## Department of 

## Energy Washington, DC 20585


### DRAFT REPORTS ON THE POTENTIAL IMPACT OFALTERNATIVE ENERGY EFFICIENCY LEVELS FOR ROOM AIR CONDITIONERSAND RESIDENTIAL COOKING PRODUCTS
The Department of Energy has recently completed the following documents: 

### "DraftReport on the PotentialImpact of Alternative Efficiency Levelsfor Room 

### Air Conditioners," "Draft Report on the PotentialImpact of Alternative efficiency Levels for Residential Cooking Products," and "General Methodology for the Analysis of Alternative Efficiency Levels for Room Air 

### Conditione~:~ and Residential Cooking Products." The reports include engineeringand economic analyses of the United Statesroom air conditioner and residential cooking markets. The reports discuss the potential impact of five different energy efficiency levels for each product on consumers,manufacturers, the environmentand utility companies. The Departmenthas initiated a comprehensive process improvement effort to examine issues surroundingthe appliance standards program including those relating.tothe planning and prioritizationprocess, data collection and analysis, and decision making criteria. As part of this effort, the Ilepartrnent is providing these reports for your review 'and comments. The Department is seeking your comments on the validity of the key results of these analyses as well as their content and the analyticalmethods used. 

### If you have any comments,please send them to: Ms. Sandy Beall, of Energy Efficiency and Renewable Energy, Mail Station EE-43 1,U.S. Department of Energy, 1000Independence Avenue, 

### SIN., Washington, 

### DC. 20585-0121. Telephone: (202)586-7574;

### FAX: (202)586-4617. 

### In order to ensure full consideration, comments must be received by the Department of Energy no later than July 1, 1996. 

### I€you have any questions,please contact Ingrid Watson at (202)586- 8119

### regarding Residential Cooking Products or Kathi Epping at (202)586-7425 regarding Room Air 

### Conditioners 

## .

~ichbel J. ~cdbe, Director 0fic6 of Codes and Standards BuildingTechnology, State and Community Programs Energy Efficiency and Renewable Energy Attachments: 

# @ Printed with soy ink on recycledpaper 

### Department of Energy Washington, DC 20585


### DRAFT REPORTS ON THE POTENTIAL IMPACT OF ALTERNATIVE ENERGY EFFICIENCY LEVELS FOR ROOM AIR CONDITIONERS AND RESIDENTIAL COOKING PRODUCTS


### The Department ofEnergy has recently completed the following documents: "Draft 

### Report on the Potential Impact ofAlternative Efficiency Levels for Room Air Conditioners," "Draft Report on the Potential Impact ofAlternative efficiency Levels for Residential Cooking Products," and "General Methodology for the Analysis ofAlternative Efficiency Levels for Room Air Conditioners and Residential Cooking Products." The reports include engineering and economic analyses ofthe United States room air conditioner and residential cooking markets. The reports discuss the potential impact offive different energy efficiency levels for each product on consumers, manufacturers, the environment and utility companies. The Department has initiated a comprehensive process improvement effort to examine issues surrounding the appliance standards program including those relatingto the planning and prioritization process, data collection and analysis, and decision making criteria. As part ofthis effort, the Department is providing these reports for your review 'and comments. The Department is seeking your comments on the validity ofthe key results ofthese analyses as well as their content and! the analytical methods used. If

### you have any comments, please send them to: Ms. Sandy Beall, Office ofEnergy Efficiency and Renewable Energy, Mail Station EE-431, U.S. Department ofEnergy, 1000 Independence Avenue, S'N., 

### Washington, DC. 20585-0121. Telephone: (202)586-7574; FAX: (202)586-4617. In order to ensure full consideration, comments must be received by the Department ofEnergy no later than July 1, 1996. If

### you have any questions, please contact Ingrid Watson at (202)586- 8119 

### regarding Residential Cooking Products or Kathi Epping at (202)586-7425 regarding Room Air Conditioners.

## s~ce2rel~' 

# "1zz Michiei J. Mc be, Director Office of Codes and Standards Building Technology, State and Community Programs Energy Efficiency and Renewable Energy Attachments: 

# ® Printed with soy ink on recycled paper

### GENERALMETHODOLOGY FOR THEANALYSIS OF ALTI5RNATIVE EFFICIENCY LEVELS FOR ROOM AIR CONDITIONERSAND

- 

### RESIDENTIAL COOKING PRODUCTS

APRIL 

### 1996

. .



### LAWRENCE BERKELEY NATIONAL LABORATORY

. 

### Energy & 

### Environment Division Technology and Market Assessment Group Berkeley, CA 

### 94720

Prepared for 

### U.S.DEPARTMENT OFENERGY Office of Codes and Standards DOElEE-XXXXX

# ~I

.' . 

### GENERAL METHODOLOGY FOR THE ANALYSIS OF AL**TI~RNATIVE** 

### EFFICIENCY LEVELS FOR ROOM AIR CONDITIONERS AND RESIDENTIAL COOKING PRODUCTS



### APRIL 1996

LAWRENCE BERKELEY NATIONAL LABORATORY Energy & 

### Environment Division Technology and Market Assessment Group Berkeley, CA 94720

Prepared for 

# u.s. DEPARTMENT OF ENERGY Office ofCodes and Standards

## PREFACE 1


### In accordance with the Energy Policy and Conservation Act, the Department of Energy has wide-ranging statutory authority to promote appliance and equipment energy efficiency. Federal energy conservation standards set minimum levels or mandatory performance levels for certain products accordingto statutorilyrequired economic and .technicalcriteria. As -part 

### of the Department's. overall regulatory reform effort, we have initiated a -comprehensivereview of the appliance energy efficiency rulemaking process. Throughout this process ilmprovement, the Department has solicited comments from stakeholdersthrough a series of public. meetings. A .constant "theme" has been the need for the. Department to seek early stakeholder input into the rulemaking process, including early review of tkhnical and economic analyses. This draft technical report outlines the general methodology used to analyze several alternativeefficiency levels forthe room air conditionerand residential cooking market in the United States. The draft 

### technical report contains the methodologies for conducting engineering analyses and dete~mining life-cycle costs, energy savings potential, energy and economic impacts for residential buildings, impacts on manufacturer profitability, environmental impacts on air-borne .emissions, and utility impacts for several energy efficiency levels. It was produced for the Department sf Energy's Oflice of Energy Efficiency and Renewable Energy by the Energy Analysis Program i~t the Lawrence Berkeley National Laboratory. This study is an extensively revised version of the Technical Support Documentpubiished in November 

### 1993, upon which the ~epartment's Notice of Proposed Rulemaking (NOPR) on March 4, 

### 1994

### for room air 

### conditionersand residential cooking equipment was based. This study relies heavily on dath'much of it from industry sources, supplied to the Department through comment; made in response to the NOPR. The Department is providing the draft technical report to' obtain comments on the input data, mqthodology and results of the analyses.

. .

Volume 1: General MetItodology Page P-1 

### PREFACE

In accordance with the Energy Policy and Conservation Act, the Department ofEnergy has wide-ranging statutory authority to promote appliance and equipment energy efficiency. Federal energy conservation standards set minimum levels or mandatory perfonnance levels for certain products according to statutorily required economic and.technical criteria. As part of the Department's· overall regulatory reform effort, we have initiated a**·comprehc~nsive** review of the appliance energy efficiency rulemaking process. Throughout this process improvement; the Department has solicited comments from stakeholders through a series of public meetings. A constant "theme" has been the need for the.**Dep~men~** to seek early stakeholder input into the rulemaking process, including early review of technical and economic analyses.- This draft technical report**outlin~s** the general methodology used to analyze several**altemativ,~** efficiency levelsfor the room air conditioner and residential cooking market in the United States. The draft technical report contains the methodologies for conducting engineering. analyses and detennining life-cycle costs, energy savings potential, energy and economic impacts for residential buildings, impacts ori manufacturer profitability, environmental "impacts on air-borne

.emissions, and utility impacts for several energy efficiency levels. It was produced for the Department ofEnergy's Office ofEnergy Efficiency and Renewable Energy by the Energy Analysis Program at the Lawrence Berkeley National Laboratory. TItis study is an extensively revised version ofthe Technical Support Document pubiished in November 1993, upon which the Department's Notice of Proposed Rulemaking .(NOPR) on March 4, 1994 for room air conditioners and residential cooking equipment**w~** based. This study relies heuvily on data,·much of it from industry sources, supplied to the Departmeni through comments made in response to the NOPR. The Department is providing the draft technical report to obtain comments on the input data,**m~thodology** and results ofthe analyses.

Volume 1: General Methodology Page pol I. 

## TABLEOF CONTEN~S

.. VOLUME 1 

# . GENERAL METHODOLOGY

.



# ........................................... CHAPTI3R 1: 

### INTRODUCTION 1

## . 1



# ................................ CHAPTER 2

## . DOCUMENT STRUCTURE 2-1 2.1 

### PURPOSE 

### AND SCOPE 

# .........................................2-1

.. 

### 2.2 

### STRUCTURE OF 

## THE DOCUMENT 

## ...... ;

## .......................2-1



## .................................... . CHAPTER 

### 3 ANALYTIC APPROACH 3-1

' 

### 3.1 

### INTRODUCTION 

## .............................................. 3-1



# ....................... 3.2 

### OVERVIEW OFANALYTIC FRAMEWORK 3-2 3.3 

### MODELS, DATA. AND 

### ASSUMPTIONS 

## ........................... 3-5



# .................... 3.3.1 

### Engineering Cost and Performance Models 3-5 3.3.2 

### LB'NLResidential Energy Model 

### (LBNL-REM) 

# ........:....... 3-8 3.3.3 

### LBNLManufacturer Analysis Model 

### (LBNLMAM) 

## ........... 3-15



## ......................... 3.3.3.1 

### Long-Run Impact Analysis 3-19 3.3.3.2 

### Short-Run Impacts 

# .................................. 3-21 3.3.3.3 

### Industry Net Present Value Analysis 

## .................. 3-22 3.3.3.4 

### SensitivityAnalysis 

## ............................... 3-22 3.3.4 

### Appliance StandardsEnvironmental and Utility Model (ASEUM) 

# . 3-27 3.3.5 

### Life-Cycle Cost Analysis. payback Period, and Cost ofConserved Energy 

# ................................. 3-30 3.3.6 

### ~nvironmental Analysis 

# ..

# .'

................................ 3-30 3.4 

### SENSITIVITYANALYSIS 

# ........................ 

# :
............. 3-32 

### CHAPTER 4

## . INDUSTRY PROFILEFOR 

### LAWRENCEBERKELEY NATION& LABORATORY MANUFACTURERIMPACT ANALYSIS' 4-1 a 

## ....... 

# . 4.1 

### DEMAND 

## .................................................... 4-1



## ..................................................... 4.2 

### SUPPLY 

## ...1 b-6

... 

### 4.3 

### :PRICES 

# ............................. / 

# .......................... 4-14



## ............... CHAPTE. R 5

## . DEVELOPMENT 

### OFBASE CASEFORECASTS 5-1 

### 5.1 :ECONOMIC

### AND DEMOGRAPHIC DATA: RESIDENTIAL 

## .......... 5-1 5.1.1 

### Occupied Households 

## ..................................... 5-1 51.2 

### Housing Starts 

# ........................................... 5-2 5.1.3 

### Disposable Personal Income 

# ................................. '5-3



# ......... ....................... . 

### 5.1.4 

### ResidentialEnergy Prices ;

### ; 5-3 5.2 

### SE~SITNITY ANALYSIS OF

### THE BASE CASE 

## ....... ;

## ...,....... 5-5



# ................ 5.2.1 

### Residential Sector Sensitivity Cases ;

## .......... 5-5 

### APPEND.IXA


### . UNCERTAINTY ANALYSIS FOR ENERGY EFFICIENCY


## ..... A- 

### 1 Volume.1: Tableof Contents:CeneralMethodology i 

### TABLE OF CONTENTS**.~** 

## VOLUME 1· GENERAL METHODOLOGY**PREFA(~E** ............... : ........ ,.: .................................. P-l CHAPTER 1: INTRODUCTION

......................'

.................. , 1-1 CHAPTER 2. DOCUMENT STRUCTURE

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

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

...... "

....................... 2-1 CHAPTER 3. ANALYTIC APPROACH

................................... 3-1

, 3.1 IN'TRODUCTION

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 3-1

3.2 OVERVIEW OF ANALYTIC FRAMEWORK

................... "

.. 3-2 3.3 MODELS. DATA. AND ASSUMPTIONS

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

.................... 3-5 3.3.2 

## LBNL Residential Energy Model (LBNL-REM)

.........'

....... 3-8 3.3.3 LBNL Manufacturer Analysis Model (LBNL-MAM)

........... 3-15 3.3.3.1 Long-Run Iffipact Analysis

......................... 3-19 3.3.3.2 Short-Run Impacts

.."

.......................'

...... 3-21 3.3.3.3 Industry Net**Prese~t** Value Analysis

.................. 3-22 3.3.3.4 Sensitivity Analysis

............................... 3-22 3.3.4 Appliance Standards Erivironmental and UtilitY Model (ASEUM)

. 3-27 3.3

..5 Life-Cycle Cost Analysis. Payback Period. and Cost ofConserved Energy ..........•........•........ , .. 3-30 3.3.& Environmental Analysis

.. ; ...........: .................... 3-30 3.4 SENSITIVITY ANALySIS

........... '

...... : ..........• : ....... 3-32 CHArn:R 4. INDUSTRY PROFILE FOR LAWRENCE BERKELEY NATIONAL

, LABORATORY MANUFACTURER IMPACT ANALySIS'

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

................**~** .......... '

......................... 4-1 4.2 SUPPLY

... : ... "

.......,.'

.............. ','

...................... '4-6 4.3 PRICES

............................. ,'

.......'

.................. 4-14

, , CHAPTER 5. DEVELOPMENT OF BASE CASE FORECASTS

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

.......... 5-1 5.1.1 Occupied Households

..................................... 5-1 5.1.2 HousingStarts

........................................... 5-2 :5.1.3 Disposable Personal Income

......................"

......... 5-3 :5.1.4 Residential Energy Prices

......... :**~** ....................... 5-3 5.2 SENSITNITY ANAL YSI8 OF THE BASE CASE

................... 5-5 5.2.1 Residential Sector Sensitivity Cases

.... : .......... : .......... 

## 5-5 APPENDIX A. UNCERTAINTY ANALYSIS FOR ENERGY EFFICIENCY A-I Volume'}: Table ofContents:General Methodology i

## ................................ . APPENDIX B FORECASTING MODELS 

# : B- 1 

### B


### .1 

### LBNL RESIDENTIALENERGY MODEL 

## ........... ;

### .... ......... B- 1



## ........................................... B

## .1.1 Overview


## ..... ; 

### B- 1



## .................................................. B

## .1.2

. 

### Housing B-2



## ............................... B

# .1.2.1 Occupied Households B-2



## ............................ B

## .1.2.2 Housing Sk ;

## ....... B-2



# ............................. B.1.3' Building Shell Thermal Integrity B-2



# ...................................... B

## .1.4 Equipment Effrciency B-3



## ............................................ B.1.5 Market Shares B-3



## ........................................ B

# .1.6 Usage Behavior 

# :
.... B-4



## ...................................... . 

### B

## .1.7 Equipment Turnover B-5



# .................................................. B

# .1.8 Outputs B-5



### ............ B

## . 1.9 Changes SincePrevious Technical Support'Document 

### B-6



# ................................................ B

# . 1.10 Input Data B-6 APPENDIX C

## . LAWRENCEBERKEFYNATIONAL LABORATORY


### . .



## ..................... MANUFA(31TURER

### ANALYSIS MODEL C- 1 C


### .1

, . 

### OVERVIEW OFTHE LAWRENCE BERKELEY NATIONAL LABORATORY MANUFACTURER ANALYSIS MODEL 

## .......

# :

.... C- 1 C.l.l Purpose 

## ................................................ c.1



# ................................... C

# . 1.2 The Economic Approach C- 1



## ....................................... C

## . 1.3 Measures of Impact C-2



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

.

. 

## ..................... 

## ......... C.2 S.m 

### UCTURE 

### OF THE LBNLMAM .' C-3



## .......................... C.2.1 Economic.Theoryand Assumptions C-3



## .................................... C.2.2

. 

### StructuralAssumptions C-13



## ............................ C.2.3 Description of Modules .' 

## ........ C- 14



## .................................... C.2.3.1 Control Panel C-16



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



# ............................ .

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



## ...................... C.2.3.4 The'~n~ineerin~ InputsModule C- 19



## ................ C.2.3.5 The StandardsLevel 

### ("REM) Module C-21 C.2.3.6 The Costs, Sales, and Revenue Module 

## ................ C-21



## ............................. C.2.3.7 i'he ~inancial Module C-23

.



### ............. C.2.3 -8

### The One-Time Cost Amortization Module C-24



## ....................... C.2.3.9 he 

### ~ong-Run ~odel ~odule C-27

.



## .................. C.2.3.10 The Short-Run Model Module ; 

## ....C-31



## .......................... C.2.3.11 Sensitivity 

### Charts Module C-32



## ...... C.2.3.12 Engineering-to-Standards Cost Conversion Module C-32



## ................... C.2.3.13 Industry- Net Present Value Module C-32

. . 

## ............................................ C.2.4 Model Inputs C-32



## ....................... C.2.4.1 Lnput GeneratingAssumptions C-32



## .................. C.2.4.2 Control Factors in the Control

. . 

### Panel C-33



## .................. ............... C.2.4.3 EngineeringData ; 

### C-34 ii

.. VolumeI: ~able'of ~ontents:~eneral Methodology

. . 

### APPENDIX B.**FORECA~TING** 

### MODELS

............... : ...............**~** 

### B., I B.l LBNL RESIDENTIAL ENERGY MODEL

........... ; .... : ......... B-1 B.1.1 Overview

.....**~** 

### ...........'

........."

...... '

... ',' .'

..... , .. B-1 B.1.2 ,Housing. "

...................... '

.. : .. ','"

...............**~** 

### B-2 B.1.2.1 Occupied Households

.............................. B-2

, B.1.2.2 Housing 

### startS

............................**~** 

### ....... B-2 B.1.3 , Building Shell Thermal Integrity

.................**~** 

### .......... B-2 B.1.4 Equipment Efficiency ...............................•..... B-3 B.l.5 Market Shares

... ','

...............'

....................... B-3 B.l.6 Usage Behavior

...........'

..........................**~** 

### .... B-4 B.1.7 Equipment Tumover

.....**~** 

### ..- .............................. B-5 ' B.1.8 Outputs,

.............. ,.....................'

... ','

........ 'B-5 B.1.9 Changes Since**Pre~ious** 

### Technical Support'Document .'

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

............................'

...............'

... B-6 APPENDIX C. LAWRENCE BERKELpY NATIONAL LABORATORY - , MANUFACTURER ANALYSIS MODEL

..................... C-l C.I " OVERVIEW OF THE LAWRENCE BERKELEY NATIONAL LABORATORY MANUFACTURER ANALYSIS MODEL

............. C-I C.l.1 Purpose

............... , ....................... ; ......... C-I C.1.2 The Economic Approach

...........................**~** 

### ........ C-I C.1.3 Measures ofImpact

.. ; .................................... C-2 C.l.4 Methodology

............................................. C-2

. C.2 STRUCTURE OF THE LBNL-MAM

........'

............. '

......... C-3 C.2.1 Economic,Theory and Assumptions

.......................... C-3 C.2.2 'Structural Assumptions

...'

........'

....................'

.... C-i3 C.2.3 Description ofModules

........................... .- ....... C-14 C.2.3.1 Control Panel

.................................... C-16 C.2.3.2 The MonteCarlo Module

...........................C-18

.C.2.3.3 The Accounting Module

............................ C-18 C.2.3.4 The'Engineering Inputs Module

...................... C-19 C.2.3.5 The Standards Level ("REM") Module

........'

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

................ C-21 C.2.3.7 The Financial Module

............................. C-23 C.2.3.8 The One-Time Cost Amortization Module

............. C-24 C.2.3.9 The Long':'Run Model Module

....................... C-27 C.2.3.lO The Short-Run Model Module

... : ............. : .... C-3l C.2.3.ll Sensitivity Charts Module

......................... C-32 C.2.3.12 Engineering-to-Standards Cost Conversion Module

..... C-32 C.2.3.13 Industry Net Present Value Module

......'

............ C-32 C.2.4 Model Inputs

..............'

............................. C-32 C.2.4.1 Input Generating As.sumptions

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

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

....**~** 

### ............•............... C-34 ii Volume 1: Tabko/Contents:GfmeralMethodologyI

. .



## .......................... . C.2.4.4 Costs. Sales. and Revenues C-35 C.2.4.5 Financial Inputs 

## ................................... C-36 C.2.5 Model Outputs 

# ......................... ;

# ............... C-38 C.2.5.1 Control Panil Outputs 

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

# ........................ C-39 C.2.5.3 EconomicAnalysis Outputs 

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

# .............................. C-40



# ..... 

# ....................... C.2.5.5 Retail Price Calculation ; 

### C-40 

### (2.2.6 Development and Integration of 

### the IndustryNPV Module 

# .... ;

## .. C-41 C.2.6.1 The Integration and Cost Iteration Pmss 

# ............. C-41 APPENDK 

### ID

# . MAINTENANCE COSTS FOR 

### INTERMllTENTIGNITION



## .................. DEVICES 

### IN GAS RANGES AND OVENS D-1 D

# .1 ANALYSIS APPROACH 

# ....................................... D- 1 D.2 

## GENERALDESCRIPTION OF

### INTERMlTTENT IGNITIONDEVICES(IIDS) 

# ................................... D-1 D.3 lvIAINTENANCECOSTS FOR 

### IID COMPONENTS 

## ................. D-2 D.4 l&UN"IENANCE 

## LIFETIMEDISTRIBUTION

## AND PRESENT 

## 'VALUE OFMAINTENANCE COST 

# ............................ D-2 APPENDIX 

### E.' ELECTRIC UTILITY IMPACT MODELING 

# .................. E- 1 E

## .1 1NT.RODUCIION

# ................................................ E- 

### 1- E.2 

### AVQIDED COST: BACKGROUNDAND CONVENTIONS 

## ...........E- 1

. 

### E.3 ENERGY COST SAVINGS

# ......................................... E-2 E.4 CAPAClTY COST SAVINGS 

# ................;

# ...................E-5 E.4.1 Value of Capacity Savings 

## ................................... E-5 E.4.2 Amount of Capacity Savings 

# .

# ,.............................. E-7 

### E.4.3 Avoided Transmission and Distiibution Capital Costs 

# ..:

# ........E-10 ENVIRONMENTALASSESSMENT FOR ALTERNATIVE

EFFICIENCY

## LEVELS FOR ROOM 

### AIR CONDFTIONERS AND KJTCHEN 

### RANGES AND 

### OVENS 

## .............................. EA- 1



## .......... 1

## . INTRODUCTION

### AND NEED FOR PROPOSED ACTION EA- 1



## ...... 2

## . METSHODS OF ESTIMATING ENVIRONMENTALIMPACTS EA-2 

### 2.


### .1 Baseline Emissions 

## ..................................... EA-3



# ............... 2.2 Sulfur Dioxide and Nitrogen OxideEmissions EA-4 2.3 Carbon Dioxide Emissions 

# ............................... EA-6 3


### . FESULTS 

# .................................................. EA-7 3.1 ROOM AIR CONDITIONERS 

# ...........................EA-8



## ........... 3.1.1 SulfurDioxide and Nitrogen Oxide ~rnissions EA-8 3.2.1 Carbon Dioxide Emissions 

# .......................... EA-8 3.3 RANGES AND OVENS 

## ................................. EA-8 3.3.1 SulfurDioxide and 

### Nitrogen Oxide Emissions 

## ........... EA-8 3.3.2 Carbon Dioxide Emissions 

## ......................... EA- 

### 10

. . VolumeI: ;Fable of Contents:GeneralMethodology iii 

### C.2.4.4 Costs, Sales, and**Reve~ues** 

### ......................... C-3S C.2.4.S Financial Inputs

..........................'

........ C-36 C.2.S Model Outputs

......................... ; ............... C-38 C.2.S.1 Control Panel Outputs

............................. C-38 C.2.S.2 Accounting Summary Outputs

...............'

........ C-39 C.2.S.3 

### Economic Analysis Outputs

.......... , .............. C-39 C.2.S.4 

### Monte Carlo Outputs

.............................. C-40 C.2.S.S 

### Retail Price Calculation

..... ; ....................... C-40 C.2.6 Development and Integration ofthe Industry NPV Module

........ C-41 C.2.6.1 The Integration and Cost Iteration Process

............. C-41 APPENDIX D. MAINTENANCE COSTS FOR INTERMITTENT IGNITION

. DEVICES IN GAS RANGES AND OVENS

... , ....**~** 

### . . . . . . . .. D-I D.I ANALYSIS APPROACH

................**~** 

### ............... ; . . . . .. D-l D.2 GENERAL DESCRIPTION OF INTERMITTENT IGNITION DEVICES (DDs)

..................................... D-l D.3**l~NANCE** 

### COSTS FOR

# no COMPONENTS.

. . . . . . . . . . . . . . .. D-2 D.4**l~NANCE** 

### LIFETIME DISTRIBtITION AND PRESENT 'VALUE OF MAINTENANCE COST

........... ; .. . . . . . . . . . . . . .. D-2 APPENDXX E: ELECTRiC UTILITY IMPACT MODELING

.................. E-I E.l INTRODUCTION

......... :**~** 

### ................ '

.. '

................. E- 

### f E.2 AVOIDED COST: BACKGROUND AND CONVENTIONS

........... E-l E.3 ENERGY COST SAVINGS

.......................... '

............. E-2

. E.4 CAPACITY COST SAVINGS

................ : ................... E-S E.4.1 Value ofCapacity Savings

.................................. E-S E.4.2 Amount ofCapacity Savings**.~** 

### .............................. E-7 EA.3 Avoided Transmission and Distribution Capital Costs

.. : ........ E-I0 ENVIRONMENTALASSESSMENT FOR ALTERNATIVE

EFFICIENCY LEVELS FOR ROOM AIR CONDITIONERS AND. KlTCHEN RANGES AND OVENS

.............................. EA-l 1.

. INTRODUCTION**AND,NEE~** 

### FOR**PRO~OSED** 

### ACTION

........ " EA-l 2. METHODS OF ESTIMATING ENVIRONMENTAL IMPACTS

...... EA-2 2.1 Baseline Emissions

......... . . . . . . . . . . . . . . . . . . . . . . . . . . . .. EA-3 2.2 Sulfur Dioxide and Nitrogen Oxide Emissions

............... EA-4 2.3 Carbon Dioxide Emissions

.. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. EA-6 3.**F~SULTS** 

### ........................**~** 

### ...........**~** 

### .............. '. EA-7 3.1 ROOM AIR CONDITIONERS

.....................**~** 

### ..... EA-8 3.1.1 Sulfur Dioxide and Nitrogen Oxide Emissions

........... EA-8 3.2.1 Carbon Dioxide Emissions

................'

...... , ... ' EA-8 3.3 RANGES AND OVENS

........ '. . . . . .

.. . . . . . . . . . . . . . ... . .. EA-8 3.3.1 Sulfur Dioxide and Nitrogen Oxide Emissions

. . . . . . . . . .. EA-8 3.3.2 Carbon Dioxide Emissions

......................... ; EA-lO Volume 1: jrable ojContents:General Methodology iii

### Table 3.1 Table 3.2 Table 3.3

. 

### Table 3.4 Table4.1 Table 4.2 -Table4.3

Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5


### Table B

## .1 Table B.2a Table B.2b Table B.2c Table B.3 Table 

### C

## .1 Table C.2 Table C.3 Table C.4 Table D

## .1 

### Table 

### E


### .1 

### Table E.2 Table 

### E.3
Table EA- 1

## . . 

### Table EA.2 

## . Table EA-3: Table EA-4


### . LIST OF TABLES Product Types and Number of Product Classes 

# ......

# :
........ :

## . 3-1 Sensitivity of ROE to 1S.E. Change in Control Variables


## ..:

## ....3-23



## ..................... Emissions Factors for Power Generation 3-31 Emissions Factors for In-House Emissions 

## ................... 3-32



# ........... ............................ Market Saturations 

### i 4-2

' 

### ~arket 'Saturations

# ........................................ 4-6 Percent of ~arket Controlled by the Largest Producers of Each Product 

# .......................................... 4-7

. 

### Acquisitionsof Appliance Manufacturers 

# .................:

## ..4-12
r 

### Occupied HouseholdsProjections (millions) 

## ................... 5-2 Housing. Starts Projections (thousands) 

## ........................5-2 ~is~osable personal Income 

### (19h$)


# ........................... 5-3 Average ResidentialEnergy Prices (1990 Dollars per Million Btu) 

## . .'5-4 Relative Electricity Price by End Use

(Average Residential Electricity Price 

### = 1.00) 

## ................ 5-4 Parameters of Thermal IntegrityICost Curves by ~ouse 

### Type 

## ...... B-3 Percent Share of Each Class ofRoom 

### Air Conditioners


## ........... B-4 Percent Share of Each Class of Electric ~ookto~s


## ................ B-4 Percent Share of Each Class of Electric and 

### Gas Ovens 

## ...........B-4



## ........... Survival Function of Room AIC. Cooktops..andOvens B-5 Accounting .Module 

## ...................................... C- 

### 19 Equations Used in the Costs. Sales. and Revenue Module


## .....;

## .. C-22 Financial Module 

# .........................; 

## .............C-24 One-Time Cost Amorti_zation Module 

## ........................C-26

. . 

### Calculations forMean Lifetime of 

## IIDUnits 

## ..................... D-4 Utility Marginal OGFs and Year of Capacity Need by NERC Region E-4 Conservation Load Factors All Products Analyzed


## ..............E-8 Sums of Baseload Energy Savings Fractions for NERC

Regions That 

### Need Capacity 

## ............................... E-9 U.S. CO,. SO2.and NOxPower Plant Emissions 

## ............... EA-4 Electricity Generation. Emissions Data. andEmissions

Rates for SO2and NOx at Fossil Fuel-Burning Power Plants


## ... EA-5 Ernissiori Rates for SO2and NQ;from In-House Combustion 

## .... EA-6

. 

### Electricity Generation Data. Emissions Data. and Emissions

Rates for C0, at Fossil Fuel-Burning-Power Plants 

## .......... EA-6 Volume I: Table of Contents:GeneraLMethodology Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 4.1 Table 4.2 ;Table 4.3

, Table:4A

table 5.1 Table 5.2 Table5.3 Table 

### 504 Table 5.5
Table B.l Table B.2a Table B.2b Table B.2c Table B.3 Table C.l Table C.2 Table C.3 Table CA Table D.I

, Table E.1

Table E.2 Table E.3

Table EA-I. Table EA-2. TableEA-3., Table EA-4.

iv LIST OF TABLES**'Produc~** Types and Number ofProduct Classes

...... : ....'

.... :. 3-1 'Sensitivity ofROE to 1 S.E. Change in Control Variables

.. 

### ,o.... 3-23 Emissions Factors for Power Generation

...."

................. 3:"31 Emissions Factors for In-House Emissions

................... 3-32**Marke~** Saturations

........... :.'.;........................ 4-2

,Market Saturations' . . . . . . . . . . . . . . • . . . . . . . . . . . . . . . . . . . . . . . . 4-6 Percent ofMarket Controlled by the Largest Producers of

, Each Product

...- ....................................'

... 4-7, Acquisitions ofAppliance Manufacturers

_ ................. :.. 4-12 Occupied Households Projections (millions)

................... 5-2

-HousingStarts Projections (thousands) . - .......... , ........... 5-2 Disposable Personal Income (1990$)

.........**~** .._ .. , ..... .- .... - 5-3 Average Residential Energy Prices (1990 Dollars per Million Btu)

.. 5-4 Relative Electricity Price by End Use

(Average Residential Electricity Price 

### = 1.00)

................ 5-4 Parameters ofThermal mtegrity/Cost Curves by House Type

...... B-3 Percent Share ofEach Class ofRoom

### Air Conditioners

.......... B-4 Percent Share ofEach Class ofElectric Cooktops "......•........ B-4 Percent Share ofEach Class ofElectric and Gas Ovens

........... B-4 Survival Function ofRoom AlC, Cooktops,and Ovens

........... B-5 AccountingModule .......................•............. C-19 Equations Used in the Costs, Sales, and Revenue Module

..... - ... C-22 Financial Module

........................, . .- ............. C-24 One-Time Cost**Amorti~ation** Module

........................ C-26' Calculations for Mean Lifetime of

## no Units

....-. . . . . . . . . . . . .. D-4

- Utility Marginal OGFsand Year ofCapacity Need by**~RC** Region E-4

Conservation Load Factors All Products Analyzed

.............. E-8 Sums ofBaseload Energy Savings Fractions for NERC

Regions That Need Capacity

.............................. E-9 U.S. CO2 , S02' and NOx Power Plant Emissions

.............. EA-4 Electricity Generation, Emissions Data, and' Emissions

Rates for S02 and NOx at Fossil Fuel-Burning Power Plants

... EA-5 Emission Rates for S02 and NOll-from In-House Combustion

.... EA-6 Electricity Generation Data, Emissions Data, and Emissions

Rates for CO2 at Fossil Fuel-HumingPower Plants

.....**~** .... EA-6 Volume 1: Table o/Contents:GeneralMethodology

### Table EA,-5. Table EA,-6. Table EA,-7. Table EA,-8. Table EA,-9. Table EA,-18. Table EA,- 11. Table EA,-12. Table EA,-13. Table EA,-14.

. . 

### Table EA,-15. Table-EA,- 16. i'able EA,-17. Table EA,- 1 

### 8. Table EA,- 1.9. Table EA,-28. Table EA,-21. Table EA.-22. Table EA.23. Table EA.-24. Table EA.-25. Emission Rates for CO, from In-House Combustion 

# .... .

# . . .. .

## . EA-7 Reduction of Pollutants for Room Air Conditioners, Level One 

## . EA- 1 1 Reduction of ~ollutank for Room Air Conditioners,Level Two 

# . EA-12 Reduction of Pollutants for Room Air Conditioners,Level 

### Three EA- 13 Reduction of Pollutants for Room 

### Air Conditioners, Level Four 

## . EA-14 Reduction of Pollutants for ~oom 

### Air Conditioners, Level Five


### . EA- 15 Reduction of Pollutants for Cooktops,Level One 

# ... . . . . . .

# . .

# . EA-16 Reduction of Pollutants for Cooktops,Level Two 

# .. ..

# .. .. .. .

## . EA-17 Reduction of Pollutants for Cooktops,Level Three 

## . ..- 

# . .. . . .

# .. EA-18 Reduction of Pollutants for Cooktops,Level Four 

# ..

# .... . . . .

# .. EA-19 Reduction of Pollutants for Cooktops,Level Five 

# . .. ....

# . ..

# . . EA-20 Reduction of Pollutants for ~onGentional Ovens, Level Ope 

# . .

# . . EA-21 Reduction of Pollutants for Conventional Ovens, Level Two 

## .. . EA-22 Reduction of Pollutants for Conventional Ovens, Level 

### Three 

## . . EA-23 Reduction of Pollutants for Conventional Ovens, Level Four. 

# . .. EA-24 Reduction of Pollutants for Conventional Ovens, Level Five 

## . . . EA-25 Reduction of Pollutants for Microwave Ovens, Level One 

# . .

# ... EA-26 Reduction of Pollutants for Microwave Ovens, Level Two 

## .. .

# .. EA-27 Reduction of Pollutants for Microwave Ovens, Level 

### Thrq 

# .. .. EA-28 Reduction of Pollutants for Microwave Ovens, Level Four 

# . . ... EA-29

, 

### Reduction of Pollutants for Microwave Ovens, Level Five 

# :

# ..

# . . EA-30

VolumeI: Table of Conlents:GeneralMethodology

. . TableEA-5. Table EA-6. Table EA-7. TableEA-8. Table EA-9. Table EA-lO. Table EA-II. Table EA-I2. Table EA-I3. Table EA-14. Table EA-15. TableEA-16. Table EA-l7. Table EA-18. Table EA-l9. Table EA-20. Table EA.-21. Table EA.-22. Table EA-23. Table EA-24. Table EA-25. Emission Rates for CO2 from In-House Combustion

. . . . . . . . . .. EA-7 Reduction**ofPollutan~** for Room Air Conditioners, Level One

. EA-ll Reduction ofPollutants for Room Air Conditioners, Level Two. EA.: 12 Reduction ofPollutants for Room Air Conditioners, Level Three. EA-13 Reduction ofPollutants for Room. 

### Air Conditioners, Level Four.' EA-14 Reduction ofPollutants for Room 

### Air Conditioners, Level Five

. EA-15 Reduction ofPollutants for Cooktops, Level One

............. EA-16 Reduction ofPollutants for Cooktops, Level Two.

. . . . . . . . . . . . . EA-17 Reduction ofPollutants for Cooktops, Level Three

............**EA~** 18 Reduction ofPollutants for Cooktops, Level Four

. . . . . . . . . . .. EA-19 Reduction ofPollutants for Cooktops, Level Five

.. :......... EA-20 Reduction ofPollutants for Conventional Ovens, Level O.ne

. . .. EA-21 Reduction ofPollutants for Conventional Ovens, Level Two

... EA-22 Reduction ofPollutants for Conventional Ovens, Level Three

.. EA-23 Reduction ofPollutants for Conventional Ovens, Level Four.

. .. EA-24 Reduction ofPollutants for Conventional Ovens, Level Five

... Et\-25 Redqction ofPollutants for Microwave Ovens, Level One

..... EA-26 Reduction ofPollutants for Microwave Ovens, Level Two

..... EA-27 Reduction ofPollutants for Microwave Ovens, Level Three:;

..... EA-28 Reduction of**~ollutants** for Microwave Ovens, Level Four

. . . .. EA-29 Reduction ofPollutants for Microwave**Ovens~** Level Five·**~** ....**E~-30**

Volume 1: Table ojCon'en's:GeneralMethodology v

### Figure 3.1 Figure 3.2 Figure 3.3 Figure 3.4

. 

### Figure 4.1

Figure 4.2
: 

### Figure 4.3 Figure 4.4 Figure 4.5 Figure '4.6 Figure 4.7 Figure 

### 4.8 Figure 4.9 Figure 4.10 Figure A


### .1 

### Figure C


### .1 Figure C.2 Figure C.3 Figure C.4 Figure 

### E


### .1 

## LIST OF

## FIGURES Analytic Framework for the Analysis of Appliance Alternative 

## .

........................................ Efficiency Levels 3-4 Activitiesin the 

### LBNGREM 

## ..............................3-10 Conceptual Approach to the ManufacturerAnalysis Model 

# ....... 3-16 How Alternative Efficiency Levels Cause a Change in ROE

andNPV 

## ............................................. 3-20 Kitchen Range Shipmentsand Housing. Starts 

## .................. 4-3 Microwave Oven Shipmentsand 

### Housing Starts 

## ................ 4-4 Room 

### Air Conditioner Shipmentsand Housing 

### Starts 

# ............4-5



### ..................... ~lectric ~ah~e ~irm ~arket shares: 1993 4-8



## ........................ Gas Range 

## Firm Market Shares: 1993 4-9 Microwave Oven Firm Market Shares: 1993 

## .................. 4-10 Room Air 

### ConditionerFirm Market Share: 1993 

# ...............4-11 National PPI and CPI Compared to Household Cooking Equipment

Product Group PPI 

## ..................................... 4-15 National CPI and PPI Compared to ~obm Air Conditioners 

## ......4-16 National CPI 

### and PPI 

### Compared to All 

### Household ~ppliances


### ....4-17 Schematic of @RISK Uncertainty Analysis for

Spreadsheet Calculations 

## ................................ A-2 Profit Maximization of Fixed Costs 

## ........................... C-5 MAM Modules and Primary Routs of Data Flow 

## ...............C- 17 

### LBNLMAM and 

### GRIM Integration Overview 

## ................C-44 Cost Comparison Factors and Their Relationships


## .............. C-45 Simplified Load-Duratjon Curve 

## .............................E-3

Volume1: Tdleof Contents:General Methodology 

### • Figure 3.1 Figure 3.2 Figure 3.3 Figure 3.4

. Figure 4.1

Figure 4.2 Figure 4.3 Figure 4.4 Figure**4~5** Figure '4.6 • Figure4.7 Figure 4.8 Figure 4.9 Figure 4.10 Figure A.l
Figure C.I Figure C.2 Figure C.3 Figure C.4 Figure E.I

IIi 

### LIST OF FIGURES Analytic Framework for the Analysis ofAppliance Alternative

Efficiency Levels

... . . : . . . . .'.

. . . . . . . . . . .. . . . . . . . . . . . . .**~** . 3-4 Activities inthe LBNL-REM

.............................. 3-10 Conceptual Approach to the Manufacturer Analysis Model

....... 3-16 How Alternative Efficiency Levels Cause a Change in ROE

and NPV

......................................... ; ..**~** 3-20 Kitchen Range Shipments and Housing,Starts

.................. 4-3 Microwave Oven Shipments and Housing Starts

................ 4-4 Room Air**~onditioner** Shipments and Housing Starts

............ 4-5 Electric Range Firm Market Shares: 1993

...'

........... : ...... 4-8 Gas Range Firm Market Shares: 1993

........................ 4-9 Microwave Oven Firm Market Shares: 1993

.................. 4-10 Room Air Conditioner Firm Market Share: 1993

............... 4-11

.National PPI and CPI Compared to Household Cooking Equipment Product GroupPPI

............. ','

...'.

. . .. . . . . . . . . . . . . . . 4-15 National CPI and PPI Compared to RoomAir Conditioners

.....**~ 4~**  16 National 

### CPI and 

### PPI**C<~mpared** to All Household AppHances

.... 4-17 Schematic of @RISK Uncertainty Analysis for

Spreadsheet Calculations

. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. A-2

- Profit Maximization ofFixed Costs

........'

.................. C-5 MAM Modules and Primary Routs ofData Flow

..............'. C-17**LB~MAM** and GRIM Integration Overview

................ C-44 Cost Comparison Factors and Their Relationships

.............. C-45 Simplified Load-Duration Curve ; ........................... E-3

Volume 1: Table ofContents:GeneralMethodology

## - [F

. . 'i

, .. 

## 'CHAPTER

## 1. INTRODUCTION
The Energy Policy and Conservation Act 

## (P.L.94-163). 

### as&ended, establishes energy conservation standards for 12of the 13

### types of consumer products specificallycoveredby the Act. The legislation requires the Departmentof~ner~~@O~) to considernew or amended standards for these andl,other types ofproducts at specified times. DOE.is currently analyzing alternativeenergy efficiency levels for two types of products: room 

### air conditioners, and kitchen.ranges and ovens (including microwave~ovens). This 

### report presents the methodology, 

### data, and rrsults fr6m the analysis of the energy and economic impacts of the efficiency levels., The womicimpact analysis is performed in four major 

### am: 

## .

a 

### An Engineering Analysis, which establishes technical feasibility and product

.

. . 

### attributes, including energy performance and costs 

### of alternative design options, which improve appliance efficiency.

o 

### A 

### ConsumerAnulysis, which-forkasts appliance sales, efficiencies, energy use, and

, ' 

### consumerexpenditures. A 

### Manufacturer Analysis, which provides an estimate of manufacturers' responses to the alternative efficiency levels. Their responses 

### are quantified by changes in several financial performance measures for a prototypical firm. An 

### ImpactAnalysis, which provides 

### an integratedframework for assessihgthe costs 

## - and benefits of implementing new appliance efficiency levels. The Impact Analysis

includes: 1) 

### an Industry ImpactAnalysis that shows the financial and competitive impacts on the respective appliance manufacturing industry; 

### 2) a Life-Cycle Cost Analysis that evaluates the savings-inoperating expense relative to an increase in purchase price for individual consumers; 

### 3) a Utility Analysis that measures the impacts of the altered energy-consumption patterns on electric utilities; 4) a 

### Cost-Benefit Analysis that collects the results of all the analyses into the net benefits and costs from a national 

### perspective; and 

### 5) 

### an EnvironmentalAssessment 

### that presents the results of the associated environmental impacts from a range of alternative efficiency levels. The Engineering Analysis segregatesproduct types into separate classes to which different efficiency levels apply. For each applianceclass, baseline units 

### are chosen representing relatively ' 

### lowefficiency units currentlybeing manufactured: The analysis identifies 

### a series 

### of design options to improve energy efficiency and estimates the factory costs to produce them. Design options are added individually or in combination to the baseline unit to evaluate units that might 

### be produced in response to having new efficiency levels implemented. ~actory costs are then marked up to

. 

### consumer
VolumeI:- General Methodology 

### CHAPTER 1. INTRODUCTION (

. , /

The Energy Policy and Conservation Act (p.L. 9+163), as mended, establishes energy conservation standards for 

## 12 ofthe 13 types ofco1.lSumer products specificaiIy covered by the Act.

. The legislation requires the Department ofEnergy (DOE) to considernew or amended standards for

thes,e andt·other types ofproducts at specified times. DOE"is currently analyzing alternative energy efficiency levels for two types of products: room air conditioners, and kitchen·ranges and ovens (includin.g microwave' ovens). This report presents the !p.ethodology, data, and results from the analysis (ofthe energy and economic impacts ofthe efficiency levels., ' The economic**~mpact** analysis is perfonned in four major areas:

tl An Engineering Analysis, which establishes technical feasibility and product" ' attributes, including energy perfonnance and costs Qf alternative design options, which improve appliance efficiency.

, ,

. ' • A ConsumerAnalysis, whichforecasts appliance sales, efficiencies, energy use, and ' consumer expenditures. • A ManufacturerAnalysis, which provides 'an estimate ofmanufacturers' responses to the alternative efficiency levels. Their responses are quantified by changes in several financial perfonnance.measures fOI: a prototypical finn.

• An ImpactAnalysis, which provides an integrated framework for assessibg the costs'

. and benefits ofimplementing new appliance,efficiency levels. The Impact Analysis

includes: 1) an Industry Impact Analysis that shows the financial and competitive

..impacts on the respeCtive appliance manufacturing industry; 2) a Life-Cycle Cost Analysis that evaluates·the savingS-in operating expense relative to an increase in p.urchase price for individual consumers; 3) a Utility Analysis that measures the impacts of the altered energy-consumption patterns on electric utilities; 4)' a Cost-BenefitAnalysis that collects the results ofall the. analyses into the net benefits and 'costs from a nationiu perspectiv.e; and 5) an Environmental Assessment that presents the results of the associated environmental impacts from a range of alternative efficiency levels. The Engineering Analysis segregates product types into separate classes to which different efficiency levels apply. For each appliance**class~** baseline units are chosen representing relatively , low-efficiency units currently being manufactured. The analysis identifies a series ofdesign opti9ns to improve energy efficiency and estimates the factory costs to produce them. Design options are added indlividually or in combination to the baseline unit to evaluate units that might be produced in response to having new efficiency levels implemented. :Factory costs are then marked up to consumer

Volume 1: GeneralMethodology Page 1-/

### prices, and life-cycle costcurves are 

### constructed foreach class. Thepayback periods 

### associated with each incremental change in efficiency are also calculated. The Consumer Analysis focuses on national energy savings and economic impacts on consumersup to the year 2030. It forecastsefficiencies 

### and sales of new appliancesby class, as well 

### as applianceusage levels in response to changes in projected energy prices and incomes. These results 

### are used to project energy use and consumerexpenditures on fuel and equipment. The net present value of the alternative efficiency levels are 

### computedby discounting the differencesin the time streams of these expenditures in the "with" and "without" efficiency level cases. The ManufacturerImpact Analysisestimatesthe overall impact of imposingnew of amended efficiency levels on manufacturers. The analysis examines long-run impacts on 1) profitability, 2) gro\tth, 

### and 3) competitiveness. To do this, two measures of impact 

### are tracked for the industry as a whole and for any segments that may exist: 1) return-onequity 

## (ROE) and 

### 2) nei income. ROE provides the primary measure of profitability; gross margin, return-on-assets (ROA), and return-on-sales (ROS) are 

### also reported. The analysis also shows total assets, shipments, average prices, and revenues. Assets and income 

### are the measures of growth (positive or negative). The Industry Impact Analysis analyzes two short-run impacts as well. First, the ability ofthe i,ndustryas a whole and of specific segments of the industry to make the one-time investments required to meet the new efficiency levels is examined. Second,if implementing new efficiency levels result 

### in decreased sales for the particular industry being analyzed, the analysis examinesthe possibility of price-cutting while the industry is adjusting to a lower sales volume. TheLife-Cycle Cost Analysisevaluates the impacts on individual consumers by determining the changes in life-cycle cost resulting from the imposition of the energy efficiency levels. The Utility Analysis focuses on revenue changes, avoided costs, and reductions in peak 

### electric loads. Utility-avoided costsrepresent the marginal value of lower fuel consumption 

### as well as the marginal value of lower investment in new generatingplants. Reduction in peak loads can lead to defemng the construction of new generating capacity. Because fuel costs and the need for additional capacity are region-specific, the Utility Analysis is conducted on a regional basis.

. . 

### The Cost-Benefit Analysis provides a picture of the sum total of all the analyses, so that the impacts of the efficiency levels may be viewed notjust as components but on a much larger scale. Finally, the Environmental Assessment measures the main environmental effects (reduced particulate emissions) resulting from the alternative efficiency levels* effect on reduced electricity and fuel demand. This assessment is completed for each appliance and for each alternative efficiency level.

Page 1-2 VolumeI: GeneralMethodology prices, and life-cycle cost curves are constructed for each class. The payback periods associated with each incremental change in efficiency are also calculated. The**~onsumer** Analysis focuses on national energy savings and economic impacts on consumers up to the year 2030. It forecasts efficiencies and sales ofnew appliances by class, as well as appliance usage levels in response to changes in projected energy prices and incomes. These results are used to projectenergy use and consumer expenditures on fuel and equipment. The net present value ofthe alternative efficiency levels are computed by discounting the differences in the time streams ofthese expenditures in the "with" and "without" efficiency level cases. The Manufacturer Impact Analysis estimates the overall impact ofimposing new ofamended efficiency levels on manufacturers. The analysis examines long-run impacts on.l)profitability, .2) groWth, and 3) competitiveness. To do this, two measures ofimpact are tracked for the industry as a whole and for any segments that may exist: 1) return-on-equity (ROE) and 2) net income. ROE provides the primary measure of profitability; gross margin, return-on-assets (ROA), and return-on-sales (ROS) are also reported. The analysis also shows total assets, shipments, average prices, and revenues. Assets and income are the measures ofgrowth (positive or negative). The Industry Impact Analysis analyzes two short-run impacts as well. First, the ability ofthe

.industry as a**who~e** and of specific segments of the industry to make the one-time investments required to meet thc? new efficiency levels is examined. Second, ifimplementing new efficiency levels result in decreased sales for the particular industry being analyzed, the analysis examines the possibility ofprice':"cutting while the industry is adjusting to a lower sales volume.

. TheLife-Cycle Cost Analysis evaluates the impacts on individual consumers by determining the changes in life-cycle cost resulting from the imposition ofthe energy efficiency levels. The Utility Analysis focuses on revenue changes, avoided costs, and reductions in peak electric loads. Utility-avoided costs represent the marginal value oflower fuel consumption as well as the marginal value oflower investment in new generating plants. Reduction in peak loads can lead to deferring the construction ofnew generating capacity. Because fuel costs and the need for additional capacity are region-specific, the Utility Analysis is conducted on a regional basis. The Cost-Benefit Analysis provides a picture ofthe sum total ofall the analyses, so that the impacts ofthe efficiency levels may be viewed notjust as components but on a much larger scale. Finally, the Environment3J. Assessqlent measures the main environmental effects (reduced particulate emissions) resulting from the alternative efficiency levels' effect on reduced electricity and fuel demand. This assessment is completed for each appliance and for each alternative efficiency level.

Page 1-2 Volume 1: General Methodology

## CHAPTER 2. 

## DOCUMENT STRUCTURE
2.1 

### PURPOSEAND 

### SCOPE The .Energy Policy and Conservation Act (P.L.94-163). 

### as amended by the National ApplianceEnergy Conservation Act of 1987 (P.L. 100-12)and by the National Appliance Energy Conservzition Amendments of 1988 

## (P.L. 100-357),provides energy conservation standards for 12 of the 13 types of consumerproducts' covered by the Act, and authorizesthe 

### Secretaryof Energy to prescribe amended or new energy standards for each 

### type (or class) of covered product. The assessment of the alternative energy efficiency levels for the two types of products2 ' 

### presented 

### in this documentis designed to evaluate their economic impacts according to the criteria in the Act. It includes an engineering analysis of the cost and performance of design options to improvethe efficiency of the products; forecasts of.thenumber and average efficiency of products sold, the amount.ofenergy the products will consume, and their prices and operating expenses; a determination of change in 

### investment, revenues, and costs to manufacturers of'the products; a calculaticw of the costs and benefits to consumers, electric utilities, and the nation 

### as a whole; and 

### an assessment of the environmental impacts of the alternative efficiency levels.


### 2.2 

### STRUCTURE OF THEDOCUMENT This report consists of two volumes. Volume 1,General Methodology,provides a 

### general description of the analytic approach, including the structure of the major models. Volume 

### 2 is specific to the two products. This volume contains the data and docdmentation specific to the analysis !ofeach product, and the results of that analysis. A reader interested 

### in one product will benefit from reading Volume 1 

### for general methodology and the specific Part in Volume 

### 2

. 

### containing analysis results for the product of interest. 

# -

' The 13 products covered.in the legislation are: 1) refrigeratbri, refrigerator-freezers,and freezers;2) room air conditioners; 3) central air conditioners and central air-conditioning heat pumps; 4) water heaters; 5) furnaces; 6)dishwaslhersi7)clothes washers; 8)clothes dryers;9) direct heating equipment; 10)kitchen ranges and ovens; 1 1) pbol, heaters; 12)television sets; and 13) fluorescentlamp ballaists. The 2 types of productsanalyzed in this rulemaking are: 1) room air conditioners; and 2) kitchenranges and ovens (including :microwaveovens). VolumeI: General Methodology Page 2-1 

## CHAPTER 2. DOCUMENT STRUCTURE

2.1 PURPOSE AND SCOPE The Energy Policy and Conservation Act (P.L.94-163.), as amended by the National Applianc:e Energy Conservation Act of 1987 (P.L. 100-12) and by the National Appliance Energy**C()nserv~ltion** AInendments of 1988 (p.L. 100-357), provides energy conservation standards for 12 ofthe 13 types ofconsumer products l co:vered by the Act, and authorizes the Secretary ofEnergy

. to prescribe amended or new energy standards for each type (or class) ofcovered product. The assessment of the alternative energy efficiency levels for the two types of products2 presented 

### in this document is designed to evaluate their econQmic impacts according to the criteria in the Ac:t. It includes an engineering analysis of the cost and performance of design options to improve the efficiency ofthe products; forecasts ofthe number and average efficiency ofproducts sold, the mountofenergy the products will consume, and their prices and operating expenses; a**determina~on** of change 

### in investment, revenues, and costS. to manufacturers 

## otthe products; a calculaticm ofthe costs and benefits to consumers, electric utilities, and the nation as a whole; and an a$sessment ofthe environmental impacts ofthe 8Iternative efficiency levels.


### 2.2 STRUCTURE OF THE DOCUMENT This report consists oftwo volumes. Volume 1, General Methodology, provides a general. descripti,on of the analytic approach, including the structure of the major models. Volume 2 is specific Ito the two products. This volume contains the data and doctlmentiltion specific to the analysis ,of each product, and the results ofthat analysis. A reader interested 

### in one product will benefit from reading Volume 1 for general methodology and the specific Part in Volume 2. containin.g analysis results for the product ofinterest.

I The 13 products**co~erecj.** in the legislation are: 1) refrigeratorS. refrigerator-freezers. and freezers; 2) room air

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

6) dishwaslilers; 7) clothes washers; 8) clothes dryers; 9) direct heating equipment; 10) kitchen ranges and ovens; J 1) pool' heaters; 12) television sets; and 13) fluorescent lamp ballasts.

.
2 The 2 types ofproducts analyzed in this rulemaking are: 1) room air conditioners; and 2) kitchen ranges and ovens (including microwave ovens).

. Volume 1: General Methodology Page 2-1

### Volume 1

### contains five chapters and five appendices:


### . Chapter 1 

### Executive Summary: presents an overview of the analysis. Chapter 2 ' 

### Introduction: outlinesthe structure of the document. Chapter 3 Analytic Approach: summarizes the methodologyused in the analysis; discusses the components of the analysis and their interrelationships; describes the models used, their data

### requirements, and their outputs; and identifies the primary assumptions of the analysis. This chapter alsoprovides an overview of the methodology employed to examine the sensitivity of the results to changes in the key assumptions, parameters, and exogenous forecasts used in the analysis. ' 

### Chapter4 

### Industry Profile: discusses appliance saturations, market shares, and industry structure. Chapter 

### 5 Development of Base Case ~orkasts: discusses population projections, housing starts, commercial floorspace projections, and energy prices. The appendices in Volume 1

### coverthe following topics: Appendix A Methodologyfor uncertainty analysis of maximum technologically feasible energy efficiency. Appendix 

### B Overviewofthe forecastingmodel: structure ofthe 

### LBNLResidential Energy Model (LBNLREM)program. The principal components of the model are discussed and the component interrelationshipis explained. 

### Iq volume 

### 2. Appendices 

# - B describe

. 

### and definethe inputs to the model specific to each respective end use. Appendix C Overviewof ManufacturerAnalysis Model (LBNLMAM): outlines how 

### an industry profile of the manufacturing firms affected

### is constructed; describes how the effects on appliance manufacturersof imposing efficiency levelsby product are quantified; definesthe results ofthe analysis

### in tek 

### of changes in gross 

### rnargin,retum on sales, return on equity, return on assets, total assets,'and laborrequirements; and presents the method for conducting a sensitivity analysis of the results. Appendix D Maintenance costs 

### are derived for several components common to more than one appliance type. Appendix E Electric utility impact modeling.



## . Page 2-2 VolumeI: GeneralMethodology Chapter 1 Chapter 2

.Chapter 3

Chapter 

### 4 Chapter 5

### V~lume 1 contains five· chapters and five

### appendice~: . Executive Summary: presents an overview ofthe analysis. Introduction: outlines the structure ofthe document. Analytic Approach: summarizes the methodology used in the analysis; discusses the components ofthe analysis and their interrelationships; 4escribes the models used, their data requirements, and their outputs; and identifies the primary assumptions of the analysis. This chapter·also provides an overview of the methodology employed to examine the sensitivity of the results to changes in the key assumptions, parameters, and exogenous forecasts used in the analysis.

. Industry Profile: discusses appliance saturations, market shares, and industry structure. Development of Base Case Forecasts: discusses population projections, housing starts, commercial floorspace projections, and energy prices. The appendices in Volume 1 cover the following topics: Appendix A Methodology for uncertainty analysis ofmaximum technologically feasible energy efficiency. Appendix B Overview ofthe forecasting model: structure ofthe LBNL Residential Energy Model (LBNL-REM) program. The principal components ofthe model are discussed and· the component interrelationship is explained. In Volume 2, Appendices B describe



### . 

# - and defme the inputs to the model specific to each respective end use. Appendix C Overview of Manufacturer Analysis Model (LBNL-MAM): outlines how an industry proflle ofthe manufacturing finns affected is constructed; describes how the effects on appliance manufacturers ofimposing efficiency levels by product are quantified; defines the results ofthe analysis 

### in terins ofchanges in gross margin, return on sales, return on eqUity, return on assets, total assets,'and labor requirements; and presents the method for conducting a sensitivity analysis ofthe results. Appendix D Maintenance costs are derived for several components common to more than one appliance type. Appendix E Electric utility impact modeling.

Page 2-2 Volume 1: GeneralMethodology

### Environmental ~ssessment:Quantifies impactsof alternative efficiency levels on emissions of carbon dioxide, nitrogen oxides, and sulfur oxides. Volume 

### 2 contains detailed analyses correspondingto particular products. Each separate ' 

### Part (A ;md 

### B) is product-specific and each 

## Pgis organized into seven chapters and three 

### appendices. Chapter 1 

### Engineering Analysis: contains detailed energy use and cost information. I 

### Chapter 2 

### Base Case

### Forecasts: describes

### national forecasts of energy consukption,efficiency 

### .

. 

### of new units, units installed in households, and

. . 

### annual appliance sales 

### in the absence of new regulations. Chapter 

### 3 Projected National Impacts of AlternativeEgciencyLevels: discussesthe choice df

. . 

### efficiencylevels to be analyzed and the projected impacts of each efficiency level. The chapter summarizes the energy savingsby fuel and product; the sales, average efficiencies,purchase costs, and operating costs of new and replacement appliances; and the net present benefit of the alternative efficiency levels: Chapter.4 ' -

### Life-Cycle Costs and Payback Periods: describesthe effects of imposed efficiency levelson individualpurchasers and users of appliances. It' compares the life-cycle cost of appliances and other measures of -consumer impact with add without'. implementing new efficiency levels. Chapter 

### 5- 

### Impacts of Alternative EfficiencyLevels 

### on

# ~~~~~~~~~~: describesthe analysis ..methodology, short- and long-run impacts, and sensitivity analysis.

. . 

### . Chapter 

### 6 Impact of AlternativeEfficiency Levels on Electric Utilities: describes the effects,

I . 

### of imposed efficiency levels on the electric utility industry, focusing on marginal costs of electricity, generating capacity growth; changes in regional capacity 'and energy demand, and changes 

### in utility revenues and'costs.. Chapter 

### 7 Environmental Effects: describes changes induced 

### by the. implementation of new efficiency levels in emissions of .oxides of carbon,,sulfur, and nitrogen from combustion of fossil fuels for electricity generation and in homes. Appendices A through C within each Part contain more detailed infohnation on the models and databasesused in the economic analysis of .tachproduct.

Volume 1: General Methodology Environmental Assessment: Quantifies impacts of alternative efficiency levels on emissions of

. carbon dioxide. nitrogen oxides. and sulfur oxides.

. Volume 2 contains detailed analyses corresponding to particular products. Each separate Part (A and B) is product-specific and each Part is organized into seven chapters and three

,**appendicc~s.**

Chapter 1 Chapter 2.

Chapter 

### 3-

Chapter 4

Chapter 5· Chapter 6

Chapter 7 Engineering Analysis: contains detailed energy use and cost information. Base Case Forecasts: describes national forecasts ofenergy consumption. efficiency ofnew units, units installed in households, and annual appliance sales in the**abse~ce** ofnew regulations. Projected National Impacts ofAlternative EffIciency Levels: discusses the choice of efficiency levels to be analyzed and the projected impacts ofeach efficiency level. The chapter summarizes the energy savings by fuel and product; the sales, average efficiencies, purchase costs, and operating costs ofnew and replacement appliances; and the net present benefit ofthe alternative efficiency**levels~**

.Life-Cycle Costs and Payback Periods: describes the effects ofimposed efficiency levels 'on individual purchasers and users ofappliances. It compares the life-cycle cost of appliances and 'other measures of ,consumer impact with arid without' implementing new efficiency levels. Impacts ofAlternative Efficiency Levels on Manufacturers: describes the analysis

..methodology, short- and long-run impacts, and sensitivity analysis

..

Impact ofAlternative Efficiency Levels on Electric Utilities: describes the effects,

. of imposed efficiency levels on the electric utility indQstry. focusing on marginal

costs of electricity, generating capacity**growth~** changes in regional capacity 'and energy demand. and changes in utility revenues and costs.· Environmental Effects: describes changes induced by the. implementation of new efficiency levels in emissions of .oxides of carbon,· sulfur, and nitrogen from**cQmbu~tion** offossil fuels for electricity**generatio~** and in homes. '

, Appendices A through C within each Part contain more detailed information on the models and dalabases used in the economic analysis of**'~ch** product.

. .

Volume 1: General Methodology

### Appendix A Energy

### Use and Cost Data: pmvides abreakdown of thecosts for each design option. This appendix also includes additional information pertinent to the Engineering Analysis. Appendix 

### B Forecasting Model 

### (LBNLREM): gives a detailed description of the data used to calculate consumer impacts. The input data 

### stream for the base case runs is shown and changes to the input data for the efficiency level cases are 

### indicated. Appendix 

### C Manufacturer Impact Analysis: provides a detailed description of the model to estimate financial impacts of the imposition of efficiency levels on the manufacturers of covered products. The appendix shows the structure of the model, the data sources used, and detailed outputs of base case, efficiency level case, and sensitivity analysis runs.

Page 2-4 Volume 1: GeneralMethodology 

### Appendix A Energy Use and Cost Data: provides a breakdown ofthe costs for each design option. This appendix also inj:ludes additional infonnation pertinent to the Engineering Analysis. Appendix B Forecasting Model (LBNL-REM): gives a detailed description of the data used to calculate consumer impacts. The input data stream for the·base case runs is shown and changes to the input data for the efficiency level cases are indicated. Appendix C Manufacturer Impact Analysis: provides a detailed description of the model to estimate financial impacts of the imposition of efficiency levels on the manufacturers ofcovered products. The appendix shows the structure ofthe model, the data sources used, and detailed outputs ofbase case,**effi~iency** 

### level case, and sensitivity analysis runs.

Page 2-4 Volume 1: General Methodology

## CHAPTER 3. 

## ANALYTIC.APPR0ACH
3.1 

### INTRODUCTION The economic impacts of alternative energy efficiency levels depend largely on the

. 

### relationshipbetween the cost of 

### a consumer product and its energy efficiency. The cost-efficiency relationships are determined for each product class based on engineering analyses. 

### DOE has segregatedproduct types into classes to which different energy efficiency levels might apply. The product types and the number of classes of each product 

### type 

### are shown in Table 3.1. Within each

, 

### class, the,

### encrgyconservingdesigns 

### are orderedby cost-effectiveness;then combinations of energysaving design options 

### are used to generatethe relationships that

### .areinputs to the other analyses of the impacts of setting new energy efficiency levels at various levels. Identification of product classes, baseline units, design options, and maximum technologically feasibleefficienciesis based on information gathered from the trade associations, manufacturers, discussions with researchers, a literature survey, and comments on the Advance Notice ofProposed Rulemaking (ANOPR)and the Notice ofProposedRulemaking (NOPR).Energy use datahave been obtained from 

### AHAM (Association ofHome Appliance Manufacturers),GAMA 

### (Gas Appliance Manufacturers Association),

### lTC(FederalTrade Commissiori),manufacturers, and simulation ~odels. Sources of factory cost data include manufacturers, suppliers, and the ANOPR and 

## NOPR comments. 

### The costestimates 

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

, \ 

### The relationshipsbetween manufacturercosts and efficiency that arepresented ih this chipter 

### are used throughout the other 

### parts of the analysis. The engineering analysis also includes determin'atisn of maintenance and installation costs for each of the design options studied (see AppendixD of

### this volume and Chapter 1

### of

## PartsAand

### B of

### thisreport).The manufacturer analysis uses manufacturercosts to produce retail prices. Retail prices, installation costs, and maintenance costs in (conjunction with the corresponding efficiencies are used 

### in the forecasting models to forecast sales and 

### efficiencies and 

### to calculate life-cycle costs and payback periods. 

### Table 3.1 

### Product Types 

### and N,umber,of Product 

### Classes

. . Product ?'= Number of Classes ~oom Air Conditioners 14 Kitchen Ranges and Ovens 8

VolumeI: General Methodology Page 3-1 

### CHAPTER 3. ANALYTIC,APPROACH

3.1 INTRODUCTION The economic impacts of alternative energy**effi~iency** levels depend largely on the ' relationship between the cost of a consumer product an4 its energy efficiency. The cost-efficiency relationships

.are detennined for each product class based on engineering analyses. DOE has segregatf:d product types into classes to which different energy efficiency levels might apply. The pI"Qduct types and the number ofclasses of**~h** product type are shown in**Tabl~** 3.1. Within each class, the energy-conserving designs are orderedby cost-effectiveness; then combinations ofenergysaving**d(~sign** options are used to generate the relationships that,are inputs to the other analyses of the impacts ofsetting new energy efficiency levels at various levels. Identification of' product classes, baseline units, design options, and maximum technologically feasible efficiencies is based on information gathered from the trade associations. manufacturers; discussions with researchers. a literature survey. and comments on the Advance Notice ofProposed Rulemaking (ANOPR) and the Notice ofProposed Rulemaking (NOPR). Energy use data have been obtained from AHAM (Association ofHome Appliance Manufacturers), GAMA (Gas ApI)liance Manufacturers Association), FfC (Federal Trade Commission). manufacturers. and simulation n;todels. Sources offactory cost data include manufacturers. suppliers, and the ANOPR and NOPR comments. The cost estimates are combined with the efficiency estimates to generate the cost-effic:iency relationships. ' Therelationships between**manufac~r** costs and efficiency that are presented in this chapter are used throughout the other parts of the analysis. The engineering analysis also includes determination of maintenance and installation costs for each of the design options studied (see Appendix D ofthis volume and Chapter 1ofParts A and B ofthis report). The manufacturer analysis uses manufacturer costs to produce retail prices. Retail prices, installation costs, and**.maint~nance** costs in conjunction with, the corresponding efficiencies are used in the forecasting models to forecast sales and efficiencies and to calculate life-cycle costs and payback periods. Table 3.1 Product Types and Number'ofProduct**CI~ses Proouctl~,** ____________________________________**~N~u~m~oo~r~o~f~C~l~~s~~~**________ __ Room Air Conditioners 14 Kitchen Ranges and Ovens 8

Volume 1: General Methodology Page 3-1

### This chapterbriefly describeshow the economicanalysisof alternativeefficiency levels was performed. Section 

### 3.2 presents an overview of the analytic methodology and discussesthe major components of the analysis: the 

### Engineering Analysis, the Consumer Analysis (national and individual), the Manufacturerand Industrial Analyses,' and the Utility 

### Analysis. The emphasisis on how these components fulfdl the seven legislative requixements listed in the Introduction. This section discusses the interrelationships among the components that ensureconsistency throughout the analysis. Section 3.3 describes the computerized models used in the analysis. The models predict consumer, manufacturer, and utility responses to future changes in the economy, including the imposition of efficiency levels. Quantitative estimates of the impacts of implementing new efficiency levels 

### are calculated from the outputs of the models. The models utilized in the analysis are: Engineering Cost and Performance Models; ConsumerImpact Models; Manufacmr Impact ~odels; Utility Impact Model. The function, 

### data sources, assumptions, and validity of the -resultsfor each model are discussed in the product-specific Parts (Parts A 

### and 

### B). Section 3.4 

### below discusses the sensitivity analysisperformed on the economic impacts and focuses on the methods used to determine the parameters that have the largest effects and to determine their range of variation. 

### 3.2 OVERVIEW 

### OF ANALYTIC FRAMEWORK The impact of implementingnew efficiency levels is determined by comparing projections of a wide range of economic variables under the existing legislation with the projections under the imposed effciency levels. For each product analyzed, thesepro~ections are 

### fmtmade for a base case (existing legislation) using the analytic models described below. 

### In each product-specific 

### Part, Chapter 

### 2.describes the data and assumptions used to calculate the baseline forecasts. The calculations are then repeated imposing the alternative efficiency levels as discussed in Chapter 3 

### of Parts A 

### and 

### B. The differencesbetween the projections of the energy consumption and economic variables in the base and efficiency level cases provide quantitative estimates of the impacts of the imposed efficiency levels. To evaluate the significanceof the differences, a sensitivity analysis is performed on the key parameters and assumptions.

Page 3-2 VolumeI: General Methodology This chapter briefly describes how the economic analysis ofalternative efficiency levels was perfonned. Section 3.2 presents an overview of**th~** analytic methodology and discusses the major components of the analysis: the Engineering Analysis, the Consumer Analysis (national and individual), the Manufacturer and Industrial Analyses, and theUtility Analysis. The emphasis is on how these components fulfIll the seven legislative requirements listed in the Introduction. This section discusses the interrelationships among the components that ensure consistency throughout the analysis. Sectiop 3.3 describes the computerized models used in the analysis. The models predict consumer, manufacturer, and utility responses to future changes in the economy, including the imposition of efficiency levels. Quantitative estimates of the impacts of implementing new efficiency levels are calculated from the outputs ofthe models. The models utilized in the analysis are:

• Engineering Cost and Performance Models;

• Consumer Impact Models;

• Manufacturer Impact Models;

• Utility Impact Model. The function, data sources, assumptions, and validity of the -results for each model are discussed in the product-specific Parts (Parts A and B). Section 3.4 below. discusses the sensitivity analysis perfonned on the economic impacts and focuses on the methods used to determine the parameters that have the largest effects and to determine their range ofvariation. 3.2 OVERVIEW OF ANALYTIC FRAMEWORK The impact ofimplementing new efficiency levels is determined by comparing projections of

. a wide range of economic variables under the existing legislation with the projections under the imposed efficiency levels. For each product analyzed. these projections are fIrst made for a base case (existing legIslation) using the analytic models described below. In each product-specifIc 

### Pan. Chapter 2. describes the data and assumptions used to calculate the baseline forecasts. The calculations are thenrepeated imposing the· alternative efficiency levels as discussed in Chapter 3 ofParts A and B. The differences between the projections ofthe energy consumption and economic variables in the base and effIciency level cases provide quantitative estimates of the impacts ofthe imposed effIciency levels. To evaluate the significance ofthe differences, a sensitivity analysis is perfonned on the key parameters and assumptions.

Page 3-2 Volume 1: General Methodology

### The economic. impact analysis is performed in seven major a&: 'An

### EngineeringAnalysis, which establishes technical feasibility and product attributes including costs of design optionsto improve appliance efficiency; 

### A Consumer Analysis at two levels:' national aggregate impacts, and impacts on individuals. Thenational aggregate impactsinclude 

### LBNL Residential Energy ~odel : 

### (LBNLREM) forecasts of appliance sales, efficiencies, energy use, and consumer

. 

### expenditures. The individual impacts 

### are analyzed by Life-Cycle Cost (LCC), 

### Payback Periods 

### (PBP), and Cost of Conserved Energy (CCE), which evaluate the savings in operating expenses relative to increases in purchase price; 

### A Manufacturerdnalysis, which provides an estimate of kufacturers' response to the alternative efficiency levels. Theirresponse is quantified by changes in several measures of financialperformance for a finn; An Industry Impact Analysis, which shows financial and competitive impacts on the appliance industry; A 

### UtilityAnalysis that measures the impacts of the altered energy-consumptionpittems 

### on electric utilities;

, 

### An Environmental Efiects Analjsis, which estimates changes in emissions of carbon dioxide, sulfur oxides, and nitrogen oxides,because of reduced energy consumption in '

### the home and at the.powerplant; and Al?egulatoryimpact

### Analysis. whichcollects the results of all the analyses into the net benefits and costs from a national perspective.

I' 

### A 

### simplifieddiagramofthe analysisis shown

### in Figure 3.1. 

### Each 

### type of analysis is performed for the two products. 

### If appliances having the maximum efficiency improvement that is
,. 

### technologically feasible show no significant energy savings in the EngineeringAnalysis, then the legislation requires 'thatno modified standard be prescribed for that product type. 

### If the appliance

, 

### efficiency can be increased to produce significant energy savings, then a detailed energy savings,

economic, and 

### impact analysis is done. For each ofthe products, the 

### analysisis perfomed for abase caseplus several alternative efficiency levels; The'selectionof efficiency levels by class is 

### described in Chapter 3 of each product-specific Part.

Volume I: GeneralMethedology .The economic. iI;t1pact analysis is performed in seven major

# areas:
• An EngineeringAnalysis, which establishes technical feasibility and product attributes including costs ofdesign options to improve appliance efficiency;

.. • A Consumer Analysis at two levels:· national aggregate impacts, and impacts .on



## • individuals. The national aggregate impacts include LBNL Residential Energy Model

. (LBNL-REM) forecasts of appliance sales, efficiencies, energy use, and consumer

expenditures. The individual impacts are analyzed by Life-Cycle Cost (LCC), Payback Periods (PBP), and Cost of Conserved Energy**(~CE),** which evaluate the savings in operating expenses relative to increases in purchase price;

. A ManufacturerAnalysis, which provides an estimate ofmanufacturers' response to the alternative efficiency levels. Theirresponse is quantified by changes in several measures offmancial performance for a frrm;

• An Industry Impact Analysis. which shows financial and competitive impacts on the appliance industry; • A UtilityAnalysis that measures the impacts ofthe altered energy-consumptionpatterns on electric utilities;

• An Environmental Effects Analysis, which estimates changes in emissions of carbon. dlioxide, sulfur oxides, and nitrogen oxides, because of**~uced** energy consumption in

,the home and at the·power plant; and • A RegUlatory ImpactAnalysis,whichcollects the results of all the analyses into the net benefits and costs from a national perspective. A simplified diagram ofthe analysis is shown in Figure 3.1. Each type ofanalysis is perfonned for the two products. H appliances having the maximum efficiency improvement that is

.. technologically feasible show no significant energy savings in the Engineering Analysis, then the legislatic,n requires that no modified standard be prescribed for that product type. Ifthe appliance efficiency can be increased to produce significant energy savings, then a detailed energy savings,· economic, and impact analysis is done. Foreach ofthe products, the analysis is perfonned for a base case plus several alternative efficiency levels; The selection of**efficien~y** levels by class is described in Chapt,er 3 ofeach product-specific Part.



## . Volume 1: General Methildology Page 3-3Mmufretu Analysis 

# * * ManufacturerCost vs. Efficiency
Retail Price vs. Efficiency ManufacturerImpacts Life-Cycle Costs National Energy Net Present Value to Society

. 

### ConsumerPayback Generating Capacity
Emissions (CO,, NO,, SO,)



### Figure 3.1 Analwc Framework for the Analysis of Appliance AlternativeEfficiency Levels Figure 3.1 

### illustrates the relationships among the Engineering,Manufacturer, and Consumer Analyses. The Engineering Analysis ytablishes appliance designs and related attributes such as efficiency and costs. Based on these costs, the Manufacturer Analysis predicts retail prices for use in the consumer analysis (the Life-Cycle Cost Analysis and Forecasts). Based on the relationship between the prices and efficiencies of design options, the consumer analysis forecasts sales and efficiencies of new and replacement appliances. These data 

### an5 used as inputs to the Manufacturer Analysis to determinefinancial impacts on typical firms 

### within the industry. The consumeranalysis also forecasts energy savings and consumer expenditures for the purchase and operation of the appliances. Consumerexpendituresare 

### used in 

### the Life-CycleCost Analysis to determineconsumer impacts. Changesin sales,revenues, investments, and marginal costs of utilities are 

### calculated from the energy savings in the Utility Analysis.

. .

. . 

### Three time frames, are 

### considered by the analysis. First, the analysis of consumer and utility 

### impacts extends over a time frame consistent with the.life of the products and includes the time required to approach market saturation. This time frame extends to 2030 

### and the new efficiency levels come into effect in 1929. Second, the Manufacturer Analysis is performed for a typical year Page 3-4 VolumeI: GeneralMetltodology**~** 

### • Manufacturer Cost vs. Efficiency

# IM4~ 

# : • Retail Price vs. Efficiency 

### • Manufacturer Impacts

, Llfe-CycJe COlt**~** ADalylia 

### • Ufe-Cycle Costs 

# ,---.1 National Energy 

### •**~** Forcc:asts**~** 

### • Net Present Value to Society 

## -I

# -1 Paybacka 

# I**~** 

### • ConsumerPayback



# y Utility 

# I ADaJYliJ 

# • • Generating Capacity

# LI"=~wl • • Emissions (C02, NOx, S02)



### Figure 3.1 Analytic Framework for the Analysis ofAppliance Alternative Efficiency Levels Figure 3.1 illustrates the relationships among the Engineering, Manufacturer, and Consumer Analyses. The Engineering Analysis**~stablishes** appliance designs and related attributes such as efficiency and costs. Based on these costs, the Manufacturer Analysis predicts retail prices for use in the consumer analysis (the Ufe-Cycle Cost Analysis and Forecasts). Based on the relationship between the prices and efficiencies of design options,the consumer analysis forecasts sales and efficiencies ofnew and replacement appliances. These data are used as inputs to the Manufacturer Analysis to determine financial impacts on typical firms within the industry. The consumer analysis also forecasts energy savings and consumer expenditures for the purchase and operation of the

. appliances. Consumer expenditures are used in the Ufe-Cycle Cost Analysis to determine consumer

impacts. Changes in sales, revenues, investments, and marginal costs ofutilities are calculated from the energy savings in the Utility Analysis. Three time frames are considered by the analysis. First, the analysis ofconsumer and utility impacts extends over a time frame consistent with the. life of the products and includes the time required to approach market saturation. This time frame extends to 2030 and the new efficiency levels come into effect in**19~9.** Second, the Manufacturer Analysis is performed for a typical year Page 3-4 Volume 1: General Methodology

### aflerthe i,mplementationof new efficiency levels. Third, the Engineering Analysis examines the technical feasibilityof improving the efficiencyof appliancesbefore the efficiency levels come into effect-within the next three 

### years.


### 3.3 

### MODELS, DATA, 

## ANDASSUMPTIONS, 33.1 

### Engineering Cost 

### and Performance Models ' 

### The Engineering Analysis provides information on efficiencies,manufacturingcosts, and other appliance characteristics foruse 'inother components ofthe analysis. Appliancefeaturesthat provide utility to the consumer are incorporated into the analysis through the creation of appliance classes. Classes 

### are a subset of appliancetypes. For example, a water heater is an appliancetype, 

### but a gasfired storage water heater is an appliance class. The ~n~inee&~ Analysis develops cost and efficiency data for a set of design options within each appliance class. These data 

### are the output of the engineering 

### performance and cost models discussed 'later in this section. The Engineering Analysis i.sperformed 

### in seven steps: 

### 1)select applisinceclasses; 

### 2) select baseline units; 3) select design options within each class;4) 

### determine maximum technically feasible designs; 

### 5)calculate the efficiency improvement provided by each design option; 

### 6) develop cost estimates; and 

### 7) generate cost-efficiency relationships. Appliance Classes The first step in the Engineering Analysis is the segregation of product types into separate classes to which different alternativeenergyefficiencylevels apply. DOE differentiates classes by the 

### type of energy used (oil, natural gas, orelectricity), and capacity orperformance-related features thatprovideutility to the consumerand affectefficiency. For specific appliances, classes 

## &defined using data.collected 

### in discus~ions with appliancemanufacturers, trade associations, other interested parties, and fromcomments received on the ANOPR and NOPR. Those classes for which no DOE test pr~etiurr: has been specified

### are omitted. The appliance classescoveredby the analysis

### arelisted in Chapter 1

### of each product-specific Part. Baseline Units

. . 

### A 

### baseline unit is the starting point for analyzing design options for improving energy efficiency. 

### To select a baseline unit, the Engineering Analysis uses information gathered fiom trade organizations,manufacturers, consultants with expertise idspecificproduct types, and 

### from public comments on the ANOPR and NOPR. For each product class, the baselineunit generally represents a model with the maximum allowable energy use specified by the National Appliance Energy Conservation Act (NAECA). Other than efficiency, features are 

### representative of the class as a whole.

VolumeI: GeneralMethodology Page 3-5 ' 

# ·

.


### after the implementatiori ofnew efficiency levels. Third, the Engineering Analysis examines the'

, technical feasibility ofimproving the efqciency ofappliances before the efficiency levels come into

effect-within the next three 

### years.


### 3.3 MODELS, DATA, AND ASSUMPTIONS 

### 3.3.1**En~:ineering** 

### Cost and Performance Models The Engineering Analysis provides information on efficiencies, manufacturing costs, and other appliance characteristics for use in other components ofthe analysis. Appliance features that provide utility to the consumer are incorporated into.the analysis through the creation ofappliance classes. Classes are a subset ofappliance types. For example, a water heater is an appliance type, but a gas- 'fired storage water heater is an appliance class. The Engineering Analysis develops cost and efficiency data 

### for a set ofdesign options within each appliance class. These data are the output of the engineering performance and cost models discussed later in this section. The Engineering Analysis is performed in seven steps: 1) select appliance classes; 2) select baseline units; 3) select design options within each class; 4) determine maximum technically feasible designs; 5) calcula:te the efficiency improvement provided by each design option; 6) develop cost estimates; and 7) generate**(~ost-efficiency relations~ps.** **Appliancl'~** 

### Classes 

### The first step in the Engineering Analysis is the segregation of product types into separate classes to which different alternative energy efficiency levels apply. DOE differentiateg classes by the type ofenergy used (oil, natural gas, orelectricity), and capacity or**performance-rela~** 

### features that provide Ultility to the consumer and affect efficiency. For speCific appliances, classes are defined using data. collected in discus$ions with appliance manufacturers, trade associations, other interested parties, and from comments received on the ANOPR and NOPR. Those classes for which no DOE test pr()!::etiure has been specified are omitted. The appliance classes covered by the**analysi~** 

### are listed in Chapter -I 

### ofeach product-specific Part. Baseline Units 

### A baseline unit is the starting point for analyzing design options for improving energy efficiency. To select

# a baseline unit, the Engineering Analysis uses information gathered from trade organizations, manufacturers,'consultants with expertise in'specific product types, and from public comments on the ANOPR and NOPR. For each product class,the baseline unit generally represents ,a model with the maximum allowable energy use specified by the National Appliance Energy Conservation Act (NAECA). Other than' efficiency, features are representative of the class as a whole.

Volume 1: General Methodology

### Design 

### Options The Engineering Analysis identifies individual or combinations of design options with a potential for improving energy efficiency. Design options that 

### are commercially available at the present time orthat 

### arepresentin prototypes 

### areconsidered. They 

### are selected after discussions with experts and an extensiveliterature review. The efficiency improvement and manufacturer cost of design options added to the baseline unit 

### are calculated. 

### Maximum TechnologicallyFeasible Designs For each product class, a maximum technologicallyfeasibledesign optian or combination of design options is identified. This option, or combination of options, results in the highest energy efficiencyfor each product class. The maximum technologicallyfeasible efficiency level is one that 

### can be 

### reached by the addition of design options,both commerciallyfeasible and in prototypes, to the baseline units. This design must be 

### possible to assemble, but not necessarily to manufacture in large numbers. Economic criteria 

### are applied to all design options in otherparts of the analysis.

For each of the product classes, the efficiency levels corresponding to various design options are determined from manufacturer data and from engineeringcalculations. Cost Estimates The 

### manufacturercost

### data are obtained through a lengthy process that included meetings and tours at manufacturing facilities, submission of formal requests to manufacturers for costing data, and review of the data received. Estimates of manufacturer cost are 

### alsoreceived in response to the ANOPR and NOPR. 

### In each product-specific Part, Appendix A contains detailed incfemental cost data disaggregated into labor, purchased parts, materials, shippinglpackaging and tooling. Cost-Efficiency Relationships The results of the Engineering Analysis are summarized in the costefficiency relationships showing the efficiency and manufacturer cost of the design options for each appliance class. Manufacturer and dealer markups derived in the Manufacturing Analysis are 

### applied to the factory costs to arrive at the purchase price of the appliance. Additional installation costs required for some designs are included in the purchase price. Additional maintenance costs associated with specific design options are 

### also estimated. The priceeff~ciency relationships are a 

### fundamental input to the Consumer Analysis. Assumptions 

### in theAnalysis Justifications for the most important assumptionsof the Engineering Analysis are 

### listed below.
Page 3-6 VolumeI: General Methodology 

### Design Options The Engineering Analysis identifies individual or combinations of design options with a potential for improving energy efficiency. Design options that are commercially available at the presenttime orthat are present in prototypes are considered. They are selected after discussions with experts and an extensive literature review. The efficiency improvement and manufacturer cost of 'design options added to the baseline unit are calculated. 

### Maximum Technologically Feasible Designs For each productclass. a maximum technologically feasible design option or combination of design options is identified. This option. or combination ofoptions. resolts in the highest energy efficiency for eachproduct ciass. The maximum technologically feasible efficiency level is one that can be reached by the addition ofdesign options. 'both commercially feasible and in prototypes. to the baseline units. This design mustbe possible to assemble. but not necessarily to manufacture in

. large numbers. Economic criteria are applied to all design options in other parts ofthe analysis. 

### Efficiency Calculation Foreach ofthe product classes, the efficiency levels corresponding to various design options are determined from manufacturer data and fromengineering calculations. 

### Cost Esti11Ultes The manufacturer cost data are obtained through a lengthy process that included meetings and tours at manufacturing facilities, submission of fonnal requests to manufacturers for costing data, and review ofthe data received. Estimates ofmanufacturer cost are also received in response to the ANOPR and NOPR. In each product-specific Part. Appendix A contains detailed incremental cost data disaggregated into labor. purchased parts, materials. shipping/packaging and tooling. 

### Cost-Efficiency Relationships The results of the Engineering Analysis are summarized in the cost-efficiency relationships showing the efficiency and manufacturer cost of the design options for each appliance class. Manufacturer and dealer markups derived in the Manufacturing Analysis are applied to the factory costs to arrive at the purchase price ofthe appliance. Additional installation costs required for some designs are included in the purchase price. Additional maintenance costs associated with specific design options are also estimated. The price-efficiency relationships are a fundamental input to the

, Consumer Analysis.

.



### Assumptions 

### in theAnalysis Justifications for the most important assumptions ofthe Engineering Analysis are listed below.
Page 3-6 Volume 1: GeneralMethodology



# •... 

### Assumption 

### '. Justification Industq production processes can be character- Manufacturing costs for small manufacturers 

### ized in 

### terms of medium and large manufactur- 

### ,ark highly varied because of dependence on . 

### ing facilities. purchased 

### parts and sensitivity to labor and

. 

### distribution costs. An effort w& made to obtain these data, but they were'.not made , 

### ,available 

### by small fms. Because of this difficulty and the fact that they represent a small fractionoftotal shipments for most of the prod.uct classes, analysis concentrated on the medium and large manufacturers. Unit energy consumption is based on DOE The baseline unit is the startingpoint to which usage estimates, unless otherwisenoted. design options 

### are added to create higher efficiency units. The selection of baseline units

. . 

### isbased on studies ofproducts on the market in

- 

### 1990and consultations with manufacturers.

Unit energy consumption is based on DOE As part of the Consumer Product Efficiency usage estimates,unless otherwise noted. Standards program, DOE was required to establish test procedures to determine average consumer utilization and energy usage for 'the covered products. Test procedures for each product 

### type have been promulgated and were

. . 

### employedto detepnineunit energy consumption throughout the ~ngin&n~ Analysis. 

### If 'field "data indicate 'that appliance energy use is

. ,



### different than -that determined 

### by the test procedure, alternative energy use estimates ' 

### based on the.field data are provided.

. . 

### Data 

### Sources , 

### Shi,pment data 

### are based on information from industry sources and published data from industry trade associations. Costs of purchased materials and parts are based on quotations from product manufacturers 

### and suppliers of these items. Data on engineering and labor costs are taken 

### from on-site visits to manufacturing plants and from manufacturer information. Data characterizing baseline units for each class are based on infonmtion from industry sources and published data.

VolumeI: General Metltodology 

### , AssumptionIndustry production processes can be characterized in terms ofmedium and large**manufactur~** ing facilities.

Unit energy consumption is based on DOE usage estimates, unless'otherwise noted.

Unit energy consumption is bas,ed on DOE usage estimates, unless otherwise noted.



### Data Sources Justification Manufacturing costs for small manufacturers 'are highly varied because of dependence on ,purchased 

# parts and sensitivity to labor and ,distribution costs. An effort was made to obtain these data, but they were', not made ,available by small fmns. Because of this difficulty and the fact that they**represen~** a

.small fraction oftotal shipments for most ofthe prod,uct classes, analysis concentrated on the medium and**l~ge** manufacturers. '

, The baseline unit is the starting point to which design options are added to create higher efficiency units. The selection ofbaseline units is based on studies ofproducts on the market in

, 1990 and consultations with manufacturers.

As part of .the Consumer Product Efficiency Standards program, DOE was required to establish test procedures to determine average consumer utilization and energy usage for ·the 'covered products. Test procedures for each product type have been promulgated and were employed to**dete~e** unit energy consumption ,throughout the, Engineering Analysis. If field • data indicate that appliance energy use 

## IS'

different than that determined by the test procedure, alternative energy use estimates based on the field data are provided.

Shipment data are based on infQnmttion from industry sources and published data from industry trade associations. Costs ofpurchased materials and parts are based on quotations from' product manufacturers and suppliers ofthese items. Data on engineering and labor costs are taken from on-site visits to manufacturing plants and from manufacturer information. Data characterizing baseline units for each class are based on infOmiation from industry sources and pub,lished data

.



### . Volume I: General Methodology 'Page 3-7

### Outpirts

### fromthe Engineering 

### Analysis For each combination of design options considered in the analysis, the models and data provide:

energy efficiency (expressed as the 

### DOE energy factor' andlorunit energy consumption); increased material, labor, and investment costs by product class for medium and large manufacturers; qnual energy cpnsumption 

### per unit (based on 

## DOE test procedures or field-based estimators); the relationship between cost and energy use by product class; and ' 

### other information on product characteristicssuch as appliance lifetimes, installation costs, and maintenance costs.


### Validation

### ofthe 

### Results Experimental data on efficiencies

### are available for some of the design options studied. For the 

### others, engineering calculationsbased on physical principles 

### are performed.


### 33.2 LBNL ~esidentia~ 

### Energy 

### Model (LBNEREM) Purpose The 

### LBNLREM models the appliancepurchase choices made in households, as well =.these households' subsequent usage behavior and energy consumption. 

## Set Appendix 

### B (herein) for

. , 

### details about 

### the model. Engineering, economic, and demographic data 

### are used 

### in 

### LBNGREM. The engineering data for appliances 

### are described in Section 3.3.1. Additional data include age distribution of existing appliance stock and retirement functions. Economic data include projected energy prices and household income and models of energy investment, appliance purchase, and usage behavior (including fuel and technology choice for each end use). Demographic data include number of households by 

### type, projected housing starts and demolitions, and appliance holdings.

-- - 

## ' The energy factor is a measurementof energy efficiency derived from the DOE test procedure for each product. Page 3-8 Volume 1: GeneralMethodology It OutpUtsfrom the EngineeringAnalysis 

### For each combination of design options considered in the analysis, the models and**d~ta** 

### provide:

• energy efficiency (expressed as the DOE energy factor' and/or unit energy consumption);

• increased material, labor, and investment costs by product class for medium and large manu.facturers;

• aI}.nual energy**~onsumption** 

### per unit (based on DOE test procedures or field-based estimators);

• the relationship between cost and energy use by product class; and'

• other information on product characteristics such as appliance lifetimes, installation costs, and maintenance costs.
Validation of the Results 

### Experimental data on efficiencies are available for some ofthe design options studied. For the 

### others, engineering calculations based on phySical principles are performed.


### 3.3.2 LBNL Residential Energy Model (LBNL-REM) Purpose 

### The LBNL-REM models the appliance purchase choices made in,households, as well as.these households' subsequent usage behavior and energy consumption. See Appendix B (herein) for details about the model.

. Engineering, economic, and demographic data are used in LBNL-REM. The engineering data for appliances are described in Section 3.3.1. Additional data include age distribution ofexisting appliance stock and retirement functions. Economic data jnclude projected energy prices and household income and models ()f energy investment, appliance purchase, and usage behavior (including fuel and technology choice for each end use). Demographic data include number of households by type, 

### projected housing starts and demolitions, and appliance holdings.

I The energy factor is a measurement ofenergy efficiency derived from the DOE test procedure for each product. Page 3-8 Volume 1: General Methodology

### HistoricalDevelopment Early energy-demand modeling focused on engineering estimates or on the relationship between energy consumption and economic growth. 

### In the 1970s. 

### Oak Ridge National Laboratory 

### (ORNL) developed the first model to integrate these two important aspects of energy demand, the Engineering-EconomicModel ofResidential Energy Use (1). The 

### OWLModel was 

### brought to 

### LBNL in 1979 and adapted to the analysis of federal appliance:alternativeefficiency levels. Further extensivechanges were made at 

## LBNL fiom 1979 to the present, resulting in the 

### LBNLREM (2). Many of these changes have already been documented (3). More recent changes for this rulemaking are described in Appendix 

### B. Structure!

### of

### theModel * 

### The: 

### LBNLREM segments annual energy consumption into house types, end uses, and fuel 

# - types. 

## he house types include single-family, multifamily, and mobile homes. Calculations are performed separately forexisting and new housing construction each year during the forecastperiod, 1980-2030.2 The end uses are space-heating (including room and central), air-conditioning,.water heating, nefrigeration, freezing, cooking,dish-washing, clothes-washing, clothes-drying, televisions, lighting, and. miscellaneous. Up to four fuels 

### are considered, 

### as appropriate to each end use: electricity, natural gas (utility gas), 

### heating'oil, and liquid petr6leum gas 

### (LPG). The national version of the model, which treats the country 

### as a singleregion, is used in .theanalysis. The. m.odel projects five types of activities:. technology/fuel choice, building shell thermal integrity choice, appliance 'efficiency choice, usage behavior, and turnover of buildings and appliances. The interrelationshipof the five types of activitiesis shown in ~igure 3.2.


### . The. initial number 

### of occupied households, by type, is taken from the 

### 1980 Census of Populati~n and

### Housing (4).

### Historical housing starts (198 1-1991)are from Census data; housing starts andl stocks (1992-2010) 

### are from

### Annual 

### Energy Outlook 1995 (5). The historical housing stocks (1981-1991) are obtained by interpolatingbetween the 1980 census figures and the 1992 figures from

### Annual Energy Outlook 1995. 

### Housing stocks and 

### starts after 2010 

### are 

### projected extrapolations. The method is fully described in Appendix 

### B. +.



## 'The model forecasts beginning in 1981. Historical data from 1981-1992 are used to check the validity of the forecast.

I. Volume 1: General Methddology

. 

### . Page 3-9 , 

# .
Historical Development Early energy-demand modeling focused on engineering estimates or on the relationship between €!Oergyconsumption and economic growth. 

### In the 1970s, Oak Ridge National Laboratory (ORNL) developed the first model to integrate**thes~** two important aspects ofenergy demand, the E!lgineerilng-Economic Model ofResidenti3J Energy Use (1).

, 

## . The: ORNL Model was brought to 

### LBNL in 1979 and adapted to the analysis of federal appliance: alternative efficiency levels. Further extensive changes were made at 

### LBNL from 1979 to the present, resulting in the 

### LBNL-REM (2). Many of these changes have already been documented (3). More recent changes for this rulemaking are described in Appendix B.**Structurf~** 

### of theModel The: LBNL-REM segments annual energy consumption into house types, end uses, and fuel

. types. The house types include single-family, multifamily, and mobile homes. Calculations are performed separately for. existing and new housing construction each year during the forecast period, 1980-2030. 2 The end uses are space-heating (including room and central), air-conditioning, water heating, I"efrigeration, freezing, cooking, dish-washing, clothes-washing, clothes-drying, televisions, lighting, and miscellaneous. Up to four fuels are considered, as appropriate to each end use: electricity, natural gas (utility gas), heating'oil, and liquid petroleum gas (LPG). The national version ofthe model, which treats the country as a single region. is used in the analysis. The: m,odel projects five types of activities: technology/fuel choice, building shell thermal integrity choice, appliance efficiency choice, usage behavior, and turnover of buildings and appliances. The interrelationship ofthe five types of activities is shown in Figure 3.2.

. The initial number of occupied households, by type, is taken from the 1980 Census of Population andHousing (4). Historical housing starts (1981-1991) are from Census data; housing starts and! stocks (1992-2010) are from Annual Energy Outlook 

### 1995 (5). The historical housing stocks (1981-1991) are obtained by interpolating .,etween the 1980 census figures and the 1992 figures from Annual ,Energy Outlook 1995. Housing stocks and starts after 2010 are projected extrapolations. The method is fully described in Appendix B. •

2 The model forecasts beginning in 1981. Historical data from 1981-1992 are used to check the validity 

## of the forecast. '

, " Volume 1: General Methddology Page 3-9

## I ,AND BOILDING USAGE STOCKS BEHAVIOR 4 DATA:

-- -- 

### OUTPUT: ANNUAL ENERGY CONSUMPTION I By Fuel



# - By Ballding Type ECONOMICS

EXPENDITURES 

# - By Emd Uae 

# - By End Uae 

# - By Fuel -By Fuel -B BnUdh T l -By Building Type 

### -

Figure 

### 3.2 Activities in the 

### LBNL-REM BUILDING EFFICIENCY CHOICE
! FUEL CHOICE 

## v 

# 1 EQUIPMENT



### Efiiency 

### ChoiceAlgorithm Historiciil efficiency data are available for selected years through 1992 for room air conditioners andkitchen ranges and ovens (depending on availabilityof data). 

### After 1992,efficiency improvements are projected 

### as a function of designs available (technological change) and of electricity, oil, or gas prices. 

### If energy increase, the lifecycle cost of more efficient designs will increase more slowlythan that of less eficient designs,

### making the more efficient designs more attractive. When the lifecycle cost of a more efficient design falls below the life-cycle cost of the current average design, then the more efficient design is projected to 

### be purchased. However, if energy prices decline, the model projects no further efficiencychange.

Page 3-10 VolumeI: GeneralMethodology ' . DATA:



# ,-IDEMOGRAPHiCSI ENGINEERING

# lGORITHMS~ . ·1

..

! I EQUIPMENT 

## I 

# I EFFICIENCY 

## I. CHOICE 

# I FUELCHOIC ECONOMICS

BUILDING EFFICIENCY CHOICE



## l i

________________

# ---i~ EQUIPMENT ND BUILDIN



### OUTPUT: ANNUAL ENERGY CONSUMPTION 

## 1- By Ead Vie

- By Fael

- By BaUdia, Type USAGE. BEHAVIOR

FUEL EXPENDITURES

- By Ead VIC • By Fael

- By B.Udialf TYDC
Figure 3.2 Activities in the LBNL-REM



### Efficiency ChoiceAlgorithm STOCKS

CAPITAL COSTS

- By Ead Vie -By**Fa~1**

- B,. BaUdiag Type

Historical efficiency data are available for· ·selected years through 1992 for room air conditioners and kitchen ranges and ovens (depending on availability ofdata). After 1992, efficiency improvements are projected as a function of designs available (technological change) and of electricity, oil, or gas prices. Ifenergy prices increase, the life-cycle cost ofmore efficient designs will increase more slowlythan that ofless efficient designs, making the more efficient designs more attractive. When the life-cycle cost ofa more efficient design falls below the life-cycle cost ofthe current averagedesign, then the more efficient design is projected to be purchased. However, if energy prices decline, the model projects no· further efficiency**chang~.**

Page 3-10 Volume 1: GeneralMethodology

### ThermalZniegri@ 

### The projection of investment in thermal..integritymeasures in new houses is based on a'

. 

### lifecycle: costcalculation analogous to thatdone forequipmentefficien~ies.~ Engineeringestimates of the inc:rementalcosts of thermal integrity measunsare used together with current fuel prices and a market discount rate for each house 

### type and heating fuel 

### type. Secondaryimpacts of appliance efficiency changes onenergy consumed to heat or cool houses are not estimated in the current analysis.

. , 

### ModelingAlte~tive EfiiencyLevels 

### The

### LBNLREMprojects the average efficiencyof new products,

### purchased each 

### year, taking account of existing federal regulations. A 

### distribution of unit energy consumptions 

### (UEC) is construct;ed amund theprojected average unit energyconsumption for each class, based 

### on relative efficiency distributions previously observed 

### in the'marketplace; -Federal energy efficiency levels would eliminateat least

### part of the distribution. A 

### new distribution

### is constructed

### in which ail 

### units below the alternativeefficiency levels 

### are increased in efficiencyto meet the new efficiency levels.. The new shipment-weighted average efficiency then characterizes the efficiencyof new units in that year. Thie same process is applied to all years after implementation of the efficiency .levels. The model is then'run again for the efficiency level case, with the adjusted average efficiencies, to calculate any changes in market .shares, usage behavior, or investment in building shell thermal improvementsthat may occur 

### as aresult of implementing new efficiency levels, 

### and to calculatethe net energy savings. Turnover

### of Appliance 

### Stocks The initial age distribution of appliances in stock is characterized from industry data about historical.annual shipments and 

### national surveysof appliance holdings. The fraction of each product that retin:s each year is based 

### on the number ofyears sincepurchase foreach age

### coho^-t.4 Each age cohort is associated with an average 

### efficiency; when older appliances retire, they 

### are ideptified 

### as less efficient. I 

### The number ofpotential purchasers of 

### an appliancein new homes is 'equal to the number of nkw 

### homes constructed each year. The number ofpotential in existing houses is equal 

### to the numbex of retiring

### appliances, plus a small fractionof those households that did not'previo~sl~ own the !product.

-. The .equipmentefficiency and thermal integrity decisions are not solved simultaneously, but recursively. The previous year's thermal integrity is assumed in projecting this year's quipment'efficiency; then this year's equipment efficiency is used to calculate this year's thennal integrity. 

## 'For example, a cohort consists ofall '8-year-old gas-fired water-heaters. Up to 30 coh.0rt.s. one for each year of purchase, iuetracked. VolumeI: GenemlMethodologV

, ' Page3-11
, 

### Thermal Integrity The! projection of investment in thennalintegrity measures in new houses is based on a; life-cyc1e: costcalculation analogous to that done for equipment efficiencies. 3 Engineering estimates

, . ofthe inc:remental costs ofthennal integrity measures are used together with current fuel prices and a market discount rate for each house type and heating fuel 

### type. ,Secondary impacts ofappliance efficiency changes on energy consumed to heat or cool houses are not estimated in the current
. analysis.

. 

### ModelingAltemotive Efficiency Levels The! LBNL-REM projects the average efficiency ofnew products, purchased each year, taking account of existing federal regulations.' A distribution of unit energy consumptions (UEC) is constructed around the projected average unit energy Consumption for each class, based on relative efficiency diistributionspreviously observed in the'marketplace:**~Federal** energy efficiency levels would eliminate at least

# part ofthe distribution. A new distribution is constructed in which all units below thl;: alternative efficiency levels are increased in efficiency to meet the new efficiency levels

.. The new shipment-weighted average efficiency then characterizes the efficiency ofnew units in**th~t** year. Tbe same process is applied to all years after implementation of the efficiency levels. The model is then run again for the ·efficiency level case, with the adjusted average efficiencies, to calculate any changes in market.shares, usage behavior, or investment in building shell thennal improvements that may occur as a result ofimplementing new efficiency levels, and to calculate the net energy savings. 

### Turnove.r of Appliance Stocks The;: initial age distribution of appliances 'in stock is characterized from industry data about historical. annual shipments and national surveys ofappliance holdings. The fraction ofeach product that**retin~s** eachyear is based on the number of

### years since purchase for each age cohort.4 Each age cohort is associated with an· average efficiency; when older appliances retire, they are ideQtified as less efficient.· '**Thc~** number of potential purchasers of an appliance in new homes is equal to the number of new hom.es constructed each year. The number ofpotential purchasers in existing houses is equal to the number ofretiring appliances, plus a small fraction ofthose households'that did not previously own the product.

3 The .equipment efficiency and thennal integrity decisions are not solved simultaneously, but recursively. The previous year's thermal integrity is assumed in projecting this year's equipment' efficiency; then this year's equipment efficiency is used to calculate this year's thennaI integrity.

4 For example, a cohort consists ofall 'S-year-old gas-fired water-heaters. 

### Up to 30 coh9rts, one for each year of purchase, nre tracked. Volume 1: GeneralMethodology Page 3-11

### Calculationof Market Shares Potential buyers may make no purchase or may buy any competing technology within an end use. For each product, the decision to purchase ornot is modeled, and the fraction of the total that choose each class, e.g., gas-fired, oil-fired, 'or gas instantaneous water heaters, is specified exogenously. Long-term market share elasticities have been assumed with respect to equipment price, operating expense, and income. Alternative efficiency levels are expectedto lower operating expenses and increase equipment prices. The percentage changes in these quantities are used, together with theelasticities,to determine changesin market shareresulting from imposed efficiency levels. Higher equipment prices will decrease market shares, while lower operating expense will increase market shares. The net result depends on the efficiency level selected and associated ' 

### equipment price and operating expense. Usage

### Behavior For someproducts, changingthe operating expense results in changes in usage behavior. For room 

### air conditioners and ranges and ovens, these elasticities are assumed to be 

### non-zero. Energy ConsumptionCakulations - ' 

### The energy consumption per appliance for each end use and fuel by house 

### type and vintage (existing or new) is the UEC. The corresponding energy consumption for all households is the consumption per appliance times the number of households of that type and vintage, times the fraction of households that owns that appliance. Aggregate energy consumption is obtained from sumniations over intermediateresults. For example,national electricity consumptionfor ranges and ovens in a particular year is the sum, over house types, classes, and vintages, of the electricity consumption of all ranges and ovens. National residential electricity consumption in that year is the sum of electricity consumption over all end uses. Assumptions in the

### Analysis The ConsumerAnalysis assumes that decisions on the purchase and use of appliances depend on operating expenses, household income, and appliance prices. Manufacturers are projected to respond to the demand for more efficient products by incorporating technologically feasible and costeffective design options in new units. Forecasts of population growth, housing starts, personal income, and energyprices from published sources are 

### utilized. Justifications for the most important assumptions in the ConsumerAnalysis are listed below.



# . Page 3-12 Volume 1: GeneralMethodology 

### Calculation of Market Shares Potentialbuyers may make no purchase or may buy any competing technology within an end use. For each product. the decision to purchase or not is modeled, and the fraction ofthe total that choose each class, e.g., gas-fired, oil-frred,'or gas instantaneous water heaters, is specified

. exogenously. Long-term market share elasticities have been assumed with respect to equipment price, operating expense, and income. Alternative efficiency levels are expected to lower operating expenses and increase'equipment prices. The percentage changes in**~ese** quantities are used, together**~th** the elasticities, to determine changes in market share resulting from imposed efficiency

. levels.' Higher'equipment prices will decrease market shares, while lower operating expense will increase market shares

.. The. net result depends on the efficiency level selected and associated

- equipment price and operating expense.'

. . . 

### Usage Behavior For some products, changing the operating expense results in changes in usage behavior. For room air conditioners and ranges and ovens, these elasticities are assumed to be non-zero. 

### Energy Consumption Calculations

.The energy consumption per appliance for each end use and fuel by house type and vintage (existing or new) is the UEC. The corresponding energy consumption for all households is the consumption per appliance times the number of households of that type and vintage, times the fraction ofhouseholds that owns that appliance.

. Aggregate energy consumption is obtained from summations over intermediate results. For example, national electricity consumption for ranges and ovens in a particular year is the sum, over house types, classes, and vintages, ofthe electricity consumption ofall ranges and ovens. National residential electricity consumption in that year is the sum ofelectricity consumption over all end uses. 

### Assumptions in theAnalysis The Consumer Analysis assumes that decisions on the purchase and use ofappliances depend on operating expenses, household income, and appliance prices. Manufacturers are projected to respond to the demand for more efficient products by incorporating technologically feasible and cost-effective design options**~n** new units. Forecasts ofpopulation growth, housing starts, personal income, and energy prices from published sources are utilized: Justifications for the most important assumptions in the Consumer Analysis are listed below.

.

. Page 3-12 Volume 1: General Methodology

### Assumption 1 

### Justification 1
'

. 

### Occupitd households will increase from 93 TheAnnual 

### Energy Outlook 1995

### projects annual

, 

### million in 11990to 131 million in 2030.

.growth

### of 1.0%. 0.6%. and 0.3% for singlefamily, multifamily, andmobile homes,respectively,from 1993 to 2010. Housing starts 

### are projected to be 

### near constant for single family and mobile homes, but to increase from the current low levels for multifamily.

. 

### Real disposable personal income is projected to grow from $4.0 billion in 1990 

# - to $8.8 billion in 2030 (in 1990 dollars). This corresponds to disposable income per household growing from $43,100 

### in 1990 to $66,400in 2030.

, 

### Residential electricity prices 

### are .projectedto increase from 8.40 cents/kWh in 1992 to 

### 8.76 cerlts/kWh 2010. Residential natural gas prirzes. are projected to increase from $5.871Wtu in 1992to $6.9UMMBtu in 20 10. Residential distillate prices are projected to increasefrom $6.8W MMBtu in 

### 1992,to. $8.71/MMBtu in 2010. Appliance purchase decisions

### are based on operating expenses, as 

### well as 

### on equipment price.

. . 

### Manufacturers are projected to respond to consumer demand for energy efficiency. Engineeringdesignsthat 

### are technologically feasible: and attractive to purchasers are assumed to be available.

. , 

### The lifetimes of appliances 

### are projected to remain the same as empirically observed in the past, independent of energy efficiency. a 

### TheAnnual 

### Energy Outlook 1995

### projects annual

.

### housing starts of 1.03 to 1.06 million for single . 

### family, from 1992-2010,of 0.17 to 0.50 million for 'multifamily, and 0.21' to 0.23 million for. mobile homes. TheAnnual 

### Energy Outlook 1995

### pr~jects~~rowth in real disposable personal income for the nation as 2.0% per 

### year from 1993 to 2010.

TheAnnual 

### Energy Outlook 1995

### projects annual growth of 0.3% for residential electriciiy prices from 1993to 2010.

Research on equipment sales foi competipg alternatives and on historical efficiency choices ,indicatesthat operating expenses ike 

### significant variables. Industry is competitive and historically has responded to changes 

### in consumer demand.

~etirement functions 

### are based on reconciliation of historical appliance stocks and 

### shipments. To

. 

### date, .efficiency improve-ments have occurred without any apparent effect on reliability.
VolumeI: General Metlzodobgy Page 3-13 

### Assumption

f •**Occupi(~** households will increase from 93 , \ million in 1990 to 131 million in 2030.

• Housing starts are projected to be near constant for single family and mobile homes, but to increase from the current low levels for multifamily. '

, • Real disposable' personal income is projecte:d to grow from $4.0 billion in 1990 to $8.8 billion in 2030 (in 1990 dollars). This corresponds to disposable income per household growing from $43,100 in 1990 to $66,400in 2030; ,

• Residential electricity prices are projected to**increasc~** from 8.40 centslkWh in 1992 to 8.76 celltslkWh in 2010. Residential natural gas prices 'are projected to increase ft:om 

### $5.871 MMBtu in 1992 to $6.921MMBtu in 2010. Residential distillate prices are projectc::d to increase from 

### $6.861 MMBtu in 199210 $8.71IMMBtu in 2010.

• Appliance purchase decisions' are based on I operating expenses, as well as on equipment price.

• " Manufacturers are projected to respond to consumer demand for energy efficiency.**Enginet~ring** designs that are technologically feasible: and attractive to purchasers are assumed to be available. ,
• The life:times of appliances are projected to remain the same as empirically observed in the past, independent ofenergy efficiency.

Volume 1: GeneralMethodology 

### Justification TheAnnual

### Energy Outlook 

### 1995 projects annual


## ,growth of 1.0%, 0.6%, and 0.3% for single family, multifamily, and mobile homes, respectively, from 1993 to 2010. TheAnnual

### Energy Outlook 

### 1995 projects annual ,housing starts of 1.03 to 1.06 million for single family, from 1992-2010, of 0.17 to 0.50 million for 'multifamily, and 0.21 to 0.23 million for' mobile homes. ' TheAnnual

### Energy Outlook 

### 1995 projects'growth

, in real, disposable personal, income for' the nation

as 2.0% per year from 1993 to 2010.

TheAnnual

### Energy Outlook 

### 1995 projects annual growth of 0.3% for residential electricil)' prices from 1993 to 2010.

Researcl'l on equipment sales for**competi~g** alternatives and on historical efficiency choices ,jndicates that operating expenses 

### are significant variables. Industry is competitive and historically has responded to changes in consumer demand.

Retirement functions are based on reconciliation ofhistorical appliance stocks and shipments. To date, ,efficiency improve-ments have occurred without any apparent effect on reliability.
Page 3-13

### Data Sources The 

### LBNLREM takes the range ofpossible energyefficienciesof new equipment from the Engineering Analysis. Thepurchase price ofthese products is derived fromthe factory cost supplied by the Engineering Analysis and adjustedfor manufacturer and dealer markups in the Manufacturer Analysis. Historical housing stocks and starts

### are from

## U.S. Department of Commerce, Bureau of the Census. 

### Projected energyprices, household incomes, and housing stocksand 

### starts are

### from

### U.S. Department of Energy, Annual Energy 

### Outlook 1995. 

### Model Outputs -

. 

### The principal outputsfrom the.LBNL-REMfor each year 

### are: National energy consumptionby end use and fuel; Per-unit equipment price and operating expense by product;

. 

### Total residential energy consumption by fuel; Projected annual shipments of residential appliances; and Differences in these quantitiesbetween a base case and kach efficiency level case.

I 

### These outputs are provided annually (or for selected years) and cumulatively for the period 

### 1999-2030.Energy savings from alternativeenergy efficiencylevels are provided annually to the 

### 'end of the period. Net present value (NPV) of alternative efficiency levels is evaluated for each regulated product. Cost-Benefit Analysis

1 

### The costs and benefits of the alternative efficiency levels from a national perspective are' quantifiedby calculating a net present value. The

### NPV is the sum of discounted savingsin operating expenses minus the sum of discounted increases in equipmentprices. DOE 

### has determined that 7% real 

### is the appropriate discountrate for calculating societal NPV.. The-NPV of alternative efficiency levels is also calculated

### atconsumer discount rates of4%

### and'10% as'sensitivityanalyses. 

# if feint assumptions about technology choice 

### are invoked when calculating energy savings of

4 

### efficiency levels on the one hand, and when calculating the nerpresent benefit of efficiency levels on the other. Energy savings are the net of any adjustments households makk in changing their technology or fuel choice or from using appliancesdifferently. For example, if, after implementing new efficiency levels, a regulated product captures larger market shares (without displacing consumption of another fuel), then the net energy savings will be 

### diminished. The energy savings Page 3-14

. '\ VolumeI: GeneralMethodology

-. , 

## !Jato Sources The LBNL-REM takes the range ofpossible energy efficiencies ofnew equipment from the

. Engineering Analysis. The purchase price ofthese products is derived from the factory cost supplied by the Engineering Analysis and adjusted for manufacturer and dealer markups in the Manufacturer Analysis. Historical housing stocks and starts 

### are from 

### U.S. Department of Commerce, Bureau of theCensus. Projected energy prices, household incomes, and housing stocks and starts are from 

### U.S. Department ofEnergy,Annual Energy Outlook 1995. Model 

### Outputs The pnncipal outputs from.theLBNL-REM for each year 

### are: -National energy consumption by end use and fuel;

- Per-unit equipment price and operating expense byproduct;

-. Total reSIdential energy consumption by fuel;

- Projected annual shipments ofresidential appliances; and

- Differences in these quantities between a base case and each efficiency level case. These outputs 

### are provided annually (or for selected years) and cumulatively for the period' 1999-2030. Energy savings from alternative energy efficiency levels 

### are provided annually to the

.end of the period. Net present value (NPV) of alternative efficiency levels is**evalu~ted** for each regulated product.

. Cost-BenefitAnalysis The costs and·benefits of the alternative efficiency levels from a national perspective are· quantified by calculating a net preseIit value. The NPV is the sum ofdiscounted savings in operating expenses minus the sum ofdiscounted increases in equipment prices. DOE has determined that7% real is the appropriate discount rate for calculating societal NPV. TheNPV ofalternative efficiency levels is also calculated at consumerdiscount rates of4% and iO% as'sensitivity analyses

.

. Different assumptions about technology choice are invoked when calculating energy savings of

. efficiency levels on the· one hand, and when calculating the netpresent benefit ofefficiency levels on the other. Energy savings are the net of any adjustments households make in changing their technology or fuel choice ocfrom using appliances differently. Forexample, if, after implementing new efficiency leveis, a regulated product captures larger market shares (without displacing consumption of another fuel), then the net energy savings will be diminished. The energy savings Page 3-14 Volume 1: GeneralMethodology

### are ~alc~liWi as the net ksult.ofimplementingnew efficiency levels,accounting for such secondary

.
. 

### effects as shifts in market share. ' , 

### NPV 

### excludes these secondary effects. Base case purchase behavior, withoutmarket share changes, 

### Is assumed in calculating the NPV, becau~ any market share shift reflects the consumer's judgment that.

### change is worth more than the direct energy savings associated with keeping market share constant. '

### NPV is calculated from per unit changes in equipment and operating costs,. multiplied 

### by efficiency level case shipments: 

### If the 

### NPV was calculated without normalizing to shipments, the results would be 

### erroneous: if implementingnew efficiencylevels caused decreased

. .

, .

### .purchase *of a 

### product, this would appear 

### as an economic benefit, 'namely less money spent'on purchasing and use ofthe appliance? and if implknientingnew efficiency levels caused 

### an increase in purchases, this would be 

### counted incorrectly 

### as a cost, when it actually reflects consumers' preference for the post-efficiency level product.


### 33.3 

### LBNL Manufacturer Analysis Model 

### (LBNEMAM)

. . ,-

.I 

### Conceptual Approach 

# . . 

### The hlanufacturerImpact Analysis estimatesboth the short- and long-run impacts of alternative efficiency levels on profitability (returnon equity) and 

### many other variables for each industry under consideration. All 

### computations used in this analysis are carried out by the Manufacturer Analysis Model 

### (LE%NLcMAM). The

### LBNLMAMis a spreadsheetcomposedofthirteen modules. 

### It consists of the earlier LBL-Manufacturer Impact Model 

### (LBL-MIM) along with Version 

### 1.2 of the 'Govemt:nt Regulatory Impact Model (GRIM) developed by the Arthur 

### D. Little Consulting Company under contract to the Association of Home Appliance Manufacturers (AHAM), the Gas ApplianceMmufacturers Association 

### (GAMA), andthe Airconditioningand RefrigerationInstitute 

### (ARI). Figure 3.3 displays inputs to 

### LBNLMAM and their interactions. 

### In addition to computing estimated values for the above-mentioned descriptive variables, the 

### LBNGMAM also computes standard errors foreachby explicitly evaluating the standardem 

### in the estimates of inputvariables and then u&ng Monte Carlo simulations~ 

### Measures of Impact Three 

### types of long-run impacts are analyzed: 1) 

### profitability, 2) 

### growth, and 

### 3) 

### competitiveness. To do this, the following six measures of impact 

### are tracked for the industry: 

### 1) shipments,2)

### price, 3)revenue, 4) 

### net income, 5)'reb on equity 

## (ROE), and 

### 6)industry net present value 

### (NPV).

Without normalization,the greatest ekonomic benefit would be obtaiied by efficiency levels that resulted in no futurepurchases ofthe product. Then, no money would be spent on purchasing the product, or on operating expenses, and the value of the savings would equal the amount of money that would have been spent without implementing the efficiency levels. Clearly, this would be 

### a misrepresentationof the NPV of alternative efficiency levels. VolumeI: Genc~ral Methodology 

## ~&d 3-15 are calculated as the net result- ofimplementing new efficiency levels, accounting for such secondary effects as shifts in market share. " . , NPV excludes these secondary effects. Base case purchase behavior, without market share changes, is assumed in calculating the NPV,**becaus~** any market share shift reflects the consumer's judgment that, change is worth more than the direct energy savings associated with keeping market share constant.
.NPV is calculated from per unit changes in equipment and**ope~ting** costs,' multiplied by efficiency level**cas~** shipments: Ifthe NPV was calculated without**nonn~izing** to shipments, the results would be erroneous: ifimplementing new efficiency levels caused decreased

.
, ,.purchase -of a product, this would appear as an economic benefit, 'namely less money spent' on

, purchasing and use ofthe appliance,S and ifimplementing new efficiency levels caused an increase

in purchases, this would be counted incorrectly as a cost, when it actually reflects consumers' preferencc! for the post-efficiency level product.


### 3.3.3 LBNL Manufacturer Analysis Model (LBNL-MAM)

. . /- 

### ConceptualA.pproach The Manufacturer Impact Analysis estimates both the short- and long-run impacts ofalternative' efficiency levels on profitability (return on equity) aJ;ld many other variables for each industry under consideration. All computations used in this analysis are carried out by the Manufacturer Analysis Model (USNlrMAM). TheLBNL-MAM is a spreadsheet composed ofthirteen modules. 

### Itconsists of the earlier LBL-Manufacturer Impact Model (LBL-MIM) along with Version 1.2 of the

.Governmtmt Regulatory Impact Model (GRIM) developed by the Arthur 

### D. Little Consulting Company under contract to the Association ofHome Appliance Manufacturers (AHAM), the Gas Appliance Manufacturers Association (GAMA), and the Air-conditioning and, Refrigeration Institute**(ARl)~** Figure 3.3 displays inputs to LBNL-MAM and their interactions. 

### In addition to computing

, estimated valuesfor the above-mentioned deScriptive variables, the LBNL-MAM also computes**standarde:~rs** for each by,explicitly evaluating the standard errors in the estimates ofinput,variables

and then VIsing Monte Carlo simulations: 

### Measures of Impact Three types of long-run impacts are analyzed: 1) profitability, 2) growth, and 3) competitiveness. To do this, the following six measures of impact are tracked for the industry: 1) shipments, 2) price, 3) revenue, 4) net income, S)'retum on equity (ROE), and 6) industry net present value (NPV).

S Without normalization, the greatest economic benefit would be obtained by efficiency levels that resulted in no future purchases ofthe prod\.lCl Then. no money would be spent on purchasing the product, or on operating expenses. and the value ofthe savings would equal the amount ofmoney that would have been spent without implementing the efficiency levels. Clearly, this would be a misrepresentation ofthe NPV ofaltemative efficiency levels. ' Volume J: 4'Jenera/MethodologyVariable

Markup

GRIM NPV

Nat Intoma 

# I I

Figure 

### 33 Conceptual Approach to the Manufacturer Analysis Model ROE is the primary measure ofprofitability, although gross 

### margin, return on assets, and return on sales 

### are also reported. Changes in assets and revenue 

### are the measures of growth (positive or negative). Because both of the industries anal* 

### are in 

### an historical process of consolidation,

### any decreasein ROE will tend to hasten thisprocess;thus, ROE also provides 

### an indication of the impact of efficiencylevels on competitiveness. Industry

## NPV is theoretically identical to ROE but has 

### been included to provide additional insight into the impact of implementing new efficiency levels. 

### A short-run calculation is also,made. 

### If the long-run calculation indicates a decrease in sales, the predicted price is reduced by an amount determinedby the industry's observed behavior during historical downturns in sales. The adjusted priceis used to calculate the other measures of impact. 

### Description of

### the LBNGW Although 

### LBNL-MAM examines many aspects of an industry, its most important role is to compute the effect on profits and indust.& net present value of 

### a cost increase caused by implementing alternativeefficiency levels. To accomplishthis, 

### the model first estimates how much Page 3-16 Volume I: GeneralMethodology Operating Co.t' Inputs Long-Ru ' Calculatl ns 

## I 

# I Standards

Capital Cost**DemaM~**____**~~**____**-+~**

Revenue

Net Income ROE GRIM NPV

Industry

NPV . Industry Data

Figure 3.3 Conceptual Approach to the Manufacturer Analysis Model ROE is the primary measure ofprofitability, although gross margin, return on assets, and return on sales are**~so** reported; Changes in assets and revenue are the measures ofgrowth (positive or negative). Because both ofthe industries analyzed are in an historical process ofconsolidation, any decrease in ROE will tend to hasten this process; thus, ROE also provides

# an indication ofthe impact ofefficiency levels on competitiveness. Industry NPV is theoretically identical to ROE but has been included to provide additional insight into the impact ofimplementing new efficiency levels.



### . . A short-run calculation is alsomade. Hthe long-run calculation indicates a decrease in sales, the predicted price is reduced by an amount detennined by the industry's observed behavior during historical downturns in sales. The adjusted priceis used to calculate the other measures ofimpact. 

### Description of the**LBN~MAM** Although LBNL-MAM examines many. aspects ofan 'industry, its most important role is to compute the effect on profits and industry net present value of a cost increase caused by implementing alternative efficiency levels. To accomplish this, the model first estimates how much Page 3-16 Volume 1: General Methodology

### of the cost increase is passed on in the form of higher prices. 

### (An accompanying effect'is the decrease in operating expense due to imposed efficiency levels, which makes the product more desirable to the consumer and, thus, increases demand forthe product.) Then, it estimates how this 

### price increase and the accompanying reduction of operating expense influence demand. Finally, given the: new price and level of demand, it computes a new level of profit. These calculations use estimated long-run costs. The 

### LBNLMAM makes an additional calculation to correct for 

### any short-run1 difficulties the industrymay have in achievingits long-run markup and alsoestimates the impact of alternativeeffi~ienc~levels on industrynet present value. It is imporiant to note from the stmi that the model does not assume perfect'competition,nor does it assume that thelevel of profit will remain unchanged. The results of the model depend entirely on the input values.

. . 

### The basic behavior of the model'can 

### be understood only in light of the' following'standard 

### economi8c consideration. 

### In the long xun, 

### an increaseinfixed costs will not be passed on'via a price increasewrcept when it is sufficientto cause some

### firmsto leave the industry? This result ishived 

### fmm the assumption that 

### firmsact individuallyto 

### aaximizeprofits and from the definition of fixed costs 

### as those that 

### arenot proportional to output. Themathematical derivation is given 

### in Appendix

, 

### C 

### of this volume.

. .

. . 

### A 

### second influenceon profits is the changein purchases resulting fromboth the change in price and the change in operating expense. These tend to offset 

### eakh other because price increases'lower quantity demanded, and increased efficiency increases demand (by loweringoperatinggexpense). The 

### LBNLNWtakes these effectsinto accountthrough the 

### use of two demand elasticities which play an important role in determining the outcome and 

### are therefore examined more closely in the sensitivity analysis. 

### Assumpt~ons in 

### 'the 

### Analysis This section begins with a series of short discussions of different aspects of the modeling procedure that require careful interpretation (such as 

### "long-run variable cost") or simplifying assumptions.. This is followed by 

### a list of more detailed assumptions. The long-run 

### costs, The 

### LBNLMAM requires input of long-run costs. A 

### firm is in long-run equilibrium when it has optimal productive ..capacity. Thus, during a recession (when firms experience excess capacity), firms are not 

### in longirun equilibrium, and the 

### theory of price 

### as a markup over long-run costs may not apply. Long-run fixed costs 

### are those that remain unchanged when a f~rm 

### moves from one long-run equilibriumto another; they do not include capital costs that are propoxtion'al to output. The latter 

### are considered long-run variable (marginal) costs and 

### are marked up 

### andpassed on. ' 

### To the extent that costs are 

### not strictly proportionalto output, but

When firms leave the industry, Ule latter becomes less competitive and the remaining 

### firms can.increasetheir markup over variable costs,thus, indirectly covering their increasedfixed costs. Volume 1: GeneralMethodology Page 3-17 of the C()st increase is passed on in, the form of higher prices. (An accompanying effect'is the' decreaSe in operating expense due to imposed efficiency levels, which makes the product more desirable to the consumerand, thus, increases demand for the product.) Then, it estimates how this price inc:rease and the accompanying reduction ofoperating expense influence demand. Finally, given the: new price and level ofdemand, it computes a new level ofprofit. These calculations use estimated long-run costs. The**LB~MAM** makes an additional calculation to correct for any short-rutll difficulties the industry may have in achieving its long-run markup and also estimates the impact ofalternative efficiency levels on industry net present value.

. It is important to note from the start that the model does not assume perfect'competition, nor does it assume that the'level of profit will remain unchanged. The results of the model depend entirely 4)n the input values. The basic behavior of the mOdel' can be understood only in light of the' following 'standard economic: consideration., 

### In the long run, an increase infixed costs will not bepassed on'via

# aprice increase -except when it is sufficient to cause Some finDsto leave the industry.6 This result is derived from the assumption that firms act individually to 

### maximize profits and from the definition offixed costs as those that are not proportional to output. The mathematical derivation is given in Appendix 

### C ofthis volume. ' , A second influence on profits is the change in**purch~s** resulting from both the change in price and the change in operating expense. These tend to offset eaCh other because price increases 'lower quantity demanped, and increased efficiency increases demand (by lowering operatingexpense). The,

,LBNL-MAM takes these effects into account through the use oftwo demand elasticities which play

an iInport8lllt role in determining the outcome and are therefore examined more closely in the sensitivity analysis. " ' Assumptions in'theAnalysis This sectiori begins with a series of short discussions of different aspects of the modeling , procedure that require careful interpretation (such as "long-run variable cost") or simplifying assumptions., This is followed by a list ofmore detailed assumptions. " The long-run costs. The LBNL-MAM requires input oflong-run costs.' A 

### fmn is in long-run equilibrium when it has optimal productive capacity. Thus, during a recession (when firms experience excess capacity), firms are not 

### in long..:run equilibrium, and the theory of price as a markUp over long-run costs may not apply. Long-run fixed costs are those that remain unchanged when a firm moves from one long-run equilibrium to another; they do not include capital costs that are proportional to output. The latter are considered long-run variable (marginal) costs and are marked up and passed on. ' To the extent that costs are not strictly proportional'to output, but

6

### Whl~n firms leave the industry, the latter

### beco~es less competitive and the remaining firms can 'increase their markup over variable costs, thus, indirectly covering their increased fixed costs. Volume 1:, GeneralMethodology Page 3-17

### decrease on average with quantity produced, there are 

### fixed costs. Also, a cost that does not depend on the scale of production, i.e., an engineering cost, is a long-run fixed cost. Use of "typical" 

### firm. The analysis estimates the impact of the efficiency levels by using "typical" firms. 

### A typical 

### firm is a hypothetical 

### fmthat is describedby industry-average values. Each one is modeled as 

### if it produced only products of the industrybeing analyzed. Thisprocedure isjustified by the observation that 

### red firms 

### generally

### are organizedinto autonomous divisions, each of which produces a different 

### type of appliance and one of which generally corresponds to the industry being modeled. Also, these divisions 

### are generallyrun on a profit-and-loss basis similar to a firm. It is not feasible to analyze interdivisionalinteractions within real firms. Aggregation. Generally,ithas been impossible to obtain data 

### on crosselasticitiesof the various product lines within an 

### industry. Therefore, data

### for tfie 

### various products are 

### aggregated before the markup is computed and applied. However, once the aggregate calculations have been made, individual prices 

### are computed for the various classes of products. How alternative Bciencylevelsare implemented in 

### the 

### model. A 

### change in energy efficiency levels affects the inputs to the model in three 

### distinctways. 

### In general, the implementation of stricter efficiency levels will require additional investment, will raise production costs, and will affect revenue through both price and demand. The most .obvious investment induced by the implementation of new efficiency levels is the purchase of new plants and equipment. This cost 

### is fmtevaluated from engineering data, and then amortized by taking into account the life of the investment, the date at which it is made, tax 

### laws, and the'appropriatecost of funds. 

### An additional, and sometimes larger, investment takes place as the old inventory is replaced with more expensivenew units. The model assumes that the ratio of inventory to revenue remains unchanged, 

### in this way both changes 

### in quantity sold and unit value 

### are taken into account. A third 

### form of investmenttracked by the 

### LBNLMAM is the change in the demand for cash that accompanies a change in revenues. 

# - Financial inputs. Several simplifyingassumptions 

### are used in the process of generating inputs for the 

### LBNL-MAM. First, all firms 

### within a particular industry segment (e.g., large manufacturers of water heaters) 

### are assumed to have the same cost structure. second, it is assumed that financial data collected for the parent company 

### are representative of the specific product division being studied. These data 

### come from publicly available sources such 

### as Value Line, Standard and Poor's, Moody's, and company reports. Impact

### Analysis:An Overview The next section provides a general discussion of the methods of the impact analysis together

. 

### with some cautions about interpreting the results. The significance of long-run impacts and how they are analyzed 

### are discussed below. These impacts are most important simply because they will either persist or 

### be so severe 

### as to cause 

### a restructuring of the industry. The meaning andanalysis 

### of short-run impacts are also discussed. Page3-18


### . Volume I: General Methodology decrease on average with quantitypr04uced, there are fIxed costs: Also, a cost 'that does not depend on the scale ofproduction, i.e., an'engineering cost,is a long-run fIxed**c~st.** Use 

## 0/ "typical"firm. The analysIs estimates the impact of the efficiency levels by using "typical" fIrms. A typical 

### f1I1Il is a hypothetical 

### f1I1Il that is described by industry-average values. ' Each one is modeled as ifit produced only productS.ofthe industrybeing analyzed. This procedure isjustifIedbythe observation that real fInns generally are organized into autonomous divisions, each of which produces a different type of**app~iance** and one.of which 'gener81ly corresponds to the industry being mOdeled. Also, these divisions are generally run on a profIt-and-Ioss basis similar to a fIrm. It is not**~easible** to analyze inter-divisional interactions within real fIrms. Aggregation. Generally, it has been**~possible** to obtain**~ta** on cross-elasticities ofthe various product lines within an industry. Therefore, data for 

### tPe various products are aggregated before the markup is computed·and applied: However, once the aggregate calculations have been made, individual prices are computed for the various claSses ofproducts. How q.ltemative efficiency levels are implemented in the model. A**~hange** in energy efficieri,cy levels affects the inputs to the model in 

### three distinct ways. In general, the implementation ofstricter

, effIciency levels will require additional investment, wili raise production costs, and will affect

, revenue through both price and demand. The most 'obvious investment induced by the implementation of new**efficien~y** levels is the purchase ofnew plants and,equipment. This cost is fIrst evaluated from engineering data, and then' amortized by taking into ae<:ount the life ofthe**inves~entt** the date at which it is made, tax laws, and the'appropriate cost offunds. ,An additional, and sometimes larger, investment takes place as the old inventory is replaced with more expensive new units. The model assumes that the ratio of inventory to revenue remains unchanged; in this way both changes in quantity sold and unit value ' are taken into account A third form ofinvestment tracked by the LBNL-MAM is the change in the demand for cash that accompanies a change in revenues.

, Financial inputs. SevelCll simplifying assumptioris are used in the process ofgenerating inputs

.for the LBNL-MAM. First, all fIrms within a particular industry segment (e.g., large manufacturers ofwater heaters) are assumed to have the same cost 'structure. Second, it is assumed that fInancial data collected for the parent company are representative of the specifIc product division being studied. These data come from publicly available sources such as Value Line, Standard and Poor's, Moody's, and company reports. ImpactAnalysis:An Overview The next section provides a general discussion ofthe methods ofthe impact analysis together , with some cautions about interpreting the results. The signifIcance of long-run. impacts and how they are analyzed are discussed below. These impacts are most important simply because they will either persist or be so severe as to cause a restructuring of the industry. The meaning and Ianalysis of short-run impacts are also discussed. Page 3-18 ' Volume 1: General Methodology

### Additionally, the various types

### of sensitivityanalysis performed on the model's results 

### are discussed. This analysis is crucial for understanding the accuracy 

### and reliability of the model's predictions, and includes Ithe use of alternative scenarios, sensitivitychecks on individual inputs, and Monte Carlo

. 

### simulations involving the random selection of all control inputs.


### 3.3.3.1 

### Long-unImpact Analysis The 

### llea~t of the model is the long-nm impactanalysis. It assumes that enough' time has passed for any changes in demand to have 

### been accommodated by changes in the industry's productive capacity. Thus, 

### in the long-run, capacity, or capital stock, is variable. Because capital is sometimes thought to be the only 

### fucd cost, it might seem

### that fixed costs would 

### be zero in the long run. This is not the case. For the purpose ofprice determination,whenever there 

### arereturns to scale,there are, effectivelly, fixed costs. This means that if the cost 

### of meeting alternative efficiency levels is less than proportional to plant capacity,there are fured costs. Design costs would typically fall into this category. See 

### Appendix ,C

### of this volume for a more detailed discussion of the long run. The fraction of increased costs of production (due to more stringent efficiency levels) that manufact~ilrers 

### are able to pass on is determined by the nature of the cost, i.e., whether it is fixed or variable. the price elasticicy of demand, and the consumer discount rate 

### as seen 

### by the price-setting 

### firm. Once

## the:price increasehas been determined, it and the operating expense decrease (obtained from engineering data) 

### are used with the price elasticity of demand and the discount' rate to determint: the 

### change in sales. These mechanisms,-are presented in'Figure3.4, which displaysthe most basic economic forces analyzed by the 

## LBNLMAM.
,. . 

# . . 

### As slhown in Figure 3.4, the two cost changes do not translate 

### directly into price changes. Instead, tihe reduction in operating costs makes the appliancemore desirable, thereby shifting the demand curve out (e.g., 

### an increase

### inproduction costs changes the 

### marginal cost curve). Together, these determine price and the number ofunits shipped (quantity). Price and quantity, together with production costs;determinenet income (profit),and these, togetherwith changes in capital stock (not shown), determine 

### ROE. The

### ISNLMAM simulatesthe implementation of alternativeefficiency levels 

### as follows. Foi each procluct, a 

### set of engineering designs is specified. ~or'each design, the engin.eering analysis provides a per-unit cost, a one-time capital cost, 

### and an energy usage. The two costs effectively determine 

### the production costs used in Figure 3.4 and the energy usage determines the operating expense. The 

## LBNLMAM calculates a weighted averageof the two costs and the energy usage for the relevant engineering designs. The weights used comspond to the relative numbers of shipments for each engineering design as determined by 

## LBNLREM. Another and more subtle point is that markup is determined by price elasticity, and because life-cycle-cost elasticity is constant,price elasticity changeswith achange in eitherprice or operating ' 

### expense. This change in markup can have an important impact on 

### ROE.

VolumeI: General Methodology Page 3-19 , \, Additionnlly. the vaPous types ofsensitivity analysis perfonned on the model's results are discussed. This analysis is crucial for understanding the accuracy and reliability ofthe model's predictions. arid includeslthe use

# of alternative scenarios, sensitivity checks on individual inputs, and Monte Carlo simulations involving the random selection of all control inputs.

3.3.3.1 Long-Run Impact Analysis The heart ofthe model is the long-run inipactanalySis. 

### It assUmes that enough time has pa$sed for any changes in demand to have been accommodated by changes in the industry's productive capacity. Thus, in the long-run, capacity, orcapital stock, is variable. Because capital is sometimes thought tobe the only fIxed cost, it might seem 

### that fixed costs would be zero in the long run. This is not the case. Forthe purpose ofprice**detennination~** whenever there are returns to scale, there are, effectivelly, fixed costs. This means that ifthe cost ofmeeting alternative efficiency levels is less than proportional to plant capacity, there are fixed costs. Design costs would typically fall into this category. See**Appe~dix** C ofthis volume for a more detailed discussion ofthe long run. The fraction of increased costs of production (due to more stringent efficiency levels) that manufactl;Jrers are able to pass on is determined by the nature o.fthe cost, i.e., whether it is fixed or variable, the priCe elasticity ofdemand, and the consumer discount rate as seen by the price-setting 

### f1Il1l. Onc::e**th~** price increase has been detennined" it and the operating expense decrease (obtained ' from engineering data) are used with the price elasticity of demand and the discount' rate to**determint~** the change in sales. These mechanisms are presented in Figure 3.4, which displays the most basic economic forces analyzed by the LBNlrMAM. ,
, , As shown in Figure 3.4,. the two cost changes do not translate directly into price cl)anges. Instead, the reduction in operating costs makes the appliance more desirable, thereby shifting the demand curve out (e.g., an increase in production costs changes the marginal cost curve'.**Tog~ther,** these dete:rmine price and the number ofunits shipped (quantity). Price**~d** quantity, together with production**costs~** determine net income (profit), and these, together with changes in capital stock (not shown), determine ROE.

. The LBNL-MAM simulates the implementation ofalternative efficiency levels as follows. For each product, a set ofengineering designs is specified. For each design, the engineering**~alysis** provides a per-unit cost, a one-time capital cost, and an energy usage. The two costs effectively determint: Ute production costs used in Figure 3.4 and the energy psage determines the operating expense. The LBNL-MAM calculates a weighted average ofthe two costs and the energy usage for the relevant engineering designs. The weights used correspond to the relative numbers ofshipments for each e:ngineering design as determined by LBNlrREM. Another and more subtle point is that markup is determined by price elasticity, and because life-cycle·-cost elasticity is constant, price elasticity changes with a change in either price oroperating expense. This change in markup can have an important impact on ROE.

Volume 1: GeneralMethodology Page 3-19

### Figure 3.4 How Alternative EficiencyLevels Cause a Change in ROE and 

### NPV Operating Cost



### The markup discussed here is a markup over economic variable costs, which is a broader category than the typical business definitionof variable costs. The manufacturermarkup listed in the model and this volume is used 

### only to calculate the baseline model price (calculated by multiplying the baseline manufacturer cost by the manufacturer markup). This is called the calibration case in 

### LBNLMAM. The manufacturer markup listed is not used in calculating the manufacturer prices for the base case or the alternative efficiency levels. 

### In these cases, the manufacturer price is calculated fromthe

### economicmarkup7which, as described above,is dependent on elasticities, discount rates, and price and operating expense changes. Thus, prices at different efficiency levels do 

### not exactly reflect the manufacturercost times the manufacturermarkup. Demand 

# * Curve

A



### Typical results of 

### an analysis of an alterantive efficiency level at aparticularlevel might be the following: wholesale price increases lo%, sales decline 2%, 

### revenue increases 8%,net income 

### increases 12%, 

### and ROE increases 

### 0.5%. Associated with each-ofthese numbers is a standarderror. The errors might be 

### as follows: 

### 4% on price, 1% on sales,4%

### on revenue, 10%on net income, and 1.5% on ROE. To interpret the prediction for ROE, one could then conclude (by checking a table of the normal distribution) that there is about a 

### 69% chance that ROE will increase, and a 31% chance that it will decrease. One could also conclude that the chance of profits declining by more than 2% 

### is only 2.3%. ROE +Quantity



# 'For a thorough description ofhowmarkups irederived and used in LB'NL-MAM,see Appendix C ofthis volume. Markup

Page 3-20 NPV

Volume 1: General Methodology Price 

### - W 

# xzziz Costs 

### >

J Operating

_

## ... Demand Cost ,. Curve 

# " ROE 

### r-+- quantity 

### ...**~I'** ,. Markup NPV 

# i,
Price

... Production Costs



### Figure 3.4 How**Alterna~ve** 

### Efficiency Levels Cause a Change in' ROE and NPV
The markup discussed here is a markup over economic variable costs, which is a broader category than the typical business defmition ofvarial>le costs. The manufacturer markup listed in the model and this volume is used only to calculate the baseline model price (calculafed by multiplying the baseline. manufacturer cost by the manufacturer markup). This is**call~** 

### the calibration case in LBNL-MAM. The inanufacturer markup listed is not used in calculating'the manufacturer prices; for the base case or the alternative efficiency levels. In 

### these cases, the

. manufacturer price is calculated from the economic markup' which, as described above, is dependent on elasticities, discount rates, and price and operating expense changes. Thus, prices at different efficiency levels do not exactly reflect the manufacturer cost times the manufacturer markup. Typical results ofan analysis ofan alterantive efficiency level at a particular level might be the following: wholesale· price increases 10%, sales decline 2%, revenue increases 8%, net income increases 12%, and ROE increases 0.5%. Associated'with each'Ofthese numbers is a standard error. The errors might be as follows: 4% onprice, 1 % on sales, 4% on revenue, 10% on net income, and 1.5% on ROE. To interpret the prediction for ROE, one could then conclude (by**chec~ng** 

### a table of the normal distribution) that there is about a 69% chance that ROE will increase, and a 31 % chance that it will decrease. One could also conclude that 'the chance ofprofits declining by more than 2% is only 2.3%.

7 For a thorough description of**h~w** markups are derived and used in LBNL-MAM. see Appendix C of this volume.

. Page3-20 Volume 1: General Methodology

### 3.3.3.2 short-~un 

### Impacts -.

. . 

### In the shortest 

### run, capacity is fixed; however, the short run of interest is, at the shortest, approximaklythree years. During this time, somechange 

### in capacitymay 

### occur, and demand, apart from the change induced by implementing new efficiency levels, will change. Demand for both types of applianceisprojectedto grow by approximately

### 2% duringthe

### threeyears, so, 

### to

### that extent, it will allrsviate any tendency toward excess capacity resulting from imposed efficiency levels. 

### In order to simplify the analysis and to take a cautious approach, the analysis has ignodthe 

### - ability of the industry to actively or passively adjust itscapacityrelative to demand. 

### ~hk estimated short-run effect is what would happen if 

### the industry converted, 

### as aresult of efficiency levels being implemerlted, all of its present capacity, and if the demandcurve did not shift except 

### as an indirect

. 

### consequence of imposed efficiency levels. The short-run analysis is based on the analogy between achangein demand caused by imposed efficiency levels and one caused by the business cycle. The business cycle periodically presents the industry with a fairlysharpdecreasein demand that is much paterthan the decrease predicted for any of the: efficiencylevels. 

### This demand shortfall 

### seems to present all of the opportunitiesfor price competitionthat would accompany a shortfallresulting 

### froma changein efficiency levels. Data for the last few business cycles 

### (18 years) have been analyzedto determine the ratio between the decline in demand (industry-wide)and the induced decline in price. 

### If, forexample, the long-run prediction is for a declinein demand of 2%. 

### then, using the above figure, 

### tht: short-run price could be 

### predicted to be 0.06% (0.03 x 

### 2%)lowerthan the long-run

### price. Actually, this 

### isjust a fmt approximation, and the model does better, as follows: the short-run price response Factor 

## (SRPR) of 0.03 means that the short-run price will be 0.06% lower than predicted with the long-run price formula 

### if the actual decline 

### in demand is 2%. 

### This is different from the long-run ]?rediction of a 

### 2% decline,because the short-run price fall will keep the long-run demand fall from fully materializing. Thus, in the shortrun, the actual fall in demand will be less than the long-run prediction of 296, 

### and the actual fall 

### in price will be 

### greaterthan the long-run pdiction, but not by a full 0.06%. Short-run deiand and price must be 

### determined simultaneously, which. occurs in the short-run module of LBNL-MAM. Shon:-runprices, profit levels, and other variables will gradually, over aperiod of several years, approachtheir 

### long-run values. How long this will take 

### depends on the 

### indusuy, on the fact that the 

### implemer~tation of new efficiency levels is announced 

### three or four years in advance, and on how fast 

### (and if) the 

### demand for the product isgrowing.

Volume1: General Methodology Page 3-21 

### 3.3.3.2 Short-Run Impacts 

### In the shortest run, capacity is fixed; however, the short run of interest is, at the shortest, approximately three years. During this**tim~.** some change in capacity may occur. and demana. apart from. the change induced by implementing new efficiency levels. will change. Demand for both types ofappliance is projected to grow by approximately 2% during thethree years. so. to that extent. it will**alll~viate** any tendency toward excess capacity**res~lting** from imposed efficiency levels.



### _ In order to simplify the analysis and to take a cautious approach. the analysis has ignored the

. ability ofthe industry to actively or passfvely adjust 

### its capacity relative to demand. The estimated

short-run effect is what would happen 

### ifthe industry converted. as a result ofefficiency levels being implemented. all ofits present capacity. and ifthe demand curve did not shift except as an indirect consequence ofimposed efficiency levels. The short-run analysis is based on the analogy between a change in demand caused by imposed efficiency levels and one Caused bythe business cycle. The business cycle periodically presents the industry with a fairly ·sharp decrease in demand that is much greater than the decrease predicted for any ofthe: efficiency levels. 

### This demand shortfall seems- to present all ofthe opportunities for price competition that would accompany a shortfall resulting from a change in efficiency levels. Datafor the last few business cycles (18 years) have been**anal~** to determine the ratio between the decline in demand (industry-wide) and the induced decline in price.

. If. fo:r example. thelong-run prediction is for a decline in demand of2%.**then~** using the above figure.**th(~** short-run price could be predicted to be 0.06% (0.03 x 2%) lower than the long-run price. Actually. this isjust a first approximation. and the model does better. as follows: the short-run price response- factor (SRPR) of0.03 means that the short-run price will be 0.06% lower than predicted with the long-run price formula 

### ifthe actual decline in demand**i~** 2%. This is different from the long-run prediction ofa 2% decline. because the short-run price fall will keep the long-run demand

. fall from fully materializing. Thus. in the short run. the actual fall in demand will be less than the long-run prediction of2%. and the actualfall in price will be greater than the long-run prediction. but not by a full 0.06%. Short-run demand and price must be determined simultaneously, which· occurs in the short-run module ofLBNL-MAM.·

- ShoI1:-run prices, profit levels, and other variables will gradually. over a period ofseveral years, approach their long-run values. How long this will take depends on the industry, on the fact that the**implemeJltati~n** ofnew efficiency levels is announced three or four years in advance, and on how fast (and if) the demand for the product is·growing.

Volume 1: GeneralMethodology Page 3-21

### 33.33 Industry Net Present Value Analysis 

### In this analysis, the LBNLMAM was modified to include 

### an analysis of the impacts of alternative efficiencylevels on industry net present value. Be-g in late 1990,a number of trade 

### associatiord contracted with the Arthur 

### D. Little consulting 

### firm to develop a model they named the Government RegulatoryImpactModel (GIRIM). Given exogenously-supplied price, shipments, and cost inputs, the GRIM provides an analysis of efficiency levels on industry net present value (NPV). Theoretically,NPV provides an alternate measure of impact that is identical to industry ROE that has been historically used by the LBN&,-MAM. 

### In response to the industry's concern, the LBNLMAM has been modified to include all the programming code from the latest version of the GRTM that is available? The GRIM has been integrated as a separatemodule within the LBNLMAM and generates the industry net present value analysis given price, shipments, and cost inputs from LBNLMAM. For a detailedexplanation of the GRIM module and its integration with the LBNLMAM,please refer to Appendix 

### C herein. 

### 333.4 Sensitivity Analysis Both because of the nature of the information required and the desire of sources to protect proprietary information, many of the data 

### used by the model represent uncertain estimates. Therefore, the effect of these uncertainties on the accuracy of the model's predictions is analyzed. Two types of questionsneed to be answered. First, how 

### do& aparticular input variable contribute to the uncertainty of the outputs? .Second, how uncertain is.the estimate of 

### a' particular output

. . 

### variable? Sensitivity charts 

### The model's sensitivity to inputs is measured by the impact of a change of one standard error (S.E.) in the input variable. This analysis ispresented only forthe impact of input variables on ROE; this standardizes and simplifies the process. The results 

### are displayed in 

## a sensitivity chart, 

### an example of which is given and explainedbelow (Table 

### 3.2).

Association of Home ApplianceManufacturers,Gas Appliance Manufacturers Association,and Air-conditioning and Refrigeration Institute. GRIM Version 1.2 dated I March 1993;received from AHAM in March 1993. Page 3-22 VolumeI:. GeneralMethodology 

### 3.3.3.3 Industry Net Present Value Analysis In this analysis. the LBNL-MAM was modified to include an analysis of the impacts of alternative efficiency levels on industry net present value. Begiqning in late 1990, a number oftrade associations8 contracted with the Arthur 

### D. 'Little**consul~ng** 

### finn to develop a model they named the Government RegulatoryImpact Model (GRIM). Given exogenously-supplied price, shipments, and cost inputs, the GRIM provides an analysis ofefficiency levels on iridustry net present value (NPV). Theoretically. NPV provides an alternate measure ofimpact that is identical to industry RC?Ethat has been historically used by the LBNYMAM. 

### In response to the iildustry's concerns. the LBNL-MAMhas been'modified to include all the programming code from the latest ,version of the GRIM that -is available. 9 The GRIM has been

,integrated as a separate module y,rithin the**~NL-MAM** and generates the industry net present value cuialysisgiven price. shipments, and cost inputs from LBNL-MAM. For a detailed explanation of the GRIM module and i.ts integration with the LBNL-MAM, please refer to Appendix C herein. 

### 3.3.3.4 Sensitivity Analysis , Both because of the nature of the information required and the desire of sources to protect proprietary information, many of the data used by the model represent uncertain estimates. Therefore, the effect ofthese uncertainties on the accuracy ofthe model's predictions is analyzed. Two types ofquestions need to be answered. First, how does a particular input variable contribute to the uncertainty of the outputs?

. Second, how uncertain is'the estimate of a particular output variable?

. 

### Sensitivity charts The model's sensitivity to inputs is measured by the impact ofa change ofone standard error (S.E.) in the input variable. This analysis is presented only for the impact ofinput variables on ROE; this standardizes and simplifies the process. The results are displayed in a sensitivity chart, an example ofwhich is given and explained below (Table 3.2).

8 Association ofHome Appliance Manufacturers, Gas Appliance Manufacturers Association, and Air-conditioning and Refrigeration Institute.

9 GRIM Version 1.2 dated I March' 1993; received from AHAM in March 1993. Page 3-22 Volume 1:, General Methodology

### Table.3.2 Sensitivityof

## ROE ta 

### 1S.E.' Change in Contro1,Variables

' Scenario =Primary - . . , . Control Variables
, . Efficiency Levels Name 

# - Value Changed 1 2 3 : ' 4 5

EP

, 0.008 0.018 -0.01% -0.01% -0.02% 0.07%. 0.15% FCA 0.098 0.157 0.00%

. 0.00% -0.01% 0.06% 0.13%. FIX 0.200 0.348 -0.09% -0.10% -0.14% -0.17% -0.30%



### Table 

### 3.2 lists the sensitivities of ROE to the control panel inputs defined below:, 1. ' 

## PE =industry price elasticity 2.

.

## RD =consumer discount rate 

### 3. ECC 

### =fm'sreal equity cost of capital 4. ' 

### EP 

### =fds economic profit 

### 5. FCA 

### = percentage 

### of 

### a fmlscosts,that 

### are fixed before the implementation of new efficiency levels 6. 

### FIX =percentage of one-time costs 

### (i.e.,costs that

## m induced by implementing new efficiency levels) that 

### are fixed 7. 

### CCN 

### =estimate of one-time capital costs induced by alternativeefficiency levels 8: ' 

### dVC.N 

### =increaseinvariable costs necessary to reach 

### an efficiency level ~otethat for CC.N and dVC.N the change of one standard error from the mean 

### is applicable for. efficiency.level 2 

### only. Thesetwo variables 

### &dollar values rather than penentages, and thus their value and the change in one standarderror are 

### different at each efficiency level. To construct the table. each control variable 

### is fmt set to its normal value. Next, one at a 

### time, each is h~creased in 

### absolutevalue by one standard error and the change in profit is recorded. Then the variible is returned to its normal value and the next variable is .tested. The matrix in the table lists the percentage point change in ROE 

### as a result of the change of one standard error in the variable's value; The change

### in ROE is listed for 

### each control panel variable and at each efficiency

.

### level 

### being analyzed.

. 

### . VolumeI: GeneralMethodology Page 3-23 . Table.3.2 Sensitivity ofROE to· 1 S.E.' Change in Control.Variables

. Scenario 

## = Primary_ Control Variables Efficiency Levels Name Value ChanSed 2 3 4 5 IPE -0.300 -0.690 0.06% 0.07% 0.15%

. -1.65% -4.27% RD 50.00% 114.96% 0.00% 0.15% 0.22% 1.32% 2.81% ECC 0.068 0.075 0.00% 0.00% 0.00% -0.01%
. -0.04% EP 0.008 0.018 -0.01% -0.01% -0.02% 0.07%· 0.15% FCA 0.098 0.157 0.00% 0.00% -0.01% 0.06% 0.1,3% FIX 0.200 0.348 -0.09% -0.10% -0.14% -0.17% -0.30% CC.N 0.394 0.480 -0.02% -0.03%

. -0.04% -0.07% -0.16% dVC.N 5.124. 6.872 0.00% 0.01% 0.01% 0.05% 0.01%

.
Table 3.2 lists the sensitivities ofROE to the control panel inputs defined below: 1. 'IPE

# = industry price elasticity

,2.

.RD

# = consumer discount rate 3. BCC 

### = fmn's real equity cost ofcapital 4. EP 

### = fmn's economic profit 5. 

### FeA 

### = percentage of a fmn's costs. that are fixed before the implementatiQn of new - efficiency levels

. 6.**~IX** 

### = percentage of one-time costs 

### (i.e., costs that 

## m:e induced by implementing new efficiency levels) that are fixed , 7. CC.N 

### == estimate ofone-time capital costs induced by alternative efficiency levels 8.. 'dVCN 

### = increase'in variable costs necessary to reach an efficiency level Note that for CC.N and dVC.N the change of one standard error from the mean is applicable for. efficiency level 2 only. These two variables 

# are dollar values rather than percentages, and thus ,their value and. the change in one standard error are different at each efficiency level.

. To construct the table, each control variable

# is first set to its norm8.I value. Next, one at a time, each is increased in absolute value by one**stan~** error and the change in profit is recorded. Then the variahle is returned to its normal value and the next variable is tested. The matrix in the taple lists the percentage point change in ROE as a result of the change of one standard error in the variable's· vallue: The change in ROE is listed for each control panel variable and at each efficiency

.level being analyzed.

.



### . Volume I: GeneralMethodology Page 3-23

### Sinceeach variable has

### its own standard error, the sensitivity

### reported in the table measures both how sensitive the model is to a change in the variable, and how uncertain the variable's value is. Note that the change in profit is simply the differencebetween long-run ROE and base case ROE. , 

### Note also that, 

### as expected, increasing the-absolutevalues of somevariables increasesROE and, for other variables, decreasesROE. The comparison of the various inputs' contributions to the uncertainty in ROE focuses attention on the parts of the model that should be 

### examined most closely to determine their accuracy and on the parts where improvement in the certainty of input variables would have the greatest payoff. Because of the differencesin the sensitivities among input variables, it will generallybe found that one to 

### three of the inputs will dominate the model's uncertainty, in the sense that perfecting all the rest of the inputs would make only a negligible differenceto the model's accuracy: 

### Alternative Scenarios For each appliance class and hypothetical efficiency level, hundreds of different scenarios

### are run for the Monte Carlo analysis described in a following section. However, a few scenariosthat involve different demand elasticities

### are singled out for special attention. If 

### the reader wishes toestimatesome other scenarionot reported here, he or shecan often simply use the sensitivity charts. Todo this, first look up how sensitive

## ROE is to 

### a one standard deviation change in the control variables that 

### are changed by the proposed scenario. Then estimate by how many standard deviations the desired scenario variables differ hm the primary scenario control variables and calculate a proportional change in ROE. 

### Lastly, add the changes in ROE resulting from the different variables. This method should be 

### reasonably accurate for changes in the control variables that 

### are likely to be 

### of interest. 

### Uncertaintyof Outputs 

### (Monte CarloAnalysis) Output uncertainty is most directly

### addressedby the Monte Carlo anaiysis.-Thisanalysisassigns an uncertainty to each of the nine control-panel input variables and then chooses a value for each based on this uncertainty. The model is then solved using these randomly chosen variables. All 

### of the important outputs 

### are tabulated on the Monte Carlo page of the model. These outputs are changes fromthe base case of price, shipments,revenue, net income, long-run ROE, and short-run ROE. Next, new values of the input variables are drawn from the same distribution. The model is run again and the new outputsrecorded. This cycle can be repeated as many as 

### 400 times (or more). After a sufficient number of runs, the mean and standard deviations of each output variable are computed. The standard deviations of the Monte Carlo output variables a& 

### the best estimate of the true uncertainty of the model's predictions. However, this does not 

### mean they are 

### absolutely reliable because they 

### are based on estimates of the uncertainty of the inputs, and these 

### are often not based on something as objectiveas the standarderrorof aregression coefficient. 

### In short, if the uncertainty in the inputs is misperceived, the estimate of uncertainty in the outputs cannot be relied upon. Page 3-24

. VolumeI: GeneralMethodology Since each variable 

### has its own 

### Standard error, the sensitivity reported-in the table measures both how sensitive the model is to 

## a change in the variable, and how uncertain the variable's value is. Note that the change in profit is simply the difference between long-run ROE 3.n.d base case ROE. Note also that, as expected, increasing the'absolute values ofsome variables increases ROE and, for other variables, decreases ROE.

, The comparison ofthe various inputs' contributions to the uncertainty in ROE focuses attention on the parts ofthe modd that should be examined most closely to determine their accuracy and on the parts where improvement in the certainty ofinput variables would have the greatest payoff. Because ofthe differences in the sensitivities among input variables, it will generally be found that one**t~** three ofthe inputs will dominate the model's uncertainty, in the sense that perfecting all the rest ofthe inputs would make only a negligible difference to the model's accuracy: 

### Alternative Scenarios Foreach appliance class and hypothetical efficiency level, hundreds ofdifferent scenarios are run for the Monte Carlo analysis described in a following section.· However, a few scenarios that involve different demand elasticities are singled out for special attention. ' Ifthe reader wishes to estimate some other scenario not reported here, he or she can often simply use the sensitivity charts. To do this, first look up how ·sensitive ROE is to a one st3.ndard deviation change in the control variables that are changed by the proposed scenario. Then estimate by how many standard deviations the desired scenario variables differ from the primary scenario control variables and calculate a proportional change in ROE. Lastly, add the changes in ROE resulting from the different variables. This method should be reasonably accurate for changes in the control variables that are likely to be ofinterest. 

### Uncertainty of Outputs (Monte Carlo Analysis)

. . Output uncertainty is most directly addressed by the Monte Carlo analysis. -This analysis assigns an uncertainty to each of the nine control-panel input variables and then chooses a value for each based on this uncertainty. The model is then solved using these randomly chosen variables. All of the important outputs are tabulated on the Monte· Carlo page of the model. These outputs are changes from the base case ofprice, shipments, revenue, net income,long-run ROE, and·short-run ROE. Next, new values ofthe input**v3riabl~s** are drawn from the same distribution. The model is run again**~d** the new outputs recorded. This cycle can be repeated as many as 400 times (ormore). After a sufficient.number of runs, the mean and standard deviations of each output variable are computed. The standard deviations of the Monte Carlo output variables 

# are the best estimate of the true uncertainty of the model's predictions. However, this ·does not mean they are absolutely reliable because they are based on estimates of the uncertainty ofthe inputs, and these are often not based on something as objective as the standard error ofa regression coefficient. In short, ifthe uncertainty in the inputs is misperceived, the estimate of uncertainty in the outputs cannot be relied upon. Page 3-24 Volume 1: GeneralMethodology

### ~onethel.ess, the standard errors generated by the Monte Cailo procedure 

### are useful for the interpretation ofthe 

### LBNGMAM outputs. 

### Assumptwnr 

### in the ~na&sis lustifications for the most important assumptions of the sensitivityAnalysis 

### are listedbelow. Assumption 

### Finns maximize revenues minus economic costs 

## (W-EC), where 

### EC includes the cost of equity and all taxes.

, 

### The lifecyclecost elasticity of demand experienced by a single 

### fm is constant. 

### This means that 

### price elasticitywill tend to decrease with operating cost. Note also that since price elasticity is finite, firms have market power. Justification 

## hisf follows from the assumptions of standard economic theory, in which firms 

### are rational profit maximizers.
There is no reason to assume that life-cyclecost elasticityeitherincreases or decreaseswith cost; thus, constancy is the base assumption.

Costs are 

### assumed to have two 'compo- A 

### linearcost function approximatesany smooth


### .. nents: one fixed, 

### and one proportional to curve over a small. region; since quantity

.. 

### quantity. changes-very little with new efficiency levels,



### .. . 

### . . 

### the 

### linear approximation should be 

### adequate. Assets 

### are assumed to 

### be a linear Same as previousjustification.

. 

### function of sales.

. 

### .

. . 

### The debt-toequityratio remains constant 

### Firms attempt to maintain 

## arelatively constant while the 

### fm finances any investment overall debt-to-equityratio, which they believe necessary to meet any imposed efficiency is optimal: Although specificnew expenditures


### . levels.

. 

### '(srich 

### as those induced by new 

### efficiency levels) may be 

### financed primarily by debt or. by equity,.inthe long 

### run,the debt-to-equity ratio

, 

### will be 

### restored to its desiredvalue.

VolumeI: Gened Methodology

f' Page 3-25

, Nonetheless, the standard errors generated by the Monte Carlo procedure are useful for the interpreultion ofthe LBNL-MAM outputs. ' 

### Assumptf,ons 

### in theAnalysis Justifications for the most important assumptions ofthe Sensitivity Analysis are listed below.



### •



### •

•



## •

• Assumption 

### Finns maximize revenues minus economic costs (R-EC), where EC includes the cost ofequity and all taxes.**Thc~** life-cycle-cost elasticity of demand experienced by a single fum is constant. This means that price elasticity will tend to decrease with oPerating cost. Note also that since price elasticity is finite, firms have market power. Costs are assumed to have two components: one fixed, and one proportional to quantity.
Assets are assumed to be a linear ' function ofsales.


### The: debt-to-'equity ratio remains cOJlstant while Ilhefmn finances any investment necessary to meet any imposed efficiency levels. ,

Volume 1: Gen.eralMethodology Justification This'follows from the assumptions of standard economic theory, in which frrms are rational profit maximizerS.
There is no reason to asslHDe that life-cyc1ecost elasticity either increases or decreases with cost; thus, constancy is the base assumption.

A linearcost function approximates any smooth curve over a small. region; since quantity changes very little with new efficiency levels, the linear approximation should be adequate. Same as previousjustification.

Finns attempt to maintain 

## arelatively constant overall debt-to-equity ratio, which they believe is optimal.' Although specific new expenditures

. (such as those induced by new efficiency levels) may be financed primarily by debt or by

. equity, ,in the long run, the debt-to-equity ratio

will be restored to 

### its desired value.

Page 3-25

### Assumption 

### . , 

### Justification


### The additional costs of parts, materials, These costs 

### are proportional to production and labor are assumed to be 

### long-run except for cases in which there 

### are marginal costs. discounts for large quantity purchases. Industry sources state that at the scales of

. 

### production under consideration, these discounts

### are very nearly exhausted.


### A percentage (that varies 

### from industry to Although industryinformants have stated industg)of capital'costs

### are long-run fixed that one-time capital costs will be 

### costs. proportional to the production capacity of the 

### plant in question, it seems inevitable that there will be some associated engineering costs that 

### are not. These will be 

### long-run fixed costs. Any fall in 

### demand caused by implementing new efficiency 

### levels (because of a price increase) will affect prices in the industryby the same amount as a normal fall in demand experienced during the business cycle. To the extent that a normal demand shortfall reduces the firm's short-run marginal cost, the demand decrease caused by implementingnew efficiency levels will be 

### indistinguishable. The crucial assumption here is that the business cycie does not influence the consumers' elasticity of demand. There is no evidence one way or the other on this. 

# -


# - Outputs 

### ofthe 

### LBNLMAM The LBNLMAM tracks six essential output variables, with emphasis on 

## ROE. The other five variables are 1)

### industry

### NPV,2)

### price, 3) 

### sales,4)

### revenue, and 5)

### net income. Base case and shortand long-run values of these 

### variables are reported on the control panel. 

### In addition, 

### LBNL-MAM constructs an output table that shows the long-run values of the variables at each of the efficiency levels under consideration. LBNLMAM alsoproduces a simplified income statement. As part of its algorithm, it calculates the markup from manufacturers' costs to consumerpurchase price. The purchase price is used in the calculation of priceefficiency curves, the lifecycle-cost curves, and the calculation of payback periods, and throughout the LBNLREM. All of these values are estimates that necessarily reflect some uncertainty. Estimates of these uncertainties and identification of their sources comprise another set of model outputs. Under each estimate of one of the five main output variables, the output table displays

# hat vari&blels change Page 3-26 VolumeI: GeneralMethodology 

### Assumption

• The additional costs of parts,**materi~s,** and labor are assumed to be long-run marginal costs.

• A percentage (that varies from industry to industry) ofcapital'costs are long-run fixed costs.

• Any fall in demand caused by implementing new efficiency levels· (because of 

## a price.increase) will affect prices in the industry by the same'amount as a normal fall in demand experienced during the business cycle.



### Outputs of the LBNL-MAM justification

These costs are proportional to production except
.for caSes in which there are discounts for large quantity· purchases. Industry sources state that at the scales of production under consideration, these

. discounts are very nearly exhausted.

Although industry informants have stated that one-time capital

. costs will be proportional to the production capacity of**th,~** plant in question, it seems inevitable that 'there will be some associated engineering costs that are not. These will be long-run fixed costs. To the extent that a normal demand shortfall reduces the firm's short-run marginal cost, the demand decrease caused by implementirig new efficiency levels will be indistinguishable. The crucial !Ssumption here is that the business cycle does not· influence the consumers' elasticity of demand. There is no evidence one way or the other on this.

The LBNL-MAM tracks six essential**o~tput** variables, with emphasis on ROE. The other five variables are 1) industry NPV, 2) price, 3) sales, 4) revenue, and 5) net income. Base case and shortand long-run values ofthese variables are reported on the control panel. In addition, LBNL-MAM constructs an output table that shows the long-run values ofthe variables at each ofthe efficiency levels under consideration. LBNL-MAM also produces a simplified income statement. As part of its algorithm. it calculates the markup from manufacturers' costs to consumer purchase price. The purchase price is used in the calculation ofprice-efficiency curves,:the)ife-cycle-cost curves, and the calculation ofpayback periods, and throughout the LBNL-REM. All ofthese values are estimates that necessarily reflect some uncertainty. Estimates ofthese uncertainties and identification oftheir sources comprise another set of model outputs. Under each estimate of one of the five main output variables, the output table displays that variable's change Page 3-26 Volume I: GeneralMethodology

### from its base level and the standard error of the estimate of that change. The 

### LBNL-MAM also computes the effect on ROE of a one-standard-deviation change in each of the control variables. This is reported for each effrciency level in 

### a sensitivity chart, designed to help discover the source of uncertidnty in .theoutput variables. 

### Datu Sources

. . 

### The 

### 1BNLMA.Muses data 

### that characterizeboth a particular industryand typical fms within that industry. Estimates of 

### data 

### are based on information from five general soums: 

### 1) the Engineering Analysis, 

### 2) the ConsumerAnalysis. 

### 3) industry consultants,

### 4) public financid data, and 

### 5) industry profiles. A detailed discuuion of model inputs appears in Appendix 

### C of each. product-specific Part.



### . . 

### 3.3.4 

### Appliance StandardsEnvironmental and ~tilit~~odel (ASEUM)

, . 

# - The two analyses used in the prior appliance standards rulemakings, the Utility Impact Model

and the Environmental Analysis have been integrated in one new model, the Appliance Standards Enviromiental and Utility Model 

### (AFEUM). 

### By calculating 

### utility avoided costs and lostrevenues, ASEUM provides marginal electricity costs to 

### be used in evaluating the societal benefits of albrnative-energy efficiencylevels. ASEUM also quantifiesimpacts on the electric utility industry's need for new generating capacity. Appendix 

### E of this volume contains more information on.the model and 

### ,dataused to perform these calculations..

.. 

### ASEIJM adopts the standard industry convention that the financial value of electricity savings ' 

### to an electric utility 

### can 

### be broken down into fuel cost savings and capacity cost savings. 

### Thesum of the two 

### is usually called avoided cost. The fuei cost elementmeasures variable production costs avoided by reduced electrical demands, valued at marginal input fuel costs. The capacity cost measures the value ofreduced loads during system

### peak periods; that is. the reduced requirement to



### . have capacity available to meet 

### peak demand. This saving is often valued at the cost of 'a combustionturbine, which is usually considered the lowest cost capacity available. This convention is explained in Appendix 

### E. ' 

### ASEUM calculates the avoided cost 

### rate per kwh of 

### energy saved. 

### These values 

### are used to calculate societal benefits from reduced electricity~consumption. 

### ASETJM calculates avoided energy costs based on a disaggregationof the national generation fuel 

### mix to the ten National Electric Reliability Council (NERC) regions and a simplified load duration curve m(LDC) 

### for each region. 

### NOattempt is made to segment the demand by region, but rather the

### electricity demand loss is assumed proportional. The fraction of the electricity that would

, 

### have to be generated at the margin from oil and gas is calculated from total regional oil and 

### gas generation and a simplified 

### LDC. Projected utilitynatural gas and coal prices. weighted by the oil and gas marginal fraction and the nonsil.and gas marginal fraction, respectively, .are 

### used to calculate ,utility marginal energy cbsts during the forecastperiod.

. 

### The a.voided-capacity cost calculation in the 

### model isbased on a conservation load factor (CLF) 

### for the eni:rgy savings attributableto the efficiencylevel, 

### as well 

### as the cost of a combustionturbine. Volume I: GeneralMethodo0logy Page 3-27 from its base level and the standard error of the estimate of that change. The LBNL-MAM also computes. the effect on ROE of a one-standard-deviation change in each of the control variables. This is reported for each efficiency level in a sensitivity chart, designed to help discover the source ofuncertainty in 'the output variables. 

### Data Sources The LBNL-MAM uses data that characterize both a particular industry and typical fmns within that industry. Estimates of data are based on information from five general sources: 1) the Engineering Analysis, 

### 2) the Consumer Analysis, 3) industry consultants, 4) public financi8I data, and 5) industry profiles. A detailed discussion of model inputs appears in Appendix C of each. product-specific Part.

. . 3.3.4 Apilliance Standards ElJvironmental and Utility Model (ASEUM)

. The two analyses used in the prior appliance standards rulemakings, the Utility Impact Model and the Environmental Analysis have been integrated in one new model, the Appliance Standards Environmental and Utility Model**(A~EUM).** By Calculating.utility avoided costs and lost revenues, ASEUM provides marginal electricity costs to be used in evaluating the societal benefits of**altemativl~** energy efficiency levels. ASEUM also quantifies impacts on the electric-utility industry's need for J!lew generating capacity. Appendix E ofthis volume contains more informationon.the· model and·data used to perform these calculations.' ASElJM adopts the standard industry convention that the financial value ofelectricity savings

. to an elecltric utility can be broken down into fuel cost savings and capacity cost savings. The sum ofthe twet is usually called avoided cost. The fuel cost element measures variable production costs avoided by reduced electrical demands, valued at marginal input fuel costs. The capacity cost measures the value ofreduced loads during system 

### peak periods; that is, the reduced requirement to have capacity available to meet peak demand. This saving is often valued at the cost of'a combusti()n turbine, which is usually considered the lowest cost capacity available. This convention is explained in Appendix E.' ASEUM calculates the avoided cost rate per kWh of energy saved.

. These values are used to calculate societal benefits from reduced electricity consumption. ASElJM calculates avoided energy costs based on a disaggregation ofthe national**gene~ation** fuel mix to the ten National Electric**Reli~bi1ity** Council (NERC) regions and a simplified load duration curve (LDC) for each region. No attempt is made to segment the demand by region, but rather the electricity demand loss is assumed proportional. The fraction ofthe electricity that would have to be generated at the margin from oil an4 gas is calculated from total regional oil and gas generation and a simplified LDC. Projected utility natural gas and coal prices, weighted by the oil and gas marginal fraction and the non-oil.and gas marginal fraction, respectively,

.are used to calculate utility marginal energy costs during the forecast period.

. The a.voided-capacity cost calculation in the model is based on a conservation load factor (eLF) for the eni!rgy savings attributable to the efficiency level, as well as the cost ofa combustion turbine. Volume 1: GeneralMethodo'logy Page 3-27

### A 

### CLFis defined as the averageload savingsofa conservationmeasure (in 

### kW) divided by itspeakbad savings. The peak savings 

### are averaged over an on-peak

### period from 0800

### to 

### 2000. This wide on-peak period yieIds a conservative estimate for peak-load savings that accounts for coincidence with system peak 

### demand. The 

### CLFis used to characterizethe 

### peak demand savings of aconservation measure. It 

### is used 

### to convert the capacity value of the alternative efficiency levels into a per kwh value described above. :Sheanalysis assumes that 

## the load shapes of appliances do not change as these appliances 

### are mad(: .moreefficient.

. 

### The

### NERC forecasts of capacity requikments for each region 

### are used to account forregional variations 

### in reserve margin. If NERC predicts an adequatereserve margin 

### in a region for a given futureyear, no reliability value is given to the 

### peak load savings in the region. The net revenue loss is equal to the difference between the revenue reductions and avoided costs. Revenue reduction is calculated by multipljing the change

### in electricityconsumptionby the average national residential 

### rate. Avoided costs 

### are calculated from the change in eltktricity consumption multiplied by the per-unit, avoided costs. The inputs needed for the utility impact calculationsinclude electricity savings,conservation load fact.ors, utility fuel prices, average electricity prices, electricity generation by fuel 

### type, and capacity need by NERC region. The outputs of 

### the analysis. 

### are the reduction in the need for new generating capacity, the net change in revenues, and the avoided energy and capacity costs for 

### an applianceper 

### MMBtu of sourceenergy. Thesemarginal costs

### areused to calculate societal costs and benefits (of implementingnew efficiency levels.

. . 

### Assumptions in 

### theAnalysis 

# -

, 

### Justifications for the most important assumptions in 

## ASEUM are listed below. These assumptions include the shape,of

### the regional LDCs, conservation factors that remain constant through Ithe analysis, the 

### regional distribution of energy savings, utility marginal costs, and the' 

### substanceof 

### NERC forecasts.

, Page 3-28



### . ' Volume 1: GeneralMethodology A CLFi:; defined as the average load savings ofa conservation 'measure (in kW) divided by its peakload saviings. The peak savings are averaged over an on-peak period from 0800 to 2000. This wide on-peak period yiefds a conservative estimate for peak-load savings that accounts for coincidence with system peak demand. ' The CLF is used to characterize the peak demand savings ofa conservation measure. It is used to convert the capacity value of the alternative efficiency levels into a per kWh value described above. The analysis assumes that the load shapes ofappliances do not change 8$ these appliances are**mad,c~more** efficient. . '

. The NERC forecasts ofcapacity requirements for each region are used to account for regional variations 

### in reserve margin. IfNERC predicts an adequate reserve margin in a region for**~** given future year, no reliability value is given to the peak load savings in the region! The net revenue loss is equal to the difference between the revenue**reduction~** and avoided costs. Revenue reduction is calculated by multiplYing the change in electricity consumption by the average national residential rate. Avoided costs are calculated from the change in electricitY consumption multiplied by the per-unit, avoided costs. The inputs needed for the utility impact calculations include electricity savings, conservation, load fact,ors, utility fuel prices, average electricity prices, el.ectricity generation by fuel type, and capacitY need by NERC region. The outputs ofthe analysis are the reduction in the need for new generating capacity. the net change in revenues, and the avoided energy and capacity costs for an

.appliance perMMBtu ofsource energy. These margmal costs are used to calculate**soci~tal** costs and benefits lofimplementing new efficiency levels. ' 

### Assumptions in theAnalysis Justifications for the most important assumptions .in ASEUM are listed below. These assumptions include the shape,of.the regional LDCs, conservation factors that remain constant through Ithe analysis, the regional distribution of energy savings, utility marginal costs, and the'**substanCl~** 9fNERC forecasts.

Page 3-28 Volume 1: GeneralMethodology

### Assumption

,

### Justification 

# he analysis calculates the kriod during whch oil and gas orcoal 

### arethe marginal fuels forc=lectricity.generation based on a simplified 

## LDC for 'each 

## NERC region. It therefore ,assumesthat this 

### LDC accurately reflects the distributionof fuels on the margin.

Oil and gas fractions 

### (OGFS)for each 

## NERC -region remain constant throughout the analysis period. ' 

### There is littleinformation by NERC region on which fuels 

### are marginal. Since 

### NERC supplies the total amount of fuel burned for generation, a method 

### is needed to convert total generation into

. 

### an estimate of marginal generation,and using an WCis the usual way to derive such 

### an estimate. Because most 

### peak generating technologies, such as gas turbioes, use light fuels, these fuels will 

### be used by utilitiesto,meet 

### peakloads; hence, oil and gas 

### are assumed to be at the top of the 

### LDC. The other fuel assumed to appear on 'themargin is coal, since nuclear, hydro, and purchases from independent generators 

### are usually .base-' loaded. Constant OGFs 

### are assumed to simplify computation. The OGFs 

### are calculated using the simplified LDCs described above and by averagingNERC forecasts of the total amount of oil 

### and gas generation for the yew 

### 1990to 

### 1995. There

### are no forecastsforthe amount of oil and gas generation 

### aft& 1995. Because these calculations are highly uncertain, and because no other forecasts are available, the analysis assumes constant OGFs. Energy savings accrue in each region in This approach is .an approximation made proportion to the region's consumption of necessary by insufficient information and 

### for heating, cooling, and baseload energy in 1980. 

### computational convenience. Information is



### .. 

### available on average saturations and appliance efficiencies,by state, that can

### be 

### aggregated to NERC regions.

, , 

### Utiliity barginal energy costs 

### are calculated Different generating units have different heat

' 

### using the sum of utility natural gas and coal rates, but the extent

### of this variation for utility priu:s weighted by the oil and gas fraction and generating systems in different 

### NERC regions the non-oil and gas fraction,respectively. This has not been 

### calculated. A 

### 34% 

### energy approach assumes that all marginal generation

. 

### conversion efficiency is typidal for current has the 

### sameheat rate. generating units.

. .

VolumeI: tfencmlMethodology Page 3-29 Assumption
• The, analysis calculates the period during which oil and gas orcoal are the marginal fuels for electricity,generation based on a simplified 

### LDe for' each NERC region. It therefore ,assumes that this,

### LDe accurately reflects the**~stribution** offuels on the margin.



### • Oil and gas fractions (OGFs) for each NERC -regi,on remain constant throughout the analysis pefi.od.

. '

• Energy savings accrue in each region in proportion to the region's consumption of heating, cooling, and baseload energy in 1980.

• Utililty marginal energy costs are calculated using the sum of utility natural gas and: coal pricc:s weighted by the oil and gas fraction and the non-oil and gas fraction, respectively. This approach assumes that all marginal generation. has the same heat rate.

Volume I: l':Ieneral Methodology ,Justification There is little information by NERC region on which fuels are marginal. 'Since NERC supplies the total amount of fuel burned for generation. a method is needed to convert total generation into' an estimate of marginal

. generation, and using an LDC is the usual way to derive such an estimate. Because most 

### peak. generating technologies. such as gas turbines. use light fuels. these fuels will be used by utilities to meet peakloads; bence. oil and gas are assumed to be at the top ofthe LDC. The ' other fuel assumed to appear on the margin is coal. since nuclear. hydro. and purcbases from independent generators are usually .base-' loaded. Constant OGFs are assumed to 'simplify computation. The OGFs are calcu.1ated using the simplified LDCs described above and by ,averaging NERC forecasts ofthe total amount ofoil and gas generation for the**ye~** 1990 to J995. There are no forecasts for the amount of oil and gas generation aftc!r 1995. Because these calculations are highly uncertain, and because no other forecasts are available, the analysis assumes constant QGFs. This approach is 'an 'approximation made necessary by insufficient information and for computational convenience. Information is available on average saturations and appliance efficiencies, by state, that can be aggregated to NERC**reg~ons.** Different generating units have different heat rates, but the extent ofthis variation for utility generating systems in different NERC regions has not been calculated. A 34% energy conversion efficiency**i~** typiCal for current generating units. '

### Assumptions Justification The

### NERC forecasts ofchanges in adjusted The 

### NERC forecasts 

### are widely accepted. reserve margin 

### are accurate. 

### They 

### are the only forecasts of adjusted reserve margin at the regional level.

1 

### 33.5 Lie-Cycle Cost Analysis, Payback Period, and Cost of Consewed Energy One measure of the effect of implementing new efficiency levels on consumers is the change in operating eFpenses as compaiedto the change in purchase prices.. These changes 

### are quantified by the difference in lifecycle costs (LCC)

### between the base and efficiency level cases for the applianceclasses 

### analyzed. The 

### LCC is the sum of the purchase price and the operating expense discounted over the lifetime ofthe appliance. It is calculated at the average efficiency foreach class in the year efficiency levels 

### are imposed using 

### real consumer discounttates of 2%. 

### 696,and 15%. The purchase price 

### is based on the factory costs in the Engineering Analysis and includes a factory markupplus a distributorandretailermarkup. Maintenance and installation costs are

### included,when appropriate. The operating expense is calculated using the unit energy consumption data in the 

### LBNLREM. Energy prices 

## &taken fromAnnual Energy Outlook 

### 1995 and applianceusage are taken from the results of the 

### LBNL-REM.

. 

### The lifecyclecost analysis also examines the payback periods (PBPs) and the cost of conserved energy (CCE) associated with the alternative efficiency levels. The PBP measures the mount of time it takes to recover additionalinvestment in increased efficiency through lower operating costs. Numerically, it is the ratio of the increase in purchase price between the base and efficiency level cases to the decrease in annual operating expenditures. Both the numerator and denominatorof this expression 

### are evaluated at the average efficiency in the year new efficiency levels come into effect and at energy prices in that year. The 

### CCE is the increase in purchase price amortized over the lifetime of the appliance, divided by the annual energy savings. Details of these calculations may be found in Chapters 4 

### of eachproduct-specific Part.


### 3.3.6 Environmental Analysis As 

### mentioned in Section 3.3.4 

### above, the ASEUM model now conductsboth the electric utility and environmental analyses. Further details of the environmental calculations and results can be 

### found in the Environmental Assessment, which is included in this volume. The environmental calculations are quite straightforward. Reductions in energy use estimated by 

### LBNL-REMare multiplied by emissions factors to estimate net emissions reductions.
Page 3-30 Volume 1: GeneralMethodology Assumptions

• TheNERC forecasts ofchanges in adjusted reserve margin are accurate. 

## .. Justification The NERC forecasts are widely accepted. They are the only foreca$ts ofadjusted

. reserve margin at the regional level.



### 3.3.5 Life-Cycle Cost Analysis, Payback Period, and Cost of Conserved Energy One measure. ofthe effect ofimplementing new efficiency levels on consumers is the change in operating**e~penses** as comparCd to the change in purchase prices.' These changes are quantified by the difference in life-cycle costs (LCC) between the base and efficiency level cases for the appliance classes analyzed. The LCC is the sum ofthe purchase price and the operating expense

. discounted over the lifetime ofthe appliance. It is calculated at the average efficiency for each class

in the year efficiency levels are imposed using real consumer discount tales of2%; 6%" and 15%. The purchase price is'based on the factory costs in the Engineering Analysis and includes a factory markup plus a distributor and retailer markup

.. Maintenance and installation costs are included, when appropriate. The operating expense is calculated using the unit energy consumption data in the LBNL-REM. Energy prices 

# are taken from Annual Energy Outlook 

### 1995 and appliance usage are taken from the results ofthe 

### IBNlrREM.

. . The llfe-cycle cost analysis also examines the payback periods (PBPs) and the cost ofconserved energy (CCE) associated with the alternative efficiency levels. The PBP measures the amount of time it takes to recover additional investment in increased efficiency through lower operating costs. Numerically, it is the ratio ofthe increase in purchase price between the base and efficiency level cases to the decrease in annual operating expenditures. Both the numerator**~d** denominator ofthis expression are evaluated at**th~** average efficiency in**th~** year new efficiency levels come into effect and at energy prices in that year. The CCE is the increase in purchase price amortized over the lifetime ofthe appliance, divid.ed by the annual**ener~** savings.

. Details ofthese calculations may be found in Chapters 4 ofeach product-specific Pan.


### 3.3.6 Environmental Analysis As mentioned in Section 3.3.4 above, the ASEUM model now conducts both the electric utility and environmental analyses. Further details of the environmental calculations and results**c~** be found in the Environmental Assessment, which is included in this volume. The environmental calculations**ar~** quite straightforward. Reductions in energy use estimated by LBNL-REM are multiplied by emissions factors to estimate net emissions reductions.
Page 3·30 Volume'}: General Methodology

# -
Themain environmentaleffectsofelectricitygenerationand distribution on 

### airand water quality result frolmemissions of sulfur dioxide (SOJ, nitrogen oxides (NO,), and carbon dioxide (CO ,). 

### With the alternativeefficiencylevels lessening the need forelectricitygeneration, in general, power plant emissions would be reduced. However, the case of SO, is complicated by the emissions cap provisio:n off the 1991 

### Clean 

### Air Act Amendments. 

### In the case of this pollutant, the reduction in emissionsreported shouldbe interpreted 

### as reduced demand for emissions. Physical emissions of SO, will most likely not change, although the price of emission allowances should be 

### lowerthan the case absent the imposition of new efficiencylevels.,The ASEUM model also estimates the effect of imple~aenting new efficiency levels on the 

### three emissions resulting from in-house combustion.
I 

### The numerous other, but more minor, effectsofpower generation, such

### as waterpollution, landuse effects, etc., 

### are not covered in the analysisof appliance energy efficiencylevels. The net effect of 

### an appliance energy efficiency level on such environmental problems 

### arejust too smdl to be measurable. Themultipliers used 

### in this report 

### are derived from areport that accompanied the 1991

### NES (6). 

### Estimarion of muitipliers over future periods is not 

### as straightforward 

### as it may seem. Our reasonat~le xpectation is of improved emission controls over time &d the effectiveness of these improvements must be 

### forecast. 

### In addition, future restrictions on emissions of greenhousegases could have 

### a major impact on CO, emissions. Table 

### 3.3 shows some sample emission factors. As is clear,the forecast emission factor for SO, falls dramatically

### asthe provisions of the Clean 

### Air Act become effective. The factor for

### NO, falls,but less dramatically, while the CO, emissions,which have no cunrentlyknown feasible abatement strategy, fall modestly. 

### Table 3.3 Emissions

### Factors for Power Generation

. 

### Clearly, the approach,usedin this axialysis assumes that the generation avoided will be similar 

### Is thisin. its environmental effects to average generation. That is, no effort is made to conduct a

, 

### marginal rather than average analysis, even though the .fractionof time that a certain fuel .is a ' 

### marginal'fuelwill not 

### be the same 

### as its shareof overall generation. -For residential

### air conditioning and 

### coolcing, this is areasonable assumption. Generationwill be 

### reduced at all

### times of the year.and

, 

### coal 

### and:gas will both be marginal for some of the time.
VolumeI: ~eneml ~e~hodilo~~ ' . Page 3-31 C02 gkm 964.2 977.5 973.2 952.1 , 

### Year

2000. 2010 2020 2030 

### SO2 gkm 3.463 2.550 1.639 0.951

. 

### NO, gfim 2.578 2.2 16

, .

1.897 1.169 The main environmental effects ofelectricity generation and distribution on airand waterquality result from emissions ofsulfur dioxide (SOJ, nitrogen oxides (NOli)' and carbon dioxide (CO2)' With the alternative efficiency levels lessening the·need for electricity generation, in gener3J, power plantemissions would be reduced. However, the case ofS02 is complicated by the emissions'cap provisio:n oj[ the 1991 Clean 

### Air Act Amendments. In the case ofthis pollutant, the reduction in

.emissions reported should be interpreted as reduced demand for emissions. Physical emissions of 

### S02 will most likely not change, although the priCe ofemission' allowances should be lower than the case**absc~nt** the imposition ofnew efficiency leve,ls. ,The ASEUMmodel also estimates the effect ofimplementing new efficiency.levels on the three emissions resulting from in-house combustion. The numerous other, butmore minor, effects 

### Qfpower generation, such as water pollution,landuse effects, etc., are not covered in the analysis ofappliance energy efficiency levels

.. The net effect ofan appliance energy efficiency level on such environmental problems arejust too smail to be

.measurahle.

. Themultipliers used in this report are denved from a report that accompanied the 1991 NBS'(6). Estimation of muitipliers over future periods·is not as straightforward as it may seem. Our reasonable expectation is of improved emission controls over time aDd the effectiveness of these improvements must be forecast. In addition,**futu~** restrictions on emissions ofgreenhouse gases could have a major.impact on CO2emissions. Table 3.3 shows'some sample emission factors. As is clear, lhe forecast emission factor for 

### S02 falls dramatically as the provisions ofthe Clean 

### Air Act become effective. The factor for NOli falls, but less dramatically, while the CO2 emissions, which· have no currently known feasible abatement strategy, fall modestly. 

### Table 3.3 Emissions Factors for Power Generation
S02 NO x CO2

. Year g/kWh /kWh 

### g. . g/kWh 2000' 3.463 2.578 964.2 2010 2.550 2.216 977.5 2020 1.639 1.897 973.2 2030 0.951 1.169 952.1

. Clearly. the approach 'used in this 'arialysis assumes that the $eneration avoided 

### will be similar Is thisin. its environmental effects to average generation. That is, no effort is made to conduct a marginal rather than average analysis, even though the fraction of time that a certain fuel -is a marginal fuel 

### will not be the same as its share ofoverall generation

.. Forresidential air conditioning and cooking, this is a reasonable assumption. Generation will be reduced at all times ofthe year.and

. coal and: gas will both be marginal for some ofthe time.

. . Volume 

### i: General Methodology

. Page 3·31

### .Table3.4 

### shows the emissions factors used for in-house emissions, which 

### are assumed fixed over the forecastperiod. 

# - Table3.4 

### Emissions F'actors forIn-House Emissions

Sensitivity studies 

### are performed to determine how changes in technical 

### and operational parameters affect key engineering and economic indicators 

### used in the evaluation of appliance energyefficiency levels. This makes it possible to .placebounds 

### on the uncertainty in the results of the analysis and to gain an unherstanding of which variables 

### are most importantin producing these results. Sensitivity analyses are 

### developed in a series of distinct steps. For each computer model in the analysis, critical input-parameters

### are identified and reasonable ranges of variation 

### are Year -


### 2000



### determined; The sensitivity-of the model to changes in the value of each importantparameter is then estimated.by running the model for.the base case and efficiency level cases. The significanceof the results is assessed in terms of the magnitude of change in each model's outputs. Sensitivity runs 

### are made with the LBNLREM to explore the effects of uncertainty in the equipment and operating cost of the products being analyzed. The costs of the baseline unit as well 

### as the incremental costs of design options are varied. Other sensitivity runs examine alternative efficiency trends in the absence of implementing new efficiencylevels. 

# - Sensitivity analysis of the results of the LBNLMAM is conducted mainly from a sensitivity chart that reports the sensitivity of ROE to each of the nine control variables. Because the LBNL-MAM is a non-linearmodel, the effects of these input uncertainties may not be 

### additive. A 

### Monte Carlo analysis addresses this problem by computing standard errors,which are displayed 

### in the output table. For each control variable, the Monte Carlo section randomly picks a value near the best estimate of its true value. As 

### a result, the outputs of Monte Carlo vary from run to run. After finishing 

### a batch of runs (typically 100 

### to 400), 

### the LBNL-MAM calculates means and standard .deviations for the five main output variables. 

### . SO2

The results of the sensitivity analyses 

### are examined to extracttwo types of information. First, the base case runs of the model 

### are analyzed to determine the sensitivity of the forecasts to exogenous variables and assumptions. Second,the sensitivity analysis of the impacts of alternative efficiency levels is performed by examining the differences between the base and efficiency level cases. A 

### variable that affects the 

### two cases similarly will have little effect on the impact of imposed 2 

### Gas 0

.Page3-32 Volume 1: GeneralMethodology 

### Oil

' 

### 134.6 

### NOx Gas 45.1 

### 'CO2 Oil 55.9 

### Gas 47291 

### Oil 72485

\. 

### . , 'Table 3.4 shows the emissions factorS used for in:-house emissions, which are assumed fixed over the forecast period.


### Table 3.4 Emissions Factors 

### for In-House Emissions 

### Year S02 NO .x CO2



# . ,Gas

. Oil Gas Oil Gas Oil



### - 2000 0 134.6 45.1 55.9 47291 72485

, 

### 3.4 SENSITIVITY ANALYSIS Sensitivity studies are performed to.determine how changes in technical and, operational parameters affect key engineering and economic indicators used in the evaluation of appliance energy efficiency levels. This makes it possible to place bounds on the uncertainty in the results of the analysis and to gain an understanding ofwhich variables are most important in producing these' results. Sensitivity an_alyses are developed in a series ofdistinct steps. For each computer model in the analysis, critical input parameters 

# are identified and reasonable ranges of variation are determined; The sensitivity.ofthe model to changes in the value ofeach important parameter is,then estimated by ruiming the model for the base case and efficiency level cases. The significance ofthe results is assessed in tenns ofthe magnitude ofchange in each model's outputs. Sensitivity runs are made with the LBNL-REM to explore the effects of uncertainty in the equipment and operating cost ofthe products being analyzed. The costs ofthe baseline unit as well as the incremental costs of design options are varied. Other sensitivity runs examine alternative efficiency trends in the absence ofimplementing new efficiency levels. Sensitivity analysis ofthe results ofthe LBNL-MAM is conducted mainly from a sensitivity chart that reports the sensitivity of ROE to each of the nine control variables. Because the LBNL-MAM.is a non-linear mOdel, the effects ofthese input uncertainties may not be additive. A Monte Carlo analysis addresses this problem by computing standard errors, which are displayed in the output table. For.each control variable, the Monte Carlo section randomly picks a value near the best estimate ofits true value. As a reSUlt, the outputs ofMonte Carlo vary from run to run. After finishing a batch of runs (typically 100 to 400), the LBNL-MAM calculates means and standard ,deviations for the five main' output variables. The results ofthe sensitivity analyses are examined to extract two**~s** ofinformation. First,

.the base case runs of the model are analyzed to detennine the sensitivity of the forecasts to

.exogenous variables and**~sumptions.** Second, the sensitivity analysis ofthe impacts ofalternative efficiency levels is performed by examinfng the differences between the base and efficiency level cases. A variable that affects the two cases similarly will have little effect on the impact ofimposed Page 3-32 Vol{lme 1: General Methodology

### eficienc:~ levels, even though it might have a significant effect on the absolute forecasts. The. 

### primary intmst in 

### the smd type 

### of sensitivityanalysis isin variables and assumptions that affect ' , 

### the two cases differently. The sensitivity analyses of the impacts of efficiencylevels 

### a& discussed in Chapters 6 ofeach product-specific Part.

VolumeI: Geneml Methodology efficienc:y levels, even though it might have a significant effect on the absolute forecasts. The' primary interest in the second type of**~nsitivity** analysis is in variables .and assumptions that affect the two c:ases differently

..The sensitivity analyses ofthe impacts ofefficiency levels are discussed in Chapters 6 ofeach product -specific Part.

;.

Volume I: GeneralMethodology Page 3-33

.. . 

### REFERENCES 1. E. Hurst and 3. 

### Carney. 1978. The 

### ORNL Engineering-Economic Model of Residential Energy Use,

### Oak Ridge National Laboratory, Oak Ridge, 

## TN. ORNUCON-24. 

### 2. J.E. McMahon. 1987. "The 

### LBL Residential Energy Model: An Improved Policy Analysis Tool," Energy Systems and Policy, 

### vol. 10,no. 1,pp. 4 

### 1-71. 3. U.S. Department of Energy (US. DOE). 1988. Energy Conservation Progrcunfor Consumer Products: Advance Notice of Proposd Rulemaking and 

### Request for Public CommentsRegarding Energy ConservationStandarak for Dishwashers, Clothes Washers, 

### and ClothesDryers," 

### Washington, D.C. Federal Register, vol. 53, 

### no. 96, 17712-17721. 4. U.S.,Department of,Comrnerce, Bureau of the Census. 1983. 1980

### Census ofPopulation and Housing. 

### Washington,'D.C. PC8O-A1. 5. U.S. DOE, Energy PC80- 1 -A 1Information Administration. 1995. Annual Energy Outlook 1995

### with Projections to 2010,

### Washington, D.C. DOEEIA-0383(95), January 6. 

### U.S. DOE, Energy Information Administration. 1991. 

### Improving Technology:Modeling Energy Futures for National Energy Strategy. 

### Service Report to the 1991

### National Energy 

### Strategy. Washington, D.C. SWNESl90-01.

Page 3-34 VolumeI: GenemlMethodology 

### REFERENCES 1. E. Hurst and 

### J. Carney. 1978. The ORNL Engineering-Economic Model ofResidential Energy Use, 

### Oak Ridge National Laboratory, Oak Ridge, TN. ORNUCON-24. 2. J.E. McMahon. 1987. "The LBL Residential Energy Model: An Improved Policy Analysis Tool," Energy Systems 

### andPolicy, 

### vol. 10, no. 1, pp. 41-71. 3.

. U.S. Department of Energy (U.S. DOE). 1988. Energy Conservation Program for Consumer Products: Advance Notice ofPropo"sd Rulemaking 

### and Requestfor Public Comments Regarding Energy Conservation Standards for Dishwashers. Clothes Washers. 

### and Clothes Dryers," 

### Washington, D.C

..Federal Register, vol.**5~,** 

### no. 96, 17712-17721. 4. U.S. Department ofCommerce, Bureau ofthe Census. 1983. 1980 Census of Population 

### and Housing. 

### Washington,

### D.C. PC80-A1.

- 5. U.S. DOp, Energy PC80-1-Allnfonnation Administration. 1995. AnnualEnergy Outlook 1995 with Projections to 2010, 

### Washington, D.C. DOFJEIA-0383(95); January 6. U.S. DOE, Energy Infonnation Administration. 1991. Improving Technology: Modeling Energy Futures forNational Energy Strategy. 

### Service Report to the 1991 National Energy Strategy. Washington, D.C. SRlNES/90-01.

, PageJ-J4 Volume 1: General Methodology

## CHAPTER 4. 

## INDUSTRY PROFILEFOR 

## LAWRENCE 

## BERKELEY NATCOVAL LABORATORY MANUFACTURER IMPACT ANALYSIS

. .

.. . 

### This section begins by examining housing starts and appliance saturation rates, with the primary fixus on annual unit shipments for each of the analyzed products. It then presents a brief descriptionof market shares followedby a discussion ofmergexs and acquisitionsin these industries, and finally a comparisonof consumer and producerprice indices.
4.1 

### DEMAND
., 

### UnitsShipped 

### and Housing 

### Starts

/ 

### One of the primary components driving the demand of most appliances is housing starts. Although the 

### changes in shipments of appliances 

### are not entirely the result of changes 

### in housing starts, housing starts can be 

### used to predict shipments of most appliances for two reasons. Hrst, almost every new house requires a new set of appliances, so fluctuations 

### in housing starts cause fluctuatio~ls 

### in shipmentsof most common appliances. (In 

### fact, most of the fluctuation in shipments ofproducrs being analyzedcanbe attributed to the fluctuations

### in housing starts.) Second, housing starts are a good indicator ofthe strength ofthe economy 

### as a whole. A healthy new-housing market is likely to 

### correspond to a healthy economy, and a healthy ecbnomy encourages replacement and discretionary purchases of appliances. Therefore, there is generally a strong correlation between new-housing starts and appliance shipments. Fi,gures4.1 through 

### 4.3' show annual shipments compared to housing starts for most products. Other factors such 

### as age of the current stock of appliances, purchase prices, fuel costs, consumer incomes, and climate conditions have varying degrees of importance in determining shipments of the various products covered by the National Appliance Energy Conservation Act of 1987 (NAECA). Further, some appliances have strongdemand in the replacement market or 

### in the aftermarket, and thus would be 

### less correlated with new housing starts. Examples of such a 

### product would be 

### microwave ovens. Some of these factors 

### an: used by the Lawrence Berkeley National Laboratory-Residential Energy Model 

### (LBNLREM) .inpredicting appliance shipments in the base and alten~ative efficiency level cases. The manufacturers impact analysis uses the LBNL-REM estimates in its analysis of financial impacts on manufacturers. The 

### explosive growth in microwave ovens is displayed in Figure 4.2. 

### After a slow period of growth in the late 1960sto early 1970s,the popularity of microwave ovens increased steeply in the 1980s, 

### probably due to their greater convenience, improved technology, and lower prices. 

## ' Housing starts data are from 'Table No. 1202. New Privately-Owned Housing Units Started-Selected Characteristics: 1970-1993,"Statistical Abstract of the unitedstates 1994,U.S. DepartmentofCommerce, 1994, p. 730. Shipments data for most products are from "Statistical Review,"Appliance, April 1980, April 1990,and April 1993.~

. . VolumeI: ' GerperulMethodology

. . page 4-1 

### CHAPTER 4. INDUSTRY PROFILE FOR LAWRENCE BERKELEY**NAT[O~AL** 

### LABORATORY MANUFACTURER IMPACT ANALYSIS

Tins section begins by examining housing starts and appliance saturation rates, with the primary focus on annual unit shipments for each ofthe**anal~** products. It then presents a brief description ofmarketshares followed by a discussion ofmergers and acquisitions in these industries, and finally a comparison ofconsumer and producer price indices.


### 4.1 DEMAND Units**Sh~pped** 

### and Housing Starts

. .. J.

. . .'

. .

.Oltle ofthe primary components driving the demand of most appliances is housing starts. Although the changes in shipments ofappliances 

## are not entirely the result ofchanges in housing starts, housing startS can be used to predict shipments of most appliances for two reasons. first, almost every new house requires a new set of appliances, so fluctuations in housing starts cause fluctuatiolls 

### in shipments ofmost common appliances. (In fact, most ofthe fluctuation in shipments ofproduct:s beiIig analyzedcan be attributed to the fluctuations in housing starts.) Second, housing starts are ugood**indic~tor** ofthe strength ofthe economy as a whole. A healthy new-housing market is likely to correspond to a 'healthy economy, and a healthy economy encourages replacement and discretionary purchases of appliances. Therefore, there is generally. a strong**correhltio~** between new-housing starts and appliance shipments.

. Figures 4.1 through 4.3 1 show annual shipments compared to housing

.starts for most products. Other factors such as .age ofthe current stock ofappliances, purchase prices, fuel costs, consumer incomes, and climate conditions have varying degrees of importance in determining sltipments, ofthe various products covered by theNational Appliance Energy Conservation Act of 1987 (NAECA). Further, some appliances have strong demand in the replacement market or 

### in the**aftennarkl~t,** and thus would be less correlated with new housing starts. Examples ofsuch a product 'Would be microwave ovens. Some of these factors are used by the Lawrence Berkeley National Laboratory-Residfmtial Energy Model (LBNL-REM) ·in predicting appliance shipments in the base and altemative efficiency level. cases. The manufacturers impact analysis uses the LBNL-REM estimates in its analysis offinanciaI impacts on manufacturers. The explosive growth in microwave ovens is displayed in Figure 4.2. After a slow period ofgrowth in the late 1960s to early 1970s, the popularity ofmicrowave ovens increased steeply in the 1980$, probably due to their greater convenience,' improv.ed technology, and lower prices.

I Housing starts data are from

..table No. 1202. New Privately-Owned Housing Units Started-Selected Characteristics: i 970-1993," SiatisticalAbstract of the Unlted States 1994, U.S. Depanment ofCommerce, 1994, p. 730. Shipments clata for most products are from "Statistical Review," Appliance, April 1980, April 1990, and April 1993.1 Volume 1:'**Ge~eral** Methodology Page 4-1 . \

### According to an industry trade source, the saturation

### rate of microwave ovens rose from 1.2% in 1973to over 

### 85%in 1993 (1).

I 

### The sales of room airconditioners have little correlationwith housing starts. We suspectthat this 

### is because room 

### air conditioners are bought primarily in the aftermarket and have a strong seasonalcomponent in their purchase. Appliunce 

### Saturation Rates 

### The saturation rate is the percentage of homes that own a particular appliance. Tables 4.1 and 4.2 list the saturationrates for the various 

### analyzed appliances

### in selectedyears during the past 

### 20 years. Note that the 

### data do not reflect the fact that somehomes have more than one appliance of aparticular

### type. In general, if most homes own 

### an appliancebut few homes have more 

### than one unit, we expect that the appliance 

### will have a relatively low elasticity of demand because the applianceis usually considered a necessity. This observation corresponds with the relatively low price elasticity of ranges and ovens and the higher price elasticityof products such 

### as microwave ovens. Table 4.1. 

### Market Saturations
Product 1973 (95) 1978 (96) 1983 (%) 1987(%) 1993 (95) Room Air Conditioners 30 28 ' 27 26 31 Ranges (Electric ). 47 5 1 58 59 58
.,

, Ranges (Gas) 52 48

, . 43 42 45

. . Microwave Ovens 1 7 33 66 86 Source:Appliance, Septkmber, 1994.

Volume 1: General Methodology According to an industry trade source, the saturation rate of microwave ovens rose from 1.2% in 1973 to over 85% in 1993 (1). The sales ofroom air

### conditi~ners have little correlation with housing starts. We suspect that this is because room air**conditio~ers** are bought primarily in the aftermarket and have a strong**seaso~al** component in their purchase. 

### AppliDnce Saturation Rates

. .

_

The saturation rate is the percentage ofhomes that own a particular appliance. Tables 4.1 and 4.2 list the saturation rates for the various analyzed appliances 

### in 

### selected years during the past 20 years. Note that the data do not reflect the fact thatsome hpmes have more than one'appliance ofa particular type. 

## ID general, ifmost homes own an appliance but few homes have more than one unit,we expect thjlt .the appliance will have a relatively ·low elasticity of demand because the appliance is usually considered a necessity. This observation corresponds with the relatively low

. price elasticity of ranges and ovens and the higher price elasticity of products such as microwave ovens. 

### Table 4.1. Market Saturations
Product 1973**!%~** 1978**!%~** 1983**~%~** 1987**~%~** 1993 !%} Room Air Conditioners 30 28 27 26 31 Ranges (Electric ) 47 51 58 59 58 Ranges (Gas) 52 48 43 42 45 Microwave Ovens 7 33 66 86 Source: Appliance, September, 1994.

Page 4-2 Volume 1: GeneralMethOdologySources:StatlstlcalAbstractoftheUnltrdStater1994.Appllanec"StatisticalReview".(Var.Yeara)YC'Z200030UJFigure-4.1KitchenRange'ShipmentsandHousingStarts,....volumei:~eneralMethodologyPage43EP.-"500-m01701'7.2124176178 180I82I84186188 19019269717375.777981.8385878991.YearHousingStarts----ElectricRanges................asRangesIIII IIIIIII4000.r-'-""-".....-----.---.-.....-.--.....-.:---.....-......-....-:---"--'..-------;'...../'........3500'-'---'.....-..-.1.\,.-......--..-.......-..u;-/\7/',/o. /I\IoI\'.. .I3000-..-.......-''-''-r--..........-\-----c-/--...-....-:'-..\._.....-:..----.-,.-.--_.__._-----------.I\.1\r/.............\,I',I.'2500......-·.-..-..-..-...-..-..-.....g,'.,J...............----.-.-...---...----""-"--'...--.--/;0'.-----.----..-.----.-----o..':J:1500cuen-E.es::.en5006973HousingStartsElectricRangesGasRanges7779Source"Statl.tlcalAb.t,.actStatu1994,Applla1fce"StatlstlcelReview",(Va•.Yea.s)Figure.4.1.KitchenRangeandHousingStartsVoillmei:GeneraiMethodology81Year91Page4-JHousingStartsMicrowaveOvensISources:Statlrtlcal Abrtraeta/ theUnltedStater1991.AppNaner"SlstistlcalReview",(Vat.Yean)Figure4.2MicrowaveOvenShipmentsand~ous'in~startsYearPage4-4VolumeI:GenemlMethodology'20000.--------------------r----------------·-f/)aaHousingStarts----MicrowaveOvensa .-15000------.-.-...-.----------.----..--.....-f/)t!cu-C/),. I_._--_._----'._-,Ir--I/'I/'IC).c.Cij10000-::::lo/............I._._._.._-_._-------------------------/",/::I:"Cccu!!cQ)E.9-..cC/)II, I, I/5000--.'._.--_....._..........._-/.._.-...--_._-.-._.._...._-._._._-.-......._..__......_........../-...,',/--_/.......------aSources:S'a"."c,1I,U."aclall".U"".dSlalu1994.Applia"CI"SlatisticalReview·..(Var.Yean)./YearI90 9289914.2MicrowaveOvenShipmentsandHousingStartsPage4-4Volume1:GeneralMethodology.Figure4.3RoomAirconditionerShipmentsandHousingStarts6000I\iOiisingSiarisVoltlrneI:GenemlMethodology'n*5000000-FwPage4-5i\----RACs\/\,''.\-..I\;?4000;\\mf-------+--/''\,\/.\5.\/r\I\0,C...v-/\:.'Z3000\3//*\'\J'LA0\1.r\I.ug2000.,\/.V_-----.--V)'CI.Cg.,...P1000,.C'V)I697173757779818385878991Sources:StatlrtlealAb#tracroftheUnlredStater1994.,,Year-Appliance"StatisticalReview".(Var.Years)..6000"-"HousingStarts\----RACs-\/\5000________g\/.\. /\.\/\i4000-. \ .I".,/\m\/"/\en·\I'/\/\m\ /'v/\/\30005\./\ /.\.-\I•"'0;2000-.!!J·cCDE.9-1000-+-----------------.cCIJI7877Source.:Stat'.tlcalAb."'acto/t",U"It,dStat,.1994,Applla"c,"StatiltlcalReview",(Vat.Years)\ I___..___.___..____.Y-._.__.__._.______.._____._.__._./.'7981838587 8991'YearFigure4.3RoomAirConditionerShipmentsandHousingStarts.Voillme1:GeneralMethodologyPage4-5I .

### Table 4.2 

### Market Saturations* Product 1981(%) 1984 (56) 1987 (9%) Electric Stove ' - 54/29 54R9.5

. . 57/77 Electric Oven 52/74 49/73 57/75 Gas Stove

. 46/23 45/20 2;7/21 Gas Oven 40117

. . 4211 8 41/21

. , Microwave 17/23 3415 1 61/76 Room Air Conditioners 32/17 30119 30116 Source: RECS, 1981; 1984.1987.' *Initial figure is for the stock of dwellings, the second is for new construction. 

### RECS figures include mobile homes, and single- and multi-family dwellings.


### 42 SUPPLY Market Concentration 

### Market concentrationis the extent to which market share is controlled by the largest 

### firms in 

### an industry; Table4.3 shows the market concentration for the products accordingto the degree of the market controlledby the two, four, and five largest manufacturers in each industry. All of the products show a high level of market concentration. The market concentrations ofthe top fiveproducers 

### are atleast 80%. 

### The largestproducercontrols from

### 40%to more than 50% 

### of the market for each of these products. Market concentrationplays a part in the analpis because it may indicate the existenceof market powef' in a particularindustry. 

### Firms with the largest market shares may have some market power, a factor that affects the markups used by these fumsboth in the base case and the alternative efficiency level case.

The relevant

### RECS reports are the data from the public use tapes for the various surveys discussed in Residential Energj Consumption Survey: Housing Charucreristics, 1981,August 1983: DOEIEIA-0314(81),U.S. Department of Energy; Residential Energy Consumption Survey: Consumptionand Expeditures, 1984, October 1986, DOEEIA- .0314(84),U.S. Department ofEnergy; and Household Consumptionand Expenditures, 1987,October 1989. DOEEIA0321lil(87).U.S. Department of Energy.

- Market power is the ability to set price within a limited range. More technically, it is the freedom that results from facinga demand curve that is not infinitely elastic. This means that if price is raised by some small amount, sales will not fall to zero, and ifprice is lowered by some small amount, sales will not become essentiallyinfinite. Page 4-6 Volume I: General Methodology 

### . . Table 4.2 Market Saturations* 1981 (%) 1984 (%)

.54129 54n9.5 Electric Oven 57ns 52n4 49n3 Gas Stove

.**2~7n.l** . 4612] 4Sn.0 Gas Oven 4112] 40IJ7 42118 17n.3 34/51 32117 30IJ9 Source: RECS, 1981; 1984, 1987. 2 ·Initial figure is for the stock ofdwellings,**~e** second is for new construction. RECS figures include mobile homes, and single- and multi-family dwellings.


### 4.2 SUPPLY. AlarkdConcenuanon Market concentration is the, extent to which market share is controlled by the largest firms in an industry:- Table 4.3 shows the marketconcentration for the products according to the**deg~e** . ofthe market controlled by the two, four, and five largest manufacturers in each industry, All ofthe products show a high level ofmarket concentration. The market concentrations ofthe top five producers are atleast 80%. The largest producer controls from 40% to more than 50% ofthe market for each ofthese Proclucts. Market concentration plays, a part in the analysis because it may indicate the existence ofmarket**powe~** in 

## aparticularindustry. Firms with the largest market shares may have some market power, a factor that affects the markups used by these fmns both in' the base case and the alternative efficiency level case.

2 The relevant RECS reports are the data from the public use tapes for the variQUS surveys discussed in Residential Energ}; Consumption Survey: Housing Characteristics. 1981, August 1983; DOElEIA-03]4(81), U.S. Department of Energy; Residential Energy Consumption Survey: COnsumption andExpeiulitures. 1984. October 1986, DOElEIA- ,0314(84); U.S. Department ofEnergy; and Household Consumption andExpenditures. 1987. October 1989. DOElEIA032I1i/(87). U.S. Department ofEnergy.

_ 3 Market power is the abilityto set price within a limited range. MoretechnicaJly, it is the freedom that results from facing a demand curve that,is not infinitely elastic. This means that ifpriceis raised by some small amollnt, sales will not fall tozer-o, and ifprice is lowered by some small amount, sales will not become essentiaily infinite. Page 4-6 Volume 1: General Methodology

### Table 4.3 

### Percent of Market Controlled 

### by the Largest ~rodueers of~aeh Product

s - Numberof Fib Product

. . Top Two (%) Top Four (%) Top Five (%) Ranges (Electric) 57 '89 95 .Ranges(Gas) 49 93 '96 '. Microwave:Ovens

. a ' . 

## . 46 74 82 Room Air Conditioners 47

. 76 82

. . Source:Appliance,September 1994. 

### Figures 4.4 

### to 4.7 

### give a picture of the manufacturermarket shares for each of the analyzed products. 

### Most products 

### are produced by 

### a few manufacturers, each of which have large market

. . 

# . shares.

Volume I: cGen~ml Methodology Page 4-7 

### Table 4.3 Percent ofMarket Controlled by 

### the Largest Producers ofEach Product

Product

. Ranges**(E~ectric)** .Ranges**(G~) Microwavt~**  Ovens Room Air Conditioners Source: AppUaflce, September 1994. TopTwo(%) 57 49 46 47 Number orFinns



### - TopFour(%) Top Five (%) 89 95 93 '96 74 82

. 76 82

Figures 4A to 4.7 give a picture ofthe manufacturer market shares for each ofthe analyzed products

.. Most products are produced by a few manufacturers, each of which**hav~** large market • shares.

.

# .

Volume J: (General Methodology Page 4·7

. 

### . .

# Electric Range Firm Market Shares

Maytag owns Jenn-Air, Hardwick and Magic Chef brand names. Electrolux owns the Frigidaire and Tappan brand names. Raytheon owns the Caloricbrand name. GE.includeswhat was Roper. Source:Appliance, september 1994


### Figure 4.4 

### Electric Range 

### Firm Market 

### Shares: 1993.

Page 4-8 Volume 1: GeneralMethodology 

# Electric Range Firm Market Shares



# IWhirlpool.18% 1

. I

1 May 

## tag 14% 1

Maytag owns Jenn-Air. Hardwick and Magic Chefbrand names. Electrolux owns the Frigidaire and Tappan brand names. Raytheon owns the Caloric brand name. GEincludes what was Roper. Source: Appliance. September 1994

Figure 4.4 Electric Range Firm Market Shares: 1993

..

Page 4-8 1 Electrolux 18%1 I Others 3% 1 

### ·1· Peerless 1% 

### I
I Brown 1% 1 1 Raytheon 6% 1

Volume 1: General Methodology

# Gas Range Firm Market Shares

Source:Appliance. ~ipternber 1994.

, ' 

### ~igure 45Gas 

### Range Firm Market 

### Shares: 

### 1993.

. .

Volume 1: General Methodology . Page 4-9

. J . 

# Gas Range Firm Market Shares

. .' . 

## - 

# IMay tag 24% 

# I I Electrolux 24% 1



# IOthers 1 % 

# I I Peerless 3% 

# I I Raytheon 20% 

# r 

# IBrown 3% 

# I·

IGE 25% 

# I

Source: Appliance, September 1994

.

. . Figure 4;5 Gas**Rang~** Firm Market Shares: 1993.

Volume 1:**G~neral** Methodology Page 4·9

# Microwave Oven 

# Firm Market Shares

Source:Appliance. September 1994.


### Figure 4.6 Microwave Oven Firm Market Shares: 1993.

Page 4-10

, f VolumeI: GeneralMethodology 

# ,Microwave Oven Firm Market Shares 

# I' San:tsung 20%, 

# I 1 Sharp 26% I



# IOthers 2% 

# I
Matsushita 16% 

# I Raytheon 2% 

# I

1 Sanyo 7% I 

# IGoldstar 12% 

# I 

# I**Ele~trolux** 8% 

# I

Source: Appliance. September 1994, ( Figure 4.6 Microwave Oven Firm Market Shares: 1993.

Page 4-10

. Volume 1: GeneralMethodology

# RAC Firm Marke't Shares



# I Matsushita 8% 

# 1

Source:A,ppliance,September 1994. 

### Figure 4.7 

### Room Air 

### ConditionerFirm Market 

### Share: 1993.

Volume I: Ger~eral Methodology . . Page 4-1? 

# RAC Firm Market Shares 

# ,I Electrolux 21 

### % 

# I 

# I Fedders 25% 

# I

lathers 8% 

# I 

# IWhii'llpooI22% 

# I I Raytheon 6% 

# I ,I Carrier 5% 

# I 

# I Friedrich 5% 

# I I Matsushita 8% 

# I

Source: Appliance, September 1994. 

### Figure 4.7 Room Air Conditioner Firm Market 

### Share: 1993.

Volume 1: Gelleral Methodology Page 4-11,

### Mergers andAcquisitions 

### The appliance-manufacturing industry has had a continuing history of consolidation. Mergers and acquisitions have two pupses: First, they produce large corporationswith the financial resources and stabilityto be 

### successfulin 

### an increasingly competitive market. Second, mergers and 

### acquisitions meaq manufacturerscan have a complete line of home appliances for product diversification and can therefore offer a complete set ofappliances to consumers, an important feature in the builder market. There is increasing worldwide competition in the major appliance market, so mergers or acquisitions 

### are likely to continue. Table4.4 

### is a partial list of 'major acquisitions of appliance manufacturers. 

### Table 4.4 

### Acquisitions

### ofAppliance Manufacturers Buyer Purchase Year Whirlpool Heil-Quaker 1964 Raytheon Co. Amana Refrigeration. Inc. 1965 White Consolidated Inc. Franklin Appliance Div. (Studebaker Cop.) 1967 White ConsolidatedInc. KelvinatorAppliance Div. (American Motor Corp.) 1968 Emerson Electric Co. In-Sink-Erator 1968 Magic Chef. Inc. Gaffers & Sanler 1968 Magic Chef. Inc. Dixie-Narco,lnc. 1968 Magic Chef, Inc. Johnson Cop. 1971 AB Electrolux (Sweden) Eureka 1974 Rockwell International White ConsolidatedInc. Caloric Corp. (Raytheon) White ~onsolidakl Inc. White ConsolidatedInc. Magic Chef, Inc. Carrier Raytheon Co. Raytheon Co. United Technologies Corp. Admiral Co. Westinghouse Electric Corp.'~, Major Appliance Opemtions Gle'lwood Range Co. Philco'Appliance Business (Ford Motor Co.) Frigidaire~~~liance Business (General Motors Corp.) Admiral Div. (Rockwell) Jenn-Air Modem Maid Co. Speed Queen (McGraw Edison) Carrier

. AB Electrolux (Sweden) Taooan 1981

Page4-12 VolumeI: General Methodology

. . 

### Mergers and-Acquisitions



### . The appliance-manufacturing industry has had a continuing history of consolidation. Mergers ,and acquisitions have two purposes: First, they produce large**corp~rations** with the fmancial resources and stability to be successful in 

## an increasingly**~ompetitive** market. Second, mergers.and acquisitions meaq manufacturers 

### can have a complete line ofhome appliances for product diversification and can therefore offer a complete set ofappliances to consumers, an important feature in the builder market. There is mcreasing worldwide competition in the major appliance market, so mergers or acquisitions are likely to continue. Table 4.4 is a partial list of**~major** acquisitions ofappliance manufacturers.

. 

### Table 4.4 Acquisitions ofAppliance Manufacturers**Bu~er** Purchase Year Whirlpool Heil-Quaker·

. 1964 Raytheon Co. Amana Refrigeration, Inc. 1965 White Consolidated Inc. Franklin Appliance Div. (Studebaker Corp.) 1967 White Consolidated Inc. Kelvinator Appliance Div. (American Motor Corp.) 1968 Emerson Electric Co. In-Sink-Erator 1968 Magic Chef, Inc. Gaffers·& Sattler 1968 Magic Chef, Inc. Dixie-Narco, Inc. 1968 Magic Chef, Inc.· Johnson Corp. 1971

.AB Electrolux (Sweden) Eureka 1974 RockwelllQtemational Admiral Co. 1974 White Consolidated Inc. Westinghouse Electric Corp.'s, 1975 Major Appliance Operations Caloric Corp. (Raytheon) Glenwood Range Co. 1978 White Consolidated Inc. Philco Appliance Business (Ford Motor Co.) 1979 White Consolidated Inc. Frigidaire Appliance Business (General Motors Corp.) 1979 Magic Chef. Inc. Admiral Div. (Rockwell) 1979 Carrier Jenn-Air 1979 Raytheon Co. Modem Maid Co. 1979 Raytheon Co. Speed Queen (McGraw Edis,?n) 1979 United Technologies CorP. Carrier 1919

. AB Electrolux (Sweden) Talman 1981

Page 4-12 Volume I: General Methodology'

. 

### Table 4.4 Acquisitionsof~pplianee Manuf&&rs Con't Buyer Purchase 

### Year

. , Dart & haft ,HobartCorp.

. . 1981 Magic chef. Inc.

. Maytag Go. Maytag C.D. , Revco

. Hardwick Stove

, , , ' Jenn-Air (CarrierIUnitedTechn)

, 

### . Hobart (Dlart 1Pr Kraft) Chambers Corp. (Rangaire Corp.) ' . 1983 Magic Ch'ef, Inc.

. Toastmaster : 1983 Admiral (Magic Chef) Chicago Pacific Corp. Warwick Manufacturing Co. - . , , 1985 The Hoover Co.

. . . . -- 1985 Mpxo Corp. Thermador-WasteKing 

### (NI Ind.) ' 1985

. , AB Elecmolux(~weden) White Consolidated

### industries' Maytag Co. Magic Chef, Inc.

. . , . 1986

. , Chicago Pacific Corp. Rowenta Group (West Germany) 1986 (AlleghenyIntl and Rothmans ~eutkhland GmbH) Whirlpool Corp. Kitchen-Aid Division @art & Kraft, Inc.) 1986 General ~~ecrrid Co.


### . ' RCA 1986 Intercity Gas Corp. (Canada) Heil-Quaker~ome Systems, Inc. (Whirlpool) 1986 Emerson Eilectrid Co., Kitchen-Aid dishwashing &nufacturingfacility (Whirlpool) 1986 White Corrsolidated Ind. (Electrolux) Design &Manufacturing Corp. Thornson :LA. (France) GURCAconsumer elektronics 1988 Toastmaseer Div. Mgt. team buys division from Maytag Co.

. 

### . 1988 Speed Qutzn Holiday-Hammond 1988 Whirlpool Corp.. Roper Cop.'s b~d name 1988 General .El,ecnic Co. Roper Corp.'s physical assets (inc. manufacturing facilities) ,1988 ' Maytag CO. Merger with Chicago Pacific Corp. 1988 Fedders ' Emerson Quiet 

### ~601 1990

MCD Mayta~ microwave oven business I 1992

, .

Volume 1: GeneralMethodology Page 4-13 

### . Table 4.4 Acquisitions ofAppliance Manufacturers**~** 

### Con't. 

### Buyer Dart & Klaft Magic Chef,**I:n~.** MaytagGo. MaytagCo. Hobart (Dart & Kraft) Magic Chef, Illlc. Admiral (Magic Chef) Chicago Pacific Corp. M!lSCo COrp. AB Electr·olux (Sweden) MaytagCo. Chicago Pacific Corp. Whirlpool Corp. General EUectric Co. Inter-City Gas Corp. (canada) Emerson ElectriC Co. White Consolidated Ind. (Electrolux) Thomson S.A. (Fr;mce) Toastmast,er Div. Speed QUf:en Whirlpool Corp

.. General.Electric Co. Maytag Co. Fedders Hayward Pools MCD

Volume 1: "]eneral Methodorogy 

### Purchase

.Hobart Corp. Revco Hardwick Stove

. Jenn-Air (CarrierlUnited Techn) Chambers Corp. (Rangaire Corp.)

. Toastmaster Warwick Manufacturing Co

.. The Hoover Co. Thermador-WasteKing (N1 Ind.)

. White Consolidated Industries Magic Chef, Inc. Rowenta Group (West Germany)

. (Allegheny Int'l and Rothmans Deutschland GmbH) Kitchen-Aid Division (Dart & KI'Ilft, Inc.) RCA Heil-Quaker Home Systems, Inc. (Whirlpool) Kitchen-Aid**dishw~hing rilanu~actUi'ing**  facility <Whirlpool) Design & Manufacturing Corp. GEJRCA consumer electronics Mgt. team buys division from Maytag Co. Holiday-Hammond Roper COl"P..'s**b~d** name Roper Corp.'s physical assets (inc. manufacturing facilities) Merger with Chicago Pacific Corp. Emerson Quiet Kool ComfortZone Maytag microwave oven business . Year 1981 1981 1981 1982 1983 1983

. 1985**~**

1985 1985 1986 1986 1986 1986 t986 1986 1986 1987. 1988 1988 1988 1988 ·1988

1988 1990 1991 1992

Page 4-13- 43 

### PRICES Appliance prices have, on average, risen more slowlythan aggregate indexes measuring consumer and producer prices. The applianceindustry has a long history of price stability even through fluctuations in the business cycle. '

### Despite increasedenergy and component prices during the 1970sand 1980s,the industryhas, through increased productivity, kept price increases lower than increases in the ConsumerPrice Index (CPI)." 

### In recent years, the industry has made 

### a strongpush toward automation to keep costs down, spendinghundreds of millions of dollars to automateproduction facilities. Figures 4.8 to 4.10 show the real Producer Mce Index (PPI) for various product groupings compa. to the CPI. Figure 4.10 shows the PPI for all co~mrnodities and for household appliances for 

### the ycars 1985to 1993. The PPI for household appliances increased about half as much as the PPI for all commodities. The real PPI for each product grouping is defined as the PPI for that product grouping,holding the overall PPI constant at 1982

### = 100. 

### In other words, this 

### figure indicates the relative increase or decrease in the price index of a product grouping, using the PPI for all commodities as the yardstick. The subsequent figures and their products are Figure4.8: 

### Household Cooking Equipment (kitchen ranges and ovens and microwave ovens),

. 

### ~i~ure 4.9: Refrigeration and 

## eating Equipment (including RACs), Figure 

### 4.10: Household Appliances.

'The consumerand producer prke indices are' from

# apersonal communication with the San Francisco, CA office of the U.S. Bureau ofLabor-Statistics, May 1995. Page '4-14 Volume 1: General Methodology \ 4.3 PRICES

, , . . Appliance prices have, on average, risen more slowly than aggregate indexes measuring consumer and producer prices. The appliance industry has a long history ofprice stability even through fluctuations in the business cycle. "Despite increased energy and component prices during the 19705 and**1980s~** the industry has, through increased productivity, kept price_increases lower than increases in the Consumer Price Index (CPl).4 In recent years, the industry has made a strong push toward automation to keep costs down, spending hundreds of millions ofdollars to automate production facilities.**F~gures** 4.8 to 4.10 show the real Producer 

## I1ice Index (PPl) for various product groupings compared to the CPl. Figure 4.10 shows the PPI for all cohlmoditiesand for ' household appliances for

# the years 1985 to 1993. The PPI for household appliances increased about half as much as the PPI for all commodities. The real PPI for each product grouping is defined as the PPI for that product grouping, holding the overall PPI constant at 1982 ::: 100. In other words, this figure indicatesthe relative increase or decrease in the price index ofa product grouping, using the

- PPI for all commodities as the yardstick.

- The subsequent figures and their products are • ' Figure ,4.8: Household Cooking Equipment (kitchen ranges and ovens and microwave ovens),

• Figure 4.9: Refrigeration and Heating Equipment (including RACs),
, • Figure 4.10: Household Appliances.**4Th~** consumer anQ producer price indices ani from a personal communication with the San Francisco. CA office of the U.S. Bureau ofLabor·Statistics. May 1995. Page4-14 Volume 1: GeneralMethodology

-rYearPPIi---CPI.............,..-HHCookingEqmtFigure4.8NationalPPIandCPI ComparedtoHouseholdCookingEquipmentProductGmupPPI----.AP..I.~olrtnteI:GenetalMethodologyPage4-15-ooor-"NClO(J)or--)(CD."CC..•...........--...--..---.J._..____. _....______._.__.__.'_______-.................................::...:.:.:.--:".":":":.-::...............................__._...._.._--_.._------_.-.:....._._-_.._._---'-------_._---..',/......,....,/'---7---.--------'-------.---.--.------.-.--------------------,///I.......---.-..._------------_._._..._---_._._-----_._----_..._-------_._-,----..-.--.--..---.--......-...--.---.---.---.---.-.:'------76PPI-'---CPIHHCookingEqmt7880'82...---_._--_..._-------_._._-----_._-------'--, r..8486889092YearFigure4.8NationalPPIandCPItoHouseholdCooking-EquipmentProductGr:oupP'PIVoillmeI:GeneralMethodologyPage4·15Figure4.9NationalCPIandPPIComparedtoRoomAir Conditioners4020-0--Page4-16VolumeI:GenemlMethodologyPPI----CPI................RAC(3585)II7981II83185878993788082848688.9092YearI160140120-g100......IINco80en......-)(.0>60-.'aC40200·-78............•__F••••••:.:-••__",.--------:-:.::-:--:::'.:::.. . . . . ......_._-_._....-._._-_.__..._----_...........__._-.._--_....._..__..__......._._.,....----.._-_.._..._._..-...•---.--80PPICPIRAC(3585)-·-T-·8486889092YearFigure4.9NationalCPIandPPIComparedtoRoomAirConditionersPage4-16VolumeI:GeneralMethodologyI...............HHAppliancesI,..jl#,.'*;..:..YearCPI----&@re4.10~ationalCPIandPPIComparedtoAllHouseholdAppliances.,.VolumeI:GenemlMelltodologyI..Page4-17.--.--...---........._----...._.._----_._--:..-..__._--_..-.,_.._..._------..'--'-"-----.---.----I . . . _ "__1_--_.,.__._..._.._.--'.---"--'-'._'--.- - - - - =--i__---------------.-.----L.................................................................................._........•••••••__•_____________..-oo..-IINil-------------.-.---..-..-..-..--.-......-.--.---------.---..-_..-.-.-.--..----.-----c__'--_____:__.._-'____...__._.:._____..___.._.:..__.____..-._'.._..______:___..._____._____________-____.-I85PPt----CPtHHAppliancesI86I87I88...I·19089YearFigure4.10NationalCPIandPPIComparedtoAllHouseholdAppliances.VolumeI:GeneralMethodology929193Page4-17

### REFERENCE 1. "A 

### Portrait of the 

### U.S. Appliance Industry 1994,"Appliance, September, 1994, p. 

### 70.

. -

Page 4-18 

### . Volume 1: GeneralMethodology REFERENCE 

### 1. "APortrait ofthe U.S. Appliance 

### Industry 1994,"Appliance, September, 

### 1994, p. 

### 70.

Page. 4-18

. Volume 1: GeneralMethodology

## CHAPTER 

## 5. DEVELOPMENT OF BASE CASE FORECASTS

, 

### Analyzing impacts of federal alternative energy efficiency levels requires comparing projected U.S. residential energy consumption with and without the efficiency levels. ll'he cases without efficiency levels are referred to as bare careprojections. 

### These base case projections 

### are comparedto projectionsofconditionsthat would be likely to prevail 

### if alternativeefficiency levels were enacted (see Chapter 3). The difference between the two projections is defined 

### as the impact of implementing new efficiency levels. Projections are made for a number of demographic, economic, and energy' variables,

. 

### including; energy prices, household income, housing stock, housing starts, 

### mix of house types (single-family, multi-family, mobile homes), building shell .thermal characteristics, appliance purchases, equipment prices, unit energy consumption, and aggregateresidential energyconsumption by fuel type., ! 

### 5.1 

### ECONOIMICAND DEMOGRAPHICDATA: RESIDENTIAL The 

### CBNL-REM (described in Appendix 

### B) .isused to derive projections of residential energy demand. These projections are dependent on assumptions about future years, including occupied households, number of annual housing starts, disposable personal income, and energy prices. These data 

### are describedin Tables 5.1 through 

### 5.5. (Economic data 

### specificto 

### each product, such 

### as equipment price, initial market shares, elasticities for market shares, efficiencychoices, 

### and usage behaviors are described in Appendix 

### C of each product-specific 

### Part.) 5.l.1 

### Occupied 

### Households Tlhe number and type of occupied households for the base year (1980) are from the 0.1% 

### Public Use Microsample (PUMS) of the 1980 Decennial Census. The occupied hokeholds projectiorl 

### from 1992

### to 2010 

### is obiained @om DOEEIA's 

### AnnualEnergy 

### Outlook 

### 1995.(1). 

### The stock of households is interpolated in years 1981to 1991.

VolumeI: GerteralMethodology Page 5-1 

## CHAPTER 5. DEVELOPMENT OF BASE CASE FORECASTS
An3.Iyzing impacts of federal alternative energy efficiency levels requires comparing projected U.S. residential energy consumption with and without the efficiency levels. The cases without efficiency levels are refeired to as 

### base caseprojections. These base case projections are compared, to projections ofconditions that would be likely to prevail 

### ifalternative efficiency levels were enacted (see Chapter 3). The difference between the two projections is defined as the impact ofimplementing new efficiency**lev~ls.** Projections are made for a number of demographic, economic, and energy variables, including energy prices, household income, housing stock, housing starts, mix of house types (single-family, multi-family, mobile homes), building shell 'thermal characteristics, appliance purchases, equilPment prices, unitenergyconsumption, and aggregate residential energy consumption by fuel type., 

### 5.1 ECONOMIC AND DEMOGRAPHIC DATA: RESIDENTIAL The LBNL-REM (described in Appendix B) is used to derive projections of residential energy demand. These projections are dependent on assumptions about future years, including occupied households, number of annual housing starts, disposable personal income, and energy prices. These data are described in Tables 5.1 through 

### 5.5. (Economic data specific to each product, such as e<luipment price, initial market shares, elasticities for market shares, efficiency choices, ana usage behayiors are described in Appendix C ofeach product-specific Part.) 

### 5.1..1 Occupied Households Tille number and type of occupied households for the base year (1980) are from the 0.1 % Public Use MicroSample (PUMS) of the 1980 Decennial Census. The occupied households projection from 1992 to 20io is obtained trom DOElEIA's

### Annual Energy Outlook 

### 1995.0). The stock ofhouseholds is interpolated in years 1981 to 1991.

Volume]: General Methodology PageS-]

### Table5.1 Occupied Households Projections (millions)

Sources: DOElEIA Annual Energy Outlook 1995. 

### U.S. Department of Commerce, Bureau of Census (2).



### Occupied Households 80.4 86.5 93.4 97.5 101.9 106.2 110.7 130.8



### 5.1.2 Housing 

### Starts Figures for past (1980-1989) 

### housing starts by state come from the Bureau 

### of 

### Census' 

### Housing Starts. Projections (1990-2010) 

### come from DOEEIA's 

### Annual Energy 

### Outlook 1995.

. 

### Housing starts in later ye& (2011-2030) 

### are extrapolated using the projected annual growth rises from 2000 

### to 2010. 

### Singlefamily 53.8

. 

### 58.3 63.8 68.0 7 1.8 75.2 78.6 91.6

Page 5-2 . . Volume 1: General Methodology 

### Multi-

s 

### family 22.7 23.6 24.5 24.2 24.7 25.6 26.6 33.4 

### Mobile Homes 3.9 4.6 5.1 5.3 5.4 5.5 5.5 5.8 

### Table 5.1 Occupied Households Projections (millions)

. 

### Occupied Single- Multi- Mobile Households family family Homes 

### 1980 80.4 53.8 22.7 3.9 1985
.**86~5** 

### 58.3 23.6 4.6 1990 93.4 ·63.8 24.5 5.1 1995 '97.5 68.0 24.2 5.3 2000 101.9 71.8 24.7 5.4 2005 106.2 75.2 25.6 5.5 2010 110.7 78.6 26.6 5.5 ,2030 130.8 91.6 33.4 5.8 Sources: ,DOFJEIAAIIIUllJIEnergy Outlook 1995

.. U.S. Department ofCommerce, Bureau ofCensus (2).


### 5.1.2 Housing starts 

### Figures for past 

### (1980-1989) housing starts by state come from the Bureau of Census' 

### Housing Starts. Projections 

### (1990-2010) come from DOElEIA's 

### Annual Energy Outlook 1995. Housing starts in later years 

### (2011-2030) are extrapolated using the projected annual growth rates from 

### 2000 to 

### 2010. ' ' Table 5.2 Housing Starts 

### Projections (thousands)

.- 

### SingleTotal family Past 

### 1980 1514 852 " 

### 1985 ,2025 1072 Projected 

### 1990 1392 901 1995 1605 1142 2000 1645' 1041 2005 1721 1063 2010 1780 1059, 2030 2110 1093 Sources: Bureau ofCensus, Housmg Starts (3). DOElEIAAnnual Energy Outlook 1995. LBNL extrapolation. 

### Multi- Mobile family Homes



### 440 222 670 284

303 188

' . 

### 230 233 388 216 435 223 496 225 775 242 

# -

Page 5-2 Volume 1: General Methodologj

### . .

. , .'
.

I, ' 

### 5.1.3 

### Disposable personal Income Disposable personal income (1980-1986) 

### is from the Survey of Current Business (4), 

### converted to 1990 

### dollars using the Consumer

### Price Index-Urban. For 2011-2030, 

### the projection is extended, assuminga

### linearincrease

### in disposablepersonal income, withthe annual increasebeing the average ofthe increases from 1993

### to 2010. 

### Annual Energy OutlookI994 (5) 

### provides the figure ' 

### for 1990 

### :and A~znuul Energy Outlook 1995 provides the most recent projections for 1992 

### to 2010. 

### The figures for 1987- 1989 

### and 1991

### are obtainedby interpolation.


### Table 

### 53 Disposable Personal Income (1990$)



### Projected

, 1990 1995. 2000 2005 2010 

### Past



# 1

- 2030 

# I 8787 

# I 66.4 Sources: (1980-1986) Survey ofCurrent Business. '(1990) DOEJEIA~&ual Energy Outlook 1994. ' (1992-2010) 

### DO= AnnualEnergy Outlook 1995. (2011-2030) 

### LBNLextrapolation. 

### Total

.. 

### (Billions)

.



### 5.1.4 

### Resiidential Energy Prices

, 

### . Per Oc'cupiedHousehol'd (Thousands)

Past 

### energyprices (1980-1988) 

### are 

### fromthe DOE/EIA's Monthly Energy Review (6).

### Energy prices for 1989 

### and 1990 

### fromDOE/EIA'sAnnual Energy Outlook1994. Energyprices for 1992

. '
' 

### to 2010

### are obtained 

### fromDOWEIA's Annul Energy Outlook 1995. Extrapolationof energy prices

(201 1-20:30) 

### assumes average annual increases of 0.3% 

### per year for electricity, 0.8% 

### per year for natural gas, 

### 1.?% 

### per 

### year for distillate,and 1.6% 

### per year for 

### LPG.

, , \ 

### ~iectricity prices 

### are further adjusted to reflect differences in effective price by specificend uses. 'Table5.5 

### shows multipliers for obtaining effective-prices for space

### heating, water heating, air conditioning, and other end uses from the average sectorprices in Table 5.4.



### . .

VolumeI: General Methodology


### . . 

### . Page 5-3

### 5~1.3 Disposable Personal Income

. . Disposable personal income 

### (1980-1986) is from. the Survey of Current Business 

### (4). converted to 

### 1990 dollars using**th~** Consumer**Pri~** Index-Urban. For 

### 2011-2030, the projection

. is extend(:d, 2\Ssuming a linear**inc~** in disposable personal income, with the annual increase being.

the average ofthe increases from 

### 1993 to 

### 2010. AnnualEnergy Outlook 1994 

### (5) provides the figure for 

### 1990 :andAnnual Energy Outlook 1995 provides the most recent projections for 

### 1992 to 

### 2010. The figures for. 

### 1987-1989 and 

### 1991 

# are obtained by interpolation.


### Table 5.3 Disposable Personal Income (1990$)

, Total

.Per Occupied Household

.. (Billions)

. (Thousands) Past 

### 1980. 2890 35.9**19~5** 

### 3435 39.4 Projected 

### 1990 4046 43.1 1995. 4478 45.9 2000 5029 49.4 2005 5481 51.6 2010 5914 53.4 2030 8787 66.4 Sources: (1980-1986) Survey o/Current Business. (1990) DOFJEIAAnnual Energy Outlook 1994. (1992-2010) DOEIEIAAnnual Energy Outlook 1995. (2011:-2030) LBNL extrapolation. 

### 5.1.4 Res:idential Energy Prices Pa:Stenergy prices 

### (1980-1988) are from the DOFJEIA's Monthly EnergyReview 

### (6). Energy

. prices for 

### 1989 and 

### 1990

# are from DOElEIA'sAnnualEnergy Outlook 1994. Energy prices for 

### 1992

to 

### 2010 are obtained from DOElEIA'sAnnualEnergy Outlook 1995. Extrapolation ofenergy prices 

### (2011-2030) assumes average annual increases of

### 0.3% per**y~ar** for electricity, 

### 0.8% per year for natural**g~s,** 1.7% per year for**di~tillate,** and 1.6% per year for LPG. , Ei,ectricity prices are further adjusted.to reflect differences in effective price by specific end uses. Table 

### 5.5 .shows multipliers for obtaining effective prices for space heating. water heating. air conditioning. and other end uses from the average sector prices

# in Table 

### 5.4.

.



## . Volume 1: .GeneralMethodology

. Page 5-3

### These multipliers captureregional differences in residential rates and applianceholdings, 

### as well 

### as effects of rate structure. 

### (Forexample, if electric heat is common in 

### areas with lower residential rates, then the effectiverate forelectric spaceheating is lower than 

### the average residential rate. Also, if the block structure offers declining 

### rates for 

### incnased consumption, then end uses associated with higher consumption, including space heating, will have lower effectiverates.) The multipliers 

### are derived from the 1990Residential

### Energy Consumption Survey 

### (RECS)(7). 

### Table 5.4 Average Residential Energy Prices (1990 

### Dollars per Million 

### Btu) Electricity* Natural Gas Distillate Oil


### . LPG Past

Projected

, '

1991 23.87 5.76 7.57 8.4 1 1992 24.62 5.87 6.86 8.52 1993 24.30 6.00 6.55 8.63 2000 23.93 5.88 7.70 9.55 2005 24.66 6.7 1 8.24 11.65 2010 25.67 6.92 8.71 12.99 2030 28.44 13.77 12.03 20.10 *34 12Btu/kWh. Source: (1980-1988) DOEMonthly Energy Review. (1989-1990) DOEIEIA AnnualEnergy Outlook 1994. 

# - (1991) interpolated by 

### LBNL: (1992-2010) DOE/EIA AnnualEnergy Outlook 1995. (201 1-2030) extrapolated by 

### LBNL.



### Table 5.5 

### Relative Electricity Price by End Use

(~vera~e Residential Electricity Price 

### = 1.00) Electricitv Natural Gas

Page

. 5-4

. Space Heating 0.87 0.98 Water Heating 0.90 1.01 Air Conditioning 0.99 - NA OtherEnd Uses 1.04 1.1 1 Source: 

### LBNL derived from 1990 R~CS.

I I VolumeI: GeneralMethodology , . These multipliers capture regional differences in residential rates and appliance holdings,as well as effects .of rate structure. (For example,.if electric. heat**i~** common in areas with lower residential rates, then the effective rate for electric space heating is**l~w(!r** than the average residential rate. Also, if the block structure offers declining rates for increased consumption, then end uses associated with higher consumption, including space heating, will have lower effective rates.) The multipliers·are derived from the 1990 Residential Energy Consumption Survey (RECS) (7).

Table 5.4 Average Residential Energy Prices (1990 DollarS per Million Btu)
Electricity· Natural Gas Distillate Oil LPG Past 1980 .22.44 5.71 11.16 12.37 1985 27.75 7.21 9.41 9.25 1990 .23.74 5.69 7.64 8.31 Projected

199] 1992 1993 2000 2005 2010 2030**Pa~e** 5-4 23.87 5.76 7.57 24.62 5.87 6.86 24;30 6.00 6.55 .23.93 5.88 7.70 24.66 6.71 8.24 25.67 6.92, 8.71 28.44 13.77 12.03 ·3412 BtulkWh. Source: (1980-1988) DOEMonthly Energy Review. (1989-1990) DOFJEIAAnnual Energy Outlook 1994. (1991) interpolated by LBNL: (1992-2010) DOFJEIAAnnuaiEnergy Outlook 1995

.

. (2011-2030) extrapolated by LBNL.

Table 5.5 Relative Electricity Price by End Use

(Average Residential Electricity Price 

### = 1.00) Electricity**Spa~** Heating 0.87 Water Heating 0.90 Air Conditioning 0.99 Other End Uses 1.04 Source: LBNL derived from 1990 RtCS. Natural Gas
0.98 1.01 NA 1.11 8.41 8.52 8.63 9.55 11.65 12.99 20.10

Volume 1: G.eneral Methodology

### 5.2 SENSITIVITY

## ANALYSIS OF 

### THEBASE 

### CASE 5.2.1 Resiidential Sectorsensitivity Cases

. . 

## hes sensitivity cases forthe residedtial sector 

### are defined as follows: Lower Equipment Price. 

### For each class, the price of the baseline unit and 

### the incremental prices associated with each other design option are reduced by the uncertainty estimated forthat cost. Higher Equipment Price. 

### For each class, the price of the baseline unit 

### and the incremental prices associated with each other design option are 

### increased by the uncertainty estimated for that cost. Lower Energy,Price. 

### Assume lower energy prices. Starting from 1996 

### to 2030, electricityprices are 3%

### lower, .whilegas and'distillateprices are"5% 

### lower than those in theAnnual Energy Outlook 

### 1995 forecast. Higher Energy Price. 

### Assume higher energy prices. Starting from 1996

### to 2030, , 

### electricity prices 

### are 3% higher, while gas and distillate prices 

### are 5%higher than 

### those in the Annual

### Energy Outlook 

### 1995 

### forecast.

. . 

### High Equipment Eficiency. 

### Assume continuing future improvement in appliance efficiencies at a rate 

### of 2% 

### per year.
8 

### Market Discount Rates .Decline. 

### Assume 'that market discount rates used to determine future efficiency choices are declining over time by 2% per year, i.e.,

. . 

### efficiency improvements appear in the marketplace sooner. 

# - The results of these sensitivitycases 

### are presented in Sections 2.3 (base Case sensitivity) and 3.6 

### (alternativeefficiency level impah)of each product-specific Part 

### of this report.

. 

### . VolumeI: Geneml ~ethodolo~~ Page 5-5 

### 5.2 SENSITMTY ANALYSIS OFTHE BASE CASE 5.2.1 Resildential Sector SenSitivity Cases

, The sensitivity cases for the residential sector are defined as follows:



### • Lower Equipment Price. For each class:, the price of the baseline unit and the incremental prices associated with eaCh other design option are reduced by the uncem;tinty estimated for that cost. '



### • Higher Equipment Price. For each class, the price of the baseline unit and the incremental prices associated with' each other (jesign option are**inc~ased** by the uncertainty estimated for that cost. '



### • Lower Energy Price. Assume low¥r energy prices. Starting from 1996 to 2030,**~lectricity** prices are 3% lower, ,while gas and 'distillate prices are'5% lower than those in the

### Annual Energy Outlook 

### 1995 forecast.



### • Higher Energy Price. Assume higher energy prices. Starting from 1996 to 2030, electricity prices are 3% higher, while gas and distillate prices are 

### 5% higher than those in the

### AnnualEnergy Outlook 

### 1995 forecast.



### • High Equipment Efficiency. Assume continuing future improvement in appliance efficienCies at a rate of2% peryear. '


### • , Market Discount Rates ,Decline. Assume 'that market discouIit rates used to

, determine future efficiency choices are declining over time by 2% per year, i.e., '

efficiency improvements appear**l~** the marketplace sooner. . The results ofthese sensitivity cases are presented in Sections 2.3 (base case sensitivity) and 3.6 (altemative efficiency level impacts) ofeach'product-specific Part ofthis report.

Volume 1: General Meihodology 'Page 5-5

### REFERENCES
1. U.S. Department of Energy (U.S. DOE), Energy InformationAdministration. 1995. Annual Energy Outlook 

### 1995withProjections to 

### 2010. Washington, D.C. DOEEIA-0383(95), January. 2. 

### U.S. Department ofCommerce,Bureau ofthe Census. 1983.1980CensusofPopulation Md 

### Housing. 

### Washington, D.C. PC80- 1-A. 3. 

### U.S. Department of Commkrce, ~"reau of. 

### the Census. 1994. Housing Stons. 

### Washington, D.C. CB-95-15 1, July. 4. 

### U.S. Department of Commerce, Bureau of Economic Analysis. 1981-1987. Survey 

### of Current Business. 

### Washington, D.C. 5. 

### U.S. DOE, Energy Information Administration. 1994. Annual Energy Outlook 

### 1994with Projections to 

### 201 0. Washington, D.C. DOEEIA-0383(94),January. 6. U.S. DOE, Energy Information Administration. 1994. Monthly Energy Review. 

### Washington, D.C. DOE/EIA-0035(94/06),June. 7. U.S. DOE, Energy Information Administration, Office of Energy Markets and End Use. 1990. Residential Energy Consumption Survey: Household Energy Consumption 

### and Expenditures 

### 1990. Washington, 

## D.C.DOE/EIA-0321(90),February.

Page .5-6

. Volume 1: GeneralMethodology 

### REFERENCES

1. 

### U.S. Department ofEnergy (U.S. DOE), Energy Informatiop Administration. 1995. Annual Energy Outlook 

### 1995 with Projections to 2010. 

### Washington, D.C: DOFJEIA-0383(95), January. 2. U.S. Departffient ofCommerce, Bureau ofthe Census. 1983. 1980 Census of Population imd 

### Housing. 

### Washington, D.C. PC80-1-A.

. . 3. U.S. Department ofCommerce,

### B~reau ofthe Census. 1994. Housing Starts. 

### Washington,

. D.C. CB-95-151, July. 4. U.S. Depanmentof Commerce, Bureau of Economic Analysis. 1981-1987.**Surve~** 

### of Current Business. 

### Washington, D.C.**5~ U.S~**  

### DOE, Energy Information Administration. 1994. Annual Energy Outlook. 

### 1994 with Projections to 2010. 

### Washington, D.C. DOFJEIA-0383(94),January.

. 6.
. U.S. DOE, Energy Information Administration. 1994. Monthly Energy Review. 

### Washington, D.C. DOFJEIA-0035(94/06), June. 7. U.S. DOE, Energy Information Administration, Office of Energy Markets and End Use. 1990. Residential Energy' ConSumption Survey: Household Energy Consumption and Expenditures 1990. 

### Washington, D.C. DOFJEIA-0321(90), February.

Page5-6 Volume 1: General Methodology

## APPENDIX A. 

## UNCERTAINTY ANALYSIS FOR 

## ENERGY EFFICIENCY Many, of the inputs to the calculations of energy savinis for each design option have some degree of uncertainty associated with them. This .uncertainty of the inputs affects the level of confidence in the estimate of energy.savings for design options. As design options 

### are combined, the effects of the uncertainty on the inputs 

### are also combined, so that the uncertainty in the energy savings becomes larger and larger. 

### in oderto understand the effects that the uncertainty of input values had on the calculation ofenergy savings,arisk-analysisprogram 

### (1)was used to quantify the uncertainty. Therisk-analysis program (See 

### Figure A.1.) 

### was 

### an add-in to the spreadsheets used to generatethe cost/efficiency tables shown in this report. 1t 

### allowed the inputs to the calculations to 

### be specifiedby 

### an assumed distribution of values with associated probabilities. After the probabilities were specified, the energy-savings spreadsheetwas recalculated severalhund times, randomlychoosing a value from each distribution. 

### The selection was done in such a way that the distribution of input values chosen matched the probabilities specified in 

### the input distribution. The baseline w& assumed to 

### be exact forpurposes of this study, therefore no distribution was applied to any of the inputs on the baseline models. The uncertaintyof the inputs forcombined or stacked1design optionsincluded (i.e., were applied on top of) the uncertainties of previous design options. Often different design options affected different parts, of the energy consumption calculatio.ns, An 

### exampleofthis would be applying heat traps to a gas-fired storagewater heater-and

, . 

### then applying a submerged combustionchamber: The heat traps reduce the standby loss, while the submerged combustion chamamber increasesthemxoveryefficiency. 

### In these cases, the uncertainties .fromprevious Zlesign optionswere used in.thecalculations, even though that part of the calculation was not being changed by the current design option. The.results of the uncertainty analysis were reported only-forthe maximum technologically feasible design options. These results include the uncertaintiesof all the design options that were applied before the maximum technologically feasible design option. The upper and 

### lower values reported for the 

### uncertainty of the efficiency indicator span the middle 95%

### of all values resulting

..



### . from the 1~ncertaint.y analysis. For room air conditioners, the method was modified slightly to accommodate the use of a simulation model. 

### An input file to the simulition model was constructed for the baseline and each design option. The datain the input fdes specified the properties and operating conditionsof a 

### room

. . 

### air conditioner. me risk-analysis, program was 'used-

### to generate several hundred simulation model input files. The distributionof values in 

### the input files that were usedto,specifychanges from the baseline by the design options reflected the probability distribution entered into the risk-analysis program. The combinedresults of all the simulationruns thus reflected the impact of uncertainty

. , 

### on the inputs. ~bst of the input values were assumed to have a normal distribution,with 

### the selected value

. 

### being the mean 

### of the distribution. ,Thestandard deviation of-the distribution 

### was then specified as VolumeI:' Ge~eral Methodology PageA-1


### . 

### APPEND][x A. UNCERTAINTY ANALYSIS FOR ENERGY EFFICIENCY
Many ofthe inputs to the calculations ofenergy savings for each design option have some degree of uncertainty associated with them. This**un~rtainty** of the inputs affects the level of confidence in the estimate ofenergy.savings for design options. As design options are combined, the effects 

### ofthe uncertainty on the inputs are also combined, so that the uncertainty in the energy savings becomes larger and larger. In order to understand the effects that th.euncertainty ofinput values had or:t the calculation ofenergy savings, a risk-analysis program (1) was used to quantify the uncertainty. Therisk-analysis program (See Figure A.l.) was ail add-in to the spreadsheets used**t~** generate the cost/efficiency tables sho·wn in this report. 

## Ii allowed the inputsto'the calculations to be speCified by an assumed distributifon IOf values'with associated**probabilities~** After the probabilities were specified,. the energy-savings spreadsheet was recalculated several hundred times, randomly choosing a value from

. each distribution. The selection was done in such a way that .the distribution ofinput values chosen matched the ']probabilities specified in the input distribution. The**basel~ne** waS assumed to 

### be exact for purpOses ofthis study, therefore no distribution was**applif~** tlO any ofthe inputs on the baseline models. The uncertainty. ofthe inputs for combined or stacked! design options included (i.e., were applied on top of) the uncertainties ofprevious design options. Often different design options affected different parts. of the**~nergy ~onsumption**  .calculations. An example ofthis would be applying heat traps to a gas-fired storage water heater·and then applying a submerged combustion chamber: The heat traps reduce the standby loss, while the submerged combustion chamber increases therecoveI)' efficiency. In these cases, the uncertainties 'from previous liesign options were used in .the calculations, even though that part ofthe calculation was not being changed by the current design option. Tberesults ofthe uncertainty analysis were reported only'for the maximum technologically

. feasible design options. These results include the uncertainties of all the design options that were

applied before the maximum technologically feasible design option. The upper and lower v8Iues reported for the uncertainty ofthe efficiency indicator span the middle 95% of all values resulting from the uncertainty analysis. For room air conditioners, the method was modified slightly to accommodate the use of a simulation model. 

### An input me to the simulation model was constructed for the baseline and each design option. The data in the input frles specified the properties and operating conditions ofa room air conditioner. 'The risk-analysis program was 'usedto generate several hundred simulation model input files. The distribution ofvalues in the input files that were used to,specify changes from the

.**base~ine** by the design options reflected the probability distribution entered into the risk-analysis,

program. The combined results of all the simulation runs thus reflected the impac.t ofuncertainty on the inputs. '

. Most ofthe input values were assumed to have a normal distribution, with the selected value being the mean**~f** the**di~tribution~** ,The standard deviation ofthe distribution was then specified as Volume 1:' GeneralMethodology Pa.geA

-1 ·

r

### the differencebetween the largest and/orthe smallestvalues considered possible and the mean. The distributions were typically truncated at two standard deviations from ~e mean. This truncation excludes the smallest 2.28% 

### and the largest 2.28% 

### of the possible values 

### in a norma1distribution. This was done to eliminatepotential outliers that could unnaturally skew the results.



### Figure 

# kl Schematic of 

### @RISK Uncettainty Analysis for Spreadsheet Calculations

PageA-2 VolumeI: GeneralMethodology the difference between the largest and/or the smallest values considered possible and the mean. The distributions were typically truncated at two standard deviations from the mean. This truncation excludes the smallest 2.28% and the largest 2.28%·ofthe possible values in a normal distribution. This·was done to eliminate potential outliers that could unilaturally skew the results.

Dl8trllIUtJon at Input C 

# A

Energy DIIItrIbulIon at Input A

Figure**A~l** Schematic of @RISK UnceItainty Analysis for Spreadsheet Calculations

PageA-2 Volume 1: General Methodology . I

### REFERENCES 1. 

## BRISK,Palisade ~or~oration,'~ewfield, NY, 1992.

Volume 1.:General Methodology Page A-3 REFERENCES 

### 1. @RISK, Palisade Corporation, Newfield, NY, 1992.

I ;



## ., Volume 1.: General Methodology

. PageA-3

## APPENDIX B. 

## FORECASTINGMODELS

. . 

### B.l LBNL 

### RESIDENTIALENERGY MODEL

, ;' 

### B.l.l Overview The ILBNLResidential Energy Model (LBNLREM)provides projections of the important characteristicsof the residential appliance market. The

### LBNLREM utilizes a databaseof significant determinantiofthe current residential appliancemarket, 

### as well 

### asparameters characterizingmarket decisions that 

### will affectthe energyconsumption of future appliances. This appendix describes-the LBNLREM, the 

### types of 

### data it uses, the calculationsit performs, and the results the LBNLREM provides.

. 

### 'I'he'JLBNLREM projects.numbers ofhouseholdsby house

### type, energy-relatedbuilding shell characteristics, average energy efficiency of appliances, fraction of households owning each

. . 

### appliance:, 

### usage behavior, and turnover of appliinces. ThesecharacteristicsAre 

### projected for each 

### yekfronn 1980

### to 2030.

. .

. , 

### TheILBNLREM combinesthese factors to project energy consumption and expendituresby fuel type: for each end use, and monetary expenditures for equipment (both replacement and new % 

### installations) and for fuels. The results 

### are reported in severalformats, for convenience, including: ' 

### annual energy consumption and expenditures, annual equipment shipments, cumulative energy. consumption and expenditures over aperiod (e-g.,

### frompolicy implementation date'tothe end of the 

### projection period), 'and net present values of equipment and energy expenditures, discounted and

. ..

### - summed overtime.

. 

### . . 0' 

### 'Ilne basic equationin the simulation model that defines residential use 

### of fuel i

### for end use
.

. *

,. k 

### in housing type m 

### during year t 

### is:

where STOKC 

### is the stock of occupied housing units, C

### is the'saturation (percent of households with

. 

### this equipment), 

## EU is the unit energy consumption', 

## U is the usage behavior factor, and 

### ,TIisthe, thermal integrity factor (for space conditioningonly). EIouseholds respond to changes in operating expenses in three differentways. 

### In the short

'

### run (see ~.1.6, "Usage Behavior"), ,they change the way they operate existing equipment 

### and structures (e.g., lower winter thermostat settings). 

### In the long run, they also change equipment by 

### switching from one fuel to another (see 

### B. 1.5, "Market 

### Shares"),by improvingthe efficiencyof their equipment (e.g., purchasing a water heater Gith more jacket insulation '(see B.1.4, "Equipment

. , Volume 1: GeneralMethodology

. , pa'ge B-1 

### APPENDIX B. FORECASTING MODELS

B.I LBNL RESIDENTIAL ENERGY MODEL B.I.I Overview lhe LBNL Residential Energy Model (LBNL-REM) provides projections ofthe important**characteri~tics** 

### ofthe residential appliance**~et.** 

### The LBNL-REM utilizes a database ofsignifi.cant determinantS ofthe current residential appliance market, as well as parameters characterizing market· decisions that will affect the energy consumption offuture appliances. This appendix describes-the LBNL-REM, the types 

### ofdata it uses, the calculations itperforms, and the results the LBNL-REM provides. lhelLBNL-REMprojects numbers ofhouseholds by house type, energy-related building shell characteristics, average energy efficiency of appliances, fraction of households owning each**appliancc~,** 

### usage behavior, and turnover ofappliances. These characteristics 

## are projected for each 

## year frol1n 1980 to 2030. lhe**lLB~REM** 

### combines these**f~ctors** 

### to**proj~t** 

### energy consumption and expenditures by fuel type: for each 'end use, and monetary expenditures for equipment (both replacement and new insta1latic)ns) and for fuels. The results are reported in several forma.ts. for convenience. including: annual energy consumption and expenditures, annual equipment shipments, cumulative energy . consumption and expenditures over a period (e.g., from Policy implementation date "to the end ofthe projection**period)~** 

### . and net present values ofequipment and energy expenditures, discounted and summed overtime.

. 

# .. lhebasic equation in the simulation**m~del** 

### that defines residential use qf

### fuel i 

### for end use k 

### in housing type 

### m during year 

### tis: . . . '
Q ikm 

## = STOKC m • C ikm •.EU ikm • U it • TI

tIt " . ttl (B.I) 

### where STOKe 

### is the stock ofoccupied housing units, C is the' saturation (percent ofhouseholds with this equipment), 

### EU is the unit energy consumption', 

### Uis the usage behavior factor, and 

### TI is' the. thermal integrity factor (for space conditioning only).

. Households respond to changes in operating expenses in three 

### different ways. In 

### the short

.run (see B.l.6,"Usage Behavior"), .they change the way they operate existing equipment and

. structures (e.g., lower winter thermost;lt settjngs). In 

### the long run, they also change equipment by switching from one fuel to another (see B.l.5, "Market Snares"), by improving the.efficiency oftheir equipment (e.g., purchasing a water heater with more**j~cket** 

### insulation (see B.I.4, "Equipmerit
Volume 1: General Methodology Page B·}

### Efficiency"), or both. Thus, the elasticity of demand for a particular fuel with respect to the price of that fuel can be separated into 

### three elements-a usage elasticity (E,), a technical efficiency elasticity (E,), and 

### an equipmentjfuelchoice (market share) elasticity (E,):



### B.1.2 Housing The model performs calculationsfor three 

### house types, (i) single-family homes, 

### (ii) multifamily homes, and (iii) mobile homes. Inputs forhousing information includes annual projections of occupied households and housing 

### starts. B.1.2.1, OccupiedHouseholds The number 

### and 

### type of occupied households for the base year 41980) 

### are from the 0.1% Public

### UseMicrosample

### (PUMS)ofthe 1980 Decennial Census. 

### Projection ofoccupied households by house type from 1992 to 20 10is obtained from DOE/EIA's Annual Energy Outlook 1995 

### (1). 

### The stock of households is interpolated in years 1981 to 1991. Household figures after 2010 are obtained by assuming constant annual 

### decay rates of 0.5%, I%, and 3.9% 

### for single-family,multifamily, 

### and mobile homes, respectively, and adding housing starb (see 

### B. 1.2.2) to the surviving housing stock.


### B.1.2.2 Housing Starts Figures forpast (1980-1989) housing startsby house 

### type come from the Bureau of Census' Housing Starts (2). Projections (1990-20 10)come fromthellnnrcalEnergy Outlook 1995. 

### Housing starts in later years (20 11-2030)

### are extrapolated using the projected annual growth rates from 2000 to 2010. 

### B.1.3 Building Shell Thermal Integrity

,

### . 

### The equation used in the LBNLREM to represent the thermal performancdcosttradeoff for structures is:

. .



### where TINis the thermal performance (relative heating energy normalized to 1980existinghouses); 

### Cis the changein the initial cost of the structure due to the change in TIN; and TINF, TTALFA, and TBETA are parameters. Table B. 1shows values of the parameters TINF/TIN@ TALFA, and TBETA. Page B-2. 

### . Volume 1: General Methodology 

## ."**Efficiency'~),** or both. Thus, the elasticity ofdemand for a particular fuel with respect to the price of that fuel can be" separated into 

## three elements-a usage elasticity (Ell)' a technical efficiency elasticity (E e

), and an equipment/fuel choice (market share) elasticity (Ems): (Bol) 

### B.I.2 Housing The model performs calculations for three house types, (i) single-family

### ho~es, (li) multi- " family homes, and (iii) mobile homes. Inputs for housing information includes annual projections

ofoccupied households and housing starts. 

### B.I.2.1 ,Occupied Households

o The number and type ofoccupied households for the base year (1980) 

# are from the 0.1 % Public UseMicroSample (PUMS) ofthe 1980 Decennial Census. Projection ofoccupied households by house tyPe from 1992 to" 2010 is obtained from DOFJEIA'sAnnual Energy Outlook 1995 (1). The stock of households is interpolated in years 1981 to 1991. Household figures after 2010 are "obtainecr by assuming constant annual decay rates of0.5%, 1%, and**3~9%** for single-family, multifamily, and mobile homes, respectively, and adding housing starts (see B.1.2.2) to the surviving housing stock. "


### B.I.2.2 Housing Starts Figures for past (198()":1989) housing starts by house type come from the Bureau ofCensus' HoUsing Starts (2)." ProjeCtions (1990-2010) come from theAnnualEnergy Outlook 1995. Housing starts in later years (2011-2030) are extrapolated using the projected annual growth**ra~es** from 2000 to 2010. ' 

### B.l.3 Building Shell Thermal Integrity The equation used in the LBNL-REM to represent the thermal performanceJcost tradeoff for structures is:
TIN TINF "[ TBETA 

## ]TALFA [ TINF"] TINo"= "TINo 

### + TBETA+C 1 - TINo (B3)

" I where TINis the thermal**pe~ormance** (relative heating energy normalized to 1980 existing houses); C is the change in the initial cost ofthe structure due to the change in TIN; and TINF, TALFA, and TBETA are parameters. "Table B.l shows values ofthe parameters TINFffINo> TALFA, and TBETA. Page B-2" Volume 1: General Methodology.

### Table

### B.l Parameters of Thermal Integrity/Cost

### dumes by House Type

The thermal integrity factor 

### (TI) is the relative energy consumption correspondingto a set of building.shel1 characteristics (e.g., ceiling insulation, floor insulation, glazing, floorinsulation, infiltration rate), associated with 

### an incremental.cost 

### (0. For spaceheating, it is calculated from the relationship each year to 

### minimize the life-cycle cost, given current energy prices, using 

### a discount rate consistent with historical market tradeoffs. The thermal iniegrity factor for air conditioning depends on, and differs from, the thermal integrity factor for heating. lbennal integrity is largely irrelevant in the analysis of ranges and ovens. The ,only interaction between energy consumption of these products and thebuilding shell characteristics is through internal loads generated by these products. Those internal loads interact with spaceconditio,ning requirements.

. . 8 

### TBETA 267 17

6067 9931 

### Single-Family Multi-Family Mobile Homes



### B.1.4 Equipment Efficiency The 

### LBNLREMpmjects the avkgeefficiency or unit energy consumption, for a new. unit purchased in each futureyear. The.methodinvolves characterizationof historical market behavior,



# . in the 

### film.of market discount rate. The.observed average efficiency of the unit purchased is TINF/TINn 

### 0.25 0.24 0.34

charactelized as a function ofthe range of designs available-hm manufactunrs, equipmentprices (setby nlanufacturers and distributors),current residential fuel prices, and 

### trade-off decisions made by purchasers (including homebuildek, contractors, landlords, and homeowners). The range of available designs and their prices are 

### from 

### the Engineering Analysis. Historical averageekciencies are reported by industry trade associations. Taking the average efficiency of units

### purchased as the 

### rninimunn life-cycle cost defines the market discount rate. 

### The average efficiency in future 

### years is projected based on the range of engineering designs available (characterized by unit price and efficiency) and 

### projected residential energy prices, given the market discount rate. ' 

### TALFA 11.14 6.87 7.56

.'. 

### B,.$.S 

### M:arketShares ' 

### T'henumber ofpotentialpu~hasers iscalculated fortwo markets: housing 

### starts andpotential replacement/retrofitin existing housing.Totalproduct salesfor

### each

### yearare determinedby applying market share elasticitiestoprojected equipment price, operating expense, and income to determine the fraction 

### of potential purchasers who will actually purchase the product. The market share elasticitiesinclude cross-price variables, where appropriate, to captureinter-fuel competition, e.g., 

### the choice between electric or gas cooktop.

VolumeI: Gt'nemlMethodology Page 8-3 

### !ableB.I Parameters ofThermallntegrity/Cost 

### Curves by House Type
TINFfI1No TALPA TBETA Single-Family ,0.25 11.14 26717 Multi-Family 0.24 6.87

. 6067 Mobile Homes 0.34 7.56 

# . 9931

The thermal integrity factor (Tl) is the relative energy consumption corresponding to a set ofbuilding'shell characteristics (e.g., ceiling insulation, floor insulation, glazing, floor insulation, infiltration rate), associated with an incrementaLcost 

### (C). For space heating, it is calculated from the relationship each year to minimize the life-cycle cost, given current energy prices, using a discount rate consistent with historical m3!ket tradeoffs. The thermal integrity factor for air conditioning depends on, and differs from, the thermal integrity factor for heating. Thennal integrity is largely irrelevant in the analysis of ranges and ovens. The.only interacti,on between energy consumption ofthese products and the building shell characteristics is through' internal loads generated by these**produ~ts.** Those internal loads interact with spaceconditio.ning requirements. ' 

### B.I.4 Equipment Efficiency 11le LBNL-REM projects the average efficiency or unit energy consumption, for a new, unit purchased in each future year. The·method involves characterization ofhistorical market behavior, in the fc)rm of market discount rate. The'observed average efficiency of the unit purchased is charactedzed as a function ofthe range ofdesigns available from manufacturers, equipment prices (set by manufacturers and**distributors)~** Current residential fuel prices, and**tra4e~ff** deCisions made by purchasers (including homebuilders, contractors, landlords. and homeowners). The range of. available**d~signs** and**~eir** prices are from the Engineering Analysis. Historical average efficiencies are reported by industry trade associations

..Taking the average efficiency ofunits purchased as the minimum life-cyc1e cost c;iefmes the market discount rate. The average efficiency in future years is projected based on the range of engineering designs available (characterized by unit price and efficiency) CIiIld projected residential energy. prices, given the market discount rate. 

### B

.•

### I.S M:arket Shares ' '1benumber ofpotential purchasers is calculated for two markets: housing starts and potential rephicementlretrofit in existing housing. Total product sales for each year,

## are determined by applying market share elasticities to projected equipment price. operating expense, and income to determine the fraction of potential purchasers who will actually ,purchase the product. The market share elasticitif:s include cross-price variables, where .appropriate, to capture inter-fuel competition',. e.g., the choice between electric or gas cooktop. , .
Volume 1: General Methodqlogy Page 8·3- 

### The market share elasticities are constant over the projection period. The market shares for each product in future years 

### are given in Chapter 

### 2, Table 

### 2.5 of each product-specific 

### Part of this 

## - xeport. Thepercent share for each class in future years is assumedto 

### be the same 

### asthat in 

### 1992and is shown 

### in Table B.2a-B.2c. 

### Table B.2a Percent Share of Each Class of

### Room Air 

### Conditioners

Table B.2b percent Share of Each Class of Electric Cooktops

Table B.2c Percent Share of Each Class.of Electric and Gas 

### Ovens coil element smooth element 85% 15%



### B.1.6 Usage Behavior Electric Ovens self-cleaning non self-cleaning Gas Ovens self-cleaning non self-cleaning



### The usage elasticities for room air conditioners and ranges and ovens 

### are given in Appendix'Bof each'product-specific

### Part.

. . 56% 44%
24% 76%

Page B-4 VolumeI: GeneralMethodology The market share elasticities are constant over the projection period. The market shares for each product in future years are given in Chapter 2, Table 2.5 ofeach product-specific Part ofthis report. Thepercent share for each class 

### in future years is assumed to be the same as that in 1992 and is shown 

### in Table B.2a-B.2c. ' 

### Table B.la Percent Share ofEach Class ofRoom Air Conditioners
less than 6 Kbtu/hr with louvres 26.7% 6 to 8 Kbtu/hr with louvres 14.4% 8 to 14 Kbtu/hr with louvres 32.8% 14 to 20 Kbtu/hr with louvres 13.1%, greaterthail 20 Kbtu/hr with louvres 6.3% , 6 to 8 Kbtulhr without louvres 2.1%

8 to 14 Kbtu/hr without louvres 2.1% heat pump**wi~** louvres 2.1% heat pump without louvr.es 0.5%


### Table B.2b, Percent Share ofEach Class ofElectric Cooktops
coil element 85% smooth element 15%


### Table B.2c Percent Share ofEach Class ofElectric and Gas Ovens
Electric Ovens self-cleaning 56%

" non self-cleaning 44% Gas Ovens self-cleaning 24%
non self-cleaning 76% 

### B.l.6 Usage Behavior The usage elasticities for room air conditioners and ranges and ovens are given 10 AppendixB ofeach'product-specific Part.

PageB-4 Volume 1: GeneralMethodology

'\

### . - 

### Chapter 

### 2 in each product-specific

### Part shows average equipmentlifetimes. These lifetimes

. , 

### are assu:medto be 

### constant over the projection period. The retirement functions (the percent of

'

. ' 

### products which survives, as a function of years afterpurchase) 

### are shown in Table 

### B.3.

. . 

### Table B.3 SurvivalFinetion

## MRoomAIC, Cooktops,and Ovens



### The 

### LBNLREM produces 

### annualprojections of:

a 

### number of houses in stockby house 

### type;

. . 

### housing starts

### by house 

### type; thermal integrity factors for stock and new houses by house type; average unit energy consumption of new units sold (byclass and by end usdfuel);


# . average unit equipment price of new units sold (by class and by end uselfiel); • 

### fraction of existing houses having each end use; fraction of new houses having each end use; 

# . usage behavior factor; by end usdfuel; fuel consumptionby housing 

### type. by fuel, and by end use; 

# .. total equipment expendituresby end use; and total fuel expenditures.

. . '..

Vilurne I: Generalhiethodology Page B-5 

### B.1.7 Equipment Turnover' Chapter 2 in each product-specific Part shows average equipment lifetimes. These lifetimes , are as'su:med to be constant over the projection period. The retirement functions (the percent of 'products which survives.,as a function ofyears after pUrchase) are shown in Table B.3. 

### Table B.3 Survival Function 

### of Room AlC, 

### Cooktops, and Ovens Age (year) RoomAlC

. Cooktop Oven Microwave Oven 1 to 6 100% 100% 100% 100%

7 100% 100% ' 100% 97% - 8 100% '. 100% 100% 83% ,

9, 96% '100% .100% 62% 10 85% 100% ' 100% 38% 11 69% 100% 100% 17% 12 '50% 100% 100% 3% 13 31% 100% 100% 0% 14 15% 100% 100%, 0% 15 4% 100% \ 100% 0% 16 0% 87% 87% 0%

, 17 0% 65% 65% 0%

18 0% 41% , 41% 0% 19 0% 20% 20% 0%' 20 0% 6% 6% 0% • '21 to 30 0% 0% 0% 0% 

### B .

.1.8 

### Outputs The LBNL-REM produCes annual projections of:

• number ofhouses**i~** stOCk by house type; 

### • housing starts by 

### house type; .' thermal integrity factors for stock and new houses by hou'se type; • average unit energy consumption ofnew units sold (by class and by end uself':lel); • average unit equipment price ofnew units sold (by class and by end use/fuel); • 'fraction ofexisting**ho~ses** having each end use; • fraction ofnew houses having each end use; •**usag~** behavior factor. by end use/fuel; " • fuel consumption by housing type. by fuel. and by end use; '. total equipment expenditures by**~nd** use; and • ' total fuel expenditures.

Volume 1': GeneralMethodology PageB-S

### The 

### LBNLREM produces cumulative values for a period (1998-2030): fuel use by end use and fuel type; / 

### discounted fuel expenditures by end use and fuel 

### type; discounted equipmentexpenditures; and discounted total (fuel and equipment) expenditures. The 

### LBNLREM produces cumulative values for new units purchased 

### in a period (1998- 2030): discounted he1expenditures by end use and fuel 

### type; discounted equipmentexpendituresby end use and fuel 

### type; ' 

### discounted total (fuel and equipment) expendituresby end use and fuel type; units installedby end use and fuel

### type; and total discounted lifecycle expendituresper unit by end use and fuel type. Product types usually conespond to end uses.
. 

### B.1.9 Changes SincePrevious

### ~echnical SupportDocument - 

### The principal changes sincethe previous Technical Support Document in 1993

### are: (1) The set of clasesbeing analyzed has 

### changed for room air 

### conditioners: certain nonlouvers classes have been removed. (2) 

### The set of design options, efficiencies, and cost data

### have changed (see 'Chapter I 

### of each product-specific Part). (3) Okupied household and housing startprojections have been revised to agree with Annual Energy Outlook 1995 

### (see Chapter 5 herein). 

# - (4) 

### Energy price projections have been updated toAnnual Energy Outlook 1995 

### (see Chapter 5 herein). (5) Historial shipments of room 

### air conditionershave been updated. 

### (6) The average equipmentlifetime of room 

### air conditionershas been re-estimated. (7) 

### A consistent set of market discount rates is used in calculatingmarket shares and in forecasting equipment efficiencies.


### B.l.10 Input Data The complete input listing is available on electronic medium in 

### ASCII format. The input database includes demographic, economic, and engineeringdata. The input listing for products in the currentproposed rule is reproduced in Appendix 

### B of each product-specific Part.

Page B-6 VolumeI: .GeneralMethodology The LBNL-REM produces cumulative values for a period (1998-2030):


### • I 

## •

• 

## • fueJ use by end use and fuel type; discounted fueJ expenditures by end use and fuel type; discounted equipment expenditures; and discounted total (fuel and equipment) expenditures. The LBNL-REM produces cumulative values for new units purchased in a period (1998- 2030):

. .
• discounted fuel expenditures by end use and fueltype; • discounted equipment expenditures by enp use and fuel type; • discounted total (fuel and equipment) expenditures by end use and fuel type; • units installed by end use and fuel type; and

. • .total dlscounted·life-cyde expenditUres per unit by end use and fuel type. Product types usually correspond to end uses.

### B.1~9 

### Changes Since·Previous Technical Support Document The principal changes since the previous Technical Support Document in 1993 are: (1) The set ofclases being analf1.ed has changed for room air conditioners: certain nODlouvers classes have been removed. • (2)

.The set ofdesign options, efficiencies, and cost data have changed (see Chapter 1 of each product-specific Pclrt).

. (3) OCcupied household and housing start projections have been revised to agree with Annual Energy Outlook 

### 1995 (see Chapter 5 herein). (4) Energy price projections have been updated to Annual Energy Outlook 

### 1995 (see Chapter 5 herein). (5) Historial shipments ofroom air conditioners have been updated. (6) The average equipment lifetime ofroom air conditioners has been re-estimated. (7) A consistent set.ofmarket discount rates is used in calculating market shares and in forecasting equipment efficiencies.·

.


### B.1.10 Input Data

.The complete input listing is available on electronic mediuin in ASCII format. The input 'database includes demographic, economic, and engineering data. The input listing for products in the current proposed rule is reproduced in Appendix B ofeach product-specific Part.

PageB-6 Volume 1:

.General Methodology

### REFERENCES

I. 

### 1. 

### U.S.Department of Energy, Energy Information Administration. 1995. hwl~ner~y' Oiitlook 1995 withPr&ctions to

### 2010. 

### Washington: D.C. DOE~EIA-0383(95). January. 2. 

### US.Departmentof~ommerce. 

### Bureau ofthe Census. 1990. Housing 

### Smm. Washington, D.C. July.

.

Volume 1: ~Gen~eral Metliodology Page B-7 

### REFERENCES

1. 

### U.S. Department of Energy, Energy Information Administration. 

### 1995. Annual Energy' Outlook 1995with Projections to 2010. Washington,' D.C. 

### DOFJEIA-0383(95).**Janu~.** 

### ·2. U.S. Department ofCommerce, Bureau ofthe Census. 

### 1990. Housing Starts. Washington,. D.C. July.

.

Volume 1: iGeneral Methodology Page B-7

## APPIENIDIX 

## C. LAWRENCE

## BERKELEY NATIONAL LABORATORY 

# - MANUFACTURER ANALYSIS 

## MODEL

. . 

### C.l 

### OlVERVIEW OF 

## THE LAWRENCE BERKELEY NATIONAL 

### LABORATORY 

# - NkWACTURER ANALYSIS 

### MODEL

. . . . 

### C.l.l 

### Purpose .*

. . I 

### The Lawrence Berkeley National Laboratory Manufacturer Analysis Model 

### (LBNGMAM) collkts iinto One spreadsheetall the caldulations necessary to determinethe idpactof a change in appliance:efficiency levels on an industry's profitabilityand scale of operation; The spreadsheet design makes it 

### possible to quickly analyze the effects of uncertainties in input values and to

.. . 

### efficient& incorporatenew 

### data into the calculations. The model h& a control panel, which makes

. , . 

### the analysis of uncertainty particularly straightfoxward for nine of the model's crucial inputs. Throughthis panel, the model provides the usernot only with the best estimates of financial impacts, but also with the uncertaintyof these estimates and a sensitivityanalysis attributing that uncertainty to certain input variables.
C.1.2 

### Thre Ecomomic Approach

, .. 

### Tlle impact of 'implementing new efficiency levels on mabufacturers depend on the interaction of four. factors: 

### 1) the costs imposed by the change'in implementing new efficiency levels, 

### 2) the price elasticity of a singletypical 

### fm, 3) the industry's price elasticity of demaxid, and 4) 

### the consumer market discount 

### rate for energy savings for a product (which a& 

### used to obtain industry cjpemting cost elasticity). The 

### LBNLMAM integrates and analyzes these four factors 

### as '



### . . 

### they apply to a single typical 

### fm. I A 

### change in imposed efficiency levels.wil1 generallycreate

### three 

### types .of costs 

### for a 

### firm. First, variable costs of goods sold 

### (VCGS) will be affected by new parts 

### and labor requirements. Second, some engineering will be 

### required 

### to design, test, and plan the manufacturing of new products. And 

### third, new products often require m-to6ling and new equipment (capital) costs. 

### The incorporation of VCGS 

### is straightforward,but engineering and capital costs must be 

### amortized to be converted from one-time costs to annual costs for a typical-year model. Markups from manufacturing costs .to ex-factory prices depend on the single-firm price elasticity. Because it is difficultto find data

### on-single-fumprice elasticities, ,the

### LBNLMAM infers them fkom 

### the fm'sfixed costs and return on equity 

## (ROE).The markup is over variable cost and 

### not fixed; hence it must 

### be greaterthan one if a 

### fm with fixed costsis to recoverits fixed costs and

. 

### . 

### turn aprofit.

, 

### The industryhas two elasticitiesofintexest: itspriceelkticityand its operatingcost elasticity.

., . Volume 1: General ~ethodolo~~ Page C-1

### APPI~NlI>~ 

### C. LAWRENCE BERKELEY NA'J'IONAL LABORATORY

- MANUFACTURER ANALYSIS MODEL

C.I OVERVIEW OF THE LAWRENCE BERKELEY NATIONAL LABORATORY· lv.lANUFACTURER ANAL 

### ys:is MODEL C.I.I Purpose (



### _ Tlhe Lawrence Berkeley National Laboratory Manufacturer Analysis Model (LBNL-MAM) , collects**il~to** 

### one spreadsheet all the calculations necessary to detennine the impact of a change in appliance:**effic~ency** 

### levels on an industry's profitability'and scale of operation: The spreadsheet design makes it possible to quickly analyze the effects Of 

### uncertainties in input values and to

, efficiently incorporate new data 

### into the calculations. The model haS a control panel, which makes

the analysis of uncertainty particularly straightforWard for 'nine of the modelis crucial inputs. Through lhis panel, the model provides the user not only with the best estimates offinancial impacts, but also with the uncertainty ofthese estimates and a sensitivity analysis attributing that uncertainty to certain input variables.
C.I.2 Thle EcOIllOmiC 

### Approach 

### The impact of 'implementing new efficiency levels on manufacturers depend on the interaction of four factors: 1) the costs imposed by the change'in implementing new efficiency levels, 2) the price elasticity ofa single typical f1llIl, 3) the industry's price elasticity ofdemand, and 

### 4) the consumer market discount rate for energy savings for a product (which 

# are used to obtain industry operating cost elasticity). The LBNL-MAM integrates and analyzes these four factors as' they apply to a single typical f1llIl. ' A change in,**impos~** 

### efficiency levels 'will generally create three types .ofcosts for a firm. First. variiable costs of goods sold (VCGS) will be affected by new parts and labor requirements. Second, some engineering will be 

### required to design, test, and plan the manufacturing of new products. Arid third, new products often require re-tooling and new equipment (capital) costs. The incorporation ofVCGS is straightforward, but engineering and capital costs must be 

### amortized to be converted from one-time costs to annual costs for a typical-year model: Markups from manufacturing costs ,to ex-factory prices depend on the single-firm price elasticity. Because it is difficult to find data 

### on'single-fmri price elasticities, ,the LBNL-MAM infers them from the f1llIl's fIXed costs and return on equity (ROE). The markup is over variable cost and not fixed; hence it must be 

### greater than one ifa f1llIl with fixed costsjs to recover its fixed costs and**~a~~** 

### , ' The industry has two elasticities ofinterest: its price elasticity and its operating cost elasticity.

Volume 1: GeneralMethodology Page C-l

### Although individual 

### firms must calculate according to their own elasticities of demand, it is ultimatelythe industry elkticitiesthat determinethe level of demand experiencedby each

### fum. This apparent paradox arises because the model assumes that all firms 

### are profit 

### maximizers and thus respond in essentially the same way to the imposition of new efficiency levels. Because of this assumption, all fumschangethe prices and operating costs of their appliancesby the same amount (i.e., the industry acts in unison). When this happens, sales for each 

### firm are determined by the 'industry elasticities.


### C.13 Measures of Impact The

### DNLMAMcomputes

### three 

### primary and several secondary

### measures of impact. The three

### primary measures, 

### ROE, industry net present value, and net incomeare p-nted because they can each move in opposite directions. For instance, if net income increases,'but assets and thus equityincrease. by a greaterpercentage, ROE

### will decline. 

### If the decline is large, the net effect will be 

### viewed as negative, but 

### if a small decreasein a healthy ROE is accompaniedby a large increase in net income, the net effect might be viewed as positive. Other outputs of the model 

### are total shipments, price, revenues, and average wholesale price. These variables help explain the origins of changes in the primary inputs.
C.1.4 

### Methodology HowAlternative-EmiencyLevels Are Implemented in 

### theModel Generally,. a change in 

### efficiency levels affects the model in three 

### distinct ways. Implementation of new efficiency levels require additional investment, raise production costs, and affect revenue. The most obvious investment inducedby new efficiency levels is the purchase of new plants and equipment. Thisxost is fvst evaluated from engineering data 

### and then amortizedby taking into ' 

### account the life of the investment,the 

### timing of the expenditures, tax 

### laws, and the cost of funds. An additional (and sometimes larger) investment is made as old inventory is replaced with more expensive new units. The model assumes that previous inventory ratios are maintained. A. third form of investment tracked 

### by 

### LBNGMAM is the change in the 

### transactions demand for cash 

### that accompanies a 

### change in revenues. Increased costs of production 

### are modeled by coupling changes in unit costs with changes

- 

### in product shipments. Changes

### in unit costs come from engineering data and shipments data 

### come

from LBNLResidential Energy Model (LBNLREM). Revenue is affected both by price and shipments. Price is determined by computing the 

### markup over long-run marginal costs, and th& using the markup to determine an optimal price. Shipments (demand) is

### determined by price elasticitiesand consumermarket discount rates, coupled Page C-2 Vohe I: GenemlMethodology , ' Althougb indlvidual flIlIlS ,must calculate according to their own elasticities of demand, it is ultimately the industry elaSticities that determine the level ofdemand experiencedby each fmn. This apparent paradox arises because the model'assumes that all flIlIlS are profit maximizers and thus respond in essentially the same way to the imposition of new efficiency levels.**B~auseof** this

.assumption, allfmns change the prices and operating costs oftheir appliances bythe same amount (i.e., the industry acts in unison). When this happens, sales for each 

### firm are determined by the

. 'industry elasticities. .'.

.



### C.l.3 Measures of

### Impact

. The tBNlJ;MAM compl:ltes three primary and several secondary measures ofimpact. The three primary measures, ROE, industry net present value, and net income are**p~nted** because they caD each move in opposite directions. For instance, if net income**increases,~** but**~setS** and thus '. equity increase_by a greaterpercentage,'ROE will decline

..H the decline is large, the net effect will

be viewed as negative, but ifa small decrease in a**h~thy** ROE is accompanied by a large increase in net income, the net effect might be viewed as positive. Other outputs of the model are total shipments, price, revenues,and average wholesale price. These variables help explain the origins ofchanges In the primary inputs.


### C.l.4 Methodology 

### How Alternative 'EfficiencyuvelsAreImplemented 

### in the Model Generally,. a change in efficiency levels affects the model in 

### tl1r'ee distinct ways. hnpleinentation ofnew efficiency levels require additional investment, raise production costs, and affect revenue. ' The most obvious**inv~tment** induced by new efficiency levels is the purchase ofnew plants and equipment. This,cost is first evaluated from engineering data and then amortized by taking into

- account the.life ofthe investment,the timing ofthe expenditureS, 

### tax laws, and the cost of funds. An additional (and sometimes'larger) investment is"made as old inventory is replaceq with more expensive new units. The model assumes that previous inventory ratios 

# are maintained. A third form ofinvestment tracked by LBNlJ;MAM is the change in the transactions demand for cash -that

. accompanies a change in**revenu~s..** . - Increased costs ofproduction are modeled by coupling changes in unit costs with changes in product shipments. Changes in unit costs**'co~e** from engineering data and shipments data come from LBNL-Residential Energy Model (LBNL-REM). Revenue is affected both by price and, shipments. Price is determined by computing the ma.rkup

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

,Shipments (demand) is determined byprice elasticities and consumer market discount rates, coupled Page C-2 Volume 1: GeneralMethodology

### with the changes induced by imposed efficiency levels 

### inprice and operating costs.

. , 

### . . 

### 2jpiccrl-yearApproach

. . . , 

### The LBNLMAM uses a "typical-year" approach rather than adynamic approach. This approach 'models'a "typical year" for the industry both in the base case and in the alternative efficiency :levelcase. 

### The year chosen for the model is typically the 

### fourth year after the 

### imposition of new efficiency levels, and is selected

### because it is believed to be long enough to capture any major impacts from the imposed efficiency level, such as profitability changes or 

### fun entrjinto or exit from the industry. Use

### ofPrc~totypical 

### Firms

, 

### Because the engineering and financial data for most firms are proprietary, 

### LBNLMAM models a prototypical firm. A 

### prototypical 

### fum is a hypothetical firm 

### that is representativeof the industry. 

### In many cases, this 

### fmmust be thought of as representing an autonomous division of a larger firnl. Prototypical firms 

### are defined by parameters that 

### are important for determining the impacts of'alternative efficiency levels and 

### are consistentwith data for the portion of the industry they represent. Important parameters used in the model include the cost structure of the 

### fm, profitabilityratios, relative costs of complying with new efficiencylevels, and marketing strategies. 

### Product S,egmentation 

### The 'L;~~NLMAM assumes that the product market is segmented. Thus, different products have different markups. 

### In general, it is found that appliance .manufacturers 

### are able to charge

. ' 

### different nlarkups for products that have different characteristics. They can chargehigher markups on produc.ts that 'have desirable characteristics. Products without ,such desirable features are 'generally lbought in larger quantities at lower prices by consumerswho 

### are more prick-conscious, and thus 

### the markups for theseproducts 

### are lower. The per-unit profits made by manufacturers for these diffe'rent products may differ significantly.


## .. 0 

### C.2 

### STRUCTURE

## OF 

## THELBNL-MAM



### .. C.2.1 

### Ec.onomicTheory 

### and Assumptions 

### Th~e 

### basic question that

### LBNLMAMmust answeris: how much of the costincreases caused by new efficiencylevels 

### can be 

### passed through to consumers? 

### If less than the normal pass-through occurs, prcbfits will suffer,.while

### an abovenonnal pass-through will increase profits. To answerthis question precisely, it is necessary to compute a markup; but, before that, a more intuitive underitancling of the relevant economics will 

### be useful. In txo~lomics, costs 

### are often divided into fixed and marginal components where fixed costs 

### arejust whiat their name suggests and marginal costs 

### are the costs of producing an additional unit of Volume I:.GeneralMethodology Page C-3 with the changes induced" by imposed efficiency levels in price and operating costs.



### " Typical-ye'arApproach The LBNL-MAM uses a "typical,.year" approach rather than a dynamic approach. 1;his approach models. a "typical year" for the industry both in the base case and in the altemapve efficiency level case. The year chosen for the model is typically the fourth year after the imposition ofnew efficiency levels, and is selected because itis believed to be long enough to capture any major impacts from the imposed efficiency level, such 

## as profitability changes or 

### fum entry into or exit from the industrY.

. " " 

### Use of Pr(,totypical Firms Because the engineering and financial data for most firms are proprietary, LBNL-MAM models a prototypical 

### fmn. A prototypical 

### fmn is a**~ypothetical** 

### fmn that is representative ofthe industry. In many cases, this 

### fum must be thought ofas representing an autonomous division ofa larger firm. Prototypical 

### fmns are defined by parameters that are important for determinilig the impacts ofalternative efficiency levels and are consistent with data for the portion ofthe industry tl)ey represent Important**paramet~rs** used in the model include 

### the cost structure of the firms,

. profitability ratios, relative costs ofcomplying with new efficiency levels, and marketing strategies. 

### Product S,egmentation

" The U3NL-MAM assumes that the product market is segmented. Thus, different products have different markups. 

### In general, it is found that appliance manufacturers are able to charge " different markups for products that have different characteristics. They can charge highermarkups on products that have desirable characteristics. Products**~ithout.**such desirable features are "generally bought in bU'ger quantities at lower prices by consumers who are more price-conscious, and thus the markups for these products are lower. The per-unit profits made by manufacturers for these different products may differ**signifi~antly.** 

### C.2· STRUCTURE OF THE LBNL-MAM C.2.1 EcDnomic Theory and AsSumptions Tbebasic question that LBNL-MAM must answer is: how much ofthe cost increases caused by new efficiency levels can be passed through to consumers? Ifless than the normal pass-through occurs, profits will suffer, while an above normal pass-through will increase profits. To answer this question precisely," it is necessary to compute a markup; but, before that, a more intuitive underStanding ofthe relevant economics will be useful. 

### In**c~onomics,** costs .are often divided into fixed and marginal components where fixed costs

. arejust what their name suggests and marginal costs are the costs ofproducing an additional unit of

. " Volume 1: General Methodology Page C-3

### output. Three 

### points 

### are true in general of thesecost distinctions: 1. 

### Under perfect competition, price equals marginal cost. 2. 

### underkperfect competition (i.e., where the market

### is characterizedby a downwardsloping demand curve) price 

## 'quais marginal cost times 

### a markup. .3. 

### In neither case.do fixed costs affect the calculation of price.'

.,. 

### Thefirstpoint may be understood by assumingfor a moment that it is false and that price is higher than marginal cost. 

### In this case, each 

### fm concludes that it is profitable to increase production (since under perfect competition each 

### fm knows that as an individual firm its own output 

### h8s no effect on price). As a result, prices would certainly fall. If 

### price is below marginal cost, the 

### reverse argument holds, so, in either case, price will inevitably change until it equals marginal cost. The second point is derived in the next section. The third point is most easily understood by examining Figure 

### C. 1, which shows a revenue curve and two totalcost curves that differ only 

### in their fixed components.



### . How will a firm respond to 

### an increase in fixed cost (reprksented by the upward shift displayed in the cost curve)? Since the fm 

### wishes to maximize the differencebetween cost and 

### revenue, it chooses the optimal output Q*. 

### This choice isclearly independent of the 

### shift in fixed cost, but once output is determined,the demand curvewill dictate the price at which that output can be 

### sold. A 

### change in fixed cost has no effect on the demand curve and thus no effect on price. The separationof costs into fixed and variable must be 

### done with 

### an eye to the time horizon under consideration,because most (if not all) costs that 

### are fured in the short run become variable in the longrun. Three 

### distincttime horizons 

### are useful: the short

### run,the longrun, and the very long 

### run. 

### In the short run, the productive capacity of the finns 

### in the industry remains fured. In the long run, capacitymay be 

### adjusted, but the number of 

### firmsremains constant. 

### In the very long 

### run, 

### fm may enter or leave the industry, with the result that an individual firm's market power, as measured by its price elasticityof demand,may change. Thus, in the very long run, markup may change. 

### In the long run, price is a markup over long-run marginal cost, while in the short run, price is a markup over short-run marginal cost. Since in the long run, capital is considered a marginal cost, how 

### can the two pricing rules be reconciled? If 

### a firm finds that its long-run plans have been 

### realized 

### (in other words, 

### if the market conditions 

### are those that it foresaw at the time it planned its 

## ' Two legitimate interpretationsmaybe given to this mathematicalresult. he first is that giventhe demand function. marginal cost determines price and fixed costs are not passed on. The second is that one may argue that fixed costs indirectlydetermine the elasticity ofdemand.and thereby the markupovermarginalcost. 

### Thus fixed costs are indirectly passed on through the markup of marginal cost. The mechanism providing this indirect link is the following: if fixed costs are not covered, then economic profits will be negative and some firms will leave the industry; this will reduce competition and thus reduce each firm'selasticityofdemand. Conversely. iffixed costs are more than covered, positive economic profits will induce entry and elasticity and markup over marginal cost will be reduced. Page C-4 Volume1: GenemlMethodology output. Three points are true in general ofthese:-cost distinctions:

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

, 2. Underimperfect competition (i.e., where the market is**charac~erized** by a downwaro-

. sloping demand curve) price equals marginal cost times a markup.

_ 3. 

### In neither case' do fixed costs affect the calcillation ofprice.' The first point may be understood by assuming for a moment that it is false and that price is higher than marginal cost. 

### In this case, each f1I1D. concludes that it is profitable to increase

.production (since**un~er** perfect competition each f1I1D. knows that as an individu'al firm its own output has no effect on price). As a result, prices would certainly fall. Ifprice is below marginal -cost, the reverse argument holds, so,' in either case, price will inevitably change untiJ it equals

. marginal cost. The second point is derived in the next section. The third point is most easily

understood by examining Figure C.l, which shows a revenue curve and two total-cost curves that differ only in their fixed components.

. . How will a firm respond to an increase in flXed cost (represented by the upward shift displayed in the costcurve)?

.Since the fIrm wishes to maximize ,the difference between cost and revenue, it chooses the optimal output 

### Q*

..This choice is dearly independent ofthe shift in fixed cost, but once output is determined, the demand curve will dictate the price at which that output can be sold. A change in fIxed cost has no effeCt on the demand curve and thus no effect on price. The separation ofcosts into fixed and variable must be done with 

## an eye to the time horizon under consideration, because mos! (ifnot all) costs that are flXed in the short run become variable in the long run. Three distinct time horizons are useful: the short run, the long run, and the very long run. 

### hi the short run, the productive capacity ofthe fums in the industry remains flXed. 

### In the long run, capacity may be adjusted, but the number offirms remains constant. In the very long run, fums may enter or leave the industry, with the result that an indiVIdual firm's market power, as measured**b~** its price elasticity ofdemand, may change. Thus, in the very long run, markup may change. 

### In the long run, price is a markUp over long-run marginal cost, while in the.short run, price is a markup over short-run marginal cost. Since in the long run, capital is considered a marginal cost, how can the two pricing rules be reconciled? Ifa fIrm fmds that its long-run plans have been realized (in other words, 

### ifthe market conditions are those that it foresaw at the time it planned its

I Two legitimate interpretations may be given to this mathematical result The first is that given the demand function, ,marginal cost determines price and fixed costs are not passed on.. The second is that one may argue that fixed costs indirectly determine the elasticity ofdemand. and thereby the markup over marginal cost Thus fixed costs are indirectly passed on through the markup of marginal cost. The mechanism providing this indirect link is the following: iffixed costs are not covered, then economic profits will be negative and some firms will leave the industry; this will reduce competition and thus reduce**ea~h** firm's elaSticity ofdemand. Conversely, if**fix~costs** are more than covered; positive economic profits will induce entry and elasticity and markup over marginal cost will be reduced. Page C-4 V()lume 

### 1: GeneralMethodology

# Q* Output 

# (Q)
Figure Ct,l

### Profit Maximization and Fixed Costs

Volume1: GeneralMethodology Page C-5 

## .---. 0 --...... U) 

# ...., U) 

## 0 

## 0



# - ca 

# ...., 0 

## l- "C 

# c ca 

# .---. 

# a:: --...... fI) eI) 

# = -c eI) 

# > eI) 

# a::



# - ca 

# .... 0

## lI 

# I

I , 

## I I I I i' 

# I I 

# I '1 i ! i 

## i I j I I 

# I' 

# I i I. I , I I ! ! I i

I . Ji' 

# ·

. 

# · · · .. 

# ·

· .. 

# · · Change 

### in C



# R.

i' l-..-

_____________________________. _ .. , 

# Q* 

# Output 

# (Q) Figure Cut Profit Maximization and Fixed Costs

Volume 1: General Methodology Page C·S

### present capacity), then long- and short-run marginal costs will be 

### equal. This is possible because although capital is not included 

### as a short-run cost, there 

### are other costs that 

### are short-run cost but not long-run costs. These 

### are the costs associated with an increase in output beyorid the planned long-run capacity level. 

### If a plant is running optimally, output has been pushed to the point that short-run marginal costs equal long-xun marginal costs; otherwise a different size plant would have been more profitable. The next section gives a theoretical overview of the 

### LBNL-MAM, followed by a derivation of some important results.and a review of their application to the long-run model. Finally, we discuss the short-run part of the model. 

### Theoryandthe LBNLMAM

. 

### From our disc?usiions with representatives of the appliance manufacturing indistries and industry consultants, we know that 

### in appliance manufacturing, unit cost decreases as quantity increases. Therefore, we know ,that ,there 

### are fured 

### costs in the production of appliances. Consequently; eachproducingfm 

### does not face 

### an infiote elasticity of demand because a small increasein price charged will not result 

### in a 100%

### loss of.sales'(because the increase in price would induke entry of new competitors). As a result, we know that the appliance-manufacturingindustry ' 

### is not perfectly competitive. Hence, the LBNLMAM 

### models typical fm 

### as having a combination of fixed and variable costs, and 

### as facing a demand curve with finite elasticity.

. . 

### The next section derives the equations relating markup, 

### fued cosk, variable cost; and economic profit. Theserelationships 

### arepart of standard economic theory, hence these derivations are included for the convenience of the reader and not as an explanation of any theoretical results new to this study. One exceptionto this disclaimeris the discussion on oligopsony power and the "effective" number of firms. 

### There is no generally accepted theory on how to model a market characterized by significant market concentration on both the seller's and the buyer's side. As a 

### result', we developed an approach that is discussed in greaterdetail below.



# . Derivation 

### of

### Markup 

### Tobegin the derivation ofthe markup equation,assume that firms 

### maximizeeconomicprofit, where economic profit is revenue minus economic cost, and economic cost includes the cost of 

### equity and all taxes. Economic costs (C) 

### are assumed to be 

### linear functions of output (Q) 

### and revenues 

### (R):

Although cost is written 

### as a linear function of output, this is not essential;anon-linear cost function

Page C-6 VolumeI: GenemlMethodology , present capacity), then long-and short-run marginal costs will be equal. This is possible because

although capital is not included as a short-run cost, there are 'other costs that are short-run cost but

_

not long-run costs. These are the costS associated with an increase in output beyond the planned long-run capacity level. If a plant is running optimally, output has been pushed to the point that short-run marginal costs equal long-run marginal costs;' otherWise a different size plant would have been more profitable. The next section gives a theoretical overview ofthe LBNlrMAM, followed by a derivation -of some important results, and a review of their application to the long-tun model. Finally, we. discuss the short-run part ofthe model. 

### Theory andthe**LBN~MAM** 

### ' From our discussions with representatives of the appliance manufactming industries and

. industry consultants, we know that in appliance manufacturing, unit cost decreases as quantity increases. Therefore, we know ,that

_ there are fixed costs in the production of appliances.**Consequently~** each producing fmn does not face an infinite elasticity of demand because a small increase in price charged will not result in a 100% loss ofsales '(because the increase in price would induCe entry ofnew competitors). As a result, we know that the appliance-manufacturing industry' is not perfectly competitive. Hence, the LBNlrMAM models typical finns as having a combination offixed and variable costs, and as facing a demand curve with finite elasticity.

. 

### . The next section derives the equations relating markup, fixed costs, variable cost, and economic profit. These relationships are part ofstandard economic theory, hence these derivations are included for the convenience ofthe reader and not as an explanation of any theoretical results new to this study. One exception to this disclainler is the discussion on oligopsony power and the "effective" number of finns. There is no generally accepted theory on how to model a market characterized by significant market concentration on both the seller's and the buyer's side.

- As 

## a**result~** we developed an approach that is discussed in greater detail below. 

### perivation of Markup Tobegin the derivation ofthe markup**equation~** assume that firms maximize economic profit, -, where economic profit is revenue minus economic cost, and economic cost includes the cost of

equity and all taxes. Economic costs (C) are assumed to be linear functions-of output (Q) and revenues (R): (C.I) Although cost is written as a linear function ofoutput, this is not essential; 

### 8: nort-linear cost function

Page C-6 Volume 1: GeneralMethodology

### wodd give a mathematically equivalent, but more difficult to exikss,result?

, 

### Next, make the standard economicsassumption that firmsmaxi& eonom@profit 

### (R-C). Since the cost of capitalis included in economic cost, a 

### firm with 

### an 

### ROE equal to the cost of capital will ftnd 

### h economicprofit to

### be 

### zero. If, however, a 

### fumcanearn a higher ROE, it is assumed that it will do so,.in which case 

### R-C will be 

### positive. The firm can 

### maximize profit by picking either

. . 

### a quantity'to produce (Q) 

### or, equivalently, a price to sell it at, but it cannot choose both. 

### The following:computations assumethat profit is maximized with respectto Q. 

### Thisgivesthe following first-order condition:

This equationcompletes the description of the supply-side assumptions. 

### The demand side is describedby 

### an elasticity of demand which 

### may vary, but which at the market equilibrium takes the value:e. 

### Elasticity of demand is the percentage change 

### in demand, caused by a 

### I%.change in price 

### P, and consequentlyis negative. 

## ath he ma tic all^, thisis expressed as follows:

All necessary assumptions have now been made, so all that remains 

### are some mathematical manipulations. From the two supply assumptions, and denoting 

### dR /dQby 

## R 'and 

### dP/dQ by 

### P 

# : we have:

or equivalently:


### By definition:

If we assume that cost is an arbitrary function of Q

# . C(Q, R), our final result, will differonly in that the two types of co:jt ( 

### A, and 

## P, ) will be replaced by C

### - Q (dCI dQ ) and dC IdR, respectively.
Volume 1: GeneralMethodology Page C-7 would give B. mathematically equivalent, but more difficult to express, result. 2 Next. make the standard economics assumption that finns maximize economic profit (R-C). Since the cost ofcapital is included in economic cost, a 

### fuin with an ROE equal to the cost ofcapital will find ilts economic profit to be zero. If, however, a 

### fum can'earn a higher ROE, it is assumed that it will do sO,.in which caSe R-C will be positive. The firm can 

### maximize profit by picking either, a quantity'to produce (Q) or, equivalently, a price to sell it at, but it cannot choose both. The following computations assume that profit is maximized with respect to Q. TIlls 

### gives the following first-order condition:

## ~(R-C)=O dQ (C.2) ,
This equation completes the description ofthe supply-side assumptions. The demand side is described by an elasticity ofdemand which may vary, but which at the market equilibrium takes the value: 

### e. Elasticity of demand is the percentage change 

### in demand, caused by a 1%'change in

. price P, 2nd consequently is negative. Mathematically, this is expressed as follows:



### . dQ P

-'- =e



### . dP 

### Q (C.3)

All necessary assumptions have now been made, so all that remains are some mathematical manipulations. From the two supply assumptions. and denoting dR/ dQ by R 'and dP/ dQ by P**~** we have: ' ' d ' _[R_A._AQJ=O 

### dQ ,",0 1-'1 • (C.4)

or equivalently:**R/=~1** (C.5) By definitioI'!: R=P'Q (C.6)

2 Ifwe a:ssume that cost is an arbitrary function of Q, C(Q. R), our final result, will differ only in iliat the two types of cost ( 

### P/I and PI) willbe replaced by C - Q (dC IdQ ) and dC IdR. respectively.

, . Volume 1: General Methodology PageC-7,

### Substituting for 

### R' in the first order condition yields:



### In this last equation, price is a simplemarkup over marginal cost. This result may 

### be found in any standard text on industrial organization. Now we define the markup:

Note that because the own-price elasticity (e) 

### is negative,p 

### is greaterthan 1. 

### Of course if e s 

### = -1, then y 

### is undefined, since the firm makes more profit the less it sells. We can now rewrite 

### Eq. C. 

### 10toprovide a simpler look at the markup equation:



### Price formation can

# de explained 

### as follows: marginal costs 

### are marked up by p 

### and fixed costs 

### are not passed on at all. It is now easy to derive andther =presentation ofp 

### which is used in the calibration stage of the 

### LBNL-MAM to estimatey 

### from 

### Eq. C. 

### 1:

Page C-8 Volume 1: GeneralMethodology 

### " R'=P'·Q+P



### R' 'p ---+P dQ**~** dPQ

R '=P + P 

### e Substitutirig for 

### R' in the fIrst order condition yields:



### e 

## p=-p , 1 +e I (C.,)

(C.S)

(C.9)

(C.IO)


### In this last equation, price is a simple markup over marginal cost. This result may be found in any standard text on industrial organization. Now we define the markup:

, e J.l=-

. 1+e (C.11)

Note thatbecause the own-price elasticity (e) is negative, J.l is greater than 1. Ofcourse if e $ 

### = -1, then J.l is undefined, since the firm makes more profit the less it sells. We can now rewrite 

### Eq. C.IO tO'provide a simpler look at the markup equation: (C.12)

Price fomiation can be explained as follows: marginal costs are marked up byJ.l and fixed costs'are' not passed on at all. It is pow easy to derive another representation ofJ.l which is used in the calibration stage of the LBNV

..MAM to estimate J.l from Eq. C.l:

.. '

Page C-8 Volume 1: GeneralMethodology

### Using 

## Eq.C.6 with 

## Eq. C. 12: 

### R =P.Q=pPIQ

so.
.



### and

Finally,

This equation shows that markup is economic income 

### (R-C)plus fixed costs, all over total variable cost. The calibrikion calculations 

### of the long-run module'uses a simple variation of this equation in which 

## F$Q is replaced by C-P, and the relation, R-C 

### = EP, where EPis economic profit, 

### is used twice. The new form of the equation is:

Markup is 

### determined.by revenue, economicprofit, and fixed 

### costs. Generally, it has 

### been found that fixed 'costsare largerelative to economic2profit, so the numerator is controlled l~gely by the 

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

### OligopsonyPower and 

### the "Eflective Number 

### ofFinns" From 

### an evaldation'ofthe 

### 'malyzed industries (for instance, 

### see Chapter4, 

### Industry Profile,

, 

### . 

### of the General Methodology section), we see that the market structures of the industries 

### are oligopolies and 

### model them accordingly. However, the situation is fuaher complicated by the .fact that the 

### demand side of the market is also heavily concentrated dueto the presence of largeretailers VolumeI: General Methodology , Page C-9 (R-C) =R-Po-p.·Q 

### (C.13)

. Using Eq. C.6 with Eq. C.12:
R =P'Q =J.lP1Q 

### (C.14)
SQ· (R -C) =J.lP.Q-PO-P1Q 

### (C.IS) and (R-C) =(J.l-l)P1Q-PO 

### (C.16)
Finally,


### (C.17)
This equation shows that markup is economic income (R-C) plus fixed costs, allover total variable cost. The calibration calculations

# ofthe long-run module uses a simple variation ofthis equation in which**t~Q** is replaced by C-POt and the relation, R-C

# = EP, where EP is economic prefit, is used twice.**Thl~** new form of the equation is: 

### (C.IS)
Markup is determined· by revenue, economic profit, and fixed costs. Generally, it has been found that fixed costs are large relative to economic profit, so the numerator is controlled**l~gely** by the fixed cost. Thus, markup is primarily determined by the. size offixed cost relative to revenue. 

### Oligopsony Power and the "Effective Number 

## 0/ Firms" From an evaluation·ofthe ·analyzed industries (for instance, see Chapter 4, Industry Profile, of the General Methodology section), we see that the market structures of the industries are oligopolies and model them accordingly. However, the situation is further complicated by the fact that the demand side ofthe market is also.heavily**concentrat~** due to the presence oflarge retailers Volume 1: General Methodology Page C-9'

### who have a great deal of market power? 

### In other words, we have an oligopolyfacing an oligopsony. Consequently, the standard oligopoly model needs to be modified. Unfortunately, there is no standarc1 theory on how to model an industry with such a market structure. After examining the evidence of oligopsony,we go on to show how we incorporate this into the model. The

### rise oflarge retailers in the home appliance industry is a relatively recent phenomenon. They 

### an: generally defined as multi-market large-volume retailers, which includes firms such as Sears,Montgomery Ward, Circuit City, and Highland. For example, the home appliance

### iietail share of Sears andMontgomery Ward alone has been estimatedtobe about 

### 30%forvarious products. One 

### indicaticwofthis oligopsony power isthat 

### if

### theoreticalmarkups based on the actual numberof firms 

### in the irtdustry 

### are estimated, we find that the markups are much too large relative to what we observein the market. We addressboth the problems of oligopsony and of "over-estimated" markups by assuming that the low observed markup is the result of oligopsonypower and is an indication of the strength of that power. Since a firm's markup is directly related to the elasticity of the demand it faces, we can find the single-firm demand elasticity that corresponds to the observed markup. This same elasticity is also implied by the industjelasticity and a Coumot oligopoly with some hypothetical number of firms. 

### This allows us to compute the hypothetical or what we refer to as the "effective" number of fmsfrom the observed markup. We then model the industry 

### as if it were a Cournot 01igopol:y facing a competitive, not oligopolistic, demand for its product, but we use the "effective" number of firms rather than the actual number of 

### firmsto reflect the impact of the oligopsony. 

### The effect of this is to reduce the market power of the individual 

### firmto a&- with our observation of markup. Although the reduced market power is a consequence of specifying too many firms,

### and not ofmtdeling oligopsony, we believe that itprovides a goodfirst-order approximationto the actual market structure and provides a way around the intractable problem of modeling a market with imperfectly competitivedemand and supply sides. We now show how markup is computedfrom economic profit and fixed costs, and how the "effective" number of fvms is computed from markup and industry demand elasticity. A wellknown result for a Cournot oligopoly is: e=NF-e,

... 

### (C.19)
where:

.


### e 

### = the 

### fm elasticity of demand, 

### e, 

### = .the industry elasticity of demand, and number of firms. 0. 

## NF =

The increasing concentration on the demand side of the mqket also agrees with comments by the home appliance industry during the comment for these and other appliances. For instance, see AHAM, "Stateof the 

### AW to the Depanmeni: ofEnergy onNotice ofProposedRulemaking on Energy Efficiency Standardsfor Clothes Dishwashers, Clothes washers and Clothes Dryers."CE-&-88-10]. October 10. 1989. . . . Page C-I0 VolumeI: GenemlMethodology · . who have a great deal ofmarket power. 3 

### In other words, we have an oligopoly facing an oligopsony. Consequently, the standard oligopoly model nee:ds to be modified. Unfortunately, there is no standard theory on how to model an industry with such a market structure. After examining the**evidenC4~** ofoligopsony, we go on to show how ,,:,e incorporate this into the model. The rise oflarge retailers in the home appliance industry is a relatively recent phenomenon. They 

### an: generally defined as multi-market large-volume retailers, which includes firms such as Sears, Montgomery Ward, Circuit City, and Highland. Forexample, the home appliance retail share ofSears andMontgomeryWard alone has been estimated tobe about 30% for various products. One indication ofthis oligopsony poweris that iftheoretical markups based onthe actual numberoffmns in the industry are estimated, we fmd that the markups are much too large relatiye to what we observe in the market. 'We address·both the problems ofoligopsony and of "over-estimated" lI].arkups by assuming that the low Observed markUp is the result ofoligopsony power and is an indication ofthe strength ofthat power. Since a firm's markup is directly related to the elasticity ofthe deinand it faces, we can find the single-firm demand elasticity that corresponds to the observed markUp. Tills same elasticity is also implied by the industrY elasticity and a Coumot oligopoly with some hypothetical

. number offinns. This allows us to compute the hypothetical or what we refer to as the "effective" number -of fmns from the observed markup. We then model the industry as if it were a Coumot oligopoly facing a competitive, not oligopolistic, demand for its product, but we use the "effective" number offinns rather than the actual number of

### finns to reflect the impact ofthe oligopsony. The effect of this is to reduce the market power ofthe individual firm· to**a~** with our observation of markup. Although the reduced market power is a consequence of specifying too many fmns, and not ofm(xleIing oligopsony, we believe that itprovides a good first-order approximation to the actual market structure and provides a way around the intractable problem of modeling a market with imperfectly competitive demand and supply sides. We now show how markup is computed from economic profit and**fix~** costs, and how the "effective" number of f1l11ls is computed from markup and industry demand elasticity. A wellknown result for a Coumot oligopoly is:

where:



### e 

### = ej 

### = NF 

# = e=NF·e. I

the firm elasticity ofdemand, the**in~ustry** elasticity ofdemand, and number offirms. 

## (c. 19)

3 The i1ncreasing concentration on the demand side ofthe market also agrees with comments by the home appliance industry during the comment for these and other appliances. For instance, see AHAM. "State of the AlHAM to the Depanment ofEnergy on Notice ofProposed Rulemaking on Energy Efficiency Standards for Clothes Dishwashers. Clothes Washers and Clothes Dryers." CE-RM-88-101. October 10. 1989. Page C·10 Volume 1: GeneralMethodology

### We can solve for

### NFif the two elasticities are known We have 

## & estimate of 

### eiand can estimate

. 

### e as follo\vs. From 

## Eq. C.11, we know that:
I



### We can fuldthe markup, p, 

### from

### Eq. C. 12if we know margin4 cost 

## fi,. Marginal cost

### can be found from the 

### fib sturnon equity, its 

### fured costs, and the following price ideatity: xlQ=P-P,+PdQ 

### , ' 

### where 

## Po is fixed cost and 

### x: denoteseionomicprofit, which is equal to revenue minus cost. (This isjust a restatementof 

## Eq.C. 13.) Noting that revenue,'

### R, equals 

### PQ, 

### we have:

This gives.a formulaformarkupp 

### based on ourmost 

### easily observed variables. SolvingEqs. C. 

### 19 and C.20 

### :For e, 

### we can now frnd the number of firms,

### NF:

. , '
.



### We find that given two elasticities estimated from actual data,

### NF is higher than 

### the actual number offms observed. 

# - Tc) suma'rize, we first look at a typical fumls.profitand fixed costs.' 

## In order to recover fixed 

### cost:s,a 

### firm must mark up over variable costs, and if 

### it can mark up 

### enough, it 

### will make . 

### above "no:rmalW 

### ROE. Fromthese consideratio@,we find its 

### markup. 

### The markup indicates a firm's industrydern'andelasticityand the effectivenumber

### ofTi in the industry. We use it in subsequent calculations.


### TheBase Case 

### In the 

### long-run model. module, the calibration case is immediately followed by long-run calculations for the state of the industry in 1996 provided that .the 1990

### NAECA standardsare unmodified. These calculations use the markup that is based on the onejust calculated and modified in keeping with the assumption that demand elasticitywith respect to lifecycle cost is constant. The modified imarkup is calculated 

### as in 

## Eq. C.5

### but from 

### a modified price elasticity. The new price

. . Volume 1: cfencd Melhodology

. Page C-I1 . We can solve for NFifthe two elasticities are known: We have 

## aD estimate of e; and can estimate



### e as follows. From Eq. C.II, we know that:

### e=~. I-J.l (C.lO)

Wecan fmd tl'ne markup, J.l, from Eq. C.I2ifwe know**margin~** cost 

## PI" 'Marginal costcan be found from the firm's return on equity, its fIxed costs. and the following price identity: 7tIQ=P-P.+PJQ. where

## Po is fIxed cost and 1t denotes eConomic profIt. w.hich is equal to revenue minus cost.· (This isjust a n:statement ofEq. C.13.) Noting that revenue, 

### R, equals 

### PQ, we have:
p. p. 

## R J.l 

## - - = = 

## PI P 0 7t 

## R-Po -7t I. 

### (C.21) p---- 

### Q Q
This gives a formula for markup J.l based on our most easily observed variables. Solving Eqs. C.19 and C.20 for 

### e, we can now fmd the number offInns. NF:


### (C.22)

We fInd that given two elasticities estimated from actual data. NFis higher than the actual number offIrms obsellVed.

- To summarize. we fIrSt look at a typical fmn's.profIt and fIxed' costs: 

### In order to recover fixed costs, a finn must mark up over variable costs, and if it can mark liP enough, it will make above "no:rmal" ROE. From these considerations, we find its markup, The markup indicates a finn's industry**df~mand** elasticity and the effective number offmns in the industry. We use it in subsequent calculatioll1s.

.
Th.e Base Case In the long-run model. module. the calibration case is immediately followed by long-run calculations for the state of the industry in 1996 provided that .the 1990 NAECA standardsare . unmodified. These calculations use the markup that is based on the onejust calculated and modifIed in keeping with the assumption that demand elasticity with respect to life-cycle cost is constant. The modified markup is calculated as in 

### Eq. C.S but from a modified price elasticity. The new price

Volume 1: (;eneralMethodology

. Page C-ll

### elasticity is obtained as follows. First, the original price and operating cost elasticities 

### are used to determine a lifecyclecost and 

### an elasticity with respect to that cost. A 

### demand curve with constant lifecycle cost elasticity is then computed. It takes this form: Q = 

### a.(p+ff*eB 

### (c-23) where 

### P is prke, 

### F is operating (fuel)cost,ff 

### is the fuelcost factor. and 

### B is the elasticity with respect to lifecycle cost. Then price and fuel cost 

### are changed from the calibration case to either the base case 

### or a long-run case. These changes 

### shift the division of demand elasticitybetween the price and operating cost. 

### In particular, the price elasticity is given by:

As price increases as a fraction of lifecycle cost, the price elasticity increases. The added costs to meet new efficiency levels are 

### divided into two categories: capital, and parts and labor. Capital costs come in two varieties, tooling and equipment. These are 

### not currently separated by the model because we do not have separate 

### data 

### from the manufacturers. Since the chief economic difference between these two is their expected life, the LBNLMAM, in its amortizationprocedure, uses an intermediatevalue for the lifetime of new capital. This procedure takes a one-time capital expense and convertsit to an annual expense,taking into account the cost of debt and equity,the tax 

### laws, the lifetime of the capital, and the age of any capital being replaced. Oncethe cost of capital has been annualized,it must be divided into fixed and vdableparts. The fixed part is then added to

# Po while the remainder is divided by Q 

### and added to

# PI. Since there are undoubtedly some -economiesof scale when parts 

### are purchased, the 

### LBNL-MAM provides for a divisionof the cost of parts and laborbetween the fixed and variable categories. However, the'savings generatedby buying an additional 

### 100,000units above-an initial million, forexample, will generallybe so 

### small that the cost ofparts and labor will be 

### referred to as a variable cost and thus can be thought of as being added dkctlyto

# PI. Oncebase case costs have been assigned, price is easily computed from 

### Eq. C.13, by using the price elasticity calculated as described above. With the new price in hand, sales (Q) 

### can be computed from price and operatingcost elasticities. Revenue is nowjust R = 

### PQ, 

### and economic incomeisjust 

### R-C.Assets 

### are those of the calibrationcase

### plus the 

### new capital resulting from 1990 

### NAECA standards, and equity is computed from the debt-toequityratio. This calculation of equity is the last essential calculation for the base case.

Page C-I2 VolumeI: ~eneral Methodology elasticity is obtained as follows. First, the original price and operating Cost elasticities are used to detennine a life-cycle cost and an elastiCity with respect to that cost. A demand curve with constant**life~yc1e** cost elasticity is then computed. It takes**~s** form:

. 

### Q 

## = a-(P+ff-F)B (C.23) where P is price, F is operating (fuel) cost,ffis the fuel-cost factor, and B is the elasticity with respect to life-eycle cost. Then price and fuel cost are changed from the calibration case to either the base case or a long-run case. These changes shift the division ofdemand elasticity between ·the price and operating cost. In particular, the price elasticity is given by:



### e 

### = B-P P+ff-F
As price increases as a fraction oflife-cyc1e cost. the price elasticity increases. ·"(C.24)

The added costs to meet new efficiency levels are dividedinto two categories: capital, and

.partS and labor. Capital costs come in two varieties, tooling and equipment. These are not currently separated by the model because.we do not have separate 

### data from the manufacturers. Since the chief econoinic difference between these two is their expected ·life, the**LB~MAM.** in its amortization procedure, uses an intermediate value for the lifetime ofnew capitaL This procedure takes a one-time capital expense and converts it to an annual expense, taking into account the cost ofdebt and equity, the tax laws, the lifetime ofthe capital, and the age ofany capital being replaced. Once the cost ofcapital has been annualized, it must be divided into fixed and**v~able** parts. The fixed part is then added to 

## PC) while the remainder is divided by Q and added to 

## Pl' Since there are undoubtedly semeeconomies of scale when parts are purcliased, the**LB~MAM** provides for a division ofthe cost ofparts and labor between the fixed and variable categories. However, the savings generated by**buyi~g** an additional 100,000 units above-an initial million, for example, will generally be so small that the cost ofparts and labor will be referred to as a variable cost and thus cari be thought ofas being**ad~ed** directly to 

## Pl. Once base case costs have been assigned, price is easily computed from Eq. C.13, by using the price elasticity calculated as described above. With the new price in hand, sales (Q) can be computed from price and**operating~ost** elasticities. Revenue is nowjust R = P 

### Q, and economic income isjust R-C. Assets are those ofthe calibration case plus the new capital resulting from 1990 NAECA standards, and equity is computed from the debt-to-equity ratio. This calculation ofequity is the last essential calculation for the base case.

Page C-12
. Volume 1: General Methodology

### TheLong-nur Aniilys&: ~hn&d 

### ~~ernative Emiency Lcvek 

## .

8. 

### . . . 

### The analysis of the 

### long-run effect of achangein efficiency levels from their 1990NAECA 

### level is essentially the same as the analysis of the base case. New costs 

### are dividedbetween fixed 

### and variable categories hi 

### the sameway and added 

### to thebase case

### costs. Fromthere,'the calculation proceeds in exactly ,theskneway as above.

The cost structure. 

### Costs 

### are assumed to have two components: fixed and proportional to quantity.

7ike

### assetstructure. 

### Assets 

### are assumed to have the

### sametwo components.

D,emQnd

### elasticity. 

### The.demand elasticity with respect to life-cycle cost experiencedby a singlefmn is assumedto be 

### independentofprice 

### and operating cost over the relevant region. This means that if 

### the industry raises price, the percentage change in demand caused by a 1 % 

### change in price remains the same as before the price increae.

. 

### . Oj7timizingbehavior.A 

### firm is assumedto 

### maxi- revenue minus economiccost (R-EC). Debt-to-equizy 

### ratio. 

### The debt-toquity ratio is assumed to remain constant kdile the 

### firm finances any investmentnecessary to'meetnew efficiencylevels. It is generally'assumed that fvms 

### have somr: Dm 

### ratio that they consideroptimal, and.thereisno reason fornew efficiency levels to change this 

### figure.



# . Anwrfization ofcapitalcosts. 

### It is assumedthat the one-time capital cost is to be 

### amortized over its life (typicallyseven years), that its cost is the weighted average after-tax cost of capital, ahd 

### that depreciationis straight-lineover the tax 

### life. Structure ofcost resulting from changes alternativeefiencylevils. 

### The cost of capital is 

### assumeclto be 

### partly fixed andpartly variable (i.e., proportional to shipments). For instance,parts and 

### labor ;are

### assumed to be 

### variable costs. .

VolumeI: General Methodology . Page C-13 The Long-runAnillysls: ChangedAltenuJtive Efficiency Levels 

### 1be analysis ofthe long-run effect ofa change in efficiency levels from their 1990 

### NAECA level is essentially the same as the**~alysis** 

### ofthe base case. New costs are divided: between fixed and variable categories in'the same way and added to the base case costs. From there, the calculation proceeds in exactly the same way as above.


### C.2.2 StructuralAssumptions The cost**struct~re.** 

### Costs are assumed to have two components: 'fixed and proportional to quantity.

.



### Th.e asset structure. 

### Assets are assumed to have the same two components.

D,emand. elasticity. 

### The·4emand elasticity with respect to life-cycle cost experienced by a single finn is assum¢ to be independent 'ofprice and operating cost over the relevant region. This means thalt if

### the industry raises price, the percentage change in demand caused by a 1 % 

### change in price remains the same as before the price increase. Optimizing behavior. 

### A firm is assumed to 

## maximize revenue minus economic cost (R-EC). Debt-to-equity ratio. 

### The debt-to-equity ratio is assumed to remain constant while the firm finances any investment necessary to meet new efficiency levels. It is generally'assumed that fmns 

### have**som(~** DIE 

### ratio that they consider optimal, and.there is no reason for new efficiency levels to change thilS 

### figure. Anwrtization of capital costs. 

### It is assumed that the one-time capital cost is to be 

### amortized over its life (typically.seven years), that its cost is the weighted average after-tax cost ofcapital. and that depreciation is straight-line over the tax 

### life. St,.ucture of cost resulting/rom changes in alternative efficiency levels. 

### The cost ofcapital is assumed to be 

### partly fixed and partly variable (i.e., proportional to shipments). For instance, parts and labor ;are 

### assumed to be 

### variable costs.
.

. Volume I: <1encrolMethodology Page C-13

### C.23 Description 

### ofModule. The 

### LBNLMAM is a Lotus 1-2-3 spreadsheet that 

### is organizedinto 9 sheets,each of which is subdivided into a nuniber of modules or "pages." A 

### flowchart depicting the main linkages between the most importantmodules appears 

### in Figure C.2, and a brief description of each is given below. Startingwith subsection C.2.3.1, each panel is given a full description. A. Primary Page. 

### 1. Control panel. 

### This module displays nine key variables and their control factors, plus a summary of crucial results. The control factors 

### are normally set to onk, 

### but can be 

### changed to test the model's reaction to any of the control variables. The change in results is immediately displayed in the lower part of the control panel. 2. Cost, sales, and 

### revenues. 

### This module uses prices, markups, market shares, and industry shipment levels to compute variable cost of goods sold and revenues for a single prototypical 

### fum. Changes in operating cost, capital cost, and engineering expense 

### are also computed here. 3. Costs

### andassets ofone-time investment.

### The one-time cost module derives amortized costs from the one-time capital cost and one-time engineering expense associated with the imposition of new efficiency levels. 4. 

### Model 

### anddemand 

### (long-run).

### This module does most of the economic calculations. Its calibrationphase estimates the markup over variable cost which is used to estimate new prices once new efficiency levels have been imposed. These prices 

### are then used to compute sales, and, from sales, all

### other descriptive variables. 5. 

### Short-runmodel. 

### This module modifies the price computed by the long-yun model in orderto take into accountthe possible short-run effects of excess capacity. 

### An estimate of the responsiveness of price to changes in demand during the business cycle is combined with the change in demand predicted by the long-run model. 6. Monte Carlo. 

### The nine control variables can be selected randomly frorfi probability distributions determined by the variables* standard errors and median (estimated) values. The Monte Carlo module is designed to 

### record many such 

### runs and display the mean value and standard deviation of six outputs: percentage change in price, sales, 

### . revenue, and net income, plus the changein ROE in tile long and shortruns. 

### 7. Variation in Inputs. 

### This module is similar to the upper portion of the control panel (Al). 

### It displays 20 inputs for the long-run model and their control factors. The control factors are normally set 

### to one, but can be 

### changed to test the model's reaction to any of the variables. 

### B. Industry Net Present Value.

### This module estimates the impacts of imposing new eff~ciency levels using 

### an industry net present value approach. This module's functionalityis exactly that of the externallydeveloped GovernmentRegulatory Impact Model 

### (GRIM).See 

### C.2.6 for more details.

VolumeI: GeneralMethodology C.2.3 Description ofModules The LBNL-MAM is a Lotus 1-2-3 spreadsheet that is organized into 9 sheets, each ofwhich is subdivided into a number of modules or "pages:" A flowchart depicting the main linkages between the most important modules appears 

### in 

### Figure C.2, and a brief'description ofeaCh is given below. Starting with subsection C.2.3.I, each**pane~** is given a full description. A. Primary Page. I; Controlpanel. This module displays nine key variables and their control factors, plus a summary ofcrucial results. The control factors are noimally set to one, but can be changed to test the model's reaction to' any of the control variables. The change in results is immediately displayed in the lower part ofthe control panel. 2. Cost, sales, and revenues. This module uses prices, markups, market shares, and industry shipment levels to COmpute variable cost of,goods sold and revenues for a single prototypical 

### fum. Changes'in operating cost, capital cost, and engineering expense are also computed here. , ' ' 3. 'Costs andassets of one-:-time investment. 

### The one-time cost module derives amortized

.costs from the one-time capital cost and one-time engineering expense associated with the imposition ofnew efficiency levels. 4. Model anddemand (long-run). This module does most ofthe economic calculations. Its calibration phase estimates the markup over v¢able cost which is used to estimate new prices once new'efficiency levels have been imposed. These,prices are then used to compute sales, and, from sales, 'all other descriptive variables. 

### 5. Short-run model. This module modifies the price**co~puted** by the long-run model in order to take into account the possible short-run effects ofexcess capacity. An estimate of the'responsiveness of price to changes in demand during the business cycle is combined with'the change in demand predicted by the long-run model. 6. Monte Carlo. The nine control variables can be selected randomly from probability distributions determined by the variables' standard errors and median (estimated) vaIues. The Monte Carlo module is designed to record many such runs and display the mean value and standard deviation of six outputs: percentage change in price, sales, revenue, and net income, plus the change in ROE in the long and short runs. 

### 7. Variation in Inputs. This module is' siiriilar to the upper portion of the control panel (AI). It displays 20 inputs for the long-run model and their control factors. The control factors are normally set to one, but can be changed to test the model's reaction to**~y** ofthe variables. B. Industry Net Present Value. This module estimates the impacts ofimposing new efficiency levels using an industry net present value approach. This niodule's functionalitY is exactly that ofthe externally developed Government Regulatory Impact Model (GRIM). See C.2.6 for more details. '

Page C-14 Volume 1: General Methodology

## - C. 

### Linking Page. The integration of 

### MAM and 

### GRIM required some modification of input

- 

### variables due to the diffexentways the 

### two models treat 

### costs. This module aligns inputs for {the two models using cost comparisonfactors. See C2.6 for more details. ' 

## D. Program. 

### LBNLMAM

### Primary Programming. The program flow is controlled through a seriesofmenus. Theprogramperforms the calculations forthe various outputs of the model, inclu.dingefficiency level cost calculations,sensitivity analyses, and a Monte Carlo analysis. outputs. 1. 

### Accounting Summary. The accounting module gives a summary, in the form of a simplified income statement, of the financial impact of alternative efficienc levels on the firm. 

### In addition, a summary of the financial impacts dueto new efficiency levels from an economic point of view is shown at the bottom of the page. 2. Output Table. The output page tabulates long-run variables, percent changes, and standarderrors from the Control Panel (Al) and Monte Carlo module (A6), and shortrun variables and percent changes from the Short-Run Model (AS). 3. ChartsofSensitivity to ControlPanel Variables.This module produces two charts. The first shows how profit is affectedby a one-standarddeviationchange in each of the control variables. The second chart shows how profits change 

### as the alternative efficiency level or engineering design level is changed, but all control variables are kept at their base value. 4. 

### Charts

### of sensitivity toModel Variables. This table shows how profit is affected by a one-standard-deviation change in a number of secondary variables. 5. 

### Retail Price Output Table. These retail prices 

### are produced 

### as inputs for the

# LB* REM. 

## F. GWM Outputs. This module displays the results

. . 

### from the industry net present value module/GRIM 

### (B).

. 

# -

,. 

### G. Clther Inputs 1



## .. Cost page source inputs. This module contains primary inputs and simplecalculations used as 

### inputs for cost page (A2) 

### calculations. 2. 

### Finance source inputs.The.fmancialmodule computes the weighted average cost of capital and a 

### factordescribing how costs depMd on revenues. 3. 

### One-time cost source inputs. This module contains primary inputs and simple calculations used'

### as inputs.for one-time cost page (A3) calculations. 

### H. CbstInputs 1. 

### Engineering cost input matrices.

## he engineering inputs.moduleaccepts 

### as input raw engineering data that 

### are used elsewhere in the model for the impacts analysis. These data include incremental variable costs, capital costs, maintenance costs, installation costs, and unit energy consumption forthe products being analyzed. 2. 

### Alternative Eficiency leveldistribution matrices. This module weights the costs given 

### by the engineering inputs module in proportion to the number of products of 

### a VolumeI: General Methodology C. 

### Linking Page. The integration of MAM and'GRIM required some modification of input variables due to the different ways the two models treat costs. This module aligns inputs for ,the two models using cost comparison factors. See C.2.6 for more details. ' D. 

### Program. LBNL-MAM Primary Programming. The program flow is controlled through a**SI~ries** ofmenus. Theprogram perfonns the calculations for the various outputs ofthe model, inclu.ding efficiency level cost calculations, sensitivity analyses, and a Monte Carlo analysis. 

### E. Outputs. , 1. 

### Accounting Summary. The accounting module gives a summary, in the form of a simplified income statement, ofthe fmancial impact ofalternative efficienc levels on . the fIrm. 

### In addition, a summary ofthe financial impacts due**~o** new efficiency levels from an economic point ofview is shown at the bottom ofthe page. '

. 2. 

### Output Table. The output page tabulates long-run variables, percent changes, and standard errors from the Control Panel (Anand Monte Carlo module (A6), and shortrun ·variables and percent changes from the Short-Run Model (AS)

.. 

### 3. Charts o/Sensitivity to Control Panel Variables. Thismodule produces two charts. The first shows how profit is affected by a one-staridard-deviation change in each of the control variables. The second chart shows how profits change as the .alternative efficiency level or engineering design level is changed, but all control variables are kept at their base value.

. 

### 4. Charts 

## 0/ Sensitivity to**Mode~** 

### Variables. This table shows how profit is affected by a one-standard-deviation change in a number of secondary variables. ., 

### 5. Retail Price Output**Table~** These retail prices are produced as inputs for the**~NL** REM:

. F. 

### GRIM Outputs. This module displays the reSUlts. from the industry net present value module/GRIM (B).

. 

### G. Other Inputs' 1" CoStpage source inputs. This module contains primary inputs and simple calculations used as inputs for cost page (A2) calculations. 

### 2. Finance source inputs. The ·fmancial module computes the weighted average cost of capital and a factor 

### describing how costs depend on revenues. ' 

### 3" One-time cost source inputs. This module contains primary inputs and simple calculations used as inputs for one-time cost page (A3) calculations. H. 

### Cost Inputs 1. Engineering cost input matrices. 

# The engineering inputs.module accepts as input raw engineering data that are used elsewhere in the model for the impacts analysis. These data include incremental variable costs, capital costs, maintenance costs, installation costs, and unit energy consumption forthe products being analyzed. 

### 2. Alternative Efficiency level distribution matrices. This module weights the costs given by the engi'neering inputs module in proportion to the number of products of a Volume 1: Ge1leralMethodolOgy PageC-lS

### design that will be produced at any given efficiency level and thereby generates the costs for that.level. These costs appear in the standards level- module (H3).

- . 

### 3. Cost inputs by alternative efficien,cylevels. The standards level module provides the same data 

### as the engineering inputs module, except that the data have been processed by the 

### LBNLREM andlor the "Cost ~onveion" module (H2) 

### and 

### are presented for ' 

### the efficiency levels being analyzed.

, 4. 

### Retail price calculation module. This module provides estimates'of retail prices of appliances at different efficiency levels. The primary input for the retail price calculation is the estimateof manufacturer prices. 

### I. Engineering Data. Raw engineeringinputs 

### are included on a 

### separate sheet. These values 

### are processed by,the- Cost Inputs (Gl)sheetbefore being passed to the rest of the model.

. 

### J. 

### GeneralNotes. 

### C.2.3.1 

### Control Panel The control panel displays the ten most importantinput variables of the model and allows the user to 

### easily adjust them. These variables 

### are describedbelow. 

### The input values are 

### the best available estimates 

### of the relevant variables. The controlpanel has one control foreach 

### input variable. The codtrols, found in the column labeled cntrl,allow the input valuesto be modified to become themodel valuesbefore they 

### are used by the model. The controls work 

### as follows: when a control is set to zero, its input value becomes a model value without any adjustment. When a control is set to 1, 

### its input value is increased (in absolutevalue)by approximately one standard deviationto become amodel value. Because the error distributions associated with the input values 

### are not normal, they are not changed by exactly one standard deviationwhen the control is set to plus or minus 

### 1. A 

### cxytrol value of 1actuallychanges an input value by an amount sufficient to make it just 

### as unlikely 

### as a point that 

### is one standard deviation from the mean using a normal distribution. Consideran input value that is known to be strictlypositive and have a mean of 5 

### and a standard deviation of 3. Setting its control to -2, 

### which would otherwise change

### its value to -1 

### (an impossibility) actuallyonly changes it to some small but positive value. This value would be

### just 

### as unlikely as apoint -2 

### in a normal distribution. . 

### The Variationcolumn shows eitherthe standard deviation of 

### an input variableif the variable is normally distributed, orthe coefficientof variation(S.DJmean) 

### if the input variable is non-normal.

Page C-16 VolumeI: GeneralMethodology particular design that will

. be produced at any given efficiency level and thereby generates the costs for that level. These costs appear in the standards level module (H3). 

### 3. Cost inputs by alternative efficienr:y levels. The standards level moduJe provides the same data as the engineering inputs module,**~xcept** that the data have been processed by the LBNlrREM and/or the "Cost Conversion" module(H2) and are presented for the efficiency levels being analyzed.

. 4. 

### Retail price calculation module. This module provides estimates 'of retail prices of appliances at different efficiency levels. The primary input for the retail price calculation is the estimate ofmanufacnirer prices. I. 

### Engineering Data. Raw engineering inputs are included on a separate sheet. These values are processed bythe Cost Inputs (Gl) sheet before being passed to the rest ofthe model. 

### J. General Notes. C.2.3.1 

### Control Panel The control.panel displays the ten most important input variables ofthe model and allows the user to easily adjust them. These variables are described below. The 

### input values are the best available estimates ofthe relevant variables. The control panel has one control for each inputvariable. The controls, found in the column labeled 

### cntrl, allow the 

### input values to be modified to become the 

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

### input value becomes a 

### model value without any adjustment. When a control is set to 1, its 

### input value is increased (in absolute value) by approximately one standard deviation to become a 

### model value. Because the error distributions associated with the 

### input values are not normal, they are not changed by exactly one standard deviation when the control is set to plus or minus 1. A cqntrol value of

### ± 1 actually changes an 

### input value by an amount**~ufficient** to make itjust as unlikely as a point that is one standard deviation from the mean using a normal distribution. Consider an 

### input value that is known to be strictly positive and have a mean-of5 and a standard deviation of3. Setting its control to -2, which would otherwise change its value to -1 (animpossibility) actually only changes it to some small but positive value. This value would bejust as unlikely as a point -2 in a normal distribution. The 

### Variation column shows either the standard deviation ofan input variable ifthe variable is normally distributed, orthe coefficient ofvariation 

### (S.D.lmean) if.the input variable is non-normal.

Page C-16 Volume 1: GeneralMethodology

# MAM'Modulesand 

# Primary Routes of 

# Data 

# Flow
Engineering 

# 1 ;Yr 

# I-.

. 

# 1 1X Cost Inputs 

## f (~3) 

## 1

VolumeI: Gene& Methodology r - . 

### - . ..

. -. i

,

# I Stam~dards 

# 1 Cost 

## i Finance

. -. . - - --

### - i .Levels (02) ! 

### i 033) ! i


### i

. 

## I Costs',Sales,

, '

Accounting

- Cost

., 

# (A4)
Tcharts i NPV (GRIM)

. .

. .

. . Conaol 

### ! shoddm 

# i Charts I peel 

# 1 

# 1 7;;; 

# 1 (E3) !. (All

. i J J

1- ! -Output 

### - 1 ! Retail 

# 1 (E2) 

# *

. t j I (A6) ! Prices (E5,H4) Figure 42.2 MAM Modules and Primary Routes of Data Flow 

# MAM

. Modules a!ld**Pri~ary** 

# Routes of

# Data Flow
j 

# I Engineering Inputs Oil) IX Cost Inputs

.

(03)

# r-~-W:d~~~ " Cost : Levels 

# "----II Conversion 

### ! 

### on)'

0 (H2) :

.**-----~** !FinanCe {-.-----. 

### '--1 

# L . .,-_(0_o2_)

__ --lr--·:-·· --. --- ..

# _"I~;~~l I

# cosu.sa~es.**~**__

# ~__________ __ 

## i and R.evenue 

## I (A2) _. ,

### '--J~ C,)st InJ'uts

.

(01) 

## L

________-' [

,**~** NPV (GRIM) (D.C)



### , ! Output (E2)**~** t



## .

'-

I

# ~ ! ! y



# .Long-Run Mode,l
,



## (A4)**~** j

. -



# - 

# i·: Sh:rt-RUft ! y Control

Panel 

### I; Model (AI)

. 

# I j (AS) I , Y ! 

### I Monte ! Carlo 

### I (A6) i ! j Figure C.2 MAM Modules and Primary Routes ofData Flow

Volume 1: (;eneral Methodology , I 

# I 

# L

.. r



# .. Accounting 

### .! (EI)

_

Tcharts

(E4) , 

## I
·1

,------- -----, Charts \

(E3) 

### i.
,**~**

## ___._._J

Retail Prices (ES.H4)

Page C-l1

### At the bottom of the control panel is a short output summary, wuch shows the five key output variables: shipments,price, revenue, net income, and ROE. The first column givkthe output values in 

### the base 

### case. The second column assumes some particular changein 

### efficiency levels and gives long-run values--that is, values that would hold after capacity adjustments (by about 

### 2003). The third 

### column gives the percentage change from the base case to the alternative efficiency level 

### case for each of the five key output variables. The fourth column, labeled Previous Change, 

### simply lists the results of the previous run to use 

### as a comparison with the results of the current run. The fifth

### column lists the results from the short-run 

### part of the model (i.e., the estimated output values for the period immediately after the imposition of the new efficiencylevels).

8.

,. 

### The following is a list of the ten key input

. . 

### variables that appear 

### as control factors in the Control Panel module: The industry's price elasticityof demand

### (IPE), The consumer discount rate 

### (RD), The market cost of capital financed through equity (ECC), - . 

### Economic profit: the indus'try'sROE-nrlinus normal 

### ROE (EP), The long-run fixed 

### part of costs and assets (FCA), 

### The long-run fixed part of one-time costs and assets (FIX), 

### One-time capital costs resulting

### fr& the change in efficiency levels (CC.N), Variable cost of goods sold after the change in efficiency levels (dVC.N) The elasticity curve parameter of the demand function, (ro.N), and The short-run price response to demand(SRPR).
C.2.3.2 

### The Monte Carlo Module This module estimates the standard errors of the six most important output variables by randomly choosing the ten control variables 

### in accordance withestimatesof their standard errors, running the model, and recording the six output variables. This procedure is repeated up to 

### 400 

## . times. Afterwards, the mean and standard deviations of the 400 computed values of each output variable 

### are computed 

### and displayed 

### near the top of the Monte Carlo module. The top row, labeled Value, 

### is the most recently computed random set of outputs. The first four output variables 

### are the percent change resulting from new efficiency levels, in output (Q), 

### price 

### (P) and revenue 

### (R). The last three 

### variables 

### are changes in net income 

### (NI) and long- and short-run return on equity (ROE).


### .
C.2.3.3 

### The Accounting Module The Accounting Summary consists of a simplified income statement that is presented in standard form, along with several key financial ratios and figures that highlight the firm's financial

, Page C-18 Volume1: GeneralMethodology 

### At the bottom of the control panel is a short output summary,**w~ch** 

### shows the five key output vanables: shipments, price, revenue, net 

### income. and ROE. 

### The first column gives the output values in 

### the base case. The second columnassunies some particular change in 

### efficiency levels and gives long-run values-thatis. valueS that would'hold after capacity adjustments (by about2(03)

.

. The third 

### column gives the percentage change from the base case to the alternative efficiency level

case for each ofthe five key output variables. The fourth column. labeled Previous Change. 

### simply lists the resUlts. of the previous run to use as a comparison with.the results ofthe current run. The fifth 

### column lists the results from the short-run part ofthe model (Le.• the estimated output values for the period immediately after the imposition ofthe new efficiency levels). The**follo~ing** 

### is a list of the ten key input variables that appear as control factors in the Control Panel module: 1. 

### The industry's price elasticity ofdemand (IPE), 2. The consumer discount rate (RD), 3. The market cost ofcapital financed through equity (BCC). 4.' Economic profit: the industry's ROEminus normal ROE (EP), 5. The long-run fixed part ofcosts and assetS (FCA). 6. The long-run fixed part ofone-time costs and assets (FIX), 7. One-time capital costs resulting from the change in efficiency levels (CC.N), 8. Variable cost ofgoods sold after the change in efficiency levels (dVC.N) 9. The elasticity curve parameter ofthe demand function. (ro.N). and 10. The short-run price response to demand (SRPR).,

C.2.3.2 The Monte Carlo Module This module estimates the standard errors of the six most important output variables by randomly choosing the ten control variables in accordance with.estimates oftheir standard errors, running the model, and recording the six output variables. This procedure is repeated up to 400 times. Afterwards, the mean and standard deviations of the 400 computed values ofeach output variable are computed and displayed near the top of

### the Monte Carlo module. The top row. labeled Value. 

### is the most recently computed random set ofoutputs. ' The frrst four output variables are the percent change resulting from new efficiency levels, in output (Q), 

### price (P) 

### and revenue (R). 

### The last three 

### variables are changes in net income (NI) 

### and long- and short-run return on equity (ROE).

C.2.3.3 The Accounting Module The Accounting Summary consists of a simplified income statement that is presented in standardform, along with several key financial ratios ,and figures that highlight the firm's financial Page C·18 Volume 1: GeneralMethodology

### position. 

### Theincome statement and financialratios are presented for the calibration case and base ' - . 

### cases that include the impact of the 

### 1990

## NAECA standards, and 

### an alternative efficiency level case that lists the firm's financial ~sults under the alternative efficiency level being analyzed. Thus, the

. 

### financial impacts of the alternative efficiency levels 

### are kityseen 'by comparing the two energy efficiency scenarios. To highlight the results, a final column 

### in FeAccounting Summary indicates the percei~tage change from the base case to the alternative efficiency level case. 

### A secondtable atthebottom of

### theAccounting summarypagelists financial

### data for the firm using figr~ks that 

### are necessary for 

### an economic analysis. Note that the assets and expenses 

### are broken clown 'into two categories: fixed 

### and proportional to quantity produced. These proportio~nalities have economic significance when the price markupis computed, and forthat

### reason they 

### are reported separatelyin the economic analysis.


### Table'C.l Accounting 

### Module Eaniing!; Before Interest and Taxes. 

## EBIT =Revenue'- Total Expenses

, 

### Total ~1;:~enses Does not indudeinterest or taxes

Taxes Net Income Gross M:argin (Interest rate Debt ratio

# - Assets) 

## -Interest earned on "Cash" assets 

## (EBIT 

## -Interest) Tax rate EBIT

# -Interest

# -Takes

. 

### 1

# -(Variable Cost of Goods Sold/Revenue) Return 

### oa S.ales Net Income/Revenue Retum on ~ssets (ROA) 

### (EBIT 

## -Taxes 

## - (Tax rate Interest))/Assets This calculation eliminates interest tax 

### shield Return on 

# -

Eguity 

### (ROE)

, 

### Net Income/Equity

Note that "one-time" (1 

### -X) in the Accounting module refers to expenses incurred because of the change in alternative efficiency levels.
C.2.3.4 

### The Engineering 

### Inputs 

### Module Thie Engineering Inputs module receives all the engineering cost data and energy consumptjon data 

### for the product classes being analyzed. The data include the incremental variable cost, change in unit energy consump'tion, maintenance cost, and installation cost for each design Page C-19

, position. The income statement and financial ratios are presented for the calibration case and base ' cases that include the impact ofthe 1990 NAECA standards, and an alternative efficiency level case that lists the finn's fmancial results under the alternative efficiency level being analyzed. Thus, the financial impacts of the alternative efficiency levels are**~ily** seen 'by comparing the two**e~ergy** efficiency scenarios. To highlight the results. a final column 

### in the Accounting Summary indicates

, I the**perceJ~tage** change from the base case to the alternative efficiency level case. A second table atthe bottom ofthe Accounting Summarypage lists fmancial data for the fJJlI1 using figures that are necessary for an economic analysis. Note that the assets and expenses are ' broken down 'into two categories: fixed and proportional to quantity produced. These, proportiolilalities have economic significance when the price markup is computed, and for

## that reason they are r,eported separately in the economic analysis.


### Table'C.1 Accounting Module Item Eaniings Before Interest and Taxes Total Expenses Interest
Taxes' Net Income Gross Margin Return on s.ales Return on Assets (ROA)
Return on Equity (ROE) Formula EBIT

# = Revenue- Total Expenses I Does not include interest or taxes (Interest rate· Debt ratio· Assets)

- Interest earned on "Cash" assets (BBIT

- Interest)

. Tax rate EBrt .,.. Interest

- Taxes 1 - (Variable Cost ofGoods SoldlRevenue) Net IncomelR.evenue (BBIT

- Taxes

- (Tax rate· Interest»/Assets This calculation eliminates interest tax shield Net IncOIl),elEquity ,
Note that "one:..time" (l-X) in the Accounting module refers to expenses incurred because of the**changl~** in alternative efficiency levels.

.


### C.2.3.4 The Engineering Inputs Module The Engineering Inputs module receives all the engineering cost data and energy consumption data for the product classes being analyzed. The data include the incremental vanable cost, change in unit energy consumption, maintenance cost, and installation cost for each design Volume 1: I']enerolMethodology Page C·19 ,

### option. They also include the one-time capital costs incurred in meeting the imposed .efficiency levels; the module assigns these costs to design options and product classes where appropriate or necessary. The various components of the module 

### are described below:

. . 

### 1. Increment in Additional UVC (Unit Variable Cost): Engineering input data. Each "incrementiis 

### measured relative to 

### theprevious engineering design. Thefigures

### arc in 

### 1990 dollars. 

### 2. Additional WC above 

### 0cost (VCS-E): "Additional" WC

### is measured relative to the ,1989

### calibration case, which is shown as level 0. 

### In other words, this table is a cumulative

. 

### version of the above "incremental"table.



## . . 

### 3. 

### Cwnulative~ainteke Cost: These engineering input 

### data are the maintenance costs, per year for each product class and engineeringdesign option. 4. 

### Annual UnitEnergy Costs per YearThese engineering input 

### dataare the energy use per year measured in 1990dollars. 5. 

### TotalAnnual Operating Costs (WS.E):

### These costs 

### are the sum of cumulativemaintenance costs and annual unit energy costs. 

### 6. Installation Costs (1NCOST.E): These 

### are engineering input data used for the calculation of lifecycle cost. 

### 7. Capital Costs(CC.E): These engineering input data 

### arc given on a per-unit b&is amortized (with zero interest) over seven years. 8. 

### Additional Capital Cost (above Level 

### 0 Cost)(ADD.E): This matrix translatesihe amortized capital costs into total one-time per 

### firm capital costs. 9. 

### Total Capitaland 

### Engineering C&S 

### (CCEE-E):This row is the sum of the columns in the "additional capital cost"matrix. Thus the 

### CCEE-E matrix gives the combined added capital cost for all classes of an appliance. The 

### CCEE.E matrix contains the capital costs that 

### are used 

### asinputs to the rest of the model. The Long-Run module calculates the average prices for all classes and for each level.

, 10. 

### Research 

### andDevelopment Costs (RD.E): These engineering input 

### data, when available,

### are treated separately from other capital conversion costs. 11. 

### Total Capital Conversion Cost,Excluding R&d (TCC.E): This matrix translates amortized capital costs into total undepreciated one-timeper industry capital costs. This is used for the 

### industry net present value calculations.

Page C-20 .. Volume 1: GeneralMethodology option. They also include the one-time capital costs incurred in meeting the imposed .efficiency levels; the module assigns these costs to design options and product classes where appropriate or necessary. The various components ofthe module are described**belo~:** 1. 

### Increment 

## i:n Additional UVC (Unit Variable Cost): Engineering input data. Each "increment" is measured relative 'to the previous engineering design. The figures are in 1990 dollars. 

### 2. Additional 

### uve above Level 0 cost (VCS.E): "Additional" 

## We is measured relative to the

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

version ofthe above "incremental" ,table. 3. 

### Cumulative Maintenance Cost: These engineering input data are the maintenance costs. per, year for each product class and engineering design option. 

### 4. Annual Unit Energy Costsper Year: These engineering input data are the energy use per year measured in 1990 dollarS. 

### 5, TotalAnnual Operating Costs (KWS.E): These costs 

## are the sum ofcumulative mai,ntenance costs and annual unit energy costs. 

### 6. Installation Costs (INCOST.E): These are engineering input data used for the calculation of

. ,life-cycle cost.



# . 7. Capital Costs (CC.E): These engineering input data are given on a per-unit basis amortized (with zero interest) over seven years. 

### 8. Additional Capital Cost (above Level 0 Cost)(ADD.E): This matrix translatesThe amortized capital costs into total one:-time per firm capital costs.

I 

### 9. Total Capital and

### EngiJ:zeering Costs (CCEE.E): This row is the sum ofthe columns in the "additional capital cost" matrix. Thus the 

### CCEE.E matrix gives the combined added capital cost for all classes of an appliance. The 

### CCEE.E matrix contains the capital costs that are used as inputs to the rest ofthe model. The Long-Run module**calculate~** the average prices for all classes and for each level. 

### 10. Research and

### Development Costs (RD.E): These engineering input data, when available; are treated separately from other capital conversion costs. 

### 11. Total Capital Conversion Cost, Excluding R&d (TCC.E): This matrix translates amortized capital costs into total undepreciated one-time per industry capital costs. This is used for the industry net present value calculations.

Page C-20 Volume 1: General Methodology

### . . 

### C.2.3.5 

### The StandardsLevee 

### ("REM") Module

. - 

### The Standards Levels module provides the same information 

### as the Engineering Inputs module exceptthat instead oflistingthe cost dnd energydata 

### forvarious engineeringdesign options, it 

### lists 

### the data forthe' various alternative efficiency levels being 

### analyzed.

' 

### The data that appear in the seven REM panel tables for each product-VCS.R, KWS.R, 

## ' INC0ST.R CC.R ADD.R, 

### RD.R and TCC.R-may. correspond exactly to some specific enginee~ing levels or, if 

### it has been determined that the manufacturer, when faced with certain efficiency levels, would use a mixture

### ofengineering

### designs,then the "StandardsLevel Distribution . 

### Module" appropriatelyaveragesthe engineering 

### data and places 

## the results 

### in the seven REM tables. ' , ' 

### The exact rxnixtures of engineering options 

### are determined 

### by the 

### LBNL-REM. .

, . 

### I'he top section ofthis module allows the user to detemiine.what alternativeefficiency levels will be 

### analyzed in 

### a particular run. The module is used interactivelyby the user, who can choose "levels" LEV.B and LEV.N; which correspond to the Base case and Long-Run case, respectively. The bottom numbers in the "LevIn"

### matrix detennine whether these levels will refer to "alternative efficiency levels" or "engineeringdesigns.': The'topnum&rs specifythe exact level or design. The values in the "LevIn" matrix determine what values are placed 

### in the bottom two matrices when the model is 

### run. For example if 

### in "LevIn," 

### LEV.B =4 

### and.

### SE.B 

### =0, then the columns of VCB, 

### KWB, CCB, and 

### INCSTB will contain 

### data for the fourth engineering design option. Changing SIE.B from 0 to 

### 1would cause all of these data to be-replaced with data for the. fourth efficiencylevel.

. .

. .


### C.2.3.6 

### 'TheCosts, 

### Sales,and Revenue Module This module 

### has two functions. First, itprovides the Long-Run Model'module with several inputs; thiese 

### are averages. Second,ituses many of

### the data

### from

### the inputpages and 

### processes these data into prices, quantities sold, and revenue. These intermediate output data 

### are then used in the financial and economic summaries to give output results, which 

### are usedl for the 

### analysis 

### of the 

### impact of 

### implementing new efficiency levels. Table C.2 

### lists the equations that 

### are used in 

### the Costs, Sales, and Revenue module.
111

### the alternativeefficiency level case, the variables 

### are named with a ".N" suffix instead of

. . 

### a 

## ":B"suffix. The ''T@alsW row gives the weighted average change in WC (dvC.~), the weighted average change in price, the riew weighted averageprice (P.B),the 

### fm'stotal revenues 

### (r(Ri), R.B),
; 

### the weighted average change in operating costs (OC%.B),

### and the total firm shipments (Q.B). 

### P.B, Q..B

### and R.B are actually computed in the Long-Run module. The first stage in this module is setting up: the calibration case, which uses known information about industry shipments,product class market shares,prototypical 

### firm market shares. Volume 1: GeneralMethodology Pagcl C-21 

### C.2.3.S The Standards Levels ("REM") Module The Standards Levels inodule provides the same information as the Engineering Inputs module c:xcept that instead oflisting the cost and energy data for various engineering design options, it lists the data for the various alternative efficiency levels being analyzed.**~** The data that appear in the seven REM panel tables for each product-VCS.R, KWS.R, INCOST.R. CC.R, ADD.R, RD.R and TCC.R-may correspond exactly to some specific engineeling levels or, if it has been determined that the manufacturer, when faced with certain efficiency levels, would use a 

### mixture ofengineering designs, then the "Standards Level Distribution Module" appropriately averages the engineering data and pla¢es the results 

### in the seven REM tables

.. The exac:t mixtures ofengineering options are determined by the LBNlrREM. 1betop section ofthis module allows the user to detemiine·what alternative efflciency levels will be analyzed in a particular run. The modUle is used interactively by the user, who can choose "levels" LEV.B and LEV.Ni which correspond to the Base case and Long-Run case, respectively. The bottc)m numbers 

### in the "LevIn" matrix determine whether these levels will refer to "alternative efficiency levels" or "engineering**designs.'~** The"top numberS specify the exact level or design.**Th~** values in the "LevIn" matrix determine what values.are placed 

### in the bottom two matrices when the model is run. For example if 

### in "LevIn," LEV.B 

# = 4 andSIE.B 

# = 0, then the columns of VCB, KWB, CCB, and INCSTB will contain data for the fourth engineering design option. Changing SIE.B from 0 to 

### 1 would cause all ofthese data to 

### be-replaced with data for the· fourth efficiency level.

.



### C.2.3.6 'The Costs, Sales, and Revenue Module

. This module has two functions. First, itprovides the Long-Run Modermodule with several**inputs~** tbese are averages. Second, it uses many ofthe data from the input pages and processes these data into prices, quantities sold, and revenue. These intermediate output data are then used in the financial and economic summaries to give output results, which are used- for the analysis of the impact of implementing new efficiency levels. Table C.2 lists the equations that are used in 

## the Costs, Sales, and Revenue module.

. hi the alternative efficiency level case, the variables are named with a ".N" suffix instead of a ".B" suffix. The "Totals" row gives the weighted average change in UVC 

### ·(dve.B). the weighted average change in price, the riew weighted average price (P.B), the fum's total revenues (L(Ri), R.B), the weighted average change in operating costs (OC%.B), and the total firm shipments (Q.B). P.B. Q.B and.R.B are actually computed in the Long-Run module. The first stage in this module is setting up. the calibration case, which uses k.oown information about industry shipments, product class market shares, prototypical firm market shares, Volume 1: GeneralMethodology Page C-2l

### Table C.2 Equations 

### Used in 

### the Costs, Sales, and Revenue Module
" 

### 1tem Calibration CaseFormula orDefinition Size - 

### Prototypical

### firm market share ' 

### Annual industry shipments for a product class Industry Shipments Relative Shipments (Q%) Product class market share (product class units sold/totalunits sold) Shipments 

## (Q: 1) Product class shipments for a prototypical 

### fm Price 

### (P.l) Unit Variable Cost (UVC) input markup 

### (MU.0) Price Shipments. Revenue 

### (R. 1)

, 

### 1 + 

### (P. 

### 1 -. 

### BB)/AA

, 

### Markup 

### (M. 1) Unit Variable Cost 

### .(UVC. 1) 

### P.1IM. 1 Weighted WC WC Relative Shipments 

### (Q%)



## ............................................................................................ ........ . 

## .... .................................................. New Shipments (Qi.B) Q% Q.B ~uledf-~humb Change 

### in 

### Price M. 1 (change in 

### WC 

# +change in variable

### part 

### of per unit investment costs) Ruleaf-Thumb Revenue (P. 

### 1

## +Ruleaf-Thumb dP) Qi.B Scaling factor to insure 

### &.B 

### =R.B
New Price (Pi.B) 

### P. 1

## + (1

# - AlphaB Rule-of-Thumb dP) Rule-ofThumb 

### dP New Revenue (Ri.B) Pi.B Qi.B Operating Cost Ratio 1996UEU1990UEC Weighted Operating Cost Ratio Operating Cost Ratio Relative Shipments
Percent Change in Operating Cost C-~td OperatingCost Ratio KWHR 

## - 1

Page C-22 VolumeI: GeneralMethodology / 

### , " Table C.2' Equations Used in the Costs, Sales, and Revenue Module Item Size Industry Shipments Relative Shipments (Q%) Shipments**(Q~l)** Price (P.I) Revenue (R.I) Calibration Case Fonnula or Definition Prototypical 

### firm market share ' Annual industry shipments for a product class Product class market share (product class units sold/toW units sold)

, Product class Shipments for a prototypical 

### fmn

Unit Variable Cost (UVC)" input markup (MU.O) Price "Shipments, Markup (M.I) I + (P.I :"BB)lAA Unit Variable Cost (UVC.I) P.l/M.I



## ...**~~~.~~~~**

## ..**~~_**

## ...__.._ ....._ ..._ ......**~._**

## .........__**!-!.Y_c;._._~~~~E.Y~~h!P.!!l~!!~.'(Q~2.**

## .._ ......._ ......................................

, , New Shipments (Qi.B) Q% "Q.B Rule-of-Thumb Chapge in Price
Rule-of-Thumb Revenue Alpha.B

New Price (PLB)
New Revenue (Ri.B) Operating Cost Ratio Weighted Operating Cost Ratio
Percent Change in Operating Cost (OC%.BO)

Page C.22 M.I " (change in UVC 

### + change in variable part ofper unit investment costs) (P.I 

### + Rule-of-Thumb dP) "Qi.B Scaling factor to insure LRi.B 

# = R.B
P.I 

### + (1- AlphaB· Rule-of-Thumb dP)" Rule-ofThumbdP PLB

. Qi-B 

### 1996 UECll990 UEC Operating Cost Ratio" Relative Shipments 

# LaassWtd Operating Cost Ratio" KWHR - I

Volume 1: General Methodology

### markups foreach ofthe product classes,and baseline unit variable costs to calculatethe prototypical firm's shipments for each product class, its cost of goods sold, and its revenue. Also calculated 

### are a weighted-averageunit variable cost, selling price, and markup. '~arku~ is calculated 

### fmm data

### on

. 

### the 

### ratio between the highest and lowest markups (ratio) and the typical markup (mid). The next &ge 

### in the Costs, Sales, and ~evenue Module calculates the changes necessary to

. 

### produce die base case and the alternative efficiency level case.4 The calibration case is used 

### as a base, and the changes in unit variable cost and unit energy consumption 

### are received'from the Standards Level cost inputs module. From these inputs, new unit variable costs and prices 

### are calculated foreach oftheproduct classes. The model then uses

### known price elasticitiesand discount rates to calculate changes in 'shipmentsfor each of the alternativeefficiency level cases. For each of the efficiency level cases, the model calculates the firm's new shipments, revenue, andthe weighted-average change in unit variable cost and price. Th~e manufacturer,markuplisted in the panel is used to calculatethe baseline model prices, 

# - but it is nc~t.used in calculating the manufacturerpricesfor

### the alternative efficiency levels. 

### In these - 

### cases, 

### LB1NL.MA.Muses an econdmicmarkup to determineprices.' This markup is on economic variable costs, which are a broader category than the typical business definition of variable costs. The markup varies depending on .price elasticities, discount rates, and changes in prices and

. 

### operating costs at different efficiencylevels. Thus, pricesat different efficiency levels do not exactly reflect the manufacturer cost times the manufacturermarkup. 

### C.23.7 

### The Financial Module The 

### Financial module lists the financial ckcteristics if the prbtotypical 

### fum being analyzed. Someof the inputs 

### are used to calculateintermediate outputs of financial characteristics of the firm. The financial inputs and intermediate outputs 

### are then used elsewhere in,the model when the impacts of

. . 

### new efficiency levels 

### are calculated. Table

### C.3gives the formulik'used in the module. . . 

### Several variables in the Financial module are not related to alternative efficiency levels and thus require explanation. First, in order to,make the model adjustable to 

### a particular point in time when 

### the size of the 

### fm may be different 

### from what it was in the year in 

### which the data were collected, some of the inputs 

### are listed as ratios proportional torevenue. For instance, the historical ratio between depreciable assets and revenues forihe cooktop manufacturing industry (calledDA:R) 

### is 

### 0.37. T'his is used by the model to compute a value for depreciable assets at some typical year in the future after efficiency levels have changed by multiplying DA:R 

### by revenue in that year.

4 The base case allows the analysis of intermediate efficiency levels or other alternate efficiency level analysis scenarios in addition to the new efficiency level case sceriarios; For the purposes of this analys$, the base case is identical to ithe calibration case. 

### . .

. For a thorough description of how maikups arederived and used in LBNL-MAM, see C.2.1.

. . . Volume 1: GeneralMethodblogy Page C-23 markups i4Jr each ofthe product Classes, and baseline unit variable costs to calculate the prototypical firm's shipments for each product class, its cost ofgoods sold, and its revenue. Also calculated are a weighted-average unit variable cost, selling price, and markup. Markup is calculated from 

### data on the ratio between the highest and lowest markups (ratio) and the typical markup (mid). Th.e next stage in the Costs, Sales, and

### Reven~e Module c3Iculates the changes necessary to
. produce tbe base case and the alternative efficiency level case. 4 the calibration case is used as a base, and the changes in unit variable cost and unit energy consumption are received

-from the Standards Level cost inputs module. From these inputs, new unit variable costs and prices are calculated for each ofthe product classes. The model then uses known price elasticities and discount rates to calculate changes in shipments for each ofthe alternative efficiency level cases. For each of the efficiency level cases, the model calculates the firm's new shipments, revenue, and' the weighted-average change in unit variable cost and price. The manufacturer-markup listed in the panel is used to calculate the baseline model prices, • but it is not.used in calculating the manufacturer prices-for the alternative efficiency levels. 

### In these' cases, LBNL-MAM uses an econoImc markup to determine priCes.s This markup is on economic variable costs, which are a broader category than the typical business definition of variable costs. The markup varies depending on price elasticities, discount rates, and changes in prices and operating costs at different efficiency levels. Thus, prices.at different efficiency levels do not exactly reflect the manufacturer cost times the manufacturer markup. 

### C.2.3.7 The Financial Module The Financial module lists the financial characteristics of the prototypical. fum being

. analyzed. Some ofthe inputs are used to calculate intermediate outputs offmancial characteristics of the firm. The financial inputs and intermediate outputs are then used elsewhere in,the model when the impacts ofnew efficiency levels are calculated. Table C.3 gives the formulas used in the module. Several variables- in the Financial module are not related to alternative efficiency levels and

. thus require explanation. First, in order to.make the model adjustable to a particular point in time

when the**~ize** of the fmn may be different from what it was in the year in which the data were

.collected, some ofthe inputs are listed as ratios**p~portional** torevenue. For instance, the historical ratio between depreciable assets and revenues for the cooktop manufacturing industry (called DA:R) is 0.37. T.his is used by the model to compute-a value for depreciable assets
_ at some typical year in the future after efficiency levels have changed by multiplying DA:R by revenue in that year.

4 The base case allows the analysis of intermediate efficiency levels or other alternate efficiency level analysis scenarios in addition to the new efficiency level case sceriarios: For the purposes of this analYSIS, the base case is identical to Ithe calibration case. -
S For a thorough description of,how markups are derived and used in LBNL-MAM. see C.2.t.

.Volume 1: (;eneral Method()logy Page C-23

### Table C3 

### Financial Module Definition Item Formula DR [ER] Debt fEquity]/@ebt+Equity) 

### DRR Depreciation Average depreciation rate depreciable assets (expressed as a percent of revenue) ECC Equity cost of capital PECC Pre-tax ECC ECCI(1 

## - Tax 

### Rate) WACC Weighted average CC DR 

### Interest rate 

## +ER PECC ATWACC After-tax WACC WACC (1

## - Tax Rate) ROE Return on Equity (expected ROE for a ATWACC 

## +Economic Profit prototypical firm)

- 

### Acomplicationmay 

### occur if a value changesin proportion to revenuebetween now and the typical 

### year, but under efficiency levels which have been implemented it is expected that a portion of the value will remain proportional to revenue while anotherportion of the value will not change with revenues because of the change in efficiency levels (i.e., it is "faed"). 

### In this case, that value is analyzed as having a "variable"and a "fixed" component, where "variable" and "fixed"are 

### used with respect to a change in efficiency levels.

. . 

### C.2.3.8 The One-Time

### CostAmortization Module Crucial to the manufacturer impact analysis are 

### the capital costs that the industry will incur because ofthe change 

### in imposedefficiency levels. This module takes as input from the Engineering Analysis the estimate of the total value of the one-time capital costs and reduces.themto annual



### . expenses.To annualize (or "amortize") these costs, the module takes into account: the expected life of the new capital, the tax life of the investment, the weighted average cost of capital, and the timing of the investment relative to any capital that is being replaced.

I 

### The amortized capital costs 

### are calculated for the change from the calibration case to whateverefficiency level case is under consideration. Also, the average value (over the amortjzation period) of the new capital is computed. Two important factors derived he=, the gross 

### tax effects and the levelized capital cost factors. Both calculations are based on a model of investment where new capital will be paid'for according to the CC SCHEDULE. This schedule gives expendituresfor the four years previous to 1993; these expenditures 

### are assumed to'occurat the middle of each year. They 

### are assumed to be 

### financed by debt and equity in the firm's normal debt-to-equity ratio.

, - Tax 

### benefits will accrue over the tax 

### life of the capital. Somepart of the capital expenditure (commonly 50%) 

### is assumed to be for new plant and equipment that do not take the place of' Page C-24 VolumeI: General Methodology 

### Table C.3 Financial Module Item DR [ER] DRR

ECC PECC WACC ATWACC ROE Definition Depreciation
Equity cost ofcapital Pre-taxECC Weighted average CC After-tax WACC .Return on Equity (expected ROE for a prototypical firm) Formula Debt [Equity] l(Debt+Equity) Average depreciation rate· depreciable assets (expressed as a percent ofrevenue) ECC/(l

- 

### Tax Rate) DR •

### Interest rate 

### + ER • PECC WACC· (1
- Tax 

### Rate) A

TW ACC 

### + Economic Profit

A complication mayoccur if

### a value changes in proportion to revenue between now and the typical year, but under efficiency levels which have been implemented 

### itis expected that a portion ofthe value will remain proportional to revenue while another portion ofthe value will not change with revenues because ofthe change in efficiency levels (i.,e., it is "fIXed"). In 

### this case, that value is analyzed as having a "variable" and a "fixed" component, where "variable" and "fixed" are used with respect to a change in efficiency levels.

.**C~2.3.8** 

### The One-Time Cost Amortization Module Crucial to the manufacturer impact analysis are the capital costs that the industry will incur because ofthe change in imposed efficiency levels. This module takes as input from the Engineering Analysis the estimate of the total value ofthe one-time capital costs and reduces them to annual expenses. To annualize (or "amortize") these costs, the module takes into account: the expected life ofthe new capital, the tax life ofthe investment, the weighted average cost ofcapital, and the timing ofthe investment relative to any capital that is being replaced. The amortized capital costs are calculated· for the change from the calibration case.to whatever efficiency level case·is under**considera~on.** 

### Also, the average value (over the amortization period) ofthe new capital is computed. Two important factors are derived here, the gross tax effects and the levelized capital cost factors. Both calculations are based on a model of investment where new capital will be paid'for according to the CC SCHEDULE. 

### This schedule gives expenditures for the four years previous to 1993; these expenditures are assumed to 'occur at the middle ofeach year. They are assumed to be 

### financed by debt and equity in the firm's normal debt-to-equity ratio.

- Tax benefits will accrue over the tax 

### life ofthe capital. Some part ofthe capital expenditure

. (commonly 50%) is assumed to be for new plant and equipment that do not take the place of

Page C-24 Volume 1: General Methodology



# ..

### existing capital; the remainder is assumed to replace inadequateexistingequipment. The replaced equipment is assumed to have had the samepotential lifetime (L) 

### as the replacing equipment and is assigned a certain age. 

### Ifthe new equipment replaces old equipmentthat would soon have needed replacing ;anyway,savings 

### are included at the time ,thenatural replacement would .have occurred. Because 

### these savings

### are not tax-related, they reduceinitial capital costbut 

### are not calculated under tax effects.

. , 

### Thecomputed annualizedcost tells what conitant rcal

### dollarpayment would.need to be 

### made each

### year in

### order that at the end ofthe capital's useful life all

### ofthe issued debtcouldhave been paid

, 

### off, and 

### all of the issued equity could have been repurchased." Since imposed efficiency levels 

### sre assumed to last indefinitely, new capital will need to 

### be purchased at the end of this cycle; but of

.

, 

### coursethe next cycle startswith a clean slate and repeats the same process we havejust described. This process.canrepeat indefinitelywith no change 

### in amortized payments. TELS procedure overestimates the burden to 

### the: 

### fm because, since industry is constantly improving the efficiency ofproduct lines, those lines would undoubtedly be 

### much nearer to meeting the implenlented efficiency levelsby the beginning of the second cycle. Thus, much of the capital equipment re-installed at the end of the firstcapital's lifetimewould have been a normal expenseby this date (around 2001). 

### For the interested reader; the details of the calculation method 

### are given in Table C.4.

. 

### TableC.4 

### shouldbe

### read in conjunction with the one-~ime CostAmortization module. Note that the caJculations 

### are all done with continuous interest ,and discount rates, which ,makes it necessary to convertATWACCto a continuous equivalent cost of'capital(AT')by taking the natural log of 

### (I $. 

### A'PWACC). The continuous rate is equivalent to, and for smali rates it is nbmerically close to, the 

### mud rate. The continuous time formula for the present value of

## $Y received t 

### years . 

### in'the future is 

### Y exp(-rt), where 

### r is the discount rate and exp denotes exponentiation. For a constant streamof income at 

## SY per year for'L

### years, the present value is 

## Y (I

# - exp(-;~))/r. 

### The:mast bportant output of this module 

### are the capital cost amortizationfactor 

### CCLF and

. 

### the capital c0s.tleveliiation tax 

### factor 

### CCLTF. CCCLF

### includes the initial cost (normalizedto 1

### in order to produce a cost factor instead of 

### an actual cost) and the present value of savings from not replacing old capital at the end of its normal life if it 

### lbs already been replaced with new capital to meet new eff~ciency levels. Thesetwo present values 

### are converted to 

### an equivalent constant sheam of payments that continues for the lifetime of the capital. 

## CCLF is calculated to take into account the fact 

### thac. investmentmuit startbefore the implementation of new efficiency levels. The capital cost 1evelii:ation tax factor CCLTF similarly levelizes the present value of the tax benefit from straight-lint: depreciation of the initial cost, the tax 

### benefit from the one-time depreciationof the old capital when it is scrapped, and the loss of tax benefit hm the old capital after it has been scrapped. The formula for these equations 

### are shown in Table C.4.

Of COU~S~ tax benefits are counted toward eliminating debt and equity.

. . VolumeI: General Methodology Page C-25 existing cupit'll; the remainder is assumed to replace inadequate existing equipment. The replaced equipment is assumed to have had the same pOtential lifetime (L) as the replacing equipment and is

,assigned a certain age. Ifthe new equipment replaces old equipment that would soon have needed replacing ;anyway, savings are included at the time the natural replacement would 'have occurred. Because these savings are not tax-related, they reduce initial capital cost but are not calculated under tax effects. ' Thecomputed annualized cost tells what constant real dollarpayment would, need to be made each 

### year in orderthat at the end ofthe capital's useful life all ofthe issued debt could have been paid off, and all ofthe issued equity could have been repurchased. 6 Since imposed efficiency levels are assumed tt:> last indefinitely, new capital will need to be purchased at the end ofthis cycle; but of ' course the next cycle starts with a clean slate and repeats the same process we havejust described. This process can repeat indefinitely with no change in amortized payments. This procedure**ov~restimates** the burden to the fum because, since industry is constantly improving the efficiency ofproduct lines, those lines would undoubtedly be much nearer to**meetin~** the**implement~** efficiency levels by the beginning ofthe second cycle. Thus, much ofthe capital equipment re-installed at the end ofthe first capital's lifetime would have been a normal expense by this date (around 2(01). For the interested**reader~** the details ofthe calculation method are given in TableC.4. TableC.4shouldberead in conjunction with the One-Time CostAmortization module. Note ' that the caJculations are all done with continuous interest and discount rates, which, makes it necessary to convertATWACCto a continuous equivalent cost of capital (ATR) by taking the natural log of (1 

### :+. ATWACC). The continuous rate is equivalent to, and for smali rates it is numerically close to,**thl~** annual rate. The continuous time formula for the present value of

### $Yreceived 

### t years in'the future is 

### y. exp(-rt), where r is the discolint rate and exp denotes exponentiation. For a constant stream ofincome at 

### $Yper year for L years, the present value is 

### Y-' (1

- exp( -rL))/r. 

### The: most important output ofthis module are the capital cost amortization factor CCLFand the capital cost levelization 

### tax factor 

### CCLTF. CCLF includes,the initial cost (normalized to

### -I in order to pmduce'a cost factor instead of an actual cost) and the present value of savings from not replacing oId capital at the end ofits nonnallife ifit

# has already been replaced with new capital to meet new efficiency levels. These two present values are converted to an equivalent constant stream of payments that continues for the lifetime ofthe capital. CCLFis calculated to**~e** into account the fact that investment must start before the implementation ofnew efficiency levels. The capital cost**levelh~ation** tax factor 

### CCLTF similarly levelizes the present value of the tax benefit from**straight-lin(~** depreciation ofthe initial cost, the 

### tax benefit from the one-time depreciation ofthe old capital when it is scrapped, and the loss oftax benefit from the old capItal after it has been scrapped. The formula for these equations are shown in**T~ble** C.4:

.

6 Ofcour.;e tax benefits are counted toward eliminating debt and equity. Volume 1: G.meralMethodology Page C-25

### Table C.4 One-Time Cost Amortization 

### Module Item Definition Formula 

### T Tax rate (36%)
' TL Tax life of existing capital L Economic life of existing capital AGE Age of existing capital %NC Percent of one time capital that is add-on (not EPlacementL 

### ............. 

## ..........- P 

## --....... 

## .....- .-----. RTL Remaining tax life max (TL

### - AGE, 0) ATR Continuous after-tax weighted average cost of capital* in(] 

### +ATWACC) EMRT e-ARTL EMRL e-ATR.L BN Tax benefit rate T/IZ BEN Tax benefit of existing capital (1 

### - %NC)

## - BN DIS PV discount factor ,-ATRiL 

### -nmns



## .......... 

## ................ 

## -"-......."......- .-.-.*.-......-.-

................. CCi

### * Capital cost I years before effcy levels have been implemented CCWi* Weighted CC lead-time factors CC~ 

## .e(~~~ 

### - o LTC Cumulative CC lead-time factor 

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

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

## .,- .S_u~CEi.~!!!e.r~P?.ears 

## ..--........... OCS* Savings from old capital not replaced later -( 1

## - %NC) 

### . e-~~~<~-ffi~) PVCC* Present value of capital cost (CC) I +OCS CCLF CC amortization factor ATR • PVC LTC/(I 

### - EMRL) PVD* Tax benefit of straight-line depreciation 

## - BN 

## .(I -EMRT)/ATR LEC 1 Loss of tax benefit 

### on existing capital 

### BEN 

# .(1 

## - e-Am'RTL)/ATR LEC2 Loss of taxbenefit on discounted existing capital in DIS BEN

## - (1 

## - ~-'T"~YA+R the future OC7Z* Loss of tax benefit from existing capital -(LEC 1 

### +LEC2) 

### LTB

### * 1X depreciation of existing capital (I-%NC). RTL 

## .BEN 

## - A'PR 

## . LTC CCLTF CC levelization tax factor LTB 

## + (ATR 

## .LTC • (PVD+OCl'L))/(I 

### -

............. 

### -..-.... ...."..".*........



## ......

## .......--. ... ".-....-.- --.. AFB Asset Factor for any new capital or asset I 

### - ATR 

## .L EMRL/(I- EMRL) 1

### - EMRL AAF ~verak asset factor -" 

## ........... ... 

### .-.-... 

## ".--..-....."..." -................. %NC 

## - AFB 

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

## ..... -.- LCC.B Gross levelized I-X CC, base CCLF 

## .CC.B LCC.TB Levelized 

### 1-X CC tax effect, base CCLTF CC:B 

### LAB Levelized 1-X assets, base AAF 

### .CC.B

' ' GLCC.N Cost comparison factor for net present value calculations LCC.N Gross levelized I-X CC, new

. (CCLF 

## .CC.N) 

## + GLCC.N LCC.TN Levelized I-X CC tax effect,new CCLTF 

## .(CC.N 

### + (GLCC-NICCLF)) L;A.N Levelized I-X assets, new AAF (CC.N 

### + (GLCC.N/CCLF)) 

### * Not a variable used in the model. Provided here for ease of exposition.

. 

## . Page C-26 Volume 1: Generalme tho do log^ 

### Table C.4 One-Time Cost Amortization Module Item Definition Formula T Tax rate (36%) TL Tax life ofexisting capital L Economic life ofexisting capital AGE Age ofexisting capital %NC

. Percentof'one time capital that is add-on (not

.___._._. _. __**~Iace!!!.~nt~{..**____ _ 

# ._--_ ....

# _- RTL Remaining tax life max (TL
- AGE, 0) ATR EMRT EMRL BN BEN Continuous after-tax weighted average cost ofcapital* In(1 +A1WACC) e·ATR

· TL e·ATR

· L Tax benefit rate Tm.. Tax benefit ofexisting capital (1- %NC)· BN ;..I;>IS

_____ 

### f'V**~j~s.Qym.f.!£tQr_** _ ____**~:~~~TL»** __ . CCi* Capital cost I years before effcy levels have been implemented CCWi*

.Weighted CC lead-time factors CCi· e(ATR'1) 

# ----..

# _._._...**J=.1:.~_.**

### ___**<;.Y.mp.l!tiv~ C<;'J~~-qme**  faC!9r • ______**~um** of**GQYi.Q)!~LU~!!'.§.**

### ___ .._._•.... OCS* Savings from old capital not replaced later -(1
- %NC)

. e·ATR'(L.AOE) PVCC* Present value ofcapital cost-{CC) 1 + OCS CCLF CC amortization factor ATR· PVC· LTC 1(1
- EMRL) PVD* Tax benefit of straight-line depreciation

- BN

. (I -EMRT)/A TR LECI Loss oftax benefit on existing**capi~1** BEN· (l
- e·ATR.RTL)/ATR • LEC2 Loss oftax benefit on discounted existing capital in DIS· BEN· (I

- e·ATR'TL)lATR the future 

### oen* LTB* CCLTF Loss oftax benefit from existing capital IX depreciation ofexisting capital. CC levelization tax factor

. -(LECI + LEC2) (I-%NC)· RTL· BEN· A'fR· LTC LTB + (ATR

. LTC· (PVO+OCTI..»/(1
- 

# --_..

# _---_..

# __......

# _-_._---_ ...

# _._---. . ____**---'=EM=RL~l.__** _ ....._ ....... AFB Asset Factor for any new capital or asset I - A TR . L . EMRL 1(1
- EMRL) I-EMRL**.~._.**__ .._.**_.~.'!.f?£~~ ~~.~U"!~12.;..r** ___ . " %NC·AFB


# -

....

# --.....

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

# ---....

### -...

## -.-.---

## .....

# -.-.---- LCC.B

. Gross levelized .I-X CC, base LCC.TB Levelized I-X CC tax effect, base LA.B

. GLCC.N

LCC.N LCC.TN Levelized I-X assets, base Cost comparison factor for net present value calculations Grosslevelized I-X CC, new Levelized I-X CC tax effect, new CCLF·CC.B CCLTF·CC:B AAF·CC.B**~CCLF** . CC.N) + GLCC.N CCLTF· (CC.N + (GLCC.N/CCLF»

. LA.N Levelized I-X assets, new AAF· (CC.N + (GLCC.N/CCLF» • Not a variable used in the model. Provided here for ease ofexposition.

Page C-26 Volume I: General Methodology

### Becausecapital depreciates,its averagevalue is lawer than 

### its initial value. 

### In orderto derive the factor

### AAF, by which the initial value should be 

### multiplied to arrive at the averagevalue, it is necessary to account forthe exactpath by which the capital depreciates. Since

### the value 

### of apiece of capital is, at any time, simply the present value of its future income stream, the value of the depreciated capital as a function of time can be found from the streamof income that the capita( will generate. 

### If that stream has 

### a constant flow for a period ofL 

### years, then the depreciated value of a unit of capital. is:

Now if that capital is held for a period of L 

### years, it is logical to ask what the 

### average value of the ' ' 

### asset is over the 

### Lyears. Because of discounting, a unit of capital now should weigh more heavily than a unit of capital later. This is properly taken into account by requiring 

### the following equivalence: PV(DF(t)) =

### PV(AAV) ' . 

### where 

### PV() is the L-year present value operator, and

### AAVis the average asset value corresponding to DF(t). *This 

### equation can be 

### solved forAA

### V. to fmd the following:

This is the average asset factor for:the productive income of any new capital or asset. 

### In order to get the factorwe want--the average asset factor for add-on capital-we must multiply

### AAV by 

### the fraction of one-time capital that is add-on rather than kplacement capital, %NC. At thebottom ofthe One-Time Cost Amortizationmodule 

### are found the capital costs for the' base and long-run levels, 

### and its coefficient of variation. 

## he three factorsjust derived, 

### CCLF, CCLTF, mdMF,

### can be 

### applied to.CC 

### (for either the base case or long-run case), to derive 

### the amortized outputs LCC, LCC.

### Tand 

### LA, respectively.,


### . I 

### C.2.3.9 

### Tlhe Ihng-Run Model Module 

### The Long-Run Model module is the heart of the 

### LBNGMAM. It does all of the computaticas

### concerning atypical fum'sbehavior. It

### proceeds in three stages. The calibration stage uses data on present costs and profit to estimatethe fm's maikup. The second stage estimatesthe industry's cun-ent situation. The third estimates a typical fim;'s situation with more stringent efficiency levels. A 

### detailed discussion of the economicassumptionsand results used by this section may be found'i? C.2.1. 

### No,fation.The following naming conventions have been followed for, the variables in the Long-Run Model module and generally throughout 

### the 

### LBNLMAM. 

## The suffixes 

## F and 

### .Q, are VolumeI: C;enesal Metlrodo1og.y Page C-27 Because capital depreciates, its average value is lower than its initial value: In order to derive the factorAAF, by which the initial value should be multiplied to arrive at the average value, it is necessary to account for the exact path by which the capital depreciates. Since the value of a piece" of capital" is, at any time, simply the present value of its future income stream, the value of the depreciated capital as a function oftime can be found from the stream ofincome that the capital 

### will "generate. Ifthat stream has a constant flow for a period of

### L years, then the depreciated value of a

unit ofcapital is: l-e -r(L-t) DF(t)=--- l-e,-rL
Now if !:but capital isheld for a period of L years, it is logical to ask what the average value ofthe asset is**oVI~r** the L years. Because ofdiscounting, a unit ofcapital now should weigh more heavily than a unit of capital later. This is properly taken into account by requiring the following equivalence: " PV(DF(t»

## = PV(AA V)
where PV() is the L-year presel)t**valu~** operator, and AA Vis the average asset value corresponding to DF(t). This equation can be solved forAAV, to fmd the following:


### 1

# ~ L -rL] AAV=" 1_[ 

### e 1

### -e -r 1

### -e -rL This is the average asset factor for the productive income of any new capital or asset. In order to get the factor we want-the average asset factor for a4d-on capital-we must multiplyAA

### V by the fraction ofone-time capital that is add-on rather than replacement capital, %NC.
- At the bottom ofthe One-Time Cost Amortization module are found the capital costs for the" base and long-run levels, and its coefficient of variation. The three factors just derived. 

### CCLF. CCLTF, aJlld AAF. can be applied to CC (for**~ither** the base case or long-run case), to derive the amortized outputs 

### Lee, Lee. Tand 

### LA, respectively." 

### C.2.3.9 The Long-Run Model Module Thl! Long-Run Model module is the heart of the LBNL-MAM. 

### It does all of the computations concerning a typical f1IlIl's behavior. 

### It proceeds in three stages. The calibration stage uses data on present costs and profit to estimate the fum's markUp. The second stage estimates the industry'S tcun:ent situation. The third estimates a typical**firm~s** situation with more stringent efficiency leve.ls. A detailed discussion ofthe economic assumptions and results used by this section may be found in C.2.1. 

### Nott:Ztion. The following naming conventions have been followed forthe variables in the Long-'Run Model module and generally throughout the LBNL-MAM. The suffixes .F and 

### .Q! are
Volume 1: G;eneral Methodology Page C-27

### used with variables that measure 

### an asset or a costthat is fixed or proportional to sales,mpectively. The suffixes

### - .B and 

## .N refer to the base case and the alternative efficiencylevels, respectively. A 

### combined suffix such as 

### .BF has 

### the natural interpretation (i.e., a fixed variable in the base case). Themost important variablenames in 

### the Long-Run Model module

### m A 

### forassets, TC 

### for total cost excluding taxes and the cost of equity, 

### EC for economic cost (including taxes and equity), 

### EI for economic income, and mu1 for markup above marginal cost, p 

# - 1 

## . Calibration. The calibration stageestimates a typical firm's markup over unit variable costs using the followingbasic economic equation: (~ixed Cost + 

### Economic hfit) 

# ' Markup = 

### Variable Cost (In this equation, fixed and variable cost 

### are components of total cost and 

### are not per-unit costs.) Economic 

### Profit is the difference between 

### ROE and 

### the equity cost of capital, both of which 

### are obtainedby observation. Fixed and Variable Costs must be deduced from accounting 

### data and from information on economies of scale in the industry. Assets and costs in.thecalibration stage are 

### both represented as proportions of revenue, so 

### firm size is irrelevant. This means, for instance, that fixedand marginal costs are not given as dollars and dollars per unit, but hstead, total fixed and total marginal costs are described as a fraction of revenue.

. 

### Depreciable assets 

### are divided between fwdand marginal according to the input parameter FCA, the "fixed part of costs and assets." Cash 

### and inventory and receivables'are classified as proportional to revenue. Now considertotal costs (i.e., those that 

### are taxdeductible). FCA is also used to determine the proportion of fixed (total) costs in revenues. All other costs 

### are marginal, and their amount is deduced from the firm's rate .ofeconomic profit. Because the costs so far 

### are taxdeductible,but revenue 

### is taxable, economic fixed costs are given by the following formula:

where 

### T is the tax 

### rate, 

### ER is the ratio of equity to assets, and ECC is the cost of assets. In principle, we could now estimatemarginal economiccost (EC.Q) directly and use this value to finish

, 

### our analysis. However, because costs cannot be measured accurately enough, there could be considerableerrorin the implied value of econqmic profit and thus in markup. (Typically a 

### 5%error in total estimated cost will cause a 10%error in implied economic profit.) It is much better to deduce marginal economic cost from economic profit, because economic profit is relatively well-known. Page C-28 Vohe I: GeneralMethodology used with variables that measure an asset or a cost that is fIXed orproportionalto sales, respectively. The suffixes·.B and .N refer to the base case and the alternative efficiency levels, respectively

.

.. A combined suffIx such as**~BF** has the ilaturafinterpretation (i.e., a fixed variable in the base case).

The most important variable names in the Long-Run Model module areA for assets, TC for total cost excluding taxes and the cost of equity, EC for economic cost (including taxes and equity), El for economic income, and mu 1 for markup above marginal cost, J.I

- 1 . Calibration. The calibration stage estimates a typical firm's markup over unit variable costs using the following basic economic equation:

.**Mar~up** 

### = (Fixed Cost + Economic Profit) Variable Cost (In this equation, fixed and variable cost are components of total cost and are not per-unit costs.) Economic Profit is the difference·between ROE and the equity cost ofcapital, both

. 

# of which are obtained by observation. Fixed and Variable Costs must be deduced from accounting data and from infonnation on economies of scale in the industry. Assets and costs in·the calibration stage are both represented as proportions ofrevenue, so finn size is irrelevant. This means. for instance, that fIXed .and marginal costs are not given as· dollars and dollars per unit, but mstead. total fixed and total marginal costs are described as a fraction ofrevenue. . - Depreciable assets are divided between fIXed and marginal according to the input parameter FCA, the "fixed part of costs and assets." Cash and inventory and receivables·are classified as proportional to revenue. Now consider total costs (Le., th()se that 

### are tax-deductible)

.. FCA is also used to detennine the proportion offixed (total) costs in revenues. All other costs are marginal, and their amount is deduced from the finn's rate .ofeconomic profit. Because the costs so far are tax-deductible, but revenue is taxable. economic fixed costs are

. given by the following fonnula:
EC.F

## = (1-T)·TC.F+A.F·ER

.ECC
where T· is the tax rate, ER is the ratio of equity to assets; and ECC is the cost of assets. In principle, we could now estimate marginal economic cost (EC. Q) directly and use this value to fmish our analysis. However. because costs cannot be measured accurately enough. there could 

### be considerable error in the implied value ofeconqmic profit and thus in markup. (Typically a 5% error in total estimated cost will cause a 10% error in implied economic profit.) Itis much better to deduce marginal economic cost from economic profit., because**eco~omic** profit is relatively well-known

.. Page C-28 Volume 1: General Methodology

### This approach gives us the equation for economic marginal cost: 

## . '



### where 

### EP' is the rate of economic profit, and 

### A:R isthe ratio of total assets to revenue. Now the markup is easily computed using the formula given at the beginning of this section on calibraltion and the model is ready to analyze the base level of altemative efficiency levels. 72le

### base case. The base case is an andysis of what may happen in thecurrent

### period. The * 

### variables in both the base cise and the alternative efficiency level case give fixed costs and assets

. . 

### in dollarsand variable costs in dollars per unit. Since most calibration case costs and assets are reported ;as 

### a percent of revenue, those values are 

### multiplied by revenue to get theirbase case counterparts. Most base case variables 

### are the same as the calibration case unless we 

### are analyzing the 

### impact of any possible 

### interimefficiency levels. 

### In that case,,new variable costs dueto the interim efficiency level 

### are added into TC.Q, and levelized one-time assets are added into the total asset calculation. Economic costs 

### are computed from A 

### and 

### TC as before, except for the addition of amortized capital costs 

### (LCC), which 

### are dividedbetween 

### fwd and variable accordingto,theinput parameter

### FIX. In addition, an adjustment is made for the changing level of working capital 

### as revenue changes. 

### The followingworking capital corrections 

### are calculated: 

### WCA.B =Total working capital comtion (assets) =

### DVC.B*Q.IR: 

### R
' 

# . W@CEC.B =Working capital correction (per unit 

### EC) =(WCA.B.ATR)/Q
I 

### WCC1.B =Total working 'apital correction (interest)=

### WCA.B.1 ' 

### These corrections are 

### added to the 

### calculations forA.B, EC.BQ, and TC.B, respectively. 

### TIE 

### alternative eficiencylevel case. For the case of changesin efficiency 

### levels, new values of

### dVC (theclhange in 

### variable cost),LA, 

### and

### LCC areused. 

### The formulas contain several terms not included 

### inthe

### base 

## &calculations. These 

### terms allow the model to converge when the industry net present value module is run (see Section 

### C.2.6). When the industry net present value model is not being run, these terms are equal to 

### zero. 

## . Th!eremaining 

### descriptive variables. For the calibration case, most of the remaining 

### descriptive? variables, Q, 

### P, R, 

### WC, TC, A,and

### ROE are

### known because they 

### are inputs or

### have

### been computed. The others are obtained 

### as follows:

Volume 1: (General Methodology Page C-29 This appr,oach gives us the equation for economic marginal cost: EC.Q= l-EC.F-T-EP·ER·A:R
where EP' is the rate ofeconomic.profit, and A:Ris the ratio oftotal assets to revenue. ' Nf)W the markup is easily computed using the fonnula given at the beginning ofthis section on calibr2ttion and the model is ready to analyze the base level ofalternative efficiency levels. 

## nil! base case. The base caSe is an an8Iysis ofwhat may happen in the' current period

.. The variables in both the base case and the alternative efficiency level case give fixed costs and assets in dollars'and variable costs in dollars per unit. Since most calibration case costs and assets are reported ellS a percent of revenue, those values are multiplied by revenue to' get their'base case counterparts. Most base case variables are the same as the c.alibration case unless we are analyzing the impact of any possible interim efficiency levels. 

### In that c.ase"new variable costs due to the interim efficiency level are added into TC.Q, and levelized one-time assets are added into the total asset calculatio,n. Economic costs are computed from A and TC as before, except for the addition of amortized capital costs 

### (LCC). which are divided between fixed and variable according to,the input parameter 

### F lX.' In addition, an adjustment is made for the changing level of working capital as revenue**c~anges.** The following working capital corrections are calculated: WCA.B =T"tal working capital correction (assets) =DVC.B·Q·IR:R WCCEC.B =Working capital correction (per unit EC) =(WCA.B·ATR)/Q
I WCCI.B =ToW working capital correction (interest) =WCA.B·! These corrections are added to the calculations for A.B, Ee.BQ, and TC.B, respectively. 

### The alternative**effici~ncy** 

### level case. For the case ofchanges'in efficiency levels, new yalues of dVe (the clhangeinvariable cost), LA, and LCC are used. The fonnulas contain several tenns not included in the base case calculations. These tenns allow the model to converge when the industry net present value module is run (see Section C.2.6). When the industry net present v&ue model is not being run, these terms are equal to zero. 

### The remaining descriptive variables. For the calibration case, most of the remaining descriptivf! variables, 

### Q, P, R, uve, Te, A, and ROEare known because they are inputs orhave been computed. The others are obtained as follows:

Volume 1: IGeneral Methodology PageC·29

### Variable Cost of 

### Goods Sold = 

### 'VCGS = 

### UVC-Q Interest, Not 1X = 

### IC = 

### 1.DR.A-C:R*R.(I-ICash) Pre-interestcost. = 

### PIC

. = 

### TC-IC 

### Pre-tax 

### Cost = 

### PTC = 

### TC Taxes = 

### TAX = 

### (R-TC):T Equity = 

### EQ = 

### ER-A Economic Income = 

### EI = 

### NI - ER 

# . A-ECC and
Net -Income = 

### Nl = 

### R-TC-TAX
For the base case the above formulas hold with the exception of the one for

### NI, but several other descriptive variables must be computed. Their formulas follow:

. 

### . Q.B = 

### QR0.B "'O = ae

### -Xro+be 

### (life -cyclecost)'0

CC.B 1X depreciation = 

### XID.B = 

## - L 1Xinterest = 

### XlLB = 

### LA.B.DR.1-LTC 

## 1X equity cost = ' 

### XIE.B = 

### LAB.ER .ECC 1Xtax 

### benefit = 

### X1T.B = 

### Lw.TE3

Page C-30 VolumeI: GeneralMethodology 

### Variable Cost of Goods Sold = 

### VCGS = 

### UVC· Q Interest, Not IX = 

### IC = 

### I·DR . A-C:R. R . (I

- ICash) Pre-interest Cost· = 

### PIC. = 

### TC-IC Pre -tax 

### Cost = 

### PTC 

## = TC

. Taxes 

## = TAX 

## =**(R-TC)~T** 

### Equity 

### = EQ 

## = ER·A Economic Income

.

### = EI 

### = NI

- 

### ER·A·ECC and
Net

. Income 

## = NI 

## = R-TC-TAX

For the base case the above fonnulas hold with the exception ofthe one for NI, 

### but several other descriptive variables must be computed. Their fonnolas follow:**Q~B** 

## = QRO.B IIro 

## = ae·Xro 

### +be·(life

.cycle costYo



### P.B 

## = (J.iI+I)·EC.BQ (l-T) R.B 

## = Q.B·P.B UVC.B 

## = UVC+DVC.B VCGS.B
IX depreciation 

## = UVC.B·Q.B



## = XID.B 

## = CC.B L IX interest 

## = XlI.B 

## = LA.B·DR·I·LTC IX equity cost =' 

### XIE.B 

## = LA.B·ER·ECC IX tax 

### benefit 

## = XIT.B 

## = LCC.TB IC.B 

## =**I·DR·(A.B-LA~B)** 

### - C:R·R.B·(l-ICASH)

Page C-30 Volume 1: General Methodology

### PTC = 

### TC.BtIEC.B



### Q.Bis calculated from the life-cycle cost using a constant elasticity of substitution (CES) demand function. 

### Xro is a linear term calculated from the calibration case, and ae 

### and be 

### are parameters. The descriptivevkiables for the changed efficiency level (long-run) case 

### are computed in exactly the same way 

### as the corresponding variables in the base case, with the exception of 

### TAX.N, which includes 

### a convergence factor for the industry net pnsent value caicula6ons (see 

### Section C.2.6.) -


### C.23.10 The Short-Run

### Model Module During a recession an appliance manufacturing industry will typically face a substantial declinein demand for its product, which will usually cause increased price competition within the industry, with a consequent fall 

### in price. By regressing-price on quantity sold and on a time trend, we &n 

### estimatethe industry's "short-run iriceresponseto demand (SRPR). The LBNLMAM 

### uses . 

### this -varialble

### as an input to the Short-Run Model module. From SRPR,. the short-run .impact calculation

### proceeds as follows. Assume for explanatory purposes that demand 

### has fallen because 

### of the change in efficiency levels. From SRPR compute the implied fall in price resulting from this

, 

### unexpectt:d drop4n demand. At this new price level, compute a new level of demand that will necessarily

### be a little higher atthe new lowerprice than at the previous higher 

### price. Now take'the new estimate of the level of (and change in) demand resulting from new efficiency levels and repeat the calculations. ~fter five or fewer repetitions of these calculations, our estimates will converge to a stable answer. At this pointithe price correction exactly agrees with the estimated fall in demand, and the estimated demand agrees with the price., At no other values ofprice and quantity would thi.s

### be: true. Onceprice and quantity are

### known, all

### &her variables 

### are easily computed using procedures that have already 

### been described.

. 

### .

. .

. 

### . VolumeI: GeneralMethodology

.Page C-31 PIC.B = TC.B+XID.B-IC.B

PTC 

## = TC.B+LCC.B TAX.B 

## = (R.B-TC.B)·T-XIT.B NI.B 

## = R.B

- PTC.B - TAX.B ·EI.B = NI.B -ER·(A.B -LA.B)·ECC-XIE.B EQ.B =ER·A.B ROE.B=ECC + ElB EQ.B Q.B is calculated from the life-cycle cost using a constant elasticity of substitution (CES) demand function. Xro is a linear term calculated from the calibration case, and ae and be are parameters. The descriptive vanables for the changed**e~ciency** level (long-run) case are computed in exactly the same way.as the corresponding variables in the base case, with**th~** exception ·of TAX.N, which includes a convergence factor for the industry net present value calculations (see Section C.2.6.)

C.2.3.10 The Short-Run Model Module During a recession an appliance manufacturing industry will typically face a substantial decline in demand for 

### its product, which will usually cause increased price competition within the industry. with a consequent fall in price. Byregressing price on. quantity sold and on a time trend. we

# can estimate the industry's "short-run price response to demand" (SRPR). The LBNL-MAM uses '**thisvari~lble** as an input to the Short-Run Model module. From SRPR, the short-run impact calculation proceeds as follows. Assume for explanatory purposes that demand has fallen because ofthe change in efficiency levels. From SRPR compute the iniplied fall in price resulting from this unexpecu!d drop'in demand. At this new price level, compute a new level of demand that will necessarily be a little higher at the new lower price than at the previous higher price. Now take'the new estimate ofthe level of(and change in) demand resulting from new efficiency levels and repeat the calculations. After five or fewer repetitions ofthese calculations, our estimates will converge to a stable alIlswer. At this point,'the· price correction exactly agrees with the estimated fall in demand, fll1d the estimated**dem~d** agrees with the price

.. At no other values ofprice and quantity would this be true. Once price and quantity are known, all other variables are easily computed using procedures that have already been described.
Volume I: GeneralMethodology

.PageC·31

### C.23.11 

### Sensitivity Charts Module

I 

### This module producesa chartwhich showshow profit is affectedby a one-standard deviation change in each of the Control Panel variables. These changes are 

### all made with the alterantive efficiency level set to its middle value.



### - C.2.3.12 

### Engineering-to-Standards Cost Conversion Module . 

### This module weights the costs given by the engineering analysis for each design option in proportion to the number of appliances of a particular design that will be 

### produced at any given efficiency level and thereby generates the costs for that level. These costs appear in the Standards Level Module.
C.2.3.13 

### Industry Net PresentValue Module - 

### The LBNLMAM 

### includes the current version of the GovernmentRegulatory ImpactModel 

### (GRIMVer. 1.2) 

### to calculate the impact of alternative efficiency levels on industry net present values. The integration of this externally developed model into the 

### LBNGMAM required the resolution of a number of issues. These 

### are covered in more detail in Section C.2.6.


### C.2.4 

### Model Inputs C.2.4.1 

### Input Generating Assumptions Many of the necessary input data 

### are not publicly available and so have been estimated from available data or estimated from averagesof subjectiveestimatesmade by industry representatives. This section describes the various origins of LBNL-MAM's data. 

### Costdata. All 

### cost-data 

### are provided by the Engineering Analysis. Elasticities and discount rates: 

### Industry price elasticity .andconsumer discount rates are supplied by the 

### LBNLREM. Financial data. 

### Financial data 

### are collected from publicly available sources such as Value Line, Standard and Poor's, Moody's, and company reports. The data 

### are often for firms whose operations in the industry under consideration

### are only part of the firm's total business operations. It is assumed that the relevant division is similar to the firm as a whole. Amortization data. 

### New capital has a seven-year am~rtiza~tion lifetime. Values relating to such issues 

### as taxation are displayed in the spreadsheet and 

### are generally derived from historical Page C-32 Volume 1: Geneml Methodology C.2.3.11 Sensitivity Charts Module This module produces a chart which shows how profit is affected by a one-standard deviation change in each ofthe Control Panel variables. These changes are all made with the alterantive efficiency level set to its middle yalue

.

. C.2.3.12 Engineering-to-Standards Cost Conversion Module This module weights the costs given by the engineering analysis for each design option in proportion to the number of appliances of a particular design that will be produCed at any given efficiency level and thereby generates. the costs for that level. These costs appear in the Standards Level Module.

C.2.3.13. Industry Net Present Value Module The LBNlrMAM includes the current version ofthe Gove(IlIIlent Regulatory Impact Modei (GRIM Ver. 1.2) to calculate the impact of alternative efficiency levels on industry net present values. The integration 

# of this externally developed model into the LBNL-MAM required the

. resolution ofa number ofissues. These are covered in more detail in Section C.2.6.

C.2.4 Model·Inputs C.2.4.1 Input Generating Assumptions Many·ofthe necessary input data are not publicly available and so have been estimated from**availa~le** data or estimated from average! ofsubjective estimates made by industry representatives. This section describes the various origins ofLBNL-MAM's data Cost data. All cost-data are provided by the Engineering Analysis. Elasticities and discount rates: Industry price elasticity and consumer discount rates are supplied by the LBNL-REM. Financial data. Financial data are collected from publicly available sources such as Value Line, Standard and Poor's, Moody's, and company reports. The data are often for firms whose operations in the industry under consideration are only part ofthe fIrm'S total business operations. It is assumed that the relevant division is similar to the firm as a whole. Amortization data. New capital has a seven-year**amorti~tion** lifetime. Values relating to such issues as taxation are displayed in the spreadsheet and are generally derived from**historic~** Page C-32 Volume 1: GeneralMethodology. .



## .. ' 

### discussio~os with industxy representatives. ~hk timing of capital ,iostscome from the Engineering

. , ' . 

### Analysis.

. 

### id

### andmarginalcosts 

### andassets. Some costs inc-e in proportion to shipments, and some 

### are fixed. 

### Industry representatives indicate that the bulk of costs and assets 

### are proportional to shipments. The specificvalues 

### are shown in the Control Panel. Tloe following sections list and describe the inputs used by the model.


### C.2.4.2 Clontrol Factors 

### in the 

### Control Panel ' 

### Industryprice elasticity. Industry price elasticity 'measuresthe way a change 

### inthe average industry !price for a product will affect the industry's shipments. For example, if the price of a product increases 

### 4% and shipments drop 

### 2% as a result, the price elasticity is-0.5. Because

. . 

### imposed efficiencylevelswill causethe prices ofproducts to increase, this input is-obviously crucial

. 

### in determiningthe impact of new efficiency levels on manufacturers. CIonsmer discount rate. 

## he consumer market discount rate is a measure of how much consumers value 

### futureoperating cost savings from energy-efficientdesign options. Becaus'e new efficiency levels mandate more efficientproducts that will have lower operating costs, this input will affect consumer demandand is therefore important in determining the impact of new efficiency levels on manufacturers. &pity costofcapital. This is the 

### rateof return expectedby 

### equity shareholders. This fip 

### is in 

### real tem~ (i.e;, with inflation subtracted). It is an after-tax rate, which means that it is the rate
. 

### of return ithe company must earn

### for its shareholders 

### aftu its 

### taxes have been accounted for. Data values come from calculations using standard finance equations and public fhancial data. 

### Economicprofit. E&omic profit is the profit earned

### by a. firm aboveits expected return on equity. 

### Irk other words, it is the profit eamed 

### after all 

### costs are 

### taken into account, including the cost of equity (themarket rate of return). .A

### fum makes economic profit only when it has some degree of markel:power. Long-runjixed

### part of

### costs and

### a.&ts. This figureis the

### portion of all 

### costs and assets that is fixed over time, that is, which does not vary with quantity sold or with revenue. It is expressed as apercent of the base case costs and assets. From 

### an economic standpoint,this figure is important ' 

### for 

### determining

## the impact of new efficiency levels on manufacturers. Lmg-runjkedpartofone-time capital cost.- 

### This figureis the portion of one-time capital costs that is fixed in ,thelong run. Although all

### capital costs are normally considered fixed in the short run.,this

### portion of the capital costs is spent regardless -of the output capacity of the tooling purchased. Tbe otherportion of one-time capital costs varies proportionallywith the output capacity of the new equipment. Volume 1: General Methodology ' Page C-33 " . discussiolllS with industry representatives. The timing ofcapital costs come from the Engineering Analysis.

. . FlXed 

### andmarginai costs 

### andassets. Some costs increase in proportion to shipments, and some are fixed. Industry representatives indicate that the bulk ofcosts and assets are proportional to**shipm~:nts,** The specific values are shown in the Control Panel. The following sections list and describe the inputs used by the model.


### C.2.4.2 Control Factors in the Control Panel

.

. Industryprice elasticity. Industryprice elasticity measures the way a change in the average industry ]price for a product will affect the industry's shipments. For example, if the price of a product increases 4% and shipments drop 2% as a result, the price elasticity is**~O.5**

### .. Because

. imposed**I~fficiency** levels will cause the prices ofproducts to increase, this input is obviously crucial in detennining the impact ofnew efficiency levels on manufacturers. Consumer discount rate. The consumer market discount rate is a measure of how much consumers value future operating cost savings from energy-efficient design options. Because new efficiency levels mandate more efficient products that will have lower operating costs, this input will affect cOllsumer demandand is therefore important in determining the impact of pew efficiency

. levels on IIUlXlufacturers

.

. rAluity cost of capital. This is the rate ofreturn expected byequity shareholders. This figure is in real t:emlS (i.e;, with inflation subtracted). It is an after-tax rate, which means that it is the rate ofreturn lthe company must earn for its shareholders after its taxes have been accounted for. Data values come from calculations using standard finance equations and public fmancial data. Economicprofit. Economic profit is the profit earned by a· fmIl above its expected return on equity. hi other words, it is the profit earned after all costs are taken into account, including the cost ofequity (the market rate ofreturn): A fmn makes**~nomic** profit only when it has some degree ofmarket power. . . . . Long-run fixedpart 

### of costs andassets. This figure is the portion of all costs and aSsets that is fixed**ove~** time, that is, which does not vary with quantity sold or with revenue.· 

### It is expressed as a perce:nt ofthe base case costs and assets. From an economic**~tandpoint,** this figure is important for determining the impact ofnew efficiency levels on manufacturers.

. Long-run fixedpart of one-time capital cost . .This figure is the portion ofone-time capital costs that is fixed iilthe long run. Although all capital costs are normally considered fixed in the short run., this' portion of the capital costs is spent regardless ofthe output capacity of the tooling purchased. The other portion. of one-time capital costs varies proportionally with the output capacity ofthe new equipment. Volume 1: General Methodowgy Page C-JJ*. 

### One-rimecapitalcoifs.'someof the design options 

### used to meet the increased efficiencies mandated by the alternative efficiency levels require additional capital investment in the form of retooling, new equipment, or other capital expenditures. The "one-time capital-costs" figure repreSents the capital costs expended to meet aspecificefficiencylevel being analyzed; this number covers all products in 

### a product type 

### and is an input 

### from 

## thkEngineering Inputs module. Unitvariablecost increase. Thisnumber is the shipment-weighted average increasein unit variable costs incurred by the manufacturer in meeting new efficiency levels. The incremental variable cost includesraw materials, directlabor,purchased parts,- and inc- transportation costs. 

### - This number is ai~ 

### input from the Costs, Sales, and Revenues module. EZasticiry Curve Parameter. This parameterof the 

### CES demand function determines the shape of the.demandcurve. Changing the value of ro permits specifications other than constant demand elasticity (to

### =0.) Short-run price response to demand

### (SRPR). When industry demand fallsby Y%, price will, in the short run, fall

### by SRPR 

### Y%. This 

### response is expectedto die out within a few years, and Y% should be 

### less than 20%when SRPR is used.


### C.2.43 Engineering 

### Data Incremental writ variable cost. The incremental unit variable costs 

### are the variablecosts associated with design optionsthat increase aproduct's energy efficiency. This input differs from the variable cost increase input for the Control Panel (Section C.2.3.1) 

### in that it lists incremental variable costs foreach design option foreach product class oftheproduct 

### type being analyzed,rather 

### than a single shipments-weightedaverage number. The 

### increased variable cost includes costs such as raw materials, direct labor, purchased parts, and increased transportation costs. The data also come in thk 

### form of a table that lists the incremental variable cost for each product class for each of the efficiency levels being analyzed. 

# - Maintenance Cost. This data comes in the form of a table which lists annual maintenance costs foteachproduct class at each engineeringdesign option. Maintenance costs 

### are measured in dollars per year. Unit energy consumption (UEC). Unit energy consumption is the annual energy cost (in dollars) of 

### a product. The input data come in the form of a table that lists the 

### UEC for each product class for each design option (and alternative efficiencylevel)being analyzed. Installation costs. The cost,(indollars)of installingeach product class- foreach design level -is provided intabularform. Installation costs are incurred by the purchaser at the time of purchase and are part of the life-cycle cost of the product.

. .

. ,
Page C-34 VolumeI: GeneelMethodology One-time capital costs. Some ofthe design options**il~ed** to meet the increased efficiencies mandated by the alternative efficiency levels require additional capital investment in the fonn of retooling, new equipment, or other capital expenditures.· The "one-time capital"costs" figure represents the capital costs expended to meet a specific efficiency level being analyzed; this number covers 

### all products ina product type and is an input from the Engineering Inputs module. Unit variable cost increase. This. number is the shipment-weighted average increase in unit variable costs incurred by the'manufacturer in meeting new efficiency levels. The incremental variable costincludes raw materials, direct labor, purchased parts,and increased transportation costs. This number is ail input from the Costs, Sales, and Revenues module. Elasticity Curve Parameter. This parameter of the CBS demand function determines the shape of the demand curve. Changing the value of ro permits specifications other than constant demand elasticity (ro 

### = 0.) . , Short-runprice response to demand(SRPR). When'industry demand falls by Y%, price will, in the short run, fall bySRPR • Y%

. .This response is expected to die out within a few years, and Y% should be less than 20% when SRPR is used.


### C.2.4.3 Engineering Data Incremental unit variable ,cost. .The incremental unit variable costs are the variable costs associated with design options that increase a product's energy efficiency. This inputdiffers from the variable cost increase input for the Control Panel (Section C.2.3.t) in that it lists incremental variable costs for each design option for each product class ofthe product type being analyzed, rather than a single shipments-weighted average number. The increased variable cost includes costs such as raw materials, direct labor, purchased parts, and increased transportation costs. The data also come in the fonn ofa table. that lists the incremental variable cost for each product class for each of the efficiency levels being analyzed. Maintenance Cost. This data comes in the fonn ofa table which lists annual maintenance .costs foreach product class at each engineering design option. Maintenance costs are measured in dollarS per year. Unit energy consumption (UEe). Unit energy consumption is the annual energy cost (in dollars) of a product The input data come in the fonn ofa table that lists the UEC for each product class for each design option (and alternative efficiency level) being analyzed. Installation costs. The cost'(in dollars) ofinstalling each product class for each design level ·is provided in'tabular fonn. InsuiIlation costs are incurred by the purchaser at the time ofpurchase and are part ofthe life-cycle cost ofthe product.

Page C-34 Volume 1: GeneralMethodology

### Capital costs. 

### Some of the design options used to m&t.the increased efficienciesmandated by the alternativeefficiency levelirequireadditional capital investmentin the form ofretoqling, new equipment, or other capital expenditures. This input is given as a table listing the cqst associated
. ' 

### with each design option that requires a capital expenditure. The input is also given in a tablelisting
. ' 

### the total capidexpenditureforthe design optionsused tonxch each of the-various efficiency levels being analyzed. Thecapital cost input covers all product classes within 

### a product 

### type, unless noted otherwise. The one-time capitalcostsxesulting hm imposed efficiency levels generally vary with the size of'themanufacturer. 

### If more than one size of manufacturer is being analyzed for a product type, then input data for all' 

### sizesbeing analyzed 

### are included.

. / 

### C.2.4.4 Costs, Sales,and Revenues Im.lUstry shipments. 

### Industry shipments

### are annual shipments for all classes of the product type being analyzed. These inputs 

### are actual data for a calibration and base.case and long-run estimates for' each of the alternative efficiency levels being analyzed. The long-run shipments estimates 

### are based on projected product price increases ihdproduct price elasticities and market discount rates which 

### are based on forecasts provid by the 

### LBNLREM.

' 

### Product 

### dawmarket shares. 

### Each of the classes within a product type 

### has a "market share" of the shipments forthat 

### product 

### type. 'Forexarhple, coil element electric~cooktops comprise about half of all.cooktop shipments.. The product class market share combined with the total industxy shipments;give a breakdown of annual shipmentsby product class. Mtznufacturer market share. 

### Each of the prototypical firms 

### in the analysis is assignedla market share appropriate .to that segment of the industry. For example, five large room air conditioner manufacturers may each 

### be assigned 

### a 

### 20% market share. The manufacturer market, share, combined with the total industry shipments, gives 

### a fum's annual shipments. This figure, combined with the product class market shares, gives a typical firm's annual shipments for each product class.



# .

. .

. 

### Mtzrklips. 

### The markup is the figwe

### used by 

### the rnanufa~~turer todeterminethe firm's selling price for ;a 

### particular product. It is an increase over,theunit.variablecost of that product, and it covers overhead, capital costs, and profit. 

### In some industries, manufacturersuse differentmarkups on different product classes. ' 

### The markup input lists the markup used for each class in the product ' 

### type being analyzed. Initialprices. This 

### input is the baseline manufacturer's selling price per unit for eachproduct class. Added to the baseline unit price 

### are the incrementalcosts of ieaching the'higherefficiency levels required by meeting new implemented efficiency levels. Energyprice. 

### This figure is the price ratio of a 1992 

### kwh to a 

### 1998 

### kwh taken from the LBNLREM; 

### energy price is used to calculate the change in operating cost from the calibration case to the'basc case 

### (OC%.B)and 

### to the alternative efficiency level case 

### (OC%.N). VolumeI: ~Geneml Methodology Page C-35 Capital costs. Some ofthe design options used to meet,the increased efficiencies mandated by the alternative efficiency levels require additional capital investment in the form ofret()()ling, new equipment, or other capital expenditures. This input is given as a table lis.ting the c<!st associated with each design option that requires a capital expenditure. The input is also given in a table ,listing the total capital expenditure for the design options used to**'~h** each ofthe'various efficiency levels being analyzed. The capital cost input covers all product classes within a product type, unless noted otherwise. The one-time capital costs resulting from imposed efficiency levels generally vary with the size ofthe manufacturer. Ifmore than one size ofmanufacturer is being analyzed for a product type, then input data for ali sizes being analyzed are included.



### C.2.4.4 Costs, Sales, and Revenues**1'IlJ.ust~** shipments. Industry shipments are annual shipments for all classes ofthe product type being analyzed. These inputs are actual data for a calibration and base.case and long-run estimates for' each of the alternative efficiency levels being analyzed. The long-run shipments estimates are based on projected product price increases and product price elasticities and market discount rates which are based on forecasts provided,by the LBNL-REM. Product class market shares. Each ofthe classes within a product type has a "market share" ofthe shipments for that product type. 'For example, coil element electriccooktops comprise about half of alI. cooktop shipments. ,The product class market share combined with the total industry shipments give a breakdown of annual shipments by product class. Manufacturer market share. Each of the prototypical 

### fmns in the analysis is assigned la market share appropriate.to that segment of the industry. For example, five large room air condition,er manufacturers may each be assigned a 20% market share. The manufacturer market' share, combined with the total industry shipments, gives a firm's annual shipments. This figure, combined with the product class market shares, gives a typical firm's annual shipments for each product c1ass. '

, " Markups. The markup is the figure used by the manufac,turer to determine the firm's selling price for 

### ;a particular product. It is an increase over, the unit' variable cost of that product, and it covers ov(:rhead, capital costs, and profit. In some industries, manufacturers use different markups

, ' on different product classes. The markup input lists the markup used for each class in the product type being analyzed. ' Initialprices. This input is the baseline manufacturer's selling price per unit for each product class. Added to the baseline unit price are the incremental costs ofreaching the higher efficiency levels reqluired by meeting new implemented efficiency levels. ETt!ergy price. This figure is the price ratio of a 1992 kWh to a 1998 kWh taken from the LBNL-REM; energy price is used to calculate the change in operating cost from the calibration case to the'**basl~** case (OC%.B) and to the alternative efficiency level case (OC%.N). Volume 1: .GeneralMethodology Page,C-35 , I

### C.2.45 

### Financial Inputs Rates offinancial costs: ~frer-t'ar equity cost ofcapital. 

### This input is explained.in Section C.2.3.1, Control Panel. Interest rate on debt. 

### This is the interest rate paid by a corporation on its debt. This figure is in real terms (i.e., with inflation subtracted),and is the yield of corporatebonds with a Moody's rating comparableto ratings in the industrybeing analyzed. Interest lost on cash. 

### Companieshold a portion of their current assets in the form of cash inbank accounts,marketable securities, and other liquid holdings, 

### all lumped under the 

### term "cash." This category of assets has a cost associated with it because of the cost of the debt and equity associated with all

### assets. However, companies manage their cash accounts vej well and 

### are able to 

### earn interest each day on the cash they 

### are not using that &y. The interest earned on the cash accounts tends to be 

### low, since the investment is only overnight and varies 

### from day to day. The interest earned is lower than the cost of the capital used to form the asset. Thus, the cash account costs the company money but the cost is lower than for any of the company's other assets. Rate ofdepreciation.

### This 

### figwe is the 

### rate of depreciation on 

### fixed assets for a typical 

### fm in the industry. It is obtained by assuming straight-linedeP&iation and an average tax 

### life of eight years for the firm's fixed assets. It is then revised by analyzing actual depreciation data from the industry under consideration. Taxrate. 

### This rate is the average tax 

### rate faced by an industry in 

### 1996. It is based on past data and recent revisions to the tax 

### law. Assets and 

### costsasapercent ofrevenue: Cash. 

### Companies, hold aportion oftheir current assets in the form of cash in bank accounts; marketable securities,and otherliquid holdings, 

### all lumped under the 

### term "cash." In the model, this input is expressed as apercent of total revenue. ' ' . 

### Inventory and 

### receivables. 

### This figure represents the inventory and receivables assets, expressed 

### asa percent of total revenue. Inventoryincludes raw materials owned by the company, work in process, and finishedgoods that have not yet been shipped. Receivablesrefers to 

### goods that have already been sold and shipped by 

### the fm 

### for which the firm has not yet been paid, but is instead owed money. Depreciable assets. 

### This figurerepresents the net depreciable, or fixed, assets of the firm, expressed as a percent of revenue. Depreciable assets 

### are tooling, equipment, production facilities, and other goods that have an original lifetime 

### iflongerthan one year and thus 

### are depreciated over several years rather than expensed in ~ne'~ear.

/ Page C-36 Volume 1: GeneralMethodology /



### C.2.4.5 Financial Inputs Rates offinancial costs: After-tax equity cost of capital. This input is explained in Section C.2.3.1, Control Panel. Interest rate on debt. This is the interest rate paid by a corporation on its debt. This figure is in real terms (i.e., with inflation subtracted), and is the yield ofcorporate bonds with a Moody's rating comparable to ratings in the industry being analyzed.

. Interest lost on cash. Companies hold a portion oftheir current assets in the form ofcash inbank accounts, marketable securities, and other liquid holdings. all lumped under the term "cash." This category of assets has a cost associated with it because of the cost of the debt and equity associated with all assets. However. companies manage their cash accounts very well and are able to earn interest each day on**th~** cash they are not using that day. The interest earned on the cash accounts tends to be low. since the.investmerit is only overnight and vanes from day to day. 1.be interest,earned is lower than the cost ofthe capital used to form the**ass~t.** Thus. the cash account costs the company money but the cost is lower than for any ofthe**c~mp3nY's** other assets. Rate of depreciation. This figure is the rate ofdepreciation on 

### fixed assets for a tYPical firm

. , in the industry

.. It is obtained by assuming straight-line depreciation and an average tax life ofeight years for the firm's fixed ·assets. It is then revised by analyzing actual depreciation data from the industry under consideration

.. Tax rate. This rate is the average tax rate faced by an industry in 1996. It is based on past data and recent revisions.to the tax law. Assets and costs as a percent of revenue: Cash. Companies. hold a portion oftheir current assets in the form ofcash in bank accounts; marketable securities, and other liquid holdings, all lumped under the term "cash." In the model, this input is expressed as a percent oftotal revenue. Inventory and receivables. This figure represents the inventory and receivables assets, expressed as a percent of total revenue. Inventory includes raw materials owned by the company, work in process,-and finished goods that have not yet been shipped. Receivables refers to goods that have already been sold and shipped by the firm for which the firm has not yet been paid, but is instead owed money. Depreciable assets. This figure represents the net depreciable, or fixed, assets ofthe firm, expressed as a percent ofrevenue. Depreciable assets are tooling, equipment, production facilities, and other goods that have an original lifetime oflonger than one year and thus are depreciated over several years rather than expensed in one·year. PageC-36 Volume 1: General Methodology ,

### General 

### and administrative expenses (G&A). 

### This figure ,represents the general and administrative and selling expenses for the firm, expressed as a percent of revenue. It is the "overheadl"of the company.

. . 

### Fixed anu! 

### variable costs: Fixed 

### pan ofall costs Md

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

. 

### Fixed

### par;!ofone-time capitalcost. This input is explainedin the Control Panel section of C.2.3.1. Other fimmcial inputs:


### ..

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

.. , 

### Debt-to-equity rutio. 

### AU of 

### a fm's assets

### are paid for either by debt or by equity. The debt-to-equity ratio is used in calculating a firm's'weighted average cost of capital. It is also associated with the risk factorof the fm and affects how much money a fum is able to borrow and at what interestrate, so it is importantin the analysis of the impacts of new efficiency levels. Mtzrkup 

### on a typical model. This figure is the markup used by a 

### fm in determiningthe manufactimr's selling price. It is the markup used on the unit variable cost of a typical product in the industry being analyzed. Ratw sfhighestto lowestmarkup. 

### In some industries,manufacturers use differeht markups on different product classes. 

### If such a situation exists 

### in the industry being analyzed, this ratio indicates the range of markups used. 

# - Costsand assets 

### ofone-time investment: One-timecapitalcost's life. This input is the 

### pmducti\;e life of the equipmentpurchased to meet the imposed efficiency levels, expressed in years. The number is also used as the period of time over which the one-time costs induced by 

### imposed efficienc levels 

### are amortkd. One-timecapital cost's tax life. This input is the tax 

### life of the equipment purchased to meet the impost:d efficiency levels, expressed in years. Percent additional one-time capital. Meeting new efficiency levels may require both the

. 

### addition of new capital that replaces nothing, and the replacement of old capital that has become inadequate:. This variable tells what percent ofnew.capitalis additional rather than forreplacement. - 

### Age 

### ofold (repfuced) capital. This input gives the age, in years, of the old capital that gets replaced hauseofthe new efficiencylevels. Undernew efficiency levels, someold equipmentwill 

### get scrappc:d 

### before the end of its productive life and replaced 

### by new equipment. Depending on the VolumeI: GenemiMethodology Page C-37



## . .

. . G.meral and administrative expenses (G&A). This figure ,represents the general and administrative and seliing expenses f<?f the firm, expressed as a percent of revenue. It is the "overhead" ofthe company. Fixed 

### andvariable costs: Fitedpart of all costs 

### anddepr:eciable assets. This input is explained in the Control Panel section ofC.2.3.t.

. '.

, Fixedpar.r of one·time capital cost. This input is explained in the Control Panel section ofC.2.3.1.

. . OtherJinttncial inputs: Economicprofit. This input is explained in the Control Panel section ofC.2.3.l.··
I**D(~bt·to-equity** ratio. All of a fmn's assets are paid for either by debt or by equitY. The debt-to-e<IUity ratio is used in calculating a fmn's' weighted average cost of capital. It is also associated with the risk**fact~r** ofthe fmn and affects how much money a firm is able to borrow and at what interest rate, so it is important in the analysis ofthe impacts of new efficiency levels.

. , MClrkup on a typical model. This figure is the markup used by a f1Illl in determining the manufacturer's selling price. Itis the markup used on the unit variable cost ofa typical product in the industry being analyzed. Ratio of highest to lOwest markup. In some industries, manufacturers use different markups on different product classes. If such a situation exists in the industry being analyzed, this ratio indicates the range ofmarkups used. Costs andassets of one-time investment: Onte-time capital cost's life. This input is the productive life ofthe equipment purchased to meet the imposed efficiency levels, expressed in years. The number is also used as the penod of time over which the one-time costs induced by imposed efficienc levels are amortized. One-time capital cost's tax life. This input is the tax life ofthe equipmeIl:t purchased to meet the**imposc~d** efficiency levels, expressed in years. Percent additional one-time capital. Meeting new efficiency levels may require both the addition of new capital that replaces nothing, and the replacement of old capital that has become inadequate:. This variable tells what percent ofnew. capital is additional rather than for replacement. Age of old (replaced) capital. This input gives the age, in years, ofthe old capital that gets replaced**bc~use** ofthe new efficiency levels. Under new efficiency levels, some old equipment will get**scrappc~d** before the end ofits productive life**~d** replaced by new equipment. Depending on the Volume 1: (,eneral Methodology PageC-37

### age of the replaced capital, tax 

### and/oreconomic consequences 

### are factored into the cakulations of the cost of the new equipment. 

### Om-time capital-cost schedule. ' 

### The one-time capital cost schedules list the costs incurred over time for preparations !o meet the imposed efficiency levels. A 

### percentage of the total cost is attributed to each year before the new.efficiency levels go into effect, because. that is when these expenses will 

### occur. '~hese cost schedules allow the model to be more accurate in calculating the present value of the amortized costs, rather than assuming ,thatthe costs all &cur when the new efficiency levels go into effect.

$

'. 

### C.2.5 

### Model Outputs' 

### C.2.5.1 Control Panel Outputs The Control Panel outputs are the summary outputs of the model. This output section enables the reader or model user to see at a glance the 

### main short-run and long-run impacts of alternativeefficiency levels on a few key variables: shipments, price, revenue, net income, and 

### return on equity. For each ofthe five variables 

### listed above, the Control Panel output table lists the base case number, the new long-run number, and the percentage changebetween the two. The table also lists the percentage changes for these vaiiables from the most recent preirious 'run for comparison purposes. The last column in the output table lists the short-run results for each of the variables. The base case values refer to the current state of the industry. The long-run numbers 

### are the model's estimates for these variables when alterantiveefficiency levels 

### are implemented and after - 

### manufacturers have had time to adjust to the changes and find new optimal selling prices and production quantities. The short-runnumbers 

### are the model's estimates ofimpacts on manufacturers in the shortrun, before they have time to find a new optimum. Shipments. Shipments 

### are the totalmits sold by a prototypical 

### fm in a given year, expressed 

### in millions of units. The shipments vhble the sum of shipments for all product classes within aproduct 

### type. Shipmentsdata 

### are calculatedon the Costs, Sales, and Revenuespage. Price. Price is the shipments-weighted average of the manufacturer's sellingprices for the different classes within the product 

### type being anal@. Price data 

### are calculated on the Costs, Sales, and Revenues page. Revenue. Revenue is the amount of money received by the manufacturerfor the products it has sold. It is the sum of the revenue foreach of the product classes being soldby the manufacturer and it is expressed in millions of dollars. It can also be calculated by multiplying hie 

### annual shipments times the shipments-weighted average price. Revenue data are 

### calculated on the Costs, Sales, and Revenues page. Page C-38 VolumeI: GeneralMethodology age ofthe replaced capital, tax andlor economic consequences are factored into the calculations of the cost ofthe new equipment. One-time capital-cost schedule. The**one~time** capital cost schedules list the costs incurred

. over time for preparations**~o** meet the imposed efficiency levels. A percentage ofthe total cost is attributed to each year before the new efficiency levels go into effeCt, because that is when these expenses will occur. 'These cost schedules allow the model to be more accurate in calculating the present value of the amortized costs, rather than assuming ,that the costs all occur when the new efficiency levels go into effect.


### C.2.5 Model Outputs C.2.5.1 Control Panel Outputs The Control Panel outputs are the sununary outputs of the model. This output section enables the reader or model user to see at a glance the 

### main short-run and long-run impacts of**altem~tive** efficiency levels on a few key variables: shipments, price, revenue, net income, and return on equity. Foreach ofthe five variables listed above, the Control Panel output table lists the base case number, the new long-run number, and the percentage change between the two. The table also lists the percentage changes for these variables from the most recent previous

. run for comparison purposes. The last column in the output table lists the short-run results for each ofthe variables. The base case values refer to the current state ofthe industry. The long-run numbers are the model's estimates for these variables when alterantive efficiency levels are implemented and after manufacturers have had time to adjust to the changes and fmd new optimal selling prices and production quantities. The short-run numbers are the model's estimates ofimpacts on manufacturers in the short run, before they have time to find a new optimum.

. Shipments. Shipments are the total-units sold by a prototypical fmn in a given year, expressed in millions of units. The shipments variable**~s** the sum of shipments for all product classes within a product type. Shipments data

## are calculated on the Costs, Sales, and Revenues page. Price. Price is the shipments-weighted average ofthe manufacturer's selling prices for the differ:ent classes within the product type.being an3Iyzed. Price data are calculated on the Costs, Sales, and Revenues page. Revenue. Revenue is the amount ofmoney received by the manufacturer for the products it has sold. It is the sum ofthe revenue for each ofthe product classes being sold by the manufacturer and it is expressed in millions of dollars. It can also be calculated by multiplying 'the annual shipments times the shipments-weighted average price. Revenue data are calculated on the Costs, Sales, and Revenues page. Page e-38 Volume 1: General Methodology. . 

### Nt!tincome. Net income is the after-tax profit made by the'company. express& 

### in millions of dollars. Net income and the associated return-on-equityare 

### primary measures of a company's financial lxrformance. 

### In LBNL-MAM, net income is calculated in the Accouriting Summary. Return on equity (ROE). Net income is a dollar amount that varies in part simplybecause of a fm'ssine, and 

### ROE adjusts fordifferent-sized companiesby giving net income as 

### a proportion of stoch~olders' equity. It is the 

### return an investor makes on his investment in the stock of a particular company. ROE is calculated 

### in the Accounting Summary.
C.2.5.2 

### Accounting 

### Summary Outputs Net income. 

### See the control Panel section above.for a description of net income. 

### In the Accounting Summary, net income is expressed in thousands of dollars rather than millions. Grossmargin. 

### Grossmargin shows, on apercentagebasis, how much revenue remains after the variable cost of goods sold is accounted for: GM ='(R-VCGS)IR 

## . Return on sales. Return on sales is net income as a percentage of revenue. 

# -

.. , 

### Total. assets. Total assets is the sum of all of the prototypical firm's assets. This includes cash, inventory, .andreceivables; depreciable assets; and in the case of alternativeefficiency levels, the one-tirne assets acquired to meet the new efficiencylevels. ~ehm on assets. Return on assets is 'the total return on the debt 

### and equity portions of a firm's asse:ts. .It.is a measure of how productively a 

### firm's assets 

### are being used. The interest paid on debt instruments and the net income that belongs to equityshareholders are the returns on the fm'sasse'ts. 

### The returns 

### are divided

### by the total assets

### of the 

### fmto calculatereturn 

## hassets.

, . s 

### ~&,uity.Equity is the portion of a fm'sassets owned by the stockholders of the 

### firm. It is the 

### difference

### between a 

### firm's assets 

### and its 

### liabilities. In 

### this model it is calculated by 

### multiplying the equity percentage obtained from the debt-toequity ratio times the 

### total assets figure. - 

### Refurnon equity (ROE). Seethe Control Panel section above for 

### a description of 

### ROE.
C.2.5.3 

### Econonnic Analysis 

### Outputs Thc economic analysis outputs give a clear and concise picture of what is gging onin the company ikon1 an 

### economic standpoint. Most of the outputs show up in the other two outputs sections. Shipments,price, and revenue 

### are discussed in the Control Panel skon. The total assets number is calculated in the Accounting Summary section, and the economic analysis output breaks down totali assets into three 

### categories: cash, inventories, and depreciable assets. Expenses are

VolumeI: GeneidMethodolog Page C739 - 

# .**N~!t** 

### income.**~et incom~**  is the after·tax profit made by the company, expressed in millions of dollars. Net income and the associated retum.on-equity are primary measures ofa company's financial performance. In LBNL-MAM. net income is calculated in the Accounting Summary.**RE~tum** 

### on equity (ROE). Net income is a dollar amount that varies in part simply because ofa finn's siz,e. and ROE adjusts for different·sized companies by giving net income as a proportion of stockbolders' equity. It is the return an investor makes on his investment in the stock of a particular company. ROE is calculated in the Accounting Summary.


### C.2.5.2**Accoun~g** 

### Summary Outputs**NE~t** 

### income. See the Control Panel section above. for a description of net income. In the Accounting Summary. net income is expressed in thousands ofdollars rather than millions. Gross 

### margin. Gross margin shows. on a percentage basis. how much revenue. remains after the variable cost ofgoods sold is accounted for: GM =·(R- VCGS)/R. Return 

### on sales. Return on sales is net income as a percentage ofrevenue. Total. assets. Total assets is the sum of all ofthe prototypical finn's assets. This includes cash, inventory•.and receivables; depreciable assets; and in the case ofalternative effidency levels. the one-time assets acquired to meet the new efficiency levels.

. Return 

### on assets. Return on assets is ·the total return on the debt and equity portions of a finn's asse:ts. IUs a measure ofbow productively a firm's assets are being used. The interest paid on debt instruments and the. net income that belongs to equity·shareholders· are the returns on the firm's assets. The returns are divided by the total assets ofthe firm to calculate return on assets.

. - . 

### -Equity. Equity is the portion ofa finn's assets owned by the stockholders ofthe firm. 

### It is the difference between a firm's assets and its liabilities. In this model it is calculated by multiplying

. the equity percentage obtained from the debt-to-equity ratio times the total assets figure

.. Return 

### on equity (ROE). See the Control Panel section above for a description ofROE:**C.~.5.3** 

### Economic Analysis Outputs

. The:: economic analysis outputs give a clear and concise picture ofwhat is gping on in the company from an economic standpoint. Most of the outputs show up in the other two outputs sections. Shipments. price. and revenue are discussed in the Control.Panel section. The total assets number is c;alculated in the Accounting Summary section. and the economic analysis output breaks down totall assets into three categories: cash. inventories. and depreciable assets. Expenses are

Volume 1: GieneralMethodology Page**C~39**

### looked at from 

### an ecohomic point of view rather than an accountingone. Thus expenses 

### arr broken down into fixed expenses and expenses that vary with the quantity produced.
C.2.5.4 

### Monte Carlo Outputs The Monte Carlo Outputs section lists the results of six key measures of impact when the Monte Carlo option is used. For key input variables, the Monte Carlo option randomly generates
- 

### values around the best available estimatesof their true values. As 

### a 

### result, the outputs vary fromrun

to 

### run,and the model 

### can calculatemeans and standard deviationsfor the key measures of impact. It is then possible to bound the estimates of impacts and attribute a degree of certainty to them. TheMonteCarlo Outputs section is atable that 

### lists foreach variablethe value of the current iteration, the mean of all the iterations done in the current 

### run, and the standard deviation of the results. It then lists in chronological order the results of all the iterations done in the current run. The measures of impacts 

### are percentage change in shipments, percentage change in price, percentage change

### in revenue, changein net income, and percentage point change in ROE forboth the long-run analysis and the short-run analysis. Seethe ControlPanel section (Section C.2.3.1) for a more complete description of each of these outputs.
C.25.5 

### Retail Price Calculation One importantrole of LBNL-MAM is to provide estimates of retail prices of appliances at different efficiency levels forthe 

### LBNLREM and the Life-Cycle Cost analysis. The primary input for the retail price calculation in 

### LBNLMAM is MAM's estimates of manufacturer prices. The manufacturerprices are 

### derived from base manufacturer costs, engineering production costs, and appropriate manufacturer markups for each product. The base manufacturer cost is derived from current retail price 

### data for appropriateproduct classes. These prices 

### are divided by 

### both the retail markup for the product and the wholesale (manufacturer) markup. This calculation is described in - 

### more detail in the description of the Costs, Sales, and Revenue module (Section C.2.3.6). The model generates a

### matrix of manufacturers' sellingprices for each product class at 

### each 'efficiency level. These manufacturers prices are 

### then multiplied by 

### an estimate of the manufacturer-to-retail markup appropriateforthe product class. This gives the estimated retail price.

Page C-40 VolumeI: General Methodology . iooked at from 

### an economic point ofview rather than 

## aD accounting one. Thus expenses are broken down into flxed expenses and expenses that vary with the quantity produced.

. 

### C.l.5.4 Monte Carlo Outputs The Monte Carlo Outputs section lists the results of six key measures of**imp~t** when the Monte Carlo option is used. For key input variables, the Monte Carlo option randomly generates

_ values around the best available estimates oftheir true values. 

### .As a result, the outputs vary from run

to run, and the model can calculate means and standard deviations for the key measures ofimpact.

. It is then possible to bound the estimates ofimpacts and attribute a degree.ofcertainty to them. TheMonte Carlo Outputs section is a table that lists fox: each variable the value ofthe current iteration, the mean of all the iterations done in the current run, and the standard deviation of the results. It then lists in .chronological order the results of all**th~** iterations done in the curreJ.1t run. The measures 

### ofimpacts are percentage change in shipmentS, percentage change in price, percentage change in revenue, change in netincome, and percentage pointchange in ROE for both the long-run analysis and the short':'run analysis. See the Control Panel section (Section C.2.3.1) for a more complete description ofeach ofthese outputs.


### C.leS.S Retail Price Calculation One important role ofLBNL-MAM is to provide estimates ofretail prices of appliances at**~erent** efficiency levels for the LBNL-REM and the Ufe-Cycle Cost analysis. The primary input for the retail price calculation in LBNL-MAM is MAM's estimates of manufacturer prices. The manufacturer prices are derived from base manufacturer costs, engineering production costs, and appropriate**m~ufacturer** markups for each product. The base manufacturer cost is derived from current retail price data for appropriate product classes. These prices are divided by botli the retail markup for the product and the wholesale (manufacturer) markup. This calculation is described in more detail in the description ofthe Costs, Sales,**an~** Revenue module (Section C.2.3.6). The model generates a matrix ofmanufacturers' selling prices for each product class at each efflciency level. These manufacturers prices are then multiplied by an estimate of the manufacturer-ta-retail markup appropriate for the product class. This gives the estimated retail price.

. Page C-40 Volume 1: General Methodology'

## . C.2.6 

### Detvelopment and Integration of 

### the Industry

## NPV Module. During 1991-1993, 

### three industry trade associations7contracted with the Arthur 

### D. Little consulting 

### finm to develop a model that presents industry impacts using industrynet present value 

### (NPV) 

### as an impact variable. Their model was named the Government Regulatory Impact Model 

## (GRIM). 'helatestversion of this model is Version 1.2 and was released 

### in April 1993. SinceNPV is theoretically identical to ROE 

### as an impact measure, the 

### U.S. Departmentof Energy decided to include the GRIM 

### as part of the analysis. Hence the entire GRIM Version 1.2 model is integrated 

### into the 

### UBNLMAM. As 

### a 

### result, if the reader were to use the cost,

### price, and shipments inputs that were used in the 

### LBNLpMAM for input to a standalone version of GRIM, they will obtain the same answer. There has been no modification of the GRIM'Sfunctionality 

### as part of the integration. ,


### C.2.6.1 

### The Integration and Cost Iteration process. The integration of GRIM presented 

### three, major issues: They were: The GRIM model's programming did calculate the net c&h flows well, however there is no price-estimation algorithm (i.e., there is'nomechanism to forecast price). The model assumes that'price and shipments inputs be supplied from some external forecasting source. The

### GRIM has a cost analysis structuresuch that it uses annual costs and revenues to computeannual cash flowsand then calculates a net present value ofthe induarybased on those cash flows. On the otherhand, while the 

### LBNLMAM recognizes different costs in different 

### years for its demand function and 

### price-formation algorithm, it "levelizes" these costs before it calculates price and quantity. Hence in integrating the ' 

### two models, the costs in the two approaches need to be converted such that both approachesrecognize

### the same costs. There 

### are several categoriesof costs that

### *GRIM calculates

### as fixed

### ratios of revenues. Hence there 

### are several categories of costs that 

### are higherin GRIM thanin 

### the 

### LB.NL , - 

### MAM. In the absence of 

### an alternate solution, the lack of a price-formation algorithm

### in GRIM was addressed by using the demand function and price formation algorithm that was developed in the 

### LBNL-MAN. This entailed the following: 

# 'ThiAssociationofHomeAppliance Manufrtww. Gas Appliance Manufacturers Association. and Airsonditioni~ and Refrigeration Institute.

. . 

### . .

. .

. . VolumeI: G;eneml Methodology Page C-41 . . C.2.6 De'velopment and Integration ofthe**I~dustry** NPV Module. During 1991-1993. three industry trade associations 7 contracted with the Arthur D. Little

. consulting firm to develop a model that presents industry impacts using industry net present value (NPV) as an impact variable. Their model was named the Government Regulatory Impact Model (GRIM). The latest version ofthis model is Version 1.2 and was released in April 1993. Since NPV is theoreti.cally identical to**~OE** as an impact measure, the U.S. Department ofEnergy decided to include thl! GRIM as part ofthe analysis. Hence the entire GRIM Version 1.2 model is integrated into the 

### UBNL-MAM.

. As a result, ifthe reader were to use the cost. price, and shipments inputs that were used in ' the LBNL-MAM for input to a standalone version of GRIM. they will obtain the same answer. There has been no modification ofthe GRIM's functionality as part ofthe integration.

C.2.6.1 The Integration and Cost Iteration Process



### •



# ..

• The integration ofGRIM presented three. majorissues: They were: The GRIM model's programming did calculate the net cash flows well, however there is no**price-es~mation** algorithIn (Le., there is no mechanism to forecast priCe). The model assumes that'price and shipments inputs be supplied from some external forecasting source. . The GRIM has a cost analysis structure such that it uses annual costs and revenues to compute annual cash flows and then calculates a net present value of,the industry based on those cash flows. On the other hand, while the LBNL-MAM recognizes different costs in different years for its defi:umd function and price-formation algot:ithm. it

. "levelizes" these costs before it calculates price and quantity. Hence in integrating the two models, the costs in:the two approaches need to be converted such that both approaches recognize the same costs. '**~'here** are several categories ofcosts that ·GRIM calculates as fixed ratios ofrevenues. Hence there are several categories ofcosts that are higher in GRIM than in the**~NL** MAM. In the absence ofan alternate solution. the lack ofa price-formation algorithm in GRIM was addressed by using the demand function and**pric~** formation algorithm that was developed in the



### . LBNL-MAM. This entailed the following:



# . 7 

### The Association ofHome Appliance Manufacturers. Gas Appliance Manufacturers Association. and Air-conditioning and Refrigeration Institute. Volume 1: G;eneralMethodology Page C-411. 

### Sincethe cost structureof GRIM is different from that of 

### LBNLMAM, the costs forboth . 

### modules need to be 

### translated into the same basis. As 

### a result, a "cost levelization" methodology was developedto translate the format ofthe 

### GRIM-style costs into MAM-style costs. (There is no change in the amount of costs,just how the costs 

### are represented as necessary for input to the cost analyses of the two modules.)



### . . 11. 

### In the absenceof GRIM-st);lecostinputs fromthe industry,we take the baseline per-unit cost inputs of materials, labor, and overhead costs, along ,with baseline per-unit price and shipments (suppliedby the Engineering Analysis), and constructed a GRIM cost input table. These samebasic cost inputs 

### are also used in the 

## LBNLMAM. iii. Using this cost input table, the LBNLMAM is run toestimate price and shipments for the alternativeefficiency level case. iv. Theprice and shipments estimated

### by LBNLMAM 

### arethen input to the GRIM module, and 

### the GRIM module isrun to generate its results. Because severalof the cost assumptionsin the GRIM module are 

### proportional to revenue, the costs for several major cost categories (i.e., variable cost of goods sold, capital costs, working capital, and selling and general administration costs) will change from the original inputs.

v. The 

### LBNGMAM then compares the costs used in 

## MAM that supplied the inputs to the GRIM module and the resulting costs from the GFUM module. These differences are incorporatedback intothe LBNLMAMcosts via "cost comparisonfactors" (definedbelow). The 

### LBNLMAM is then run again to generate new price and shipments estim~tes based on these revised costs. vi. 

### This iterativeprocess isrep! until the cost differencesbetween the major cost categories used in the GRIM module and that of the LBNLMAMrun that generated the Gputs is equal to zero. vii. 

### After convergencehas been obtained, the final industry net present value results from the 

### GRIM module are reported.

. . 

### The data flows and linkages between the LBNLMAM, the GRIM module, and the cost comparison factors used to link the modules are illustrated in Figures C.3 

### and C.4.


### Cost Comparison Factors Since the cost structure in the 

### LBNLMAM and GRIM modules may differ due to the differing assumptions in the two models, cost comparison factors 

### are used in the integration of the two mode&to properly align and adjust their costs. For instance; if the GRIM assumptionsresulted in a lower capital maintenanceexpense,the inkal GRIM cost assumption would be 

### adjusted by 

### a cost comparison factor.



### . Page C-42 VolumeI: GeneralMethodology 'i. ' Since

# the cost structure ofGRIM is different from that ofLBNlrMAM, the costs for both modules need to be translated into the same basis. As a result, a "cost levelization" methodology was developed to translate the format ofthe GRIM-style costs**in~o** MAM-style costs. (There is no change in the amount of costs,just how the costs are represented as necessary for input to the cost analyses ofthe two modules.) , ii. 

### In the absence ofGRIM-stYle cost inputs from the industry, wetake the'baseline per-unit cost inputs of materials, labor, and overhead costs, along with baseline per-unit price and shipments (supplied by'the Engineering Analysis), and constructed a GRIM cost input table. These same basic cost inputs are also used in theLBNlrMAM.

111. Using this cost input table, the LBNL-MAM is run toestimate price and shipments for the alternative efficiency level case.
IV. The**pri~** and shipments estimated by LBNlrMAM are then input to the GRIM module, and the GRIM module is run to generate its results. Because several ofthe cost'assumptions in the GRIM moduie are proportional to revenue, the costs for several major cost categories (Le., variable cost of goods sold, capital costs, working capital, and selling and general administration costs) will change from the original fnputs. '

v. The LBNlrMAM then compares the costs used in**~** that supplied the inputs to the GRIM module and the resulting costs from the GRIM module. These differences are incorporated back into thy LBNL-MAM costs via ·jcost comparison factors" (defmed below). The LBNlrMAM is then run again to generate new price and shipments**estim~tes** based on these revised costs. vi. This iterative process is repel'ted until the cost differences between the major cost categories used in the GRIM modUle and that ofthe LBNL-MAM run that generated the iiiputs is equal to zero. vii. After convergence has been obtained, the final industry net present value results from the' GRIM module are reported. The data flows and linkages between the LBNL-MAM, the GRIM module, and the cost comparison factors used to link the modules are illustrated in Figures C.3 arid C.4.



### Cost Comparison Factors Since the cost structure in the LBNL-MAM and GRIM modules may**~iffer** due to the differing assumptions in the two models, cost comparison factors are used in the integration ofthe two**mode~** to properly align and adjust their costs. For**instance~** ifthe GRIM assumptions resulted

, 

# ., in a lower capital maintenance expense, the initial GRIM cost assumption would be adjusted by a cost comparison factor. Page C-42 Volume 1: GeneralMethodology

### In this 

### section,we discuss each of the ComparisonFactors in the 

### MAM Long-Run page that are used for the adjustment of 

### MA&l and GRIM, why they 

### are needed, and how they 

### are .implementedin the model. 

### Levelized Capital Costs (LCC): GRIM'S assumptions of the level of capital maintenance expense is higher,than MAM's. Hence 

### an initial adjustment is needed to align the costs between the two approaches. Further, , 

### GRIM'Sleivel of capital expenditureis a 

### fixedpercentage of revenues, hence when quantity changes (e.g., when other cost adjustments

### are made),

### LCC will change. I, 

## MAM already computes per unit levelized capital costs. for alternative effciency levels (LCC.N) in the model. Hence we create a new variable, GLCC.N, which is the per unit difference between 

### h4A.Mand GRIM, which we add to LCC.N. Further, we need to take GLCC.N %toaccount for tax 

### effixts and new assets. 

### Since

### LCC.TN and

### LAN (thetax 

### effectsof one-timecapital cost 

### and

. 

### assets duo: to one-time capital costs, respectively) 

### are obtained by applying levelization factors to CC.N (unleveliredcapital costs), the adjustmentto 

### these variablesis madeby "unlevelizing" the Lost comparison factor GLCC.N and adding it to CC.N before the appropriate levelization factor is applied.

. .

.-

Verne I: GeneralMethodology .. . Page C-43 · In this section, we discuss each ofthe Comparison Factors in the**~** Long-Run page that are used for the adjustment of MA¥ and· GRIM, why they are needed, and how they are .implemented in the model.'

.
Levelized Capital Costs (LCC): GRIM's assumptions of the level of capital maintenance expense is higher than MAM's. Hence an initial adjustment is needed to align the costs between the two approaches. Further, GRIM's le:vel ofcapital expenditure is a fUed percentage ofrevenues, hence when quantity changes (e.g., when other cost adjustments are made).**~CC** will change. MAM already computes per unit levelized capital costs. for alternative efficiency**level~** (LCe.N) in the model. Hence we create a new variable, GLCC.N, which is the per unit difference between MAM and GRIM, which we add to LCe.N. Further, we need to take GLCC.N**~to** account for tax effiects arid new assets. Since LCe. TN and LA.N(the tax effects ofone-time capital cost and assets duc:: to one-time capital costs, respectively) are obtained by applying levelization factors to Ce.N(unlevelized capital costs), the adjustment to these variables is made by "unlevelizing" the cost comparison factor GLCC.N and. adding it to Ce.N before the appropriate levelization factor is applied..

.

Volume 1: (leneralMethodology Page C-43 '..GRIMCOSTFACTORSMAM/GRIMCostIteration DetailL-----GRIMCOSTS-ManufacturincostsCGS+Labor+Mat.+OH+Dep.7'Fixedandvariable&---costsafternew-CapitalmstsfCCf.~L(CE~~T~~CO")+(CE~~.erncon,-Workingcapital,[ARCC51foreachyearstandardsdistributionsonly.[Nooperatingcosts.]Alsoneedtobescaledtoindustry-level costs'I-SGA/R&D,CC7+(R)ffD%,whereCC7=totalannualSGAGRIMCash-Taxes,Pre-taxnF.2,whereF22=faxrate.flow&NPV.-modules.GRIMcostsarereportedonanPerUnit CostInputs:For baselineandnewengineering design options(i.e.,conversioncosts):-Variablecosts(labor,OH,materials).-Fixedcosts(capital)-Operating costs-NewstandardsdistributionmatricesmodulesMAMlQRiMThl8proerr88RouldbedoneloratSwitchCostFactoqIea8t8avmItrnUoni(I.,.,prfcrSet costfactors=1-LevellzedcaItalcostsend quantity and coat adju8tment8needtobedonertlaart8evenwhen offelseusecost-Wo&ingfactonwhenrnnning-~anulactu&~coststimesto~IOW~OnVeWenCe.GRIM.-SGAlOthercosts-TaxesFigureC.3LBNL-MAM'andGRIMIntegrationOverviewPageC-44VolumeI:GenemlMethodologyGRIMCOSTFACTORS-R&D,%ofR{CC.8L-Deprec/atlon,%ofR,(CC101-Workingcapital,%{CCS}-CapitaleXf'.endlture,%ofR,fCC11J-Taxrate,[CC3!-Interestrate,{CC4}MAM/GRIMCostIterationDetailcost-,§npu--ndCostFactor-Fixedandvariable---costsafternewGRIMCOSTS-Manufacturingcosts-Capitalcosts(CCf'Rl+(CE38TempCon)+(CE39·rempCon)standards.distributionsonly.[Nooperatingcosts.]Alsoneedtobescaledtoindustrylevelcosts---;....--..:PerUnitCostInputs:Forbaselineandnewengineeringdesignoptions(i.e.,conversioncosts):-Variablecosts(labor,OH,materials).-Fixedcosts(capital)-Operatingcosts-Newstandardsdistributionmatricesp,a-Worfdngcapital,{41R•CCS}foreachyear-SGAIR&D,CC7+(R)-RD%,whereCC7=totalannualSGAGRIMCash-Taxes,pre-tax"•F22,whereF22=taxratenow&NPV·f--------.modules1jGRIMcostsarereportedonanI"IndustrylevelandmustbslevellzedL_____,;.NPV('O,"erencertoperfirmlevel.\MAMreprtscostsonperfirmlevel.MAMprice'--_-C/oBothareensubsequentlyfonnatlon&1---"ROElconvertstoperunit.demandfunc.--____....JMAMCOSTS-Capitalcosts[LCC.BandLCC..Manufacturingcosts[VCGS)..i-WorkIngcapital,TC.B.'R:R.(1-R)-I;ThisIsBaseWC::.variable.-.(p.TC+TaleSHVCGS+LCC.+WC+Taxes)L<>-[-··---·---·----rI'Adjuster_____._JMAMIGRIMThl.proce..ahouldbedoneforatSwitchCostFactors'ea.t.eveniteration.(I.e.,priceSetcostfactors=1 -levelizedcapitalcostsandquantityandco.tadju.tment.whenoffelseusecost-Workingcapital.needtobedoneatle..t..venfactorswhenrunning-Manufacturingcoststime.toaltowconvergence.GRIM.-SGAlOthercosts-TaxesFigureC.3LBNL.,MAMandGRIMIntegrationOverviewPageC-44Volume1:GeneralMethodologyYalor YAY Coal Out~ub - Thefirsf 4 CWf Cat8 d8S fbw @'redly brt0 the 1X C~phl c08- mellBno8r. 1~18"Catepty torthe ~emr Ls theOMER COSTS categorywhfch is celwletedasthe ditfemncebetnwn the first 4

VC0S.N 

## I-,

Fh.d w P- and QumnWy whlch worklno cap. am kput tn QRIM for 0p.n- Cmb mnd Domand Functlon uah(low anoIyak of WCA. WCCEC. abomhrn 

# + Adjusterand Diierenmr (Cost tador generation) I

CostFactors Affeded Variables for 8s IMlIy netationsas 

## -------

# +[GI J Y -0' P.n(10r)l*hIm hputto ORlY for OanandFuncbonAlOorfim cuhf%w .naly.b Of WItm (MODEL 

# '7 Adjusterand DHferanoer Cash-florX;osls from GRIM (Cost fador generation)



### Figure C.4 

### Cost Comparison Factors and 

### Their Relationships Volume 1: Gene& Methoddogy Page C45. 

### . Irandi neer



# - Cash-f1owCos1 from GAl M s Major MAM Coat Output.

Cost factors
GLCC.N Olllor Coote The first4 cost categories flowdif8C1lyInto /he difference,. The fifth categoryfor/he dllferenoe, Is /he OTHER COSTS categorywhJch is csJcuIBtedas the dlfferenoe between /he filSt 4 cost categories aM the total costs (mctuding taxes) calculated byMAM.

MAM Prtce Formotlon**I----~**..**r:::a~=:ond** Function (MODEL pogo) P_ ond Quonllty wlllch oro Input to GRIM for coall flow onolya" of _ate**~**

## ~ Adjuster and Differencer (Cost factor generation)
TO.NQ. TC.NF.I--------

.... EC.NQ. EC.NF
Affected Variables



## -**\~.~**

## ~~-- Repeat fo, as many Iterationsas necessary P

.... OWlllclltl

.. Input ID GRill for WOIk. c.p. wfEC.WC.An, IC.TC.EF.WC MAM PrIce FonnaIIon and cullnow ....Ip

.. Demand Func:IioII AlgorIIIIm 01 COIla

### I~ !OIhar Coats !(1

.. )**j--~**

### ~--**~(M_oo~a~~** __

)

______

### ~i~

__

# ~ __

# ~I=IOTAX.N 

# eJ+ •
Adjuster and Differencer OIherCosIa - - _ -**~** EC.NF. EC.NQ, Te.· (Cost factor generation) TC.NO



### Figure C.4 Cost Comparison Factors and Their Relationships Volume 1: {,eneral Methodology PageC4S. .',

### Manufacturing Costs: 

### ~anufacturing costs are the sum of materials, labor, and overhead costs. 

### In MAM, this is the Variable Cost of 

### Goods Sold (VCGS.N)

### variable..

### In 

### GRIM, it is the samethree 

### cost inputs, but it also includes depreciation. However in 

### GRIM, depreciation is only included for the purpose of tax 

### calculation and it is netted out later in the cash flow calculation. Since it plays no role in the 

### NPV calculation (aside from taxes), we do not include it.inthe Manufacturing Costs calculation. Thebaseline per unit costs of materials, labor, and overhead 

### are inputs into the models. But in the case afternew efficiency levels have been implemented, the 

### GRIM Overhead cost component 

### . has a portion that varies with shipments.' Since shipments will vary when other adjustments are 

### made, overheadcosts and consequently manufacturing costs, will change. 

### In MAM, the per unit cost of alternative efficiency levels is calculated in the change in variable cost variable, dVC.NO. 

### ~ence we created a new variable, WG, 

### where we input the per unit difference in manufacturing costs which is added to dVC.NO. Working Capital: In GRIM, 

### working capital is 

### a fixed percentage of revenues. Since revenues are likely to change 

### as aresult of other cost adjustments,

### an adjustmentforworking capital is 

### needed. Hence we define a variable, WC. 

### EC, which is the sum of the per unit differencesin working capital costs from each iteration. The model adds WC-EC

### to the variable part of economic costs, EC.NQ. 

### Next the total difference in working capital assets is calculated as: WC.ECi

# . Qi, 

# wcm = 

### ATR



# . where (WC.ECi 

## .Q, ) 

### is the sum of each per unit difference times the Q.N 

### from that respective iteration. 

### ATR is the after-tax weighted cost of capital. 

## WCM is then added 

### to new efficiencylevel assets,

### A.N. The total "accounting cost of working capital is:



# '

The 

### GRIM Overhead formula is OH 

### =OH,,+ (OHf 

### Q.N), where OH, is the baseline Overhead cost, OH,is the per unit difference between Overhead costs in the pre- and post-efficiency level case, and 

### Q.N is shipments in the post- 'eficiencylevel case. Page C-46 VolumeI: GenemlMethodology Manufacturing Costs: Manufacturing costs are the sum ofmaterials, labor, and overhead costs. In 

### MAM, this is the Variable Cost ofGoods Sold (VCGS.N) variable. ' In GRIM, it is the same three cost inputs, but it also includes depreciation. However in GRIM, depreciation is only included for the purpose of tax calculation and it is netted out later in the cash flow calculation. Since it plays no role in the NPV calculation (aside from taxes), we do not include itin the Manufacturing Costs calculation. ' The baseline per unit costs ofmaterials, labor, and overhead are inputs into the models. But' in the case after new efficiency levels have been implemented, the GRIM Overhead cost component has ,a portion that varies with shipments. 8 Since shipments will vary when other adjustments are made, overhead costs and consequently**manufac~uring** costs, will change. , ' In MAM, the per unit cost of alternative efficiency levels is calculated in the change in variable cost variable, dVC.NO. Hence we created a 'new variable, dVC.G, where we input the per unit difference in manufacturing costs which is added to dVC.NO.

.

.Working Capital: In GRIM, working capital is a fixed percentage ofrevenues. Since revenues are likely to change as a result ofother cost adjustments, an adjustment for working capital is needed. Hence we defme a variable. WC.EC. which is the sum ofthe per unit differences in working capital costs from each iteration. The model adds WC.EC to the variable part ofeconomic costs, EC.NQ. Next the total difference in working capital 'assets is calculated as: WC.EC,·Q. WCAN= 1 10 ATR
where (WC.EC j • Qio ) is the sum ofeach per unit difference times the Q.N from that respective iteration. ATRis the after-tax weighted cost ofcapital. WC.ANis then added to new efficiency level assets, A.N. The total "accounting" cost ofworking capital is: WC.TC=WCAN·(I-ER)-;

8 The GRIM Overhead formula is OH = OlIt,+ (ORr Q.N), where OH b is the baseline Overhead cost, OH,is the per unit difference between Overhead costs in the pre- and post-efficiency level case, and Q.N is shipments in the postefficiency level case. Page C·46 Volume 1: GeneralMethodology

### where

### ER is the equity ratio and 

### iisthe intuestrate. ~owevir, as we 

### mentioned earlier,to facilitate convergence with GRIM, we assume (1 

## - ER) = 1. 

### WC.TCis added to total costs in the alternative efficiency level case, 

### TC. N. 

### Finally, from 

### an accounting cost standpoint, 

### WC.TCwill probably 

### be less 

### than total cost of working capital. Hence to facilitate convergence, we define one more "extra factor" variable: 

### EFNC =

### (WCM*A TR) 

### -WC. TC
where 

### (WCAN ATR) is the total costs of working capital and 

### WC.TC is the accounting portion' recognized earlier. 

### EF.NC is.also added to 

### TC.N.

, ,

. .

. 

### Adjustme~it for Other Variable Costs 

### (MAM) and 

### SGALR&D (GRIM):

-. 

### M.Mcontains Other Variable Costs which 

### are the vaiiable portion of fixed costs such 

### as

capital costs? GRIM also contains explicit variable costs for selling and general administration expense and research and development. These GRMcosts 

### are a 

### fixedpercentage of revenue and thus are vzuiable costs also. Sincethey are

### both relativelyclose

### in size,both 

### are variable costs, and

. ' 

### .bothare cost categories.which 

### are not in the other model, we align 

### these two costs.

. . 

### Some iteridon will be 

### necessary because the GRIM

### costs.are a proportion of revenue. And so 

### as

. 

### othercost adjustments are made, revenue will change and hence these costs will change. - 

## - When we 

### align these costs, we want to affect both the accounting and the economic costs. The

. . 

### accounting costs 

### are defined 

### as:
. 

### TCN=

### TCNF+(TC.ATQ)Qfl where Tc.N 

### is total costs in the 

### alternative efficiency level case, TC.NF 

### is the total fixed part of 

### costs, TC.JVQ 

### is the per 

### unit variable part of costs, and 

### Q.Nis shipments. These 

### accounting costs are 

### important ,in

### the deterinination

### of financial variables Such 

### asNet Income,

### Taxes, and 

## ROE. Economic costs 

### are defined 

### as:

.9This is 1-use in MAM, even' fixed costs such ascapital costsareconsideredto havea variable portion in the long fun. VolumeI: Gene& Methodology Page C-47 where ER is the equity ratio and iis the interest rate. However, as we mentioned earlier, to facilitate convergence with GRIM, we assume (1

- ER) 

### = 1. We.TC is added to total costs in the alternative efficiency level case, TC.N.

. . . Finally, 

### fr,om an accounting cost standpoint, We.TC will probably be less than total cost ofworking capital. Hence to facilitate convergence, we derme one more "extra factor" variable: EF.NC

## =(WCAN'ATR)- WC.TC
where (WeAN· ATR) is the total costs of working capital and we. TC is the accounting portion· recogqized earlier. EF.NC is.also added to TC.N. Adjustrru!lJtjor Other Variable Costs (MAM) andSGAIR&D (GRIM): 

## MAM contains Other Variable Costs which are the variable portion offixed costs such as

. capital costs. 9 GRIM also contains explicit variable costs for sellhlg and general administration

.expense a:nd research and development. These GRIM.costs are a fixed ·percentage ofrevenue and thus are**v~ariable** costs also. Since they are both**~latively** close in size, both are variable costs, and .both are Clost categories· which are not in the other model, we align these two costs. Some iter:ation will be necessary because the GRIM costs.are a proportion ofrevenue. And so as other cost adjustments are made, revenue will change and hence these costs 

# will change. 'Yhen we align these costs, we want to 8ffect both the accounting and the economic costs. The accounting costs are defined as:

. TC.N

## =TC.NF+(TC.NQ)· Q.N
where TC

..N is total costs in the alternative efficiency level case, TC.NF is the total fixed part of costs, TC.NQ is the per unit variable part ofcosts, and Q.Nis shipments. These accounting costs are importantin the detertnination offmanciaI variables**~uch** as Net Income, Taxes, and ROE. Economic costs are defined as: EC.N

## = EC.NF +(EC.NQ

. Q.N)

.9 This is because in MAM, even fixed costs such as capital costs are considered to have a variable portion in

## the long run. Volume J: GeneralMethodology PageC-47

### whereEC.N 

### istotal knomiccosts,EC.NF 

### is the total fixed part of economic costs, EC.NQ 

### is the per unit variable part of economic costs, and 

### Q.Nis shipments. Economic costs.areimportant for the determination ofprice.

, 

### Since the MAM Long-Run page calculates 'fixed and variable costs separately and the cost

components 

### are used as 

### inputs to other areas 

### of the models, we define a factor, e, which will be 

### applied to each of the cost components. Hence we will have: TC. 

### NF

. -. 

### (1, 

## + e), TC. NQ (1

## + e), EC. NF (1

# + e), ' 

### EC.NQ 

# - (1

# + e) Generally, the'MAMOther Variable Costs need to be 

### scaled up to align with'the

## GRIM SGAR&D costs. We 

### can derive a formula 

### as follows. We want to find a fomiulafor'afactorthat,doesthe following:

where OTHER- is MAM 

### OtherVariable Costs, TC.N

### is total costs, e is the adjustment factor, and SGA,, 

### is GRIM SGALR&D 

### costs. Solving for e, we have: ' . 

### SGAM; 

### OTHERe = 

### Thus, iq the model, we input the 

### per unit values for

### SGA,, 

### ,OTHER-, and TC.N. 

### Wkscale each of the per unit values to firm values by 

### multiplyingby Q.N 

### and then calculating e. (For successive iterations, e is the sum of the individual calculations.) Cost

### Levelizlrtion 

### Most costs in the 

### GRIM are annualizedcosts and are 

### thus in 

### the same format 

### as' those of the 

### LBNLMAM. ,However, there 

### are two types of costs which will 

### differ from year to year. These 

### are: Conversioncosts: 

### In 

### GRIM, conLersion costs 

### are broken into two types: design costs and

.

### capital costs, which 

### are incurred

### in the three years leading up to new efficiency levels 

### in the 

### SGA and Capital Expenditure categories,respectively. Workingcapital: 

### Working capital is incurred 

### as a one-time cost when there is a 

### change in revenue 

### as in the first year of new efficiency levels when price and quantity changes.



## . Page C-48 Volume 1: GenerolMethodology where EC.Nis total economic costs,EC.NF is the total fixed part ofeconomic costs, EC.NQ is the per unit variable part ofeconomic costs, and Q.Nis shipments. Economic costs

## ,are important for the**d~termination** ofprice.

, Since the MAM Long-Run page calculates 'fixed and variable costs separately and the cost**compone~ts** are used as inputs to other areas of the models, we define a factor, 

### e, which will be

applied to each ofthe cost components. Hence we

### will have: TC.NF °f1+ e). TC.NQ ° (1 

## +e). EC.NF ° (1 

### + e). 'EC.NQ ° (1 

### + e) Genenilly, the'MAM Other Variable Costs need to be scaled up to align with'the GRIM 

### SGAIR&D costs. , ., 

## .
We

## can derive a formula as follows. We want to fmd a formula fora factor thatdoes the following: OTHER"",,+TC.N·(e} 

### =SGAgriin
where OTHERmam is MAM OtherVariable Costs, TC.Nis total costs, e is the adjustmentfactor, and SGA grim is GRIM SGA/R&D costs. ' Solving for e, we have: ' 

### SGA ,- OTHER ' e 

## = grim nun TC.N' Thus, it). the model, we input the perunit values for 

### SGA grim ,**OTH~R_,** and TC.N. 

### We scale each. ofthe perunit values to firm values by multiplying by Q.N and then'calculating e. (For successive iterations, 

### e is the sum ofthe individual calculations.) 

### Cost Levelization Most costs in the GRIM are annualized costs and are thus in the same format as those ofthe LBNL-MAM.However, there are two types ofcosts which will differ from year to year. These are:

• Conversion costs: In GRIM, conversion costs are broken into two types: design costs and

,capital costs, which are incurred in the three years leading up to new efficiency levels in the 

### SGA and Capital Expenditure categories, respectively.

• Working capiial: Working capital is incurred as a one-time cost when there is a change in revenue as in the first year of new efficiency levels when price'and quantity changes.

PageC-48, Volume 1: General**~ethodology**

### To convert these "one-times"costs into continuous, annualized costs, we "levelize" them via the followingprocedures: 

### I. 

### Ifthey 

### an alternative efficiency level costs incurred in 

### years prior to new efticiency levels, future value them to the 

### fmt year that newefficiency levels, take 

### affect. This may be 

### accomplished by the following formula'where the total future value C,, is:

11. 

### 'Ib 

### derivethe formula forlevelized costs (L) from the future valued costs (C,, ), 

### we perform tlie following:

...

Volume 1: GenemlMethodology . To conVI!rt these**"one-times~'** costs into continuous, annualized costs, we "levelize" them via the

following procedures:

. 

### I. Ifthey are alternative efficiency level costs incurred in 

# years prior to new efficiency levels, future value them to the 

### first year that new'efficiency levels. take affect. This may be accomplished by the following formula where the total future value 

### Co is:


### C=rC '(l+r)n

o LJ 

### '-n



## .. n. To derive the fonnula for levelized costs (L) from the future valued costs (Co), we Perform

. the following:

L . L+--+

... =C 1+r 0 . L . Co

.. L+-"+ .... 

### =(C -L)'l +'. 1+r 0

Co

. -=C -L l+r 0

. . Co' Co(1 +r)-C o

' r' 

### L=C--=

. =-C o 1+r 1+r 1+r 0"

Volume 1: General Methodology .Page C-49

## A:I?PENDIX 

## D. MAINTENANCE COSTS FOR INTERMITTENT IGNITION DEVICES 

## IN GAS RANGES AND 

## OVENS.

,


### This appendix descriks the analysis used to characteriie the maintenance costs for intennittent ignition devices 0s) for gas cwktops and ovens.

. . . I 

### D.l ANA,LYSI[S APPROACH Maintenance costs have been characterizedfor this analysis =.an annual incremental cost,
' 

### referred tohere 

### as the annualizedmaintenance cost, MC,, tobe 

### added to the annual fuel cost for the gas 

### cooktopor oven. 

### In order to derive MC, , 

### 'thepresent value of the maintenance cost. MS., is first derived. 

### The value of MC, is. based on assumed probability distribution of maintenance costs over.the 

### appliance lifetime. Given the present value MC, 

### , the annualized maintenance cost iscalculated using the so-called capitalrecovery factor. 

### CRF. as MC, 

### =CRF

# - MC, 

# . The capital recovery factor is calculated as.
CRF = 

### d 

### 1' 

## - (1

### +4)" 

### National ApplianceEnerbConservationAct (NAECA),PublicLaw 100-12, March 17,1987.where n 

### is the appliance lifetime, and d

### is the discountrate.

. . 

### D.2 

## GENERAL DESCRIPTION 

### OF 

## INTERMITTENTIGNITION DEVICES-(IIDs) The cost analysis here was generated specificallyfor two types of pilotless ignition systems; direct spafk and spark-to-pilot

### IDS. 

### IDS are 

### common to gas-fired furnaces, water heaters, direct heating equipment, clothes dryers, and 

### cooktops. With regard to ovens, most use electric glo-bar ignition syitemsrather 

### thanm>s. Although electric g1o;bar ignitioa devices 

### are a 

### type ofhot surface -ignition(IISI) device, their control systems differ considerably from those used by typical .HSI devices. %:lecFJic glo-bar ignition systems are designed to draw electricalpower for 

### as long 

### as the burner remains in operation while almost 

### all other HSI systemdrawpower onlyto light the burner. Although the glo-bar's ,control system uses a considerable amount of electrical energy, it is a relatively :simple design which, unlike most IlDs and HSI systems, does not require 

### the use of electronics.. Therefore, the maintenance costs for gas ovens using electric glo-bar ignition devices are 

### not treated in this Appendix. The cost analysis generated here is not directly applicableto typical HSI devices, either. The main differencebetween HSI and 

### IID devices is the actualignitor element itself; cont:rol modules, gas valves, and 

### ignition sensors appear to be 

### similar in function and maint;nanc:e across all systems. Therefore,the assumptionis made here that 

## HSI and spark ignitors
Volume1: General Methodology Page D-1 

### A:PPENDIX D. MAINTENANCE C;OSTS FOR INTERMI'ITENT IGNITION DEVICES IN GAS RANGES AND OVENS,

This appendix describes the analysis used to characteriZe the maintenance costs for**int~rmittent** ignition devices (DDs) for gas cooktops and ovens.


### D.l ANALYSIS APPROACH " M;aintenance costs have been characterized for this analysis as,an annual incremental cost, referred to here as the annualized maintenance cost, MC •• to be added to the annual fuel cost for the gas cooktop or oven. In order to derive MCa, the' present value ofthe maintenance cost, Mc;,v, is first derived. The value ofMCpv is, based on

# an assumed probability. distribution of maintenance costs over the appliance lifetime. Given the present value MCpv , the annualized maintenance cost • is calculated using the so-called capital recovery factor, 

### CRF. as Mea 

### = CRF

. MCpv . The capital

recovery factor is calculated as d CRF = -....,.....-- 

### 1 - (1 

### +4)-n

National Appliance Energy Conservation Act (NAECA), PublicLaw 100-12, March 17, 1987. where n is the ap:pliance lifetime, and d is the discount rate.


### D.2 GENERAL DESCRIPTION OFINTERMITTENT IGNITION DEVICES-(llDs) The c:ost analysis here was generated specifically for two types ofpilotless ignition systems; direct spark and spark-to-pilot DDs. DDs are common to gas-frred furnaces, water heaters, direct, heating equipment, clothes dryers, and cooktops. With regard to ovens, most use electric glo-bar ignition**sy!~tems** rather than DDs. Although electric glO:-bar ignition devices are a type ofhot surface ignition (HSl) device, their control systems differ considerably from those used by typical .HSI devices. 'Electric glo-bar ignition systems are designed to draw electrical power for as long as the

, burner remains in operation while almost 

### all other HSI systems 'draw power only to light the burner.

Although the glo-bar's control system uses a**~onsiderable** amount of electrical energy, it is a relatively simple design which, unlike most lIDs and HSI systems, does not require the use of electronics.. Therefore, the maintenance costs for gas ovens using electric glo-bar ignition devices are not treated in this Appendix. The cost analysis generated here is not directly applicable to typical HSI devices, eifuer. The main difference between HSI and 

# no devices is the actual ignitor element itself; control modules, gas valves, and ignition sensors appear to be similar in function and maintenance across all systemS. Therefore, the assumption is made here that HSI and spark 

### ignitors

Volume 1: (,eneral Methodology ,PageD-)

### have the same maintenance costs. While HSI ignitor breakage rates were probably substantially higher than spark ignitor failure rates in 

### the recent past, the assumptionin 

### this analysis is,thatHSI 

### ignitor breakage rates will have been significantly reduced by the time the subj.ect alternative efficiency levels take effect (1). Regardless~of the system 

### type, 

## IIDsystems were consideredfor this analysis ticonsist of three 

### main subcomponents: ignitor device, control module, and ignition sensor. After discussions with equipment maintenance companies, it was decided to assign negligible maintenance costs to the ignitor components. This was based,onthe assumption that past problems with hot surface ignitors will disappearby the time that the new efficiency levels take effect for cooktops and ovens. Sensors and control modules wereconsidered separately in terms of maintenance costs.


### D.3 MAINTENANCE 

## COSTS 

## FOR IDCOMPONENTS The maintenancecosts for sensors &d controlmodules were obtained directly from a survey of equipment maintenance organizations in the San Francisco area: Sensorreplacement service call: $ 

### 79 Control module replacementservicecall: $170 

### Total Cost: 

### $249 These values represent the total amounts, in 

### the year of the maintenance occurrence and in 1990 dollars, that 

### an appliance owner in the San Francisco area would have to pay for replacement of a defectivepart. The sensorand controlmodule costshave been added above due to the unavailability of separatelifetime informationfor sensors. Both sensorand control module were assigned the same lifetime as the control module. After obtaining the regional (San Francisco area) cost of $249, a location multiplier of (111-367 =) 

### .732 was used to.adjust the costs to a national average value, $182. The 1.367 factor was obtained from a listing of location multipliers for mechanical construction work cost estimation databy the R.S. Means Company (2).


### D.4 MAINTENANCE 

## LIFETIMEDISTRIBUTION AND 

### PRESENT

## VALUE OF MAINTENANCE COST Maintenance lifetime was estimated from infomation obtained from IID manufacturers and not from the survey results. This approach was taken because it ,appeared that 

### IID systems in particular were still evolving rapidly, and that the estimates for lifetimes obtained from services organizations would be 

### faulty if based on experience from the past. Thepresentvalue ofmaintenancecost forIIDs was characterizedin this analysis as the average present value forall.possiblefailure times. 

### In orderto achieve such 

### an average, a probability density Page 0-2 VolumeI: GeneralMethodology have the same maintenance costs. While HSI ignitor breakage rates were probably substantially higher than spark ignitor failure rates in the recent past, the assumption in this analysis is that HSI ignitor breakage rates will have'been significantly reduced by the time the subject alternative efficiency levels take effect (1). '

, 

## . Regardless'ofthe system type, no systems were considered for this analysis to consist ofthree main subcomponents: ignitor device, control module, and ignition sensor. After discussions with equipment maintenance companies, it was decided to assign negligible maintenance costs to the

. ignitor components. This was based.on the assumption that past problems with hot surface ignitors will disappear by the time that the new efficiency levels take effect for cooktops and ovens.' Sensors and control modules were considered separately in terms ofmaintenance costs.


### D.3 MAINTENANCE COSTS FOR 

## un COMPONENTS The maintenance costs for sensors.and control modules were obtained directly from a survey ofequipment**mainten~ce** organizations in the San Francisco area: Sensor replacement service call: $ 79 Control module replacement service call: $170 Total Cost: $249 These values represent the total'amounts, in the year of the maintenance occurrence and in 1990 dollars, that an appliance owner in the San Francisco area would have to pay for replacement ofa defective part. The sensor and control module costs have been added abOve due to the unavailability ofseparate lifetime infonnation for sensors. Both sensor and control module were assigned the same lifetime as the control module.,

. After obtaining the regional (San Francisco area) cost of $249, a location multiplier of (1/1.367 =) .732 was used to. adjust the costs to a national average value, $182. The 1.367 factor was obtained from a listing oflocation multipliers for mechaIiical construction work cost estimation data by the R.S. Means Company (2).


### D.4 MAINTENANCE LIFETIME DISTlUBUTION AND PRESENT VALUE OF MAINTENANCE COST Maintenance lifetime was estimated from infomiation obtained from no manufacturers and not from the survey results. This approach was taken because it ,appeared that no systems in' particular were still evolving rapidly, and that the estimates for lifetimes obtained from services organizations would be faulty ifbased on experience from the past. The, present value ofmaintenance cost for nos was characterized in this analysis as the average present value for allpossible failure times. In orderto.achieve such an average, a probability density ,PageD-2 Volume 1: GeneralMethodology

### function (IPDF) of.failures over time was generated based od information obtained from telephone interviews with manufacturers of 

### IID control modules. 

### A separate lifetime

### was not available for sensors, so the lifetime for control modules was chosen for .sensorsas well. Convers@ons with service 01-gktionsindicated that sensor lifetimes 

### are probably.sho~er 

### .than control module lifetimes. The following information was 

### obtained from conversations with 

### ID control module manufacturers:Control module average lifetime was estimated to be on the order of 14years. The average control module failurerate during the first year of operation was found to be below 

### 2.5%. The value 

### 2.5 was 

### used for this analysis. Failures in the fmt year are covered under warranty and do not represent a cost to the consumer. After 10 

### years of production, yearly 

### IID sales for the aftermarket (repairs) were detednedto'beon the order of 

### 5% of total yearly sales. TableD.l presents a tabular summary of the probability density function (Columns A 

### and 

### B) developed using the above information. The values 

### are calculated on the basis of unitary

. 

### maintenancecost ($I), a discount rate of 6%.,appliance installation in year 0, and maintenance cost

for an 

### individual year equal to $1 times the probability of failure for the,year. The summation (in Column 

## B)is calculated based on 

### an assumed lifetime of 

### .18years for gas cooktops and ovens. It does not include

## thecost of failures occurring in the first year. Using the total cost of $182 '(given above),the present value for 

### IID maintenance is.calculated as follows:

, .


### The annualized maintenancecost (6%discount rate) derived from

. 

### this

. 

### value is 

### MC,=

### MC,, CRF 

### = $81 

# ..092 

### = $7.45 / 

### year

Volume 1: General Methodology

. . Page 0-3 function (IPDF) of.failures over time was generated based oli information obtained from telephone interview:; with manufacturers of 

## no control modules. A separate lifetime was not available for sensors, so the lifetime for control modules was chosen 'for ,sensors as well. Conversations with service organizations indicated that sensor lifetimes are probably' shoI1er

. than control module lifetimes.

. The following information was obtained from conversations with 

## no control**m~dule** manufacturers:

• .. Control**modu~e** average lifetime was estimated to

## be on the order of 14 yearS.

• The average control module failure rate during the'first year ofoperation was found to be below 2.5%. The value 2.5 was used for this analysis. Failures in the fIrst-year are covered under warranty and do not represent a cost to the consumer.

• After 10 years of production, yearly TID sales for the aftermarket (repairs) were determined to 

### be on the order of5% oftotal yearly sales. Table 0.1 presents a tabular summary ofthe probability density function (Columns A and B) developed using the above information. The values are calculated on the basis of unitary

. maintenance cost ($1), a discount rate of6%, appliance installation in year 

### 0, ami maintenance cost

for an indilvidual year equal to $1 times the probability offailure for the'year. The summation (in Column E) is calculated based on an assumed lifetime of .18 years for gas cooktops and ovens. It does not include the cost offailures occurring in the first year. Using the total cost of$182 '(given above), the present value for TID maintenance is'calculated as follows: • MCpy 

### = $182

.. ($.44/$1.0) 

### = $81 The**annu~llized** maintenance cost (6% discount rate) derived**fro~** this value is

. MCa == MCpv • CRF 

### = $81

.. 092 

### = $7.45 / year

Volume 1: General Methodology PageD.3

### Table D.l Calculations for 

### Mean Lietime 

### of IID Units

. 

### . Year After Yearly Fraction of Present Value Average Present.Valueof SummationofPresent Purchase Units Failing (F) Discount Factor (f) Maintenance ($1-Ef) Value ofMaintenance Column A Column B Column C Column D Column 

### E 1' 0.025 na na 2 0.0 1 0.890 $0.009 3 0.01 0.840 $0.008 4 0.01 0.792 $0.008 5 0.0 1 0.747 $0.007 6 0.0 1 0.705 $0.007. 7 0.0 1 0.665 $0.007 8 0.0175 0.627 $0.01 1 9 0.0275 0.592 $0.01 6 10 0.045 0.558 $0.025 11 0.0575 0.527 $0.030 12 0.075 0.497 $0.037

. 

# . Page 04 Volume I: GeneralMethodology 

### Table D.I Calculations for Mean Lifetime oflID Units
Year After Yearly Fraction of Present Value Average PresentValue of Summation ofPresent Purchase Units FailinS**~** Discount Factor

# m Maintenance**~$I'F'n** Value ofMaintenance ColumnA ColumnB ColumnC ColumnD ColumnE 1 0.025 na na 2 0.01 0.890 SO.OO9 3 

## om 0.840 SO.OO8 4 0.01 ' 0.792 SO.OO8 .5
.. 0.01 0.747 SO.OO7 6 0.01 0.705 SO.OO7. 7 0.01 0.665 SO.OO7 . 8 0.0175 0.627 SO.OII

9 0.0275 0.592 SO.016 10 0.045 0.558
. SO.025 11 0.0575 0.527 $0.030 12 0.075 0.497 SO.037 13 0.0875 0.469 SO.041 14 '0.105 0.442 SO.046 15 0.115 0.417 $0.048 16 0.1225 0.394 SO.048 17 0.1275 0.371 SO.047 18. 0.135 0.350 $0.047 $0.44

.. PageD-4 Volume 1: General Methodology

### REFERENCES
1. 

### Ilzierwa, 

### R. 1990. "KindlingAppliance Fires."Appliance, 

### December; 1990,pp. 

### 20-26. 2. 

### It. S.Means 

### Company, Inc. 1990. Means Mechanical CostData. Kingston, MA.

Volume I: 'GeneralMethodology Page D-5 

### REFERENCES
1: Dzierwa, R! 1990. "Kindling Appliance Fires." Appliance, 

### December; 1990, pp. 20-26. 2. R. 

### S. Means Company, Inc. 1990. Means Mechanical Cost Data. 

### Kingston, MA.

. Volume 1:'General Methodology PageD-Se 

## APPENDIX E ELECTRIC 

## UTILITY IMPACT 

## MODELING

..

## - E.l INTRODUCTION 

### IRis appendix summarizesthe methods used to calculate

### utility avoided 

### &for the energy savings [that result from the impositionof applianceenergy efficiencylevels. Usually, the appliance being analyzed is electrically fueled; however, efficiency levels imposed on gas or oil-fired appliances may also inclirectly affect electricity demand. The conventions 

### and background information used in the analysis, and the basis for valuing the'energy andcapacity savings of the efficiency levels 

### areexplained. The goal ofthis analysisisto calculatethe

### peak and capacity savings and the ztvoided costs, in dollarsper million Btu ($/MMBtu) of electricity saved, where the energy is expressed 

### asprimary energy.' Avoided costs containboth the marginal fuel and marginal &pacity value of each kwh saved by the alternative efficiencylevels in each year of the period analyzed.


## E.2 AVOIDED COST: BACKGROUNDAND CONVENTIONS The analysis adopts the standard utility conventibn that the value of electricity savings, commonly calledaioided cost,can 

### be broadly separatedinto energy or variable costsavings, and capacity or fixed cost savings. The variable impact measures the production costs avoided by, , 

### reduced electrical generation. The largest variable componentby far is the cost of the fuel not burned, but there 

### are also avoided labor and variable operation and maintenance costs to be considend: The 

### fixed component isintended to 

### measure the benefit of reduced system load during peak 

### periods. Since

### electricitycannotnormallybe 

### stored on the large scale associated with utilities, all

### electriicity must 

### be generated at the same moment it is consumed. One way to measure the cost avoided l>ya lower

### peak demand is to estimatethe lowest possible .costof maintaining the cheapest generato]:availableon hand year-around simply to generate the last

### kWdemanded at the 

### peak hour. By convt:ntion, this hypothetical generatoris a lowcost gas turbine, and its capital cost per kW is called

### thc:gas turbine 

### proxy. 

### In additionto the generating capacityitself,lowering

### peak demand will also deliver a saving in the need for 

### gaktransmission and 

### distribution 

### (T&D) capacity, which should be 

### included in the 

### capacity savings.

. , , ' Am 

### appliance alternative efficiency level analysis typically relies on disaggregation by ' 

### National Electric Reliability Council (NERC) region of capacity savings and energy savings, followed by re-aggr&ation to thenational level. Disaggregation is necessary because different regions need capacity at different times,.and the relative energy savings attributableto each region for heating, cooling, and all other appliances vqxy substantially. For the purposes of calculating electric utility avoided costs, heatingappliances

### are defined 

### as electricheat pumps in heating mode and 

### electric resistance heat, coolingappliances

### are defmed as 

### room and central air-conditioningplus heat pumps in cooling mode, and baseload appliances are defined 

### as all other appliances affected by new efficiency levels, including all residential refrigeration.

VolumeI: GeneralMethodology Page E-1

, 

### APPENDIX E. ELECTRIC UTILITY IMPACT MODELING

E.I INTRODUCTION Jrhisappendix summarizes the methods used to calculate utility avoided costs for the energy savings lthat result from the impoSition ofappliance energy efficiency levels. Usually, the appliance being aJlalyzed is electrically fueled; however, efficiency levels imposed on gas or oil-fired applianc:es may also indirectly affect electricity demand. The conventions and background

. information used in the analysis, and the basis for valuing the'energy and' capacity savings ofthe

efficiency le.vels are explained. The goal ofthis analysis is to calculate thepeak and capacity savings and the avoided costs, in dollars per million Btu ($lMMBtu) ofelectricity saved, where the energy is expre!:sed as primary energy. Avoided costs contain both the marginal fuel and marginal**Capaei~** value ofeach kWh saved by the**al~emative** efficiency levels in each year ofthe period analyzed.


### E.2 AVOIDED COST: BACKGROUND AND CONVENTIONS

, . The analysis adopts the standard utility convention that the value of electricity savings, commonly called 

### avoided cost, can be broadly separated into 

### energy or variable cost' savings, and 

### capacity or fixed cost savings. The variable impact measures the production costs avoided**~y** , reduced electrical generatiQri. The largest variable component by far is the cost of the fuel not burned, but there are also avoided labor and variable operation and maintenance costs to be considem<:t The fIXed component is intended to measure the benefit of**~uced** system IQad during 

### peak periods. Since electricity cannot nonnally be stored on the large scale associate<i with utilities, all electrilcity must be generated at the same moment it is consumed. One way to measure the cost avoided by a lowerpeak demand is to estimate the lowest possible cost ofmaintaining the cheapest generator available on hand year-around simplyto generate the last kW demanded at the 

### peak hour. By**convc~ntion,** this hypothetical generator is a low-cost gas turbine, and its capital cost per kW is called**thc~** 

### gas turbineproxy. In addition to the generating capacity itself, lowering

### peak demand will also deliver a saving in the need for 

# peak transmission and distribution (1'&D) capacity. which should**b~** included in the capacity savings. An appliance alternative efficiency level analysis typically relies on disaggregation by National Electric Re,liability. Council (NERC) region of capacity savings and energy savings, followed by re-aggregation to the national level. Disaggregation is necessary because different regions need capacitY at different times, and the relative energy savings attributable to each region for heating, cooling, and all other appliances vary substantially. For the purposes of calculating elt:Ctric utility avoided costs, 

### heating appliances are defined as electric heat pumps in heating mode and electJic resistance heat, 

### cooling appliances are defmed as room and central air-conditioning plus heat pum.ps in cooling mode, and 

### baselocid appliances are defined as all other appliances affected by new efficiency levels, including all residential refrigeration.

Volume 1: General Methodology PageE-l

### E.3 

### ENERGY COST 

### SAVINGS- ' -.

.

### . 

### The energy value ofload shapemodificationsis 

### measured by the marginal cost of electricity, which depends on the variable cost of operating the power plants that will change their output in response to a change 

### in load. This variable cost consists primarily of the cost of fuel for these marginal units. 

### U.S. utilities 

### are characterizedby a substantial difference among the prices of fuels used to generate electricity on the margin. Marginal resources of a utility can 

### be 

### either more expensive oil and gas br less expensive non-oil-and-gas resounvs, typically coal or economy

. 

### . 

### purchaseofpower generated by coal. Very low variable-mst nuclear oinurof-riverhydro resounrs 

### are, usually base loaded and thus only rarely contribute to marginal energy costs. Even though the

-
. 

### relative cost of natural gas as-ageneration fuel has decreased in 

### recent years, and seasonal price

., 

### fluctuationsoften make itcheaperthan coalbought under long-termcontract, this oil and gas fraction approach is still valid because gas-frred resources 

### are more likely 'tobe 

### marginal- because of their greater operating flexibility. The relevant marginal cost is the cost of each marginal fuel weighted by the fraction of time each fuel is on the margin plus any variable operation and maintenance (O&M). Formally, this relationship can be 

### simplified as follows: Marginal Cost 

### ,=OGF, Natural Gas Price 

## +NOGF, 

# - Coal Price +Variable O&M To calculatethe 

### oil-and-gas fraction on the

### margin, OGF,,,, the total amount of oil- and gasgenerated electricityis divided by the total 'forecasted amount of electricitygeneration from NERC data (1) to obtain the 

### total oil 

### andgasfraction, OGF,. OGF, 

### can then 

### be related to OGF, with a simplified loadduration curve 

### (LDC) foreach region. Figure 

### E. 1

### shows this 

### LDC, where 

### B 'is the 0%. The non-oil-and-gas fraction (NOGF,) is simply 1

# - OGF,. The purpose of the following derivation,is to calculate the OGF,,,

### (B'). First, note that

and 

### B 

### = 1

### .O, which implies that A=BtanS2=tad 

### The normalized 

### area of the triangle at the top of the 

## LDC represents the fractionof total generation from oil and gas (OGF,). Calculatingthe area of.this triangle yields QGF, = 

### O.SA1B' = 

### O.5(B1td)B'

. 

### B 'can thus be 

### expressed as follows:
Page E-2 Volume 1: General Methodology E.3 ENERGY COST SAVINGS

, The energy value ofload shape modifications is ineasured by the marginal cost ofelectricity, which depends on the variable cost of operating the power plants that will change their output in response to a change in load. This variable cost consists primarily of the cost of fuel for these marginal units. ,U.S. utilities are characterized by a substantial difference among the prices offuels used to generate electricity on the margin. Marginal resources of**~** utility 

### can be either more expensive oil and gas or less expensive non-oil.-and-gas resources; typically ,coal or economy purchase of**po~er** generated by coal. Very low variable-cost nuclear orrun-of-river hydro resources are,usually base loaded and thus only rarely contribute to marginal energy costs. Even though the relative cost ofnatural gas as-a generation fuel has decreased in recent years, and seasonal price' fluctuations often make itcheaperthan coal bought under long-term contract, this 

### 011 and gaS fraction approach 

### is still valid because gas-fIred resources are more likely to be marginal,because of their greater operating flexibility. The relevant marginal cost 

## IS the cost ofeach marginal fuel weighted by the fraction oftime each**fu~l** is on the margin plus any variable 'operation and maintenance (O&M). Formally, 

### this relationship can be simplifIed as follows: Marginal Cost,= OGF m ' Natural GasPrice + NOGF m • Coal Price +Variable O&M To calculate th,e oil-and-gas fraction on the margin, OGF m, the total amount ofoil- and gasgenerated electricity is divided bythe total 'forecastecI amount ofelectricity generation from NERC data (I) to obtain the 

### total oil 

### andgasfraction. OGF,. OGF, 

### can then 'be related to OGE'". with a**~implilled** load-duration curve (LDC) for each region. Figure E.1 shows 

### this LOC. where 

### B I is the OGF m• The non-oil-and-gasfraction (NOGF m) is simply 1 - OGF m• The purpose ofthe following derivation is to calculate the OGF m (B '). First, note that

and B 

# =1.0, which implies that 

### tanO A B

A = B 

### tanO = 

### tanO
The normalized area ofthe triangle at the top oftheLOC represents the fraction oftotal generation from oil and gas (OGF,). Calculating the area of. 

### this triangle yields OGF, 

## = 0.5A'B' 

## = 0.5(B ,tanO}B, ,
B ' can thus be expressed as follows:
PageE.2 Volume 1: General Methodology

## Normalized Duration

. 

## (in fraction of a 

## year)



# . Figure 

### E,.l SimplifiedLoad-Duration Curve.
The appropriate value forA 

### =tan 

### dais determinedempirically:when 

## OGF, 2 

### 0.4, 

## OGF, = 1 

### .O. The value ofA. 

### must therefore be greater than or equal to 0.8. Note that 

### whenA 

### = 0.8, 

### Normalizedpeak demand 

### = 1.4 mean load and Normalized minimum load 

### = 0.6 mean load.

VolumeI: General Methodology Page E-3**~ ~**  0**~** 

# =**~** Q)

### ~ 

# 1.0 0 

# ..,**~** Q) N 

# .- 

# -**~** 

## E Iou4 0 

## Z 

# 0.0 B'=OGF

..

# =~2=,



# ---- ------- ------------------f

--------- ---- -------- 

# -r',' A'

...**~~---** 

# B'**----I~~**



# 0.0 B

Normalized Duration (in fraction ofa year) 

# !

1.0.

Figure E.l Simplified Load-Duration Curve.
The appropriate value forA 

# = tan 

### Ois detennined empirically: when OGF,**~** 

### 0.4, OGF m 

# =1.0. The value ofA. 

### must therefore be greater than or equal to 0.8. Note that when A 

# = 0.8, Normalized peak demand::: 1.4· mean load and Normalized minimum load 

# =0.6' mean load.

Volume 1: Ger.leral Methodology PageE-3

### A 

### weighted-averagemarginal energy cost is determined for each of the 

### NERC regions using the above formulas, and a national weighted-akge marginal cost for heating and baseload energy savings is calculated using the regional fuel price multipliers and energy savings fractiohs described above. This approach assumes that the marginal oil-and-gas fractions remain constant over the analysisperiod. For cooling energy savings, the appropriate marginal energy cost 

### (MEC) will exceed the annual average MEC because the 

### OGF, is typically higher in summerthan in winter. The annual average 

### OGF, is approximately

### 52% when calculatedusing 

### the energy savings fractions forheating, cooling, and baseload energy. Cooling MECs calculated using a national 

### OGF, of 

### 60% to account for the seasonal differences in marginal fuel usage. Table 

### E. 1 shows the .annual average 

### OGF, for each 

### NERC region. 

### Table 

## E.l Utility Marginal 

### OGFs and Year of Capacity 

### Need by NERC Region

The calculation 

### of MECs 

### in year) can be summarized 

### as follows: 6 NERC Marginal Year Capacity Region 

### OGF is Needed

N 

### OGF, = 

### ESF, 

# . OGF m.1. . L=l NPCC MAAC ECAR MAIN

SPP 

### SERC ERCOT MAPP WSCC National Avg.



### NOGF, = 

### 1 

### - OGF, MECj 

### = (OGF, gas pricej

## +NOGF, coal pricej 

## + Variable O&M,) 

### 1.06 0.88 0.61 0.16 0.19 0.77 0.47 0.92 0.15 055 

# - 0.52

Page E-4 now now now now 1999 now 1999 1997 1999

VolumeI: GeneralMethodology A weighted-,average marginal energy cost is detennined for 'each ofthe NERC regions using the above fonnulas, and a national weighted-average marginal cost for heating and baseload energy savings is calculated using the regional fuel price multipliers and energy savings fractiohs described above. This approach assumes that the marginal**oil~and-gas** fractions remain constant over the analysis period. For cooling energy savings, the appropriate marginal energy cost (MEC) will exceed the annual average MEC because the OGF m is typically higher in summer than in winter. The annual**~verage** OGF m is approximately 52% when calculat¢ using the energy savings fractions for heating, cooling, and baseload energy. Cooling MECs 

### are calculated using a national OGF m of 60% to account for the seasonal differeIicesin marginal fuel usage. Table E.1 shows the .annual average OGF m for each NERC region.



### Table E.I Utility Marginal OGFs 

### and Year of

### Capacity Need 

### by NERC Region
NERC Marginal Region OGF
NPCC 0.88 MAAC 0.61 ECAR 0.16 MAIN 0.19

SPP 0.77 SERC 0.47 ERCOT 0.92 MAPP 0.15 WSCC 0.55 National Avg. 

# . 0.52 The calculation ofMECs

# in year

### j can be summarized as follows:

PageE-4 N OGF rn 

## = 

# L ESFj • OGF rn. j 1=1 Year Capacity is Needed

now now now now 1999
now 1999 1997 1999

Volume 

### 1: GeneralMethodology, 

### where: i 

### denotes a particular

## NERC region, ' 

### ESF, represents the energy savings fraction for region 

### i, and 1.06adjusts for transmission and'distribution losses. Tlhe 

### ESF for region,i 

### is the sum of .productsof t-he heating, cooling, and baseload energy ' 

### savings fntctionswith the heating, cooling. and baseload factors. Variable OM is calculated using

' . 

### references 

### (2) and (3). Average O&M 

### (fixed and variable) for all fossil-fuel 

### fired steamelectric power plants forinvestor-owned utilities 1990is reported 

### in (4) 

### as 09$/kWh (in 1990 dollars). This can 

### be split into fixed and variable components using (5). The analysis uses the fraction of O&M that is variable for a supercritical coal steam unit 

### (50%)and for a natural gas combustion turbine (:!O%), 

### weights these by the appropriate marginal oil and gas fractions, and applies the resulting l~rcentage (variableOM 

### as a percent of-totalOM) to the total O&M (O.5@kWh). This calculatio~n results 

### in a variable 

### O&M cost of 0.33$/kWh (in 

### 1990dollars). 

### In keeping with industry practice, labor and other variable cost savings &e 

### ignored in this analysis. 

### This practice has arisen fmt,because labor costs 

### are very low relative to fuel and capital, and, seco:nd,because the regulatory treatment of labor cost recovery in the general rate case cycle makes savings difficult to estimate.

. 

### E.4' 

### CAPACITY 

## COST SAVINGS 

### E.4.1 Value of Capacity Savings The value of capacity 

### is the value associated with having additional generating capacity

, 

### availableI:O

### meet load increases. For utility systems with excess capacity, this value may be equal. ' 

### to or close: to 

### zero. When capacity does 

### have value, this value

### is commonly measured by the cost of the margir~al investment 

### a utility would make to expand system capacity, such as the purchase of a combusticlnturbine 

### (6). Thisinvestment

### addsreliability to the 

### systemeven if it

### never 

### operates, 

### since

' 

### it is ready to prevent outages if anothergeneratingunit fails. If 

### the utility chooses to build another

type of plant with higher capital costs, such 

### as a baseload coal unit, it must be basing this choice on considerations other than.meeting

### peak demand alone. Baseload coal plants, for example,can have' ' 

### lower operating costs than gas-fired generation and can be used to displacegenerators with higher

. 

### variablecost,per 

### kwh.The additional capital costof acoal plant is thus not related to capacity alone but to enera benefits 

### as well 

### (7). The use of capacity valuation based on the gas turbine proxy, therefore, has a rationalebehind it and is widespread in the industry.

. . 

### The additional coal plant capital cost, sometimes called energy-related capital, is contained implicitly in the marginal energy cost described above. Consider a utility with 

### a large number of expensive oil and gas units on the margin and dwindling adjusted reserves. Such a utility would need capacityto preserve the system's reliability at

### 'an acceptable level. It would also benefit from installing 

### a baseload unit that would reduce oil and 

### gas consumption at the margin. This reduction Volume 1: GeneralMethodology

. Page ES · where: i denotes a particular NERC region, 

### ESF1represents the energy savings fraction for region i, and 1.06 adjusts for transmission and'distribution losses. The 

### ESF for region. i is the sum of_products of the heating, cooling, and baseload energy . savings fractions with the heating, cooling, and baseload factors. Variable O&M is calculated usjilg references (2) and (3). Average O&M (fixed and variable) for all fossil-fuel fired steam-electric pOwer plalIlts for investor-owned utilities in· 1990 is reported in (4) as 0.5¢IkWh (in 1990 dollars). This can be split into fixed and variable components using (5). The analysis uses the-fraction of O&M thnt is variable for a supercritical coal steam unit (50%) and for a natural gas combustion turbine (20%), weights these by the appropriate marginal oil and gas fractions, and applies the

, resulting percentage (variable O&M as a percent ohotal O&M) to the total O&M (0.5¢IkWh). This

calculatioln results in a variable O&M cost ofO.33¢IkWh (in 1990 dollars).

- - 

### In**kec~ping** with industrY practice, labor and other variable cost savings are ignored in this analysis. This practice has arisen first, because labor costs are very low relative to fuel and capital, and, second, because the regulatory treatment oflabor cost recovery in the general rate case cycle makes savings difficult to estimate.

E.4

-CAPACITY COST SAVINGS E.4.1 Value ofCapacity Savings The value of capacity is the value associated with having additional generating capacity

. available to meet load increases. For utility systems with excess capacity, this value may be equalto orclose: to zero. When capacity does have value, this value is commonly measured by the cost of the marginal investment a utility would make to expand system capacity, such as the purchase of a combusticln turbine (6). This investment adds reliability to the system even 

### if itneveroperates, since it is ready to prevent outages if another- generating unit fails. Ifthe utility chooses to build another type ofpblDt with higher capital costs, such as a**basel~acl** coal unit, it must be basing this choice on considerations other than-meeting 

### peak demand alone. Baseload coal plants, for example, can have

-

. lower operating costs than gas-rued generation and can be used to displace generators with higher variable cost perkWh. The additional capital cost ofa coal plant is thus not related to capacity alone but to energy benefits as well (7). The use of capacity valuation based on the gas turbine proxy, therefore, has a rationale behind it and is widespread in the industry. The additional coal plant capital cost, sometimes called energy-related capital,.is contained implicitly in the marginal energy cost described above. Consider a utility with a large number of expensive oil and gas uni!S on the margin and dwindling adjusted reserves. Such a utility would need capa(:ity to preserve the system's reliability at'an acceptable level. It would also benefit from installing (l baseload unit that would reduce oil and gas corisumption at the margin. This reduction Volume 1: General Methodology PageE-S

### in fuelcosts compensates for expending capital in addition to the cost of a combustion turbine. A 

### utility with coal on the margin a large fraction of the time would not have the same incentive and would probably install a combustion turbine to preserve reliability. The utility will choose to have abaseload plant come on line when the present value of the energy-related capital cost of the plant isjust equal to the present value ofthe reduction 

### in fuel costs from the operation of 'the plant. The reduction 

### in fuel costs and hence the incentive to build a baseload plant depend on the marginal oil and gas 

### fraction. Thus, the marginal energy cost contains 

### an avoided capital component in addition to an avoided fuel component.
, 

### The following ho rules 

### are applied to determine the value of capacity for the year 1998 based on DOESinterpretationof

### NERC data 

### (8): 1. 

### If the 

## NERC region is forecastto have an adjusted reserve margin larger'than 

### 5%of forecasted

### peak demand, there is no reliability value for capacity.' 2. If 

### the 

### NERC region is forecast to have a capacity deficiency (operating adjusted reserves of less than 5%),

### the capacity value for the combustion turbine is applied. After 1998,

### DOE has not calculated adjusted reserve margins and it 

### is assumed that every region will need capacity. Table 

### E. 1 

### shows the 

### year that each region will need capacity based on these rules. The calculations

### in this analysis follow standard industry practice developed for detemning the 

### value of reductions in 

### peak load caused by cogenerators and other small power producers (9). 

### First, the present value ofrevenue requirements is calculated for a combustion turbine based on its capital cost ($400 per 

### kWin 1990dollars), 

### a discount rate equal to the utility rate 

### of disadvantage or ROD (10.0%). a fixed charge 

### rate (13.0%): a turbine lifetime of 

### 25 

### years, and the assumption of straight-linedepreciation. The calculatipn 

### is repeated for

### each

### year of the analysis assurxring no real capital cost escalation for the combustion turbine. Next, the present values 

### are spread over time using 

### an economic carrying charge (ECC). 

### The 

### ECC technique yields a current dollar stream of costs, escalating at 

### an inflationrate of 

### 5%that has the same present value, when discounted at the

. . 

### ' An equivalent rule of thumb for adequatereliabilityis that the overall reserve margin should equal about 20%of forecasted peak demand. Adjusted reserves are defined by 

### DOE as [Planned Capacity 

### + Net Power Transfers(in) 

### - (Forced Outages 

### + ScheduledMaintenance

### +Other Outages)

### - Forecasted Peak Demand]. Overall reserves are defined as [Planned Capacity 

### + Net Power Transfers(in)

## - Forecasted Peak Demand. This analysis does not account for $e possibility that new efficiency levelscould cause a significantchange in a region'sadjusted reserves, and hence change the date when capacity is needed. 'The estimates of ROD and fixed charge rate have been calculatedbased on current interestrates and the corporate tax rate (34%)adopted in the 1986 tax revisions. The standard utility capital structure fromEPRI is used (45%debt, 10% preferred stock!and 10% commonstock). Nominal costs ofcapital are 10%for debtand preferred stock and 13.4% for common stock. Page E-6 VolumeI: General Methodology in "fuel costs compensates for expending capital in addition. to the cost ofa combustion turbine. A" " utility with coal on the margin a large fraction ofthe time would not have the same incentive and would probably install a combustion turbine to preserve reliability.

. The utility will choose to have a baseload plant come on line when the present value ofthe energy-related'capital cost ofthe plant isjust equal to the present value ofthe reduction in fuel costs from the operation of the plant. "The reduction in fuel costs and hence the incentive to build a baseload plant depend on the marginal oil and gas traction. Thus, the marginal energy cost contains an avoided capital component in addition to an avoided fuel component. The following two rules are applied to determine the value of capacity for "the year 1998 based" on DOE's**interpretati~n** ofNERC data (8): I. 

### Ifthe NERC region is forecast to have

# an adjusted reserye margin larger'than 5% of forecasted 

### peak demand, thereis no reliability value for capacity. 1 2. 

### If the NERC region is forecast to have a capacity deficiency (operating adjusted reserves ofless than 5%), the capacity valueJor the combustion turbine is applied.

After 1998, DOE has not calculated adjusted reserve margins and it is assumed that every . region will need capacity. "Table E.l shows the year that each region will need capacity based on these rules.

- The calculations in this analysis follow standard industry practice developed for detennining the value ofreductions in 

### peak load caused by cogenerators and other small power producers (9). First, the present value ofrevenue requirements is calculated for a combustion turbine based on its capital cost ($400 per kW in 

### 1990 dollars), a discount rate equal to the utility rate ofdisadvantage or ROD(10.0%), a fixed charge rate (13.0%),2 a turbine lifetime of25 years, and the assumption of straight-line depreciation. The calculation is repeated for each year ofthe analysis assuming no real capital cost escaiation for the combustion turbine. Next, the presentvalues are spread over time using an economic carrying charge (ECC). The ECC technique yields a current dollar stream of costs, escalating at an inflation rate of5% that has the same present value, when discounted at the

, An equivalent rule of thumb for adequate reliability is that the overall reserve margin should equal about 20% of forecasted peak demand. Adjusted reserves are defined by DOE as [Planned Capacity + Net Power Transfers(in)

- (Forced Outages + Scheduled Maintenance + Other Outages)

- Forecasted Peak Demand]. Overall reserves are defined as [Planned Capacity + Net Power Transfers(in)

- Forecasted Peak Demand. This analysis does not account for**~e** possibility that new efficiency levels could cause a significant change in 

## a region's adjusted reserves, and hence change the date when capacity is needed.

2 The estimates ofROD and fixed charge rate have been calculated based on current interest rates and the corporate tax rate (34%) adopted in the 1986 tax revisions.· The standard utility capital structure from EPRI is used (45% debt, 10% preferred**stock~** and 10% common stock). Nominal costs ofcapital are 10% for debt and preferred stock and 13.4% for common stock. PageE.6 Volume 1: GeneralMethodology, . 

### ROD, 

### as the original stream. A 

### stream ofcosts that escalates at the inflation

### .rateis also constant in real 

### terms, so this technique levelizesthe costs to some constant dollar value ($47.07 per 

### kWIyr a '.

### . in 1990clollars). This cog is then converted to dollars per

## kWh of electricity savings by.themethod detailed in the next section. 

### E.4.2- 

### A,mountof Capacity 

### Savings- Am 

### analysis of the load shape impacts of improved efficiencies for residential appliances must considerboth 

### the magnitude and timing of the impacts relative to periods 

### of high system loads -*because :residentialclass load shape changes only have reliability value for the system when they coincidewith times of high system loads. Thisrelationship is approximated from modeling runs of the 

### LBNlLHourly and 

### PeakDemand Model (10) and from two 

### LBNLutility case studies (1 1),;(12). 'I'hene 

### arelargebut varying degrees of coincidencebetween residential

### peak loads and system peak 

### loacis. The &e studies showthat the average change 

### inthe 250highest residential hourly loads is a reasonable measure of the system-levelcapacity value of applianceefficiency levels.

d 

### The results of these studies also indicated that load shape impacts, measured as a conservation load factor 

### (CLF), vary in a predictable fashion across utility service territories. A 

### conservation load factor is the annual average load savings divided by the 

### peak savings of the conservation measure (13). Peak savings 

### are defined as the forecasted differences between the average of the highest 250 hourly loads before and after the appliance efficiency levels are implemented. Thepeak savings for residential appliances calculated using the 

## LBNLResidential Hourly and Beak Demand Model. For the purposes of this analysis, the U.S. is assumed to be a summer peaking system. While there 

### are individual utilities in the U.S. which are not summer peaking, these are unusual and, by and large, small.

.. 

### . 

### Conservation load factors can be used to characterize the peak load impacts of heating, cooling, imd baseload energy savings' More formally, a conservation load factor can be expressed as:



# . CLF = 

### Annual Load Savings 

### Peak Load Savings Multiplying both 

### ~umerator and denominatorby 

### 8760hours gives: Annual Energy Savings Peak Load Savings =

, 

### CLF 8760



### CLF 

# . 8760 = 

### Annual Energy Savings Peak Load Savings

VolumeI: GeneralMethodology

. Page E-7 ROO, as the original stream. A stream ofcosts that escalates at the inflation .rate is also constant in real tenns, so this technique levelizes the costs to some constant dollar value ($47.07 per kW 

### Iyr a in 1990 dollars). This**co~t** is then converted to dollars per

### kWh ofelectricity savings by the method detailed in the next section.

. 

### E.4.2· Amount of Capacity Savings. An analysis of the load.shape impacts of improved efficiencies for residential appliances must consider both the magnitude and timing ofthe impacts relative to periods ofhigh system loads .' because :residential class load shape changes only have reliability value for the system when they

coincide with times ofhigh system loads. This relationship is approximated from modeling runs of the LBNlL. Hourly and Peak Demand Model (10) and from two LBNL utility case studies**(11),.(12~.** lhereare large but varying degrees ofcoincidence between residential 

### peak loads and system peak loads. The case studies showthat the average change in the 250 highest residential hourly loads is a reasonable measure ofthe system-level capacity value ofappliance efficiency levels. The results' of these studies also indicated that load shape impacts. measured as a conservation load factor (CLF), vary in a predictable fashion across utility service territories.' A conservation load factor is the annual average load savings divided by the peak savings of the conservation measure .(13). Peak savings 

## are, defined as the forecasted differences between the average of the highest 250 hourly loads before and after the appliance efficiency levels are implemented. The peaksavings for residential appliances are calculated u.sing the LBNL Residential Hourly and Peak Demand Model. For**~e** purposes of this analysis, the U.S. is assumed to be a summer peaking system. While there are individual utilities in the U.S. which ,are not summer peaking. these are unusual and, by and**large~** small.

. . Conservation load factors can be used to characterize the peak load impacts of heating, cooling, ;md baseload energy savings: More formally. a conservation load factor can be expressed 

### as: eLF 

## = Annual Load Savings Peak Load Savings Multiplying both numerator and denominator by 8760 hours gives: Peak Load Savings

CLF·8760

Volume 1: General Methodology = Annual Energy Savings CLF

. 8760

= Annual Energy Savings Peak Load Savings

Page B·7

### The 

### peak load savings 

### can be expressed as a function of the annual energy savings and the CLF. Another useful form of this equation is as 

### follows: CLF = 

### Annual Energy Savings Peak Load Savings 

# . 8760

CLF 

### 8760 =kwh of energy savings needed to yield 

### 1kW of 

### peak load savings. The conservationload factorsused forall productswith alternative efficiency levels 

### are listed in Table 

### E.2. Conservation load factors for mobile home furnaces, pool heaters, and direct heating equipment 

### are undefined 

### as they do not contribute to savings in 

### peak load. 

### Table 

## E.2 Conservation

### Load Factors AUProducts Analyzed

Page E-8

. 

### Appliance Cooktop/Oven/Microwave

Room 

### Air Conditioner Pool Heater Direct Heating Equipment

Mobile Home Furnaces Oil & 

### Gas Water Heaters Central Air Conditioner Lamp Ballast Clotheswasher

Clothesdryer Dishwasher ElectricWater Heater Refrigerator Television

Volume1: GeneralMethodology 

### ConservationLoad Factor 0.46 

### 0.14



## --


## -- - 0.89 0.15

.

- 

### 0.57



## . 0.63 0.63 0.76 0.89 0.89 0.95 The peak load savings can be expressed as a function of the annual energy savings and the CLF. Another useful form ofthis equation is as follows:

. CLF = Annual Energy Savings Peak Load Savings' 

### 8760

CLF

. 

### 8760 

# = kWh ofenergy savings needed to yield 

### 1 kW ofpeak load savings. Theconservation load factors used for all products with alternative efficiency levels 

### are listed in Table E.2. Conservation ioad factors for mobile home furnaces, pool heaters, and direct heating equipment 

### are undefmed as they do not contribute to savings in peak load. 

### Table E.2 Conservation Load 

### Factors AlI·Producfs Analyzed**~** Appliance
.Conservation Load Factor Cooktop/OvenlMicrowave 

### 0.46 Room 

### Air Conditioner 

### 0.14 Pool Heater 

# -- Direct Heating Equipment 

# --

Mobile Home Furnaces

. 

## - Oil & Gas Water Heaters 

### 0.89 Central Air Conditioner 

### 0.15 Lamp Ballast 

### 0.57 Clotheswasher " 

### 0.63 

## . Clothesdryer 

### ·0.63 Dishwasher 

### 0.76 Electric Water Heater 

### 0.89 Refrigerator 

### 0.89 Television 

### 0.95

PageE.8 Volume 1: General Methodology

### Tlherefore, the calculation of avoided capacity costs can be 

### summarized in the following equation:



### - Capacity Value ARc~~~~ 

## - CLF,,,, 

# . 8760 0.3 1.2 

## Z ESFBm
where:



### =Avoided reliability costs 

### in 1990 

### $/MMBtu in year

### j, 

### ~~C~mebd~ Capacity Value 

### =value of 1

### kW of 

### peak 'load savingsin 1990 

### $/kW/yr, . 

### 0.3 

### =energy conversion efficiencyincluding 

### T&D losses, 

### CLFSmelawi =conservation load factor forbaseload appliances, 1.2 

### =reserve marginadjustmentneedk for adequatereliability, 

### xEsf'~mi!! =sum of baseload ~nergy Saving Factors forregions that .needcapacity in yearj.

. 

### Capacity savingsare peak load savings for only those regions that need capacity in a given year. 

### Load savings in regions that do not need capacity contribute to deferring capacity additions in futureyears, but this analysis does not account forthe value of such deferrals..


### Table E.3 

### Sums ofBaseload Energy SavingsFractions for

## NERC Regio'ns

, . 

### That

### Need Capacity

I I

. . Volume I: GeneralMethodology 

### Year

. 

### 1998

. 

### 1999 

### 2000

Page E-9

. . 

### Baseload

. 

### ESF

. 

### 0.596 1.000
1

# .ooo Regions Needing Capacity 6

, . 

### ,9

a 

### 9 Therefore, the calculation of avoided capacity costs can be summarized in the following equation:
ARCBft••1oadJ = Capacity Value

. 0.3

. 1.2

. 

### :E ESF

.

- CLF

. . 8760 Baseload,J Baseload where:
ARCBaseloadJ

. 

# = Avoided reliability costs in 1990 $lMMBtu in yearj, Capacity Value 

### = value of 1 kW of

### peak load savings in 1990 $IkW /yr, 0.3 

# = energy conversion efficiency including T&D losses,



### = conservation load factor for baseload appliances,

, , 1.2 

### = reserve margin.adjustment needed for adequate reliability,



### = sum ofbaseload Energy Saving Factors for regions that need capacity in

yearj.

.**C~Lpacity** savings·are peak load savings for only those regions that need capacity in a given year. 

### Load savings in regions that do not need capacity contribute to deferring**capaci~** additions

. . 

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


### Table E.3 Sums ofBaseload Energy Savings Fractions for NERC Regions

. That Need 

### Capacity Regions Year Baseload Needing ESF Capacity

.. 1998 0.596 

### 6
1999 1.000 

# .. .9 

### 2000 1.000 9

Volume 1: General Methodology Page E·9

### E.43 Avoided Transmission and Distribution Capital Costs Recently, utility planners have begun to account forcapital costs ofthe T&D systemthat can

, 

### be avoided by reductions in electricity use. There 

### are a wide range of estimates of such costs,

dependingon theparticularcharacteristicsofthe utility 

### system and of the consewationprograms and 

### policies that lead to such reductions 

### in electricity use. Krause et

### al. (14) review studies on this topic and settleon arange forT&D savingsof from $10

### to $40kWlyr. Krauseet 

### al. also citereasons why this range is probably too low and suggest that furtherwork will probably raise these values. For example, they cite marginal T&D cost estimates of nine differentutilities that (on average)totaled more than $lOOkW/yr. We adopt the lower end of Krause et 

### al.'s range ($lOkWlyr) 

### as our estimate for avoided 

### T&D capital costs. We choose this lower bound estimate because of the substantialuncertainties in applying suchestimates (which 

### are derived at the level of individual utilities) in national analyses.

Page E-I0 Volume1: GeneralMethodology 

### E.4.3 Avoided Transmission and Distribution Capital Costs Recently, utility planners have begun to account for capital costs ofthe T&0,system that can be avoided by reductions in electricity use. There,are a wide range of estimates of such costs, depending on the particular characteristics ofthe utility system and ofthe conservation progi'ams and policies that lead to such reductions in electricity use. Krause et aI. (14) review studies ,on this topic and settle on a range for T&0 savings offrom $10 to $4OIkW /yr. Krause etaL also cite reasons why this range is probably too low and suggest that further work will probablyraise these values. For example, they Cite marginal T&0 cost estimates ofnine different utilities that (on average) totaled more than $1OOIkW/yr. We adopt theJower end of Krause et al.'s range ($lOIkW/yr) as our estimate for avoided T&D capital costs. We choose this lower bound estimate because ofthe substantial uncertainties in applying such estimates (which are derived at the level 'ofindividual utilities).in nationai analyses.

Page E-I0, Volume 1: GeneralMethodology

### 1. North American ~lectric~eliabili~ Council (NERC). 1990; 

### ~lectricity Supply and

### Demamf for 

### 1990-99. Princeton, NJ. 2. ' 

### ,Electric Power Research Institute (EPH). 1989.' 

### TAG-~echnical Assessment Guide: Vol.I: EJectricity Supply-1

### 989. Palo Alto, CA. EPRl P-6587-L. September 1989. 3. U.S. Department of Energy. 1992. 

### Electric Plant Cost and Power Production Expenses 

### 1!290..Energy Information Administration. Washington, D.C. DOE~EIA-0455(90). June. . . 

### 4. 

### U.S. Department of Energy. 1990. 

### StaflReport:ElectricPower Supply and 

### Demand for the Contiguous UnitedStates, 

### 1989-98. Washington, D.C. DOEIIE-0008. 

### 5. EPRI., 1989. 

### Op. cit. 

### 6. ~ational Economic Research Associates. 1977. 

### How to Quantifjl Marginal Costs: Topic 4. 

### PdoAlto, CA. 

### EPRI Report No. EURDS-23.

. . 

### 7. Ibid. 8. 

### US.Department of Energy, 1990. 

### Op. cit. 9. 

### Ibid. 10. Verzhbinsky, G., 

### H. ~udeian, 

### and M. D. LRvine. 1986. 

### The Residential Hourly and Peak Demand Model: Description and Validution, Lawxence Berkeley *Laboratory Report L.BL18698.. Berkeley, CA'. 11. Eto, 

## I., J. Koomey, 

### J. McMahon, 

### P. Chan. 1986. 

### Financial Impacts on Utilities of toad 

### Shape Changes: Tk Nevada 

### Power company. Lawrence Berkeley Laboratory Report 

### U3G2 1597. Berkeley, CA.

. 

### . 12. 

### Eto, J., J. 

### Koomey, J. 

### McMahon, P. Chan. 

### 1986. 

### Financial Impacts on Utilities of 

### Load 

### Sllape Changes: The Texas Utilities Electric Cornpanj, Lawrence Berkeley Laboratory Report LBL-2 1598. Berkeley, CA. 13. Koomey, 

### J., A. 

### H. Rosenfeld and A. 

### K. Gadgil. 1990. Conservation Screening Curves to Compare Efficiency Investments to Power Plants.

### Energy Policy. vol. 18, no.8. p. 

### 774.

' 14. 

### Ksause, 

### F., 

### J:F. Busch and 

### J. G. 

### Koomey. 1992.

### Incorporating Global WarmingRisks 

### in Power Sector Planning: A 

### Case Study of the New England Region. Lawrence ~erkele~ ' 

### Laboratory LBG30797 (vols. 

### I and 11). 

### November, 1992. Berkeley, CA.

Volume 1: ,General Methodology Page E-11**REFERl~NCES** 

### 1, North American Electric Reliability Council (NERC). 1990: Electricity Supply 

## arid Demand

. fG'r 

### 1990·99. Princeton, NJ.

. 2. ,Electric Power Research Institute (EPRI). 1989: TAG-TechnicalAssessment Guide: Vol. I: EJectricity Supply·1989. 

### Palo Alto, CA. EPRI P-6587-L. September 1989. 3. U.S. pepartment of Energy. 1992. Electric Plant Cost and Power Production Expenses 

### 1990.. Energy Information Administration. Washington, D.C. DOFJEIA-0455(90). June. 4. U.S. Department ofEnergy. 1990. Staff Report: Electric Power Supply andDemand for the Contiguous United States, 

### 1989·98. Washington, D.C. DOElIE-0008. 5. EPRI, 1989. Op. cit.

. . 

### 6. National Econoinic Research Associates. 1977. How to Quantify Marginal Costs:· Topic 

### 4. Palo Alto, CA. EPRI Report No.**EURDS~23.** 

### . 7. Ibid. 

### 8. 

## U.S. Department ofEnergy, 1990. Op. cit. 9. Ibid. 

### 10.**V(~rzhbinsky.** 

### G.• H. Ruderman. and M. D. Levine. 1986. The Residentiallfourly and**Pl~ak** Demand Model: Description and Validation, 

### Lawrence Berkeley Laboratory

.**Rt~port** 

### LBL-18698

.. Berkeley, CA.

. . 11. Eto, ] .• J. Koomey. 

### J. McMahon. P. Chan

.. 1986. Financial Impacts on Utilities of Load Shape Changes: The· Nevatlo Power Company. 

### Lawrence Berkeley Laboratory Report U3L-21597. Berkeley. CA. 12. Et.o. Jl., J. 

### Koomey. J. 

### McMahon, P. Chan. 1986. Financial Impacts on**Utilitie~** ofLoad Shape Changes: The Texas Utilities Electric**Company~** 

### Lawrence Berkeley Laboratory**R(~port** 

### LBL-21598. Berkeley. CA. 13. Koomey. 

# J.: A. H. Rosenfeld and A. 

### K. Gadgil. 1990. Conservation Screening Curves to Compare Efficiency Investments to Power Plants. Energy Policy. 

### vol. 18, no.8. p. 774. 14. Krause, F., J.F. Busch and J. G. Koomey. 1992.**Incorporat~ng** ($lobal Wanning Risks in Power Sector Planning: A Case Study ofthe New England Region. 

### Lawrence Berkeley uLboratory LBL-30797 (vols. I and 

# m. November. 1992. Berkeley, CA.

Volume 1: ·GeneralMethodology Page £·11

### DOE/EA-XXX 

## EW.CR(3NMENTAL 

## ASSESSMENT 

## FOR 

## ALTERNATIVE EFFICIENCY 

## LEVELS FOR ROOM 

## AIR CONDITIONERS AND 

## KITCHEN 

## RANGESAND 

## OVENS

. . . . 

### 1. 

### INT.RODUCTION

## AND NEED 

### FOR PROPOSED ACTION This Environmental ~sseisment 

### (EA) on the candidate alternative efficiency levels forroom 

### air conditionersand kitchen ranges and ovens was prepared pursuant to the National Environmental policy Act of 1969 (NEPA), regulations of the Council on ~nvimnmental Quality, 

### and Title 40,

.
' 

### Code of :FederalRkgulations, Parts 1500through 1508.-The-candidate alternativeefficiency levels

. 

### arebeing analyzedpursuant to the Energy Policy and ConservationAct, 

### as amendedby the National Energy Conservation Policy Act'and the National ApplianceEnergy Conservation Act (1): The EA presents the results of the associated environmental impacts from new candidate alternativeefficiencylevels fortwo types ofhousehold appliances: room 

### airconditionersand kitchen 

### : ranges mid ovens. Each measure ofpossible environmental change is an altemative action, and it iscompared to what is expected to happen if no new efficiency levels for these products were implemented, i.e., the "no action" altemative. The primary environmental concern addressed is atmospheric einissions .both from fossil-fueiledelectricitygeneration and 

### fromcombustion 

### in 'thehome. The design options forroom. air conditioners and kitchen ranges and ovens result in .decreasedelectricity use.and, therefore, a reduction 

### in power plant emissions. The altkmative efficiency levels will generallyd~crease air pollution by decreasing 

### future energy demand; 

### 'Amajor benefit of lower energyuse is the reduced need to elnit sulfurdioxide, SO,. However, the Clean 

### Air Act Amendments (1990)placed anational

. .



### . ceiling on emissions of this acid 

### rain precursor. 

### In this report, reductions in SO2

### are reported; however,, these should be interpreted 

### as a reduced needto pollute, rather than reduced physical. emission!;. ~eductiork of nitrogen oxides (NO,) 

### and carbon dioxide emissions (CO,) 

### will actually occur as il

### result ofthe new efficiency levels and are listed by weight ofNO, and CO,, respectively. 

### NO, also contributesto acid de'&sition and is apncursor to urban photochemical smog, 

### as well 

### as

. 

### being directly 

### harmful if breathed. CO, emissions 'from fossil-fuel burning is considered 

### an environmentalhazard 

### because itcontributesto the ''greenhouse effect" by trapping heat energy from the earth 

### that is emitted 

### as infrared radiation. 

### The greenhouseeffect is expected to gradually raise the mean global temperature.

., . 

### Although the quantity of raw materials used per appliancewill remain relatively constant, in most scenarios increasedinitial cost is expected to slightlydeaeasethe number of appliances sold; resulting in small decbesin raw materials used. The main effect of the.applianceproduction decrease is reduced 

### SO, emitted in steel production. That reduction is small, however, in ' 

### comparison to the SO2

### decreases 

### from avoided fuel-buming at power plants. 

### The contribution from

VolumeI: EnvironmentalAssessment PageEA-I DOElEA-XXX 

### ENVIRONMENTAL ASSESSMENT FOR ALTERNATIVE EFFICIENCY

. LEVELS FOR ROOM AIR CONDmONERS AND KITCHEN RANGES AND OVENS
1. INTRODUCTION AND NEED FOR PROPOSED ACTION

.

, This Environmental Assessment (EA) on the candidate alternative efficiency levels for room air conditioners and kitchen ranges and ovens was prepared pursuant to the National Environmental Policy Act of 1969 (NEPA). regulations of the Council onEnvironmentai QUality. and Title 40•. Code of Federal Regulations. Parts 1500 through 1508. The candidate alternative efficiency levels are being analyzed pursuant to the Energy Policy and Conservation Act, as amended by the National Energy Conservation Policy ACfand the National Appliance Energy Conservation Act (1);

. The EA preSents the results of the associated environmental impacts from new candidate alternative efficiency levels for two types ofhousehold appliances: I'<?0m airconditioners and kitchen " ranges and ovens. Each measure of·possible environmental change is an alternative action. and it .is**comp~tred** to what is expected to happen if no new efficiency levels for ,these products were implemented. i.e., the "no action" alternative. The primary environmental concern addressed is atmospheric einissionsboth from fossil-fue:led electricity generation and from combustion in the home. The design options for room' air conditioners and kitchen ranges and ovens result in ,decreased electricity use, and. therefore. a reduction 

### in power plant emissions. The alternative efficiency levels will genenl1lydecrease air pollution by decreasing future energy demand: . A major benefit oflower energy use is the reduced need to elllit sulfur dioxIde, 

### S02' However, the Clean**~** Act Amendments (1990) placed a national ceiling o.n emissions of this acid rain precursor. 

### In this report. reductions in 

### S02 are reported; however,. these should be interpreted as a reduced need'to pollute. rather than reduced physical. emissions. ReductionS ofnitrogen oxides (NO,) and carbon dioxide emissions (C02) will actually occur as n result ofthe new efficiency levels and are listed by weight ofNOx and CO2, respectively. NOx also contributes to acid deposition and is a precursor to urban photochemical smog. as weil as being directly harmful if breathed

.. CO2 emissions from fossil-fuel burning is considered an environmental hazard because itcontributes to**th~** "greenhouse effect" by trapping heat energy from the 

### earth 1that is emitted as infrared ra:diation.The greenhouse effect is expected to gradually raise the mean global temperature. Although the quantity ofraw materials used per appliance will remain relatively constant, in most scenarios increased initial cost is expected to slightly decrease the number ofappliances sold, resulting in small decreases in raw materials used. The main effect of the appliance production decrease is reduced 

### S02 emitted in steel production. That reduction is small, however. in comparison to the 

### S02 decreases from avoided fuel-burning at power plants. The contribution from**Volum~** 1: En\'ironmentalAssessment PageEA

...1

### steelproduction is not included in the estimates for net SO, 

### dsrrascsnsulting fromdesign changes

. 

### . in these products. The focus of the environmental analysis is onjust two 

### airpollutants, SO, and NO,, because the effects ,of changes in total electric power generation 

### are primarily seen in emissions of these pollutants. In-house combustion is an important source of NO,. Additionally, since the greenhouse effect is of such major internationalconcern, the effect on emissions of CO, is also 

### reported. 

### - Of the six criteria pollutants, power generation ,contributes major shares (70% 

### and 32%, 

### respectively) of total 

## U.S. SO, and NO, emissions,but only 

### 2% oflead emissions, and insignificant amounts ofparticulate', carbon monoxide (CO), and volatile organic compounds (VOCs) 

### (2). 

### Inhouse combustion is an important contributorto overall NO, 

### emissiBns 

### in urban areas, which can 

### be important during smog episodes, but is 

### an in~i~cant source of SO, and the other criteria pollutants. Power generation contributes 34% of all U.S. CO, emissions, and in-house combustion 

### 19%(3). Reductions in particulate emissions accompaniedby decreases in SO, and NO, would have other beneficial effects on the environment. The resultant improvement to air quality and the dec& potential of acid rain formation would help improve the qualityof wetlands and fish and wildlife as well as 

### aid in the preservation of historical and archaeological sites. Reductions in NO, emissions within warm urban 

### areas is particularly beneficialbecause it is an urban smog precursor 

### as well 

### as an acid rain precursor, and an 

### airpollutant 

### in its 

### own right. Reduced in-house fuel consumption will decrease the amount of gas or oil burned within some homes, thereby decreasing the impact of combustion on indoor

### airquality. Indoorair

### problems are usually due to acombination offactors, including

### atight house envelope, insufficient ventilation forcooking appliances, presence of sources such 

### as cigarette smokers or formaldehyde-containing products, and radon diffusion from soil. 

### In comparison to the above factors, and because fuelburning appliances 

### arenormallyvented to the outside,the projected changesin in-house fuel-burning applianceuse is expected to have little effect on indoor 

### air quality.
2. 

### METHODS OF ESTIMATING ENVIRONMkNTALIMPACTS The greatest impacts of implementing the new efficiency levels would be a reduction in electricity demand growth. 

## The main environmental effects ofpowerplants on air 

### and water quality result from emissions of SO,, NO,, and CO,. With the alternativeefficiency levels lessening the need for electricity generation, powerplant emissionswould be 

### reduced. A 

### second source of these pollutants is fuel-burning household appliances. Pollutants from fuel-burning household appliances will be 

### termed "in-house" emissions, and are reported below. 

# ' Based on the PM-10-plus PM-I0fugitive dust definition.



## . .IPage EA-2 Volume 1: Environmental Assessment steel production is not included in the estimates for net 

### S02 decreases resulting from design changes in these products.

. . The fOCus ofthe environmental analysisis onjust two airpollutants, 

### S02·and NOx, because the effects 'of changes in total electric power generation 

### are primarily seen in emissions of these pollutants

.. In-house combustion is an important source ofNOx' Additionally, since the greenhouse effect is ofsuch major international concern, the effect on emissions ofCO2 is also reported.

. Of the six criteria pOllutants, power.generation ,contributes major shares (70% and 32%, respectively) oftotal 

### U.S. S02 and NOx emissions, but only 2% oflead emissions, and insignificant amounts ofparticulateI , carbon monoxide (CO), and volatile organic compounds (VOCs) (2). Inhouse combustion is an important contributor to overall NOx emissibns 

### in urban areas, which can be important during smog episodes, but is an insignifiCant source of 

### S02 and the other criteria pollutants. Power generation contributes 34% of all 

### U.S. CO2 emissions, and in-house combustion 19% (3). Reductions in particulate emissions accompanied by decreases in 

### S02 and NOx would have other beneficial effects on the environment. The resultant improvement to air quality and the decreased potential ofacid rain formation would help improve the quality ofwetlands and fish and wildlife as well as aid in the preserVation ofhistorical and archaeological sites. Reductions in NO x emissions within warm Ul'ban areas is particularly beneficial because it is an urban smog precursor as well as an acid rain precursor, and an air pollutant in its own right. Reduced in-house fuel consumption will decrease the amount of gas or oil burned within. somehomes, thereby decreasing the impact ofcombustion on indoor airquality. Indoor airproblems are usually. due to a combination offactors, including a tight house envelope, insufficient ventilation for cooking·appliances, presence ofsources such as cigarette smokers or fonnaldehyg.e-containing products, and radon diffusion from soil. In comparison to the above factors, and because fuelburning appliances are normally vented to the outside, the projected changes in in-house fuel-burning appliance use is expected to have little effect on indoor air quality

.



### . 2. METHODS OF ESTIMA TI:NG**~NVIRONMENTAL** 

### IMPACTS The greatest impacts of implementing the new efficiency levels would be a reduction in electricity demand growth. The main environmental effects ofpower plantS on air and water quality result from emissions of 

### S02' NOx, and CO

### 2 , With the alternative efficiency levels lessening the need for electricity generation, power plant emissions would be reduced. 'A second source ofthese pollutants is fuel-burning household applianCes. Pollutants from**fuCl-bum~ng** household appliances will be termed "in-house" emissions, and are reported below.

I Based on the PM-lOpJus PM-lO fugitive dust defjnition. Volume 1: EnvironmentalAssessment

### Note that the effect of implementing new efficiency levels will not be 

### to reduce emissions in all cases. There 

### are two major ways in which emissions can rise as the result of 

### an imposed efficiency level. First, the changing economics'ofappliancesmay cause consumers to fuel-switch or applial&-switch. For example, higher 

### momair conditioner prices could prompt a switch to central air 

### conditioning, raising both overall energy use and emissions. Second,the efficency level may also change the thermodynamics of the home. For example, more efficient cooking appliances could lower the heat released in-house, increasing the load on the heating system. 

### If the. homeis

. 

### heated with oil, the effect would be to raise.S02 emissions.

. . 

### 2.1 

### Baselline

### Emissions Icithe 

### ServiceReportthat accompanies the 1991 National Energy Strategy

# m), the impact on power plant emissions as 

### a result ofrevisions to Title 

### V of the 1990 Clean,AirAct 

### Amendments, 

### P. 

### L. 101-549,

### are estimated (4). The emission foht that 

### appears 

### in that report is presented below as Table 11. 

### NO, emissions 

### arerequired to be reduced by 1.8 Mt (2 million short tons)by the year 2000 under the legislation. However, no emissions cap exists forNO,, and the goals of the legislation 

### are implemented through command-and-control emissions rates that vary by fuel burned and type of boiler. Therefore, while NO, emissions per 

## kwh generated 

### are expected to fall, they will fall less than emissions of SO,. Also, it is 

### less clear whether the Act's emissionsreduction target will be 

### met because enforcement

### is indirect, through various performance 

### stan&. 

### In this case, it is reasonable to 

### report predicted reductions 

### in physical NO, emissions as 'aiesult ofthe appliance efficiency levels, and that qpproachis adopted here. Tkr c&e of SO2 emissions is more complex. The legislation calls for2S0 emissions reductions in.two phases. 

### .Inthe first phase of the reductions (beginning December 

### 31, 1995, and carrying through the year 2000). electric utilities will have several options for Mucing their SO2 emissions to comply with the allowance constraints imposed by the provisions of the Clean Air Act. 

### The major options 

### a& 1)to decrease their use of high-emission units and increase the use ' 

### of their clean 

### units, 2) 

### to switch units using high-sulfur coal to low-sulfur coal, 3) 

### to retrofit plants emitting iit a high rate with emissions-reduction technologies (e.g., scrubbers), 

### 4) t~.~urchase, allowanct:~ 

### from other utilities who reduce their emissions below their permitted levels, and 5)

### to purchase power rather than generate it.

.

### Most utilities will make use of a combination of these options to minimize the cost of complying with the allowance constraints. Total SO2 ehissionsby utilities cannot exceed 8.1 Mt (8.9 million short tons) after December 31,2000. 

### Xn the second phase of the planned reductions (beginning December 

### 31,2000). the options available to electric utilities for maintaining the 8.1 Mt SO2emissions cap will broaden with the expected introduction.of new, advanced generating technologies. However, during this period utilities &e 

### less able to reduce emissionsby changingthe way they utilize their plants; Since most plants will:be 

### fully utilized, there will be few opportunities forreducing emissionsby decreasing the use of a hiigh-emissionplant or for furtherfuel switching.

. . Volume I: EnvironmentulAssessment PageEA-3 Note iliat the effect ofimplementing new efficiency levels will not be to reduce emissions in all**casl~S.** There are two major way's in which emissions can rise as**~e** result of an iinposed efficiency level. First. the changing economics of appliances may cause consumers to fuel-:switch or appliance-switch. For example. higherroom . air conditioner prices could prompt a switch to central ail' conditioning. raising both overall energy use and emissions. Second, the efficency level may also c:hange the thermodynamics ofthe home. Forexample, more efficient cooking appliances could lower the heat released in-house, increasing**th~** load on the heating system. 

### Ifthe homejs heated with oil, the effect would be to raise,S02 emissions.

.
2.1 Baselline Emissions In the Service Report that accompanies the 1991 National Energy Strategy (NES), the**impa~t** on power plant emissions as a result ofrevisions to Title V ofthe 1990 CleanAir Act Amendments, P. L. 101-549. are estimated (4). The emission**fo~t** that appears in that report is presented below as Table ll. NOx emissions are required to be reduced by 1.8 Mt (2 million short tons) by the year 2000 under the legislation. However, no emissions cap exists for NOx • and the goals ofthe legislation are implemented through command-and-control emissions rates that vary by fuel burned and type of boiler. Therefore. while NOx emissions per kWh generated are expected to fall, they will fall less than emissions of S02' Also. it

### is less clear whether the Act's emissions reduction target will be met because enforcement 

### is indirect, through various performaIice 

### stand8rds. In this case. it is reasonable

, to report predicted reductions in physical NOx emissions asaresult ofthe appliance efficiency levels,

and that approach is adopted here. The case of S02 emissions is more complex.

_ The legislation calls for2SO emissions reductions in'two phases.

_ 

### In the first phase of the plann¢ reductions (beginning December 

### 31, 1995. and carrying through the year 2000), electric utilities will have several options for reducing their S02**c~missions** to comply with the allowance constraints imposed by the provisions ofthe Clean Air Act. 'irhe major options are 1) to decrease their use ofhigh-emission units and increase the use· oftheir**clc~** units, 2) to switch units using high-sulfur coal to low-sulfur coal, 3) to retrc?fit plants emitting at a high rate with emissions-reduction technologies (e.g.• scrubbers). 4) to**purchase~** allowancc:s from other utilities who reduce their emissions below their permitted levels. and'?) to purchase power**rathe~** than generate it.

-Most utilities will make use of a combination of these optioQ.s to minimize the cost ofcomplying with the allowance constraints. Total S02 erilissions by utilities cannot exceed 8.1 Mt (8.9 million short tons) after December 

### 31,2000.

. . In the second phase ofthe planned reductions (beginning December 

### 31, 2000), the options available to electric utilities for maintaining the 8.1 Mt 

### S02**e~ssions** cap will broaden with the**expecte~** introduction of new. advanced generating technologies. However. during this period utilities ar.e less able to reduce emissions by changing the way they utilize their plants; Since most plants wilI be fully utilized, there will be few opportunities for reducing emissions by decreasing the use of a hiigh-emission plant or for further fuel switching. Volume I: Environm!!ntalAssessment PageEA-3

### The adoption or non-adoption of the efficiency level forroom air conditioners and kitchen ranges and ovens will likely not affect the physical emissions of SO2,which will hover near the ceiling. This is not to say that there is no SO, emissions benefit to 

### be derived from the lowered electricity demand implied by appliance efficiency levels. Actual physical emissions will not be 

### lowered,but the demand for SO, allowancesby electricity generators will be reduced, resulting in lower allowance prices, and lower electric utility compliance costs. 

### In other words, lowered generation is a costless contribution towards the SO, clean-up 

### required by the Act. Estimating these effects 

### as they reverberate through SO, 

### allowance trading, however, is too ambitious for this analysis. Here, therefore, emissionsreductionsby weight 

### are simplyestimated and reported, 

### as if 

### the allowance trading market did not exist. Further, no effort is made to estimate the reduced compliance cost, which may ultimately benefit utility companies, their stockholders, or their ratepayers. The actual distributionofbenefits depends upon the regulatory regime. 

### In the Service Report accompanying'the 1991

## NES,two possible outcomes 

### are presented, a flexible case and a restricted case, so that 

### the effect of different levels 

### of permitted trading of emission allowances 

### canbe 

### evaluated. (Thereport does not go beyond this explanationin defining the differencesbetween the twocases.) As presented in the report, the results forthe two cases are 

### 'virtually identical. Because the two cases 

### are so similar, only the U.S. power plant emission projections for the 

### thne effluents under the assumptions made in the flexible case are presented.


### Table EA-1. 

## -US. CO,, SO2,

### and 

### NO, Power Plant Emissions

2.2 

### sulfur~ioxide and 

### ~itroien 

### oxide Emissions Year 1995 2000 2010 2020 2030



### In order to capture the effects of cleaner-burningpower plants in futmyears, emission rates (tons1Quad) for power plant fuel-burning 

### are cdculated from projected emissions and electrical generation data. The electrical generation data is translated below into energy use (Quads) by assuming a 30%overall energy conversion efficiency. The source of these projected emissions and electrical generation data is the Service Report. Table 2 presents these data ind the calculated emission rates for SO2and NO,.

Page EA-4 co2 

### I

## 06 short tons 2233 2506 3219 3964 4804

VolumeI: Environmental Assessment 

# so2 106 

### short tons 13.8 9.0 8.4 

### 6.7 4.8 NO, 106

### short tons 8.4 

### 6.7 7.3 

### 6.7 5.9 The adoption or**~oil-adoption** ofthe**effici~ncy** level for room air conditioners and kitchen ranges and ovens will likely not affect "the physiCal emissions of S02' which will hover near the ceiling. This is not to say that there is no S02 emissions benefit to be derived from the lowered electricity demand implied by appliance efficiency levels; Actual physical emissions wiil not be lowered, but the demand for S02 allowances by electricity generators will be reduced, resulting in lower allowance prices, and lower electric utility compliance costs. 

### In other words, lowered generation is a costless contribution" towards the S02 clean-up required by the Act. EStimating these "effects as they" reverberate through S02 allowance trading."**however~** is "too ambitious for this analysis. Here, therefore, emjssions reductions by weight are simply estimated and reported, as ifthe allowance trading market did not exist. Further, rio effort is made to estimate the reduced compliance cost, which may ultimately benefit utility companies, their stockholders, or their ratepayers. The actual distribution ofbenefits depends upon the regulatory regime. 

### In the Service Report accompanying"the 

### 1991 

## NEs, two ppssible outcomes are presented. a flexible case and a restricted case, so that the effect of different**lev~ls** 

### ofpermitted trading of eIIlission allowances can be evaluated. (The report does not go beyond this**expl~ation** in defining the differences between the twoc8.ses.) As presented in the rePort, the results for the two cases are virtually identical. Because the two cases are so similar, only the U.S. power plant emission projections for the three effluents under the assumptions made in the flexible case are presented.

Table 

### EA·l.U.s. COl' SOl' and NOs Power Plant Emissions CO2 S02 NOx Year 

### ](1 short tons 

### ](1 short tons 1(fshort tons 

### 1995 2233 13.8 8.4 

### 2000 2506 9.0 6.7 2010 3219 8.4 

### 7.3 2020 "3964 6.7 6.7 2030 4804 4.8 

### 5.9

2.2 SulfurDioxide and Nitrogen Oxide Emissions In

### ~rder to capture the effects ofcleaner-burning power plants in future years, emission rates (tons/Quad) for power plant fuel-burning are**c~culated** from projected emissions and electrical generation data. The electrical generation data is translated below into energy use (Quads) by assuming a 

### 30% overall energy conversion efficiency. The source ofthese projected emissions and " electrical generation data is the Service Report. Table 

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

PageEA-4 Volume 1: EnvironmentalAssessment

### TableEA-2. Electricity Generation, EmissionsData; and Emissions Rates 

### for SO2

### and NO,

, ' 

### at 

### Fossil Fuel-BurningPower Plants

,



### Year 

# I109kWh ricity Generation Energy 

# I use Emissions Rates Oil 

### ~o~k~h

Tlhe calculated emissions rate data listed in Table 

### 2 represents the average SO2and NO, emissions rates for all fossil fuel-burning power plants in the United States. Emissions rates were not calculated for each fuel-burning source because the emissions data supplied by the Service Report 

### were not disaggregated accordingto powerplant 

### type (i.e., coal, oil, gas). To obtain emission 

### rate values, the amountof emissionswas dividedby the total energy use of fossil fuel-burning power plants. Tlne total 

### energy use by fossil fuel-burning power plants was calculated from the electrical generation data supplied by the ServiceReport. 

### The electrical generation data was disaggregated by 

### fuel source. To obtain the total energy use (input),the electrical generation data 

### from each fossil fuel sounx was summed 

### and then divided by the assumed efficiencyof fossil fuel-burning power plants (301%), which includes transmission and distribution losses. This fossil fuel-burning power plant effkiencyisconsistentwith that used by the 

### LBNL Residential Energy Model 

### (LBNL-REM). The amount of SO, and NO, 

### emissions abated for any particular year is determined by multiplyillg the estimates of energy saved through reduced electricity generation in that year by the emission rate for that particular year. For years not covered in the Service Report, linear Gas 

### 109kwh

interpolationwas used to deriveemission rates and, in turn, the corresponding abated emissions. Total ~wrds



### Table 3

### presents the emission factors (rates)that were used for SO, and NO, forin-house gas and oil combustion. The values for reduction of SO2and NO, emissions from in-house gas 

### and oil combustion

### are produced by multiplying in-house fuel savings for gas'and oil by the corresponding , 

### emission rates. Emission factors that appear. in Table 3 

### are from a 

### Lawrence Berkeley National Laborato~y report 

### (5). Emission factors for gas in-house combustion were assumed to equal the average 

### ofthose for residentialgas space heaters and water heaters. Emission factors foroil in-house combustion were assumed to equal those associated ~5th a residential #2 

### oil boiler.

VolumeI: ~nvironmental hsessment PageEA-5 

### Table EA·2. Electricity Generation, Emissions**Data~** 

### and Emissions Rates for S02 

### and NOx 

### at Fossil Fuel.Buming Power Plants

, Electricity Generation Energy Emissions Emissions Rates Use (primary) Coal 

### Oil Gas Total 

## sen NOx 

## sen NOx Year 

### 10 9 

### kWh**l~kWh** 

### 10 9 

### kWh Quads 

### 1Cfshort 1Cfshort 1(j tons/ 1(j tons/ tons tons Quad Quad t995 1l602.2 193.7 442.0 25.74 13.8 8.4 536.1 326.3 2000 1l814.0 179.8 605.0 29.89
..9.0 6.7 301.1 224.1' 2010 2660.6 149.9 482.5 37.88 ' 8.4 7.3 221.8 192.7 2020 3727.8 67.2 292.3 47.01 6.7 6.7 142.5 142.5 2030 4837.3 ' 29.0 179.2 58.03 4.8 5.9 82.7 101.7 The calculated emissions rate data listed in Table 

### 2 represents the average S02 and 

### NO x emissions rates for all fossil fuel-burning power plants in the United States. Emissions rates were not calculated for each fuel-buming source because the emissions data supplied by the Service

, Report**w(~re** not disaggregated according to powerplant type (Le., coal, oil, gas). To obtain emission

.

rate values, the amount ofemissions was divided bythe total energy use offossil fuel-burning power ·plants. The total energy use by fossil fuel-buming power plants was calculated from the electrical generation data supplied by the Service Report

..The electrical generation data was disaggregated \ by fuel source. To obtain the total energy use (input), the electrical generation data from each fossil fuel soun:e was summed and then divided by the assumed efficiency offossil fuel-1?uming power plants, (30%). which includes transmission and distribution losses. This fossil fuel-buming power' plant efficiency is consistent with that used bythe LBNL Residential Energy Model (LBNL-REM). The amount of S02 and NOli emissions abated for any particular year is determined by multiplying the estimates ofenergy saved through reduced electricity generation in that year by the emission rate for that particular year. For years not covered in the Service Report, linear interpolation was used to derive emission rates and, in turn, the corresponding abated emissions

.



### . ' Table 3 presents the emission factors (rates) that were used for S02 and 

### NOz for in-house gas and oil combustion. The values for reduction ofS02 and 

### NOxemissions from in-house gas and oil combusti()n are produced by multiplying in-house fuel savings for gas' and oil by the corresponding

,emission rates. Emission factors that appear in Table 3 are from a Lawrence Berkeley National Laboratory report (5). Emission factors for gas in-house combustion were assumed to equal the average o:fthose for residential gas space heaters and water heaters. Emission factors for oil in-house combustit)n were assumed to equal those associated With a residential #2 oil boiler.

Volume 1: EnvironmentalAssessment PageEA-S

### Table EA-3. Emission 

### Rates for 

### SO, and NO, from In-House Combustion SO, Gas Emission SO, Oil Emission NO, Gas Emission NO, Oil Emission



### lo' 

### tons/euad lo' 

### tons/euad lo' 

### tonr/euad 

### lo' tons/Qd 0.0 156.5 52.5 65.0



### - 2.3 

### Carbon Dioxide Emissions Emissioniates for C02 were derived

### in the same manner as those derived for SO, and NO,. As 

### presented in Table 1, 

### the ServiceReport accompanying the.1991 

### NES also provided emissions

. 

### data with 

### regard to COP2 Table 4 

### presents the C02 emission

### rate data 

### as derived from tbe electrical generation data and emissions data suppliedby the report.


### Table EA4. Electricity Generation Data, Emissions Data, and Emissions Rates for CO,,

### at Fossil Fuel-BurningPower Plants

As with the SO2 and NO, emissions,the amount of CO, emissions abated for any particular year is determined by multiplying the estimates of energy saved through reduced electricity generation by the emission rate for that particular 

### year. For years not covend in the service~e~ort, linear interpolation was used to derive emission rates and, in turn, the corresponding abated

I. 

### emissions. h



### Year 1995 2000 2010 2020 2030


### .

From phone conversationswith David Streets at Argonne National Laboratory (February 1992). it was determined that the carbon emissions data provided in the Service Report accompanying the 1991 NES were mistakenly reported as tons ofcarbonemitted. David Streets was one of authors at Argonne who contributed to the report.
Page EA-6 Volume 1: EnvironmentalAssessment 

### Emission 

# co2 106 

### short 

### tons 2232.5 2506.2 3219.3 3964.2 4804.4 

### Energy Use Total 

### Quads 25.74 29.89 37.88 47.01 58.03 

### Electricity Generation Emission Rate co2 

### 106 

### tons/Quad 86.37 83.85 85.00 84.32 82.79 

# - Coal 1@kWh

1602.2 1814.0 2660,6 3727.8 4837.3 

### Oil l@kWh

193.7 179.8 149.9 67.2 29.0 

### Gas l@kWh

442.0 605.0 482.5 292.3 139.2 

### Table EA-3. Emission Rates for 

### S02 andNO. ·from In-House Combustion
S02 Gas Emission

](j tons/Quad 

### 0.0 S02 Oil Emission

.](j tons/Quad 

### 156.5

2.3 Carbon Dioxide Emissions NO. Gas Emission J(j tons/Quad 

### 52.5 NOx Oil Emission 

### .1 (j.tonS/Quad 

### 65.0

Emission rates for CO2were derived in the same manner as those derived for S02 and NOx• As presented in Table 1, the Service Report accompanying the. 

### 1991 NES also provided emissions data with regard to CO2 

### ? Table 4 presents the CO2emission rate data as derived from the electrical generation data and emissions data supplied by the report.
.


### Table EA-4.Electricity Generation Data, Emissions Data, and Emissions Rates for CO2 

### at



### . Fossil Fuel-Burning Power Plants

. . Emission Electricity Generation Energy Use CO2 Emission Rate

.. Coal Oil Gas Total 

### ](1 

### soon CO2 Year ](jJ kWh ](jJ kWh ](jJkWh Quads tons 

### ](1 tons/Quad 

### 1995 1602.2 193.7 442.0 25.74 2232.5 86.37 2000 1814.0 179.8 605.0 29.89 2506.2. 83.85 2010
. 2660.6 149.9 482.5 37.88 3219.3 85.00 ,.- "'1 

### 2020 3727.8 67.2 292.3 47.01 3964.2 84.32 2030 4837.3 29.0 119.2 58.03 4804.4 82.79

.. As with the S02 and NOx emissions, the amount ofCO2emissions abated for any particular

.year is determined by multiplying the estimates of energy saved through reduced electricity generation by the emission rate for that particular year. For years not covered in the Service Report, linear interpolation was used to derive emission rates and, in tum, the corresponding abated emissions.

2 From phone conversations with David Streets at Argonne National Laboratory (February 1992), it was determined that the carbon emissions data provided in the Service Report accompanying the 1991 NES were mistakenly reported as tons ofcarbon emitted. David Streets was one ofauthors at Argonne who contributed to the report. PageEA-6 Volume 1: EnvironmentalAssessment

# -

.I ' 

### Talble 5 

### presents,theemission factors (rates)that were used for CO, for in-house gas and oil combusticw. The values for the reduction of CO, emissions from in-house gas and oil combustion are produ!qed by multiplying in-house fuel savings for gas and oil by the corresponding emission rates. Emission factors that appear in Table 

### 5 are from a Lawrence Berkeley National Laboratory report 

### (6). The emission factor for gas in-house combustionwas assumed to equal ,the average of those for residential gas space heaters and water heaters. The emission factor for oil in-house combustion was assumed to equal the one associated with a residential #2 

### oil boiler.


### TableEA-5. Emission Rates for CO, 

### from 

### In-House Combustion 

# .

. .

' . 

### CO, Oil 

### Emission ' 

### CO, Gas Emission.

. 

### 1

### O-' tondQuud lO-' 

### tons/Quad 55.000 

### 84.300 I 

### 3: 

### RESULTS

. 

### Thefollowingresults in Tables 6through 25 indicatethe 

### potential changes in amounts of emitted 

### CO,, SO,andN0, resulting

# hornnew efficiency levels for the appliances considered in this analysis. A 

### table is,presentedfor each of the alternativeefficiency levels. Each table details the changes that occur to each of the three emissions (i.e., CO,, SO,, andNO,) through the imposition of a new efficiency level for this type of appliance. Each table shows, for a specific year between 2000 and 2030, '.

### the amount 

### of emission abated from power plant generation, the amount abated fromin-house generation, the net change

### in the emission, and the percent the net change comprises of total 

### U.S. residential emissions. Also included 

### are the cumulative changes of each pollutant (between the years 

### 2000 and 2030). Fclr each section that follows, only the results from the highest efficiency level 

### are discussed for each appliance. 

## In orderto view the results for each efficiency level, tables for each level are 

### provided afterthe discussion. It 

### :should be noted that the alterantiveefficiencylevels studied are 

### not consistentbetween the two appliances. The number of efficiencylevels that are analyzed 

### for aparticular appliancedepends on the nurnber and 

### type of technologies that were considered forit. For a detailed explanation of the specific ttxhnologies considered for each appliance, please refer to the appropriate sections and appendices of each product-specific Part 

### in this report.

VolumeI: EnvironmentalAssessment Page EA-7 J Talble 5 presents,the emission factors (rates) that were used for CO2for in-house gas and oil combustion. The values for the reduction ofCO2emissions from in-house gas and oil combustion are produ!:::ed by multiplying in-house fuel savings for gas and oil by the corresponding emission

- rates. Emission factors that appear in Table 5 are from a Lawrence Berkeley National Laboratory report (6)

.. The emission factor for gas in-house combustion was assumed to equal the average of those for residential gas space beaters and water heaters.

.The emission factor for oil in-house combustion was assumed to equal the one associated with a residential #2 oil boiler.

. , 

### Table EA-S. Emission Rates for CO2 

### from In';House Combustion

3.· RESULTS CO2Gas Emission, J(j tons/Quad 55,000 , CO2Oil Emission 

### 1(j tons/Quad 

### 84,300

The following results in Tables 6 through 25 indicate the potential changes in amounts of emitted CO2 , S02,andNOxresulting from new efficiency levels for the appliances considered in this analysis. A table is .presented for each of the alternative efficiency levels. Each table details the changes that occur to each ofthe three emissions (Le., CO2 ,S02' andNOx) through the**i~position** of a new**(~fficiency** level for this type of appliance. Each table shows, for a specific year between 

### 2000 and 

### 2030,·the amount of emission abated from power plant generation, the amount abated from in-house generation, the net change 

### in the emission, and the percent the net change**~omprises** of total U;S. residential emissions. Also included are the cumulative changes of each pollutant (between the years 2000 and 2030). FClr each section that follows. only the results from the.highest efficiency level are discussed for each appliance. In order to view the results for each efficiency level, tables for each level are provided after the discussion. It should be noted that the alterantive efficiency levels studied are not consistent between the two appliances. The number ofefficiency levels that are analyzed for a particular appliance dePends

. on the number and 

### type oftechnologies that were considered for it. For a detailed explanation ofthe

specific**tc~hnologies** considered for each appliance. please refer to the appropriate sections ·and appendices ofeach product-specific Part in this**~port.**

Volume 1: EnvironmentalAssessment PageEA-7

### 3.1 ROOM 

### AIR CONDITIONERS' 3.1.1 SulfurDioxide and Nitrogen Oxide Emissions Sulfur dioxide emissions would be decreased by a cumulative total of up to 33 kt (37 thousand shorttons) between 2000 and 2030 in the level 

### 5 scenario. 

### In the year 2000, decreases in SO, will represent about 0.03% of the SO, emissions estimatedto come from the residential sector in that year. 

### In the year 2030, decreases in SO, emissions will represent about 0.10% of the SQ emissions estimated to come from the residential sector 

### in that 

### year. As discussed earlier, the

. . 

### possible reductions of SO2 emissions caused by new efficiency levels will reduce the utility's need to purchase allowances or permit it to save them for future use, orsell them. To the extent saved allowances 

### are used for future emissions, the 

### efficiency levels' net effect on those SO2 emissions would be only 

### a reduction 

### in the demand for emissions allowances. Level 5design changes to room 

### airconditioners would result in 

### an estimated decreasein 

### NO, emissions of 5 1kt 

### (56thousand shorttons) between 2000 and 2030. NO, decreases would represent 0.03% and 0.12% of the NO, emissions estimated to come from the residential sector in the years 2000 and 2030, respectively.

. 

### 3.2.1 Carbon Dioxide Emissions The cumulativereduction 

### in COzemissionsfromlevel 

### 5 design changes is 5 1Mt (56 million short tons).



# - 3.3 RANGES AND OVENS ~ecrekes in the amounts of CO,, SO,, and NO, 

### are presented for cooktops, conventional ovens, and microwave ovens. Alternativeefficiency levels 1

### through 5

### for cooktops

### are summarized in Tables 11 through 15, alternative efficiency levels 1 through 

### 5 for conventional ovens 

### are summarized in Tables 16through 20, and alternative efficiency levels 1through 

### 5 for microwave ovens 

### are summarizedin Tables 21 through 25. 

### 3.3.1 SulfurDioxide and Nitrogen Oxide Emissions 

### cooktops Need for sulfur dioxide emissions would be 

### decreased by a cumulative total of up to 67 kt 

### (73 thousand short tons) between 2000 and 2030 in the level 5 

### scenario. 

### In the year 2000, decreases in sulfur dioxide will represent about 0.02% of the SO, emissions estimated to come from.the residential sector in that year. 

### In the year 2030, decreases in SO, emissions will represent about 0.10% of the 

### SO, emissi~ns~estimated to come from the residential sector in that 

### year.

.

### As

, 

### discussed PageEA-8 Volume 1: EnvironmedAssessment 

### 3.1 ROOM AIR CONDmONERS'

. 3.1.1 Sulfur Dioxide and Nitrogen Oxide .Emissions Sulfur dioxide emissions would be decreased by a cumulative tO,tal of up to 

### 33 let 

### (37 thousand short tons) between 

### 2000 and 

### 2030 in the level 

### 5 scenario. In the year 

### 2000, decreases in S02 will represent about 

### 0.03% ofthe S02emissions estimated to coine. from the residential seCtor in that year. In the year 

### 2030, decreases in S02emissions will represent about 

### 0.10% of the 

## sq emissions estimated to come from the residential sector. in that year. As discussed earlier, the possible reductions of S02emissions caused by new efficiency levels will reduce the utility's need to purchase allowances or permit it to save them for future use, or sell them. To the extent saved allowances are used for future emissions, the efficiency levels' net effect on those S02 emissions would be only a reductiCln in the demand for emissions allowances. Level 5 designchanges to room airconditioners would result in an estimated decrease in NOx emissions of

### 51 kt 

### (56 thousand short tons) between 

### 2000 and 

### 2030. NOx decreases would represent 

### 0.03% and 

### 0.12% ofthe NOxemissions estimated to come from the residential sector in the years 

### 2000 and 

### 2030, respectively.

. 

### 3.2.1 ,Carbon Dioxide Emissions The cumulative reduction in CO2 emissions from level 

### 5 design changes is 

### 51 Mt 

### (56 million short tons).

. . .


### 3.3 RANGES 

### AND OVENS Decreases in the amounts of CO2 , S02' and NO" are presented for cooletops,**co~ventional** ovens, and microwave ovens. Alternative efficiency levels 1through 5 for cooletops are summarized in Tables 

### 11 through 15, alternative efficiency**l~vels** 1 through 5 for conventional' ovens are summarized in Tables 1 

### fl through 

### 20, and alternative efficiency levels 1 through 5 for microwave ovens are summarized in Tables 

### 21 through 

### 25.

. 

### 3.3.1 Sulfur Dioxide and Nitrogen Oxide Emissions Cooktops Need**~or** sulfur dioxide emissions would be decreased by a cumulative total ofup to 

### 67 kt 

### (73 thousand short: tons) between 

### 2000 and 

### 2030 in the level 

### 5 scenario. In the year 

### 2000, decreases in sulfur dioxide will represent about 

### 0.02% of the S02 emissions estimated to come from. the residential sector in that year. In the year 

### 2030, decreases in S02 emissions will**repr~seni** about 

### 0.10% ofthe**S02emissions~estimated** to come from the residential sector in that year..**~** discussed fage EA-8 Volume 1: EnvironmimtalAssessment

.

### earlier, the possible reductions of S0,emissions caused by new efficiency levels can be 

### earned 

### as excess adlowances. To the extent these allowances 

### are used for future emissions, the efficiency ' . , 

### levels' net effect on those SO, eniissions would be 

### only a postponement.

I 

### live1

### 5 design changes to c00kt~~s would result in 

### an estimated decrease

### in NO, emissions of65 kt 

### (72 thousand short tons) between 2000and 2030. N0,decreases would repment 0.02% and

. . 

### O.lO%'ofthe NO, emissions estimated to come from the residential sector in the years 2000 and 2030, respectively.

* 

### ConvenrknalOvens

. . 

### Sulfur dioxide emissions would be decreased by a cumulative total of up to 241 kt 

### (266 thousancl short tons) between 2000 and 2030

### in the level 

### 5 scenario. 

### In the year 

### 2000, decreases in sulfur dioxide will represent about 0.05% .of the SO, emissions estimated to come from the ' 

### residenti.alsector 

### in that year. 

### In the 

### year 2030, decreases in SO, 

### emissions will represent about 0.43% of the S0,emissions estimatedto comefrom the residential sector in 

### that 

### year. As discussed

' 

### above, tl~e possible reductions of SO2 emissions caused by new efficiency levels can be earned as excess allowances. To the extent these allowances 

### are used for future kmissions, the efficiency levels' net effect on those SO2 emissions would be 

### only apostponement. Level 

### 5 design changes to conventionalovens would result in 

### an estimated decrease in NO, emissions of 239 kt 

### (263 thousand short tons) between 2000 and 2030. NO, decreases would represent 0.05% and 0.41% of the NO, emissions estimated to come from the residential sectorin the years 2000 and 2030,~.respectively. 

### Microwave Ovens sulfur dioxide emissions would be decreased by 

### a cumulative total of up to 53 kt (58 thousand shorttons) behueen 2000 and 2030 

### in the level.5 scenario. 

### In the year 2000, decreases in sulfur dioxide will represent about 0.02% of the SO2emissions estimated to come from the resident@ sector in that year; 

### In the year 2030, decreases in SO, emissions will.represent about 0.08%

### of'the 

### S0,ernissions estimated to come 

### from the residential sectorin that year. As discussed above, the possible reductions of SO, emissions caused by new efficiency levels can 

### be earned 

### as excess allowances. To the extent these allowances 

### are used for future emissions, the efficiency levels' nct effect on those SO, emissions would be only apostponement. I 

### Levef 5 

### design changes to microwave ovens would result in 

### an estimated decrease in NO, emission:; of48 kt 

### (53 thousand short tons) between 2000 and 2030. NO, decreases would represent 0.0256 ar~d 0.07% of the NO, emissions estimated to come from the residential sector in the years 2000 and 2030, respectively.

. , 'Volume1: EnvironmentalAssessment

.- 

### . PageEA-9 earlier. the possible reductions ofS02emissions caused by new**effi~ien~y** levels can be earned as 'excess alilowances. To the extent these allowances are used for future emissions. the efficiency' levels' net effect on those S02 emissions would be only a postponement. ' LevelS design changes to cooktops would result in an estimated decrease in NOJ; emissions of65 kt (72 thousand short tons) between 2000 and 2030. NOxdecreases would represent 0.02% and 0.10%'o,f the NOxemissions estimated to come from the residential sector in the years 2000 and 2030, respectively.



### ,Conventional Ovens

, Sulfur dioxide emissions would be decreased by a cumulative total of up to 241 kt (266 thousand short tons) between 2000 and 2030 in the levelS scenario. 

### In the year 2000, decreases in sulfur dioxide will represent about 0.05% 'of the S02 emissions estimated to come from the residenti.al sector in that year. In the year 2030, decreases in S02emissions will represent about 0.43% olfthe S02emissions estimated to come from the residential sector in that year. As discussed above, the possible reductions of S02emissions caused by new efficiency levels can be earned as excess allowances. To the extent these allowances are used for future emissions, the efficiency levels' net effect on those S02 emissions would be only a postponement. levelS design changes to conventional ovens would result in an estimated decrease in NO" emissioDIS of 239 kt (263 thousand short tons) between 2000 and 2030. NOx decreases would represent: 0.05% and 0.,41 % ofthe NOxemissions estiIrtated to come from the residential sector in the years 2000 and 2030,, respectively. , . 

### MicrowQ:ve Ovens Sulfur dioxide emissions would be decreased by -a cumulative total of up to 53 kt (58 thousand short tons) between 2000 and 2030 in

# the level,S scenario. 

### In the year 2000, decreases in sulfur dioxide will represent about 0.02% of the S02 emissions estimated to come from the residenti:3:l sector In that year: In the year 2030, decreases in S02emissions will, represent about 0.08% ofthe S02emissions estimated to cOme from the resIdential sector in that year. As discussed above, the possible reductions of S02emissions caused by new efficiency levels can 

### be earned as excess allowances. To the extent these allowances' are used for future emissions, the efficiency levels'**nt~t** effect on those S02 emissions would be only a postponement. Lever 5 design changes to microwave ovens would result in an estimated decrease in NOx emissions of48 kt (53 thousand short tons) between 2000 and 2030. NO" decreases would represent 0.02% and 0.07% ofthe NOx emissions estimated to come froIp the residential sector in the years 2000 and 2030. respectively.

'Volume 1: EnvironmentalAssessment PageEA-9

### 33.2 Carbon Diokide 

### Emissions Cooktops Thecumulative reduction in 

### C0,emissions from level 5 

### design changes is 36 

### Mt (39 

### million short tons) of CO,. For the 

### year 2000, 

### the estimated CQ reduction is 0.16 

### Mt of CQ or about 0.01% 

### of estimated 

### U.S.powerplant C0,emissions 

### in 2000. 

### For the 

### year 2030, 

### the estimated CO, reduction is 1.47 

### Mt of CO, or about 0.09% 

### of estimated 

### U.S. power plant CO, emissions in 2030. 

### ConventionalOvens The cur&ative reduction 

### in CO, emissions from level 

### 5 design changes is 133 

### Mt (47 

### million short tons) of CO,. ' 

### For the year 2000, 

### the estimated CO, reduction is 0.52 

### Mt of Cq or about

### 0.04%of estimated

### U.S. power plant C0,emissions 

### in 2000. 

### For the year 2030, 

### the estimated CO, reduction is 6.77 

### Mt of CO, or about 0.38% 

### of estimated

### U.S.power plant Cq emissions in 

# - 2030. / 

### Microwave Ovens The cumulative reduction in 

### C0,emissions from

### level 5 

### design changes is 25 

### Mt (28 

### million short tons) of CO

# ,. For the 

### yeg 2000, 

### the estimated 

### Cq reduction is 0.16 

### Mt of Cq or about 0.01% 

### of estimated

## U.S.powerplant C0,emissions 

### in 

### 2000. For the 

### year 2030, 

### the estimated CO, reduction is 1.02 

### Mt of CO 

# , or about 0.06% 

### of estimated

### U.S. power plant CO 

# , emissions in2030.

PageEZL-10 .Volume I: EnvironmentalAssessment 

### 3.3.2 Carbon DioXide Emissions Cooktops The cumulative reduction 

### in CO2 emissions from level 5 design changes is 36Mt (39 million

.short tons) of CO2 , For the year 2000, the estimated 

## cq reduction is 0.16 Mt of

## cq or about 0.01 % ofestimated U.S. pOwer plant 

### CO2 emissions 

### in 2000. For the year 2030, the estimated 

### CO2 reduction is 1.47 Mt of

### CO 2or about 0.09% ofestimated U.S. power plant 

### CO2 emissions in 2030. 

### ConventionalOvertS The cumulative reduction

### in CO2emissions from level 5 design changes is 133 Mt (47 million short tons) of 

### CO2 ,. For the year 2000, the estimated 

### CQz reduction is 0.52 Mt of**C~** or about 0.04% ofestinlated U.S. power plant 

### CO2 emissions

# m 2000. Forthe year 2030, the estimated 

### CO2reduction is 6.77 Mt of 

### CQz or about 0.38% ofestimated U.S. power plant**C~** emissions in 2030.

.



### . Microwave Ovens

. The cumulative reduction 

### in CO2emissions from level 5 design changes is .25 Mt (28 million short tons) of 

### CO 2' For the**y~** 2000; the estimated**C~** reduction is 0.16 Mt of 

## cq or about 

### Q.OI% ofestimated U.S. power plant**C02e~ssions** 

### in 2000. For the year 2030, the estimated 

### CO2 reduction is 1.02 Mt of

### CO 2 or about 0.06% ofestimated U.S. power plant 

### CO 2emissions in-2030.

PageEA-10 ·Volume 1: EnvironmentalAssessment

# - Table

### EA-6.~eduction of Pollutants for 

### Room Air 

### Conditioners,Level One a

, .

. .



### 59 65 .'000 Cum~llative SO2 reduction (kt): (short tons):


### NOx

Volume I: Environmental Assessment Page EA-?I Curnl~lati ve NOxreduction (kt): 

### 55 (short tons): 

### 61 000 uctlon ::a g..of Total Residential ~~issions 

### 0.02 

## - 0.06 0.09

' 

### Oi09

. 

### 0.08 0.08

. 

### 0.08 ~batd frdm Power Plants

Curnl-dativeC02 reduction (Mt): 

### 30 (short tons): 33 

### 000 000 8 uctlon 

# t: % 

### of Total Residential Emissions 

### 0.02 0.05 0.08 0.08 0.07

, 

### 0.07

. 

### 0.08 .

. 

### kt 0.64 1.57

' 

### 2010 2.37 2015 ' 

### 2.03 2020 1.89 2025 1.59 1.45

.

. ~otal Reduction in

, Abated from Power Plants thousand short tons 

### 0.70 1.73 2.62 2.24 2.08 1.75 1.59 Abated from In House

Mt 

### 0.29 0.64 1.05 1.09 1.12

' 

### 1.11 1.30

. Mt 

### 0.24 0.64 1 

### -05 2015 I .04 

### 2020 1.12 2825 1.11

, 

### 2030

. 

### 1.18 Abated from In House Total Reduction in ' Emissions kt 

### 0.05 0.00 '0.00 0.05 0.00 0.00 0.10

million short tons 

### 0.31 

### 0.71 

### 1.15 1.20 1.23

. 

### 1.22

. 

### 1.43 million short tons 

### 0.26 0.71

. 

### 1.15 1.15 1.23 1.22 

### I .30 Mt 

### 0.05 0.00 0.00 0.05 0.00 0.00 0.12 kt 

### 0.68 1.57 .2.37 ' 

### 2.08 1.89 1.59 1.55 thousand short t~s 

### 0.05 0.00 0.00

. 

### 0.05

0.00 0.00 0.11

million short tons 

### 0.05 0.00 0.00 0.05 0.00 0.00' .0.13 thousand short tons 

### 0.75 ' 

### 1.73

2.62 2.29 2.08 ' 

### 1.75 1.70 

### Table EA-6. Reduction ofPollutants for Room**Ab~ Co~ditioners,**  

### Level One S02 Abated from Power Total Reduction in Reduction Y,w Abated from In House asa%of Plants Emissions Total , thousand thousand thousand Residential kt short tons kt short tons kt' short tons Emissions 2000 0.86 0.94 0.00 .0.00 . 0.86 0.94 0.02 2005 1.95 2.15 0

..00 0.00 1.95 2.15 0.06 2010 2.13 3.01 0.00 0.00 2.73 3.01 0.10 2015 2.19 2.42 0.00. 0.00 2.19 2.42 0.09 2020 1.89 , 2.08 0.00 0.00 1.89 2.08 0.09

. 2025 1.45 1.60 0.00 0.00

. 1,45 1.60 0.09

2030 1.18· 1.30 0.13 0.15 1.31 1.44 0.10 Cumulative S02 reduction (kt): 59 (short tons): 65·000


### NOx Abated from Power Total Reduction in Keductlon YIW Abated from In House asa%of Plants Emissions Total thousand thousand thousand Residential kt short tons kt short tons kt short tons Emissions 2000 0.64 0.70 0.05 0.05 0.68 0.75 0.02 

### . 2005 1.57 1.73 0.00 0.00 1.57 ' 1.73 0.06 2010 2.37 2.62 0.00 0.00 2.37 2.62 0.09 2015 2.03 2.24 0.05 0.05 2.08 2.29 0;09 2020 1.89 2.08 0.00 0.00 1.89 2.08 0.08 2025 1.59 1.75 0.00 0.00 1.59 1.75 0.08 2030 1.45 1.59 0.10

. 0.11 1.55 1.70 0.08 Cumlliative NOx reduction (kt): 55 (short tons): 61000
C02 Abated from Power Total Reduction in Keductlon**YI~r** Abated from In House as a % of Plants Emissions Total million million million Residential Mt short tons Mt short tons Mt short tons Emissions 

## -Uioo 0.24 0.26 0.05 0.05 0.29 0.31 0.02 2005 0.64 0.71 0.00 0.00 0.64 0.71 ·0.05 2010 1.05 1.15 0.00 0.00

. 1.05 1.15 0.08 2015 1.04 1.15 0.05 0.05 1.09 1.20 0.08 2020 1.12 1.23 0.00 0.00 1.12 1.23 0.07 2025 1.11 1.22 0.00 0.00 1.11 1.22 0.07 2030 1.18 1.30 . 0.12 0.13 1.30

. 1.43 0.08 Cumulative CO2reduction (Mt): 30 (short tons): 33 000 000 Volume 1: Environmental**A~sessment** PageEA-H

### Table 

### EA-7. Reduction of Pollutantsfor 

### Room Air 

### Conditioners,Level Two

. . SO, Abated from Power



# '1 2030 

# I 1.72 1.90 

# -1 0.13 

# 1 0.15

Cumulative SO2 reduction (kt): - 86 Abated from In House
kt 1.14

' 2.92

3.78 .3.20 2.70 2.18

2020

Cumulativ 

### NOx thousand short tons

, 1.26

' 3.22 4.17 3.53 .2.98 2.40

Abated from Power Plants 

# I Abated from In House kt 0.00 0.00 0.00 0.00 0.00 0.00 thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00

kt 0.85 2.35 3.29 2.97 2.70 2.39 2.12 

# 1 2.34 

# 1 0.10 

# 1 0.11 4Ox reduction (kt): 80 thousand short tons 0.94 .2.59 3.62 3.27 2.98 2163 kt 0.05 0.00 0.00 0.05 0.00 0.00 thousand short tons 0.05 0.00 0.00 0.05 0.00 0.00

2030 Cumulative Cot reducti 

# I year 

# I Abated from Power Plants

-- million short tons 0.35 1.06 1.60 1.68 1.76 1.83 1.90 Abated from In House

,n (Mt): 44 Page EA-12 Reduction Total Reduction in 

# I

- as a % of

I Emissions Total

1.86 

### ( 2.05 

# 1 0.14 (short tons): 95 000 kt 1.14 - 2.92 3.78 3.20 2.70 2.18



# -.--. . . . Keduction 

## 1 10ta1 Keaucuon in asa%of Total thousand short tons 1.26 3.22 4.17 3.53 2.98 2.40

2.22 

# I 2.45 

# I 0.12 

# I (shorttons): 89 000 Residential Emissions 0.03- 0.09 0.13 0.13 0.13 0.13

million short tons 0.05 0.00 0.00 0.05 0.00 0.00 0.13

Volume 1: EnvironmentalAssessment

. , Reduction Total Reduction in 

### as a of Emissions 

# 1 Total 

# I

1.85 

# 1 2.03 

# 1' 0.11 

# I (shorttons): 48 000 000 Mt 0.37 0.96 1.45 1.57 1.60 1.66 million Residential short tons Emissions 0.40 0.03 1.06 0.07 1.60 0.1 1 1.73 0.11 1.76 0.11 1.83 0.1 1 

### Table EA

..'. Reduction ofPollutantS for Room Air Conditioners, Level Two' S02 Abated from Power Total Reduction in Reduction Year Plants Abated from In House Emissions . asa%of Total thousand' thousand thousand Residential kt short tons kt short tons kt short tons Emissions 2000 1.14
. 1.26 0.00. , 0.00 1.14 1.26 0.01 2005 2.92 3.22 0.00 0.00 2.92 3.22 0.09 2010 3.78 4.17 0.00 0.00 3.78 4.17 0.13 2015

. .3.20 3.53 0.00 0.00 3.20 3.53 0.13 '2020 'f.70 2.98 0.00 0.00 2.70 2.98 0.13 2025 2.18 2.40 0.00 0.00 2.18 2.40 0.13 2030 1.72 1.90 0.13 0.15 1.86 2.05 0.14 Cumulative S02reduction (kt): 86
.(short tons): 95000
NOx Abated from Power Total Reduction in KeductlOn Year Plants . Abated from In House Emissions as a % of Total thousand thousand thousand Residential kt short tons kt short tons kt short tons Emissions 2000 0.85 0.94 0.05 0.05 0.89 0.99 0.03 2005 2.35 .2.59 0.00 0.00 2.35 2.59
. 0.09 2010 3.29 3.62 0.00 0.00 3.29 3.62 0.12 2015 2.97 3.27 0.05 .0.05 3.02 3.32 0,12 ·2020 2.70 2.98 0.00 0.00 2.70 2.98 0.12

2025 2.39 2.63 0.00 0.00

. 2.39 2.63 0.12 2030 2.12 2.34 0.10 0.11 2.22 2.45 0.12 Cumulative NOx reduction (kt): 80 (short tons): 89000


### C02 Abated from Power Total Reduction in Reduction Year Abated from In House as a % of Plants Emissions Total million million million Residential Mt short tons Mt short tons Mt short tons Emissions 2000 0.32 0.35 0.05 0.05 D.37 0.40 0.03 2005 0.96 1.06 0.00 0.00 0.96 1.06 0.07 2010 1.45 1.60 0.00 0.00 1.45 1.60 0.11 2015 1.52 1.68 0.05 0.05 1.57 1.73 0.11 2020 1.60 1.76 

### 0·90 0.00 1.60 1.76 0.11 2025 ·1.66 1.83 0.00 0.00 1.66 1.83 0.11 2030 1.73 1.90 0.12 0.13 1.85. 2.03 0.11 Cumulative CO2 reduction (Mt): 44 (short tons): 48 000 000 PageEA-12 Volume 1: EnvironmenltJlAssessment

### Tiable EA-8. 

### ReductionofPollubnts for RobmAir Conditioners,Level Three SO2

. Abated from Power 

# IAbated from in House Plants thousand thousand kt

. short'tons kt shorttons 

### 1.43 ' 

### 1.57

. 

### 0.00 0.00 3.65 4.02 0.00 0.k :20 10 

### 5.04 5.56 0.00 0.00 :20 15 4.22 4.65 0.00 0.00

. 

### 3.51 3.87 0.00 0.00 :2025

. 

### 2.91 3.20 0.00 0.00 2.27 0.13 0.15 Cumulative SO2 reduction (kt): 111 - 

### NOx Abated from Power 

# IAbated from In House thousand 

### 'kt thousand kt short tons short tons 

### 1.06 1.17 0.05 0.05 2.94 3.24 0.00 0.00 - 

### 4.38 4.83 0.00 0.00' 3.91 4.31 

### 0.05 0.05 3,5 1 

### 3*87 0.00 ., 

### 0.00 3.18 3.5 1 

### 0.05 0.05 2.79 3.08 0.10
. 0.11 Curnulative.NOx reduction (kt): 

### 104 Emissions Total - - thousand Residential 

# 'TI Emissions

# 1

Emissions Total - 

### 1.43

3.65

. 

### 5.04 4.22 3.5 1 

### 2.91 .2.41

.

- - thousand Residential Emissions

# 1

1.11 

### 1.22 0.04 2.94 3.24 0.11 

### 4.38 4.83 0.16 3.95 4.36 0.16 3.51 3.87, 0.16 3.23 3.56 

# - 0.16 2.90 3.19 0.15

. (shorttons): 1 

### 15 000 (short tons): 

### 122 000 . 

### 1.57 4-02,. 5.56

, 

### 4.65 3.87

. 

### 3.20 2.65 0.04 0.12 

### . 0.18 0.17 0.17' .0.17 0.18



## c02
'Year



### :!000 :!005 :!010 :!015 :!020 :!025 :!030 *a

# a

Cunlulative C02 

### 2.28 reduction (Mt): 

### 2.51 

### 57 0.12 0.13 (short 

### 2.40 tons): 63 000 

### 2.64 000 

### .O.15
Volume 1: Environmental Assessment Page EA-13 Abated from Power Plants
Mt 

### 0.40 1.20 1.93,. 2.01 

### 2.08 2.22 million short tons 

### 0.44 1.32 2.13 2.21 2.29 2.44 Abated from In House



### Mt 0.05 0.00 0.00 0.05 0.00 0.05 uction dpg of Total Residential Emissions 

### 0.04 0.09 

### 0.14 0.14 0.14 0.15 million short tons 

### 0.05 0.00 0.00 0.05 0.00 0.05 Total Reduction in Emissions
Mt 

### 0.44 1.20 1.93 2.05 2.08 2.26 million short tons 

### 0.49 1.32 2.13 2.26 2.29 2.49 

### Tablte EA·8. Reduction ofPollutBnts for RoomAir Conditioners, Level Three SOl Abated from Power Total Reduction ,in' .KOOuctJOn Year Abated from In House asa%of Plants Emissions Total thousand thousand thousand Residential kt short'tons kt short tons kt short tons Emissions :2000 1.43 1.57 0.00 0.00 ' 1.43 1.57 0.04 :2005, 3.65 4.02 0.00 0.00 3.65 4.02', 0.12 :2010 5.04 5.56 0.00 0.00 5.04 5.56 0.18 :2015 4.22 4.65 0.00 0.00 4.22 4.65 0.17 :2020 ' 3.51 3.87 0.00

. 0.00 3.51 3.87 0.17' :2025 2.91 3.20 0.00 0.00, 2.91 3.20 ,0.17 :2030 2.27 2.50 0.13 0.15 2.41 2.65 0.18 Cumulative S02reduction (kt): 111 (short tons): 122000

NOx Abated from Power Total Reduction in KeauctJOn YeaI!' Abated from In House asa % of Plants Emissions Total thousand thousand thousand Residential kt short tons let short tons kt short tons Emissions 2000 1.06 1.17 0.05 0.05 1.11 1.22 0.04 2005 2.94 3.24 0.00 0.00

- 2.94 3.24 0.11


# . 2010 ' 4.38 4.83 0.00 0.00 4;38 4.83 0.16 2015 3.91 4.31 0.05 0.05 3.95 4.36' 0.16 2020 3,.51 3.87 0.00 0.00 3.51 3.87 0.16 2025 3.18 3.51 0.05 0.05 3.23 3.56

- 0.16 2030 2.79 3.08 0.10 0.11 2.90 3.19 0.15 CUJ:llulative,NOx reduction (kt): 104 (short tons): 115000


### C02 Abated from Power Total Reduction in Keauction Year Abated from In House , as a % of


# . Plants Emissions Total million million ' million Residential Mt short tons Mt short tons Mt short tons Emissions 2000 0.40 0.44 0.05 0.05 0.44 0.49 0.04 2005 1.20 1.32 0.00 0.00 1.20 1.32 0.09 2010 1.93, ' ' 2.13 0.00 0.00 1.93 2.13 0.14 2015 2.01 2.21 0.05 '0.05 2.05 2.26 0.14 2020 2.08 2.29 0.00· 0.00 2.08 2.29 0.14 2025 2.22 2.44 0.05 0.05 2.26 2.49 0.15 2030 2.28 2.51 0:12 0.13

. 2.40 2.64 ,0.15

. Cumulative CO2 reductIOn (Mt): 57 (short tons): 63 000 000 Volume 1: EnvironmentalAssessment PageEA.13

### ~nble 

### EA-9. Reduction 

### ofPollutants for 

### Room Air 

### Conditioners, Level Four SO,

Abated from Power 

# IAbated from In House 

# I I kt 

# I I kt 

# 1 thousand short tons short tons Year



### 2000 2005 2010 2015 2020

- 

### 2025

2030 II CumulativeSO2reduction (kt): 

### 149 (short tons): 

### 164 000

Abated from Power 

# I Abated from In House - - Abated from In 

### House Abated from Power Plants



### 2000 2005 2010 2015 2020 2025 2030 

# I Mt 

# I million 

# I,,Mt 

# I million short tons short tons kt 

### 0.00 0.00 0.13 0.13 0.13 0.13 0.13 kt 

### 2.00 4.87 6.52 5.56 4.73 3.84 3.06 Reduct~on 

### as a 9% of

Total Residential Emissions 

### 0.06 0.16 0.23 0.24 0.24 0.24 0.23 thousand ' &ort tons 

### 0.00 0.00 0.15 0.15 0.15 0.15 0.15 thousand short tons 

### 2.20 5.36 7.18 6.13 5.21 4.23 3.37 Total Reduction in Emissions

Cumulative NOx reduction (kt): 

### 141 1.49 3.92 5.66

. 

### 5.16 4.73 4.21 3.76 Emissions kt 

### 2.00 4.87 6.65 5.70 4.86 3.97 3.19

2025 2030 thousand short tons 

### 2.20 5.36 7.33 6.28 5.36 '4.38 3.52

1.64

, 

### 4.32 6.24 5.69 5.21 4.63 4.14

Keducuon Total Residential short tons Emissions 

### kt 1.53 4.01 5.81 5.35 4.87 4.40 3.95

Cumulative COz reduction (Mt): 

### 79 i 

### 2.93 3.06 0.05 0.09 0.15 0.19 0.15 0.19 0.19

. 

## . Page EA-I4 VolumeI: Environmental~ssessmknt (short tons): 

### 156 000 thousand shorttons 

### 1.69 4.42 6.40 5.90 5.37 4.84 4.35 0.05 0.10 0.16 0.21 0.16 0.21 0.21



### .O.W 1.70 2.66 2.86 2.96 3.14 3.27 3.23 '3.37 Residential Emissions 

### 0.05 0.15 6.22 0.22 0.22 0.21 0.21

(short tons): 88 000 000 

### 0.67 1.87 2.94 3.15 3.27 3.46 3.61 .0.21 0.21 0.05 0.13 0.19 0.20 0.20 0.20 0,20 

# . 0.24 0.24 

### Table EA-9. Reduction ofPollutants for Room Air Conditioners, Level Four S02 Abated from Power Total Reduction in Reduction Year Abated from In House asa%of Plants Emissions Total -thousand thousand' thousand Residential kt kt , kt Emissions short tons short tons short tons 2000 2.00
_2.20 0.00 0.00- 2-.00 2.20 0.06 2005 4.87 5.36 0.00 0.00 4.87 5.36 0.16 2010 6.52

- 7
..18 0.13 0.15 6.65 7.33 0.23 2015 .5.56

- 6.13 0.13 0.15 5.70 6.28 0.24 2020 4.73 5.21 0.13 0.15 4.86 5.36

- 0.24

- 2025 3.84 4.23 0.13 0.15 3;97 '4.38 0.24

2030 3.06 3.37 0.13 0.15 3.19 3.52 0.23 Cumulative**S~** reduction (kt): 149

_(short tons): 164 000
NOx Abated from Power ,Total Reduction in Reduction Year Abated from 'In House asa%of Plants Emissions

- Total thousand thousand

- thousand Residential kt short tons kt short tons kt short tons Emissions 2000 1.49 1.64 0.05 0.05 1.53 1.69 0.05 2005 3.92 4.32 0.09 0.10 4.01 4.42 0.15 2010 5.66**6.~_** -,- 0.15 0.16 5.81 6.40 0.22 2015 5.16 5.69 0.19 0.21 5.35 5.90 0.22 2020 4.73 5.21 0.15 0.16 4.81 5.37 0.22 2025 4.21

- 4.63. 0.19 0.21 4.40 4.84 0.21 2030 3.76 4.14 0.19 0.21 3.95 4.35 0.21 Cumulative N:Ox reduCtion (kt): 141 (short tons): 156000


### C02 Abated from Power Total Reduction in KeduCtiOn Year Abated from In House as a % of Plants Emissions Total million million million Residential Mt short tons Mt short tons Mt short tons Emissions 2000 0.56 0.61 0.05 0.05 ,0.60 0.67 0.05 2005 1.60 1.77 0.09 0.10 1.70 1.87 0.13 2010 2.50 2.75 0.17 0.18 2.66 2.94 ·0.19 2015 2.65 2.92 0.21 0.24 2.86 3.15 0.20 2020 2.80 3.08- 0.17 0.18 2.96 3.27 0.20 2025 

## • 2.93 3.23 0.21 0.24 3.14 3.46 0.20 2030 3.06 -3.37 0.21 0.24 3.27 3.61**0~20** Cumulative CO2reduction (Mt): 79

- (short tons): 88 000 000 Page EA-14 Volume 1: EnvironmentalAssessment

## TI JleEA-10. Reduction of Pollutants for

### Room Air Conditioners, Level Five

. : 

## so2 

# I

' Year 

# I
'Om Abated from In House Plants 

# I

L2030 

# 1 0.47 

# 1 0.52 

# 1 0.94 

# 1 1..04 Cunwlative SO2 reduction (kt): 33 

# -- 2000 21005 2010 201

### 5 2020 2!025 Total Reduction in Emissions 

# -lmziG
as a % of

Total

. ~esiden tial Emissions

' 

### 0.03 0.04

, 

### 0.03

0.02 0.05 0.08 0.

## ro kt 

### 0.86 0.73 0.00 -0.34 0.14 0.42 kt 

### 0.99 1.13 0.81 

### 0.47 1.08 1.36 1.41


### NOx kt 

### 0.13 0.40 0.81 0.81 0.94 0.94 thousand short tons 

### 0.94 0.80 0.00 -0.37 0.15 0.46 thousand short tons 

### 1.09 ' 

### 1.25 0.89 0.52
. 1.19 1 

### .SO 1.56 thousand short tons 

### 0.15 0.44 0.89 0.89 1.04 1.04 (short tons): 

### 37 000

volume I: EnvironmentalAssessment PageEA-15 Year 

# ni

C02 

### Cum~ulalive 

### NOxreduction (kt): 

### 51 (short tons): 

### 56 

### 000 Abated from Power Plants - Abated from 

### In House

. Sear

' 

### 2005 2010 2015 2020 2025 21030 kt 

### 0.64 0.59 0.00 -0.31 0.14 0.45 0.58 kt 

### 0.28 0.84 1.42 1.51 1.56 1.65 1.74

CumulativeC02 reduction (Mt): 51


### . . (shorttons): 

### 56 000 

### 000

, . uction



### aB of Total Residential Emissions 

### 0.03 0.05 0.05 0.05 0.08 0.10 0.12 thousand 5hort tons 

### 0.70 0.65 0.00 -0.34 

### 0.15 0.50 0.64 thousand short tons 

### 0.31 0.93

. 1.x 1.66 1.72 1.82 1.92 TotaI Reduction in Emissions '

. kt 

### 0.92 1.43 1.42 1.20 1.70 2.1 1 

### 2.32

Abated fmm 

### Power Plants shorttons 

### 1.01 1.58 1.56 1.32 1.87 2.32 2.56

Mt 

### 0.24 0.24 0.00 -0.16 0.08 0.32 ' 

### 0.47 million short tons 

### 0.26 0.26 0.00 -0.1 8 0.09 0.35 0.52 Abated from 

# InHouse
Mt.,', ' 

### 0.31 

### 0.93 1.57 1.66 1.74 1.83 ' 

### 1.93 uction da% of Total Residential

.Emissions 

### 0.04 0.09 0.11 

### 0.10 0.12 0.14 0.15 million

. short tons 

### 0.34

. . 

### 1.02 1.73 1.83 1.91 2.02 2.12 Total Reduction in .Emissions
Mt 

### 0.55 1.17 1.57 1.50 1.82 2.15 ' 

### 2.40 million short tons 

### 0.60 1 

### ;29 1.73 

### 1.66 2.00 2.37 2.64 

### TllbleEA-lO. 

### Reduction ofPollutants for Room Air Conditioners,Level Five·

,. I

.
Abated from Power Total Reduction in Ke(Juctlon Year Abated from In Hou$e asa%of Plants Emissions Total. thousand thousand thousand Residential kt short tons kt short tons kt short tons Emissions

### ~~OOO 0.86

. 0.94 0.13 0.15 0.99 1.09

. 0.03**2~OO5** 0.73 0.80 0.40 0.44 1.13 1.25

. 0.04**2~010** 0.00 0.00 0.81 0.89 0;81 0.89 0.03 2:ot5 -0.34 -0.37 0.81 0.89 0.47 0.52 0.02**2~020** 0.14 0.15 0.94 1.04 1.08 1.19 0.05 2025 0.42 0.46 0.94 1.04 1.36 1.50 0.08 2030 0.47 0.52 0.94 1.04 1.41 1.56 0.10 Cumulative S02reduction (kt): 33 (short tons): 37000 NOx Abated from Power rotal Reduction in Keductlon Year " Plants Abated from In House Emissions \ as a % of Total thousand thousand thousand Residential. kt ··short tons kt short tons kt short tons Emissions 2000 O.M 0.70 0.28 0.31 0.92 1.01 0.03 2005· 0.59 0.65 0.84 0.93 1.43 1.58 0.05

. 0.00 

# .- 2010 0.00 1.42 1.56 1.42 1.56 0.05 2015 -0.31 -0.34 1.51 1.66 1.20 1.32 0.05 2020 '0.14 0.15 1.56 1.72 1.70 1.87 0.08 2025 0.45 0.50 1.65 1.82 2.11 2.32 0.10 ·2030 0.58 0.64 1.74

.. 1.92 2.32 2.56 0.12 Cumulative NOx reduction (kt): 51 (short tons): 56000 C02 Abated from Power 

### .. Total Reduction in KedUCUOn Year Plants Abated from In House ·Emissions as a %of Total million million million Residential· Mt Mt .. ' Mt short tons .. s)tort tons short tons Emissions 

## 2000 0.24 0.26 0.31 0.34 0.55 0.60 0.04 2005 0.24 0.26 0.93 .1.02 '1.17 1:29 0.09 2010 0.00 0.00 1.57 1.73 1.57 1.73 0.11 2015 -0.16 -0.18 1.66 1.83 1.50 1.66 0.10 2020 0.08 0.09 1.74 1.91 1.82 2.00 0.12 2025 0.32

. 0.35 1.83 • 2.02 2.15

. 2.37 0.14 2030 0.47 0.52 1.93 2.12 2.40 2.64 0.15 Cumulative CO2reductIon (Mt): 51 (short tons): 56000 000 Volume 1: EnvironmentalAssessment PageEA-1S

### Table 

### EA-11. Reduction of Pollutantsfor

### cooktops,Level One SO* 

### Year

2000 2005 2010 2015 2020 2025 2030



# I . . 'bated Abated 

### from In House

# I Total Reduction in Reduction as Plants Emissions a % of Total 

# I I Year

2000 2005 2010 2015 2020 2025 2030 Cumulative SO2 reduction (kt): 0 (short tons): 0000

- 2030 

# 1 0.00 

# 1 0.00 

# 1 0.00 

# 1 0.00 

# 1 0.00 

# I 0.00 

# I 0.00 

# I Cumulative (2% reduction (Mt): 0 (short tons): 0 000 000 Abated from Power Plants

Cumulative NOxreduction (kt): 0 (short tons): 0000

2000

PageEA-I6 kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Abated from Power Plants

VolumeI: EnvironmentalAssessment thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Abated from In House

kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00 kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00

thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Abated from In House

Mt 0.00 Reduction as a 96 of Total Residential Emissions 0.00 0.00 0.00 0.00 0.00 0.00 0.00 thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Total Reduction in Emissions

kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00

million short tons 0.00 kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00'

Reduction as a 96 of Total Residential Emissions 0.00 0.00 0.00 0.00 0.00 0.00 0.00 thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Total Reduction in Emissions

Mt 0.00 thousand Shorttons 0.00 0.00 0.00 0.00 0.00 0.00 0.00

. kt

0.00 0.00 0.00 0.00 0.00 ' 0.00 0.00

million short tons 0.00 thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Mt 0.00 Residential Emissions 0.00 million short tons 0.00 ' 

### Table EA-11. Reduction ofPollutants for Cooktops, Level One SOl Year Abated from Power

- Abated from In House TotalReduction in Reduction as Plants Emissions a % ofTotal thousand thousand thousand Residential kt short tons kt short tons kt short tons. Emissions 2000 0.00 0.00 '0.00 0.00 0.00 0.00 0.00 2005 0.00
. 0.00 0.00 0.00 0.00 0.00 0.00 2010 0.00'· 0.00 0.00 0.00 0.00 0.00 0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2030 0.00 0.00 0.00 0.00 0.00- 0.00 0.00 Cumulative S02reduction (kt): 

## o (short tops): 0000 

### NOx
Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal kt thousand kt thousand kt thousand Residential short tons shOrt tons short tons Emission...S. 2000 . 0.00 0.00 0.00 0.00' 0.00 0.00 0.00 2005
. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2015

. 0.00 0.00 0.00 '0.00 0.00 0.00 0.00 2020 0.00 .0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00

. 0.00 0.00 0.00 2030 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cumulative NOx reduction (kt): .0 (short tons): 0000

, , Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal Mt million Mt million Mt million Residential short tons short tons short tons Emissions 2000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2005 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2010 0.00 0.00 0.00 '. 0.00 0.00 0.00 0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ·2030 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cumulative**C~** reduction (Mt): 

## o (short tons): 0 000 000 PageEA-16 Volume 1: EnvironmentalAssessment

### Table 

### EA-12. Reduction of Pollutants for Cooktop$,

### Level Two

Abated from Power Year

. Plants thousand ' kt short tons 

### 0.14 0.16 0.51 0.56 2010 0.63 ~0.69 2015 0.52 0.58 0.26 0.28 0.04 0.05 0.00 0.00



# I I

, 

# I *bated from PO& Plants Abated from 

### In &use 

### NOx Cumulative SOzreduction (kt): 11 (short tons): 

### 12 000 , . Abalsd fmm in House

, 

# p

20:15 

### 2020 2C!5 

### 2030 kt

. ' 

### -0.01 0.00 0.00 -0.0 1 

### -0.01 0.00 0.00

CumulativeNOxreduction (kt): 

### 9 (short tons): 

### 10 000



# 1,

20 10 

### 20:20 20:25 20:30

Volume 1: .Environmental ~ssessrnent Page EA-17 Reduction as a % of Total Residential Emissions 

### 0.00 0.02 0.02 0.02 0.01 0.00 

### Q.OO Total Reduction in Emissions ' thousand short tons 

### -0.01 0.00 0.00 -0.01 -0.01 0.00
- 

### 0.00'

Reduction as a % of Total Residential Emissions 

### 0.00

Cumuilative C02 reduction (Mt): 

### 4 

### . Total Reduction in

' Emissions



### 0.16 0.23 0.23 0.14 0.03 0.00 ,kt 

### 0.13 0.5

### 1 0.63 0.51 

### 0.24 0.04 0.00

Abated from Power Plants Abated from In House

Mt .'



### 0.17 24 04 0.25 0.15 0.03 0.00 

### Mt 0.03 thousand

. tons 

### 0.14 0.56 0.69 0.56 0.27 0.05 0.00

kt 

### 0.11 0.41 

### 0.55 0.48 0.26 0.05 0.00 kt 

### -0.01 -0.01 -0.01 -0.02 -0.0 1 

### 0.00 0.00

-0.01 -0.02, -0i02 0.00 0.00 ' million short tons 

### 0.04 19 27 0.27 0.17 0.03 0.00 million short tons 

### 0.04

(short tons): 

### 5 000 

### 000 0.17 0.25 0.25 0.15 0.03 0.w Reduction as a % of Total Residential Emissions 

### 0.00 0.01 0.02 0.02 0.01 

### 0.00 0.00 thousand short tons 

### 0.12 0.45 0.60 0.53 0.28 0.05 0.00 thousand short tons 

### -0.01 -0.01 -0.01 -0.02 -0.02 0.00 0.00 Total Reduction in Emissions

million short tons



# . -0.01 2

-0.01

-0.02 -0.02 -0.01 0.00 0.01 0.02 0.02 0.01

, 

### 0.00

0.00 kt 

### 0.10 0.40 0.53 0.47 0.24 0.04 0.00 thousand short tons 

### 0.1 1 

### 0.44 0.59 0.51 

### 0.27 0.05 0.00 

### Table EA-12. Reduction ofPollutants for**CooktOp~,** 

### Level Two

Year· Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal thousand' thousand thousand Residential kt short tons kt short tons kt

.short tons Emissions 2000 0.14 0.16 -0.01 -0.01
. 0.13 0.14 0.00 2005 0.51 0.56 0.00 0.00 0.51 0.56 0.02 2010 0.63 ,0.69 0.00 0.00 0.63 0.69 0.02 .2015 0.52 0.58 -0.01 -0.01 0.51 0.56 0.02 ·2020 0.26 0.28 -0.01 -0.01 0.24 0.27 0.01 2025 0.04 0.05 0.00 0.00 0.04 0.05 0.00 20:30 0.00 0.00 0.00

- 0.00' 0.00 0.00 0.00 Cumulative S02reduction (kt): 11 (short tons): 12000 

### NO" Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal kt thousand kt thousand kt thousand Residential short tons short tons short tons Emissions 2000 0.11 0.12 -0.01 -0.01**0~1O** 0.11 0.00 2005 0.41 0.45 -0:01 -0.01 0.40 0.44 0.01 20'10 0.55**0~60** -0.01 ",0.01 0.53 0.59 0.02 2015 0.48 0.53 -0.02 -0.02 0.47 0.51 0.Q2 2020 0.26 0.28 -0.01 -0.02 0.24 0.27 0.01 2025 0.05 0.05 0.00 0.00 0.04 0.05 0.00 2030 0.00 0.00 0.00 0.00 0.00 0.00 0.00 CumulatIve NOxreductlon (kt): '9 (short tons): 10000

Abated from Power " Total Reduction in Year. Plants Abated from 

### In House Emissions Reduction as a % ofTotal Mt million Mt million Mt million Residential short tons short tons short tons Emissions 2000 0.04 0.04 -0.01 -0.01 0.03 0.04 0.00 2005 0.17 0.19 -0.01

. -0.01 0.16 0.17 0.01 2010 0.24 0.27 -0.01 -0.02 0.23 0.25 0.02 2015 0.25 0.27 -0.02 -0.02 0.23 0.25 0.02 2020 0.15 0.17 -0:02 -0.02 0.14 0.15 0.01 2025 0.03 0.03 0.00 -0.01**0~03** 0.03 Q.OO 2030 0.00 0.00 0.00 0.00 0.00 0.00 ; 0.00 Cumullatlve CO2 reductIon (Mt): 4. (short tons): 5 000 000
Volume 1: .EnvironmentalAssessment PageEA-17\. 

### Table

## EA-13. Reduction of Pollutants for Cooktops,Level Three

NOx Year

2000 2005 2010 2015 2020 2025 2030 Cumulative SO2 reduction (kt): 5 (short tons): 6000

' Year

2000 2005 2010 2015 2020 2025 2030

Page EA-18 Volume I: Environmental Assessment Abated from Power Plants

. kt 0.1 1 0.41 , 0.50 0.37 , 0.15 -0.04 -0.08

CumulativeNOxreduction (kt): 1 1 (short tons): 12 000 thousand short tons 0.13 0.46 0.56 0.4 1 0.16 -0.05 -0.09 Abated from In House

Abated from Power Plants

Cumulative 

### C@ reduction (Mt): 8 (short tons): 8 000 000 Year

2000 2005

. 2010

2015 2020 2025 2030, kt -0.01 -0.04 -0.05 -0.09 -0.08 -0.07 -0.08

kt 0.09 0.33 0.44 0.34 0.15 -0.05 -0.10 Reduction as a 9%of Total Residential Emissions 0.00 0.01 0.02 0.01 0.00 -0.01 -0.01 thousand short tons

-0.0 1 -0.04 -0.06 -0.10 -0.09 -0.07 -0.09 Total Reduction in Emissions

thousand short tons 0.09 0.37 0.48 0.38 0.16 -0.05 4.11 - Abated from In House kt 0.10 0.37 0.45 0.28 0.07 -0.11 -0.16

kt 0.02 0.06 0.13 0.19 0.21 0.23 0.25

Abated from power . Plants thousand short tms 0.1 1 0.4 1 0.50 0.3 1 0.07 -0.12 -0.18


# budon ar a % of Total Residential Emissions

# _ 0.00 0.01 0.02 0.02 0.02 0.01 0.01 thousand short tons 0.02 0.07 0.14 0.21 0.24 0.26 0.27 Total Reduction in Emissions



### Mt 0.03 0.14 0.19 0.18 0.09

' -0.03

-0.08 . kt 0.10 0.40 0.56 0.53 0.36 0.19
, 0.15

million short tons 0.04 0.15


### . 0.2 1 0.19 0.10 -0.03 -0.09

. 

### . . Abated from In House thousand short tons 0.11 0.44 0.62 0.58 0.40 0.21 0.17

Mt 0.02 0.06 0.1'3 0.19 0.22 0.24

. 0.25 ' as a % of ~otal Residential Emissions 0.00 0.02 0.02 0.03 0.02 0.01 0.01 million short tons 0.02



## . 0.07 0.14 0.20. ,024 0.26 0.28 , Total Reduction in Emissions Mt 0.05 0.20 0.32 0.36 0.30 0.21 0.17 million short tons 0.05 0.22 0.35 0.40 0.34 0.23 0.19 

### Table**EA-~.** 

### Reduction ofPollutants for Cooktops, Level Three

Year . Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal thousand thousand thousand Residential kt short tons kt short tons kt short tons Emissions 2000 0.11 0..13 -0.01 -0.01 0.10 0.11 0.00 2005 0.41, 0.46 -0.04 -0.04 0.37 0.41 0.01 2010 0.50' -0.56 -0.05 -0.06 0.45 0.50 0.02 2015 0.37 ' 0.41 -0.09 -0.10 0.28 0.31 0.01

, 2020 0.15 0.16 -0.08 -0.09 0.07 0.07 0.00

2025 -0.04 -0.05 -0.07 -0.07 -0.11 '-0.12

. -0.01 2030 -0.08 -0.09 -0.08 -0.09 -0.16 -0.18 -0.0] Cumulative S02reduCtIon (kt): 5

. (short tons): 6000 NOx

, Abated from Power Total Reduction in Year Plants Abated from In House Emissions Reduction as a % ofTotal kt thousand kt thousand kt thousand Residential short tons short tons short tons Emissions 2000 0.09 0.09 0.02 0.02 0.10 0.11 0.00 2005 0.33 0.37 0.06 0.07 0.40 0.44 0.01 2010 0.44 0.48 0.13 0.14 0.56 0.62 0.02 2015 0.34 0.38 0.19 0.21 0.53 0.58 (}'02 2020 0.15 0.16 0.21 0.24 0.36 0.40 0.02 2025 -0.05 -0.05 0.23 0.26 0.19 0.21 0.01 2030 -0.10 

## ..a. I 1 0.25 0.27 0.15 0.17 0.01 Cumulative NOx reduction (kt): 11 (short tons): 12000

' , Abated from Power Total Reduction in Year Abated from In House Reduction as Plants Emissions a % ofTotal Mt million Mt million Mt million Residential short tons short tons short tons Emissions 2000 0.03 0.04 0.02 0.02 0.05 0.05 0.00 2005 0.14 0.15 0.06 0.07 0.20 0.22 0.02 2010 0.19 0.21 0.13 0.14 0.32 0.35 0.02 2015 0.18 0.19 0.19 0.20. 0.36 0.40 0.03 2020 0.09 0.10 0.22 -0.24 0.30 0.34 0.02 2025
. -0.03 -0.03 0.24 0.26 0.21 0.23 0.01 2030. -0.08 -0.09 ' 0.25 • 0.28 0.17 0.19 0.01 Cumu]atlve**C~** reduction (Mt): 8 (short tons): 8 000 000 PageEA·18 Volume I: EnvironmentalAssessment

### Table 

## EA-14. Reduction of

### Pollutantsfor 

### Cooktops, Level Four

, . 

### NOx 

## so2

Volume 1: EnsironmentulAssessment Cumulative S& reduction (kt): 13

. (short tons): 14'000 Year 

# 'F

2005 2010

. 2015 2020 2025 2050



## -

Page EA-19 Reduction as .a % of ~otal Residential Emissions 0.00 0.02 0.03 0.03 0.02 0.0 1 0.00 . . Abated from In House 'Total Reduction in Emissions '

CumulativeNOxreduction (kt): 18 (short tons): 20 

# - 

# &

2010 2015


### . 2025 2030

Reductionas a % of Total Residential Emissions 0.00 0.02 0.03 0.04 0.03

, 0.02 0.02 - - Abated from Power Abated from In House Yeiu Plants. million

. ' million Mt short tons 

### Mt short tons 0.05 0.05 0.01 0.0 1 0.20 0.22 0.06 0.06 0.3 1 0.34 0.11 - 0.12 2015 0.34 0.38 0.16 0.1 

## a 0.26

. 0.29 0.19 ' 0.21 2025 0.15 0.17 0.20 0.22

. 2030 0.12 0.13 0.22 0.24 kt ' -0.01 -0.04 -0.05 -0.09 -0.08 -0.09 -0.08 kt 0.16 0.57 0.74

. 0.63 0.37 0.10 0.04 Abated from Power Plants

Cumu1:ativeC02 reduction (Mt): 11 (short tons): 12 000 000

. ' thousand short tons

-0.01 -0.04 ' -0.06 -0.10 -0.09

. -0.10 -0.09 thousand shorttons 0.17 0.63 0.82 0.70 0.40

. 0.11 0.04 kt 0.17 0.61 0.80 0.73 0.45

. 0.20 0.12
,

Abated 'Om Plants - Abated fmm In House

Total Reduction in Emissions thousand short tons 0.19 0.67 0.88 0.80 0.49 0.22 0.13

kt 0.13 0.49 ' 0.69 0.67 0.45 0.22 0.14 kt 0.01 0.06 0.1 1 0.16 0.19 0.20 0.22



### Mt. 0.06 ' 0.26 0.41 0.5 1 0.46 0.35 0.34

. Reduction as a % of Total Residential Emissions 0.0 1 0.02 0.03 0.03 0.03 0.02 0.02 thousand short tons 0.14 0.54 0.76 0.74 0.49 0.24 0.16 thousand short tons 0.01 0.07 0.12 0.18 0.21 0.22 0.24 Total Reduction in Emissions

million short tons

, 0.07 0.29 0.46 0.56 0.50 0:39 .' 0.37 - kt ' 0.14 0.55 0.80 0.84 0.64 0.42 0.36 thousand short tons 0.15 0.6 1 0.88 0.92 0.70 0.46 0.40 

### Table EA-14. Reduction ofPollutants for Cooktops, Level Four SOz

- Year Abated from Power Abated nom In House Total Reduction in Reduction as Plants Emissions 'a % ofTotal thousand thousand thousand Residential kt short tons kt short tons kt short tons Emissions 2000 0.17 0.19 -0.01 -0.01 0.16 0.17 0.00 2005 0.61 0.67 -0.04 -0.04 0.57 0.63 0.02 2010

. 0.80 0.88 -0.05 -0.06 0.74 0.82 0.03 2015 0.73 0.80 -0.09 -0.10 0.63 0.70 0.03 2020 0.45 0.49
. -0.08 -0.09 0.37 0.40

. 0.02 2025 0.20 '0.22 -0.09 -0.10 0.10 0.11 0.01**20~;0** . 0.12 0.13 -0.08 -0.09 0.04 0.04 0.00 Cumulative S(hreduction (kt): 13
. (short tons): 14000 NOx Yenr Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal kt thousand kt .thousand kt

. thousand Residential short tons short tons short tons Emissions 2000 0.13 0.14 0.01 0.01 .0.14 0.15 0.01 2005 0.49 ' 0.54 0.06 0.07 0.55 0.61 0.02 2010 ·0.69 0.76 0.11 '0.12 0.80 0.88 0.03 201'5 0.67· 0.74 0.16 0.18 0.84 0.92 0.03 2020 0.45 '0.49 0.19 0.21 0.64 0.70 0.03 2025 0.22 0.24 0.20 0.22 0.42 0.46 0.02 2030 0.)4 0.16 0.22 0.24 0.36 0.40 0.02 Cumulative NOx reduction (kt): 18 (short tons): 20 '. COz Abated from Power " Total Reduction in**Ye~tr** Abated from In House Reduction as Plants' Emissions a % ofTotal 

# . Mt million Mt' million Mt million Residential short tons short tons short tons Emissions 2000 0.05 0.05 0.01

. 0.01 0.06 0.07 0.00 2005 0.20 0.22 0.06 0.06 0.26 0.29 0.02 2010 0.31 0.34 0.11· 0.12 0.41 0.46 0.03 2015 0.34 0.38 0.16 O.IS. 0.51 0.56 0.04 2020 0.26 0.29 0.19 0.21 0.46 0.50 0.03 2025 0.15 0.17 0.20 0.22 0.35 0:39 0.02 2030 0.12 0.13 0.22 

# . 0.24 0.34 0.37 0.02 Cumul::itlve CO2reductIon (Mt): II (short tons): 12000 000. Volume 1: Ent'ironmentalAssessment PageEA.19

### Table EA-15. 

### ReductionofPollutants for Cooktops,LevelFive

L Year

2000 2005 2010 2015 2020 2025 2030


### NOx

PageEA-20 Volume 1: EnvironmentnlAssessment Cumulative SO2 reduction (kt): 67 (short tons): 73 000

L Year

2000 2005 2010 2015 2020 2025 2030

Year

2000 2005 2010 2015 2020 2025 2030 Abated from Power Plants
kt 0.57 1.92 2.86 3.10 ' 2.57 1.89 1.46

Cumulative NOxreduction (kt): 65 

# - (short tons): 72 000

Cumulative C02 reduction Wt): 36 (short tons): 39 000 000 thousand shorttons 0.63 2.12 3.15 3.42 2.83 2.08 1.61 - Abated from In House

Abated from Power Plants

Abated from Power Plants kt -0.01 -0.04 -0.05 -0.1 1 -0.09 -0.09 -0.08

kt 0.42 1.55 2.48 2.88 2.57 2.07 1.79

Mt 0.16 0.63 1.10 1.48 1.52 1.44 1.46 

# . Reduction as 

### a 96 ofTotal Residential Emissions 0.02 0.06 0.10 0.12 0.12 0.1 1 0.10 thousand short tons

-0.01 -0.04 -0.06 -0.12 -0.10 -0.10 -0.09 Total Reduction in Emissions

Reduction as a % of Total Residential Emissions 0.02 0.06 0.09 0.12 0.1 1 0.10 0.10 thousand short tons 0.47 1.70 2.74 3.17 2.83 2.28 1.97

million short tons 0.18 0.70 1.21 1.63 1.67 1.59 1.61 Abated from In House kt 0.56 1.88 2.80 3.00 2.47 1.79 1.38

Abated from In House

Mt 0.00 -0.01 -0.01 -0.02 -0.01 0.00 0.01 thousand 

### st,ort tms 0.6 1 2.07 3.09 3.30 2.72 1.98 1.52

Total Reduction in Emissions kt 0.00 -0.01 0.00 -0.0 1 0.00 0.01 0.02

Reductionas a 8of Total Residential Emissions 0.01 0.05 0.08 0.10 0.10 0.09 0.09 Total Reduction in Emissions million short tons 0.00 -0.0 1 -0.0 1 -0.02 -0.01 0.00 0.01 kt 0.42 1.54 2.48 .2.87 2.56 2.07 1.81 thousand short tons 0.00 -0.01 0.00 -0.01 0.00 0.01 0.02

Mt 0.16 0.62 1.09 1.46 1.51 1.44 1.47 thousand short tons 0.47 1.70 2.73 3.16 2.83 2.29 1.99 '

million short tons 0.17 0.68 1.20 1.60 1.66 1.58 1.62 

### Table 

### EA-lS. Reduction ·ofPollutants for Cooktops,

. Level Five

Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions fl % ofTotal thousand thousand thousand Residential kt short tons kt short tons kt short tons Emissions 2000 0.57 0.63 -0.01 -0:01 0.56 0.61 0.02 2005 1.92 2.12 -0.04 -0.04 1.88 2.07 0.06 2010 2.86 3.15 -0.05 -0.06 2.80 3.09 0.10 2015 3.10 3.42 -0.11 -0.12 3.00
. 3.30 0.12 2020 ' 2.57 2.83 -0.09 -0.10 2.47 2.72 0.12 2025 1.89 2.08 -0.09 -0.10 1.79 1.98 0.11 2030 1.46 1.61 -0.08 -0.09 1.38 1.52 0.10 Cumulative S02reduction (kt):' 67 (short tons): 73000 

### NOx Year Abated from Power

. Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal kt thousand 'kt thousand kt thousand Residential short tons short tons short tons Emissions 2000 0.42 0.47 0.00' 0.00

. 0.42 0.47 0.02 2005 1.55 1.70 -0.01 -0.01 1.54 1.70 0.06 2010 2.48 2.74 0.00 0.00 2048 2.73 0.09 2015 2.88 3.17 -0.01 -0.01 ·2.81 3.16 0.12 2020 2.57 2.83 0.00 0.00 2.56 2,83 0.11 2025 2.07 2.28 0.01 0.01 2.07 2.29 0.10 2030 1.79 1.97 0.02 0.02

. , 1.81 1.99 0.10 Cumulative NOxreduction (kt): 65 (short tons): 72000

Year Abated from Power Abated from In House Total Reduction in Reduction as PlantS Emissions a % ofTotal million .. million million Residential Mt short tons Mt short tons Mt short tons Emissions 2000 0.16 0.18 0;00 0.00 0.16 0.17 0.01 2005 0.63 0.70 -0.01 -0.01 0.62 0.68 0.05 2010 1.10 1.21 -0.01 -0.01 1.09 1.20 0.08 2015 1.48 1.63 -0.02 -0.02 1.46 1.60 0.10 2020 1.52 1.67 -0.01 -0.01 1.51 1.66 0.10 2025 1.# 1.59 0.00 0.00 1.44 1.58 0.09 2030 1.46 1.61 0.01 0.01 1.47 1.62 0.09 CumulatIve CO2 reductIon (Mt): 36 (short tons): 39 000 000 PageEA-20 Volume 1: EnvironmentalAssessment

\

### Table 

### EA-16. Reduction ofPollutantsfor ~onventidnnl .ovens,Level One SO2



### NOx Abated from Power Total Reduction in Reduction as Abated from In House Plants Emissions a % of Total thousand thousand 

### kt thousand Residential

. kt shorttons kt short tons short tons Emissions 0.06 0.07

. 0.00 0.00

. 0.06 - 0.07

, 0.00 

## - 8 2!005 0.23 0.26 0.00 0.00 0.23 0.25 0.01 0.42 0.46 ' ,401 -0.0 1 0.4 1 0.45 0.02


### .. 0.57 -0.02 -0.02 0.50 0.55 -0.02 2020 0.49. 0.54 -0.02 -0.03 0.46 0.5 1 0.02 2025 0.43 0.48 -0.02 -0.02 0.4 1 ' 0.46 0.02 ' 0.38 0.4 1 403 0.35 21030 -0.02 0.39

. 0.02 CunlulaniveNOxreduction (kt): 11 I (short tons): 13000

,

Abated Power Abated from In House 'tear Plants thousand thousand kt short tons kt short tons 0.09 0.09 0.00 0.00 0.29 0.32 0.00 0.00 0.48 0.53 0.00 0.00 0.56 0.61 -0.0 1 -0.01 0.49


### . 0.54 -0.01 -0.01 0.39 0.43 0.00 0.00 0.3 1 0.34 -0.01 -0.01



# I liear 

# I Abated from Power Plants Abated 

### frc million

.

# lTzzJz&&z

Cun~ulative SO2 reduction (kt): 12 (short tons): 13 000 Reduction as a % of Total Residential Emissions 0.00

. 0.01

0.02 0.02 0.02 0.02 0.02 Total Reduction in Emissions kt 0.09 0.29 0.48 0.54 0.47 0.39 0.29

0.10

. 2!010 ' 0.19

2:O 1 5 0.26 2020 0.29 0.30' 0.3 1 thousand short tons 0.09 0.32 0.53 0.60 0.52 0.43 0.32



### n 

### In House million short tons 0.00

Volume 1: ~nvironmentul ~ssessment PageEA-21 -0.01 -0.01 -0.02 -0.03 -0.02 

## 1 -0.03

' Cunrulative C02 reduction (Mt): 6 0.1 1 0.20 0.29 0.32 0.33 0.34 0.00 -0.01 -0.02' -0.03

. -0.02 -0.03 

### Reduction as a % of Total Residential Emissions 0.00 Total Reduction in Emissions

(short tons): 

### 7 000 000 0.09 0.18 0.24 0.26 0.28 0.28 Mt 0.02 million short tons 0.03 0.10 0.19 0.27 .0.29 0.3 1 0.31 0.01 0.0 1 0.02 0.02 0.02 0.02 

### Table 

### EA·16. Reduction ofPollutants for Conventional.Ovens, Level One

Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotill thousand thousand thousand Residential kt short tons kt short tons kt short tons Emissions 2000 0.09 0.09 0.00 0.00 0.09 0.09 0.00 2005 0.29 0.32 0.00 0.00 0.29 0.32 .0.01

. ·2010 0.48 0.53 0.00 0.00 0.48 0.53 0.02

2015 0.56 0.61 -0.01 -0.01 0.54 0.60 ·0.02' 2020 0.49 0.54 -0.01 -0.01 0.47 0.52 0.02 2025 0.39 0.43 0.00 0.00 0.39 0.43 0.02 2030 0.31 0.34 -0.01 -0.01 ·0.29 0.32 0.02 Cumulative S02**reduc~on** (kt): 12 (short tons): 13000 

### NOx Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal kt thousand kt thousand kt thousand Residential short tons short tons short tons Emissions 2:000 0.06 0.07 0.00 0.00 0.06 0.07 0.00- ' 2:005 0.23 0.26 0.00 0.00 0.23 0.25 0.01 2010 0.42 0.46 ·-0.01 -0.01 0.41 0.45 0.02 2015 '0.52 0.57 -0.02 -0.02 0.50 0.55 ,0.02 2020 0.49· 0.54 -0.02 -0.03 0.46 0.51 0.02 2075 0.43 0.48 -0.02 -0.02 0.41 0.46 0.02 2030 ' 0.38 0.41 -0.02 -0;03 0.35 0.39 0.02 Cumulative NOx reduction Oct):

, 

### " 11 (short tons): 13000 C02

. Year Abated from Power Abated from 

### In' House Total Reduction in Reduction as Plants Emissions a % ofTotal ' Mt million Mt million Mt million Residential short tons short tons short tons Emissions 2:000 0.02·· 0.03 0.00 0.00 0.02 0.03 0.00 2005 0.10 0.11 0.00 -0.01 0.09 0.10 0.01

. 2:010 0.19 0.20 -0.01 -0.01 0.18 0.19 0.01

2:015 0.26 0.29 -0.02' -0.02 0.24 0.27 0.02 2020 0.29 0;32 -0.03 -0.03 0.26 ,0.29 0.02 2:025 0.30' 0.33 -0.02 -0.02 0.28 0.31 0.02 2:030 0.31 0.34 -0.03 -0.03 0.28 0.31 0.02 Cumulative CO2reduction (Mt): 6 (short tons): 7 000 000
Volume 1,' EnvironmentalAssessment PageEA.21

### Table 

### EA-17. Reduction of Pollutantsfor Conventional Ovens, Level Two

NOx b Abated from Power Abated from In House Year

2000 2005 2010 2015 2020 2025 2030 Cumulative SO2 reduction (kt): 25 (short tons): 28 000

CumulativeNOx reduction (kt): 23 

# - Abated from Power Plants 

### - Abated from In House

2000 2005 2010 2015 2020 2025 2030 

# I Year 

# I Abated Power 

# IAbated from 1; '~ouse Plants

, kt 0.17 0.63 0.97 1.15 1 -00 0.81 0.63 kt -0.01 0.00 -0.01 -0.03 -0.03 -0.01 -0.01 Reduction as a 96 of Total Residential Emissions 0.00 0.02 0.03 0.05 0.05 0.05 0.04 thousand short tons 0.19 0.70 1.07 1.26



### . 1.10

0.89 0.69 thousand short tons -0.01 0.00 -0.01 -0.03 -0.03 4.01 -0.0 1 Total Reduction in Emissions kt 0.16 0.63 0.95 1.12 0.97 0.80 0.61

kt 0.13 0.5

### 1 0.84 1.06 1.00 0.89 0.77

2000 thousand shmtons 0.17 0.70 1.05 1.23 1.07 0.88 0.68



### Irt -0.01 -0.0 

### 1 -0.03 -0.04 -0.05 -0.05 -0.05 thousand short tons 0.14 0.56 0.93 1.17 1.10 0.98 0.85

2005 2010 2015 2020 2025 2030 ~ ~ -- Total Reduction in Emissions thousand short tons -0.01 -0.01
- -0.04 -0.05 -0.06 -0.06 -0.06 -- Total Reduction in - Emissions

Mt 0.05 million short tons 0.04 0.04

kt 0.12

. 0.50

. 0.81 1.02 0.95 0.84 0.72

million Mt million short tons short tons 0.05 -0.01 -0.01

Cumulative C02 reduction (Mt): 0.21 0.37 0.55 0.59 0.62 0.63 0.59 0.56 0.62 0.63 (short tons): 14 000

### OW thousand short tons 0.13 0.55 0.89 1.12 1.04 0.92 0.79

0.23 0.41 0.60 0.65 0.68 0.69 Reduction as I a % of Total

Residential

(short tons): 26 000 Emissions 

# -1

Reduction as a 5% of Total Residential Emissions 

## 0.001

Page EA-22 Volume 1: EnvironmentalAssessmeni 

### Table EA-17. Reduction ofPollutants for Conventional Ovens, Level Two

Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal



# - thousand thousand

. thousand Residential kt short tons kt short tons kt short tons Emissions 2000 0.17 . 0.19 -0.01 -0.01 0 ..t6 0.17 0.00 2005 0.63 0.70 0.00 0.00 0.63 0.70 0.02 2010 0.97 1.07 -0.01 -0.01

. 0.95 1.05 0.03 2015 1.15 1.26 -0.03 -0.03 1.12 1.23 0.05 2020 1.00 1.10 -0.03 -0.03 0.97 1.07 0.05 2025 0.81 0.89 -0.01 -0.01 '0.80 0.88 0.05 2030 0.63 0.69 -0.01 -0.01 0.61 0.68 0.04 Cumulative S02reduction (kt): 25 (short tons):

. 28 000 NOx
Year Abated from Power Abated from In House Total Reduction in


### - Reduction as Plants Emissions a % ofTotal kl thousand kt thousand kt thousand Residential



### - short tons short tons short tons Emissions 2000 0.13 0.14 -0.01 -0.01 0.12 0.13 0.00

- 2005 0.51 0.56 -0.01 -0.01' 0.50 0.55 0.02 2010 0.84 0.93 -0.03 . -0.04 0.81 0.89 0.03 2015 1.06 1.17 -0.04 -0.05 1.02 1.12 0.04 2020 1.00 1.10 -0.05 -0.06 0.95 1.04 0.04 2025 0.89 0.98 -0.05 -0.06 0.84 0.92 0.04 2030 0.77 0.85 -0.05 -0.06 0.72 0.79 0.04 Cumulative NOx reduction (kt): 23 (short tons): 26000 C02 Abated from Power " Total Reduction in Year Abated from In House Reduction as Plants Emissions a % ofTotal ; Mt million Mt million Mt million Residential short tons short tons short tons Emissions 2000 0.05 0.05 -0.01

. -0.01 0.04 0.04 0.00 2005 0.21 0.23 -0.01 -0.02 0.19 0.21 0.01 2010 0.37 0.41 -0.04 -0.04 0.33 0.37 0.02 2015 0.55 0.60 -0.05 -0.05 0.50 0.55 0.03 2020 0.59 0.65 -0.06 -0.06 0.53 0.59 0.04 2025 0.62 0.68 -0.05
. -0.06 0.56 0.62 0.04 2030 0.63 0.69 -0.05 -0.06 0.57 0.63 0.04 Cumulative CO2reduction (Mt): 13 (short tons): 14000000 PageEA-22 Volume 1: EnvironmentalAssessment. . 

### Table EA-18.'

### Reduction ofPollutants for ~onventionsl Ovens, Level 

### Thm

. . 

### . 

## so2 Abated from Power Total Reduction in Reductionas ,Plants Emissions a 8 ofTotal . thousand thousand thousand Residential kt short tons kt

. .short tons kt short tons, Emissions 

### 0.09 0.09 -0.03 -0.03 0.06 0.06 0.00

. 

### ,2005 0.27 0.29 -0.07 -0.07 0.20 0.22 0.01 

### 0.46 0.5 1 

### -0.09 -Oil0 0.37 0.41 

### 0.01 20:15 ' 

### 0.56 0.61 -0.15 -0.16 0.41 

### 0.45 0.02 2020 ' 

### 0.50 0.55 -0.16 -0.18 0.34 0.37 0.02 2025 0.40 0.45 -0.16 -0.18 0.24 0.27 0.01 

### 2030 0.31 

### 0.35 -0.15 -0.16 '0.17 0.18 0.01 Cumullative SO2 reduction (kt): 

### 9 (short tons): 

### 10 000 

### NOx

2010 201 5 20;!0 20;!5 20510

Volume I: i~nvbonm'enhl ~ssessk PageEA-23 

### co2 

# 0;

Cumulative NOx 

### 0.39

reduction (kt): 

### 0.42 19 0.34 0.38 (short 

### 0.73 tons): 

### 0.80 21 000 0.04 ' Abated from Power Plants

Abated it am Power Plants. kt 

### 0.06 0.22 0.40 0.52 0.50 0.44

Cumulative 

### C4 reduction (Mt): 

### 14 (short tons): 

### 15 000 

### 000 Mt 

### 0.02 0.09 0.18 2015
. 0.26 20210 0.30 2025 0.31 

### 0.31 thousand short tons 

### 0.07 0.24 0.44 0.57 0.55 0.49 Abated from In House

million shorttons 

### 0.03 0.10 0.20 0.29 0.33 0.34

; 

### 0.35

. Abated from In ~ouse kt 

### 0.02 0.11 0.20 0.29 0.31 

### 0.33

Mt 

### 0.02 0.11 0.20 0.28 0.31 

### 0.32

' 

### 0.35 Reduction as a 8of Total Residential Emissions 

### 0.00 0.01 0.02 0.03 0.04 0.04 thousand short tons 

### 0.02 0.12 0.23 0.31 

### 0.34 0.36 Total Reduction in Emissions

Reduction

# as a 9% of Total Residential Emissions

. 

### 0.00 0.01 

### 0.03 0.04 0.04 0.04
% 

### 0.04 , million short tons 

### 0.02 0.12. 0.23 0.31, 0.34 0.36 0.38 Total Reduction in Emissions kt 

### 0.08 0.32 0.61 0.80 0.81 0.77



### Mt 0.W 0.19 0.38 0.55 0.61 0.63 0.66 thousand short tons 

### 0.09 0.36 0.67 0.88 0.89 0.85

million short tons 

### 0;05

. 

### 0.21 0.42 0.61 0.67 0.70 0.73 

### Table EA-18. Reduction ofPoUutants for Conventional Ovens, Level Three

Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal thousand thousand thousand Residential kt short tons kt short tons kt short tons. 'Emissions

### ~)o 0.09 0.09 -0.03 -0.03 0.06 0.06 0.00 ·2005 0.27 0.29 -0.07 -0.07 0.20 0.22 0.01

20:10 0.46 0.51 -0.09 -0.10 0.37 . 0.41 0.01 2015 0.56 0.61 -0.15 -0.16 0.41 0.45 0.02 2020 0.50 0.55 -0.16 -0.18 0.34 0.37 0.02 2025 0.40 0.45 -0.16 -0.18 0.24 0.27 0.01 2030 0.31 0.35 -0.15 -0.16 '0.17 • 0.18 0.01 Cumullative S02 reduction (kt): 9 (short tons): 10000 

### NOx Year Abated from Power Abated from In House Total Reduction in

. Reduction as Plants Emissions a % ofTotal kt thousand kt**~ousand** kt thousand Residential short tons short tons short tons Emissions 

# - 2000 0.06 0.07 0.02 0.02 0.08 0.09 0.00 2005 0.22 0.24 0.11 0.12 0.32 0.36 0.01 2010 0.40 .0.44 0.20 0.23 0.61 0.67 0.02 2015 0.52 0.57 0.29 0.31 0.80 0.88 0.03 2020 0.50 0.55 0.31 0.34. '0.81 0.89 0.04. 2025 0.44 0.49 0.33 0.36 0.77· 0.85 0.04 2030 0.39 0.42
. 0.34 0.38 0.73, .0.80 0.04 i Cumulative NOx reduction (kt): 19 (short tons): 21000 C02 Year Abated**~l**om Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal Mt million Mt million Mt million Residential short tons short tons short tons Emissions


# - 2OCI0 0.02 0.03 0.02 0.02 0.04. 0;05 0.00 20CIS 0.09 0.10 0.11 0.12' 0.19 0.21 0.01 2010 0.18 0.20 0.20 0,23 0.38 0.42 0.03 2015

. 0.26 0.29 0.28 0.31 0.55 0.61 0.04 202:0 0.30 0.33 0.31 0.34 0.61 0.67 0.04 202:5 0.31 0.34 0.32 0.36 0.63 0.70 0.04 ' 2030 0.31 , 0.35 0.35 0.38 0.66 0.73 0.04 CumulatIve CO2 reduction (Mt): 14 (short tons): 15000 000

, .

. Volume I: EnvironmentalAssessment PageEA-23

### Table EA-19. 

### Reduction of Pollutantsfor Conventional Ovens, Level Four

NOx 

## so2 Year

2000 2005 2010 2015 2020 2025 2030

Abated from Power 

# I I Plants Cumulative SO2 reduction (kt): 247 (short tons): 273 000



### fn

2000 Abated from Power Plants

2005 2010 2015 2020 2025 2030 thousand 

## I

. 

### ,.- 

## I thousand kt 1.94 6.25 9.63 11.26 10.04 8.03 6.3 1

n In House Reduction as a % of Total Residential Emissions 0.05 0.20 0.33 0.46 0.47 0.46 0.44 thousand short tons 2.14 6.89 10.6 1 12.41 11.06 8.85 6.95

Total Reduction in Emissions 

### kt -0.03 kt 1.45

CumulativeNOx reduction (kt): 236 5.03 8.36 10.44 10.04 8.80 7.76 (short tons): 260 00( Abated from In House

thousand short totis 1.59 short tons -0.04. Reduction as a % of Total Residential kt -0.04 -0.11 -0.19 -0.27 -0.30 -0.30 -0.30 Total Reduction in Emissions

5.55 9.22 11.51 11.06 9.69 8.55 Emissions 

# TI thousand short tons

-0.04 -0.12 -0.21 -0.30 -0.33 -0.33 -0.33 kt 1.90 6.15 9.44 11.00 9.74 7.74 6.01

Kt 1.41 -0.10 -0.19 -0.27 -0.31 -0.33 -0.33 thousand short tons 2.09 6.77 10.40 12.12 10.73 8.52 6.63

short tons - 1.55

PageEA-24 I

VolumeI: ~nviron~ental ~sseisment CumulativeC02 reduction (Mt): 129 (short tons): 142 000 000 Year

2000 2005 2010 2015 2020 2025 2030 Abated from Power Plants Mt 0.54 2.06 3.69 5.36 5.94 6.12 6.32 million short tons 0.60 2.27 4.07 5.90 6.54 6.75 6.96 Abated from In House
Mt -0.04 -0.12 -0.21 -0.32 -0.36 -0.37 -0.38 

# . as a % of Total Residential Emissions 0.04 0.15 0.25 0.35 0.37 0.37 0.37 Total Reduction in Emissions million short tons

-0.04 -0.13 -0.24 -0.35 -0.39 -0.41 -0.42 Mt 0.50 1.94 3.47 5.04 5.58 5.75 5.93 million short tons 0.55 2.14 3.83 5.56 6.15 6.34 6.54 

### Table EA-19. Reduction ofPollutants for Conventional Ovens, Level Four

Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal thousand thousand thousand Residential kt short tons kt short tons kt short tons Emissions 2000 1.94 2.14 -0.04 -0.04 1.90 2.09 0.05 2005 6.25 6.89 -0.11 -0.12 6.15

. . 6.77 0.20 2010 9.63 10.61 -0.19 -0.21 9.44 10.40 0.33 ·2015 11.26 12.41 -0.27 -0.30 11.00 12.12 0.46

2020 10.04 11.06 -0.30 -0.33 9.74 10.7.3 0.47 2025. 8.03 8.85 -0.30 -0.33 7.74 8.52 0.46 2030 6.31 6.95 -0.30 -0.33 6.01**6.6~** 0.44 CumulatIve S02reduction (kt): 247 (short tons): 273000 NOx Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal kt thousand kt thousand kt thousand Residential short toris short tons short tons Emissions 2000 1.45 1.59

. -0.03 -0.04 1.41 1.55 0.05 2005 5.03 5.55 -0.10 -0.11 4.93 5.43 0.18 2010 8.36 9.22 -0.19 -0.21 8.18 9.01 0.31. 2015 10.44 11.51 -0.27 -0.30 10:17 11.21 0.42 2020 10.04 11.06 -0.31 -0.34 9.72 10.72 0.43 2025 8.80 9.69 -0.33 -0.36 8.47 9.34 0.41 2030 7.76 8.55 -0.33 -0.37 7.42 8.18 0.40 Cumulative NOx reduction (kt): 236 (short tons): 260000 

### C02 Abated from Power .. Total Reduction in Year Abated from In House Reduction as Plants Emissions a % ofTotal Mt million Mt million Mt million Residential short tons short tons short tons Emissions 2000 0.54 0.60 -0.04 -0.04 0.50 0.55 0.04 2005 2.06 2.27 -0.12 -0.13 .1.94 2.14 0.15 2010 3.69 4.07 -0.21 -0.24 3.47 3.83 0.25 2015 5.36 5.90 -0.32 -0.35 5.04 5.56 0.35 2020 5.94 6.54 -0.36 -0.39 5.58 6.15 0.37 2025 6.12 6.75 -0.37 -0.41 5.75 6.34 0.37 2030 6.32 6.96 -0.38 • -0.42 5.93 6.54 0.37 Cumulative CO2 reduction (Mt): 129 (short tons): 142000 000 PageEA-24 Volume 1: EnvironmentalAssessment

### Talble 

### EA-20. Reduction of Pollutants for Conventional .Ovens,Level Five 

# I ,irnr 

# I ~batedfiyn Power Plants 

# I

, -. 

# 17
short tons

La30 

# I 6.23 

# 1 6.87 Cun~ulative SO2 reduction (kt): 

# ( , 

# I Abated fmm Power Plants

2010 2.015 10.30 2.020 9.90 2025 8.69 CummlativeNOx reduc thousand short tons 1.57 5.46 9:10 11.35 10.91 9.58 8.44 

# - In (kt): 

# I 1 Abated 

### fmmPower Piants million 

# - short tons 2000 

### 053 0.59 -0.04 -0.04 1.87 

### 2.06 0.05 -0.15 -0.16 6.01 ' 6.62 0.19 -0.24 -0.27 9.26 10.20 0.33 -0.36 -0.40 10.75 11.85 0.45 -0.40 -0.44 9.50 

### . 10.47 0.46 -0.40 -0.44 7.53 8.30 0.45 -0.39 -0.43 5.84 6.44 0.43 24 1 (short tons): 

### 266 000 NOx Total Reduction in Reduction as Abar

### fro; In H6"se

# 1

kEmisrs 

# 1 1
a % of Total thousand thousand Residential short tons short tons Emissions -0.01 -0.01 1.42

. 1.56 0.05

-0.05 

# I -'-0.05 - 

# 1, 7.61 

### ( 8.39 

# 1 

# - 0.41

. 

# 1 239 (short tons): 263 

### 000

Abated from In House a % of

### Total million Mt short tons -0.01 -0.01 0.52 0.57 0.04 1.99 3.60 5.21. 5.76 5.95 6.15 (short tons):

\ VolumeI: EnvironmentalAssessmeit Page EA-25 

### TaUde EA-20. Reduction ofPollutants for Conventional Ovens, Level Five

, .' 

### . S02

. Year Abated'fi:om Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal thousand thousand thousand Residential kt short tons kt shortctons kt short tons Emissions 2000 . 1.91 2.11 -0.04 -0.04 1.87 2.06 0.05 2005 6.16 6.79 -0.15 -0.16 6.01 6.62 0.19 2!O10 ·9.50 10.47 -0.24 -0.27 9.26 10.20 0.33 2015 11.11 12.25 -0.36 -0.40 10.75 11.85 0.45 2020 9.90 10.91 -0.40 -0.44 .9.50 .10.47 0.46**~~025** 7.94· 8.75 -0.40 -0.44 7.53 8.30 0.45**~!O30** 6.23 6.87 -0.39 -0.43 5.84 6.44 0.43 Cumulative S02reduction (kt): 241 (short tons): 266000 NOx Year Abated from Power Abated from In House Total Reduction in Reduction as Plants Emissions a % ofTotal kt thousand**k~** thousand kt thousand Residential short tons short tons short tons Emissions

### ~Ooo 1.42 1.57 -0.01 -0

..01 1.42· 1.56 0.05 2005 4.95 5.46 -.0.02 -.0.02 4.94 5.44 .0.18 201.0 8.26**9~l.o** -0.01 -0.02 8.24 9.08 0.31' 2015 10.30 11.35 -0.03 -0.04 10.27 11.32 • .0.42 2020 9.9.0 10.91 -0.05 -0.06 9.85 10.85 .0.44 2025 8.69 9.58 -0.05 -0.06 8.64 9.52 .0.42 2030

. 7.66 8.44 -0.05 ':.0.05 . , 7.61 8.39 _ .0.41

. Cumulauve NOx reduction (kt)": 239 (short tons): 263000 

### C02 Year Abated from Power Abated from In,**~ouse** Total Reduction in Reduction as Plants Emissions a % ofTotal Mt .million Mt million Mt million Residentiai short tons short tons short tons Emissions 2000 0:53 0.59 -0.01 -0.01 0.52 0.57 0.04 2005 2 ..03 2.23 -0.03 -0.04 1.99 2.20 0.15 ,2.010 3.64 4.01 -0.04 -0.04 3.60 3.97 0.26 2.015 5.29 5.82 -.0

..07 -0

..08 5.21, 5.74 0.36 2020 5.86 6.46 -0.10 -0.11 5.76 6.35 0,38 2.025 6 ..05 . 6.67 -0.10 -0.11 5.95 6.56 . .0.38 2030 6.24 6.87 -0.09 -0.10 6.15 6.77 0.38 

# ---- Cum.ulative CO2reduction (Mt): 133 (short tons): 147 000 000

Volume J: EnvironmentalAssessment PageEA-25

### - Table 

### EA-21. Reduction of Pollutants for M.ic,mw&eOvens, Level One

NOx Year

2000 2005 2010 2015 2020 2025 2030 Cumulative SO2 reduction (kt): 0 (short tons): 0000

Year

2000 2005 2010 2015 2020 2025 2030

Page EA-26 Volume 1: Environmental Assessment 

# - Abated from In House Abated from Power Plants

b Year

2000 2005 2010 2015 2020 2025 2030 kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00 kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Cumulative NOx reduction (kt): 0 (short tons): 0000 uctton 

# ra dpLb of Total Residential Emissions 0.00 0.00 0.00 0.00 0.00 0.00 0.00 thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Total Reduction in Emissions

Abated from Power Plants

Cumulative COz reduction (Mt): 

### 0 (short tons): 0 000 000 kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00

kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00 thousand short tons 0.00 0.00 0.00 0.00


### . 0.00 0.00 0.00

thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Abated from In House

Abated from Power Plants 

### kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00

UCtlOn 

# ya 4b of Total Residential Emissions 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Abated from In House Total Reduction in Emissions 

### Mt 0.00 0.00 0.00 0.00 0.00 0.00 0.00 uctlon d,W of Total Residential Emissions , 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Total Reduction in Emissions thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Mt 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mt 0.00 0.00 0.00 0.00 0.00 0.00 0.00 million short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

### kt 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00

million short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 million short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

### Table EA-ll. 

### Reduction ofPollutants for**Microw~ve** 

### Ovens, Level One

Abated from Power Total Reduction in Keductlon Year Abated from In HouSe asa%of Plants Emissions Total thousand thousand thousand Residential kt short tons kt short tons kt short tons Emissions 2000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2005 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2010 0.00' 0.00 0.00 0.00" 0.00 0.00 0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00

. 0.00 2020 0.00 0.00 '0.00 0.00 0.00 0.00 0.00 2025 0.00 . 0.00 0.00 0.00 0.00 0.00 0.00 2030 0.00 0.00 0.00 '0.00 0.00

. 0.00 0.00 Cumulative S02 reduction (kt): 

## o (short tons): 0000 NOx Abated from Power Total Reduction in KeauctJon Year Abated from In House asa%of Plants Emissions Total kt thousand kt thousand kt . thousand Residential short tons short tons -short tons Emissions 2000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2005 0.00 0.00 0.00 0.00 0.00 0.00, 0.00 2010 0.00 0.00 0.00 ·0.00 0.00 0.00 0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ·2025 0.00 0.00 0;00 0.00 0.00 0.00 0.00

2030 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cumulative NOx reduction (kt): 

### o (short tons): 0000 

### C02 Abated from Power Total Reduction in KeauctJon Year Abated from 'in House as a % of pIimts Emissions Total Mt million Mt million Mt million Residential short tons short tons short tons Emissions 2000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2005 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2010 0.00 0.00 0.00 0.00 0.00 .0.00 0.00 2015 . 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2030 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cumulative CO2redllctlon (Mt): 

## o (short tons): 0000 000 PageEA-26 Volume 1: EnvironmentalAssessment

### TnbleEA-22. ReductionofPollutants

### for ~iemwrbe Ovens, Level Two

. .



# 's

,~ 

### 2005 2015 2020 2025 2030 

## :

VolumeI: EnvironmentalAssessment Page EA-27 Cumulative S@ reduction (kt): 

### 0 (short tons): 

### 0000 Year



### 2000 2005 2010 2015 2020 2025 2030 educt~on 

# Ksa qb of

Total Residential Emissions 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

### - Abated from In House Total Reduction in Emissions

Cumulntive.NOx reduction (kt): 

### 0

, ' (short tons):

. 

### 0 000 kt 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00 kt 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Abated from Power Plants

Cumulative C4 reduction (Mt): 

### 0 (short tons): 

### 0 000 

### 000 AbateJ from Power Plants thousand short tons 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00 thousand short tons 

### 0.00 0.00 0.00



## . 0.00

0.00 0.00 0,00 kt 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Abated from Power



### 0.00Plants

,short tusand 

### 0.00 tons

ucuon dsg6 of Total R&idential Emissions 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mt 

### 0.00 0.00 0.00 2015 

### 0.00 2020 0.00 2025 0.00 '2030 0.00 - -- Abated from 1" House Total Reduction in Emissions thousand short tons 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00

uction ::a d,qb of

Emissions 

### 0.00 Total ~esid~ntial



### 0.00 0.00 0.00 0.00 

# - 0.00

0.00 

## - .

## -.- Abated from In House
kt


### 0.00 0.00 0.00 0.00

. 

### 0.00 0.00 Total Reduction in

million short tons 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mt 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mt 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

kt 

### 0.00

. 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

### . 0.00 0.00

. - .

million short tons 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00 million short tons 

### 0.00 0.00 0.00 0.00 0.00 0.00 0.00 short tousand, 

### 0.00 tons 

### 0.00 ' 

### 0.00 0.00 0.00 0.00 0.00 short 

### 0.00 tons Emissions kt


### 0.00 0.00 0.00 0.00 0.00 0.00 thousand



### 0.00- 

### 0.00 0.00 

### . 0.00 0.W 0.00 

### TableEA-22. Reduction ofPollutants for Microwave Ovens, Level Two

Abated from Power Total Reduction in KeouCtlOn Year Abated 'from In House asa%of Plants Emissions Total' thousand thousand thousand Residential kt short tons kt short tons kt ,short tons Emissions 2000 0.00 '0.00 0.00 0.00 0.00 0.00 0.00 2005 0.00 0.00 ,0.00 0.00 0.00 0.00 0.00 2010 0.00 0.00 '0.00 0.00 0.00 0.00 ,0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00 0.00 '0.00 0.00 2030 0.00 0.00 0.00 0.00 0.00 0:00 ' 0.00 Cumulative**S~**reduction (kt): 

## o ' (short tons): 0000 

### NOx Abated from Power Total Reduction in Reduction Year Plants Abated from In House Emissions asa%of Total kt thousand kt thousand kt thousand**Resid~ntial** short tons short tons short tons Emissions 2000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2005 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2010 0.00 0.00 0.00 0.00 0.00 0.00 • 0.00

, 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00 0.00 0.00 _ 0.00 2030 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cumulative,NOx reduction (kt): 

## o (short tons): , 0000



### , C02 !"'"-'"- Reduction AbateJ from Power " Total Reduction in Year Plants Abated from In House
, Emissions as a% of Total Mt million Mt million Mt million Residential short tons short tons short tons Emissions 2000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2005 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 .2020 0.00 0.00 ' 0.00 0.00 0.00 0.00 0.00 :2025 0.00 0.00 0.00 0.00 0.00 0.00 0.00 :2030 0.00 0.00 0.00 ' , 0.00 0.00 0.00 0.00 ' Cumulative CO2reduction (Mt): 

## o (short tons): 0 000 000
Volume 1: EnvironmentalAssessment PageEA.27 '

### Table 

### EA-23. Reduction of Pollutantsfor Microwave Ovens, Level 

### Three SO, Abated from Power 

# I ~otal Reduction in ' 

# I ~b?ted fmm In House Emissions Keduct~on as a % of

Total Residential Emissions 2000 2005 2010 2015 2020 2025 2030


### NOx Cumulative SO2 reduction (kt): 0 (shorttons): 000(



# I

, yar 

# I Abatai horn Power Plants kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Year

2000 2005 2010 2015 2020 2025 2030 thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00



# I Total Red Abated fmm

### h House

, kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Cumulative NOxreduction (kt): 0 (short tons): 0000

2000 2005 2010 2015 .2020 2025 2030 million short tons thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 kt 0.00

. 0.00 0.00 0.00 0.00 0.00 0.00

Abated 

### Power Plants 

# 1;

(shorttons): thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00

kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00

CumulativeCOzreduction (Mt): 

### Mt 0.00 0.00 0.00 

### - 0.00 0.00 0.00 0.00 uctlon Total million Residential Reduction

. as a % of

Total Residential Emissions. 0.00 0.00 

# - 0.00 0.00 0.00 

# - 0.00 0.00 thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00

million short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 short tons Emissions 

# +I Abated from In House

Page EA-28 kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Total Reduction in Emissions

VolumeI: Environmentul Assessment thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00 thousand short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

### Table EA-23. Reduction ofPollutants for Microwave Ovens, Level Three 

### S02 Abated from Power Total Reduction in Keductlon Year Abated from In House asa % of Plants Emissions Total thousand thousand thousand Residential kt short tons kt short tons kt short tons Emissions 2000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2005 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ·2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00 Q.OO 0.00 0.00 2030 0.00 0.00 0.00 0.00 ·0.00 0.00 0.00 Cumulatlve S02reductlon (kt): 

### o (short tons): 0000 NOx Abated from Power Total Reduction in KOOUCtion Year Abated from In House asa%of Plants Emissions Total kt thousand kt thousand kt .thousand Residential short tons short tons short tons Emissions 2000 0.00 0.00 0.00 0.00 0.00 0.00 0.00

.. 2005 0.00 0.00 0.00 0.00 0.00 0.00

. 0.00 2010 .0.00 0.00· 0.00 0.00 0.00· 0.00 

# . 0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00 0.00 0.00 

# - 0.00 2030 0.00 0.00 0.00 0.00 0.00 0.00 ·0.00 -**Cumulativ~** NOx reduction (kt): 

### o (short**~ons):** 0000 

### C02 Abated from Power Total Reduction in Reduction

. Year Abated from 

### tit House as a % of Plants Emissions

. Total Mt million Mt million Mt million Residential short tons short tons short tons Emissions 2000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2005 0.00. 0.00 0.00 0.00 0.00 0.00 0.00 2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 .2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2030 0.00 0.00

. 0.00 0.00 0.00 0.00 0.00 Cumulative**C~** reduction (Mt): 

## o (short tons): 0 000 000 PageEA.28 Volume I: EnvironmentalAssessment. , 

### Table 

### EA-24. Reduction of Pollutantsfor Microwave Ovens, Level Four


### . SO,



## - .--- uctlon Abated from Power Abated from In House Tota~ Reduction in 

# ya of Plants Emissions Total thousand thousand kt thousand Residential kt short tons kt short tons 

### . short tons Emissions 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 , 0.00 0.00 0.00 2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 CumulativeNOxreduction (kt): 0 (short tons): 0000 ,

.

Volume 1: EnvironmenUrlAssessment

. . PageEA-29 Cumr~lative SO2 reduction (kt): 0 (short tons);: ' 0'000 uction fdpg of Total Residential Emissions 0.00 0.00 0.00 0.00 0.00

. 0.00 0.00

.

, . c02 ucuon Abated from Power Total Reduction in %

### of Abated from 

### In House ' Year

.. .:# 2Cl10 2C115 2020 2025

Plants Emissions Abated

. .~lants - - Abated from In House

Cumu.lative C02 reduction (Mt): 0 (short tons): 0 000 000 Total

. Residential Emissions

. 0.00 0.00

. 0.00

0.00 0.00 0.00 0.00 million Mt million Mt Mt short tons short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.1 0 0.00 0.00 0.00 

## . 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0;Oo 0.00 0.00 0.00

. 0.00 0.00 0.00 0.00 0.00 ,0.00 2030

. 0.00 0.00 0.00 0.00 0.00 Total Reduction in

..Emissions

, kt 0.00 0.00 0.00 ' 0.00' 0.00 0.00 0.00 'kt 0.00 0.00 0.00 0.00 0.00 0.00 0.00

. '

. million short tons 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

# kt 0.00. 0.00 0.00 0.00 0.00

. .0,00

, 0.00 thousand short tons 0.00 0.00

. 0.00 0.00 0.00 0.00

. 0.00 thousand short tons 0.00

. 0.00

0.00 0.00 0.00 0.00 0.00 thousand short tons 0.00 ' 0.00 0.00 0.00 0.00

, 0.00 0.00 

### Table EA-24. Reduction ofPollutants for Microwave**Ovens~** 

### Level Four S02 Abated from Power Total Reduction in ReductIon**Y4~r** Abated from In House asa%of 'Plants " Emi.ssions Total thousand thousand thousand Residential kt short tons .kt short tons kt short tons Emissions 2(00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2(105 0.00 0.00 0.00 .0.00 .0.00 0.00 0.00 2010 0.00 0.00 ·0.00

. 0.00 0.00 0.00 0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00

. 0.00 0.00 . ' 0.00 0.00 0.00 2030 0.00 "0.00 0.00 0.00 0.00 0.00 0.00

. Cumulative S02reduction (kt): 

## o (short tons): 0'000 

### NO" Abated from Power Total Reduction in ReductIon Ymrr 'Plants Abated from In House

.Emissions as a %of Total kt thousand kt thousand kt thousand Residential

. short tons short tons short tons Emissi'ons**~bo** 0.00 0.00 0.00 0.00 0.00' 0.00 0.00 2005 0.00 0.00 '0.00 0.00 ' 0.00 0.00 0.00 

### 2010 '0.00 0.00 0.00 0.00 0.00 0.00 0.00 2015 0.00 .0.00 0.00 0.00 0.00 0;00 0.00 2020 0.00 0.00 0.00 0.00 0.00 0.00 0.00

. 2025 0.00 0.00 0.00 0.00 0.00 0,.00 0.00 2030 0.00 0.00 0.00 0.00 0.00 0.00 0.00 CumulatIve NOx reductlon (kt): 

### o (short tons): 0000 

### C02 Abated from PQwer Total Reduction in Reduction· 

### Yc:ar Abated from In House asa%of Plants Emissions Total Mt million Mt million Mt million Residential short tons short tons short tons Emissions 2000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2005 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20'lO 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2015 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2020 0;00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2030 0.00 0.00 0.00 0.00 0.00 0.00 0.00 CumulatIve CO2 reduction (Mt): 

### o (short tons): 0000000

Volume 1: EnvironmentalAssessment Page El·29

### Table 

### EA-25. Reduction ofPollutants for 

### Microwave Ovens, Level Five SO, -Abatedhorn Power Plants 

# I Abated hm In House 

# I kt '

# I thousand 

# I kt short tons thousand short tons -0.01

-0.04 -0.04 -0.03 -0.04 2000 2005 2010 2015 2020 2025 2030 Keductlon Emissions

J 0.61 ' 0.68 0.02 1.90 2.09 0.06 2.39 2.63 0.08 2.02 

### . 2.22 0.08 1.70 1.88 0.08 1.38 1.53

. . 0.08 1.06 1.17 0.08 (shorttons): 58 000 Cumulative 

### S4 reduction (kt): 53 0.63 

### .I.92 2.42 2.06 1.74 1.41 1.1-1

.

' 

### NOx 

# I year 

# I Abated hom Power Plants 

# I Abated from In House . 0.69 2.12 2.66 2.27 -1.92 1.56 1.22 -0.01 -0.03 -0.03 -0.04 -0.04 -0.03 -0.04

2030 

# 1 1.36 - 

# 1 .1.50 

# 1 -0.08

. 

# 1 -0.09 CumulativeNOxreduction (kt): 48 2000 Emissions Total C

1.50 2.04 1.84 1.67 1.47 1.28

PageEA-30 Volume I: EnvironmentalAssessment kt 0.47 kt -0.01

Year

2000 2005 2010 2015 2020 2025 2030 thousand short tons 0.52 thousand short tons

-0.02

(shorttons): 53 000 1.65 2.25 2.03 1.84 1.62 1.41

CumulativeC02 reduction (Mt): 25 (short tons): 28 000 000. 0.06 ' 0.08 0.08 0.07 0.07 0.07 ,

Abated from Power Plants uctlon % of Total Residential Emissions 0.01 0.04 0.06 0.06 0.06 0.06 0.06 Abated fromlnHouse
Mt 0.17 0.63 0.93 0.98 1 .O? 1.08 1.11 Total Reduction in Emissions 

### Mt -0.02 -0.05 -0.07 -0.07 -0.08 -0.08 -0.09 million short tons 0.19 0.70 1.02 1.08 1.14 1.19 1.22 Mt 0.16 0.58 0.86 0.90 0.95, 1.00 1.02 million short tons

-0.02 

### -0.06 -0.07 -0.08 -0.09 -0.09 -0.10 million short tons 0.17 0.64 0.95 1.00 1.04 1.10 1.12 

### Table EA-25. Reduction ofPollutants for Microwave Ovens, Level Five S02 Abated from Power TOtal Reduction i'n Ke<luctton Year Plants Abated from In House Emissions as a % of Total thousand thousand thousand Residential kt short tons kt short tons kt short tons Emissions 2000 0.63 0.69 -0.01 -0.01 0.61 0.68 0.02 2005 '1.92 2.12 -0.03 -0.03 1.90 2.09 0.06 2010

. 2.42 2.66 -0.03 -0.03 2.39 2.63 0.08 2015 2.06 2.27 -0.04 -0.04 2.02 2.22 0.08 2020 1.74 1.92 -0.04 -0.04 1.70 1.88 0.08 2025 1.41 1.56 -0.03 -0.03 1.38 1.53 0.08 2030 1.11

. 1.22 -0.04 -0.04 1.06 1.17 0.08 Cumulattve SOz reduction (kt): 53 (short tons): 58000 NOx Abated from Power TotalReduction in ,. Reduction Year Plants Abated from In House Emissions as a % of Total kt thousand kt thousand' kt thousand Residential short tons short tons short tons Emissions 2000 0.47 0.52 -0.01 -0.02 0.45
. 0.50 0.02 2005 1.55 1.70 -0.05 -0.05 1.50 1.65 0.06 2010 2.10 2.31 -0.06 -0.07 2.04 2.25 0.08 2015 1.91 2.10 -0.07 -0.07 1.84 2.03

. 0.08 2020 1.74 1.92 -0.08 -0.08 1.67 1.84 0.07 2025 1.55 1.70 -0.07 -0.08 1.47 1.62 0.07 2030 1.36· .1.50 -0.08. -0.09 1.28 1.41 0.07 Cumulative NOx reduction (kt): 48 (short tons): 53000 C02 Abated from Power Total Reduction in Reduction Year Abated fronl'In House as a % of Plants Emissions Total Mt million Mt million Mt million Residential short tons short tons short tons Emissions 2000 0.17 0.19 -0.02 -0.02 0.16 0.17 0.01

- 2005 0.63 0.70 -0.05 -0.06 0.58 0.64

. 0.04 2010 0.93 1.02 -0.07 -0.07 0.86 0.95 0.06 2015 0.98 1.08 -0.07 -0.08 0.90 1.00 0.06 2020**1.0~** 1.14 -0.08 -0.09 0.95' 1.04 0.06 2025 1.08 1.19 -0.08 -0.09 1.00 1.10 0.06 2030 1.11 1.22 -0.Q9 -0.10 1.02 1.12 0.06 Cumulative CO2reduction (Mt): 25 (short tons): 28000 000'
Page EA-.30 Volume 1: EnvironmentalAssessment

### 1.

. 

### National Appliance Energy Conservation Act (NAECA), Public. Law 100-

### 12, March 17, 1!987. 

### 2. U.S. ~e~artrnent of Commerce, Bureau of the Census. 1994. 

# tati is ti& ~bstract ofthe U'nitedStates: 1994. 

### ('1 14thEdition). Washington, D.C. 3. 

### U.S. Department of Energy (U.S. DOE), 1994. Energy Information Administration. Emissions of Greenhouse Gases in the United States 1987-1992, 

### Washington, D.C. DOEIEIA-0573, November 1994. 4. 

## U.S.

## DOE, Improving Technology: Modeling Energy Futures for Natidnal Energy Strategy. 

### 1991 ServiceReport to the 1991National Energy Strategy,Washington,D.C. SRlNESl90- 0;1. 

### 5. 1. 

### KMmey, 

### 1990. Comparative Analysis ofMonetary Estimutes ofExtemal ~nbironmental

, ' 

### CostsAssociated with CombustionofFossil Fuels.

### Lawrence'Berkeley National Laboratory, Berkeley, CA. 

### LBL-283 13. 

### Ibid. 6.

VolumeI: .Environmental Assessment . .

. .. Page EA-31 

### REFERlB:l'lCES

1. ' National Appliance Energy Conservation Act (NAECA), Public,Law 109-12, March 17. 1987. ' 2. U.S. Department of Commerce, Bureau of the Census. 1994. StatisticalAbstract o/the United States:, 

### 1994. (114th Edition). Washington, D.C. 3. U.S. Department of Energy (U.S. DOE), 1994.' Energy Information Administration. Emissions 

## 0/ Greenhouse Gases in the United States 

### 1987-1992, Washington, D.C. ' DOElEIA-0573, November 1994.

, 4. U.S. DOE, Improving Technology: Modeling Energy Futures/orNational Energy Strategy. 1991 Service Report to the 1991**Nation~** Energy Strategy, Washington, D.C. SRlNES/90- 

### 01.

. 5. 

### J. Koomey, 1990. Comparative Analysis of Monetary Estimates 

## 0/ External Environmental CostsAssociatedwith Combustion of Fossil Fuels. Lawrence Berkeley,National Laboratory,**B.~rkeley,** CA. LBL-28313. ' 6. Ibid.

Volume 1: .EnvironmentalAssessment Page EA-31 .**~** 'l .'