantsthatthe secondary findings were inadequatebecause the samples contained inthe surveys were not representative ofthe nation, not allthe relevantclothes washerattributeswerecovered, andthemethodologies of the different surveys were not comparable. A Clothes WasherConsumerAnalysis Working Group (Working Group) was formed representing manufacturers, energy efficiency groups, national laboratories, and other stakeholders. In addition to developing a list ofrelevant clothes washer attributes, the Working Group reviewed different approaches such as focus groups, surveys, and a conjoint analysis. (A conjoint analysis is research technique that lets the consumer determine the combination offeatures that the product (or service) must have to appeal to them.) The Working Group recommended the use offocus groups and conjoint analysis. Results from these will be reviewed by the Department to better understand the value consumers place on clothes washer attributes, such as cycle options, door placement, temperature options, etc. The Department willalso be studying purchase price increases to avoid negative impacts to " identifiablepopulationgroups and to assess thelikely impacts of purchaseprice increases onproduct sales and fuel switching. This information will then be considered by the Department when it proposes energy efficiency for clothes washers · In order for the Department to conduct a consumer survey and/or focus group, the Paper Reduction Act of 1995 requires that the Department seek comments on the proposed information
10-1collectionina Federal Registernotice, and seekOfficeofManagement and Budget (OMB) approval through submissionofthe form OMB 83-1 upon completion ofthe comment period ofthe Federal Register notice.


### 10.1.1 Focus Groups and Conjoint Analysis The DOE will hire a marketing research firm that will collect clothes washer consumer data ina two-phase process. 

### Inthefirstphase, theresearch 

### firm will convene 10 focus groups comprised of8-10 individuals (sample of 100 respondents) at five different geographic sites in the U.S. (sites ·to be determined) to refine the initial list of32 clothes washer attributes that were developed by the Working Group. The goal is to refine the initial list to 8-12 attributes for use in a conjoint analysis thatwill begivento arepresentative sample of500respondents atfive sitesintheUnited States (sites to be determined). Conjoint analysis is a method thatpermits OCS to identifythe value a respondent placesona particularattribute ofa clothes**wash~r.** This analysis is accomplished through a trade-off procedure in which the respondents are asked to give up various attribute levels to achieve other attribute levels. The attributes data are collected through personal interviews with the respondents interacting with a**pre-pro~ed** microcomputer that is provided by the research company. 

### 10.2 CONSUMER SUB-GROUP ANALYSIS The consumer analysis evaluates impacts to any identifiable groups or consumers, such as householdsofdifferent income levels, who maybedisproportionately affectedby any national energy efficiencystandard level. This will be accomplished in partby analyzingthe life-cycle cost, payback and internal rate ofreturn for those households that fallinto any identifiable groups or consumers. (Inputs to the spreadsheet used 

### in determining life-cycle-cost and payback periods are explained in detail in Chapter 7.)


### 10.2.1 Life-Cycle Cost Analysis (LCC) · The spreadsheet model used for the LCC analysis can be· used with different data inputs. Among the inputs are the data from the 1993 RECS published by·the EIA. The standard LCC analysis( described in Chapter 7) includes all the households having both a washer and a dryer. The LCC for any subgroup, such as low-income households, could be analyzed by using the LCC spreadsheet model by only sampling that subgroup.

10-2 ,-,CHAPTER 11:. UTILITY IMPACT ANALYSIS
11.1 INTRODUCTION Analysisoftheeffects of proposed clothes washer standard levels onboththe gas and electric utilityindustries will be conducted using a variant ofEnergy Information Administrations ( EIA) National Energy Modelling System (NEMS), togetherwith some minor external calculations. 

### '.fhe versionofNEMS to be used for appliance standards analysis is calledNEMS-NAECA. NEMS was. used by the EIA to produce the I998AnnualEnergy Outlook (AE098). The electricity and gas utilityanalyses will consist ofa comparisonbetweenmodel results for the base case and policy cases inwhich proposed standards are in place. Outputs ofthe utility analysis are presented in a format similar to the results in AE098 [1]. Assumptions The use ofNEMS for the .utility analysis offers several advantages. As the official DOE energy forecasting model, it relies upon a set ofassumptions which are fairly transparent due to the exposure and scrutiny the AEO receives. NEMS also offers a picture ofthe effect·of appliance standards since its scale allows it to measure the interactions between the various energy supply and demand sectors and the economy as a whole. In addition,the scale ofNEMS allows an analysis of the effects ofstandards on both the electric and gas utility industries.

· The entire utility analysis will be conducted as a policy deviation from the AE098, with the samebasic setofassumptions applied. Forexample, the operating characteristics (energy conversion efficiency, emissions rates, etc.) of future electricity generating plants are exactly those used in AE098, as are the prospects for natural gas supply. Deviationsfrom some ofthese assumptions have also been explored through a few scenarios that represent alternative futures. Two ofthese alternative scenarios are based on the economic growth cases ofAE098 and use variations in economic growth assumptions, These growth cases include a set ofboth high and low growth assumptions, with the moderate case serving as the reference scenario. The growth assumptions for each case are based on macroeconomic forecasts prepared by Design Research, Inc. {DRl)/McGraw-Hill and demonstrate the effects ofalternative growth assumptions onenergy markets. The high growth case incorporates higher growthrates for population, labor force and laborproductivity, which cause lower inflation and interest rates and an increasedprojected economic outputof2.4percenta year. Conversely, the lowgrowth caseis based on lower growth rates for population, labor force and productivity, resulting in higher prices and interest rates and lower growth in industrial output. Economic output in the low growth case increasesby 1.3 percent a year from 1996 through 2020. While supply-side growth determinants are varied in these cases, AE098 assumes the same reference case energy prices for all three economic growth cases.

11-1

### 11.2 METHODOLOGY NEMS is alarge multi-sectoral partial equilibrium model ofthdJ.S. energy sector that 

### has been developed over several years by the EIA primarily for the purpose ofpreparing 

### theAnnual Energy Outlook. NEMS produces a widely recognized baseline forecast for the U.S. through 2020 and is available in the public domain. NEMS-NAECA is the model that is used for appliance standards analysis and is based on the AE098 version ofNEMS with minor modification$. The electric utility industry analysis will consist ofNEMS-NAECA forecasts for generation, installed capacity, and prices
.. Since the AE098 version ofNEMS forecasts only to the year 2020, a method. for extrapolatingprice datato 2030 is required. Theadopted method uses the EIA approachto forecast fuel prices for the Federal Energy Management Program (FEMP). These are the prices used.by FEMP to estimate life-cycle costs offederal equipmentprocurements. Forpetroleum products, the average growth rate for the world oil price overthe years 2010 to 2020 is used in combination with the refinery and distribution markups from theyear 2020 to determine the regional price forecasts. Similarly,natural gas prices are derived from an average growth rate figure in combination with regional price margins from the year 2020. 

### 11.3 RESULTS Results will include sales and price forecasts for the electric and gas utilities. Below is an exampleoutputtable ofbasic NEMS-NAECAresults for the AE098 case. Final results will include equivalenttables for the AE098 case with residential energy demand reduced by the levels forecast by the National Energy Savings spreadsheet models, and all results will be extrapolated to 2030, as described above. Further, similar tables will be presented for the alternative scenarios.

1 EIA approves use ofthe nameNEMSonly to describe an AEO version ofthe model without any modification tci code or data. Since, in this work, there are some minor code modifications and the model is ruri under various policy scenarios that deviate from AEO assumptions, Berkeley Lab proposed the use ofthe name NEMS-NAECA for the model as used here. 

### 11-2

### Table 11.1 Examele Outeut Table ofNEMS-NAECA Results for Utili!l'. Anall'.sis NEMS-NAECA: AE098 Baseline 2000 2005 2010 2015 2020 2025 2030 Residential Consumption Electricity Sales (PwhJ 1.16 1.26 1.35 1.45 1.55 1.65 1.77 Natural Gas (EJ) 5.641 5.60 5.76 5.97 6.11 6.29 6.48 LPG {EJ) 0.47 0.50 0.52 0.53 0.54 0.55 0.56 Oil (EJ) 0.90 0.83 0.81 0.78 0.76 0.73 0.71 Natural Gas (Quads) 5.35 5.31 5.46 5.66 5.79 5.96 6.14 LPG (Quads) 0.45 0.47 0.49 0.50 0.51 0.52 0.53 "' Oil (Quads) 0.85 0.79 0.77 0.74 0.72 . 0.70 0.67 Residential Prices Electricity (¢/kWh) 7.82 7.51 7.30 6.96 6.83 6.83 6.83 Natural Gas ($/GJ) 6.09 5.86 5.74 5.60 5.77 6.06 6.36 LPG($/GJ) 12.20 12.88 . 13.12 13.12 13.26 13.73 14.22 Oil ($/GJ) 7.54 7.88 7.98 8.07 8.13 8.42 8.72 Natural Gas ($/MBtu) 5.77 5.55 5.44 5.31 5.47 5.74 6.03 LPG ($/MBtu) 11.56 12.21 12.44 12.44 12.57 13.01 13.48 Oil {$/MBtul 7.15 7.47 7.56 7.65 7.71 7.98 8.27 1Trillions ofkWh.

11-3

### REFERENCES 1. U.S. Department ofEnergy, Energy Information Administration. Annual Energy Outlook 1998: With Projections Through 2020. DOE/EIA-0383(98), December 1997.



### 11-4

### CHAPTER 12: ENVIRONMENTAL ASSESSMENT
12.1 INTRODUCTION Theclotheswasherenvironmentalanalysisuses avariantofEIA

# sNationalEnergy Modelling System (NEMS), plus some minor additional analysis. Outputs ofthe environmental analysis mirror the results ofAE098[1]," with some additions. Purpose of.theAnalysis The environmental analysis is intended to provide emissions results to policymakers and interveners, and to fulfill requirements that the environmental effects ofall new Federal rules be properly quantified and considered. The environmental analysis considers only two pollutants, SQ and NOx, and one emission, carbon. The only form ofcarbon tracked by NEMS-NAECA is 

## cq, so the carbondiscussed inthis reportis only 

### inthe form of

## cq. Foreach of the standard levels, total emissions will be calculatedusing NEMS-NAECA in part, with some external analysis required. Assumptions The environmental analysis will be conducted as a policy deviation from the AE098, with the samebasic setofassumptions applied. Forexample, the emissions characteristicsofanelectricity generating plant will be exactly those used in AE098.


### 12.2 METHODOLOGY Carbonemissions aretrackedinNEMS-NAECAby adetailedcarbonmodule; this gives good results because of its broad coverage of all sectors and inclusion of interactive effects. Past experience with carbon results from NEMS suggests thatemissions estimates are somewhat lower thanemissions based onsimple average factors. One ofthe reasons for this divergence is thatNEMS tends to predict that conservation displaces renewable generating capacity in the out years. Or.the whole,NEMS-NAECAprovides carbonemissionsresults of reasonable accuracy, a level consistent with other Federal published results. Thetwo airborne pollutantemissions thathave beenreported inpast analyses, 

## sq andNOx, are reported by NEMS-NAECA. The Clean

### AirActAmendments of 1990 setan SQ emissions cap onall powergeneration. Theattainmentof thistarget, however, is flexible amonggeneratorsthrough the use ofemissions allowances and tradable permits. NEMS includes a module for SQallowance trading and delivers a forecast ofSD.z allowance prices. Please note that accurate simulation of

## sq trading tends to imply that physical emissions effects wilbe zero because emissions will always be at the cejling. This fact has caused considerable confusion in the past. However, there is an SQ benefitfrom conservation inthe form ofa lower allowance price and, ifbig enough to be calculable
12-1byNEMS-NAECA, this value willbereported; One small effectthatNEMS-NAECAmustconsider in addition to AE098 calculations is the effect of standards on 

## sq emissions from in-house combustion ofoil, since the emissions cap does not apply to households. This effect is calculated using simple emissions factors. NEMS also has an algorithm for estimatingNOx emissions from power generation, but it is somewhat inadequate for this analysis because it does not estimate in-house emissions. In-house emissionsaccount for the combustionoffossil fuels, primarily natural gas, within individual homes. Sincehouseholds thatuse natural·gas, fuel oil orcoal contributetoNOxemissions, LBNLhas added a separatehouseholdNOxemissionsestimationcapability, basedonsimpleemissionsfactors derived from general literature.


### 12.3 RESULTS As described, the results for the environmental analysis are similar to a complete NEMSNAECArun. Somekey results are summarized inthe table below. The outcome ofthe analysis for eachcase arereported as deviations from the AE098result. Allresuits are extrapolatedto 2030 and include household emissions.



### Table 12.1**Exam~le** 

### Ou!]!ut Table ofNEMS-NAECA Results for Environmental Analysjs NEMS: AE098 Baseline 1000 2005 2010 2015 2020 2025 2030 Jn-house Combustion

. NOx(Mt/a) 0 13.3 16.8 30.8 44.9 73.4 120.0

Carbon.(Mt/a) 11.1 54.7 88.5 134.7 165.7 226.7 310.2 $02 (Mt/a) Power Sector Emissions NOx(Mt/a) 5.05 5.16 5.32 5.52 5.71 5.92 6.13 Carbon (Mt/a) 1577 1685 1798 1882 1950 2031 2115 S02 (Mt/a) 10.19 9.69 9.22 9 9 9 9 S02Trading Price {$/t} ·120.9 173.l 218.9 181.1 165.2 143.5 124.7

12-2

### REFERENCES l. U.S, Department ofEnergy, Energy Information Administration. Annual Energy Outlook 1998: With Projections Tlrrough 2020. DOE/EJA:.0383(98), December 1997.

12-3------- -- ------



### CHAPTER 13: REGULATORY IMPACT ANALYSIS
13.1 INTRODUCTION DOE will be preparing a draft regulatory analysis pursuant to E.0. 12866, 'Regulatory Planningand Review," which will be subject to review under the Executive Order by the Office of Informationand Regulatory Affairs (OIRA) 58 FR51735 (October4, 1993). Sixmajor alternatives

. . were identified by DOE as representing feasible policy options to achieve consumer product energy efficiency. Each alternative will be evaluated interms of abilityto achieve significantenergy savings at a reasonable cost and will be compared to the effectiveness ofthe rule. Aspart ofthe docket forthe RefrigeratorProducts Energy Conservation Standards (Docket No. EE-RM93-801) AHAM stated that the Department needs to improve the evaluation ofnonregulatory means ofachieving energy savings. (AHAM, No. 207 at 7). Under the Process Rule policies, the Department is committed to continually explore nonregulatory alternatives to standards. Listed 

## ill Table 13.1 below are the non-regulatory means of achievingenergy savings, proposed for analysis by DOE. The final TSD will include a complete quantitative analysis ofeach alternative, the methodology for which is discussed in briefbelow. 

### Table 13.1 Non-regulatory Alternatives -No new regulatory action -Consumer tax credits -Manufacturer tax credits -Performance standards -Rebates· -Voluntary energy efficiency targets -Early replacement -Mass government purchases


### 13.2 METHODOLOGY To compare each alternative quantitatively to.the proposed conservation standards in terms ofenergy savings and netpresentvalue (orcost savings), itis necessaryto quantify the effect ofeach alternativeonthe purchase and use ofenergy-efficient consumerproducts. Inthe final TSD (NOPR TSD) each alternative to the proposed regulation will be quantified so that inputs to the accounting model can be determined, The accounting model 

### will be used to calculate the energy use and the net present value corresponding to each alternative. Thekey inputs to the accounting model will be:

### • energy prices and escalation factors;
• implicitmarket discount rates for trading·offpurchase price against operating expense when choosing appliance efficiency;
• consumer demand and Income elasticities;
• price versus efficiency relationships for each consumer product;
• appliance stock data (purchase ofnew appliances or turnover rates for inventories). The key measures ofthe impact ofeach alternative will be:
• ResidentialEnergy Use (EJ = 

### 10 18

### joule): Cumulative, from the effective date ofthenew standardto the year 2030 for the appliance product Electricity consumption is reported asprimaryenergy. DOEagrees withtheAdvisoryCommitteesrecommendationthatthe assumption ofa constant conversion factor should be dropped in favor ofa conversion factor that changes from year-to-year.
• Energy Savings. Cumulative energy use from base-case projection less alternativepolicy-case projection.
• Net Present Value. Value offuture savings from appliances bought 

### in the period from the effective date ofthe new standard to the year 2030. Calculated as difference: present value of equipment and energy expenditures in base case less present value of expenditw:es in alternative policy case. Future fuel and equipment expenditures are discountedto 1998 using 7% real discountrate. Fuel expenses are calculated for the life
· ·ofthe appliance. ·

13-2

### APPENDIX A: APPROACH FOR UNCERTAINTY AND VARIABILITY
A.I INTRODUCTION Analysisofan energy efficiency standard involves calculations of**impacts~** for example the impact ofa standard on consumer life cycle cost. In order to perform the calculation, the analyst mustfirst: 1) specify the equation ormodel thatwill 

### be used; 2) define the·quantities inthe equation; '- and 3)prov:ide numerical values for eachquantity. Inthe simplest case, the equation isunambiguous , (contains all relevant quantities and no others), each quantity has a single numerical value, and the calculationresults in a single value. However, unambiguity and precision are rarely the case. 

### In almost 

### all cases the model and/or the numerical values for each quantity 

### in the model are not completely known (i.e, there is uncertainty) or the model and/or the numerical values for each quantity in the model depend upon other**condi~ons** (i.e., there is variability). Thorough analysis involves accounting 'for uncertainty and variability. While the simplest analysisinvolves a singlenumerical value foreachquantityina calculation, argumentscanarise about what the appropriate value is for each quantity. Explicit analysis ofuncertainty and variability is intended to provide more complete**informatio~** to the decision process.


### A.2 UNCERTAINTY When. making observations of past events or speculating about the future, imperfect knowledge is the rule rather than the exception. For example, the energy actually consumed by a particularappliancetype (suchas the averageU.S. clotheswasher) is notdirectlyrecorded, butrather estimatedbaseduponavailableinformation. Evendirectlaboratorymeasurementshave some**mar~** oferror.. When estimating numerical values expected for quantities at -some future date, the exact

. outcome is rarely known in advance.



### A.3 VARIABILITY Variabilitymeans that different applications or situations produce different numerical values when calculating a quantity. Specifying an exact value for a quantity may 

### be difficult because the valuedepends on something else. Forexample, the numberofloads oflaundryperweek washed by a household depends uponthe specific circumstances and behaviors ofthe occupants (e.g., number ofpersons, personal habits about how often clothes are washed, etc.). Variability makes specifying anappropriatepopulationvalue more difficultinas much as anyone value may notbe representative ofthe entire population. Surveys canbe helpful here, and analysis ofsurveys can relate the variable ofinterest (e.g., laundry loads perweek)to othervariables thatarebetterknown oreasierto forecast (e.g., persons per household).

A-1

### A.4 APPROACHES TO UNCERTAINTY AND VARIABILITY Two approaches to consider uncertainty and variability are described here:
• scenario analysis and • probability analysis. 

### Scenario analysis uses a single numerical value for each quantity in a calculation, then changesone (ormore) of thenumerical values andrepeatsthe calculation. A number ofcalculations are done, which provide some indication of the extent to which the result depends upon the assumptions.Forexample, the life cycle costofan appliance could be calculated for energyrates of 2, 8, and 14 cents per kWh. The advantages ofscenario analysis are that each calculation is simple; a range ofestimates isused; and crossoverpoints canbe identified. 

### (An example ofa crossover point is the energy rate above which the life cycle cost is reduced, holding all other inputs constant. That is, the crossover pointis the energy rate atwhichthe consumer achieves savings in operating expense thatmore than compensate for the increased purchase expense.) The disadvantage ofscenario analysis is thatthere 

### is no information about the likelihood ofeach scenario. 

### Probabilityanalysis considerstheprobabilitieswithin a range ofvalues. Forquantities with variability( e.g., electricityrates in**differe~t**households), surveys canbeusedto generatea frequency distributionofnumerical values (e.g., the number ofhouseholds with electricity rates at particular levels) to estimate the probability of each value. For quantities with uncertainty, statistical or subjectivemeasures, suchas a triangularprobability**distribution~** canbeused to provideprobabilities (e.g., manufacturing costto improve energy efficiencyto some level may be estimated to be $10 

### +/- $3).
. The major disadvantage ofthe probability approach is that it requires more information, namely information about the shapes and magnitudes ofthe variability and uncertainty of each quantity. The advantageof theprobability approach isthatitprovidesthe greatestinformation about the outcome ofthe calculations, that is, the probability that the outcome will be in any particular range is provided. · Scenarioandprobabilityanalysis provide.someindicationof therobustness of thepolicygiven the uncertainties and variability. A policy is robust when the impacts are acceptable over a wide range ofpossible conditions.

A-2

### APPENDIX B: CLOTHES WASHERMANUFACTURERS Corporation Addre5ses Brands Whirlpool Estate North American Appliance GE Group Kenmore 2000M63N Kitchen Aid Benton Harbor, MI Roper 49022-2692 Whirlpool Phone: (616) 926-5000 

### Fax: (616) 926-3622 General Electric Appliances GE GEAppliance Park Hotpoint Louisville, KY 40225 RCA Phone: (502) 452-4311 Phone: (800) 626-2000 · 

### Fax: (502) 452-0471 Maytag Admiral 

### - 403 W. 4th St., N. Maytag Newton, 

### IA 50208 Admiral Design· Phone:(515)792-8376 Crosley ·Jenn-Air Hardwick Magic Chef Signature

B-1

### AB Electrolux Flex-Line Luxbacken 1, Frigidaire Lilla Essingen GE S-105-45-Stockholm Gibson Sweden Kelvinator Internation Kenmore Frigidaire Roper Frigidaire HQ Tappan 600 Perimeter Dr. White-Westinghouse Dublin, OH4317**Phone:(614)792~100** 

### Fax:(614)792-4079 Raytheon Amana 141 Spring St. Caloric Lexington, MA 02173 Glenwood Phone: (617) 862:.6600 Modem maid 

# . Phone: (800) 843-0304 Roper Fax: (617) 860-2172 Speed Queen Sunray Whirlpool Andi-Co AEG {imports) Applianees, Inc. 65 Campus Plaz.a Raritan Center**Ediso~** 

### NJ 08837 Phone: (908)225-8837 Fax: (908) 225-8839 Miele Appliance Inc. Miele 22D Worlds Fair Dr. Sommerset, NJ 0899 ·. 

### Phone:(908)560-0899 Fax: (908) 560-9649 Miele 

### & Cie/Gmbh Postfact 2400 4830 Guetersloh Germany


### B-2BSH Home Appliances Bosch 120 Bosch Boulevard New Bern, NC 28562 Phone: (919) 6364303 General Domestic Appliances Limited Creda Morley Way, Peterborough PE2 9JB England Phone: 44(0) 1733 568989 Fax: 44(0) 1733 341783 Staber Industries, Inc
.. Staber 4411 Marketing Place Groveport, Ohio 43125 Phone: (800) 848-6200



## ..

B-3

# .. APPENDIX C: PRELIMINARY LIFE-CYCLE COST AND PAYBACK PERIOD ·RESULTS
Thisappendix contains the complete set oflife-cycle cost (LCC) and paybackperiod results for all the efficiency improvement levels analyzed for clothes washers. For details on the methodology used to calculate the LCC and payback period refer to Chapter 7. 

### C.l LIFE-CYCLE COST RESULTS Thefollowing sectioncontainsthepreliminaryLCCresults for allthe efficiencyimprovement
. levels. For each efficiency level, :frequency and cumulative charts are provided showing the distribution ofexpected LCC differences from the baseline design
.

C-1

### 10,000 Trials .048
.036

### ~ 

# ·- 

# - ·- .Cl .024 00 .Cl 

### c 

# ....

.012 

# c. .000**~** ($90} 

### Forecast: LCC Difference Frequency Chart·

($68} ($45) $ ($23) 

### . 0 Outliers 477

llllU-Nl----l- 357.7 

# .,, ... 

# " 238.5-§ 

# " = .__--I- 119.2**~ .~~___,_**  0 $0 

### Figure C.15% Efficiency Level:**Frequ~ncy** 

### Chart ofLCC Differences



### 10,000 Trials**~** 

# ·- 

# - ·- 1.00u- .750
.Cl .500 00 .Cl 

## c**~** .250 .000**~** ($90} 

### Forecast: LCC Difference
.

($68) 

### Cumulative Chart

- ($45) $ •I .•1111111 _I Mean= ($19) ($23) 

# ............

Figure C.2 5% Efficiency.Level: Cumulative Chart ofLCC Differences

C-2 

### 0 Outliers 10000



# .,,. 

# ... 

# " .c c 

# " =**~**

..__ 0**~** $0

## 10,000 Tria.ls Forecast: LCC Difference Frequency Chart 0 Outliers .057**-r--~~~~~~~~~~~~~~~..---=-~~~---,_555** .042

### ~ 

# ·- 

# - ·- .c .028 o:i 

## .c 

# = a.. .014 

# a. .000**~** ($250) ($181) ($113) ($44) 

# -----t- 424.5

. "'Tl



# ... n fffffH------1- 283 

### .c 

### c n**~** MIHIH-----+- 141.5**~**
$25 Certainty is 99.49% from -Infinity to $0 $ 

### Figure C.3 10% Efficiency Level: Frequency Chart ofLCC Differences



## 10,000 Trials 1.00

.750

### ~ 

# ·- 

# - ·- .c .500**~** 

### .c 

# = a.. .250 

# a. .000**~** ($250) 

### Forecast: LCC Difference Cum·u1ative Chart

($181) ($113) ($44) Certainty is 99.49% from -Infinity to $0 $ 

### Figure C.4 10% Efficiency Level: Cumulative Chart ofLCC Differences

C-3 $25

### 0 Outliers ·10000

### Forecast: LCC Difference 10,000 

### Trials Frequency Chart O

### Outliers

## ~ 

# - ·- .c 

# = .c Q t.. 

## c.. .069 

# ..r-----------------,----:--------... 690 .052-i----------------- .035+------------------

.000 ..L_
_________ ......,..a111111W

.. ($450) ($313) ($175) "------·- 517.5 

# .,,

...... n 

# -------l- 345 

### .ca c n 

# = 

# -------+- 172.5

### ~
$100 Certainty is 95.92% from -Infinity to $0 $ 

### Figure C.5 15% Efficiency Level: Frequency Chart ofLCC Differences



### 10,000 Trials 1.000

.750

### ~ 

# ·- 

# - - .c .500 

# = .c Q t.. .250 

## c.. .000 ($450) 

### Forecast: LCC Difference Cumulative Chart

($313) ($175)

Certainty is 95.92% from -Infinity to $0 $ $100 

### Figure C.6 15% Efficiency Level: Cumulative Chart ofLCC Differences

C-4 

### 0 Outliers 10000

0 

### -n



## ..... n 

### .ca c 

### n 

# =**~**



## .•

### ·- 

### 10,000 Trials Forecast: LCC Difference frequency Chart 0 Outliers .072**~~~~~~~~~~~~~~-.....,,~~~~~~~111** .054

### ~ 

# ·- 

# - ·- .Cl .036 

### co .Cl 0 L. .018 

## c. .000**~** ($600) ($400) ($200)**~------** 537.7 -r'!**~** 

### n 

# -------r 358.5**~** 

### n 

## == fffftlttr------+ 179.2

### ~
$200 Certainty is 86.65% from -Infinity to $0 $ 

### Figure C.

### 7 20% Efficiency Level: Frequency Chart ofLCC Differences



### 10,000 Trials**~** 

# ·- 

# - ·- 1.000
.750
.Cl .500 

# = .Cl 0 

# a: .250
.000**~** ($600) 

### Forecast: LCC Difference Cumulative Chart

I

# -···~ I ($400) ($200)

# ~JdU
I,11

Mean=**($6~).UW ~**  $0 Certainty is 86.65% from -Infinity to $0 $ 

### Fig-.re C.8 20% Efficiency Level: Cumulative Chart ofLCC Differences

C-5 

## 0 Outlie.rs 10000



### - 0 $200 -r'!**~** 

### n 

## .c c C"D 

## ==**~**

### 10,000 Trials .047 

### Forecast: LCC Difference Frequency Chart 0 Outliers 471

### ~ 

# ·- 

# - ·- .c «:J 

## .c .Q



# ... c. .035-i----------------+ fllllll-----f- 

### 353.2 "'Tl**~** 

### n .024;---------------:- ffffffffffllfffftt----+ 

### 235.5**~** 

### n 

# = .012-1-------------tttttttttttttttttltltttHttttttt------r 117.7

### ~**.000.L_...-~----..-u-** .......**llllllWJUWlllllllQWWWWUllllmm~ili::::::::...-1** O ($1,500)· ($1,063) ($625) ($188) 

### $250 Certainty is 89.23% from -Infinity to $0 $ 

### Figure C.9 25% Efficiency Level: Frequency Chart ofLCC Differences



### Forecast: LCC Difference 10,000 Trials Cumulative Chart 0 Outliers**1.000·-r---------------~.----:;;;;rnun-1-** 10000

### ~ 

# ·- 

# - ·- .c .500-1---------------....l..lilUUllUWIWUllUUU--l- «:J 

## .c Q**~** 

### .250;--------------:ttttfflttilfffflttttfflfffllfflffff--+
.000**~** ($1,500) ($1,063) 

### ($625) ($188) Certainty is 89.23% from -Infinity to $0 $ 

### Figure C.10 25% Efficiency Level: Cumulative Chart ofLCC Differences

C-6 ' -0 

### $250 "'Tl**~** 

### n 

## .c c n 

# =**~**

### 10,000 Trials .049

- 

### Forecast: LCC Difference Frequency Chart 0 Outliers 489
f-------+- 366.7**~** .**~** 

# ·- 

# ... = n**~** .024 tttt--------1- 244.5**~ ~**  

### n 0 

# = ct .012-t-----------fttfffffffilfffflfltttilffffl..-----+ 122.2

### ~ .000**~** ($2,000) ($1,313) ($625) Certainty is 83.42% from -Infinity to $0 $



### . Figure C.11 35% Efficiency Level: Frequency Chart ofLCC Differences



### 10,000 Trials

## ~ 

# - ·- 1.000- .750**~** .500**~** ..Q C) 

# ... a. .250 .000**~** {$2,000) 

### Forecast: LCC Difference Cumulative Chart ·

# ·····~1111111 ($1,313) ($625) Mean 

# = ($25211111111**~·** $63 Certainty is 83.42% from -Infinity to $0 $ 

### Figure C.12 35% Efficiency Level: Cumulative Chart ofLCC Differences

C-7 $750



### 0 Outliers 10000



## -

0 $750**~** 

# .... n .Cl. 

### c n 

# =**~**

### 10,000 Trials
. .055

### ~ 

# - 

# - ·- .c Forecast: LCC Difference Frequency Chart

Cl 

### .c Q**~** 

## a.**.014-1-----------~** 

### .000**~** ($2,250) ($1,500)
. ($750) Certainty is 83.70% from -Infinity to $0 $ 

### Figure C.13 40% Efficiency Level: Frequency Chart ofLCC Differences



### Forecast: LCC Difference 0 Outliers 552

- 414 "'Tl**~** 

## n

- 276 

## .c c 

## n 

# =

- 138

## ~

0 $750



### 10,000 Trials c·umulative Chart 0 Outliers**1.0Q·nJ----------------.-----~=--...** 10000**·~** 

# ·- 

# - ·- .c Cl .Cl Q**~** 

## a. .500-r----------------hfiltttt11tttt11tttt11ttt 

### .250+-------------"TIHIHIHHIHHfftltlfft:Hfff Mean= $253**.ooo.L...-~ ---,---...-~~ullllJ,U.UJWJW!IUllJUiiil~~~mmijiif**  ($2,250) ($1,500) ($750) $0 Certainty is 83.70% from -Infinity to $0 $ 

### Figure C.14 40% Efficiency Level: Cumulative Chart ofLCC Differences

C-8 $750 "'Tl**~** 

### n



## . .c c n 

# =**~**

### 10,000 Trials .052 

### Forecast: LCC Difference Frequency Chart**~** 

# ·- 

# - - .ca**~** 

### .ca Q '- 

## a. .039+---------------t .026+---------------Hf**.013+--------------~** 

### OOutliers 520



# .,, ... n

- 260 

# -§ n 

# = ----130

### ~ .000..L
....**~.-----.....,.a..1..&&JIJldWIUl.l,UWUl~WllllblD:IJJmllllDi:qm!C:__,..._J_** 0 ($2,250) ($1,500) ($750) $750 Certainty is 73.64% from -Infinity to $0 $ 

### Figure C.15 45o/o Efficiency Level: Frequency Chart ofLCC Differences



### Forecast: LCC Difference 10,000 Trials Cumulative**C~art** 

### 0 Outliers 1.oonr---------------.------:--==::---l.. 10000

### ~ 

# ·- 

# - ·- .750+---------------+-----::


### .ca .500+-------____;_
______ __ 

# = .ell Q 

# £t**.250-t-------------~fffffffffftttfft** Mean= $199**.000..L_r-~ ---=------:--~~ulllll:llJlllllWll.μWllllDllllJ~~m'.lil~WWJ.,_J..**  0
. ($2,250) ($1,500) ($750) $0 $750

. Certainty is 73.64% from -Infinity to $0 $ 

### ·Figure C.16 45% Efficiency Level: Cumulative Chart ofLCC Differences

.C-9 

# .,, ... n 

## .c c n 

# =**~**

### Forecast: LCC Difference 10,000 Trials Frequency Chart 

### OOutliers**~** 

# ·- 

# - -**.049..r------------------.-~----------,_493** .037-+--------------

------- 369.7 "'Tl


# .... n 

### .c o::i 

### .c 0 a.. .025-t---------------t fffftHfftll---------1- 246.5 

## "§ n :::I 

### D.**----123.2~** .000**~** ($2,250) ($1,500) ($750) Certainty is 74.16% from -Infinity to $0 $ 

### Figure C.17 50%, Efficiency Level: Frequency Chart ofLCC Differences



### Forecast: LCC Difference $750



### 10,000 Trials Cumulative Chart 0 Outliers**1.oon.r---------------..--------==~-,_** 10000

### ~ 

# ·- 

# - ·- -= .500.;-----------------ttlfftfflfftffllfffffffffllfffffffff--l- o::i 

### .c 0 

# ct .250.;-:--------------=ff**.ooo..l_~~--..--...---~...u11WJ.U.** ($2,250) ($1,500) ($750) $750 Certainty is 74.16% from -Infinity to $0 $ 

### Figure C.18 50°./c, Efficiency Level: Cumulative Chart ofLCC Differences

C-10 "'Tl


# .... n .c c: 

## n :::I

## ~**-----------~---** -------- - -- - ----


### C.2 PAYBACK PERIOD RESULTS The following section contains the preliminary payback period results for all the efficiency improvement levels.


### 10,000 Trials .765

## ~

. 

# -

·- .ca 

# = .ca Q 

# "" c.. .574 .383 .191
.000**~** 0.0 Mean= 0.111.0 

### Forecast: Payback Frequency Chart

2.0 years 3.0 

### Figure C.19 5% Efficiency Level: Distribution ofPayback Periods



### 10,000 Trials .615 

# ·- .461 .307 .154 11 JMean =0.61 .000 I**~** 0.0 

# . 4.4 

### Forecast: Payback Frequency Chart



# . 8.8 years 13.1 

### Figure C.20 10% Efficiency Level: Distribution ofPayback Periods

C-11 

### ·0 Outliers 7651**~** '4.0**~** 17.5 0 

# .,, ... n**~** 

### c n 

# =**~**

OOutliers 6148

0

### 10,000 Trials**~** 

# ·- - 

# ·- .717 .537
.cl .358 ti:! .cl 

### c 

# d: .179 .000 

### I**~** 0.0 Mean= 1.41 11.3 

### Forecast: Payback Frequency Chart

22.5 years 33.8**~** 

### O.Outliers 7165



### - 0 -rt 

# ... C'D .Cl 

### c C'D 

# =**~**
45.0 

### Figure C.21 15% Efficiency Level: Distribution ofPaybackPeriods



### 10,000 Trials

## ~ 

# - ·- .Q o:::i .Q Q 

### a.. 

# c. .512
.384
.256
.128 .000 

### Im**~** 0.0 .I Mean= 3.61 15.0 

### Forecast:.Payback Frequency Chart

30.0 years 45.0 

### Figure C.22 20% Efficiency Level: Distribution ofPayback Periods

C-12 

# • 60.0

### 0 Outliers 5115

0 

# .,, ... C'D 

### .c c C'D 

# =**~**

•.

### Forecast: Payback 10,000 Trials Frequency Chart OOutliers .054 544
.041 408

### ~ 

# .,, ·- ... 

# - n 

# ·- .ca .027 272 

### .c**~** 

### c .ca n C) 

# = ... .014 136

## ~ 

# c. .000**~** 0 8.8 17.5 26.3 35.0 years 

### Figure C.23 25% Efficiency Level: Distdbution ofPaybackPeriods



### Forecast: Payback 10,000 Trials Frequency Chart OOutliers .096 961 .072

- 720.7

### ~ 

# .,, ·- 

# ... 

# - n 

# ·- .ca .048

- 480.5**~ ~**  

### .ca n C) 

# =· 

# ... .024 240.2

### ~ 

# c. .000**~** 0 0.0 12.5 25.0 37.5 50.0**.~** years 

### Figure C.24 35°/o Efficiency Level: Distribution ofPaybackPeriods

C-13

### Forecast: Payback 10,000 Trials Frequency Chart 0 Outliers .115 1151 ;086 863.2

### -~ ""Tl 

# ·- ... 

# - ni 

# ·- .Cl .058 575.5 

# -g 

# = .Cl ni 0 

# = 

# .. 287.7

### ~ 

## c.

.. 000**~** 0 0.0 15.0 30.0 45.0 60.0 years 

### Figure C.25 

### 40o/o Efficiency Level: Distribution ofPaybackPeriods



### Forecast: Payback 10,000 Trials Frequency Chart 0 Outliers .071 712
.053 . 534

### ~ ""Tl 

# ·- ... 

# - n 

# ·- .Cl .036 356 

### .c 

# = c .Cl 

### n 0 

# = 

# .. .018. 178

## ~ 

## c. .000**~** . 0 0.0 13.8 27.5 41.3 55.0 years 

### ··- Figure C.26 45% Efficiency Level: Distribution ofPaybackPeriods

C-14

### Forecast: Payback 10,000 Trials Frequency Chart 0 Outliers .073 732 .055 549

### ~ 

# .,, - ... 

# - n 

# - .c .037 366 

### .c**~** 

### c 

### .c n 0 

# = ... .018 183

## ~ 

# a. .000**~** 0 

### 0.0 13.8 27.5 41.3 55.0 years 

### Figure C.27 50°/o Efficiency Level: Distribution ofPaybackPeriods

C-15

### APPENDIX D: NATIONAL ENERGY SAVINGS (NES) SPREADSHEET OUTPUT· · The NES spreadsheet (NES
_CWV2.l .XLS) calculates the energy and water.savings by comparingtheenergyandwaterconsumptionassociatedwithclotheswashers inabasecasewiththat ina specified standards case. The following pages containtables and figures showingthe results for each of the standards cases assessed in this analysis. For each standards case two tables are presented. The first showscumulativeenergy savingsbyfuel 

### type (electricity, gas, and oil) andwater savingsfor years 2010, 2020 and 2030. The second shows netpresent values (including energy and water savings and equipment cost) from2003 to 2030 discounted at 7% real to 1997. Benefit and cost ratios are. also presented. The corresponding figure for each standard case shows the annual benefit/costpatternof national savings and costs resulting from standards overtime. Theheavy line runningjust below. the energy and water savings bars indicates the undiscounted net national con5umer impact ofstandards over time. It·is important to note that the net saving shown in each plot only captures a snapshot ofeachyear. Forexample, the benefit ofthe energy and water savings from the equipment expenditure in year 2005actually continues through the lifetime ofthe clothes washers bought in 2005.



### D-1Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) Total Energy & Water Saving Total**~uipment** Cost Net Present Benefit

Benefit/Cost Ratio

from 2003 to 2010 to 2020 to2030

1000



### - 0.04 0.19 0.36

1005 0.03 0.13 0.25

2010 0.2 c::=:I Price Ch an g e 0.01 0.05 0.10

l!ll!!!'!!!!IEnergy & Water Saving 0.2 

## - -Net Saving 

# "" .,,, r! .,,, .! ;; 0.1**~** 

### - .,,, .,,, 

## ... °' .,,, 

### °' 

# - "" i: 0.1**~** 

### il5 0.0 

## - 

# "" 

# A11"'

-0 .1 

### - Figure DJ 5% Efficiency Level: Net Saving

D-2 1015

,,,. 

### ,,,. 0.00 0.00 0.00



### .... $1.03 $0.01 $1.02

113.23

2020 0.05 0.24 0.46

1015 1030

### - .. - -- -------**-----------..-------~·**

Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) Total Energy & Water Saving Total Equipment Cost Net Present Benefit
Benefit/Cost Ratio

from 2003 to 2010 to2020 to 2030 0.14 0.62 1.18 0.09 0.42 0.78 0.04 0.20 0.38 0.00 0.01 0.02 $2.50 $0.09 $2.41
27.39

0.05 0.24 0.46

co·st and)Sa,·ing of Ncw·C.Jothcs W ashcrs
2000 2005 2010 2015 2020 2025 

### o.s 

## - c:=:::JPrice Change 0.4 l!!l!!!EEnergy & Water Saving



### - ,....- --Net Saving 

### .... 

## "" "" a: 0.3**~** 

### c c::i 

### - 

# " "" 

# " ,... 

### °' 0.2**~** 

# "

- 

# " c 

# " :5 0.1 iii 

## ""



## - 

# " 0.0 

# .Kl -



# ... 

# - .. .. .. -0 .1 

### - Figure D.2 10% Efficiency Level: Net Savings



### D-3 2030

### D.1.515% Efficienc Level: Cost and Net Present Values

Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) Total Energy & Water Saving Total Equipment Cost Net Present Benefit

Benefit/CostRatio

from 2003 to 2010 to 2020 to2030 0.26 1.15 2.18 0.17 0.73 1.38

1000 1005 2010 0.8 

### - c:::::=I P rice Change 0.09 0.40 0.77

0.7 

## - Eiiii!5!!'i3Energy & Water Saving 0.6 

### - -Net Saving f**~·** GI 0.5 

## - 

# ,.**~** 

## "" Q 0.4 

### - 

# ,. ... °' 

# ,. °' 

### .... 0.3 

## -**~** II: 

## 5 

# ,. ' ii 0.1 

## - 

# ,. / 0 .1 

### - 

## "" 0.0 IV 

## u**~** '**~** ... 

# . ..**~** -0 .1 

## - . 

### Figure D.315% Efficiency Level: Net Savings

D-4 1015

!-"**~- ~**  -

. .;
... 0.00

0.02 0.03.



### .,.,

.:i $4.i4 $0.44 $3.80

9.67

1020



# . 1 0.05 0.23 0.45

1025

: 

# . 2030**~ ~** --- --- ----------------- ------

Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) Total Energy & Water Saving Total Equipment Cost Net Present Benefit

Benefit/CostRatio

from 2003 to 2010 to2020 to2030 0.31 1.40 2.66 0.20 0.87 1.64

2000 2005 2010 1.0

- c==::JPrice Change 0.11 O.Sl 0.98

0.8

- !Ei!!!51Energy & Water Saving -Net Saving 0.6

- 1--' 

## ,, 0.4

- 

## ,, ,, ,, 0.2 

### ""' ""' o.o II
-0 .2

- -0.4

- 

### Figure D.4 20% Efficiency Level: Net Savings



### D-5 2015

i;



### .... 0.00 0.02 0.04 $5.20 $1.53 $3.67

3.40

2020 0.07

. 0.31

0.59

2025 2030



# . .. 

# .Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) Total Energy & Water Saving Total Equipment Cost Net Present Benefit

Benefit/Cost Ratio

from 2003 to 2010 to2020 to2030

2000



### - 0.60 2.68 5.09

2085 0.38 1.67 3:13

2010 0.21 0.97 1.88

3.5 3.0 2.5 

## -

- c:::=::J Price Ch an g e l'iiEil!lil!!IEnergy & Water Saving -Net Saving r! 2.0 

## -**~** ""' 

### c Q 1.5 

### - ii"'" 

### °' 1.0 

## - °' 

# - i: 0.5 

## - 5 iiS 0.0 -0 .5 

## - -1.0 

## - -1.5 

## - Figure D.5 25% Efficiency Level: Net Savings

D-6 2015

""' 0.01 0.04
. 0.08 $17.57 $6.50 $11.07

2.70

2020



# . 1.15 5.26 10.13

2025 2030->

•, 

## ------------------.-----

Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) Total Energy & Water Saving Total Equipment Cost Net PresentBenefit

Benefit/CostRatio

from 2003 to 2010 to 2020 to2030

2000



## - 0.93 4.14 7.85

2005 0.65 2.85 5.35

2010 0.27 1.24 2.39

6.0 5.0 4.0 

### - c:=:::JPrice Change lii&!!!IEnergy & Water Saving --Net Saving



### - 

### z: 

### • 3.0 

### :; - Cl



### .... 2.0 

### "' 

### "' .,, 

# - r::: 

## 5 1.0 

## - iii 0.0 -1.0 

## - -2.0 

## -

· Figure D.6 35% Efficiency Level: Net Savings



### D-7 2015 0.01 0.05 0.10 $26.46 $12.99 $13.47

2.04

2020 1.66 7.59 14.62

2025 2030Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) Total Energy & Water Saving Total Equipment Cost Net Present Benefit

Benefit/Cost Ratio

from 2003 to 2010 to 2020 to 2030

2000 O.!J4 4.17 7.90

2005 0.65 2.87 5.40

2010 0.27 1.24 2.40

6.0

. 5.0

-

4.0

- · c::=J Price Ch an g e**~Energy** & Water Saving_**~Net** Saving z: m 3.0**~** 

## - Q r-- 

### °' 2.0 

## - °' 

### ""

..... 

# ... s: :! 1.0 

## - 

# s 

# .. "" "" "" I-"" 0.0 -1.0 

## -**~ ~**  

## .. • -2 

### .o 

## - Figure D.7 40% Efficiency Level: Net Savings

D-8 2015



# .. ...

,,; 0.01 0.05 0.10 $26.56 $13.04 $13.53

2.04

2020 1.66 7.59 14.62
.

2025 2030



### ·.Discounted from ioo3 to 2030 at 7% real to 1997 (in billion 1997$) Total Energy & Water Saving Total Equipment Cost NetPresent Benefit

Benefit/Cost Ratio

from 2003 to 2010 to2020 to 2030 1.12 4.99 9.49 0.81 3.60 6.80 0.29 1.34 2.59 0.01 0.05 0.10 $27.70 $18.89 $8.81

1.47

1.43 6.50 12.47

Cost an-d, Sa.ving-·-of N.ew:
..C lothes"W as hers

. 

# - -
6.0 5.0 4.0 .f .!! 3.0 c Q

..... 2.0 

# ... 

# ... 

# - 1::1 

### :5 1.0 ii 0.0 -1.0 -2 

### .o 2000 2005 2010



### -



## -

-

-

-

- c=:::J Price Ch an g e IEEi5i\IBEnergy & Water Saving --Net Saving



### Figure 

### D.8 45% Efficiency.Level: Net Savings

D-9 2015 2020 2025 2030**~**

### a e 

# . . 0 

### 1c1encv T bl D 1 17 SOo/c Effi
. 

### eve: os an e 

### L I C t dN tP resen

### t 

### V I a ues Cost and Net Present Values**Discount~d** from 2003 to 2030 at 7% reaJ to 1997 (in billion 1997$) Total Energy & Water Saving $28.75 Total Equipment Cost $19.68 Net Present Benefit $9.07

Benefit/Cost Ratio 1.46



### a e 

# . . 0 

### c1encv T bl D 1 18 SOo/c Effi
. 

### L IE eve: nerl!V an

### dW t 

### S a er aVIDS?S Energy Saving in Quads Water Saving Total Elec Gas from 2003 to 2010 1.18 0.87 0.30 to 2020 5.28 3.85 1.38 to2030 10.06 7.28 2.67

2000 2005 2010 6 

### .o - 5.0

- 4.0

- 3.0

- 2.0

- 1.0

- 0.0 -1.0

- -2 .0

- -3 .0

- c:::::::J Price Ch an g e • -+>Energy & Water Saving -Net Saving



# .. 

# - .... '."" .;



# - 

# - Figure 

### D.9 50°/o Efficiency Level: Net Savings 2015



## ...

D-10 · Oil (Trillion 2als) 0.01 1.43 0.05 6.50 0.11 12.47

2020 2025**I~**



# - 

# - 2030



### ..**I~** , ,



# - ..

### APPENDIX E: GOVERNMENT REGULATORY IMPACT MODEL (GRIM)
E.1
. INTRODUCTION AND PURPOSE 

### The purpose ofthe Government Regulatory Impact Model (GRIM) is to.help identify the effects ofvarious efficiency regulations and other regulations on manufacturers. The basic mode of analysisis to determine the change invalue of the manufacturers(s) following aregulation or a series ::- ofregulations. The model structure also allows an analysis ofmultiple products with regulations taking effect over a period oftime, and ofmultiple regulations on the same product. Industry value is defined, for the purposes ofthis analysis as the present value ofcash flows for the manufacturer(s) in question. Cash flow iscalculated by the user entering shipment volumes and manufacturerprices (i.e., prices from manufacturers to the first customer, such as a wholesaler) andthen selecting user-defined regulatory levels. The model calculates the actual cash flows by year andthendetenlμnes thepresentvalue of those cashflows bothwithoutregulations (thepre:-regulation

.base case) and with regulations (the post-regulation standards case). Output comes from the model in terms ofsummary statistics, graphs ofmajorvariables, and, when appropriate, access to the complete cash flow calculation.
E.2 MODEL DESCRIPTION 

### The basic structure ofthe GRIM is a standard annualcash flow analysis that uses price and volume information as an input, builds on fundamental base cost information, and accepts a set of regulatory conditions as changes in costs andinvestments.The cash flow analysis is separated into two major blocks: income and cash flow. The income calculation detennines the profit after taxes butbefore financial charges. The cash flow calculation converts profitaftertaxe.s into an annual cash flowby including investmentand non-cash items. Beloware definitions oflisteditems onthe printout ofthe output sheet. (see Section E.3) 

### (1) Income Statement: Overall calculation 

### ofNetIncome Before Financing (17) using revenue and cost items subject to income tax effects as described in (2) through (17).

E-1(2) 

### Price/Unit: Actual manufacturer unit price charged to the first level of distribution (distributor, retailer, etc.) Price ofBaseline Unit= $285.90 Price ofhigher efficiency unit= Price ofBaseline Unit+ (Incremental Mfg. Cost)* (Mfg. Markup) It can.also be entered as an input by the user (3) 

### Unit Sales: Annual shipments for the industry will be obtained from the National Energy Savings Spreadsheet. Baseline Industry Shipments (1997) = 7.065 million It can also be entered as an inputbythe user (4) 

### Revenues: Annual revenues; computed by

### multiplyingPrice/llnit (2) by 

### Unit Sales (3). (5) 

### Base Costs: Costs per unit priorto regulations. (6) 

### New Costs: Costs per unit following regulations. 

### (7) Labor: Factory direct labor and fringe benefit costs; Baseline Labor Cost= $29.3 1 New Labor Cost = Baseline Labor Cost + Incremental Labor Cost (8) 

### Material: Purchased materials and components; Baseline Material Cost= $148.1 NewMaterial Cost= Baseline Material Cost + Incremental Material Cost (9) 

### Overhead: Factory overhead excluding depreciation. Overhead is treated as both fixed and variable, with the percentage of overhead (excluding depreciation) thatjs variable set under the Major Assumptions. Baseline Overhead Cost= $33.5 1 · New Overhead Cost= Baseline Overhead Cost + Incremental Overhead Cost

1 Source: "Mark-ups for Clothes WasherRulemakingAnalysis."This documentwaspresented inthe Clothes Washer Workshop held in March, 1998. E-2 

## -

. .,,

### (10) Deprecia#on:

(11) Cost of Sales: (12) SG&A: (13) R&D: (14) Product Conversion:

(15) ProfitBefore Tax:
(16) Taxes:
(17) NetIncome Before Financing: Annual depreciation on pre-regulation assets; computed as a percentage of 

### Revenues (4). Annual depreciation on postregulation assets is computed by 

### dividingConversion CapitalExpenditures (25) by a useful life fromMajor Assumptions. Forpost-regulation years, 

### Depreciation is the sum ofdepreciation onpre- and post-regulation assets. Pre-regulation: Depreciation= 

### % ofannual revenues Post-regulation: · Depreciation = Depreciation_ Pre-regulation 

### + (Conversion Capital Exp./Useful_ life) Total cost ofsales, CQmputed by adding

### Labor (7), 

### Material (8), 

### Overhead (9) and

### Depreciation (I0). Selling, general and administrative costs are computed as a percentage of

### Revenues ( 4). Research and development costs unrelated to regulations; computed as a percentage of

### Revenues (4). Expensable costs related to meeting a regulation, often including product redesign costs and expensable factory conversion expenses. Also includes costs incurred for new product literature and catalogs, product obsolescence, and various related marketing expenses. GRIM allocates these costs over a number ofyears. Profit before taxes and any financing costs; computed by subtracting 

### Cost of Sales (II), 

### SG&A (12), 

### R&D (13), and 

### Product Conversion (14) from 

### Revenues (4). Taxes on

### Profits Before Tax; computed by multiplying the 

### tax rate contaitted inMajor Assumptionsby 

### ProfitBefore Tax (15). Profits after taxes; computed by subtracting 

### Taxes from 

### ProfitBefore Tax (15).

E-3

### (18) Cash Flow Statement: (19) NetIncome: (20) Depreciation: Overall assessment including·net income, other cashrelated items and adjustments and investments. Net income; identical 

### toNetIncomeBefore Financing (17). Depreciation is a non-cash cost and is added back 

### intoNet Income (19) as part ofthe cash flow calculation; identical to 

### Depreciation (10). 

### (21) Change in Working Capital: Additional accounts receivable, inventory, and other cash investments necessary to support increased revenues; computed bymultiplying a percentage fromMajor Assumptionsby the change 

### inRevenues (4). 

### (22) Cash Flow from Operations: The cash flow from operating activities; computed by addingNet

### Income (19), 

### Depreciation (20) and subtracting 

### Change 

### in Working Capital (21)
.



### . (23) CapitalExpenditures: Investment to 

### maintain and replace existing production assets; computed as a percentage of 

### Revenues (4) using a percentage contained inMajor Assumptions. Postregulation capital expenditures equal pre-regulation expenditures plus depreciation 

### onConversion Capital Expenditures (24). Pre-regulation: Capital Exp. = 

### % ofannual revenues Post-regulation: Capital Exp. =Capital Exp._Pre-regulation 

### + (Conversion Capital**Exp./Useful~** life) 

### (24) Conversion Capital Exp.: Capital costs for meeting regulations, typically including plant, equipment, tooling and the like. These have been provided by AHAM for each efficiency level.
. 

### (25) Cash Used in Investments: Cash required for assets; computed by adding

### Capital Expenditures (23) and 

### Conversion Capital Expenditures (24).

E-4

### (26) Net Cash Flow:
(27) Post-Regulation Value:

•, Annual cash flow from operations and investments; computed by subtracting 

### C.ash Used in Investments (25) from 

### Cash Flow from Operations (22). Value·ofthe relevant industry following adoption and implementation ofthe regulatory scenario; the present value ofthe annual

### Net Cash Flow (26) from the base year to the year 2012, plus a**~rminal** value based onthe final years cash flow valued as an annuity, are computed by model and listed. The discounted rate for the present value is the "Discount Rate for NPV''contained in Major Assumptions. Pre-regulation value is computed in a similar way using base case

### Net Cash Flows(26) and terminal value. ·



### E-5

### E.3 INCOME STATEMENT

,

# '"itif"~4lSlt~**.~** . 

# I Announce 

# l j Standard 

# I Bau Year Year i l Year (I)**~** 1998 1999 ' 2000 2001 ' 2002 I· 2003 ' 2004

(4) Revenues 2,020.1 2,063.S i 2,107.9 2,153.2 j 2,1995 1 2,246.8 ! 2.295.1 !

. : (11) Cost of Sales (7) Labor 207.0 21151 216.0 220.7 i 225.4 230.3 ! 235.2 ! (8) Material 1,050.7 l,073.3 i 1,096.3 1,119.9 ! 1,144.0 1,168.6 ! 1,193.7 j (9) Overhead

- Fixed (9) Overhead

- Variable 165.7 1693 ! 172.9 '176.6 ! 180.4 184.3 l 188.21 .• (10) Depreciation 70.9 72.4 ! 74.0 75.6 ! 77.2 78.9 ! 80.6 i Se/Unit, General and.Administrative 0.035 ! 0.035 0.035 ' (12) Standard SG&A (13)

- R&D 363.6 371.4 i 379.4 387.6 ! 395.9 '404.4 36.4 37.1 i 37.9 38.8 i 39.6 40.4 413.1 _j 41.3 i _, (14) Product Conversion Expense (IS) Profit Before Tall 125.8 128.5 i 131.3 134.1 i 137.0 139.9 ! 142.9 06) Taxes 53.9 S5.0 i S6.2 S7.4 ! S8.6 S9.9 i 61.2 I 

# .. I (17) Net Income Before Financing 72.0 73.5 i 75.1 I 76.7 i 78.4 i 80.0 l 81.8 i l 72.0 73.5 i 75.1 76.7 i 78.4 81.8 j (20) Depreciation 78.9 ! 70.9 72.4 ! 74.0 75.6 ! 77.2 80.6 ! (21) !Change in Workinl! Canital (3.2)[ (2.9)! (3.0)• (3.l)i (3.1) (3.3)[ 142.9 143.0 ! 146.1 ' 149.2 ! 152.4 ! 159.I i (89.S)**(~_!_:_~}!** (93.4ii**~95.~1** (97.4)! (9;.4)1 (89.S) (91.4)! (95.4)! (97.4)1 i (26) Net Cash Flow 53.4 51.6 l 52.7 53.8 i 55.0 56.2 ! 57.4 i Terminal Value Present Value Factor 1.0000 0.9324 l 0.8694 0.8106 l 0.7558 0.7047 i 0.6571 Discounted Cash Flow S3.4 48.1 ! 45.8 43.6 i 41.6 39.6: 37.7 (27) Industry Value {Net Present Value) S 905.8

E-6

### E.4 MAJOR ASSUMPTIONS Inmaking the cash flow calculations, the model uses several major assumptions, each of which can be modified. The major assumptions are outlined below.

Base Year Announcement Year Standard Year Tax Rate DiscoW1t Rate for NPV Inflation Rate Working Capital Base Year Unit Price Base Year Unit Sales Unit Sales Growth Rate Standard SG&.A Research and Development Ordinary Depreciation Ordinary Capital Expenditures Variable Overhead as % ofTotal Standards Case Efficiency Level => Efficiency Improvement of
Manufacturin Costs Materials I Unit Labor/Unit Overhead I Unit (excl. Depreciation)**r'.!.'\ftf~;'i/;'..:'::'** :'19!18 Beginningyur for analyticlll pwposa**~>:i\l~** Year in which the regulation fllUIOllllUlllelll iswide**@~i!i:\'0** /:;.:2()()2 Year in which the regulation tllkaeffect**:;~;,,~;J':'·'"''".42'.So.4 ·'k':~~~~::.:~~;~~~;7.25%** **~>~P·'r:** ,:;:0:00A.1na annum**'{'0'°'0-~:'iU>.:'.6'.76%** of Revenue**!::~:::i>,t'~.,'.285;90;** ($)**"·}:~;<~ ~:·~** ..; :·'
.... ·1.01.; (000,000) .r<i'..'.<:.':: 2;15%1.DUannum 

# r.'':: : :18.00%10/Revenue



### .·'. ·>·.. .·;1;800.4.of Revenue '·· '

...;' ;,.;"3.51% of Revenue ·: :·.·:':'',''•·.:**~;** ·.4.43% of Revenue I ·.· ;' :.• ·' <JOOOA.I 

### ' y· i .,.

# ·-~~~;; iaseline MEFo/0.817 Base Incremental Due to Year Standard**"":'"""\""'~?,.,..'::,...,:·:"""·~··"""n""'"4g~_~10,....,,""' :?>'-Li:.~·.y29.30.**  ?>'>YI/ '23A5; . __ ($)per unit ($)per unit 

## aii!m1JJI ($)per unit Industry Mark-up (over full production costs) Standard Year Unit Price Conversion Costs Coversion Capital Expenditure Useful Life Design &. Marketing Expenses



### E-7 ·.

### APPENDIX F: CONSUMER RESEARCH REPORT

F-1

### DRAFT REPORT .ON CONSUMER RESEARCH FOR CLOTHES WASHERS

March 27, 1998

LAWRENCE BERKELEY NATIONAL LABORATORY Environmental Energy Technologies Division

Technology and Market Assessment Group Berkeley, CA 94720

Prepared·for U.S. DEPARTMENT OF ENERGY Office ofCodes and Standards

### CONSUMER RESEARCH SUMMARY
......................................... . 1.0 INTRODUCTION
. . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

. 2.0 SOURCES OF INFORMATION
...................................... 1

3.0 SOURCES and PROJECTS
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3.1 Major Studies by Consortiums
................................... 2 3.1.1 THELMA
.......................................... 2 3.1.2 Bern, Kansas Washer Study
. . . . . . . . . . . . . . . . . ·. . . . . . . . . . . . 9 3.1.3 WashWise·.
. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.4 Northeast Energy Efficiency Partnership (NEEEP)
........... 13 3.2 Individual Utility Demand Side Management 

### I Market Transformation Programs
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2.1 Pacific Gas & Electric (PG&E)-. Utility Survey
............. 14 3.2.2 Commonwealth Electric
. . . . . . • . . . . . . . . . . . . . . . . . . . . . . . . 15 3.3 Consumer Publications
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.3.l Consumer Reports 

### I Consumers Union
.................... 15 3.3.2 Consumer- NewZealand publication
................... 16 3.3.3 CHOICE
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.4 Trade Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7 3.4.1 The Soap and Detergent Association (SDA)
................ 17 3.4.2 Arthur D. Little (AOL) Report on the behalfofAHAM, 1991
... 18 3.4.3 EPRI (Electric Power Research Institute) Journal
............. 19 3.4.4 American Society ofMechanical Engineers, Journal.
.......... 19 3.5 Government Projects .·. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.5.l Energy Star Program
.................................. 20 3.5.2 Commercial Laundry Field Study
........................ 21 3.5.3 MacDill Air Force Base
............................... 21 3.5.4 Fort Hood Demonstration Study
......................... 22 3.5.5 Fort Hood Government Procurement, DOD
................ 23

•

### CONSUMER RESEARCH SUMMARY
1.0 Introduction

- Presentation ofConsumer Research This report is a summary ofconsumer research on clothes washers.


### 2.0 Sources ofInformation Listed beloware sources ofinformation regarding consumer issues. Some programs are applicable to more than one general heading. Major Studies 

### by Consortiums The High Efficiency Laundry Metering & Marketing Analysis (THELMA) Bein, Kansas 

### I Maytag demonstration project Wash Wise

- Northwest Energy Efficiency Alliance (NWEEA) Northeast Energy Efficiency Program (NEEP) · Individual Utility DemandSide Management (DSM) I Market Transformation Programs Pacific Gas & Electric (PG& E, California) Commonwealth Electric Consumer Publications ConsUD)er Reports (USA) Consumer (NewZealand) CHOICE (Australia) Trade & Professional Organizations Soap & Detergent Association American Home Appliance Association (AHAM) Electric Power Research InstitUte (EPRI) American Society ofMechanical Engineers (ASME) Government Programs Energy Star Appliance Programs (DOE, EPA)- D&R International and other ORNL

- Commercial Laundry Field Study MacDill Air Force Base, DOD Fort Hood Demonstration Study, DOD Fort Hood Government Procurement, DOD

1

### 3.0 SOURCES and PROJECTS 3.1 Major Studies by Consortiums 3.1.1 THELMA (fhe High-Efficiency Laundry Metering & 

### Marketing Analysis Project) Sponsors: The High-Efficiency Laundry Metering & Marketing Analysis (THELMA) study is an Electric Power Research Institute Tailored Collaborative project sponsored by a consortium of28 electric, gas, water and sewer utilities, as well as the Electric Power Research Institute (EPRI) and the**U~S.** Department ofEnergy. The project is being coordinatedthrough EPRI. Contractors: Hagler Bailly Consulting Inc., Phone: (608) 232-2800
· Dethman & Associates, Phone: (206) 545-0717

SBW Consulting, Inc., Phone: (206) 827-0330 National Center for Appropriate Technology, Phone: (206) 324-8064 Arthur D. Little, Phone: (617) 498-5000 Objectives: The broad goals ofTHELMA are to acquire the information necessary for leveraging a lasting market transformation to efficient clothes washers and to design effective incentive programs and other promotional tools for simulating the purchase ofefficient washers. Specific objectives include:

.. Characterize status ofH-axis technology.

.. · Confirm savings estimates.

.. Assess customer laundering habits.

.. Identify market barriers.

.. Suggest strategies for utility actions. Methodologies:
. . . The THELMA report includes the following volumes (not all are currently published): Volume 1: Final Research Plan Volume 2: Laboratory Testing ofClothes Washers Volume 3: General MarketAssessment Volume 4: Distribution Channels Volume 5: Product Demonstrations Volume 6: Product Users: In-Home Interviews Volume 7: Product Users: Field Monitoring Volume 8: Product Benefits and Target Markets Volume 9: Product Users: Further Evaluations
2

### " Individual THELMA report volumes, organizedbytasks, are discussed below. Future Work: Findings ofthe THELMA project will be published in a nine volume set. Four volumes were made available to LBNL; all but the Laboratory Testing Report were draft documents. It is not certain when the other eight report volumes will become available to the general public. Additional field testing with the Neptune, Maytag was done but this report is not available.yet. There are currently no plans for EPRI to do additional work.


### 3.1.1.1 THELMATask2.1 Final Report Title: 

### Results ofFocus Groups with 

### Consumers Date: June 1995 Report leader: Dethman 

### & Associates Objectives:

... Qualitatively assess the current market for H-axis washers.

... To explore consumer attitudes, rather than to measure them.

... Identify and explore the educational needs ofconsumers.

... Inform other THELMA research components. Methodologies:
. Washers: prior to introduction ofNeptune (Maytag); both top-loading and front-loading Haxis washers were discussed. Description: Focus Groups with Consumers. Date: February 6-9, 1995. Location: Bellevue, Washington & Concord, California Sample size: Four focus groups of 10 people each (2 in each location). Sample demographics: selected by wash loads and income Rebates:.None Topics covered included:

... satisfaction with current washer

... reactions to H-axis washer

... interest in buying a H-axis washer

... consumerdescriptions ofH-axis washers Findings: [from Executive Summary] Barriers to Acceptance ofH-axis Washers Awareness ofH-axis washers was extremely low among focus group participants. Participants reactions were initially quite negative to front-loading H-axis washers, based on old associations. They also were resistant to certain features, citing concerns about the neeq to bend; leaking; child safety; and ease ofuse. (Participants were shown photographs offront-loading and top-loading models; information and
3

### diagrams that compared the mechanical action and cleaning technique ofY-axis and H-axis washers; specific details ofresource and cost savings)
.
.,. Participants viewed top-loading H-axis washers as a confusing hybrid. They also had concerns about specific features (e.g., double door entry; the hatch always rotating to the top). Even when given basic information about H-axis washers, participants had a multitude ofquestions and concerns. The Appeal ofH-axis Washers

.,. Most participants inthe.focus groups said they might consider buying an H-axis washer ifit was equal to their V-axis washer in terms ofreliability, capadty, price, and convenience
.
.,. Participants who spontaneously suggested H-axis benefits were few in number and quite tentative
.
.,. Participants perceived that resource conservation is less important than other washer attributes such as ease ofuse and cleaning ability.

.. Participants rarely mentioned energy and water efficiency as key buying criteria; rather, they mentioned cost, reliability, large capacity, versatility, and easy use. Key Buying Criteria[froni Results ofFocus Groups with Consumers, Draft] The most frequently mentioned buying criteria were:

.. Cost

.,. Reliability and durability ·.,. Large capacity

.,. Versatile features (e.g., water levels; wash speeds; temperature settings; rinse

. cycles; delicate settings; dispensers; etc.)

.,. Controls that were easy to use Factors that were occasionally mentioned included: ease ofhome maintenance; safety features; gas vs. electricity; type ofagitator; noise level; aesthetics; length ofwash cycle. Energy and water efficiency were rarely mentioned, except in the context ofselecting the water level to match the load size.
3.1.1.2 THELMA Task 2.3 Draft Title: Laundry Demonstration Center Date: April 1996 Report leader: Dethman 

### & Associates 

### ·Objectives: The Demonstration Centers·purpose was to introduce potential H-axis consumers to two types ofH-axis washers, and to collect their reactions to and insights about these washers. The primary objectives ofthe Demonstration Center were to assess respondents:

... Awareness and initial impressions ofH-axis washers. 

### 4 --. 

### · .. Preferences for top-loading vs. front-loading H-axis washers.

.. Reactions to trying out an H-axis washers when they conducted a 'test wash".

.. Likelihood ofbuying an H-axis washer.

.. Questions about H-axis washers and reactions to H-axis utility program ideas. Methodologi'es: Washers:

.. Participants were shown a top-loading and a front-loading machine, and they performed a test wash on the front loader.

.. Neptune washer (Maytag) was not involved. Description: Surveyed participants after demonstratingwashers. Time period ofsurveys: November 7, 1995 to February 21, 1996. Location: Lynnwo9d, Washington (a suburban areajustnorth ofSeattle) Sample size: one hundred in depth interviews were conducted. Sample demographics:

.. Random from phone books covering neighborhoods near the demonstration center.

.. Participants who had some potential for buying an H-axis washer were selected.

.. Homeowners with at least $30,000 income

.. Currently owned V-axis machines

.. Household did four or more wash loads per week Findings: [from Executive Summary] Whats Important in a New Washer?

.. Capacity (51%)

.. Specific features such as dispensers, cycles ( 48%)

.. Efficiency (35%) (e.g., selecting cold wash rinse or adjusting water level)

.. Reputation/reliability (31% )

.. · Purchase price (28%) What Initial Impressions Did Participants Have ofH-axis Washers?
·.. One-third had heard ofresidential H-axis washers; 15% had used one.

.. Just over a quarter ofrespondents.said they would be extremely or very likely to buy one.

.. Those less predisposed to H-axis washers most often cited 'tack ofinformation"as the reason for their negative ratings; other concerns included front-loading issues (e.g., bending, leaking) and inferior performance. What Difference Did Hands-on Experience Make? Findings are based on two specific brands ofwashers, and therefore, may not apply equally well to other H-axis machines, especially findings on specific product features
. Response to Front-load Washer (WasherA)

.. Perceptions on capacity changed. Over a third ofparticipants remarked that they could have added more clothes. 

### 5Many asked for reliable data on the true capacity ofH-axis washer$, and on the relationship between capacity and cleaning performance. • About halfthought the cfothes would get as clean and rinse as well as with their V-axis washer. 

## •
• About halfwere unsure or expected inferior cleaning and rinsing. Respondents were confused about how clothes could get clean without immersion in water, or without the scrubbing action ofan agitator. Respondents wanted more hands-onproofabout the machiness performance under a variety ofreal life settings.
. Appealing features included: overall appearance, quietness, gentleness on Clothes, familiar controls, better balance, and the convenient dispensers for bleach/softener. Areas ofconcern included: bending to load and ullload, fear ofleaks, accessibility of controls to children, and dispenser spills. Response to Top-Load Washer (Washer B) This was a top-load, horizontal axis washer. • Said they preferred the top-loading H-axis washer in theory but preferred the front-loading H-axis washer because they felt the overall design was more logical, more familiar from laundromat experiences, and more user-friendly. • • The majority ofrespondents who said they preferred a top loader said they would never buy Washer B. Reaction to Washer B was dominated by negative response to the latched opening used to enter

## tlie drum, as well as concerns about the machines basic quality and finish. How Did Participants View H-axis Washers in the End? • Respondents were most likely to believe that H-axis wa5hers did reduce wear and tear, but were less sure about their superior cleaning/rinsing ability and equivalent capacity. • Respondents were evenly split between those who said that the higher purchase price ($600-$800 purchase price) would deterthem from buying an H-axis washer, and those who said it might be worth paying more, ifthe savings, superior performance and reliability ofH-axis washers were proven. • Two-thirds were impressed with improved efficiency and financial savings; others wanted prooforthought the savings were too small. • The number ofparticipants who rated themselves extremely/very likely to buy an H-axis washer had more than doubled (from 28% to 67%), after the interview and test wash. • Most respondents said they would need more information before actually buying an H-axis washer. Respondents required proven data about the H-axis washers'track record; ease of repair; efficiency/savings; cleaning, rinsing, wear 

### & tear; and capacity.


### 3.1.1.3 THELMA Task4 Draft Title: 

### Impact Analysis Date: October 1996 Report leader: SBW Consulting, Inc. (for EPRI Retail Market Tools and Services) 

### 6Objectives: To assess energy and water savings in an 'in-home"environment as compared to a laboratory setting. Laboratory testing can provide savings estimates but must make assumptions about peoples'laundry practices. In-home monitoring can provide estimates under more realistic and diverse conditions. Methodologies: Washers: Two

# types ofH-axis washers were used. • WasherA was il)Stalled in 46of50 sites. It is a front-loading model that wa5 officially released to the U.S. market in September 1996 by a major U.S. appliance manufacturer. It has a maximum spin speed of650 rpm. • Washer Cwas installed in 4 of50 sites. It is a front-loading model manufactured in Europe and available in the United States. Washer C requires 220 Volt service and only a cold-water connection**b~cause** it has an internal electric water heater. Description: Existing washer and dryer were monitored for a 6-week period.
Dates: An H-axis washer was then substituted for the V-axis washer, and the H-axis washer and dryer were then monitored for a 7-week period. Phase I (existing washer monitored) Sept. 1995

- Dec. 1995, Phase II (H-axis washer monitored) May 1996

- Aug. 1996. Locations: Phase I: 22 sites within a 10-mile radius in the Greater-Seattle, Washington area 4 sites in Tacoma, Washington Phase II: 19 sites in Salem, Oregon 

### 5 sites in the Bay Area ofCalifornia (Fremont, San Rafael, Richmond) Sample size: 50 homes were monitored. Fmdmgs: J • Water savings ranged from 11to74 percent with an average water savings of36 percent. • Energy is saved ifwarm or hot water is used for washing or ifthe washer features a high spin speed. Average savings were 1.02 kWh/wash load. • Participants with the H-axis Washer A reduced their load size by 4 percent. Those with Washer C reduced on average reduced their load size by 10 percent. • There was no significant change in the wash temperatures selected between the V-axis and H-axis washers.


### 3.1.1.4 THELMA, Final Report Title: 

### Laboratory Testing ofClothes Washers, Final Report Date: April 17, 1997 Contractor: Arthur D. Little, Inc. (ADL) Principle investigators: 

### J Dieckmann, DetlefWestphalen Project Managers: R. Gillman, 

### J. Kesselring; Customer Systems Group, EPRI

7This report was a continuation ofan earlier report ofthe same title, published in December 1995. Principal investigators for the earlier report (also by ADL) are: J Dieckmann, W. Murphy. An additional washer not previously available was tested in the updated report. 

### Objectives:
. · To test energy and water consumption and otherperformance parameters discussed below. 

### Methodologies: Washers: Number ofclothes washers tested: seven H-axis and one Y-axis washers. Washers were labeled A through H. It is believed that a prototype Neptune (Maytag) was included in the testing. Clothes washers could be identified by noting their unique specifications. Laboratory testing ofwasherto determine (for 8 washers):

... DOE washer capacity

... Hot and cold water consumption

... Energy consumption by the washer mechanical drive

... DOE energy factor

... Water extraction effectiveness

... Effect ofwater extraction effectiveness on dryer energy

... Soil removal effectiveness

... Rinsing effectiveness

... Gentleness ofaction 

### Findings: [from report Conclusions]

... The normal wash cycle times ofthe eight H-axis machines ranged between 40 minutes and 110 minutes. The Y-axis normal wash cycle reqμired 3 7 minutes.

... Th.e energy factor ofthe H-axis machines, as measured by the DOE test procedure, was approximately 95% higher than the Y-axis machine that was tested (i.e., H-axis energy consumption normalized to the basket volume is approximately 45% less than Y-axis).

... The average water consumption ofthe eight H-axis machines, normalized to the basket volume, is 25% less than the Y-axis machine at maximum fill level and l0% less than the Y-axis at minimum fill level.
.

... The H-axis washers tested all had higher spin Gs than the Y-axis machine, resulting in measurably lower remaining moisture content (RMC).

... Electric clothes dryer energy decreases linearly with decreasing RMC.

... . As a group, the soil removal effectiveness ofthe H-axis washers, as measured by the AHAM soil removal test, was 50% greater than for the Y-axis machine.

... The average rinsing effectiveness ofthe H-axis washers was 50% greater.than the Y-axis washer.

... The AHAM gentleness ofaction test results were comparable for H-axis and Y-axis machines.



### 8

### 3.1.2 Bern I 

### Kansas Study Title: 

### Bern Clothes Washer Study

- Final Report Date: March 1998 Report: ORNL/M 6382 Sponsors: Department ofEnergy, Maytag Contractor: Oak Ridge National Laboratory 

### Contacts: • John Tomlinson, Oak Ridge National Laboratory (ORNL), Phone: (423) 574-0291 • D.T. Rizy, Oak Ridge National Laboratory • Linda Eggerss, Maytag, Phone: (515) 791-8518 

### Web Site: www.energystar.gov 

### Objectives: • To evaluate the energy and water savings ofhigh-efficiency, H-axis washers in a community which 

### has been converted to the new design. • To determine the impact ofthe new washer on customers'laundry habits and perceptions • To demonstrate the findings and to develop information helpful to utilities (energy and water) and others with an eye towards moving the current clothes washer market to higher efficiency options. 

### Methodologies: Washers: Monitoring was done with the existing V-axis waslting μiachines in Phase 1and with the Maytag Neptune H-axis washer in Phase 11. Description: Demonstration/monitor & metering study. • · H-axis washers were installed in 105 homes inBern, Kansas
.. • Interviews and questionnaires were used to determine the machines users satisfaction with the washer. Dates: monitoring took place between June, 1997 and September 1997. Location: Bern, Kansas; a rural community with a population ofabout 200 Sample size: 105 homes Conditions: participants were allowed to keep washers after the study free ofcharge 

### Data collectedon: 

## • impacts ofload size 

## • water consumption 

# • energy consumption 

## • detergent use and consumption 

## • impacts on other additives 

## • impacts on laundry habits 

## • impacts on machine settings 

## • cleaning performance 

## • impacts on load dampness 

### 9

### .. impacts**~>n** 

### drying habits • municipal utility impacts 

### Findings:. [from Executive 

### Summary and Conclusions]

.. Overall, it was found that the changeoverto the H-axis washer reduced the average water consumption from 41.5 gallons/load to 25.8 gallons/load - a water savings ofabout 38%. • The H-axis washers energy consumption including washer energy and hot water energy fell by 58% due to hot water savings and the impact ofa highly efficient motor in the H-



# ..



# ..

.. axis. The remaining moisture content ofdamp loads removed from the H-axis washers was, on average, 7% lowerthan for loads removed from participants'V-axis washers. The data and subsequent analyses also showed that across all loads, temperature settings, use ofdetergent and other additives, participants found the cleaning performance ofthe Haxis technology to be generally superior to their Phase I washer irrespective ofits age. Participants seeined to adapt easily to the H-axis design, and laundry habits (average load weights, detergent use, how loads were dried, when loads were washed during the week, wash/rinse temperatures and other.factors) remained largely unchanged from Phase I to ·Phase II. • Taken together, these findings suggest that a changeover to H-axis technology delivers large savings in energy and water to the customer with an improvement in cleaning performance and utility.
.

.. The fraction ofloads in which participants were 'tompletely satisfied" with the.cleaning performance increased**thr~efold** 

### from 15 to 45% in the changeover from the average Vaxis washer to the H-axis washer 

### in the study.

.. Overall, the study showed that the changeover to the H-axis washer has little impact on the way in which customers washed and dried clothes.
3.1.3 WasbWise Title: Coming Clean About Resource-Efficient Clothes Washers: An Initial WashWise Program and Market Progress Report

- Final Report Report Number: E98.,003 · Date: January 28, 1998 

### Commissionedby. • Northwest Energy Efficiency Alliance (NWEEA); comprised primarily ofelectric utilities 

### in the Pacific Northwest.



### · Contractors:

• Portland Energy Conservation Inc. (PECI) • Gilmore Research • Pacific Energy Associates (PEA)
10

### Contacts: • Northwest Energy Efficiency Alliance Tom Eckman, Phone: (503) 827-8416 Gabriel Foulkes, Phone: (503) 827-8416 x225 Ben Bronfinan, Phone: (503) 827-8416 x226 • Portland Energy Conservation Inc. (PECI) Lois Gordan, Phone: (503) 248-4636 x207 • Pacific Energy Associates (PEA) David Hewitt, Phone: (360) 571-0497 Gary Smith, Phone: (503) 590-4609 • Gilmore Research Group

. Denise Bauman, Phone: (503) 236-4551 • Oregon Department ofEnergy Charlie Stevens, Phone: (503) 229-5625 

### Web site: http://www.nwalliance.org 

### Objectives: • To dramatically increase the market share ofresource-efficient-clothes washers (RECWs) in the ·program area overthe next three years. • To influence the development and support ofmaximum energy and water efficiency levels for revised federal clothes washer standards. 

### Methodologies: Washers: At the time the surveys below were done, the Maytag, Neptune H-axis washer was not on the market. Frigidaire, Staber and Europeans had horizontal axis · washers on the market. Description: This project 

### has several aspects to it:

... a telephone survey of419 participants/purchasers ofRECWs as part ofthe WashWise rebate program (a rebate program funded by utilities)

... an intercept survey of42 consumers shopping for laundry equipment

... a telephone survey of205 appliance retailers • a telephone survey ofqualified representatives ofnine major appliance manufacturer and importers ofEuropean products Location: Pacific Northwest; Washington, Oregon, Idaho, and western Montana Gilmore Researchconducted the following surveys:

... Participant Survey

- , those who participated in the rebate program and bought an H-axis clothes washer. Consumers got a $130 instant rebate and dealers got a $20 rebate from the sponsoring**u~jlity.**

... Intercept Surveys

- what people thought ofH-axis washers before they became participants and before they used the H-axis washers. • Retailer Survey (dealers)- how-many they think they could sell.

... Manufacturer Survey- predicted future H-axis sales. 

### 11

### Pacific Energy Associatesevaluated the program and discussed key findings in the report: 

### Coming Clean AboutResource-Efficient Clothes Washers: AnInitial WashWise Program andMarket Progress Report.-· Final**Repor~;** 

### January 28, 1998 This report includes analysis of: • reasons for purchasing a RECW; • satisfaction with Wash Wise program; • product pricing and RECW cost barrier; • manufacturers perception ofthe RECW market; • opportunities for increasing RECW sales. 

### Findings: Purchaser Survey This consisted of419 interviews with people who had purchased a RECW in the beginning months ofWash Wise. • Demographics: 36% had incomes ofless than $40;000 peryear, 39% had household incomes between $40, 000 and $75,000 per year. • Main reasons 

### for purchasing a RECW were: energy or water savings, 74%; cleaner clothes, 18%; and gentler on clothes , 15%. • 76% said that they would have purchased a front loading washing machine even if the rebate was not offered. • 95% said they were at leastsomewhat satisfied with each RECW feature 

### • 81 % said the washer was worth the price



### ., 91 % said they would recommend the machine to others 

### Intercept Surveys (42 interviews) Of the forty-two (42) intercept interviews conducted, thirty-two (32) were customers shopping primarily for top-loaders, and ten (I0) interviews were with customers looking 

### for tumble-action washers. • Top-load shoppers**mentic~ned** 'high cost" more than anything else as

# a reason for buying a top-loading washer rather than· a tumble-action washing machine. • Thirteen (13) top-load shoppers said they were shopping for a top-loader rather than a tumble-action washer because the tumble-action machines were too expensive. • Customers also reported that they did not want to bend over to load and unload the clothes (mentioned by 8 top-load shoppers). • Six (6) top-load shoppers reported that they were simply used to top-loaders. 

### Retailer Survey (205 dealers) • Retailers believed that about halfofthe RECW purchasers. would have purchased a standard, top loading machine ifthe rebate were not available. • lfthe rebate were not offered, 57% thought the demand would slacken, then pick up again. 12... When retailers were asked to estimate thf: average price oftop loading machines that have similar capacity and features to the most popular tumble actions washers. The median response was $500.

... Retailers thought 3'0% ofcustomers would be willing to pay $200 more for a RECW, and 50% would pay $100 more.

... About halfofthe retailers believe that tumble action washers will dominate the market as consumers become more familiar with them. 

### Manufacturer Survey (6 U.S., 3 Importers) "" Six U.S. based manufacturers ofclothes washers and three major importers were interviewed.

... Manufacturers'estimates for RECW market share ranged from 2% to 25% in five years. Future Work: Follow-up participant, consumer awareness/acceptance, appliance dealer, and manufacturers surveys will be conducted in the Spring of 1998, and an evaluation/marketing report will be published in the second quarter of 1998 to document program success in terms ofmarket indicators and program accomplishments.


### 3.1.4 Northeast Energy Efficiency Partnership (NEEP) Reports: None at this time Sponsors: A consortium ofutilities. Contractors: Sub-contractor: RLW Analytics Contact: Bruce Wall, Phone: (860) 620-0076 Web Site: future web site to be called:**www.tumblewashi~r.org** Objectives: The market for high efficiency clothes washers will be evaluated. High efficiency clothes washers will be promoted. Methodology: Washers: Any washer meeting Energy Star performance levels is eligible; currently this means H-axis washers. Description:

... Could characterize as a market pull program; some participants will have incentives, also consumer education will be done, point ofsales marketing promotions/education.
13... Baseline market research will include: surveys, focus groups, interviews with reta.ilers, intercept interviews with consumers leaving the store. ( will follow up with repeat interviews ofthe same people 12 months later, who bought or didnl buy a horizontal-axis washer) Location:

... Currently there are 18-20 participating utilities and retailers covering 5.4 million customers in all ofNew England, excluding Maine.
.

... NewJersey is expected tojoin in 1999. NewJersey's 1998 program will include only new construction. • Dates: Program will officially start on April 21, 1998, although some members ofthe partnership have already begun the program (e.g., Con-Electric). Rebates: Some participants will have incentives to purchase efficient washers. 

### Findings: The baseline market research is completed. It is anticipated that results ofresearch will 

### be made available to the public in the future.


### 3.2 Individual Utility Demand Side Management I 

### Market Transformation Programs 3.2.1 Pacific Gas & Electric (PG&E)

- Utility Survey Contact: Ted Pope, Phone: (415) 973-4856 

### Methodologies: Washers:

... Currently all eligible machines are H-axis.

... . The Neptune was available in the June/July time frame. Description:

... Sur\rey questions focused on participants satisfaction with their purchased H-axis machines.

... Rebate program participants responded to an 

### l l question survey. Dates: Results from April through August 1997 Location: PG&E service territory (northern California) Sample size: 643. Rebates:

... PG&E had a customer program where they gave rebates to consumers who bought energy efficient washers. ($75-$175 rebates).

... In addition, water utilities also provided rebates up to $75. 

### Findings: Over 90% ofparticipants in a rebate program responded excellent or very good when asked ifthey were satisfied with their washer.
14

### Future work: Additional survey data is being collected.


### 3.2.2 Commonwealth Electric

- Utility Survey Contact: Dick Moran, Phone: (508) 291-0950 x 3548 Methodologies: Washers: energy efficient washers Description: survey ofparticipants in a rebate program Date: a rebate program has been running since September 1997. Sample size: 175 Rebates: yes Questions relevant to this study include: • Would you have purchased this washer without a rebate? • Rate your washer (in comparison to old washer) in terms ofease ofuse, appearance, time to wash, and quality ofwash). • Have you had any problems? Findings: Responses have not been officially tabulated, however, responses are leaning toward easier to use and overwhelmingly towards cleaner clothes.


### 3.3 Consumer Publications 3.3.1 Consumer Reports I 

### Consumers Union Contact: Mark Connelly, Division Head- Home Environmental Products, Phone: (914) 378- 2325 Publication: July 1997 issue ofConsumer Reports magazine Methodology: • Clothes washers were evaluated and rated • Evaluates: noise, washability and features • Compared 16 top-loading (Y-axis) and 2 H-axis clothes washers • H-axis washers evaluated were: Frigidaire FWT445GE and ASKO 8005 (compact) Findings: • 'To get the best results with front-loaders, you need to use special detergent that can be expensive". 15

### • 'Little is yet known about their [H-axis models] reliability". • Spot checks on wear and tear didn

### t verify that front-loaders were more gentle on clothes. • The Frigidaire did not produce dryer clothes at the end ofthe spin cycle. • Front-loaders are generally quieter during filling and washing and a bit noisier during the spin cycle. • The two front-loaders used less energy. • H-axis washers handled unbalanced loads better than others.
3.3.2 Consumer- NewZealand publication Contact: Hamish Wilson, email: hamish@consumer.org Publication: September 1997 issue ofConsumer magazine. Methodology: • Rated soil removal, spin efficiency, energy use, water use, gentleness, ease ofuse, cycle time, noise, detergent used. • Rated more H-axis washers than U.S. consumer publications. • Tested four (4) H-axis washers and six (6) Y-axis washers Findings: (resultspresented in bar chart form) • Overall, H-axis machines scored better on energy and water usage. • Overall, H-axis machines scored better on gentleness. • Soil removal for H-axis and Y-axis were similar.
3.3.3 CHOICE-(Australian consumer magazine) Publication: September 1997 issue ofCHOICE magazine Methodology: • 21 washing machines were rated; 7 H-axis, front-loaders and 14 Y-axis, top-loaders • Washers were rated/tested for: wash capacity, cycle time, noise, water used, energy efficiency, spin efficiency, gentleness, and dirt removal. Findings: • Based on a combination ofreasonable performance, purchase price and 10-year running costs, the Best Buys were the GE and a Hoover front-loaders. • "Notably higher scores than the top-loaders (Y-axis) for gentleness and water and energy efficiency." • Top-loaders (Y-axis) wash and spinjust as well as front-loaders, and they tend to have faster cycle times." • Top-loaders (Y-axis) are usually less gentle on your clothes. 16

### .-**---------~~---·----------** ---- --- -- ---



### :t4 Trade Organizations 3.4.1 The Soap and Detergent Association (SDA) Publication: Detergents & Laundry Additives in High-Efficiency Washers Contacts: Janet Donohue, Director ofCommunication, Phone: (212) 725-1262 Jane Meyer, Director Consumer Education, Phone: (212) 725-1262 Background: The Soap and Detergent Association is a national trade association (139 member), founded

.. in 1926. The Association represents the manufactlirers ofhousehold, industrial and institutional cleaning products and those companies that supply ingredients and finished packaging to the industry. Over 90% ofthe cleaning products sold in the United States are made by SDA members. Methodology: Not described, however, this document relies largely on data from tumbler washer use in Western Europe. Powder detergent is used as the basis ofcomparison. Summary: The Soap and Detergent Association made available a publication detailing information on detergents and their use in H-axis machines. (October 1996) Discussed are: • excessive sudsing • formulating detergents for high-efficiency H-axis machines • ·water temperature • lower water volume • clothes-to-water ratio • powder detergents and laundry additives • -load sizes • · amount ofdetergent required • availability ofhigh-efficiency detergent Findings: • High-efficiency tumbler washers raise special challenges when formulating effective detergents and laundry additives a. Suds are more readily created. Excessive suds can overflow the machine or interfere with the washers

# sproper operation. b. Water temperature can be lower because there is less water to heat the wash tub and the clothes. c. Lower water volume makes it more difficult to keep soils from redepositing on fabrics. 17• "Little is yet known about their [H-axis models] reliability". • Spot checks on wear and tear didnt verify that front-loaders were more gentle on clothes. • The Frigidaire did not produce dryer clothes at the end ofthe spin cycle. • Front-loaders are generally quieter during filling and washing and a bit noisier during the spin cycle. • The two front-loaders used less energy. • H-axis washers handled unbalanced loads**bett~r** than others.
3.3.2 

### Consumer

- New Zealand publication 

### Contact: Hamish Wilson, email: hamish@consumer.org 

### Publication: September 1997 issue of

### Consumer magazine. 

### Methodology: • Rated soil removal, spin efficiency, energy use, water use, gentleness, ease ofuse, cycle time, noise, detergent used. • Rated more H-axis washers 

### than U.S. const.imer publications. • Tested four (4) H-axis washers and six (6) Y-axis washers 

### Findings: (resultspresented in bar chartform) • ·Overall, H-axis machines scored better on energy and water usage. • Overall, H-axis machines scored better on gentleness. • Soil removal for H-axisand Y-axis were similar.
3.3.3 

### CHOICE -(Australian consumer magazine) 

### Publication: September 1997 issue ofCHOICE magazine 

### Methodology: • 21 washing machines were rated; 7 H-axis, front-loaders and 14 Y-axis, top-loaders • Washers were rated/tested for: wash capacity, cycle time, noise, water used, energy effic_iency, spin efficiency, gentleness, and dirt removal. 

### Findings: • Based on a combination ofreasonable performance, purchase price and l0-year running costs, the Best Buys were the GE and a Hoover front-loaders. · • "Notably higher scores than the top-loaders (Y-axis) for gentleness and water and energy efficiency." • Top-loaders (Y-axis) wash and spinjust as well as front-loaders, and they tend to have faster cycle times." • Top-loaders (Y-axis) are usually Jess gentle on your clothes. 16 .:;Future work: / Additional survey data is being collected.


### 3.2.2 Commonwealth Electric

- Utility Survey Contact: Dick Moran, Phone: (508) 291-0950 x 3548

. Methodologies: Washers: energy efficient washers Description: survey ofparticipants in a rebate program Date: a rebate program has been running since September 1997. Sample size: 175 Rebates: yes Questions relevant to this study include: • Would you have purchased this washer without a rebate? • Rate your washer (in compatjson to old washer) in terms ofease ofuse, appearance, time to wash, and quality ofwash). • Have you had any problems? Findings: Responses have not been officially tabulated, however, responses are leaning toward easier to use and overwhelmingly towards cleaner clothes.


### 3.3 

### Consumer Publications 3.3.l 

### Consumer Reports I 

### Consumers Union Contact: Mark Connelly, Division Head- Home Environmental Products, Phone: (914) 378- 2325 Publication: July 1997 issue ofConsumerReports magazine Methodology: • Clothes washers were evaluated and rated • Evaluates: noise, washability and features • Compared 16 top-loading (V-axis) and 2 H-axis clothes washers • H-axis washers evaluated were: Frigidaire FWT445GE and ASKO 8005 (compact) Findings:

... "fo get the best results with front-loaders, you need to use special detergent that can be expensive". 15.. Baseline market research will include: surveys, focus groups, interviews with retailers, intercept interviews with consumers leaving the store. ( will follow up with repeat interviews ofthe same people 12 months later, who bought or didnt buy a horizontal-axis washer) Location:

.. Currently there are 18-20 participating utilities and retailers covering 5.4 million customers in all ofNew England, excluding Maine.

.. New Jersey is expected tojoin in 1999. New Jersey's 1998 program will include only new construction. Dates: Program will officially start on April 21, 1998, although some members ofthe partnership have already begun the program (e.g., Con-Electric). · Rebates: Some participants will have incentives to purchase efficientwashers. Findings: The baseline market research is completed. It is anticipated that results ofresearch will be made available to the public in the future.


### 3.2 Individual Utility Demand Side Management I 

### Market Transformation Programs 3.2.1 Pacific Gas & Electric (PG&E)-. · Utility Survey Contact: Ted Pope, Phone: (415) 973-4856 Methodologies: Washers:

.. Currently all eligible machines are H-axis.

.. The Neptune was available in the June/July time frame. Description:

.. Survey questions focused on participants satisfaction with their purchased H-axis machines. ·

.. Rebate program participants responded to an 11 question survey. Dates: Results from April through August 1997 Location: PG&E service territory (northern California) Sample size: 643 Rebates:

.. PG&E had a customer program where they gave rebates to consumers who bought energy efficient washers. ($75-$175 rebates).

.. In addition, water utilities also provided rebates up to $75. Findings: Over 90% ofparticipants in a rebate program responded excellent or very good when asked ifthey were satisfied with their washer.
14 

# .... "Little is yet known about their [H-axis models] reliability".

.. Spot checks on wear and tear didnt verify that front-loaders were more gentle on clothes.

.. The Frigidaire did not produce dryer clothes at the end ofthe spin cycle.

.. Front-loaders are generally quieter during filling and washing and a bit noisier during the spin cycle. • The two front-loaders used less energy. • H-axis washers handled unbalanced loads better than others.
3.3.2 

### Consumer

- New Zealand publication 

### Contact: Hamish Wilson, email: hamish@consumer.org 

### Publication: September 1997 issue of

### Consumer magazine. 

### Methodology: • Rated soil removal, spin efficiency, energy use, water use, gentleness, ease ofuse, cycle time, noise, detergent used. • Rated more H-axis washers than U.S. consumer publications. • Tested four (4) H-axis washers and six (6) Y-axis washers 

### Findings: (resultspresented in bar chartform) • Overall, H-axis machines scored better on energy and water usage. • Overall, H-axis machines scored better on gentleness. • Soil removal for H-axis and Y-axis were similar.
3.3.3 CHOICE

- (Australian consumer magazine) 

### Publication: September 1997 issue ofCHOICE magazine 

### Methodology: • 21 washing machines were rated; 7 H-axis, front-loaders and 14 Y-axis, top-loaders • Washers were rated/tested for: wash capacity, cycle time, noise, water used, energy effic.iency, spin efficiency, gentleness, and dirt removal. 

### Findings: • Based on a combination ofreasonable performance, purchase price and 10-year running costs, the Best Buys were the GE and a Hoover front-loaders. • "Notably higher scores than the top-loaders (Y-axis) for gentleness and water and energy efficiency." • Top-loaders (Y-axis) wash and spinjust as well as front-loaders, and they tend to have faster cycle times." • Top-loaders (Y-axis) are usually less gentle on your clothes. 16

### • •

•

• Washing machine price is the major detenninant ofconsumer utility or satisfaction
. No substantial differences in utility patterns exist between environmentally**~ensitive** and average consumers. However, low and medium income sub-groups ofconsumers have greater expected sensitivity to price. Conjoint Results (Appendix A) : Consumer preference for purchasing a washing machine 

### are: 

### • price, 49.1

### %; • energy costs, 20.3%; • ability to add more clothes, 11%; • loading method, 10.7%; • water use, 5.6%; and • detergent use, 3.3%. See (Appendix B) for detailed results ofthe selfadministered survey results
. 

### 3.4.3 EPRI (Electric Power Research Institute) Journal Publication: EPRI Journal, November/December 1997 Issue "The New Line on Laundry"by Leslie Lamarre Findings:
. • 'Consumers also like the absence ofthe agitator (on H-axis machines), which frees up room for comforters, rugs, and other bulky items they might otherwise have to take to the laundromat." This is an insightgleaned from the Four-year EPRI, THELMA project. • At this time, 84% ofconsumers are very satisfied or somewhat satisfied with their own washing machines. Their biggest complaints are unbalanced loads and clothes tangling around the agitator. · • After in-home use, tests.indicated that the capacity concern was more visual than actual. • The biggest barrier cited to buying an H-axis machine was the higher cost ofthe machines.


### 3.4.4 Mechanical Engineering

- Journal ofthe American Society ofMechanical Engineers Article title: Energy-efficient appliances Author: Steven Ashley,**Associa~e** Editor Date: March 1998 Summary: • The author discusses the design ofthe Neptune washer (Maytag). Findings: • The Maytag, Neptune has a maintenance-free, low-noise, high-efficiency motor with infinitely variable-speed control. • The Maytag team solved the bend-and stoop problem by tilting the spinner axis ofthe stainless.:steel tub up 15 degrees and making the opening as large as possible. 19 

# ..3.5 Government Projects 3.5.1 Energy Star Program Contacts: Bill Noel- Partnerships manager, DOE, Phone (202) 586-6149 Lee Bodner

- Program manager; D&R International, Phone: (301) 588-9387 Mary Myers- D&R International, Phone: (301) 588-9387 Jamie Treworgy

- D&R International, Phone: (30I) 588-9387 Marc Lafrance

- DOE, Phone: (202) 586-8423 Background: Energy Star is ajoint project ofthe U.S. Department ofEnergy (DOE) and the Environmental Protection Agency (EPA). This collaboration concentrates on educating consumers about the benefits ofhighly energy efficient home appliances, consumer electronics, office equipment and building materials. Programs: • Demonstrating new Technologies in 'teal world" settings Example: Bern, Kansas 

### I Maytag Washer study
• Volume Purchases Large institutional buyers, such as housing authorities, are organized to pool their resources.
• Energy Star Appliance Program (formerly the Retailer Initiative) A retailer initiative pilot program to promote high efficiency washers. The DOE will place "Energy Star"labels on efficient washers to help consumers differentiate them from other machines; making it easier for consumers to recognize efficient appliances. A list ofenergy efficient clothes washers is listed at the energy star web site: http://www.energystar.gov.
• Financing Programs The DOE is also working on financing programs with Fannie Mae, and working with utilities and retailers to determine the best ways to communicate efficiency benefits to consumers.
• Energy Star Manufactured Homes Program DOE, Washington State University, and the Idaho Department ofWater Resources are working with regional manufacturers to include high efficiency residential appliance, including clothes washers, in manufactured homes. Energy Star Participants reportedAugust 97: • SMUD (Sacramento Municipal Utility District) 20• Southern California Edison • SDA&E (San Diego Gas & Electric) • Potomac Electric Power Co. • Wisconsin Electric Power Company • Wisconsin Public Service • Portland Energy Conservation Inc.


### 3.S.2 Commercial Laundry Field Study Commissioned by: Cooperative Research and Development Agreement(CRADA) with Maytag 

### Contractor: 

### John Tomlinson, ORNL,Phone: (423) 574-0291 

### Sub-contractor: Ayers Associates, CH2M Hill 

### Objective: To measure and compare energy and water use before and after changing to an energy efficient clothes washer. 

### Methodology: Washers: 10 residential V-axis washers were replaced by Maytag H-axis washers Description: • Energy monitoring and metering study • Main laundry room selected with 10 washers • Washers average 115 cyeles per month Location: Tampa, Florida apartment complex with 100 units 

### Findings: (ContactTomlinson for more detail) After replacing V-axis washers with H-axis washers: 

### • water use was reduced 

### by 38%, • hot water use was reduced by 40%, • machine energy reduced by 47%, • 69% were satisfied or very satisfied with the cleaning performance ofthe V-axis washer, 

### ·• ·97% ofcustomers interviewed were either satisfied or very satisfied with the cleaning performance ofthe H-axis machine.


### 3.S.3 MacDill Air Force Base Commissioned by: Department ofDefense 

### Contact: John Tomlinson, ORNL, Pbone: (423) 574-0291 21 •Subcontractor: Ayers Associates Objectives: [from presentation by Tomlinson on July 18, 1997] • Improved awareness ofbenefits. • Information dissemination to facility managers. • Information on load size. • Technical evaluation Methodology: Washers: Y-axis large Speed Queen replaced by 

### 5 Frigidaire, 1 Creda Description:**.~** • Energy and water use study in two apartment laundry rooms used by Army barracks. • Measure energy/water consumption; measure load size

... Customerimpressions/habits Incentives: H-axis machines contributed to project by manufacturers Findings: (Contact Tomlinson for more detail)(from presentation by Tomlinson on July 18, 1997) • .**H-~is** washer (B) showed an increase in cu.ft 

### I kWh of 190% over V-axis washer 

### (A). • H-axis washer (B) showed an increase in lb I kWh of230% over V-axis washer (A).


### 3.5.4 Fort Hood Demonstration Study (metering study) Commissionedby: Department ofDefense, Forces Command- Department ofArmy Contacts: • Greg Sullivan, Pacific Northwest National Laboratory (PNNL), Phone: (509) 372-6212 • Graham Parker, PNNL, Phone: (509) 375-3805 Methodology: Washers: • Baseline washers are Roper residential washers. • On premise laundry machines (like commercial washers without a coin box). • Plan to monitor Staber, Maytag, 

### & Amana washers (a total of 18 washers). Descriptipn: • Will monitor: hot & cold water and mechanical energy. • Will examine operation ·and maintenance expense (washers expected to be treated roughly). Dates: • · A 6 month project starting in December 1997. 22

### • Will monitor H-axis washers January-June. Location: Fort Hood

- Texas (near Austin), Army Base. Sample size: • On-premise laundry for the barracks. • Each barrack has 6 

### machines. • Each machine is estimated to do 5-7 

### turns a day. Findings: Current Status (3-23-98): 

### • Have monitored/metered Roper baseline washers for two months. • Are currently monitoring Staber washers and have two months worth ofdata. • Report expected in early swiuner of 1998. · 3.S.S Fort Hood Government Procurement

- Department 

### ofDefense Commissioned

### by: 

### Department ofDefense, Department ofArmy Contact: Summary: 

### • Government procurement program. • Army bought approximately 1,200 Staber H-axis washing machines during the summer/fall of 1997.

23

### APPENDIX G: MANUFACTURER INTERVIEW GUIDE
G.1 INTRODUCTION The Departmentproposes to conductinterviews withmanufacturerrepresentatives to gather key information on the effects that potential new energy efficiency standards for clothes washers mighthave on their company's revenues and finances, and on their workforce. This interview guide • provides a starting point to identify relevant issues and qualitatively and quantitatively describe the impacts ofpossible candidate standards levels.


### G.2 CURRENT ORGANIZATIONAL CHARACTERISTICS The Department's interest concerning manufacturer impact is focused at the plant or profit center level directly pertinent to clothes washer production. The context within which the plant operates and the details ofplant pro.duction and costs are not readily available from the published literature, however, so the Department invites the manufacturers to provide these details confidentiallyin their own words to the extent practical. The organizational setting around the clotheswasherprofitcenteris ofinterestto round outthe Department's understandingof theprobable future ofthe clothes washer manufacturing activity without and with energy efficiency standards. G.2.1 Identify names of parent companies and any subsidiaries relevant to the clothes washer industry. G.2.2 Pleaseprovide the locations ofyour clothes washer production facilities and the product mix ·manufactured there. G.2.2.1 How would energy efficiency standards affect each production facility mix? G.2.3 How has your company incorporated energy efficiency improvements in clothes washer technology into its product line and marketing strategy? G.2.3.1 Are horizontal-axis clothes washers currently being produced by your company? G.2.4 Howwould a new energy efficiency standard on clothes washers affect yourproductmix arid

. marketing strategy?


### G.3 INDUSTRY INFRASTRUCTURE As part ofan attempt to better understand the industry infrastructure and the impact energy efficiencystandards might have on it, the Department would like for manufacturerrepresentatives to discuss their major distribution channels and major sources ofsupply.
G-1Major Customer Types The following questions are intended to gain an understanding ofhow potential energy standards levels might affect customer mix and distribution channels. G.3.I Generallyhowwould a new clothes washer standard impactyour customermix, distribution channels and corresponding profit margins? G.3.2 Do you see any changes in emphasis on export sales, and would the presence ofnew energy efficiency standards have an impact on production ofclothes washers for export sale? Major Suppliers The Departmentrecognizes thatmanufacturers do not operate in insolation and that changes inproductionlevelsoreconomic healthof a manufacturercanhave significantimpactsonits suppliers and other trade allies. The Department plans to be available to meet with representatives ofthese trade allies and to consider their impacts. · G.3.3 Who are the suppliers that may be affected by changes in your product mix as a result of standards? How might they be affected?


### G.4 MANUFACTURER CASH FLOW ANALYSIS Each manufacturer has been given the Government Regulatory Impact Model (GRIM) to develop its own cash flow model to compare with that ofthe "industry" cash flow. Thiswill allow each manufacturer to use its own financial information to arrive at the expected differences in cash flowdue to newenergy efficiency standards. These results, togetherwith an adequate understanding ofhowthey were developed. will help the Departmenttoestimate industry impacts for different types ofmanufacturers. G.4.1 Costs How do you explain differences. ifany, between your manufacturing costs with and without new energy efficiency standards and those presented in the sample results for the industry as . a whole? G.4.1.1
G.4.l.2 Is the "Industry" cost structure (materials, labor, overhead, tooling, etc.) representativeof your firm's costs? What are the differences and theircauses? How might new standards alter this cost structure?

G-2

### G.4.2 Prices How do you explain differences, if any, between your expected product prices with and without new energy efficiency standards and those presented in the sample results for the industry as a whole? · G.4.2.l
G.4.3 Shipments Are

### your gross margins.(% revenue) differentpost standards? 

### Ifso, what are the causes?
How do you explain differences, ifany, between your expected product shipments with and without new energy efficiency standards? · G.4.3.l C.4.3.2
G.4.4 Financial What assumption(s) did you make about your firm's market share post-standards? What Was your reasoning? Is your product mix significantly different from the overall industry proportions?
How do you explain differences, ifany, between your financial data and those presented for the industry as a whole? G.4.5 Impacts on the Business Please discuss**y~ur** 

### interpretations ofthe cash flow analyses results for the standards cases. What do the results imply about the future ofthe business? Availability ofcapital? Are there additional financial impacts on new standardsthat are not being captured inthe cash flow analysis? Please explain? (i.e., Tax Rate, Discount Rate, Working Capital, SG&A, R&D, Depreciation) G.4.6 Additional Economic Measures Please discuss any additional' measures, ifany, that you might have developed that provide insight into the economic effects ofchanges in energy-efficiency standards.
G.S COMPETITIVE IMPACTS ASSESSMENT Competitive impacts are assessed mainly in terms of market power, which is defined as the ability to set a given product price within a limited range. The Department is interested in manufacturer viewpoints on the extent to which new energy efficiency standards would increase market power and thereby lesson competition within the clothes washer industry. G.5.l How would new energy efficiency standards affect your ability to compete m the marketplace?

G-3G.5.2 Do you believe that newenergy efficiency standards would unfairly assist orharm the ability ofsome firms in the industry to compete? G.5.3 Please discuss examples, ifany, wherein substantial consumer subgroups would experience economic or performance penalties as a result ofnew standards. G.5.4 Can your firm meet potential efficiency levels without changing your products in ways that consumers might care about? G.5..S Does your company have specific information for the Department on consumer preferences for product attributes potentially affected by new standards?


### G.6 EMPLOYMENT IMPACTS ASSESSMENT The impact ofnewenergy efficiency standards on employment is an important consideration in the rulemaking process. This section ofthe interview guide seeks to explore current trends in clotheswasherproductionemploymentand solicitmanufacturerviews onhowdomestic employment

.patterns might be affected by new energy efficiency standards. G.6.1 How would your employment levels change under new energy efficiency standards? G.6.2 Would the workforce skills required under new energy efficiency standards be different enough to require extensive retraining or replacement of**employees~** 

### G.7 MANUFACTURING CAPACITY IMPACTS ASSESSMENT The informationdiscussedhere is importantfor developinganunderstandingof the investment cost inputs to GRIM. G.7.1 How would the imposition ofnewenergy efficiency standards affect your capacity utilization and manufacturing assets levels at your domestic production facilities? G.7.1.1 Would any facilities be closed or downsized? G.7.1.2 Would any facilities be added or upgraded? G.7.2 

### If current capacity were to be made obsolete, would it be remodeled to accommodate a new product mix? _- G.7.3 Please estimate the value ofstranded assets resulting froma new standard, and/or the costs of refitting the facility and carrying out the research, engineering, and other supporting activities required to accommodate the new product mix.

G-4

### APPENDIX H: LIST OF ACRONYMS AND ABBREVIATIONS 

### §: Section 

### Act 

### or EPCA: Part B ofTitle III ofthe Energy Policy and Conservation Act, Public Law 94-163, 

### as amended by the National Energy Conservation Policy Act, Public Law 95-619, the National Appliance Energy Conservation Act, Public Law 100-12, the National Appliance Energy
I' 

### ConservationAmendments of 1988, Public Law 100-357, and the Energy Policy Actof1992,Public Law 102-486 ADL: Arthur D. Little 

### AEC: Annual Energy Consumption AWC: 

### Annual Water Consumption 

### AEO: Annual Energy Outlook
. AGA: American Gas Association AHAM: Association ofHome Appliance Manufacturers 

### ANOPR: Advance Notice ofProposed Rulemaking 

### ARIMA: Auto Regressive-Moving Average 

### ASME: American Society ofMechanical Engineers AWW 

### A: American Water Works Association 

### BLS: Bureau ofLabor Statistics 

### BOM: Bill ofMaterials Btu: British thermal unit 

### CAPM: capital asset pricing model 

### CEE: Consortium for Energy Efficiency 

### CES: Consumer Expenditure Surveys

H-1CIR: Current Industrial Reports 

### CRADA: Cooperative Research and Development Agreement 

### CRF: capital recovery factor 

### CFR: Code.ofFederal Regulations 

### C02: carbon dioxide 

### cu.ft.: cubic feet 

### CW: clothes washer 

### DOD: Department ofDefense 

### DOE: Department ofEnergy 

### DOJ: Department ofJustice



### · EA: Environmental Assessment ·



### EEi: Edison Electric Institute 

### EF: Energy Factor EIA: Energy Information Administration 

### EJ: exajoules (1018joules) 

### ELCAP: Elrick & Lavidge Computerized Audit Program 

### E.O.: Executive Order 

### EPA: Environmental Protection Agency



### . EPRI: ·Electric Power Research Institute



### FASH: Financial Accounting Standards Board 

### FEMP: Federal Energy Management Program 

### ft.3 : cubic feet H-2f.o.b.: free on board FR: Federal Register G & A: General and Administrative GAAP: Generally Accepted Accounting Principles GJ: gigajoules 

### (1<>9joules) GRI: Gas Research Institute GRIM: Government Regulatory Impact Model H-axis: horizontal axis kWh: kilowatt-hour 

### LBNL: Lawrence Berkeley National Laboratory 

### LCC: Life-Cycle Cost LPG: Liquid petroleum gas MIA: Manufacturer Impact Analysis MEF: Modified Energy Factor MMBtu: million Btu Mt/a: megatons (10 6 metric tons) per year NAECA: National Appliance Energy Conservation Act
nC)·cic: Cycles per year NEEP: Northeast Energy Efficiency Partnership NEMS: National Energy Modeling System NEPA: National Environmental Policy Act of 1969 NES: National Energy Savings H-3NWEEA: WashWise- Northwest Energy Efficiency Alliance NOPR: Notice ofProposed Rulemaking NOx: nitrogen oxide NPV: net present value NYSE: New York Stock Exchange 

### OIRA: Office oflpformation and Regulatory Affairs OMB: Office ofManagement and Budget ORNL: Oak Ridge National Laboratory · PBP: Payback period PEA: Pacific Energy Associates PECI: Portland Energy Conservation Inc. PG&E: Pacific Gas & Electric PNNL: Pacific Northwest National Laboratory PVC: Present Value ofCosts PVS: Present Value ofSavings Pwh: petawan-hours (10 15 wan-hours) Quads: quadrillion Btu R&D: Research and Development RECS: Residential Energy Consumption Survey ·RECW: resource-efficient clothes washers RMC: remaining moisture content S & P: Standard & Poor's H-4Working Group: Clothes Washer Consumer Analysis Working Group

H-6